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TRENDS IN THE PREVALENCE OF FRAILTY IN JAPAN: A META-ANALYSIS FROM THE ILSA-J

 

H. MAKIZAKO1, Y. NISHITA2, S. JEONG3, R. OTSUKA4, H. SHIMADA5, K. IIJIMA6, S. OBUCHI7, H. KIM8, A. KITAMURA9, Y. OHARA8, S. AWATA8, N. YOSHIMURA10, M. YAMADA11, K. TOBA12, T. SUZUKI13

 
1. Department of Physical Therapy, Faculty of Medicine, School of Health Sciences, Kagoshima University, Kagoshima, Japan; 2. Department of Epidemiology, National Center for Geriatrics and Gerontology, Obu, Japan; 3. Department Community Welfare, Niimi University, Niimi, Japan; 4. Section of NILS-LSA, National Center for Geriatrics and Gerontology, Obu, Japan; 5. Department of Preventive Gerontology, National Center for Geriatrics and Gerontology, Obu, Japan; 6. Institute of Gerontology, The University of Tokyo, Bunkyo-ku, Japan; 7. Research Team for Human Care, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 8. Research Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 9. Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 10. Department of Joint Disease Research, 22nd Century Medical and Research Center, The University of Tokyo, Bunkyo-ku, Japan; 11. Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan; 12. Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 13. National Center for Geriatrics and Gerontology, Obu, Japan & Institute of Gerontology, J.F. Oberlin University, Machida, Japan.
Corresponding author: Hyuma Makizako, epartment of Physical Therapy, Faculty of Medicine, School of Health Sciences, Kagoshima University, Kagoshima, Japan, makizako@health.nop.kagoshima-u.ac.jp

J Frailty Aging 2020;in press
Published online December 22, 2020, http://dx.doi.org/10.14283/jfa.2020.68

 


Abstract

Objective: To examine whether age-specific prevalence of frailty in Japan changed between 2012 and 2017. Design: This study performed meta-analyses of data collected from 2012 to 2017 using the Integrated Longitudinal Studies on Aging in Japan (ILSA-J), a collection of representative Japanese cohort studies. Setting: The ILSA-J studies were conducted on community-living older adults. Participants: ILSA-J studies were considered eligible for analysis if they assessed physical frailty status and presence of frailty in the sample. Seven studies were analyzed for 2012 (±1 year; n = 10312) and eight studies were analyzed for 2017 (±1 year; n = 7010). Five studies were analyzed for both 2012 and 2017. Measurements: The study assessed the prevalence of frailty and frailty status according to 5 criteria: slowness, weakness, low activity, exhaustion, and weight loss.Results: The overall prevalence of physical frailty was 7.0% in 2012 and 5.3% in 2017. The prevalence of frailty, especially in people 70 years and older, tended to decrease in 2017 compared to 2012. Slight decreases were found in the prevalence of frailty subitems including weight loss, slowness, exhaustion, and low activity between 2012 and 2017, but change in the prevalence of weakness was weaker than other components. Conclusions: The prevalence of physical frailty decreased from 2012 to 2017. There are age- and gender-related variations in the decrease of each component of frailty.

Key words: Frailty, aging, cohort study, older.


 

Introduction

Frailty is defined as a clinically recognizable state of increased vulnerability in older adults resulting from age-associated declines in physiologic reserves and function across multiple organ systems (1). Although it is recognized as a multidimensional construct, comprising psychological and social conditions and symptoms in addition to physical, the physical frailty phenotype is well defined and its impact on adverse health outcomes such as disability, hospitalization, and death has been examined in many prior studies (2-5). Clinical practice guidelines based on the current evidence base provide recommendations for identifying and managing frailty in older adults (6). Reducing the risk and prevalence of frailty may play an important role in extending healthy life expectancy in the aged population.
The most common components used to assess physical frailty are the frailty phenotype proposed by Fried et al. using data from the Cardiovascular Health Study (CHS) (2). Based on the Fried criteria, a wide prevalence of frailty has been reported among community-dwelling people aged 65 years and older, ranging from 4% to 27% (7, 8). In Japan, with a rapidly increasing aging population, the overall prevalence of frailty was 7.4%, with a similar prevalence in men (7.6%) and women (8.1%) (9). These prevalence rates increased with advancing age (1.9%, 3.8%, 10.0%, 20.4%, and 35.1% for people aged 65 to 69, 70 to 74, 75 to 79, 80 to 84, and 85 or older, respectively) (9).
In the past several decades, both life and health expectancy have increased in many countries. In Japan, the average life expectancy was 81.3 years for men and 87.3 years for women in 2018, according to data from the Ministry of Health. There may be improvement in physical health status among older adults based on increased life and health expectancy. Although previous studies indicated the prevalence of frailty in a large cohort or meta-analysis, no studies focused on trends in the prevalence of frailty and assessment years.
This study performed meta-analyses using data from the National Center for Geriatrics and Gerontology’s Integrated Longitudinal Studies on Aging in Japan (ILSA-J), a collection of 13 longitudinal cohort studies on aging in Japan involving community-dwelling older adults, to test whether the age-specific prevalence of frailty changed in Japan between 2012 and 2017.

 

Methods

Data Sources

This study performed meta-analyses using ILSA-J data on frailty. The ILSA-J included a total of 13 longitudinal cohort studies conducted throughout Japan (Table 1). Studies were considered eligible for inclusion in the present analysis if they assessed physical frailty status and prevalence of frailty in the sample using the Fried criteria (2) (e.g., slowness, weakness, exhaustion, low activity, and weight loss). Of the 13 cohort studies, 7 (total n = 10312; 4611 men and 5701 women) were analyzed for 2012 (±1 year), and 8 (total n = 7010; 2662 men and 4348 women) were analyzed for 2017 (±1 year). Finally, only 10 of the 13 cohort studies in the ILSA-J project were included in this meta-analysis, because 3 cohort studies did not provide data on frailty status in 2012 and 2017.

Table1
Characteristics of the cohort studies included in the meta-analysis

 

Main Outcome Measures and Operational Definition of Frailty

The main outcome measures in this study were the prevalence of frailty status and the five frailty sub-items (%). This study determined physical frailty status according to the 5 criteria of physical frailty suggested by the Japanese version of the CHS (J-CHS) (10, 11) and a slightly revised criterion: weight loss, slowness, weakness, exhaustion, and low activity. Participants whose responses did not correspond to any of these target criteria were considered to be robust; those who responded positively for 1 or 2 criteria were considered pre-frail; and those with 3 or more positive criteria responses were considered frail.
Although all the cohort studies included in the current meta-analysis used the same 5 criteria to assess frailty status, there were differences in the subcriteria (Appendix table 1). The 5 criteria defining physical frailty were assessed as follows. Weight loss was identified by a response of “yes” to the question (12), “Have you lost 2 kg or more in the past 6 months?” Slowness was identified by a normal walking speed of <1.0 m/s (10). Weakness was identified according to grip strength of the subject’s dominant hand: <26 kg for men and <18 kg for women (13). Exhaustion was identified by a response of “yes” to the question (12), “In the last 2 weeks, have you felt tired for no reason?” Low activity was identified by a response of “no” to both the following questions (10): “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?”

Data Collection

All ILSA-J cohort studies were approved by the ethics committee of the relevant university or institute. Among the 13 total cohort studies, those that assessed frailty provided data on the prevalence of frailty status (frailty, pre-frailty, and robust) and the 5 frailty subitems for meta-analyses. Thus, no author of the present study could access participants’ individual data.

Statistical Analysis

A two-step approach was used in the current study. First, we obtained the frailty prevalence in each cohort study separately, then, we calculated the combined prevalence using meta-analysis. The prevalence rates of frailty and pre-frailty for the years 2012 and 2017 were calculated by age group and gender. The 5 frailty items were also included to calculate prevalence. The present meta-analysis used a two-step approach. First, Cochran’s Q test was used to assess the presence of heterogeneity across cohorts, indicated by p<0.05, and I2 statistic values of 25%, 50%, and 75% indicated low, moderate, and high degrees of heterogeneity, respectively (14). Then, prevalence and 95% confidence intervals (CIs) were calculated for frailty and pre-frailty using a random-effects model if heterogeneity was present (p<0.05) and a fixed-effects model if heterogeneity was absent based on Cochran’s Q test (9). In addition, we performed a sensitivity analysis restricting the meta-analysis to surveys performed at both time-points, 2012 and 2017. Statistical analyses were completed using Comprehensive Meta-Analysis software (Version 3; Biostat, Englewood, NJ, USA).

 

Results

Table 2 presents the data on the presence of heterogeneity across cohorts and the prevalence of physical frailty among each age group in 2012 and 2017. There was a slight decrease (1.7%) in overall prevalence of physical frailty between 2012 and 2017. The overall prevalence of physical frailty was 7.0% (95% CI 5.4-9.0%) in 2012 and 5.3% (95% CI 4.3-6.6%) in 2017. The sensitivity analysis restricted to surveys with data at both time-points (2012 and 2017) provided similar results to the main analysis (Appendix table 2). Greater decreases in the prevalence of frailty were found in adults aged 75 years and older. Specifically, in 2012, the prevalence of frailty was 7.4% in the 75-79 age group, 12.6% in the 80-84 group, and 23.2% in the 85-89 group. In 2017, a 3.0% decrease was found in the 75-79 age group, a 4.2% decrease in the 80-84 group, and a 6.2% decrease in the 85-89 group.

Table 2
Prevalence of physical frailty by age group

 

Among men, frailty prevalence increased with advancing age in both 2012 and 2017. In 2012, prevalence was 6.3% in the 75-79 age group, 9.9% in the 80-84 group, and 24.6% in the 85-89 group ; in 2017, a decrease of 3.1% was found in the 75-79 age group (prevalence of 3.2%), 3.1% in the 80-84 group (prevalence of 6.8%), and 8.2% in the 85-89 group (prevalence of 16.4%).
Similar trends were observed in women. The prevalence of frailty in 2012 was 8.1% in the 75-79 age group, 14.8% in the 80-84 group, and 26.3% in the 85-89 group. In 2017, a decrease of 3.1% was found in the 75-79 age group (prevalence of 5.0%), 5.4% in the 80-84 group (prevalence of 9.4%), and 8.7% in the 85-89 group (prevalence of 17.6%).
The gender-stratified prevalence of physical frailty subitems is shown in Tables 3 and 4. Regardless of gender, slight decreases (less than 5%) in the subitems were found between 2012 and 2017 among young old groups (ages 65-69 and 70-74), with the exception of low activity in men aged 65-69 and women aged 70-74. Differing trends between men and women were found among old groups (ages 75-79, 80-84, and 85-89). In men, subitems with greater decreases (more than 5%) included exhaustion, which decreased 6.0% in the 75-79 age group, 5.2% in the 80-84 group, and 8.9% in the 85-89 group; slowness, which decreased 7.7% in the 85-59 group; and low activity, which decreased 7.2% in the 85-89 group).

Table 3
Prevalence of physical frailty components (Men)

Note. Sample sizes for 2012 age groups were as follows: 65-69, n=1540 (6 studies); 70-74, n=1434 (6 studies); 75-79, n=942 (6 studies); 80-84, n=519 (6 studies); 85-89, n=176 (6 studies). Sample sizes for 2017 age groups were as follows: 65-69, n=357 (5 studies); 70-74, n=629 (6 studies); 75-79, n=882 (7 studies); 80-84, n=565 (7 studies); 85-89, n=229 (7 studies).

Table 4
Prevalence of physical frailty components (Women)

Note. Sample sizes for 2012 age groups were as follows: 65-69, n=1808 (6 studies); 70-74, n=1518 (6 studies); 75-79, n=1205 (7 studies); 80-84, n=892 (7 studies); 85-89, n=278 (7 studies). Sample sizes for 2017 age groups were as follows: 65-69, n=835 (6 studies); 70-74, n=1115 (7 studies); 75-79, n=1400 (8 studies); 80-84, n=756 (8 studies); 85-89, n=242 (7 studies).

 

Compared with men, women were found to have decreased prevalence in many components. In the 75-79 age group, all components expect for weakness decreased (weight loss, 9.7%; slowness, 5.8%; exhaustion, 7.3%; low activity, 6.4%). All components decreased in the 80-84 and 85-89 groups (weight loss, 7.5% and 8.1%, respectively; slowness, 12.1% and 16.6%; weakness, 6.1% and 5.5%; exhaustion, 9.3% and 5.8%; low activity, 5.4% and 5.9%).

 

Discussion

This study performed meta-analyses using data from ILSA-J cohort studies and showed that the prevalence of frailty tended to decrease in 2017 compared to 2012, especially in adults 75 years and older. The sensitivity analysis confirmed the main findings and indicates that this increases the robustness of the findings.
A recent systematic review of articles published in 28 countries estimated the global incidence of frailty among community-dwelling adults (15). Among robust individuals who survived a median follow-up of 3.0 years, 13.6% became frail, with a pooled incidence rate of 43.4 cases per 1000 person-years (15); incidence rates varied by diagnostic criteria and country income level. Previous systematic review and meta-analysis studies have also suggested variation in the prevalence of frailty based on diagnostic criteria (16), country income level (17), and residential environment (18, 9). Additionally, the prevalence of frailty among community-dwelling older adults has been reported to differ based on race (9, 19). Therefore, the influences of those characteristics should be considered when discussing the prevalence of frailty and prevention strategies.
Most systematic review and meta-analysis studies that examine the prevalence of frailty include articles published after 2000. Worldwide, there were 901 million people aged 60 years or over in 2015, an increase of 48% over the global total of 607 million older people in 2000 (20). The global number of people aged 60 years or over increased by 68% in urban areas, compared to 25% in rural areas, from 2000 to 2015 (20). In Japan, approximately 12% of the population was 65 years or older in 1990, about the same as the total in the USA in 1990 (21). By 2010, the 65 and older population in Japan doubled, rising from 15 million to 29 million and comprising 23% of the total population, the highest proportion in the world (21). The percentage rose to over 28% in 2019. Although the number of older people in Japan is increasing rapidly, their latent capabilities and background factors can be changed. Health-related measures among Japanese community-dwelling older adults from 2007 to 2017 indicate that a phenomenon of “rejuvenation” is occurring among the new generation of older Japanese adults (22). In the United States, dementia declined significantly between 2000 and 2012, and one associated factor was an increase in educational attainment (23). Thus, better change in older adults’ latent capabilities and background factors may lead to a decrease in the prevalence of frailty.

Several important factors, such as comorbidities, low socioeconomic position, poor diet, and sedentary lifestyle, increase the risks of frailty (24). Some of these are modifiable. Therefore, it may be possible to reduce the prevalence of frailty by controlling or improving risk factors. Although this study’s meta-analyses had a relatively short observational term of 5 years, decreasing trends in the prevalence of frailty may become clearer based on long-term observation.
Among 5 components of frailty, weakness and slowness may have greater impacts on increased risk of disability (11, 25). In this study, there was a decreasing trend in the prevalence of almost all items, however there was less change in the prevalence of weakness compared with other items. No change or a slight increase in the prevalence of weakness was observed in men in all age groups, whereas for women, only a decrease in the 80 years and older group was observed.
This study found significant differences in frailty prevalence between men and women. Older women, especially in the old-old population (aged 75 years and over), were found to have decreased prevalence in almost all frailty items. Recently, the ILSA-J reported differences between the years 2007 (± 2 years) and 2017 (± 2 years) in several indices (e.g., body composition, walking speed, and grip strength) that are related to the health and functioning of older adults (22). Better health status and a slower decline in most of the health-related measures were observed in 2017 compared with a decade ago. Japanese older adults living in the community have been consistently increasing their walking speed over the past 25 years, and the improvement in walking speed is especially striking in women (26, 27). In a previous study that analyzed IADL performance in 17,680 older adults with dependency in basic ADL, the men were found to have 3 times higher prevalence of poor performance of IADL compared with the women (28). Older adult women may reduce age-related decline in functional level by increasing or maintaining the multidimensional aspects of their lives, such as social and leisure time activities. In addition, our data showed higher study participation rates in women than in men for both 2012 and 2017. These findings may indicate that women have more interest in their health compared with men. Increased interest in personal health may prevent or delay the progression of frailty.
Consistent “female disadvantage” in physical performance among older adults has been demonstrated (29). One previous study with 4683 Japanese nondisabled community-dwelling older adults demonstrated increasing significant gender differences in one-legged stance performance and gait speed with age. In contrast, gender differences significantly decreased in hand-grip strength with increasing age (30). In other words, strength may be more affected by advancing age in older adult men than in older adult women. Thus, preventing or delaying the progression of weakness with age may be difficult in men. Weakness was determined according to grip strength of the subject’s dominant hand, with cutoff values of 26 kg for men and 18 kg for women. Although the average values of grip strength may decrease slightly in new generation of Japanese older adults (22), the changes may not reach sufficient levels, indicating that this component is less susceptible to generation changes than the others.
Several limitations of the present study should be noted. First, the meta-analyses in the present study used cross-sectional data from 7 cohort studies in 2012 (±1 year) and 8 cohort studies in 2017 (±1 year). Therefore, the study design was not longitudinal, following the same individuals and cohorts. Second, the current study used data from 2012 and 2017, analyzing the trends in prevalence over a period of 5 years. This may be too short to fully examine trends of change. Third, the number of participants varied widely by age group, especially participants in the 85 and older group, which had a relatively small sample size (fewer than 200 men in 2012). Finally, assessment protocols were dependent on each cohort study, not unified across all cohorts. We believe that the cohort studies included in the current meta-analysis had high data quality, but not all of the studies were designed using probabilistic samples. For instance, recruitment methods (e.g., random sampling, direct mail to all citizen, and volunteers) varied. In addition, more knowledge on the prevalence of the risk factors for frailty and those components, such as comorbidities, nutritional status, and cognitive function will support the phenomenon of decreasing frailty in the new generation of Japanese older adults.
Although this study examines a relatively short period of time (5 years), it has several strengths. First, it is, to our knowledge, the first study to describe trends in the prevalence of frailty. Second, the prevalence of frailty and subitems were assessed through a meta-analysis of 10 Japanese cohort studies, which provided data from 287 to 4779 older adults. Third, frailty status was assessed not only by questionnaires but also by objective measures such as grip strength and walking speed; therefore, are results may reflect functional status.
In conclusion, the current meta-analyses suggested that the prevalence of frailty has shown a decreasing trend in the new generation of Japanese older adults, especially in adults aged 75 years and older. This finding may indicate physical rejuvenation in older adults. Progression of this trend may improve health expectancy and shorten the gap between life expectancy and health expectancy. Future studies with more long-term follow-up period and a larger sample will be needed to clarify the trends in the prevalence of frailty among community-dwelling older adults.

 

Acknowledgement: This work was funded by the National Center for Geriatrics and Gerontology (Choujyu 29-42). We are grateful to all the participants for their valuable contribution to this study. The authors thank Dr. Katsunori Kondo of the National Center for Geriatrics and Gerontology & Chiba University, Dr. Yoshinori Fujiwara of the Tokyo Metropolitan Institute of Gerontology, and Dr. Shuichiro Watanabe of J.F. Oberlin University who are members of the ILSA-J project for their contributions in study progression. We also thank to Dr. Takehiko Doi of the National Center for Geriatrics and Gerontology, Dr. Tomoki Tanaka of the Institute of Gerontology, The University of Tokyo, Dr. Hisashi Kawai, Dr. Yu Nofuji, Dr. Takumi Abe and Dr. Susumu Ogawa of the Tokyo Metropolitan Institute of Gerontology, Dr. Yu Taniguchi of National Institute for Environmental Studies, and Dr. Yutaka Watanabe of the Faculty of Dental Medicine, Hokkaido University for their helpful supporting data sharing process and Ms. Shiho Fujii of the National Center for Geriatrics and Gerontology for her help in preparing the tables.
Conflicts of Interest: None declared.
Ethics Statement: This study was conducted in compliance with the current laws of Japan.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

SUPPLEMENTARY MATERIAL1

SUPPLEMENTARY MATERIAL2

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SARCOPENIA IN PRIMARY CARE: SCREENING, DIAGNOSIS, MANAGEMENT

 

S. Crosignani1, C. Sedini1, R. Calvani2, E. Marzetti2, M. Cesari3

 
1. Fellowship in Geriatrics and Gerontology, University of Milan, Milan, Italy; 2. Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Catholic University of the Sacred Heart Rome, Italy; 3. IRCCS Istituti Clinici Scientifici Maugeri, University of Milan, Milan, Italy
Corresponding author: Silvia Crosignani, MD, Email: silvia.crosignani@unimi.it

J Frailty Aging 2020;in press
Published online December 7, 2020, http://dx.doi.org/10.14283/jfa.2020.63


Abstract

Detection of sarcopenia in primary care is a first and essential step in community-dwelling older adults before implementing preventive interventions against the onset of disabling conditions. In fact, leaving this condition undiagnosed and untreated can impact on the individual’s quality of life and function, as well as on healthcare costs. This article summarizes the many instruments today available for promoting an earlier and prompter detection of sarcopenia in primary care, combining insights about its clinical management. Primary care physicians may indeed play a crucial role in the identification of individuals exposed to the risk of sarcopenia or already presenting this condition. To confirm the suspected diagnosis, several possible techniques may be advocated, but it is important that strategies are specifically calibrated to the needs, priorities and resources of the setting where the evaluation is conducted. To tackle sarcopenia, nutritional counselling and physical activity programs are today the two main interventions to be proposed. Multicomponent and personalized exercise programs can (and should) be prescribed by primary care physicians, taking advantage of validated programs ad hoc designed for this purpose (e.g., the Vivifrail protocol). It is possible that, in the next future, new pharmacological treatments may become available for tackling the skeletal muscle decline. These will probably find application in those individuals non-responding to lifestyle interventions.

Key words: Skeletal muscle, aging, geriatrics, physical function, muscle strength.


 

Introduction

“Sarcopenia” is a term referred to the progressive loss of skeletal muscle mass typically occurring with advancing age, as defined by Irwin Rosenberg in 1989 (1). Since then, the term has been used to more broadly embrace the age-related skeletal muscle decline, including both decrease in mass as well as reduction in strength and performance. To date, several definitions of sarcopenia have been proposed in the literature, and different consensus articles have tried to operationally frame this condition. Unfortunately, despite the fact that sarcopenia has even received a specific ICD-10 code in October 2016 (2), there is still no agreement in the scientific community about the gold standard definition to adopt for capturing this condition (3). Table 1 presents the most widely used definitions of sarcopenia currently available in the literature.

Table 1
Main definitions of sarcopenia

 

Sarcopenia still represents an underdiagnosed condition in daily practice, leaving untreated many cases amenable of interventions. Given the aging of the population, it is important that primary care physicians become familiar with the management of this condition for multiple reasons:
1) Detecting sarcopenia should be part of the routine visit due to the simplicity of the necessary tools and for the limited time required;
2) Sarcopenia is considered a reversible condition and can be contrasted by correct nutrition advices and personalized physical activity programs (4, 5);
3) Interventions directed against geriatric conditions, such as sarcopenia, are usually developed with long-term objectives (6), thus likely to involve the co-management by the primary care physician;
4) The management of a clinical condition, especially at advanced age, is strongly facilitated when the primary care physician (the one who best knows the clinical characteristics and behaviours of the patient) plays an active role;
5) Tackling sarcopenia is of primary importance in the community, where the vicious cycle of disability may still be amenable of reversion;
6) Recognizing sarcopenia in primary care may improve the design of the optimal care plan for the older person.

The present article is aimed at summarizing available evidence about the diagnosis and therapeutic process that can be activated for sarcopenia in primary care. The available diagnostic tools to recognize and quantify sarcopenia will be critically discussed. In particular, it will be considered that the operational definition of sarcopenia in primary care should be balanced to the limited availability of resources and time in this specific setting.

 

Prevalence, clinical relevance and costs

According to the World Health Organization (WHO), in 2050 there will be at least 2 billion persons aged 65 years or older, compared to the current 600 million. The increasing life expectancy is a worldwide demographic phenomenon, parallel to the growing number of persons affected by age-related chronic conditions (including sarcopenia).
In the absence of a gold standard for capturing sarcopenia, the estimate of its prevalence remains quite variable. Furthermore, the prevalence of sarcopenia is also highly influenced by the studied population and the setting where the condition is looked for, thus limiting the availability of single and reliable estimates. Nevertheless, a relatively robust evaluation of the phenomenon sets the prevalence of sarcopenia to be between 8.4% and 27.6% in community-dwelling older persons (7, 8), 14-33% in long-term care residents and 10% in acute hospital care population (9).
Sarcopenia is more likely to be present in men than in women and tends to increase with advancing age. Asians, persons with low body mass index, and those with low education represent other groups of people at higher risk of sarcopenia (7).
Sarcopenia has been associated with many negative health-related outcomes, including disability, poor physical function, falls, fractures, loss of independence, hospitalizations, institutionalization, and mortality. In patients with several comorbidities and clinical conditions (e.g., patients with cancer or undergoing surgery), sarcopenia has shown to represent a negative prognostic factor (10, 11).
Analyses conducted on Third National Health and Nutrition Examination Survey (NHANES III) database have calculated the direct costs of sarcopenia. Sarcopenia was found to cost about$18,5 billion ($10.8 billion in men, $7.7 billion in women) per year in the United States, and it represented about 1.5% of total direct health care costs calculated in the year 2000 (12). Reducing the prevalence of sarcopenia by 10% would result in about $1.1 billion savings per year. And this without considering the indirect costs of sarcopenia, such as the loss of productivity for the individual as well as for the eventual caregivers(12).Another example of how burdensome is sarcopenia for public health is brought by a Portuguese study showing that sarcopenia is independently related to hospitalization costs, independently of age. Sarcopenia was responsible for adding €884 per patient (95% confidence interval [95%CI] €295-€1,476) to hospital care costs, that represents a 58.5% increase. Again, these figures are likely underestimating the economic burden of sarcopenia because not taking into account the indirect costs (13).
In order to adequately tackle sarcopenia and prevent its detrimental consequences (for both the individual and the healthcare system), it is mandatory to design and implement an effective plan of action. In fact, it is important to preventively track sarcopenia when it is still reversible, and before its vicious cycle might cause the onset of frailty and disability.In this context, it is noteworthy that not everyone with sarcopenia is disable, but the condition substantially increases the risk of disability (14). Not surprisingly, sarcopenia is frequently considered as a condition to target for avoiding the most negative consequences of the disabling process. At the same time, the positioning of sarcopenia at the initial phases of physical dysfunction automatically indicates this as a condition of special interest for primary care professionals. In other words, the detection of sarcopenia (or, at least, the suspicion of it) in primary care might promote the implementation of successful interventions when the person is still independently living in the community.

 

Screening

It is recommended that adults aged 65 years and older should be screened annually for sarcopenia, or after the occurrence of major health events (falls, hospitalization).
It is also advisable screening older adults on the occasion of the first consultation or, for instance, at annual health check-up or flu vaccination appointments (15).
For the screening of sarcopenia in primary care, several instruments and methodologies have been developed over the years. It is generally recommended that the presence of sarcopenia should be suspected in every individual aged 65 years or older, presenting signs or symptoms suggestive of skeletal muscle impairment (3). A recent consensus paper promoting the identification and management of sarcopenia in primary care has proposed the so-called “Red Flag Method”(3) (Table 2). The purpose of this method is to generate alerts about those physical manifestations typically caused by sarcopenia that can be 1) reported by the subject, or 2) evaluated by the physician during the clinical assessment. In other words, the Red Flag Method may represent a sort of checklist for supporting the physician at the identification of several neglected signs, symptoms and conditions behind which sarcopenia might be hidden(3). The pedagogical value of the method should also be acknowledged. In fact, healthcare professionals may find in it a way for being trained at the clinical manifestation of sarcopenia, becoming more familiar with it, and introducing the process in the daily routine.

Table 2
The“red flag” method, SARC-F, and other instruments for the assessment of sarcopenia in the primary care setting

BIA: Body Impedance Analysis; DXA: Dual-energy X-ray Absorptiometry; CT: computed tomography; MRI: magnetic resonance imaging; SPPB test: Short Physical Performance Battery test; TUG: Time Up and Go.

 

Alternatives to formal/structured assessments might also be found in actions made by the individual during the clinical contact. For example, hints about the possible presence of sarcopenia might be provided by the strength of the individual’s handshake, his/her walking speed from the waiting room to the office, or observing how the person sits down and stands up from the chair.
If the Red Flag Method is based on a relatively long list of items to consider in the identification of possible sarcopenia, John Morley recently developed an ad hoc instrument (i.e., the SARC-F questionnaire) for a more rapid screening of the condition(16). SARC-F is the acronym of Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls. Each of these items receives a score ranging between 0 (absence of the sign) and 2 (inability or severe issue). A total score equal to or higher than 4 points is predictive of sarcopenia and poor health-related outcomes. The SARC-F can be used to identify individuals in the need of a more detailed and careful assessment of sarcopenia, and potentially lead to a more in-depth analysis of the case through the comprehensive geriatric assessment. Interestingly, in the revised version of the European recommendations for the definition and diagnosis of sarcopenia, designed by the European Working Group on Sarcopenia in Older People (EWGSOP), the use of SARC-F is suggested for the early identification of individuals amenable of further evaluation (17). This choice is motivated by the low sensitivity and high specificity of the instrument (17, 18).
Another opportunity for promoting the inclusion of the sarcopenia assessment in primary care can be found in a wider use of anthropometry. Although they would be useful to assess the body composition, the most commonly considered imaging methods might be unfeasible in primary care. Anthropometry (i.e., the measurement of body mass index, waist circumference, calf circumference, mid-upper arm circumference, and/or skinfold thickness) may provide easily applicable, inexpensive, and non-invasive techniques for identifying individuals at risk of presenting low muscle mass (19, 20). Recently, the Yubi-Wakka (finger-ring)test has also been proposed in this context. This is a simple self-screening method to quickly assess sarcopenia, comparing the calf circumference with the ring generated by the individual’s fingers (21).Table 2 lists several methods to be considered for the screening of sarcopenia in primary care.

 

Diagnosis

As mentioned, a gold standard definition to diagnose sarcopenia is today not yet available. In general, the available recommendations coming from different panels of experts and task forces tend to indicate the need of combining a quantitative dimension (capturing the skeletal muscle mass) and a qualitative one (assessing the skeletal muscle function). Whereas the assessments of skeletal muscle strength and/or physical performance are relatively easy to be conducted, the body composition evaluation might be challenging in the primary care setting. In fact, general practitioners may not have easy/immediate access to the suggested methodologies for measuring the skeletal muscle mass, or (at best) may have to rely on suboptimal techniques. For this reason, the accurate diagnosis of sarcopenia is likely to require the referral to specialized centres, where the dual energy X-ray absorptiometry (DXA) or other (more sophisticated) techniques (e.g., magnetic resonance imaging or computerized tomography) are available. At best, the quantification of the skeletal muscle mass in primary care might be estimated using the bioelectrical impedance analysis (BIA). This technique is inexpensive, easy to use, and readily reproducible, although its results might be inaccurate, especially in the presence of certain clinical conditions (e.g., in the presence of fluid retention).
Nevertheless, a lot can still be done in primary care to detect the sarcopenia condition. The identification of individuals with sarcopenia might also start by measuring some neglected signs or symptoms of muscular poor health, for example by formally and routinely testing muscle strength/performance. In this context, the routine adoption of the handgrip strength is widely recommended and relatively easy to implement in primary care and represents a cornerstone parameter for the diagnosis of sarcopenia (22–24). In case a dynamometer is not available, the Chair Stand Test can be a valid and reliable alternative for measuring the muscle strength (17).
It is likely that, in the next future, novel methodologies will be developed for supporting Physician to diagnose sarcopenia. One of themost promising ones is represented by the deuterated creatine (D3-creatine) dilution method, which is able to provide a direct quantification of the individual’s muscle mass via the ingestion of deuterium-marked creatine and is the only technique providing a direct and unbiased estimate of muscle mass.
Although its use is currently limited to the research setting(19), this method has relevant potential for diffusion in primary care because 1) based on the simple administration of a pill and a urine analysis (to be performed after 24-48 hours), and 2) overcoming the need of the above-mentioned diagnostic tools for body composition assessment.

 

Management

Primary care physicians may play a crucial role in the identification of individuals exposed to the risk of sarcopenia or already presenting this condition. They may preventively act providing recommendations for managing reversible risk factors (e.g., sedentary behavior, unhealthy diet) and eventually referring them to specialists for further evaluation.
To date, no pharmacological agent is available for the treatment of sarcopenia, but several molecules (at different stages of development) are in the pipelines of pharmaceutical industries. Thus, physical activity and nutritional interventions currently represent the basis of the clinical management of sarcopenia(25,26). Unfortunately, there is still a general lack of knowledge among healthcare professionals for correctly prescribing personalized interventions of physical activity and/or healthy diet.

Physical activity

The design ofa person-tailored physical activity program for tackling sarcopenia is not easy, especially if considering 1) the clinical complexity of older persons presenting this condition, and 2) the lack of adequate training that healthcare professionals may receive for this task during the curriculum of traditional study. Nevertheless, the beneficial effects that a physical exercise program may exert in frail and/or sarcopenic individuals is very well documented (27).
In general, multicomponent/combined exercise programs including aerobic activities, resistance training, and flexibility exercises are recommended. These should be proposed by primary care physicians to frail and/or sedentary community-dwelling persons as part of clinical routine (15). In this context, the material produced by VIVIFRAIL project is important to be mentioned (28). VIVIFRAIL was designed to provide support to primary care physicians in the prescription of personalized programs of physical activity. The program is based on a preliminary assessment of the individual’s physical performance, muscle strength, balance, and risk of falls. The results of such evaluation are then used to design an intervention that is tailored to the individual’s capacities and deficits. Importantly, VIVIFRAIL is designed for empowering the individual at monitoring his/her progresses (29). The VIVIFRAIL material is available at the project website (www.vivifrail.eu), and an app has also been developed for supporting the individual and the healthcare professionals.
Another project to be mentioned for its potential of reshaping the management of sarcopenia is “The Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies” (SPRINTT) study(30). This project, funded by the Innovative Medicines Initiative (IMI), is aimed to developing an operational definition of sarcopenia that might be acceptable by regulatory agencies. The project includes a randomized control trial designed to test the effects of a multidomain lifestyle intervention (mainly based on physical activity and nutritional counselling) on a condition combining physical frailty and sarcopenia. Interestingly, the target condition was theoretically framed in order to mirror the nosological conditions that are traditionally object of observation by regulatory agencies. The developed operational definition has been preliminarily endorsed by the European Medicines Agency before the beginning of the SPRINTT randomized controlled trial. At the end of the trial, investigators will be in the position of 1) estimating the prevalence of the novel condition in the general population, 2) ascertain the reversibility of the condition after implementation of lifestyle changes promoting healthy ageing, and 3) identify a subgroup of individuals resistant to the beneficial effects of physical activity and healthy diet. In particular, this latter point is of special interest because paving the way towards the profiling of future candidates to pharmacological interventions against sarcopenia (31).

Nutrition

Malnutrition is a condition due to a protein or other nutrient imbalance, responsible for negative effects on body composition, physical function, and clinical outcome. It plays a key role in the pathogenesis of sarcopenia and fragility. It is necessary to recognize malnutrition early in older adults to plan nutritional programs aimed at improving the outcome (32).
In hospital settings Nutrition Risk Screening-2002 (NRS-2002) or Malnutrition Universal Screening Tool (MUST) are used for the screening of malnutrition whereas Mini Nutritional Assessment (MNA) is considered the gold standard for the older adults hospitalized or in an outpatient setting. In the subject at risk of malnutrition, the evaluation of the nutritional status must be carried out.
These screening tools help to have a patient-centered approach, provide adequate nutritional advice, and monitoring nutritional status over time (33, 34).
An example of malnutrition prevention is the “Health Enhancement Program (HEP)”, a randomized trial with robust results. After an initial assessment conducted by a trained staff of each participant’s health and functional status, a personalized plan was carried out to counteract disability risk factors. The program consists in motivational strategies to promote behavioral changes in depression, poor nutrition, and a sedentary lifestyle. At one year follow up, compared with enrollment, a reduction of risk factors was registered (35).
An attempt of intervention in frail older adults in a clinical setting is the program of the Geriatric Frailty Clinic (G. F. C.) at the Gerontopole of Toulouse. Older adults, considered as frail by their General Practitioner, underwent a multidisciplinary evaluation at the G.F.C where the team members proposed a Personalized Prevention Plan (PPP); in case of malnutrition, detected by the MNA, a nutritionist was asked for improve dietary intake with specific recommendations. A follow-up, consisted of a nurse call after one month and three months, was organized to determine the intervention’s efficacy. After one year the Geriatrician reassessed the patient’s improvements with a multidisciplinary evaluation (36).
Recently, two consensus papers (promoted by the European Society for Clinical Nutrition and Metabolism and the PROT-AGE study group) agreed that people aged 65 years or older require a higher intake of proteins compared to what usually recommended for activating muscle protein synthesis and maintaining muscle health. Therefore, both groups recommended the assumption of at least 1–1.2 g of proteins/kg/day in older persons, pushing even higher this minimum threshold in the presence of catabolic or muscle wasting conditions (37, 38).
About the quality of proteins, essential amino acids (EAAs; in particular leucine) are recognized as providing an important anabolic stimulus. In fact, leucine is able to increase muscle protein synthesis in older people, as also confirmed in a recent meta-analysis. In fact, its consumption has been found to be directly correlated with muscle mass in healthy older people (39).
β-hydroxy β-methylbutyrate (HMB) is one of the metabolites of leucine that is able to exert anabolic effects. HMB is frequently used by athletes to improve their physical performance and has also showed promising results in improving muscle mass and strength in older adults. When applied to bed resting older people, HMB stimulated muscle mass preservation. HBM supplementation combined with exercise seems to promote the regenerative capacity of skeletal muscles (25).
For what concerns vitamin D, its supplementation is surely useful for correcting states of insufficiency or deficiency (40, 41). Nevertheless, no evidence supports its use in individuals with normal vitamin D concentrations for improving muscle health.

Drugs

No drugs are currently registered for use in the treatment of sarcopenia, and no pharmacological intervention can be accepted as first-line therapy of sarcopenia (15). However, several new molecules are currently under study at various stages of development. It is noteworthy the special interest devoted by regulatory agencies in this field. Both the Food and Drugs Administration and the European Medicines Agency are paving the way for structuring pharmacological research on this topic.
Despite the urgency of the problem, the development of pharmaceutical therapies for sarcopenia and frailty has lagged, in part because of the lack of consensus definitions for the two conditions. In 2015,an experts’ group gathered during the International Conference on Frailty and Sarcopenia Research (ICSFR) to discuss challenges related to drugs designed to the target the biology of frailty and sarcopenia (8).
Based on the available evidence, myostatin antagonists, like Bimagrumab, may be promising candidates to treat people with low lean muscle mass, in particular people older than 70 years. Bimagrumab is a monoclonal antibody that blocks the binding of myostatin to activin, thus blocking its negative regulation of muscle growth (42). Young men treated with a single dose of Bimagrumab may experience an increase in muscle mass similar to that induced by 12 week of high-intensity resistance training(43,44), while sedentary adults may receive a benefit equivalent to 9 months of jogging 12-20 miles per week (45).
Researchers are also focused on selective androgen receptor modulators (SARMs). These are a class of androgen receptor ligands that increase low lean muscle mass by binding to the androgen receptor in muscles. Different molecules have already undergone phase I, II and III trials, but at the moment longer studies are required to demonstrate the long-term safety and the efficacy of these drugs (8).
Inflammatory modulators, such as those acting on the tumour necrosis factor-α (TNFα) and interleukin-1 (IL1), are also under study. Systemic inflammation and the increasing of TNFα and IL1 in blood lead to muscle atrophy (46). Inflammatory modulators could limit the reduction of skeletal muscle by reducing pro-inflammatory cytokines.

 

Conclusions

Sarcopenia is the age-related progressive decline of skeletal muscle. It is a common age-related condition, and has a relevant impact on the person’s quality of life and functioning, as well as on healthcare costs.
Primary care physicians may play a pivotal role in the identification of the risk of sarcopenia in the aged population. Indeed, the primary care physician may detect the early manifestations of this condition and lead to its fast diagnosis and care. In this framework, multiple instruments have been developed for promoting the detection of sarcopenia in primary care. Once sarcopenia is identified, a comprehensive assessment of the individual may lead to person-tailored interventions based on nutritional counselling and physical activity programs. In the next future, the availability of pharmacological therapies could be able to prevent the skeletal muscle decline in those individuals resistant to the benefits of healthy lifestyle prescriptions.

 

Conflicts of Interest: Matteo Cesari received honoraria from Nestlé for presentations at scientific meetings and as member of scientific advisory boards. No other conflict of interest declared by the Authors.

 

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42. Rooks D, Praestgaard J, Hariry S, et al. Treatment of Sarcopenia with Bimagrumab: Results from a Phase II, Randomized, Controlled, Proof-of-Concept Study. J Am Geriatr Soc. 2017;65(9):1988-1995. doi:10.1111/jgs.14927.
43. Davidsen PK, Gallagher IJ, Hartman JW, et al. High responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expression. J Appl Physiol. 2011;110(2):309-317. doi:10.1152/japplphysiol.00901.2010.
44. Hudelmaier M, Wirth W, Himmer M, Ring-Dimitriou S, Sänger A, Eckstein F. Effect of exercise intervention on thigh muscle volume and anatomical cross-sectional areas–quantitative assessment using MRI. Magn Reson Med. 2010;64(6):1713-1720.
45. Durheim MT, Slentz CA, Bateman LA, Mabe SK, Kraus WE. Relationships between exercise-induced reductions in thigh intermuscular adipose tissue, changes in lipoprotein particle size, and visceral adiposity. Am J Physiol-Endocrinol Metab. 2008;295(2):E407-E412. doi:10.1152/ajpendo.90397.2008.
46. Ebner N, Steinbeck L, Doehner W, Anker S, von Haehling S. Highlights from the 7th Cachexia Conference: muscle wasting pathophysiological detection and novel treatment strategies. J Cachexia Sarcopenia Muscle. 2014;5(1):27-34.
47. Cruz-Jentoft A, Baeyens J, Bauer J, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412-423. doi:10.1093/ageing/afq034.
48. Fielding R, Vellas B, Evans W, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc. 2011;12(4):249-256. doi:10.1016/j.jamda.2011.01.003.
49. Morley J, Abbatecola A, Argiles J, et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc. 2011;12(6):403-409. doi:10.1016/j.jamda.2011.04.014.
50. Studenski S, Peters K, Alley D, et al. The FNIH Sarcopenia Project: Rationale, Study Description, Conference Recommendations, and Final Estimates. J Gerontol Ser A. 2014;69(5):547-558. doi:10.1093/gerona/glu010.

“SAY NINETYNINE”: IT’S NEVER TOO LATE TO RECOVER FROM COVID-19

 

M. Tosato1, F. Varone1, A. Ciccullo1, R. Calvani1, D. Moschese1, A. Potenza1, M. Siciliano1, M. Fantoni1

 

1. Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy; ORCID: https://orcid.org/0000-0001-5750-9746.
Corresponding author: Matteo Tosato, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy, email: matteo.tosato@policlinicogemelli.it

J Frailty Aging 2021;10(1)70-71
Published online August 7, 2020, http://dx.doi.org/10.14283/jfa.2020.41

 


Abstract

COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, showed higher severity and lethality in male older adults . There are currently no specific treatments. Studies are evaluating the efficacy of monoclonal antibodies against interleukin-6 receptor. Here we present the case of a 98-years old man admitted to our COVID-Hospital with acute respiratory failure. Comprehensive geriatric assessment showed no signs of frailty. First-line therapy with hydroxychloroquine and anticoagulants was not effective. Patient was administered intravenous monoclonal antibodies, and he showed remarkable clinical improvement. This case suggests that age alone should not preclude access to new therapeutic approaches. Comprehensive, multisciplinary, multidomain approaches are needed to develop patient-tailored treatments against COVID-19.

Key words: Frailty, covid-19, comprehensive geriatric assesment, aging.


 

Introduction

In late 2019, a novel betacoronavirus (SARS-CoV-2), emerged in the province of Hubei, China. The disease related to SARS-CoV-2, named COVID-19, rapidly spread all over the world and was declared a global pandemic by the World Health Organization (WHO). While the majority of cases present with no or mild respiratory symptoms, about 20% of the cases could develop severe forms of the disease, with life-threatening complications such as respiratory failure and multiorgan dysfunction (1). Fatality rates vary between studies, but large cohort observational studies reported a greater disease severity in older patients (1-2).
We have recently observed a demographic shift towards older ages in COVID-19 patients admitted to our COVID Hospital. This phenomenon was partly due to newly discovered outbreaks in nursing homes and rehabilitation units. Older patients present specific characteristics, such as frailty, multimorbidity, polypharmacotherapy, and other conditions that render their management particularly difficult. Their unmet clinical needs represent the “new challenge” in COVID-19 pandemic.
Hereby we report a case of severe COVID-19 pneumonia in an old man admitted to our COVID-hospital in Rome, Italy.

 

Case representation

A 98-years old man was admitted to our hospital on April 15th, following a 2-day history of cough and dyspnea. The patient came from a rehab-unit where he was admitted following hip fracture. He was independent in daily activities before hospital admission and not frail (Clinical Frailty Scale score was 3) (3). Chronic obstructive pulmonary disease, hypertension, chronic lymphocytosis and benign prostatic hypertrophy were also present in his medical history. Blood tests at admission showed C-Reactive Protein 221.7 mg/L (Reference Value [RV] <5.0), D-dimer 4425 ng/mL (RV <500), ferritin 1286 ng/mL (RV 21-275), IL-6 105.8 ng/L (RV <4.4). Chest X-rays documented signs of interstitial pneumonia (Figure 1). Patient presented in severe clinical conditions, hypoxemic, requiring oxygen supplementation with a FiO2 of 0.6. Patient was treated immediately with hydroxychloroquine and anticoagulant therapy with no clinical benefits. At day 5, he was administered intravenous sarilumab (400 mg as single dose). The day after, patient started showing remarkable clinical improvement. Over the following weeks, we observed a full consciousness recovery and improvement in respiratory function, with progressive reduction of oxygen supplementation. On May 8th he was discharged from the hospital after blowing out his 99 candles.

Figure 1
Patient’s chest X-rays at hospital admission

 

Discussion

As of 9 May 2020, the current COVID-19 pandemic has already caused over 270,000 deaths worldwide (4). Older patients are more vulnerable to COVID-19 as witnessed by their higher hospitalization and mortality rate; in particular, institutionalized older persons appear particularly at risk of experiencing negative outcomes. COVID-19 has now become the main challenge in geriatric care. Thus, geriatricians are increasingly recognized as key figures in multidisciplinary hospital teams dealing with the COVID-19 pandemic (5). There are currently no specific treatments available for COVID-19. Several studies are underway to evaluate the efficacy of monoclonal antibodies against the interleukin-6 receptor (tocilizumab and sarilumab) in mitigating the cytokine cascade and improving the clinical course of the disease. However, older adults are usually excluded from clinical trials and therefore are at risk, during this pandemic phase, of not having access to the treatments being studied. In our case, a 98-years old patient optimally responded to off-label sarilumab with marked improvement in clinical conditions and no adverse reactions reported. Although clinical trials will be necessary to assess safety and efficacy of sarilumab in the treatment of COVID-19, our finding may be promising.

 

Conclusion

This case suggests that age alone should not preclude access to new therapeutic approaches. Comprehensive, multisciplinary, multidomain approaches assessing, among others, comorbidity burden and frailty status, are needed to develop patient-tailored treatments against COVID-19.

 

Funding: This case report received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest.
Ethical approval: Not applicable. No research study involved.
Statement of human and animal rights: All procedures performed in the study were in accordance with the ethical standards of the institutional or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards and conformed to the Declaration of Helsinki on human research.
Informed consent: The patient included in the study gave written informed consent for the publication.

 

References

1. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention [published online ahead of print, 2020 Feb 24]. JAMA. 2020;10.1001/jama.2020.2648.
2. Chen R, Liang W, Jiang M, et al. Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China [published online ahead of print, 2020 Apr 15]. Chest. 2020;S0012-3692(20)30710-8. doi:10.1016/j.chest.2020.04.010
3. Rockwood K, Song X, MacKnight C, Bergman H, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005 Aug 30;173(5):489-95.
4. John Hopkins University – Coronavirus resource center. Accessed on May 9th. Available at https://coronavirus.jhu.edu/
5. Landi F, Barillaro C, Bellieni A, et al. The New Challenge of Geriatrics: Saving Frail Older People from the SARS-COV-2 Pandemic Infection. J Nutr Health Aging. 2020;24(5):466-470. doi:10.1007/s12603-020-1356-x

CHARACTERIZING INTERVENTION OPPORTUNITIES AMONG HOME-DELIVERED MEALS PROGRAM PARTICIPANTS: RESULTS FROM THE 2017 NATIONAL SURVEY OF OLDER AMERICANS ACT PARTICIPANTS AND A NEW YORK CITY SURVEY

 

M. El Shatanofy1, J. Chodosh1,2,3, M.A. Sevick1,2, J. Wylie-Rosett4,  L. DeLuca5, J.M. Beasley1

 

1. Department of Medicine, New York University School of Medicine, New York, New York, USA; 2. Department of Population Health, New York University School of Medicine, New York, New York, USA; 3. VA New York Harbor Healthcare System, New York, New York, USA; 4. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA; 5. Department of Psychology, Ferkauf Graduate School, Bronx, New York, USA.
Corresponding author: Jeannette M. Beasley, PhD MPH RD, Assistant Professor, Division of General Internal Medicine and Clinical Innovation, NYU School of Medicine, 462 First Avenue, 6th Floor CD673, New York, NY 10016, T: 646-501-4681, jeannette.beasley@nyumc.org

J Frailty Aging 2020;9(3)172-178
Published online May 11, 2020, http://dx.doi.org/10.14283/jfa.2020.25

 


Abstract

Background: The Home Delivered Meals Program (HDMP) serves a vulnerable population of adults aged 60 and older who may benefit from technological services to improve health and social connectedness. Objective: The objectives of this study are (a) to better understand the needs of HDMP participants, and (b) to characterize the technology-readiness and the utility of delivering information via the computer. Design: We analyzed data from the 2017 NSOAAP to assess the health and functional status and demographic characteristics of HDMP participants. We also conducted a telephone survey to assess technology use and educational interests among NYC HDMP participants. Measurements: Functional measures of the national sample included comorbidities, recent hospitalizations, and ADL/IADL limitations. Participants from our local NYC sample completed a modified version of the validated Computer Proficiency Questionnaire. Technology readiness was assessed by levels of technology use, desired methods for receiving health information, and interest in learning more about virtual senior centers. Results: About one-third (32.4%) of national survey HDMP participants (n=902) reported insufficient resources to buy food and 17.1% chose between food or medications. Within the NYC HDMP participant survey sample (n=33), over half reported having access to the internet (54.5%), 48.5% used a desktop or laptop, and 30.3% used a tablet, iPad, or smartphone. Conclusion: The HDMP provides an opportunity to reach vulnerable older adults and offer additional resources that can enhance social support and improve nutrition and health outcomes. Research is warranted to compare technological readiness of HDMP participants across urban and rural areas in the United States.

Key words: Home-delivered meals program, aging, nutrition, health behaviors, technology.


 

Introduction

The Home-Delivered Meals Program (HDMP) is a public-private partnership dedicated to reducing hunger and isolation among older adults and supports over 5,000 community-based senior nutrition programs nationwide (1). The Older Americans Act (OAA) Title III, federal legislation first passed in 1965, provides nutrition programming that includes both congregate and home-delivered meals for adults aged 60 and over. In 2018, 225 million home-delivered meals were provided to 2.4 million older adults (2).
The HDMP provides more than just daily nutrition to older adults. It is designed to improve food security, social connectedness, and health care utilization. Recipients of home-delivered meals report that nutrition programs are essential to helping them remain in their communities; however, there is still a gap to be filled (3, 4). Compared to both congregate meal recipients and the general public, home-delivered meal recipients are more likely to self-report worsening health over the past twelve months, self-rate their health as fair to poor, and have five or more medical conditions (5). Assessing HDMP participants’ health status and interest in further assistance is important to addressing the mismatch between services and outcomes. If the mismatch is exacerbated by limited access to services, then perhaps technology can help us tailor interventions with high potentials for scaling up (6).
While technology has been used in interventions to alleviate undernutrition in different age groups, older adults are often excluded from such projects because they are assumed to lack the basic technological skills (7, 8). Data suggest that there is a digital divide between older adults and the rest of the population as well as within the population of adults aged 65 and older (8), but that difference is shrinking (9). Between 2000 and 2016, internet use among a nationally representative sample of older adults rose from 14% to 67% (9). From 2013 to 2016, ownership of a smartphone also rose from 18% to 42% and use of social networking sites like Facebook or Twitter rose from 27% to 34% (9). Overall, use of the internet, smartphones, tablets, and social media among older adults have grown over the past two decades; however, the pace of growth is slower among HDMP participants (16, 17). In order to develop useful interventions for HDMP participants, it is important to understand what health behaviors older adults are interested in learning about and how they would prefer to receive this information.
There is limited knowledge on how HDMP participants would respond to combined nutritional support and technological interventions (7). This is important because undernutrition impedes healthy aging, aspects of daily living, and has been associated with increased morbidity and mortality (10-13). To improve older adults’ overall health, we need to understand barriers to proper diet quality and access to food. The purpose of this paper is to characterize the health and functional status of a national sample of HDMP participants and to characterize the technology-readiness of a local sample of NYC HDMP participants.

 

Methods

Cross-sectional data from the 2017 National Survey of Older Americans Act Participants (NSOAAP) (n=902) were used to characterize the health and functional status and demographic characteristics of HDMP participants. The NSOAAP is a telephone survey that has been conducted annually since 2005 (14). Its goal is to evaluate the effectiveness of home-delivered and congregate meals, transportation, case management, and other programs on aging funded by Title III of the Older Americans Act. The NSOAAP is conducted using a two-stage sampling design and sample weighting to achieve data output based on a representative sample of HDMP participants. Base weights are computed by taking the inverse of the selection probability for each sampled participant, then adjusting for non-response, trimming the extreme weights, and completing a post-stratification adjustment using available control totals.
To characterize technology use, educational interests, and preferred methods for receiving health information among older adults, we also completed a telephone survey with a random sample of NYC HDMP participants. Encore Community Services, a program that provides a range of social, recreational, and educational activities for older adults as well as preparing, packing, and delivering home-delivered meals, provided the home phone numbers and cell phone numbers for 109 HDMP participants. Five attempts between 9 am and 2 pm were made to reach each participant (Figure 1). We tried contacting each participant by house phone first and then cell phone. Not all participants provided both house and cell phone numbers. Of the 79 people who answered the phone, 41.8% (n=33) provided verbal consent to participate in the survey. Four people could not complete the survey due to language barriers and three people could not participate due to reported cognitive problems such as dementia. Four surveys were incomplete due to missing/refused responses for questions on educational interests and demographics.

Figure 1
Flow chart of Local New York City technology survey responses

 

To assess the impact of computer and internet access and training on the well-being of older adults with limited computer experience, we administered a modified version of the Computer Proficiency Questionnaire (CPQ). The original CPQ contains 33 questions grouped into 6 subscales: computer basics, printing, communication, internet, scheduling software, and multimedia use (15). The CPQ was shortened to prevent respondent fatigue, as many participants did not want to answer questions for more than ten minutes. We piloted the modified CPQ among 10 HDMP recipients and made edits to arrive upon a 26-item survey that focused on technology use and methods of receiving health information. Data were analyzed using SPSS (Version 25, IBM Corp., Armonk, NY).

 

Results

National (US) Data

The NSOAAP sample was largely white, high school-educated women who were living alone, with nearly a third being 85 years of age and older (Table 1). A substantial number of respondents reported comorbidities; almost three-quarters reported hypertension and arthritis, half reported hyperlipidemia, about two-fifths had heart disease, and over a third reported diabetes. Health care utilization was also common, with a third reporting a hospital stay in the past year. Nonetheless, more than half of the sample described their health as good or better.
Four-fifths of participants reported at least one limitation with activities of daily living and nearly one-third reported three or more limitations (Table 2). Most commonly, participants reported difficulty walking (67.2%), followed by bathing (37.4%) and bed/chair transfer (33.7%). Furthermore, half of the participants reported three or more limitations with instrumental activities of daily living. Most commonly, participants reported difficulties going outside the home (53.3%), preparing meals (43.5%), and doing light housework (43.0%).

Table 1
Home-Delivered Meals Participant Characteristics, National and Local Level

Note. Weighted to account for the sampling design within the nationwide sample. Some participants selected more than one race, so percentages do not add up to 100%.

 

Over two-thirds of participants reported having enough resources to buy food (67.6%), and 14.8% skipped meals due to inadequate resources over the past month (Table 2). More than four-fifths of participants reported that the home-delivered meals helped them live independently (82.3%), feel more secure (82.2%), and feel better able to care for themselves (81.2%; see Table 2).

 

Table 2
Health and Functional Status of Nationwide HDMP Participants (n=903), 2017

Note. Weighted to account for the sampling design within the nationwide sample. ADL=activities of daily living; IADL=instrumental activities of daily living.

 

Local New York City (NYC) Data

The local NYC sample was mostly comprised of white women who were living alone, with nearly a third being 65 to 74 years old (Table 1). The mean body mass index was 26.6 ± 5.7 kg/m2 per self-reported height and weight. Most participants were classified as overweight or obese (55.5%), while 40.7% had a BMI in the normal weight range. No participants reported “excellent” self-perceived general health or self-perceived diet quality. Similar to the national sample, the most common response for self-perceived general health and self-perceived general diet was “good” (33.0% for the national sample and 37.9% for the local sample; see Table 1).

Almost half of the participants reported finding information about health on the internet (Table 3), but this rose to 88.9% among the subset of participants having access to the internet (n=18). Less than one-fifth of participants said that they use a computer for activities such as entering events into a calendar, video chatting with others via web-cam, or posting messages to social media. Nearly one-third of participants said that they use a tablet such as an iPad (30.3%; see Table 3). Similarly, about one-third of participants said that they use a smartphone (30.3%; see Table 3). Almost half of the participants indicated that they did not use any types of computers. All 16 people who reported computer use said that they use desktops or laptops.
Most participants reported that they would like to receive their health information from in-person (home or office) visits with a health professional (90.0%; see Table 3). Other desired methods for receiving health information included: telephone calls with a health professional (63.3%), email (36.7%), and videos through computer, smartphone, or iPad (36.7%). Almost three-quarters of participants reported other desired methods for receiving health information. Responses were recorded and grouped into three categories (Table 3): mail (33.3%), media (television/newspapers/newsletters) (23.3%), and peers and family members (26.7%).

Table 3
Technology Use Among Local NYC HDMP Participants,
n (%)

Note. Sample size differences are due to missing survey responses.

 

Over half of the participants reported educational interests in exercise, improving sleep, meeting new people, and virtual senior centers (58.1%, 54.8%, 51.6%, and 55.2%, respectively; see Table 4), but only one-quarter wanted to learn more about losing weight (25.8%; see Table 4). Others (n=14; see Table 4) wanted to learn more about medical problems such as arthritis, knee replacements, prosthetics, gastritis, irritable bowel syndrome, neuropathy, strokes, Parkinson’s, and memory problems like Alzheimer’s. Interest in diabetes was also common, with 38.7% of participants reporting that they would like to learn more about the disease (Table 4).

Table 4
Educational Interests Local NYC HDMP Participants, n (%)

Note. Sample size differences are due to missing survey responses.

 

Discussion

This study expanded research on the demographics of HDMP participants in the United States as well as technology use and educational interests among HDMP participants in the NYC area. More than half of the nationwide participants self-rated their health as good or better and about half of the NYC participants (n=31) reported an interest in learning more about healthy eating, improving sleep, and exercise. In addition, 94% of the participants surveyed in NYC described an interest in learning more about one or more health topics. The purpose of this study was to analyze the demographics of HDMP participants and to identify preferred methods for receiving health information among a local NYC sample. Our local survey suggests that more than half of older adults are interested in learning more about technological services such as virtual senior centers, and barriers for internet access could be explored and addressed among those lacking internet access.
Many scholars have advocated for the expansion of virtual-based senior centers to help older adults age in place (16, 17). Before expanding these services, however, we need to understand how familiar older adults are with technology and how willing they are to learn about virtual senior centers. In our local sample, almost 90% of those who had access to the internet said that they found information about health online. Some participants said that they were interested in learning more about technologies but expressed concerns over the user-friendliness and affordability of certain devices. Most concerns were shared as side-notes during our telephone interviews. Other concerns, which have been reported in past research, include low self-efficacy among disabled older adults and sensory and ergonomic problems that hinder ease of use (16, 17). Among older adults with cognitive impairments, there is also a greater risk of unintentionally violating privacy rights through technology-assisted health care services (18).
Two recent studies in the Netherlands have underscored the value of using technology to help older adults at risk of malnutrition. Lindhardt and Nielsen (7) completed a quasi-experimental study to better understand the effects of combining technology and nutritional support for older adults and found that older adults at nutritional risk experienced better strength, intake, appetite, and relationships with family after receiving enriched meals for 12 weeks after discharge and using a tablet for goal setting, self-monitoring, and feedback. In a similar study, van Doorn-van Atten, de Groot, Romeaet al. (19) also found that older adults at risk of undernutrition showed improvements in nutrition after undergoing a home dietary monitoring intervention comprised of tele-monitoring and nutrition education. Taken together, these studies suggest that technology use can address more than just nutritional needs among older adults. It can leverage solutions to poor diets, health problems, and social isolation.
To rapidly scale up successful interventions and improve connectedness among older adults, we should consider how technological services could be integrated with the HDMP (20-24). Past studies have shown that, separately, home-delivered meals and technologies can help older adults age in place (3, 4, 20-24). Combined, home-delivered meal services and technologies can work synergistically to help older adults attain better overall health outcomes. The goal is to improve access to health information and the growing number of telehealth and telemental health interventions as well as to encourage participation in free chronic disease self-management programs and support groups (16, 18, 23, 25, 26).
One limitation to integrating technological services with the HDMP is the insufficient funding of the HDMP by the OAA (5, 27). The OAA covers less than a quarter (23%) of the total cost to provide meals, safety checks, and visits to over 174,000 seniors (28). Adjusted for inflation, federal funding has decreased by 19% while the population of older adults has increased by 34% over the past 20 years (28). Consequently, many programs across the United States have experienced growing waiting lists that are disproportionately comprised of widowed, less educated, older, Black, Hispanic, and Medicaid-receiving seniors (29).
We acknowledge that this study had several limitations. First, due to the low response rate of the telephone survey, it is unclear whether the local sample represents the broader community of HDMP participants across the United States. We cannot generalize our findings to rural populations across the United States, which may have lower access to the internet and therefore lower levels of computer literacy. In the future, technology use among urban and rural populations in the United States should be compared. Since rural areas tend to have fewer modes of transportation than urban areas, we predict that increasing computer use in rural areas will improve connectivity among peers and medical professionals.
Another limitation of this study was the lack of questions focused on attitudes toward computer/internet use. These questions would have enhanced our understanding of how older adults perceive the usefulness of technology for health management. In addition, responses to the national and local surveys were self-reported, and self-reported responses to health and healthcare utilization tend to be affected by recall and social desirability response bias.
Future work should incorporate weekly diaries of technology use and sample more representative groups within NYC and across the United States. This would help us target where technology could contribute the greatest health benefits (20-24). Partnering with existing social programs such as the HDMP can enhance services through technology training and supportive health interventions. Ultimately, this can help us provide the most vulnerable members of our society the care they deserve.

 

Conclusion

Data from our national sample of older adults revealed multiple comorbidities and ADL/IADL limitations such as going outside the home, but data from our NYC survey suggest that HDMP participants could benefit from technological interventions that could support nutrition, social connectedness, and healthy aging. Past studies have shown that technological interventions can improve access to health information; however, technology use among older adults, particularly HDMP participants, has been lagging (9, 30-33). Future work should compare technology use among different populations of HDMP participants in the United States and explore how additional supports, such as videoconferencing, could improve health outcomes and maintenance of positive behaviors.

 

Ethics approval and consent to participate: This study was approved by the Institutional Review Board at New York University Langone School of Medicine. All participants provided verbal informed consent.
Availability of data and material: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publication of this paper.
Funding: This study was funded by the New York Center for Diabetes Translational Research (P30DK111022-01).
Acknowledgements: The authors appreciate the contributions of New York City’s Department for the Aging in general, and Jose L. Sanchez from Encore Community Services in particular, for their assistance with this project.

 

Supplementary material1

Supplementary material2

 

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21. Coughlin JF. Technology needs of aging boomers. Issues in Science and Technology 1999 [Available from: https://issues.org/coughlin/.
22. Goldwater J, Harris Y. Using technology to enhance the aging experience: A market analysis of existing technologies. Ageing International. 2011;36(1):5-28.
23. Koch S. Healthy ageing supported by technology–a cross-disciplinary research challenge. Inform Health Soc Care. 2010;35(3-4):81-91.
24. Satariano WA, Scharlach AE, Lindeman D. Aging, place, and technology: toward improving access and wellness in older populations. J Aging Health. 2014;26(8):1373-89.
25. Wilcox ME, Adhikari NK. The effect of telemedicine in critically ill patients: systematic review and meta-analysis. Crit Care. 2012;16(4):R127.
26. van den Berg N, Schumann M, Kraft K, Hoffmann W. Telemedicine and telecare for older patients–a systematic review. Maturitas. 2012;73(2):94-114.
27. Shan M, Gutman R, Dosa D, et al. A New Data Resource to Examine Meals on Wheels Clients’ Health Care Utilization and Costs. Med Care. 2019;57(3):e15-e21.
28. Ziegler J, Redel N, Rosenberg L, Carlson B. Older Americans Act Nutrition Programs Evaluation: Meal Cost Analysis. Administration for Community Living. 2015:1-31.
29. Thomas KS, Smego R, Akobundu U, Dosa D. Characteristics of Older Adults on Waiting Lists for Meals on Wheels: Identifying Areas for Intervention. J Appl Gerontol. 2017;36(10):1228-42.
30. Peels D, Mudde A, Bolman C, Golsteijn R, de Vries H, Lechner L. Correlates of the intention to implement a tailored physical activity intervention: perceptions of intermediaries. International journal of environmental research and public health. 2014;11(2):1885-903.
31. Peels DA, Bolman C, Golsteijn RH, de Vries H, Mudde AN, van Stralen MM, et al. Long-term efficacy of a printed or a Web-based tailored physical activity intervention among older adults. Int J Behav Nutr Phys Act. 2013;10:104.
32. Peels DA, Hoogenveen RR, Feenstra TL, et al. Long-term health outcomes and cost-effectiveness of a computer-tailored physical activity intervention among people aged over fifty: modelling the results of a randomized controlled trial. BMC public health. 2014;14:1099.
33. Lukasik S, Tobis S, Wieczorowska-Tobis K, Suwalska A. Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users. Int J Environ Res Public Health. 2018;15(11).

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VASTUS LATERALIS MOTOR UNIT RECRUITMENT THRESHOLDS ARE COMPRESSED TOWARDS LOWER FORCES IN OLDER MEN

 

R.M. Girts1, J.A. Mota2, K.K. Harmon1, R.J. MacLennan1, M.S. Stock1

 

1. Neuromuscular Plasticity Laboratory, School of Kinesiology and Physical Therapy, University of Central Florida, Orlando, FL, USA; 2. Department of Exercise and Sports Science, University of North Carolina at Chapel Hill, USA.
Corresponding author: Matt S. Stock, Ph.D., School of Kinesiology & Physical Therapy, University of Central Florida, 12805 Pegasus Drive, HPA 1 – Room 258, Orlando, FL 32816-2205, Phone: (407) 823-0364, Fax: (407) 823-2596, E-mail: matt.stock@ucf.edu
J Frailty Aging 2020;9(4)191-196
Published online April 16, 2020, http://dx.doi.org/10.14283/jfa.2020.19

 


Abstract

Background: Aging results in adaptations which may affect the control of motor units. Objective: We sought to determine if younger and older men recruit motor units at similar force levels. Design: Cross-sectional, between-subjects design. Setting: Controlled laboratory setting. Participants: Twelve younger (age = 25 ± 3 years) and twelve older (age = 75 ± 8 years) men. Measurements: Participants performed isometric contractions of the dominant knee extensors at a force level corresponding to 50% maximal voluntary contraction (MVC). Bipolar surface electromyographic (EMG) signals were detected from the vastus lateralis. A surface EMG signal decomposition algorithm was used to quantify the recruitment threshold of each motor unit, which was defined as the force level corresponding to the first firing. Recruitment thresholds were expressed in both relative (% MVC) and absolute (N) terms. To further understand age-related differences in motor unit control, we examined the mean firing rate versus recruitment threshold relationship at steady force. Results: MVC force was greater in younger men (p = 0.010, d = 1.15). Older men had lower median recruitment thresholds in both absolute (p = 0.005, d = 1.29) and relative (p = 0.001, d = 1.53) terms. The absolute recruitment threshold range was larger for younger men (p = 0.020; d = 1.02), though a smaller difference was noted in relative terms (p = 0.235, d = 0.50). These findings were complimented by a generally flatter slope (p = 0.070; d = 0.78) and lower y-intercept (p = 0.009; d = 1.17) of the mean firing rate versus recruitment threshold relationship in older men. Conclusion: Older men tend to recruit more motor units at lower force levels. We speculate that recruitment threshold compression may be a neural adaptation serving to compensate for lower motor unit firing rates and/or denervation and subsequent re-innervation in aged muscle.

Key words: Motor unit, recruitment threshold, aging, electromyography.


 

Introduction

In 1977, Tomlinson and Irving observed drastic morphological differences in the spinal cords of older adults, noting that individuals over the age of 60 possessed significantly fewer functioning motor neurons compared to younger adults (1). More recently, age-related changes to the structure and function of the neuromuscular system have garnered significant interest due to the implications for independence and quality of life of older adults (2), a growing percentage of the population (3). Age-related impairments in the neuromuscular system include reductions in maximal motor unit firing rates (4), the inability to voluntarily activate muscle (5), and corticospinal hypoexcitability among weak older adults (6). These changes may be accompanied by neurodegenerative processes, resulting in motor neuron loss, leaving corresponding muscle fibers denervated and orphaned (7). Many of these fibers are subsequently reinnervated by remaining neighboring motor units (8). How motor unit recruitment patterns may be affected by this process is not completely understood.
There is evidence suggesting that the force level at which a motor unit is recruited (i.e., recruitment threshold) provides insight into the control of the neuromuscular system. For example, motor unit recruitment thresholds progressively decrease as a muscle fatigues as a compensatory mechanism to offset decline in twitch force amplitude (9). To further our understanding of the aging neuromuscular system, we set out to examine differences in motor unit recruitment thresholds for younger versus older men. Using recently developed surface electromyographic (EMG) signal decomposition algorithms [10], we observed that vastus lateralis motor unit recruitment thresholds are compressed towards lower forces in older men. We propose that recruitment threshold compression may be a compensatory strategy used by the central nervous system to control force output following denervation and subsequent reinnervation of aged muscle.

 

Methods

Participants

Twelve younger (mean ± SD age = 25 ± 3 years, BMI = 22.0 ± 1.9 kg/m2) and twelve older (age = 75 ± 8 years, BMI = 25.9 ± 3.1 kg/m2) men completed this study. Participants cleared a health screening, medical history, and physical activity readiness questionnaire (PAR-Q) before enrollment. Individuals were excluded if they exceeded a body mass index (BMI) of 30 kg/m2, presented metabolic or neuromuscular dysfunction, been diagnosed with knee osteoarthritis, required the use of a walking device, or had lower extremity surgery within the previous year. During the six months prior to enrolling, participants refrained from lower-body resistance training (< three times monthly) or other structured exercise (e.g., jogging) more than 30 minutes per day, three times per week (11). Participants were instructed to maintain their daily pattern of sleep and dietary intake, including caffeine. Participants refrained from physically demanding tasks for ≥ 24 hours prior to data collection. Exclusion due to medication was determined on a case-by-case basis. A physician was consulted prior to enrollment of two older participants. The following medications were permitted, with the number of participants using them in parentheses: Esomeprazole (one), Levothyroxine (three), Finasteride (one), Omeprazole (one), Irbesartan (one), Tamsulosin (one), Allopurinol (one), Propranolol (one), and Atorvastatin (one). The Texas Tech University Human Research Protection Program approved the study (Study ID #504976). All participants read, understood, and signed an informed consent document prior to participation.

Isometric Force Assessments

A custom knee extension chair was used for testing. Participants were seated and restrained in the chair with straps secured around their chest, abdomen, and hips. Arms remained crossed throughout testing. An ankle cuff was secured to the dominant ankle joint and attached to a calibrated tension/compression load cell (Model SSM-AJ-500; Interface, Scottsdale, AZ). All testing was performed at a knee joint angle of 120º. Following a warm-up, participants performed three, five-second maximum voluntary contractions (MVC) separated by three minutes of rest. Strong verbal encouragement was provided. The highest force from the three contractions was used to standardize submaximal testing. Following MVC determination, participants performed trapezoidal isometric contractions by tracing visual templates on a computer monitor. Participants increased isometric force from 0–50% MVC in five seconds (10%/second), held 50% constant for 15 seconds, and decreased isometric force from 50–0% MVC in five seconds (10%/second). All participants demonstrated the ability to gradually and linearly increase force prior to data collection. Multiple attempts were performed, each followed by a three minute rest period.

Surface EMG Signal Recording

Surface EMG signals were recorded from the vastus lateralis during each submaximal contraction via Bagnoli 16-channel Desktop system (Delsys, Inc., Natick, MA). Prior to testing, the skin over the muscle and patella was shaved and cleansed with rubbing alcohol. Oil, debris, and dead skin cells were removed with tape. The sensor was placed over the muscle in accordance with recommendations described by Zaheer et al. (12). A reference electrode was placed over the patella. Signals were detected with a surface array EMG sensor (Delsys, Inc., Natick, MA) that consisted of five pin electrodes (10). Four of the five electrodes are arranged in a square, with the fifth electrode in the center of the square and at an equal distance of 3.6 mm from all other electrodes. Pairwise subtraction of the five electrodes was used to derive four single differential EMG channels. These signals were differentially amplified, filtered with a bandwidth of 20 Hz to 450 Hz, and sampled at 20 kHz. Signal quality (i.e., signal-to-noise ratio > 3.0, baseline noise value ≤ 2.0µV root-mean-square, line interference < 1.0) was verified for a 20% MVC assessment prior to data acquisition.

Surface EMG Signal Decomposition

The four separate filtered EMG signals served as the input to the Precision Decomposition III algorithm, which was utilized via dEMG Analysis software (version 1.1, Delsys, Inc., Natick, MA). For further information concerning the technical aspects of this algorithm, see the work of De Luca et al. (13) and Nawab et al. (10). Briefly, this algorithm allows investigators to extract motor unit firing rate, recruitment/derecruitment, and action potential morphology data from surface EMG signals obtained during isometric contractions. Surface EMG signals were decomposed into their constituent motor unit action potential trains. The trains were used to calculate a time-varying firing rate curve for each detected motor unit. All firing rate curves were smoothed with a one-second Hanning filter. The mean number of pulses per second (pps) for a two-second interval corresponding to the steadiest force (i.e., lowest coefficient of variation) of each motor unit firing rate curve was calculated. Each motor unit’s recruitment threshold was calculated as the absolute (N) and relative (% MVC) force level when the first firing occurred (Figure 1). High threshold motor units recruited during the constant-force portion of the contraction were not considered for analysis. Contractions in which motor unit activity was detected prior to the onset of muscle force (i.e., electromechanical delay) were not considered. Similarly, erratic contractions with decreases in force during the ascending portion of the trapezoid were not analyzed. Decompose-Synthesize-Decompose-Compare testing was used to remove motor units with detection accuracy < 93.0% (10). For a contraction to be considered for analysis, the decomposition output must have yielded at least five motor units.

Figure 1
Examples of decomposed trapezoidal isometric contractions

The black line represents the ascent of isometric force up to 50% MVC, whereas each colored circle is indicative of the first firing of a motor unit. The blue arrow points to the first active motor unit and the red arrow indicates the last recorded motor unit. Descriptive data from each contraction is provided in the table. The morphology of each motor unit action potential is displayed to the left.

Statistical Analyses

All variables were assessed for normality with Shapiro-Wilk’s tests. Mean, median, and range values for recruitment threshold were compared between younger and older men in both absolute (N) and relative (% MVC) terms. For each contraction, the relationship between the mean firing rates and recruitment thresholds of decomposed motor units was examined using linear regression. The resulting linear slope coefficient (pps/% MVC) and y-intercept (pps) values were then quantified. Independent samples t-tests were used to compare differences between age groups. Cohen’s d effect size statistics were used to examine differences, with values of 0.20, 0.50, and 0.80 reflecting small, medium, and large effects (14), respectively. An alpha level of P ≤ 0.05 was used to evaluate statistical significance. JASP software (version 0.9.0.1, JASP, Amsterdam, The Netherlands) was used for statistical analysis.

 

Results

All variables were normally distributed. The mean ± SD number of motor units detected was 17 ± 5 for younger and 13 ± 4 for older men. MVC force was significantly greater in younger vs. older men (709.6 ± 197.8 vs. 520.8 ± 121.6 N [p = 0.010; Cohen’s d = 1.15]). The relative median recruitment threshold values were significantly greater for younger (26.6 ± 9.1% MVC) compared to older (15.6 ± 7.9% MVC [p = 0.005; d = 1.29]) men. Younger men also demonstrated greater median recruitment threshold values when expressed in absolute terms (198.0 ± 99.2 vs. 81.2 ± 43.0 N [p = 0.001; d = 1.53]). Similarly, large differences in mean recruitment thresholds were found when expressed in both relative (25.9 ± 7.7 vs. 16.2 ± 7.8% MVC [p = 0.005; d = 1.27]) and absolute (191.4 ± 87.5 vs. 85.1 ± 44.9 N [p = 0.001; d = 1.53]) terms. The relative recruitment threshold range was not significantly different (p = 0.235) between younger (22.6 ± 9.5% MVC) and older (18.5 ± 6.4% MVC) men, but the effect size was moderate (d = 0.50). However, the absolute range was considerably larger for younger (167.6 ± 92.4 N) compared to older (95.7 ± 36.5 N [p = 0.020; d = 1.02]) men. Univariate scatterplots displaying individual participant data have been provided in Figures 2-4.

Figure 2
Individual participant data depicting median recruitment threshold expressed in relative and absolute terms for both younger and older men. Each data point represents the median recruitment threshold value from all of the successfully decomposed motor units within a contraction

Regarding the relationship between firing rate and recruitment threshold, the mean y-intercept value was significantly greater for younger (28.7) compared to older men (22.3 [p = 0.009; d = 1.17]). The mean slope coefficients were not significantly different (-0.52 vs. -0.32 [p = 0.070]); however, a moderate effect was observed (d = 0.78). Example relationships for two participants have been displayed in Figure 5.

 

Figure 3
Individual participant data depicting mean recruitment threshold expressed in relative and absolute terms for both younger and older men. Each data point represents the mean recruitment threshold value from all of the successfully decomposed motor units within a contraction

Figure 4
Individual participant data depicting recruitment threshold range expressed in relative and absolute terms for both younger and older men. Each data point represents the recruitment threshold range from all of the successfully decomposed motor units within a contraction. Range was calculated as the difference between the greatest observed recruitment threshold value and the lowest observed value

Figure 5
Individual participant data for the vastus lateralis mean firing rate versus recruitment threshold relationship in one younger and one older man. Note the flatter linear slope coefficient and lower y-intercept for the older participant. The slope and y-intercept values shown in this figure are only slightly different from the means within each age group

 

Discussion

This study compared vastus lateralis motor unit recruitment thresholds in younger versus older men. Our findings demonstrated that older men tend to recruit a greater proportion of motor units at lower isometric forces. This conclusion was based on moderate-to-large age-related differences in median recruitment threshold values, and was consistent for both relative and absolute force levels. These results were further supported by age-related shifts in the mean firing rate versus recruitment threshold relationship, which would be suggestive of lower firing rates for low threshold motor units of older men. We propose that recruitment threshold compression towards lower forces serves as a compensatory neural strategy that offsets motor unit remodeling in aged skeletal muscle (15). We will attempt to explain the mechanistic contributors to these findings in subsequent paragraphs.

In the 1970’s, Tomlinson and Irving (1) quantified limb motor neurons within the lumbrosacral cord in 47 humans aged 13 to 95 years. They noted considerably fewer motor neurons for those ≥ 60 years, with some lumbrosacral cords displaying counts equal to half of those evident in early adulthood or middle age. Shortly thereafter, investigators began investigating the effects of aging on skeletal muscle. Cross-sectional studies noted reduced muscle size (16), a reduction in fiber size and number (17), and a greater proportion of slow-twitch fibers (18). Given the significant fiber type grouping in aged muscle (19), the aging process likely results in substantial denervation of dormant muscle fibers and subsequent reinnervation by the more active, low threshold motor neurons, thereby increasing motor unit size. This concept is also supported by the presence of extra large motor unit potentials observed in the EMG recordings of older adults. Thus, the notion that voluntary control of motor units may be affected by the aging process is well-grounded in physiology (20).

Motor unit recruitment compression towards lower forces appears to be consistent with other concepts in the aging literature. Erim et al. (21) reported lower recruitment thresholds in the first dorsal interosseous of older adults, with particularly pronounced differences at 50% MVC. Their findings were accompanied by a number of age-related differences in motor unit behavior, such as reduced common drive, and exceptions to the typically inverse relationship between mean firing rate and recruitment threshold. Second, our findings seem to be in agreement with the work of Fling et al. (22), who noted preservation of the size principle in aged muscle. Fling et al. (22) postulated that if the contractile properties of the reinnervated fibers are suboptimal, a decrease in the net twitch force amplitude could result, despite the motor unit becoming larger. Earlier motor unit recruitment might also be a marker of neuromuscular inefficiency at the onset of an isometric contraction. Kamen and De Luca (23) noted that, despite sufficient familiarization, high threshold motor units displayed prolonged activity during force descent in older adults, which they attributed to excessive antagonistic coactivation. Another explanation for force being controlled by lower threshold motor units may be related to a compensatory mechanism to offset decreased firing rates. This hypothesis is well-supported by our results, as well as the work of Watanabe et al. (24). These authors (24) examined age-related differences in the firing rates of the vastus lateralis at 30% and 70% MVC, finding that the dissimilarity between age groups in firing rates was particularly evident for low threshold motor units. This suggests that, at low forces, older adults must recruit a greater proportion of motor units in order to overcome their slower firing rates. In line with our findings, Kamen and De Luca (23) and Erim et al. (21) also reported a flatter (i.e., less negative) slope for the mean firing rate versus recruitment threshold relationship (21, 23). The results of the current study appear to support these previously observed differences in firing rates, particularly the lower threshold motor units, as well as the less negative slope in older men.
Much of the work in this area has relied on intramuscular recordings, whereas our analysis relied on surface EMG signals. Thus, it is important to be mindful that EMG results are a reflection of the motor units within the vicinity of the recording electrodes. Autopsy studies have demonstrated that vastus lateralis fast-twitch muscle fibers (innervated by large, high threshold motor neurons) are likely to be located near the surface of the skin (25). Therefore, it has been suggested that surface EMG signals may be biased towards high threshold motor units (26). If surface fibers previously innervated by high threshold motor neurons become reinnervated by low threshold motor neurons, this might suggest that the surface EMG signal in older adults reflects a greater balance of motor unit types. Potential differences between groups in subcutaneous fat thickness may have also affected these results (12).
In summary, the results of this study revealed important age-related differences in MVC force, absolute and relative median recruitment thresholds, absolute recruitment threshold range, and the mean firing rate versus recruitment threshold relationship. These results imply that older men tend to recruit motor units of the vastus lateralis at lower isometric force levels compared to younger men. This is in line with past observations for the first dorsal interosseous (21) and tibialis anterior muscles (22). We speculate that motor unit recruitment threshold compression towards lower forces is a result of age-related motor unit remodeling. Future studies should investigate whether this observed behavior is prevalent in women or middle-aged men, as well as whether these properties carry over to dynamic movements.

 

Conflict of interest: The authors declare no conflicts of interest.
Ethical standards: This study was carried out in accordance with the ethical standards of the university Institutional Review Board.

 

References

1. Tomlinson, B., & Irving, D. The numbers of limb motor neurons in the human lumbosacral cord throughout life. Journal of the Neurological Sciences, 1977;34(2), 213-219.
2. Jones, D. A., & Peters, T. J. Caring for elderly dependants: Effects on the carers’ quality of life. Age and Ageing, 1992;21(6), 421-428.
3. Vespa, J., Armstrong, D. M., & Medina, L. Demographic turning points for the United States: Population projections for 2020 to 2060 US Department of Commerce, Economics and Statistics Administration, US Census Bureau, 2018.
4. Kamen, G., & Knight, C. A. Training-related adaptations in motor unit discharge rate in young and older adults. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 2004;59(12), 1334-1338.
5. Clark, B., & Taylor, J. Age-Related Changes in Motor Cortical Properties and Voluntary Activation of Skeletal Muscle.Current Aging Science, 2011;4 (3), 192–199.
6. Clark, B. C., Taylor, J. L., Hong, S. L., Law, T. D., & Russ, D. W. Weaker seniors exhibit motor cortex hypoexcitability and impairments in voluntary activation. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 2015;70(9), 1112-1119.
7. Doherty, T. J., Vandervoort, A. A., Taylor, A. W., & Brown, W. F. Effects of motor unit losses on strength in older men and women. Journal of Applied Physiology (Bethesda, Md.: 1985), 199374(2), 868-874.
8. Doherty, T. J., Vandervoort, A. A., & Brown, W. F. Effects of ageing on the motor unit: A brief review. Canadian Journal of Applied Physiology, 1993;18(4), 331-358.
9. Contessa, P., De Luca, C. J., & Kline, J. C. The compensatory interaction between motor unit firing behavior and muscle force during fatigue. Journal of Neurophysiology, 2016;116(4), 1579-1585.
10. Nawab, S. H., Chang, S., & De Luca, C. J. High-yield decomposition of surface EMG signals. Clinical Neurophysiology, 2010;121(10), 1602-1615.
11. Lanza, I. R., Larsen, R. G., & Kent-Braun, J. A. Effects of old age on human skeletal muscle energetics during fatiguing contractions with and without blood flow. The Journal of Physiology, 2007;583(3), 1093-1105.
12. Zaheer, F., Roy, S. H., & De Luca, C. J. Preferred sensor sites for surface EMG signal decomposition. Physiological Measurement, 2012;33(2), 195.
13. De Luca, C. J., Adam, A., Wotiz, R., Gilmore, L. D., & Nawab, S. H. Decomposition of surface EMG signals. Journal of Neurophysiology, 2006;96(3), 1646-1657.
14. Cohen, J. Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ, USA: Lawrence Earlbaum Associates, 1988.
15. Brooks, S. V., & Faulkner, J. A. Skeletal muscle weakness in old age: Underlying mechanisms. Medicine and Science in Sports and Exercise, 1994;26(4), 432-439.
16. Lexell, J., Henriksson-Larsén, K., Winblad, B., & Sjöström, M. Distribution of different fiber types in human skeletal muscles: Effects of aging studied in whole muscle cross sections. Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, 1983;6(8), 588-595.
17. Lexell, J., Taylor, C. C., & Sjöström, M. What is the cause of the ageing atrophy?: Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15-to 83-year-old men. Journal of the Neurological Sciences, 1988;84(2-3), 275-294.
18. Larsson, L. Histochemical characteristics of human skeletal muscle during aging. Acta Physiologica Scandinavica, 1983;117(3), 469-471.
19. Lexell, J., Downham, D., & Sjöström, M. Distribution of different fibre types in human skeletal muscles: Fibre type arrangement in m. vastus lateralis from three groups of healthy men between 15 and 83 years. Journal of the Neurological Sciences, 1986;72(2-3), 211-222.
20. Deschenes, M. R. Effects of aging on muscle fibre type and size. Sports Medicine, 2004;34(12), 809-824.
21. Erim, Z., Beg, M. F., Burke, D. T., & de Luca, C. J. Effects of aging on motor-unit control properties. Journal of Neurophysiology, 1999;82(5), 2081-2091.
22. Fling, B. W., Knight, C. A., & Kamen, G. Relationships between motor unit size and recruitment threshold in older adults: Implications for size principle. Experimental Brain Research, 2009;197(2), 125-133.
23. Kamen, G., & De Luca, C. J. Unusual motor unit firing behavior in older adults. Brain Research, 1989;482(1), 136-140.
24. Watanabe, K., Holobar, A., Kouzaki, M., Ogawa, M., Akima, H., & Moritani, T. Age-related changes in motor unit firing pattern of vastus lateralis muscle during low-moderate contraction. Age, 2016;38(3), 48.
25. Johnson, M., Polgar, J., Weightman, D., & Appleton, D. Data on the distribution of fibre types in thirty-six human muscles: An autopsy study. Journal of the Neurological Sciences, 1973;18(1), 111-129.
26. Gabriel, D., & Kamen, G. Essentials of Electromyography Champaign, IL: Human Kinetics, 2010.

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PRACTICAL IMPLICATIONS FOR STRENGTH AND CONDITIONING OF OLDER PRE-FRAIL FEMALES

 

N.W. Bray1, G.J. Jones1, K.L. Rush2, C.A. Jones3, J.M. Jakobi1

1. School of Health and Exercise Sciences, Faculty of Health and Social Development, University of British Columbia Okanagan, Kelowna, British Columbia, Canada; 2. School of Nursing, Faculty of Health and Social Development, University of British Columbia Okanagan, Kelowna, British Columbia, Canada; 3. Southern Medical Program, Faculty of Medicine, University of British Columbia Okanagan, Kelowna, British Columbia, Canada.
Corresponding author: Jennifer M. Jakobi, School of Health and Exercise Sciences, Faculty of Health and Social Development, University of British Columbia Okanagan, Kelowna, British Columbia, Canada, V1V 1V7, jennifer.jakobi@ubc.ca

J Frailty Aging 2020;9(2)118-121
Published online March 30, 2020, http://dx.doi.org/10.14283/jfa.2020.15

 


Abstract

Approaches to and benefits from resistance training for non-compromised older adults are well known. Less is understood about resistance training with pre-frail older adults, and even less information is available on the practical approaches to delivery. Herein, we describe an approach in pre-frail females who undertook a multi-component exercise intervention, inclusive of high-intensity, free-weight, functional resistance training. Capitalizing on the principle of overload is possible and safe for pre-frail females through constant reassurance of ability and adjustments in technique. Making exercise functionally relevant, for example, a squat is the ability to get on and off a toilet, resonates meaning. Older pre-frail females are affected by outside (clinical) influences. The exercise participant, and extraneous persons need to be educated on exercise approaches, to increase awareness, debunk myths, and enhance support for participation. Identification of individuality in a group session offers ability to navigate barriers for successful implementation.

Key words: Multi-component exercise, females, aging, strength, muscle.


 

Introduction

This Research Note supplements the original research article, “Multi-component exercise with high-intensity, free-weight, functional resistance training in pre-frail females: A quasi-experimental, pilot study” (1). The primary aim of this study was to examine the feasibility and safety of multi-component exercise (MCE), inclusive of resistance training that is high in intensity, uses free-weights, and is based upon functional movement. Previous exercise interventions in pre-frail females have primarily used low-intensity, single-joint resistance training exercises (2), likely because of the commonly held, yet unsupported belief that the alternative is unsafe (3). Researchers and clinicians working with frail older adults can apply principles of our training program to exercise interventions.
Older adults are challenged with chronic health conditions and functional deficits that can render them frail (4). The incidence of frailty increases with age and is more prevalent in females (5); however, this syndrome can be positively influenced by interventions (6), especially exercise (7,8). Current recommendations to reverse frailty provide evidence to support an exercise strategy based upon the individual’s level of frailty (9). However, few studies report on the practical applications of effective delivery. Hence, we openly describe our experience administering a MCE intervention, inclusive of resistance training utilizing free-weight functional movements at high intensities, in a group of pre-frail older females.

 

Main text

Progression – Resistance exercise programs benefit muscle strength, power, and endurance when there is a progressive increase in work, balanced with appropriate recovery (10). During the early stages of resistance training, progression for pre-frail females may involve increasing the range of motion (ROM), as opposed to a traditional model of increasing the weight lifted. It is likely advantageous to first increase ROM as it has greater application to everyday tasks. We used a combination of custom built, stackable, “plyo-boxes” and free-weight plates to adjust the ROM when performing the squat and deadlift (Figure 1-2). The squat and deadlift are rarely implemented to train older adults yet, when boxes 12-24ˮ are used to adjust the ROM, these exercises can be safely undertaken.

Figure 1
A. Box heights (12-24ˮ) used to progressively and safely increase range of motion (ROM) for the squat and deadlift exercises.
1B and 1C highlight individuality, as it shows each participant squatting to a ~20ˮ and 12ˮ box, respectively. The participant squatting to the 20” box was working to achieve full ROM, while the participant squatting to the 12ˮ box is holding a dumbbell because they had progressed to the maximal ROM required of the training program, and this was the next level of progression

 

The squat exercise was initiated by squatting to a 24ˮ box. Progressive overload was applied by having the participant complete squats through a greater ROM (deeper squat) until the full-squat (~90 degree flexed knee bend/12ˮ box) was completed. Progression then involved the traditional approach of adding weight (dumbbell held in goblet position). The squat exercise should not be overlooked in designing programs for older females as it simulates functional activities, such as rising from a toilet.
Deadlifts, using a 35lb barbell, followed an identical progression. However, once participants reached the 12ˮ box, a 10lb bumper plate was added to each side of the barbell (total = 55lbs) instead of progressing to the ground, as the latter would likely represent a greater challenge. Once participants performed the prescribed sets and repetitions from the 12ˮ box, with the additional weight (total = 55lbs), they were then progressed to the ground. Exercise progression then included increasing the barbell load (Figure 2). The deadlift exercise should also not be overlooked as it simulates functional activities, such as picking up grandchildren.

 

For both the squat and deadlift, when a 6ˮ difference in box height represented too great of an increase in ROM, weight-plates (Virgin Rubber Grip Olympic Plates, Element Fitness; Latvia) ~2ˮ in width were used to safely progress (Figure 1B).
Overload – Refers to the gradual increase in stress that is placed upon the body during training, and is likely of particular importance to combatting the natural deterioration that occurs with aging. Exercise leaders, inclusive of Clinical Exercise Physiologists, were responsible for prescribing overload by increasing the weight/ROM and/or repetitions for each exercise over the course of the intervention. For example, if a participant performed three sets of eight repetitions during block one (weeks 1-4), they were then encouraged to complete nine repetitions on the next visit. The number of repetitions were increased until the participant reached 12, at which point resistance was increased and repetitions reduced back to eight. Not all participants willingly accepted the recommended progressive overload, and the exercise leaders needed to regularly build confidence for the participants to undertake this training principle.
Individuality – All exercises were modified based upon individual need (limited ROM, joint problems), as well as self-perceived ability. For example, during the early stages of the program, participants were instructed to lower the incline leg press sled (60lbs) to a point that they felt strong enough to still return it to the starting position (leg press guard ensured safety). Eventually, ROM was increased and then weight added to the sled. Continual monitoring assists in ensuring that resistance is added when participants can complete the full ROM safely.
Self-perceived Intensity – After the final set of every exercise, the OMNI Resistance Exercise Scale (OMNI-RES) was used to quantify Rating of Perceived Exertion (RPE; 11). RPE is a subjective indicator of how hard an individual is working. The OMNI-RES permits exercisers to report a measure from 0(extremely easy)-10(extremely hard). Participants often found it difficult to distinguish levels 3-8; frequently reporting a rating of 1-2 or 9-10, indicating that they could only perceive their level of exertion as very easy or very hard. The incongruity between completing more of the exercise and self-perceived scoring resulted in the decision to switch to the RPE method proposed by Zourdos (12), which has the exerciser rate their exertion based upon the number of repetitions they believe they could have executed before muscle failure. This type of RPE scale was more easily understood, potentially because of the objectivity, and thus, appeared to be more accurate in self-assessment.
Group Dynamics – Groups often adopt a ‘team’ approach, encouraging each other to improve and recognizing milestones. Positive group exercise dynamics can also foster social activities, further enhancing quality of life for older adults. The opposite situation may occur in random group assignment. Participants who question the exercise intervention and the necessity of progressive overload can minimize the development of positive group dynamics. Group settings bring together different personalities and it is not realistic to believe that they will consistently exist in harmony. The role of group dynamics should be considered in future frailty exercise interventions/programs (13).
Outside Clinician Influence – Pre-frail older females have co-morbidities that require clinical monitoring. Some participants made clear, to the certified exercise leaders, that their clinicians held negative, preconceived notions towards older females performing high-intensity, free-weight, functional resistance training. This negative influence phenomenon is quite common, despite evidence that exercise is beneficial in mitigating frailty. We observed that participants who relied on clinical suggestions were more apprehensive in the exercise program and tenuous in overloading. Importantly, our exercise intervention did not have any adverse events. Progressively overloading exercises for pre-frail older females is challenging, but it is feasible, safe, and beneficial.
Prior to the start of the intervention, researchers should hold a mandatory information session. During this session, participants should be educated about the intervention and granted the opportunity to pose concerns, as well as understand the qualifications of those administering the program. The participants primary clinician should be offered educational material (i.e. brochure) outlining the program as it could help address concerns and prevent contradictory recommendations.

Limitations

This research note offers guidance to those undertaking exercise with frail older adults. However, the applicability might be limited to females. Researchers were aware that the OMNI-RES can be poorly understood given that new trainees often report less accurate perception of their exertion than more advanced exercisers (14). Therefore, careful explanation was undertaken prior to beginning and during the exercise session. Switching to an adapted RPE might have slowed exercise progression and influenced confidence in the exercise leaders. Across frailty scales, there is considerable confusion in classification, therefore, observations may vary with the level and scale used to identify frailty (15). This progressive exercise program was successful in inducing positive adaptations, yet, the approach needs to be applied in a large randomized controlled trial.

 

Discussion

Trainers should create options for participants to work in a functional ROM, and to teach the squat and deadlift. Boxes of varying heights supported the prescription of the squat and deadlift exercise. When there was an improvement in strength, the boxes were lowered/ROM was increased. However, the height of boxes used for progression should be limited to ≤ 3″. Overall, this approach offers flexibility in exercise prescription for those with joint limitations. The following fitness equipment is also beneficial in creating progressive resistance: 1) a barbell weighing < 35lbs; 2) dumbbells increasing in 2.5lbs increments; and 3) an inclined leg press with a starting weight < 60lbs. Across all elements, pre-frail older females benefit from understanding the functional application of exercises.
Frailty is often an unfortunate reality for an aging population, its characteristics are all synonymous with lack of fitness. Strength and conditioning specialists are well suited to address frailty. To be most effective, exercise specialists need to tailor the exercise intervention, and constantly use monitoring approaches to create small progressions that promote meaningful strength gains and in-turn, enhance functional ability.

 

Funding: Partial funding for this study through the Canadian Institutes for Health Research (CIHR) Grant # 385692. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Acknowledgments: We wish to acknowledge the support from Flaman Fitness™ and the Okanagan Men’s Shed Club for generously donating the exercise equipment, graduate students (Rowan Smart and Sam Kuzyk) and senior undergraduate students (Anup Dhaliwal, Brett Yungen, Savannah Frederick, Paul Cotton and Cydney Richardson), and all the participants involved in this study.
Ethics approval and consent: All participants read and signed a letter of informed consent. Ethical approval was granted by the institutional Research Ethics Board (H16-00712).
Availability of the data and materials: The original data and materials are available through the institutions open access graduate thesis repository https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0353165
Competing interests: None
Trial Registration: This study was prospectively registered with ClincalTrials.gov (NCT02952443) on October 31, 2016.

 

References

1. Bray NW, Jones GR, Rush KL, Jones CA, Jakobi JM. Multi-component exercise with high-intensity, free-weight, functional resistance-training in pre-frail females: A quasi-experimental, pilot study. J Frailty Aging 2019;In Press.
2. Puts MTE, Toubasi S, Andrew MK, et al. Interventions to prevent or reduce the level of frailty in community-dwelling older adults: A scoping review of the literature and international policies. Age Ageing 2017;46(3):383-392.
3. Watson SL, Weeks BK, Weis LJ, Horan SA, Beck BR. Heavy resistance training is safe and improves bone, function, and stature in postmenopausal women with low to very low bone mass: novel early findings from the LIFTMOR trial. Osteoporos Int 2015;26(12):2889–94.
4. Hicks GE, Shardell M, Alley DE, et al. Absolute strength and loss of strength as predictors of mobility decline in older adults: The InCHIANTI study. J Gerontol A Biol Sci Med Sci 2012;67(1):66–73.
5. Bandeen-Roche K, Seplaki CL, Huang J, et al. Frailty in Older Adults: A Nationally Representative Profile in the United States. J Gerontol A Biol Sci Med Sci 2015;70(11):1427–34.
6. Bray NW, Doherty TJ, Montero-Odasso M. The Effect of High Dose Vitamin D3 on Physical Performance in Frail Older Adults. A Feasibility Study. J Frailty Aging 2018;7(3):155–61.
7. Theou O, Stathokostas L, Roland KP, et al. The effectiveness of exercise interventions for the management of frailty: a systematic review. J Aging Res 2011;2011:569194.
8. Jones GR, Jakobi JM. Launching a new initiative. Appl Physiol Nutr Metab 2017;42(9):iii–iv.
9. Bray NW, Smart RR, Jakobi JM, Jones GR. Exercise prescription to reverse frailty. Appl Physiol Nutr Metab 2016;41(10):1112–16.
10. Riebe D, Ehrman JK, Liguori G, et al. ACSM’s Guidelines for Exercise Testing and Prescription. 10th ed. Baltimore (MD): Lippincott Williams & Wilkins; 2018. 249-257 p.
11. Gearhart RF, Lagally KM, Riechman SE, et al. Strength Tracking Using the OMNI Resistance Exercise Scale in Older Men and Women. J Strength Cond Res 2009;23(3):1011–5.
12. Zourdos MC, Klemp A, Dolan C, et al. Novel Resistance Training-Specific Rating of Perceived Exertion Scale Measuring Repetitions in Reserve. J Strength Cond Res 2016;30(1):267-75.
13. Beauchamp MR, Eys M. Group dynamics in exercise and sport psychology. New York, NY: Routledge, 2014.
14. Testa M, Noakes TD, Desgorces F-D. Training state improves the relationship between rating of perceived exertion and relative exercise volume during resistance exercises. J Strength Cond Res 2012;26(11):2990–6.
15. Theou O, Brothers TD, Peña FG, et al. Identifying common characteristics of frailty across seven scales. J Am Geriatr Soc 2014;62(5):901–6.

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CONTRIBUTION OF PROTEIN INTAKE AND CONCURRENT EXERCISE TO SKELETAL MUSCLE QUALITY WITH AGING

N.D. Dicks1,2, C.J. Kotarsky1, K.A. Trautman1, A.M. Barry1,3, J.F. Keith1,4, S. Mitchell1,5, W. Byun1,6, S.N. Stastny1, K.J. Hackney1

1. Department of Health, Nutrition, & Exercise Sciences, North Dakota State University, Fargo, ND, USA; 2. Department of Nutrition, Dietetics and Exercise Science, Concordia College, Moorhead, MN, USA; 3.Department of Health, Human Performance, and Recreation, Pittsburg State University, Pittsburg, KS, USA; 4. Department of Family and Consumer Sciences, University of Wyoming, Laramie, WY, USA; 5. Sanford Health, Fargo, ND, USA; 6. Department of Health, Kinesiology, and Recreation, University of Utah, Salt Lake City, UT, USA;.
Corresponding author: Nathan D. Dicks, Concordia College, Department of Nutrition, Dietetics and Exercise Science, 901 8th St. S., Moorhead, MN 56562; 218-299-4440 Email: ndicks@cord.edu
J Frailty Aging 2019;in press
Published online November 26, 2019, http://dx.doi.org/10.14283/jfa.2019.40


Abstract

Background: The use of magnetic resonance imaging (MRI) derived functional cross-sectional area (FCSA) and intramuscular adipose tissue (IMAT) to define skeletal muscle quality is of fundamental importance in order to understand aging and inactivity-related loss of muscle mass. Objectives: This study examined factors associated with lower-extremity skeletal muscle quality in healthy, younger, and middle-aged adults. Design: Cross-sectional study. Setting and Participants: Ninety-eight participants (53% female) were classified as younger (20-35 years, n=50) or middle-aged (50-65 years, n=48) as well as sedentary (≤1 day per week) or active (≥3 days per week) on self-reported concurrent exercise (aerobic and resistance). Measurements: All participants wore an accelerometer for seven days, recorded a three-day food diary, and participated in magnetic resonance imaging (MRI) of the lower limbs. Muscle cross-sectional area (CSA) was determined by tracing the knee extensors (KE) and plantar flexors, while muscle quality was established through the determination of FCSA and IMAT via color thresholding. Results: One-way analysis of variance and stepwise regression models were performed to predict FCSA and IMAT. KE-IMAT (cm2) was significantly higher among sedentary (3.74 ± 1.93) vs. active (1.85 ± 0.56) and middle-aged (3.14 ± 2.05) vs. younger (2.74 ± 1.25) (p < 0.05). Protein intake (g•kg•day-1) was significantly higher in active (1.63 ± 0.55) vs. sedentary (1.19 ± 0.40) (p < 0.05). Sex, age, concurrent exercise training status, and protein intake were significant predictors of KE FCSA (R2 = 0.71, p < 0.01), while concurrent exercise training status and light physical activity predicted 33% of the variance in KE IMAT (p < 0.01). Conclusion: Concurrent exercise training, dietary protein intake, and light physical activity are significant determinants of skeletal muscle health and require further investigation to mitigate aging and inactivity-related loss of muscle quality.

Key words: Protein, functional CSA, IMAT, physical activity, aging.


 

Introduction

Skeletal muscle quality is challenging to define as the cross-sectional area (CSA) of the muscle itself does not reflect health or function (1). For example, inter and intramuscular adipose tissue (IMAT), defined as the visible adipose tissue beneath the muscle fascia and between muscle groups, as well as functional cross-sectional area (FCSA), the area of muscle isolated from fat within the CSA, maybe more useful indicators of muscle quality as they reflect muscle composition (2, 3). IMAT, in particular, has been linked with many metabolic abnormalities (4). Previous research has shown that individuals who are obese or have conditions such as diabetes or peripheral neuropathy have higher amounts of IMAT when compared to healthy adults (5). Additionally, age-associated changes in muscle composition, including increased fatty infiltration and reduced fat oxidation within skeletal muscle tissue, results in elevated levels of IMAT in older adults (6). However, IMAT may be used as an endogenous fuel source as individuals increase their physical activity level (5), and various interventions have been shown to reduce IMAT, including multimodal exercise programs (6-9), dietary interventions (10), and both dietary and exercise cooperatively (11).
Dietary protein intake in combination with exercise, in particular, can influence cross-sectional muscle area and may be related to FSCA in conjunction with interactions with other factors, including physical activity, health status, age, and body mass and composition (12). For instance, a single meal that contains ~30 g of high-quality protein can stimulate muscle protein synthesis in healthy adults, leading to increases in muscle size (13, 14). Protein intake also has a positive association with IMAT in younger individuals (27-31 years) (15), and there is a correlation between muscle size and IMAT regardless of age (16). Thus, the purpose was to identify factors that were related to skeletal muscle quality in healthy, younger, and middle-aged men and women. We hypothesized that concurrent exercise training status, the time within various physical activity intensities, age, sex, and dietary protein intake would be significant contributors to FCSA and IMAT quantity.

 

Methods

Participants completed three lab sessions, including two in the research laboratory at North Dakota State University campus and one at Sanford Broadway Clinic, Fargo, North Dakota, during this cross-sectional study. The initial visit involved completing a training session on the three-day food diary, along with an informed consent and health history questionnaire. The return visit to the laboratory included the provision of a three-day food diary and an accelerometer. Participants’ third visit was to the local hospital to complete magnetic resonance imaging (MRI) of the lower extremity. There was an average of four weeks between visits one and two, due to recruitment meetings and the capacity of the research laboratory, and there were less than two weeks between visits two and three.

Participants

Participants were recruited from the local area through information sessions, flyers, and word of mouth over two years. Participants were included in this study if they were generally healthy: 1) without any conditions that limit protein consumption and regular physical activity, 2) were not taking medications that may influence anabolism (e.g., testosterone therapy), and 3) classified into one of the two age groups: younger (20-35 years of age) and middle-aged (50-65 years of age). They were excluded if they did not meet the above criteria. Eligible participants after being categorized into two age groups: younger and middle-aged and were further sub-classified by determining their concurrent exercise training status. Sedentary individuals were not participating in resistance or aerobic exercise more than one time per week. Physically active individuals were participating in three or more days per week of aerobic and resistance exercise at a moderate-to-vigorous intensity. Before starting the study, all participants provided written informed consent approved by the university’s Institutional Review Board for the protection of human participants.

Muscle CSA and FCSA Measurements

MRI provides a highly detailed image of skeletal muscle tissue that can be analyzed to quantify IMAT, FSCA, and CSA (2, 16). In this capacity, multi-slice analysis of the mid-thigh has been shown to reduce the standard error of the estimate, increase the improvement of the prediction (4), and to assess CSA, FCSA, and IMAT accurately. For this investigation, serial axial plane MRI scans from a 3.0 T Siemens Skyra Intera whole-body scanner (Siemens Healthcare Headquarters, Erlangen, DE) were obtained at the local hospital (Sanford Broadway Clinic, Fargo, ND). Licensed radiology technicians, in collaboration with researchers, obtained images. Participants were positioned with elevated heels and knees to minimize the distortion of the muscles to be analyzed. The MRI settings were as follows: repetition time = 3730 m/s, 10 mm slice-to-slice interval, 420-500 mm x 328-390 mm field of view. ImageJ version 1.42 (National Institutes of Health, Bethesda, MD) was used to analyze MRI-derived muscle CSA on a personal computer (MacBook Pro, Apple Inc., Cupertino, CA). Images of the left leg from the MRI were analyzed. Knee extensor (KE) images selected for analysis started with the first image in which the rectus femoris was visible proximately to the first slice before the appearance of the gluteal muscles. Depending on the height of the participant, the research started with seven to nine slices. Three slices were used starting with the midpoint slice, with one slice proximal and one slice distal for the upper leg. Plantar flexor (PF) images started with the first analyzable slice for the lateral gastrocnemius and ended when there was no clear distinction between the lateral gastrocnemius and soleus resulting in four to five slices for analysis. Methods for measuring CSA were used by encompassing the KE (vastii group and rectus femoris) and PF (lateral gastrocnemius, medial gastrocnemius, soleus, and flexor longus). Determination of muscle CSA was estimated by carefully tracing the edge of each group of muscles using the freehand tool in ImageJ. The average CSA for each participant was between the selected slices.
FCSA involved an image-analysis thresholding technique, measured using gray-scale thresholding to analyze those regions of the muscle cross-sections corresponding to dark, lean muscle mass. One trained researcher conducted the analysis (NDD). Previous analysis showed that repeated measurements performed by the individual were reliable and reproducible, with an intraclass correlation coefficient of 0.98. The sampling of each slice for pixel intensity of muscle tissue in three locations on both the KE and PF was used to determine the appropriate thresholds for discriminating different tissue types. The threshold value previously collected was applied to the CSA to estimate FCSA, and the difference was the estimated composition of IMAT expressed in cm2 and a percentage of muscle area.

Dietary Intake and Physical Activity

To examine dietary intake, participants completed a three-day food diary estimating portions sizes from picture series. Participants were asked to log everything they ingested on two typical days (e.g., weekdays) and one atypical day (e.g., weekend day). Once completed, registered dietitians analyzed energy and macronutrients, including protein intake both as grams per subject and g•kg−1 per subject, using Food Processor Nutrition Analysis software (ESHA, Salem, OR). Completion of all three days of the food diary was required to be included in the analysis.
Habitual physical activity was assessed using an Actigraph GT3X+ accelerometer (Actigraph, Pensacola, FL) for seven consecutive days. Participants were instructed to wear the accelerometer on their right hip during all waking hours except for water activities (e.g., bathing or swimming), and to keep a sleep log to record the time that the accelerometer was removed at night and put back on in the morning. The accelerometers were initialized to collect raw acceleration at 80 Hz, and raw acceleration data was processed using the R-package called GGIR, specially designed for reducing multiday raw acceleration data (17). The acceleration summary data were used to calculated the amount of time (min/day) spent in sedentary behavior (1.5 to < 3 METs), and moderate-to-vigorous physical activity (MVPA, ≥ 3 METs), based on the intensity-specific milli-g cut-points derived from previously validated regression equations (18). Non-wear time was defined as intervals of at least 90 minutes of zero counts, allowing a two-minute interval of non-zero counts with a 30-minute window (19). A minimum wear time of four days with ten hours/day was required to be included in the statistical analysis.

Statistical Analyses

All values are reported as means and standard deviations. One-way analysis of variance (ANOVA) with Bonferroni corrections for multiple comparisons was used to compare variables of physical activity levels and FCSA and IMAT measures between groups defined by concurrent exercise training status, sex, and age. Standard Q-Q plots were consulted to check for the variables’ normal distribution. Stepwise regression models were used to examine the relationship of sex, protein intake, concurrent exercise training status, physical activity intensity, and age with FCSA and IMAT. The sample size was selected based on previous studies using regression methods to predict muscle size/quality (20). The two-tailed level of significance was set at p < 0.05. All statistical analyses were performed using IBM SPSS Statistics (version 24, SPSS, Inc., Chicago, IL).

 

Results

All of the recruited 98 participants (46 male, 52 female) completed the study, including 49 sedentary individuals (n = 25, age = 26.3 ± 4.7 years; n = 24, age = 57.9 ± 4.5 years) and 49 active individuals (n = 25, age = 23.0 ± 3.1 years; n = 24, age = 57.3 ± 4.0 years). Protein intake (g•kg•day-1) was significantly higher in active (1.63 + 0.55) vs. sedentary (1.19 + 0.40) (p < 0.05) and among the active-younger group compared to all of the sedentary groups (Figure 1). Figure 2 depicts the FCSA and IMAT in the KE and PF. IMAT (cm2) was significantly higher in sedentary (3.74 + 1.93) vs. active (1.85 + 0.56) as well as in older (3.14 + 2.05) vs. younger (2.74 + 1.25) (p < 0.05). Table 1 shows the differences in physical activity intensity of participants based on accelerometry. Sex, age, concurrent exercise training status, and protein intake significantly predicted 71% of the variance in FCSA (F (1, 94) = 58.12, R2 = 0.714, adjusted R2 = 0.702, p < 0.001, Table 2). Physical activity intensity indicators were removed from the stepwise model, given they were not significant predictors: sedentary (t=-0.483, p =0.629); light (t=-0.093, p =0.926); MVPA (t = -0.355, p = 0.724). Concurrent exercise training status and light physical activity predicted 34% of the variance in KE IMAT (F (1, 95) =24.84, R2 = 0.343, R2 adjusted = 0.330, p < 0.001, Table 2). Sex was the only significant predictor of PF FCSA (F (1, 96) =47.23, R2 = 0.330, R2 adjusted = 0.323, p < 0.001, Table 3). Concurrent exercise training and age significantly predicted PF IMAT (F (1, 96) = 39.04, R2 = 0.451, R2 adjusted = 0.440, p < 0.001, Table 3).

Figure 1 Protein intake in Active and Sedentary Younger and Middle-Aged Adults. Values are presented as mean + SD

Figure 1
Protein intake in Active and Sedentary Younger and Middle-Aged Adults. Values are presented as mean + SD

* Significant difference vs. SYM (p < 0.05). † Significant difference vs. SYF (p < 0.05). ‡ Significant difference vs. SMM (p < 0.05). § Significant difference vs. SMF (p < 0.05). # Significant difference vs. AMM (p < 0.05). White and gray bars represent sedentary and active, respectively; AMF = Active Middle-Aged Female, AMM = Active Middle-Aged Male, AYF = Active Younger Female, AYM = Active Younger Male, SMF = Sedentary Middle-Aged Female, SMM = Sedentary Middle-Aged Male, SYF = Sedentary Young Female, SYM = Sedentary Young Male

Table 1 Physical Activity (PA) and Sedentary Behavior (SED) Levels of Participants

Table 1
Physical Activity (PA) and Sedentary Behavior (SED) Levels of Participants

Values are presented as mean + SD. * Significant difference from SYM (p < 0.05). ‡ Significant difference from SMM (p < 0.05); Note: Sedentary individuals were not participating in resistance or aerobic exercise more than one time per week, Active individuals were participating in three or more days per week of aerobic and resistance exercise at a moderate-to-vigorous intensity, Younger (20-35 years of age), Middle-aged (50-65 years of age), SMM = Sedentary Middle-aged Male, SYM = Sedentary Young Male

Figure 2 A) Knee Extensor Muscle Functional Cross-sectional Area, B) Knee Extensor Muscle Intermuscular Adipose Tissue, C) Plantar Flexor Muscle Functional Cross-sectional Area, and D) Plantar Flexor Muscle Intermuscular Adipose Tissue in Active and Sedentary Younger and Middle-Aged Adults. Values are presented as mean + SD

Figure 2
A) Knee Extensor Muscle Functional Cross-sectional Area, B) Knee Extensor Muscle Intermuscular Adipose Tissue, C) Plantar Flexor Muscle Functional Cross-sectional Area, and D) Plantar Flexor Muscle Intermuscular Adipose Tissue in Active and Sedentary Younger and Middle-Aged Adults. Values are presented as mean + SD

* Significant difference vs.  SYM (p < 0.05). † Significant difference vs.   SYF (p < 0.05). ‡ Significant difference vs. SMM (p < 0.05). § Significant difference vs. SMF (p < 0.05). || Significant difference vs. AYM (p < 0.05). { Significant difference then AYF (p < 0.05). # Significant difference vs. AMM (p < 0.05). White and grey bars represent sedentary and active, respectively; AMF = Active Middle-Aged Female, AMM = Active Middle-Aged Male, AYF = Active Younger Female, AYM = Active Younger Male, SMF = Sedentary Middle-Aged Female, SMM = Sedentary Middle-Aged Male, SYF = Sedentary Young Female, SYM = Sedentary Young Male

Table 2 Stepwise regression determinants for predicting KE muscle quality

Table 2
Stepwise regression determinants for predicting KE muscle quality

Note: FCSA = Functional Cross-Sectional Area, IMAT = Intramuscular Adipose Tissue, KE = Knee Extensors

Table 3 Stepwise regression determinants for predicting PF muscle quality

Table 3
Stepwise regression determinants for predicting PF muscle quality

Note: FCSA = Functional Cross-Sectional Area, IMAT = Intramuscular Adipose Tissue, PF = Plantar Flexor

Discussion

In this cross-sectional investigation, younger and middle-aged adults were classified as active or sedentary based on the self-reported concurrent exercise training status, physical activity intensity of participants was quantified with accelerometry, and dietary protein intake was estimated from three-day food diaries. Muscle quality was examined using MRI derived FSCA and IMAT measurement. The main finding of this study was that 71% of the variance in KE FCSA was predicted by age, sex, concurrent exercise training, and protein intake; leaving only 29% of factors unaccounted. Dietary protein intake increases skeletal muscle protein synthesis, and over time, can increase and maintain muscle mass as we age (21). Protein intake, in this investigation, was highest in active, younger, males (2.05 ± 0.46) compared to all other groups (1.32 ± 0.43), and all groups were well above the recommended daily allowance (g • kg • d-1) (22). These data, in particular, emphasize the importance of concurrent exercise training in combination with adequate dietary protein intake for muscle quality with early aging as recent groups have advocated for higher protein intake when both healthy (1.0 -1.2 g) and when malnourished or with illness (1.2 -1.5 g) (23).

Concurrent Training Exercise -3.09 .366 15%) but significant differences in myofibrillar and sarcoplasmic muscle protein synthesis rates between slow-twitch versus fast-twitch muscles (25). Further, the muscles of the lower limb do not hypertrophy to the same degree as the KE; thus, limiting the predictability of muscle quality in the PF with aging (25).
The strengths of this investigation were the determination of IMAT and FSCA from MRI images and the objective measure of PA through accelerometry. The limitations were the assessment of self-reported concurrent exercise training status that could have been assessed with objective measures, and the estimates of protein intake were based off participant estimates in their food logs associated analysis in dietary software.

 

Conclusion and Implications

Concurrent exercise training, protein intake, and light physical activity are significant determinants of skeletal muscle health in younger and middle-aged adults. This knowledge may help mitigate age- and inactivity-related loss of muscle quality. Understanding the factors that may impact muscle quality with aging represent important areas of investigation in the future, given the healthcare-related burden that will be present as a higher proportion of the population becomes 65 years and older.

Source of Funding: We acknowledge research funding support from Sanford Health/ NDSU Collaborative Research Seed Grant Program.
Conflicts of Interest: There are no conflicts of interest.
Ethics declaration: The host university’s Institutional Review Board for the protection of human participants approved all procedures.
Acknowledgments: The authors gratefully acknowledge the contributions of Rachel Iverson and Dan Streeter.

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10. Yaskolka Meir A, Shelef I, Schwarzfuchs D, et al: Intermuscular adipose tissue and thigh muscle area dynamics during an 18-month randomized weight loss trial. J Appl Physiol (1985) 2016, 121:518-527.
11. Manini TM, Buford TW, Lott DJ, et al: Effect of dietary restriction and exercise on lower extremity tissue compartments in obese, older women: a pilot study. J Gerontol A Biol Sci Med Sci 2014, 69:101-108.
12. Mamerow MM, Mettler JA, English KL, et al: Dietary protein distribution positively influences 24-h muscle protein synthesis in healthy adults. J Nutr 2014, 144:876-880.
13. Symons TB, Sheffield-Moore M, Wolfe RR, Paddon-Jones D: A moderate serving of high-quality protein maximally stimulates skeletal muscle protein synthesis in young and elderly subjects. J Am Diet Assoc 2009, 109:1582-1586.
14. Phillips BE, Hill DS, Atherton PJ: Regulation of muscle protein synthesis in humans. Curr Opin Clin Nutr Metab Care 2012, 15:58-63.
15. Sjoholm K, Gripeteg L, Larsson I: Macronutrient and alcohol intake is associated with intermuscular adipose tissue in a randomly selected group of younger and older men and women. Clin Nutr ESPEN 2016, 13:e46-e51.
16. Akima H, Yoshiko A, Hioki M, et al: Skeletal muscle size is a major predictor of intramuscular fat content regardless of age. Eur J Appl Physiol 2015, 115:1627-1635.
17. van Hees VT, Fang Z, Langford J, et al: Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985) 2014, 117:738-744.
18. Hildebrand M, VT VANH, Hansen BH, Ekelund U: Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc 2014, 46:1816-1824.
19. Choi L, Liu Z, Matthews CE, Buchowski MS: Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc 2011, 43:357-364.
20. Asp ML, Richardson JR, Collene AL, Droll KR, Belury MA: Dietary protein and beef consumption predict for markers of muscle mass and nutrition status in older adults. J Nutr Health Aging 2012, 16:784-790.
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24. Manini TM, Clark BC, Nalls MA, Goodpaster BH, Ploutz-Snyder LL, Harris TB: Reduced physical activity increases intermuscular adipose tissue in healthy young adults. Am J Clin Nutr 2007, 85:377-384.
25. Mittendorfer B, Andersen JL, Plomgaard P, et al: Protein synthesis rates in human muscles: neither anatomical location nor fibre-type composition are major determinants. J Physiol 2005, 563:203-211.

MOBILITY IN COMMUNITY DWELLING OLDER ADULTS: PREDICTING SUCCESSFUL MOBILITY USING AN INSTRUMENTED BATTERY OF NOVEL MEASURES

 

L. McInnes1, E. Jones2, L. Rochester3, S. Lord3, S.F.M. Chastin4, A.W. Watson2, L. Little2, P. Briggs1

 

1. Northumbria University, Newcastle, United Kingdom, 2.  Northumbria University, Newcastle, UK for duration of study, United Kingdom, 3. Institute of Neuroscience, Newcastle University, United Kingdom, UK, 4. Glasgow Caledonian University, Glasgow, United Kingdom
Corresponding author: Lynn McInnes, Department of Psychology, Northumberland Building, Northumbria University, Newcastle upon Tyne, NE1 8ST, Tel: +44 1912273238,
email: lynn.mcinnes@northumbria.ac.uk

J Frailty Aging 2020;9(2)68-73
Published online October 10, 2019, http://dx.doi.org/10.14283/jfa.2019.35

 


Abstract

Mobility in older adults is associated with better quality of life. However, evidence suggests that older people spend less time out-of-home than younger adults. Traditional methods for assessing mobility have serious limitations. Wearable technologies provide the possibility of objectively assessing mobility over extended periods enabling better estimates of levels of mobility to be made and possible predictors to be explored. Eighty-six community dwelling older adults (mean age 79.8 years) had their mobility assessed for one week using GPS, accelerometry and self-report. Outcomes were: number of steps, time spent in dynamic outdoor activity, total distance travelled and total number of journeys made over the week. Assessments were also made of personal, cognitive, psychological, physical and social variables. Four regression models were calculated (one for each outcome). The models predicted 32 to 43% of the variance in levels of mobility. The ability to balance on one leg significantly predicted all four outcomes. In addition, cognitive ability predicted number of journeys made per week and time spent engaged in dynamic outdoor activity, and age significantly predicted total distance travelled. Overall estimates of mobility indicated step counts that were similar to those shown by previous research but distances travelled, measured by GPS, were lower. These findings suggest that mobility in this sample of older adults is predicted by the ability to balance on one leg. Possible interventions to improve out-of-home mobility could target balance. The fact that participants travelled shorter distances than those reported in previous studies is interesting since this high-functioning subgroup would be expected to demonstrate the highest levels.

Key words: GPS, mobility, aging, monitoring.


 

Introduction

Aging is associated with a reduction in mobility, with people over 70 years making fewer and shorter journeys than younger people (1).  Increasing age and lower income levels have been associated with reduced out-of-home mobility, which can have widespread, detrimental effects in older people for example reduced quality of life, independence and well-being (2, 3-5).
Out-of-home mobility has historically been assessed using self-report (e.g. diaries, questionnaires and surveys (6-8)). The accuracy of such methods is unknown (9) with bias and memory constraints influencing responses. Wearable devices offer new opportunities for researchers to objectively and unobtrusively monitor activity over extended periods.
Accelerometry has been increasingly used to measure mobility in adults across the lifespan (10-13). Different studies report different accelerometry outcomes (e.g. percentages of sedentary and active behaviour, amount of moderate activity and/ or step counts), which can result in difficulties comparing activity levels between populations. Accelerometry can be a useful tool for assessing mobility, however, because it is based on limb movement it gives no indication of context and whether the person is inside or out-of-home.
Novel technology provides a more ecologically valid tool. Global positioning system (GPS) technology enables the measurement of out-of-home mobility using an unobtrusive, portable device that is readily available and affordable with claims of excellent spatial and temporal accuracy (14).  Recently it has been used to track and map older individuals in health and social care settings (15), with dementia (16) and in the community (17-20).
This increasing range of tools to assess out-of-home mobility in older adults provides a range of mobility outcomes to explore and this study is unique in using all three methods to do so.
Of further interest is the identification of predictors that may impact on mobility outcomes.  These characteristics are broadly divided into five categories that have been shown to influence mobility, quality of life and/or mortality in older adults: personal (e.g. age) (4), cognitive (21), physical (22), psychological (e.g. fear of falling) (23) and social (19). As such, a broad range of characteristics from these categories were assessed in this study.  Each of these has been shown to individually influence mobility, however, the extent to which they interact and, which, if any, makes the largest contribution to levels of mobility have yet to be established.
The first aim of this study was to use a novel battery of measures including GPS to explore mobility in a group of community dwelling, cognitively intact older adults. Secondly, we wished to identify personal, cognitive, psychological, physical and social factors that predict mobility.

 

Methods

Participants

Participants were recruited from a larger longitudinal study: North East Age Research (NEAR), which is a 29 year study of cognitive aging in healthy, community resident older people in North East England (24). 308 of the ‘oldest old’ volunteers were invited to participate in the study. One hundred and forty-one individuals accepted (45.7%), of which 100 were contacted to participate and 86 (66 for GPS) of those participated in the study (63 female and 23 male). All participants gave informed written consent before participating. This study was approved by the School of Life Sciences Ethics Committee, Northumbria University.

Procedure

Experimental Protocol

Prior to measurement of mobility a comprehensive battery of standardised tests were administered to assess personal, cognitive, psychological, physical and social variables. Measures included age, gender and health, cognitive function, memory, sleep quality, depression and anxiety, gait speed, balance and social network size (11, 25).

Mobility Measures
Three mobility measurement tools were used:

GPS Location-based Tracking

Participants were visited at their home and fitted with an i-locate GPS tracking device (Trackaphone, UK). The GPS tracking kit used in this study (manufactured by H&G Communications Ltd and supplied by Trackaphone, UK) consisted of: a portable unit (100g, 180*95*13mm) containing a GPS receiver, a charge stand with mains adapter and a material outer sleeve that enables the unit to be worn visibly or discreetly around the arm, shoulders or waist.  For a period of seven days, participants were asked to wear the device every time they left their home. One charge lasted for approximately 48-hours and when not being used (i.e. when participants were in their home) the device was stored on its charger. When not charging, location was sampled every two minutes.
For the week that the device was worn the following outcomes were measured:
i.    Number of journeys outside home – Data points within a 50m radius of home were defined as “home” anything more than 50m was deemed to be out-of-home. A journey was indicated by home coordinates (coordinates within 50m of home) followed by out-of-home coordinates (coordinates that were >50m from home) followed by home coordinates all on the same day. Due to communication issues with the GPS, there were occasions where home was not recorded at the beginning or end (or both) of a journey. For these occasions, home coordinates were manually inserted, to ensure the journeys could be used in the analysis
ii.    Total distance travelled (km) – The distance between successive coordinates was calculated and the summed over the 7-days. All distances were included including those that were within the home radius (50m).
iii.    Furthest distance travelled (km) – The coordinates, reached by the participant, that were furthest from their home coordinates.

Accelerometry

Participants also wore an ActivPALTM activity monitor, which included an accelerometer (PAL Technologies Ltd, Glasgow, UK). This was worn throughout the day and only removed for sleeping, bathing, swimming and showering (11,) Seven variables were extracted from ActivPAL data to represent typical characteristics of active and sedentary behaviour (26, 27).

Diary (self-report)

Activity for each day was recorded using an activity diary, which incorporated a grid divided into 15-minute intervals. There were 30 possible activities for participants to select. Diary data were reduced to 4 types of activity: sedentary indoor (SI), sedentary outdoor (SO), dynamic indoor (DI) and dynamic outdoor (DO).  Two diaries were completed, one prior to the study and one during the study week. Comparison of the diaries at the two time points revealed no differences between participants’ SO, DI and DO (F (1,77)=0.03, 0.13 and 2.28 respectively, all p-values >0.05) showing that their behaviour on these measures was not significantly different when they had the devices compared to when they did not have the devices. However, SI was significantly higher at the first time point (before the devices; F (1,77) = 9.71, p=0.003). DO was selected as the outcome for this measure.

Data analysis

Preliminary analysis was conducted to identify key mobility outcomes. Pearson product correlation coefficients were calculated for each outcome (three from GPS, four from diary and seven from accelerometer) with all possible predictors (personal, cognitive, psychological, physical and social). Four outcomes emerged: two from GPS: total distance travelled and number of journeys; one from accelerometry (number of steps) and one from diary data (‘dynamic outdoors’).

Correlational analysis

Pearson product moment correlations were then used to explore associations between the four outcomes (see above) and all potential predictors. For each outcome, the variables with which it significantly correlated (p<0.05), were entered into a multiple regression model. Four models were calculated. If a number of predictors all correlated with the DV and the predictors were highly correlated then one predictor variable was selected to represent them. Selection was based on theoretical grounds (e.g. TUG for gait measures) or strength of correlation with the outcome. Consequently, each model contained different predictors which were deemed to be the best fit for the individual outcomes (Table 1).

Table 1 Variables entered into each regression model based on univariate analysis

Table 1
Variables entered into each regression model based on univariate analysis

Abbreviations: MMSE=Mini Mental State Exam, NART=National Adult Reading Test, STAI=State-Trait Anxiety Inventory, TUG=Timed Up and Go (Correlation coefficient of variable and outcome. Significant at *p<0.05, **p<0.01, ***p<0.005)

 

Regression analysis

Four forced entry, linear regression models were calculated (one each for outcome: steps, distance, journeys and DO). Collinearity diagnostics (VIF and tolerance) were examined to check for multicollinearity and Durbin-Watson statistic was used to test for autocorrelation. All data met the assumptions for regression analysis. The alpha level was set to 0.05. IBM SPSS Statistics for Windows (V22) Armonk, NY: IBM Corp. was used to analyse the data.

 

Results

Sample characteristics

Table 2 shows that participants were aged 70-91 years (mean age 79.8) and were cognitively intact (Mini Mental State Exam (MMSE) mean = 27.78). Observed daily step counts were 5775.46. Diaries showed just over two hours per day of dynamic outdoor activity (with a maximum of 5.45 hours per day). The GPS data showed that, over the week, participants travelled 49.6Km (30.8 miles) and they made 6.3 separate journeys.

Table 2 Descriptive statistics for the four outcome measures and all predictors used in the models

Table 2
Descriptive statistics for the four outcome measures and all predictors used in the models

Abbreviations: GPS=Global Positioning System, Km= kilometres, mins=minutes, MMSE=Mini Mental State Exam, NART=National Adult Reading Test, secs=seconds, TUG=Timed Up and Go, IQR=Inter-Quartile Range

 

Linear Regression Analysis: prediction of mobility

Balance (single leg stance), MMSE and age significantly predicted the four mobility outcomes, explaining between 32% (GPS: journeys made) and 43 % (accelerometry: number of steps and diary data: amount of time spent in dynamic outdoor activity) of variance in outcome (Table 3). Balance emerged as a significant predictor of all four outcomes.

Table 3 Regression models

Table 3
Regression models

Abbreviations: MMSE= Mini Mental State Exam, NART= National Adult Reading Test, TUG=Timed Up and Go; *p<0.05, **p<0.01, ***p<0.005

 

Discussion

This study used a novel combination of mobility measures to evaluate mobility in community-dwelling, older adults. The keys findings are that the ability to balance on one leg significantly predicts how mobile older adults are; this includes all movement within and outside the home. Moreover, in terms of out-of-home mobility (as measured by GPS and Diary) MMSE (a measure of cognitive ability) is also a significant predictor.
The mobility profile of the group showed lower step counts (5775.46 steps per day) than previous findings of 6000-8500 per day for healthy elderly adults (13, 28) but within an observed range (18) and higher than that found for residents in continuing care (12). In terms of the diary assessment participants reported spending an average of two hours per day engaging in dynamic outdoor activity (with a maximum of 5.45 hours per day). The GPS data showed that on average, participants travelled 49.6 km (30.8 miles) over the week (average daily distance is 7.09km – 4.4 miles) and they made 6.3 separate journeys per week. These distances are considerably lower than those reported for healthy elderly in Israel (approximately 33km/day for 66-72 year olds and around 23km/day for 77-90 year olds) (21) but greater than the average distance travelled from home (0.71 miles/1.14 km) in New York (19). These discrepancies may be due to cultural, climate or location differences.
In this study we sought to determine the factors that can predict mobility in the older age-group. Physical characteristics, specifically the ability to balance on one leg, predicted all four mobility outcomes. Number of journeys and dynamic outdoor activity were both predicted by cognitive ability and balance. Total distance travelled was predicted by age and balance; and steps were predicted by balance only.
The fact that balance influences mobility is not surprising given that balance impairment increases the risk of falls in high-functioning older adults (29). In addition, balance improvement interventions have successfully reduced falls in some groups of older adults (30). However, fear of falling did not predict mobility, which is surprising given the relationship between balance and falls. Moreover, fear of falling did not correlate with either of the GPS outcomes. This may be due to the fact that these participants are a high functioning group with none of them scoring at the higher end of the FES-I scale (the maximum score achieved in this study was 49 out of a possible 64).
Better cognitive ability was associated with increased dynamic outdoor activity and increased number of journeys. There was also a trend for better cognitive ability to be associated with increased distance travelled. Completion of diaries probably relies on aspects of cognitive function so this relationship is not surprising. However, given that cognitive ability also predicted an objective measure of mobility suggests that cognitive ability is directly associated with mobility rather than artefact of diary completion. In addition, this is consistent with previous findings (31) whereby cognitively impaired participants (MCI or dementia) tended to remain closer to home and spent less time out-of-home than healthy participants.
Age predicted walking pattern in this group of participants (22)  and this is consistent with earlier work (32).  The fact that age influences mobility (i.e. total distance travelled) is consistent with previous work (21) which demonstrated that healthy older adults travel shorter daily distances than younger elderly adults. Possible reasons for this have been suggested (17, 19)
Limitations of this study include the low response rate and small sample size along with a broad age range.  The low response rate may mean there is participant bias within the volunteer sample so the findings may not generalise to other subgroups of older adults. Data collection issues and missing data reduced the sample size from 100 to 86 (66 for the GPS study). This is larger than other samples reported in the literature (31) but may have resulted in under-powered analysis, as could the broad age range for the sample size. The post hoc power of the models ranged from 0.49 to 0.85 with the lowest power from the GPS models. The self-report diary methods may be viewed as out-of-date due to the advancement and availability of technology such as smartphones to assess mobility. However, ownership by older adults is still low compared to younger populations (33) so mobility metrics used must be available, feasible and acceptable for the population.
Overall this study demonstrates the importance of a few factors in predicting mobility in this group of successfully aging adults. However, there is a need to further explore the contribution of balance to overall mobility in a larger, more diverse sample. In addition the potential benefit of simple balance interventions to increase mobility are considerable and they should be examined further. A Cochrane review (34) concluded that there was a need for randomised controlled trials to examine the efficacy of balance interventions to reduce falls in older adults. The current research suggests that future research should also examine the long-term effects of balance interventions to improve mobility and consequently improve quality of life and wellbeing of older adults.

 

Funding: This work was supported by the New Dynamics of Ageing initiative, a multidisciplinary research programme supported by AHRC, BBSRC, EPSRC, ESRC and MRC: Grant number RES – 352 -25 – 0023. We acknowledge the support of the UK NIHR Biomedical Research Centre for Ageing and Age-related disease award to the Newcastle upon Tyne Hospitals NHS Foundation Trust.
Acknowledgements: We acknowledge assistance from Trackaphone who supplied the GPS equipment and data. We would also like to thank Dr Nicola Hopley, Joanne Forster and Julie Khan for their help with data collection and the volunteers from North East Age Research who participated in this research.
Conflict of Interest: The authors have nothing to disclose.
Ethical standards: This study was approved by the School of Life Sciences Ethics Committee, Northumbria University.

 

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IMPACT OF FAT-FREE ADIPOSE TISSUE ON THE PREVALENCE OF LOW MUSCLE MASS ESTIMATED USING CALF CIRCUMFERENCE IN MIDDLE-AGED AND OLDER ADULTS

 

T. Abe1, S.J. Dankel2, Z.W. Bell1, E. Fujita3, Y. Yaginuma3, T. Akamine3, R.W. Spitz1, V. Wong1, R.B. Viana1,4, J.P. Loenneke1

 

1. Department of Health, Exercise Science, & Recreation Management, Kevser Ermin Applied Physiology Laboratory, The University of Mississippi, University, MS 38677, USA;
2. Department of Health and Exercise Science, Rowan University, Glassboro, NJ 08028, USA; 3. Department of Sports and Life Sciences, National Institute of Fitness and Sports in Kanoya, Kagoshima, Japan; 4. Faculty of Physical Education and Dance, Federal University of Goiás, Goiânia, Brazil.
Corresponding author: Takashi Abe, PhD, 224 Turner Center, University, MS 38677, USA, Phone: +1 (662) 915-5521, Fax: +1 (662) 915-5525, Email:  t12abe@gmail.com

J Frailty Aging 2020;9(2)90-93
Published online October 10, 2019, http://dx.doi.org/10.14283/jfa.2019.34

 


Abstract

Previous studies proposed calf circumference cutoff values for predicting dual-energy X-ray absorptiometry (DXA)-derived low muscle mass. However, DXA-derived appendicular lean mass (aLM) includes non-skeletal muscle components such as the appendicular fat-free component of adipose tissue fat cells (aFFAT). The purpose of this study was to compare the calf circumference method of classification before (Model #1) and after (Model #2) eliminating the influence of FFAT in healthy Japanese adults (50 to 79 years; mean age 70 (SD 7) years). Model 1, and Model 2 for classifying low muscle mass had a sensitivity of 78% and 64%, specificity of 76% and 75%, positive predictive value of 31% and 28%, and negative predictive value of 96% and 93%, respectively. Appendicular fat-free component of adipose tissue has the potential to influence the ability of calf circumference to accurately classify individuals with low muscle mass. Consideration should be made when using this as a screening tool for low muscle mass.

Key words: Aging, anthropometry, dual-energy x-ray absorptiometry, muscle mass, sarcopenia.


 

Introduction

Sarcopenia refers to low muscle mass and poor physical function in older men and women. In a recent consensus, low muscle mass has been defined by the amount of appendicular lean soft tissue mass (absolute or relative to height squared) an individual has as measured by dual-energy X-ray absorptiometry (DXA) (1). These definitions are diagnostically useful for healthcare professionals who treat individuals at risk for low muscle mass and provide clinically relevant target goals for those working with these individuals. However, DXA measurements are costly and the device has limited availability. Therefore, the DXA is impractical for screening large populations and creates a need for a simple, evidence-based, screening tool for identifying individuals who have low muscle mass (2, 3).
Anthropometric measurement is a simple method that can be used when it may be impractical or cumbersome to use more sophisticated measurements. When height is identical, body mass (fat mass plus fat-free body mass) may be positively related to limb and trunk circumferences of the body. Previous studies have reported that there is a moderate to strong correlation between calf circumference and DXA-derived appendicular lean mass (aLM) and aLM index in Japanese middle-aged and older men and women (4), Brazilian older men and women (5) and French old women (6). Further, two studies (4, 5) indicated that a calf circumference less than 34 cm in men and 33 cm in women is associated with low muscle mass. However, DXA-derived aLM includes non-skeletal muscle and non-organ components, such as fat-free adipose tissue (FFAT) in the extremities (7, 8). This FFAT, if not accounted for, could falsely inflate the aLM in individuals with a relatively high amount of adipose tissue mass (9). Therefore, it is unknown whether FFAT impacts the diagnostic utility of calf circumference for correctly classifying low muscle mass. In the present study, we compare the calf circumference method of classification before and after eliminating the influence of FFAT.

 

Methods

Participants

Two-hundred and sixty-five apparently healthy Japanese adults (108 men and 157 women) aged 50 to 79 years volunteered for this study (Table 1). Written informed consent was obtained from all participants after the explanation of the study purpose and potential risks. The participants were well-functioning middle-aged and older adults evaluated by using usual walking speed (>1.0 m/s), and were free of chronic health conditions (e.g. myocardial infarction, stroke, cancer, myositis, and neuromuscular disorders) as assessed by self-report. This study was approved by the Institutional Review Board of the National Institute of Fitness and Sports in Kanoya.

Anthropometric measurements

Body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively, by using a height scale (Yagami YG-200, Tokyo, Japan) and an electronic weight scale (Tanita WB-260A, Tokyo, Japan). Body mass index was calculated as body mass (kg) / height squared (m2). Calf circumference was measured to the nearest 0.1 cm by using a tape measure at 30% proximal of lower leg length (between the lateral malleolus of the fibula and the lateral condyle of the tibia) on the right side of the body. This 30% position was nearly identical to the maximum circumference of the lower leg.

DXA measurements

A whole-body DXA scan was performed using a DXA scanner (Discovery A, Hologic Inc., Bedford, MA, USA) to determine percentage of body fat, total body fat mass, appendicular fat mass (aFM), total body lean tissue mass, and aLM. Quality assurance testing and calibration were performed the morning of data collection days to ensure that the DXA scanner was operating properly. Participants were asked to refrain from vigorous exercise for at least 24 hours prior to the scans. Also, participants were asked to refrain from eating for at least 3 hours prior to scans. Appendicular fat mass and aLM calculations were based on the sum of fat mass or lean mass in all four limbs. Test-retest reliability (minimal difference; 0.58 kg aLM) of the DXA measurements was reported previously (7).

Fat-free adipose tissue mass (FFAT) estimation

Appendicular FFAT (aFFAT) was calculated based on a model proposed by Heymsfield et al. (8). The model described that 85% of adipose tissue is fat and the remaining 15% of adipose tissue is calculated fat-free component.  According to the model, appendicular adipose tissue mass can be calculated as appendicular adipose tissue mass = DXA derived aFM ÷ 0.85. Then, aFFAT can be calculated as aFFAT = appendicular adipose tissue mass × 0.15. Adjusting for the influence of aFFAT on DXA determined aLM, we calculated an index: aLM minus aFFAT (aLM-aFFAT).

Diagnosing criterion of low muscle mass

We used the diagnosing criterion of low muscle mass proposed by the Asian Working Group for Sarcopenia (7.0 kg/m2 in men and 5.4 kg/m2 in women) for the aLM index. According to the previous study (10), we also used new cutoff values of low muscle mass which eliminated appendicular FFAT (6.7 kg/m2 in men and 4.9 kg/m2 in women) for the aLM-aFFAT index.

Cutoff values of calf circumference

Previous studies have proposed the optimal calf circumference cutoff values (34 cm in men and 33 cm in women) for predicting low muscle mass (4, 5). In this study, we used these cutoff values for the analysis.

Statistical analysis

Confusion matrices were constructed to test the ability of calf circumference to predict low muscle mass before (Model #1) and after (Model #2) correcting for aFFAT. Following construction of each confusion matrix, we determined the sensitivity, specificity, positive predictive value, and negative predictive value for each model.

 

Results

In model #1 (Table 2), 12% of the overall sample was classified as having low muscle mass (15% of men and 10% of women). The calf circumference method of assessing low muscle mass had an overall sensitivity of 78% (69% in men and 88% in women), specificity of 76% (84% in men and 71% in women), positive predictive value of 31% (42% in men and 25% in women), and negative predictive value of 96% (94% in men and 98% in women).

Table 1 Physical characteristics and body composition of the participants

Table 1
Physical characteristics and body composition of the participants

Values are means ± standard deviations (SD); aFFAT, appendicular fat free adipose tissue mass; aLM, appendicular lean tissue mass; aLM-aFFAT, appendicular lean mass minus aFFAT; SD, standard deviation

 

In model #2 (Table 2), 14% of the overall sample was classified as having low muscle mass (18% of men and 11% of women). The calf circumference method of assessing low muscle mass had an overall sensitivity of 64% (58% of men and 71% of women), specificity of 75% (83% of men and 69% of women), positive predictive value of 28% (42% of men and 22% of women), and negative predictive value of 93% (90% of men and 95% of women).

Table 2 Confusion matrix for the classification of low muscle mass using the calf circumference method. Model #1 is using appendicular lean mass (aLM) as the criterion and Model #2 is using appendicular lean mass following the elimination of fat free adipose tissue as the criterion (aLM-FFAT)

Table 2
Confusion matrix for the classification of low muscle mass using the calf circumference method. Model #1 is using appendicular lean mass (aLM) as the criterion and Model #2 is using appendicular lean mass following the elimination of fat free adipose tissue as the criterion (aLM-FFAT)

 

Discussion

In the last few years, several studies investigated the utility of anthropometric measurements in the prediction of skeletal muscle mass, especially for the lower extremity (4, 5, 11). Two of these studies proposed the same calf circumference cutoff values (34 cm in men and 33 cm in women) for predicting low muscle mass as measured by DXA. These studies reported that the calf circumference cutoff values had relatively high sensitivity (67-88%) and specificity (73-91%) for predicting low muscle mass in middle-aged and older men and women (4, 5). In the present study, we found that eliminating the influence of FFAT had minimal influence on specificity but did impact the sensitivity of the test (reduced from 78% to 64%). That is, the proportion of individuals with low muscle mass who test positive for having low muscle mass is reduced following FFAT correction. Further, the positive predictive value, the fraction of the positive predictions that are actually positive, was only 31% in model #1 and 28% in model #2. When interpreting the positive predictive value, it is important to consider the prevalence of low muscle mass. In contrast, the sensitivity and specificity are properties of the test and are not usually influenced by the prevalence of the condition (12).
A simple estimation method for diagnosing low muscle mass is desired, and anthropometric measurements are the most affordable method. However, the calf circumference may be affected not only by lower limb muscle mass but also by the structure of the ankle joint such as plantar flexor moment arm (13). In addition, daily physical activity at moderate and vigorous intensities measured by an accelerometer was associated with higher muscle size in the lower leg, but not in other body sites (14). For a simple measurement, the single site assessment of calf circumference may serve as a reasonable screening tool for identifying individuals who may need an in-depth evaluation using more sophisticated measurements.
In conclusion, eliminating the influence of aFFAT impacted the sensitivity of calf circumference in detecting low muscle mass; but not specificity. Prior to the elimination of aFFAT, the test correctly classified 8 out of every 10 individuals with low muscle mass. Following the elimination of aFFAT, calf circumference only correctly classified 6 out of every 10 individuals with low muscle mass. These findings suggest that aFFAT can influence the ability of calf circumference to accurately classify individuals with low muscle mass. Failure to correct for aFFAT may result in a false inflation of sensitivity.

 

Ethics declaration: All experimental procedures were conducted in accordance with the guidelines in the Declaration of Helsinki and approved by the university’s institutional review board.
Conflict of Interest: No conflicts of interest, financial or otherwise, are disclosed by the authors.
Funding: This study received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Acknowledgements: Our appreciation is extended to the volunteers who participated in this study. We also thank the graduate students for their assistance in the testing of this study.

 

References

1.    Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019;48:16–31.
2.    Paris M, Mourtzakis M. Assessment of skeletal muscle mass in critically ill patients: considerations for the utility of computed tomography imaging and ultrasonography. Curr Opin Clin Nutr Metab Care 2016;19:125–130.
3.    Abe T, Thiebaud RS, Loenneke JP. Simple chart for practical screening of low muscle mass in well-functioning middle-aged and older men and women. Geriatr Gerontol Int 2018;18:657–658.
4.    Kawakami R, Murakami H, Sanada K, et al. Calf circumference as a surrogate marker of muscle mass for diagnosing sarcopenia in Japanese men and women. Geriatr Gerontol Int 2015;15:969–976.
5.    Pagotto V, Santos KF, Malaquias SG, Bachion MM, Silveira EA. Calf circumference: clinical validation for evaluation of muscle mass in the elderly. Rev Bras Enferm 2018;71:322–328.
6.    Rolland Y, Lauwers-Cances V, Cournot M, et al. Sarcopenia, calf circumference, and physical function of elderly women: A cross-sectional study. J Am Geriatr Soc 2003;51:1120–1124.
7.    Abe T, Thiebaud RS, Loenneke JP, Fujita E, Akamine T. DXA-rectified appendicular lean mass: Development of ultrasound prediction models in older adults. J Nutr Health Aging 2018;22:1080–1085.
8.    Heymsfield SB, Gallagher D, Kotler DP, et al. Body-size dependence of resting energy expenditure can be attributed to nonenergetic homogeneity of fat-free mass. Am J Physiol Endo Metab 2002;282:E132–E138.
9.    Abe T, Dankel SJ, Loenneke JP. Body fat loss automatically reduces lean mass by changing the fat-free component of adipose tissue. Obesity (Silver Spring) 2019;27:357–358.
10.    Abe T, Loenneke JP, Thiebaud RS, Fijita E, Akamine T. The impact of DXA-derived fat-free adipose tissue on the prevalence of low muscle mass in older adults. Eur J Clin Nutr 2019;73:757–762.
11.    Diano D, Ponti F, Guerri S, et al. Upper and lower limbs composition: a comparison between anthropometry and dual-energy X-ray absorptiometry in healthy people. Arch Osteoporos 2017;12:78.
12.    Ranganathan P, Aggarwal R. Common pitfalls in statistical analysis: Understanding the properties of diagnostic tests – Part 1. Perspect Clin Res 2018;9:40–43.
13.    Baxter JR, Piazza SJ. Plantar flexor moment arm and muscle volume predict torque-generating capacity in young men. J Appl Physiol 2014;116:538–544.
14.    Abe T, Mitsukawa N, Thiebaud RS, et al. Lower body site-specific sarcopenia and accelerometer-determined moderate and vigorous physical activity: the HIREGASAKI study. Aging Clin Exp Res 2012;24:657–662.

CLINICALLY MEANINGFUL CHANGE FOR PHYSICAL PERFORMANCE: PERSPECTIVES OF THE ICFSR TASK FORCE

 

J. Guralnik1, K. Bandeen-Roche2, S.a.r. Bhasin3, S. Eremenco4, F. Landi5, J. Muscedere6, S. Perera7, J.-Y. Reginster8, L. Woodhouse9, B. Vellas10 and the ICFSR Task Force

 

1. University of Maryland School of Medicine, Baltimore, MD, USA; 2. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4 – Critical Path Institute, Tucson, AZ, USA; 5. Fondazione Policlinico A. Gemelli, Catholic University, Rome, Italy; 6. Queen’s University, Kingston, Ontario, Canada; 7. University of Pittsburgh, Pittsburgh, PA, USA; 8. University of Liege, Liege, Belgium; 9. University of Alberta, Edmonton, Alberta, Canada; 10. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.
Corresponding author: Jack Guralnik, University of Maryland School of Medicine, Baltimore, MD, USA, jguralnik@epi.umaryland.edu.

Task force members: Samuel Agus (Paris, France), Islene Araujo de Carvalho (Geneva, Switzerland), Mylène Aubertin-Leheudre (Montréal, Canada), Karen Bandeen-Roche (Baltimore, USA), Ann Belien (Hesusden-Zolder, Belgium), Shalender Bhasin (Boston, USA), Heike Bischoff-Ferrari  (Zurich, Switzerland), Andreas Busch (Vevey, Switzerland), Ryne Carney (Washington, USA), Matteo Cesari (Milano, Italy), Caroline Couleur (Epalinges, Switzerland), Alfonso Cruz Jentoft (Madrid, Spain), Susanna Del Signore (London, United Kingdom), Carla Delannoy (Vevey, Switzerland), Waly Dioh (Paris, France), Sonya Eremenco (Tucson, USA), Bill Evans (Durham, USA), Toby Ferguson (Cambridge, USA), Roger Fielding (Boston, USA), Jack Guralnik (Baltimore, USA), Ludo Haazen (Hesusden-Zolder, Belgium), Joshua Hare (Miami, USA), Aaron Hinken (Collegeville, USA), Darren Hwee (South San Francisco, USA), Lori Janesko (Uniontown, USA), Kala Kaspar (Vevey, Switzerland),  Francesco Landi (Roma, Italy), Valérie Legrand (Nanterre, France), Bradley Morgan (South San Francisco, USA), John Morley (St Louis, USA), John Muscedere (Kingston, Canada), David Neil (Collegeville, USA),  Marco Pahor (Gainesville, USA),  Marika Paul (Columbus, USA), Subashan  Perera (Pittsburgh, USA), Suzette Pereira (Columbus, USA), John Rathmacher (Ames, USA), Jean Yves Reginster (Liège, Belgium), Leocadio Rodriguez Manas (Getafe (Madrid), Spain), Yves Rolland (Toulouse, France), Michelle Rossulek (Cambridge, USA), Jorge Ruiz (Miami, USA), Lisa Tarasenko (Cambridge, USA), Effie Tozzo (Cambridge, USA), Heber Varela (Miami, USA), Bruno Vellas (Toulouse, France), Jeremy Walston (Baltimore, USA), Debra Waters (Dunedin, New Zealand), Linda Woodhouse (Edmonton, Canada)

J Frailty Aging 2019 ;in press
Published online October 10, 2019, http://dx.doi.org/10.14283/jfa.2019.33

 


Abstract

For clinical studies of sarcopenia and frailty, clinically meaningful outcome measures are needed to monitor disease progression, evaluate efficacy of interventions, and plan clinical trials. Physical performance measures including measures of gait speed and other aspects of mobility and strength have been used in many studies, although a definition of clinically meaningful change in performance has remained unclear. The International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and sarcopenia, met in Miami Beach, Florida, USA in February 2019 to explore approaches for establishing clinical meaningfulness in a manner aligned with regulatory authorities. They concluded that clinical meaningful change is contextually dependent, and that both anchor- based and distribution-based methods of quantifying physical function are informative and should be evaluated relative to patient-reported outcomes. In addition, they identified additional research needed to enable setting criteria for clinical meaningful change in trials.

Key words: Sarcopenia, frailty, aging, disability, physical performance, clinically meaningful change, outcome measures.


 

 

Introduction

Clinical research studies in older populations have shifted over the last two decades from assessing biological indicators and disease status to measuring physical function as a primary endpoint. This shift reflects the World Health Organization’s (WHO’s) framework for health and disability, the International Classification of Functioning, Disability and Health (ICF) (1), which provides a multidimensional framework for conceptualizing and understanding functioning and disability by integrating medical and biopsychosocial models. Using a patient-focused approach, the ICF promotes the use of language that frames disablement not in terms of disease but in how people live with their conditions across three domains: body function and structure, activity, and participation, i.e., movement in three-dimensional space, interacting with other people, and socialization (2).
With new interventions for sarcopenia on the horizon, the concept of clinical meaningfulness has emerged as an important concern for researchers, clinicians, and regulators. Thus, the International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and age-related muscle loss (sarcopenia), convened a group of experts on February 19, 2019 to explore approaches for establishing clinical meaningfulness and related regulatory considerations.
Clinical meaningfulness, as defined by the U.S. Food and Drug Administration (FDA), requires that an outcome assessment measure something that is clinically important and that substantively affects how the patient feels, functions, or survives. Thus, clinically meaningful outcome measures for sarcopenia and frailty typically assess physical function, quality of life, and survival. Muscle strength and muscle mass may also be considered as outcome measures but only if they correlate with improved function or predict other relevant health outcomes such as reduced mortality, morbidity, institutionalization, and disability (3-7).
Clinically meaningful measures may be used to monitor adults in clinical settings and in observational studies, to evaluate efficacy in clinical trials, and to compute sample size and power when planning a clinical trial (8). However, meaningful change in an observational study may differ from meaningful change in an intervention trial where change can occur much more rapidly in the positive direction and must have both clinical and public health relevance. Since rapid changes may be perceived as being much greater in magnitude than those that occur more gradually, objective measurement is important.

 

Defining a clinically meaningful change in physical performance

Meaningful change can be defined as a change that has clinical or practical importance, has an impact on an individual’s self-perceived health status or quality of life, or as a fraction of the standard deviation representing a certain level of movement across the distribution of measurements in the population. Clinically meaningful change depends on the outcome on which it is based. Physical performance measures regularly used in clinical trials include various measures of gait and balance parameters and/or the Short Physical Performance Battery (SPPB), a composite measure of walking speed, standing balance, and sit-to-stand performance (9). Gait performance measures include the 4-meter gait speed test (4MGS), the 6-minute walk distance test (6MWD), the 10-meter walk test (10MWT), the timed 400-meter walk (400MW), and the 3-meter timed “Up & Go” test (TUG) (10), (11).  Other possible measures such as gait variability, unipodal balance, and stair negotiation performance may also be used to assess mobility impairments (12, 13). Most evidence has been gathered for the 4MGS, which can be performed in a reasonably small space. For example, in a prospective cohort study of older adults, Perera and colleagues showed that a decline in gait speed of 0.1 m/s on the 4MGS  or 1 point on the SPPB over a one-year period was associated with an increased risk of subsequent mortality (14).
Clinically meaningful changes in outcomes may be expressed as changes that exceed minimally clinically important differences (MCID), clinically meaningful differences (CMD), or minimally important changes (MIC) (15). To determine the MCID and Minimally Clinically Important Improvement (MCII), either distribution-based or anchor-based measures may be used. Distribution-based methods use statistical and psychometric properties of a measure to estimate effect size and standard error of measurement (SEM=σ(1-r)1/2, where σ=standard deviation and r=reliability (16)) as functions of variability and reliability, while anchor-based methods use a change in the patient’s or provider’s perception to identify the corresponding magnitude of change in a selected measure (8).
Preliminary work by Perera and colleagues estimated what constitutes a meaningful change for three performance measures: gait speed, SPPB, and 6MWD using data from varying populations enrolled in both observational and clinical studies: 1) a basic training data set of a 3-month clinical trial of strength training intervention in people with mild-to-moderate limitations; 2) 1-year data of participants in the Predicting Elderly Performance (PEP) study dataset; and 3) 3-month data from the Stroke Rehabilitation (REHAB) randomized clinical trial of a therapeutic exercise program (8). Using both distribution- and anchor-based approaches, they concluded that small but meaningful changes are near to 0.05 m/s for gait speed, 0.5 points for SPPB, and 20m for 6MWD; and that substantial changes were near to  0.10 m/s for gait speed, 1.0 point for SPPB, and 50m for 6MWD.
They also found that meaningful changes are not affected by gender, race, or baseline performance in the Health ABC study. While men tended to have greater magnitudes for meaningful change in 400MWT and there were health and disease differences (e.g. substantial change estimate for SPPB for those with greater body mass index (BMI) when the anchor of walking ¼ mile was used, but not using other anchors), they did not show a consistent pattern and were limited by dropout bias in 400MWT (17).
In the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) study of exercise as an intervention, investigators examined the relationship between self-reported and performance measures and estimated the magnitude of meaningful change in 400MWT, 4MGS, and SPPB (18). They used both distribution-based and anchor-based methods, two magnitudes of change, and multiple indicators of self-perceived mobility. Relationships between self-reported and performance measures were consistent between treatment arms. Minimally significant changes were 20-30 seconds in the 400MWT, 0.03-0.05 m/s in the 4MGS, and 0.3-0.8 points in the SPPB. Substantial changes were 50-60 seconds in the 400MWT, 0.08 m/s in the 4MGS, and 0.4-1.5 points in the SPPB.

 

A validation approach to define meaningful change

A crucial first step in defining meaningful change is to clarify what is meant by the concept of meaningful change. A clinically important change in physical functioning should be large enough that a person perceives the change or that participation (e.g., daily roles) is affected. In clinical trials, a clinically important change indicates a treatment effect large enough to support market authorization of a drug. The analytical approach chosen should be driven by how meaningful change is defined for a particular study depending on its main purpose.
Defining meaningful change may be challenging for several reasons. First, meaningful change varies according to context, including baseline level of function as well as demographic and disease considerations. Second, when no gold standard exists with which to make a comparison, the measures by which meaningful changes are judged may not reflect the true state.
One method for assessing the ability of a measure to discriminate individuals by their anchor status is to determine meaningful adverse change (MAC) that achieves both good sensitivity and specificity (19). The Women’s Health and Aging Study (WHAS), an observational study on the characteristics and progression of disability in older, functionally limited women (20) provides an example of a validation framework for evaluating change over the course of one year using usual pace walking speed as the performance measure and self-reported walking difficulties as the anchor. Participants were dichotomized into those who worsened in any one of seven categories of walking difficulty and those who did not worsen in any category, and walking speed change was compared for those two groups. The difference in mean change between those two groups was estimated at -0.091 meters/sec (95% confidence interval [CI] of -0.128 to -0.054), with a mean change among those not worsening of 0.011 (95% CI of -0.014 to 0.035). A decline of 0.10 m/sec (substantial change), however, had a sensitivity of .41 and specificity of 0.73 for self-perceived worsening, and receiver operating characteristic (ROC) analysis of the ability to discriminate clinical change yielded an area under the curve (AUC) of only 0.59, suggesting that other considerations would be needed to adjudicate whether this is good enough for clinical practice in the community-dwelling context of the WHAS. Reanalyzing the data by evaluating empirical cumulative probability distributions of walking speed stratified by decline in 3 categories of walking difficulty all the way to improving in 3 categories of walking difficulty yielded overlapping curves (except when contrasting perception changes transitioning across multiple categories), indicating that either the anchor is inappropriate or a more sensitive performance measure is needed. In such a context, building performance indices combining multiple measures simultaneously may prove useful for enhancing precision.

 

Combining performance and patient reported outcome measures

Patient reported outcome measures (PROMs) have been advocated by regulatory agencies because they provide information about what is meaningful to patients. For example, fear of falling is one possible patient-reported measure that might correlate well with balance, strength, and other mobility-related functions. Many studies combine PROMs with performance measures since they provide complementary information (21). In a prospective cohort study, Perera and colleagues showed that performance change and self-reported change were both independently associated with 5-year survival (14).
Studies comparing self-reported versus activity-based performance measures of function such as self-paced walk, TUG, and stair tests have shown moderate correlations (22-25), suggesting that the measures provide complementary information. Moreover, these studies show that the selection of measures is condition specific. For example, in these studies the TUG was shown to be the most sensitive measure to change in patients who have undergone total hip replacement, while in patients undergoing knee arthroplasty the stair measure was more responsive to change.

Case study: Determining meaningful change in physical function in testosterone trials in older men (TOM)

The Testosterone in Older Men with Mobility Limitations (TOM) trial was designed to determine the effect of testosterone administration on physical function and lower extremity strength in older men with mobility limitations and low serum levels of testosterone. The trial was terminated early as a result of an increase in adverse cardiovascular events among participants in the treatment group (26). The trial included both a self-reported measure, the Late-Life Function and Disability Instrument (LLFDI), and several performance-based measures including handgrip strength, bilateral leg and chest press (a measure of strength and power), 12-step stair climb, the 40-meter walk test, and the SPPB. The LLFDI assesses participants’ ability to complete discrete actions or activity and their performance of socially-defined tasks (activity and participation in the ICF framework).
Both anchor-based and distribution-based methods were used to determine the MCID for physical function. To assess anchor-based responsiveness, participants were grouped according to self-reported global rating of improvement (better versus no change or worse). The distribution-based responsiveness analysis provided an estimate of effect size, minimal detectable change based on a 90% CI (MDC90), and the percentage of participants exceeding MDC90 by group.
These analyses demonstrated that loaded walk and stair climb were the most sensitive, with anchor and distribution-based measures being similar. The SPPB balance assessment was the least sensitive measure. Handgrip strength and LLFDI were not responsive to change while both the Foundation of the National Institutes of Health (FNIH) and European guidelines advocate using handgrip strength to identify participants for sarcopenia trials (27, 28).  These results suggest that this measure may be less useful to measure responsiveness to an intervention.

 

Regulatory considerations of clinically meaningful change

Regulators prefer hard clinical endpoints to surrogate endpoints (e.g. biomarkers) when making decisions about market authorization. For example, in osteoporosis trials, a statistically significant difference in fracture rates – a hard clinical endpoint – is considered meaningful (29), whereas a surrogate endpoint such as bone mineral density would not in and of itself be considered meaningful, although it may be used to bridge studies for extension of indications.
The European Medicines Agency (EMA) guideline on clinical investigation of medicinal products used pain and function as co-primary endpoints in the treatment of osteoarthritis (30). The expert consensus committee that developed the guidelines suggested the threshold for minimal perceptible clinical improvement in pain as a 10 mm improvement on a 100 mm visual analog pain scale for drugs intended to improve symptoms or at least a 5 mm mean difference between placebo and active groups (31). These criteria were applied in a trial of chondroitin sulfate compared to placebo and the non-steroidal anti-inflammatory drug (NSAID) celecoxib, which showed that both drugs produced a statistically significant and clinically relevant improvement, yet whether the magnitude of the effect is sufficient to justify granting market approval remained an unanswered question (32).
A PROM, the SarQoL, has been developed to assess quality-of-life in sarcopenia patients (33). While it has demonstrated the ability to detect statistically significant change, the MIC has not yet been determined; thus, the clinical significance is not clear.
Whether to use continuous or dichotomous variables may also be discussed with regulators. For example, the FRActure in postmenopausal woMen with ostEoporosis (FRAME) study of the bone-forming agent romosozumab assessed percent change in BMD from baseline, demonstrating that the treatment results in a rapid increase in BMD in comparison to bone loss in the placebo group and at the same time reduces fracture risk (34). When using percent change the clinical significance of the observed absolute change must also be considered.
In addition to data on clinically meaningful change used to support marketing authorization for a treatment, payers and policy makers may require additional real-world data and cost-effectiveness studies to support reimbursement (35). For example, validation of the FRAX risk assessment tool was achieved by the Screening for Osteoporosis in Older Women for the Prevention of Fracture (SCOOP) study in the United Kingdom, which showed that screening with FRAX resulted in a reduced risk of hip fracture, i.e., that the tool is medically relevant (36). Another real-world study conducted by the French Ministry of Health – the Pharmaco-Epidemiology of GonArthroSis and coxarthrosis (PEGASus) study — assessed the ability of multiple symptomatic slow-acting drugs for osteoarthritis to reduce the consumption of NSAIDs, which are associated with substantial adverse reactions. Only glucosamine sulfate showed a significant reduction in consumption of NSAIDs.
The FDA has a somewhat different perspective on meaningful change in that they focus on within-patient anchor-based change. Distribution-based approaches may be used as supportive or supplementary information. Moreover, they require changes to be meaningful to the patient, using terms to which patients can relate. This has led them to incorporate patient preferences into their deliberations and selection of outcome measures.
The Aging in Motion (AIM) coalition has been working with FDA for several years on a project to qualify gait speed alone and the SPPB as acceptable and endorsed measures of function. However, the agency has stressed the need for both an objective measure such as SPPB and a self-report approach, which has increased the complexity of the qualification process.
PROMs present many potential challenges for sponsors.   The correlation between PROMs and objective performance measures is modest, and the FDA suggests using them together as joint outcomes. Multiple primary outcomes may increase trial sample size requirements. PROMs are also subject to differences in beliefs and behaviors, making them more difficult to compare across diverse populations. One suggested approach would be to use a goal attainment scale in which the patient sets goals as well as metrics for success.
PROMs, including QOL measures, also must be very specific to the indication. For sarcopenia, this means that PROMs should relate to how low muscle mass affects how patients feel, function, and survive. Used in combination with performance measures, they could provide a powerful way of demonstrating efficacy. While there has been a reluctance of pharmaceutical companies to embed context-specific PROMs in Phase 2 and 3 studies, doing so would produce an enormous body of data that could help establish relevant anchors to estimate MCID and validate other measures.

 

Moving Forward

One problem for research into how the ICF guidelines are interpreted is that structure and function are typically assessed with clinical measures applied in a controlled environment while assessment of activity and participation require capturing the patient perspective, which is heavily influenced by the environment, adaptation mechanisms, resilience, and coping. Moreover, meaningful change is context, perspective, and purpose dependent.
The Task Force identified several key areas for future research that should be considered when setting the criteria for a clinically meaningful change in a clinical trial:
•    Published estimates of MCID derived from study participants who are only mildly functionally limited may have limited value for studies that enroll participants at high risk of physical disability. In substantially impaired participants, a small improvement in a performance test may translate into a large benefit in daily life and be perceived by the participant. Future work should address MCID in subsets of the population stratified by ability, with the instruments chosen being appropriate for that level of ability.
•    The validation framework described above offers a paradigm for thinking carefully about the ideal definition of clinically meaningful change and then working backwards to identify how to measure and assess meaningful change.
•    To measure clinically meaningful changes in real-world performance, it may be appropriate to incorporate into trials continuous digital technologies such as accelerometers as well as novel analytical techniques to determine MCID, CMD, and MCII. Signal processing of accelerometer data may also identify additional features predictive of adverse or beneficial outcomes.

 

Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.
Conflicts of interest:  The Task Force was partially funded by one educational grant from the Aging In Motion Coalition and registration fees from industrial participants (Biogen, Biophytis, Cytokinetics, Glaxosmithkline, Longeveron, Pfizer and Rejuvenate Biomed NV). These corporations placed no restrictions on this work. S. Eremenco, F. Landi declare there are no conflicts. Dr. Guralnik reports personal fees from Pluristem , personal fees from Viking Therapeutics, personal fees from Novartis Pharma, outside the submitted work. K. Bandeen-Roche reports grants from National Institutes of Health,  during the conduct of the study.
S.A.R.  Bhasin reports grants from AbbVie, grants from Alivegen, grants from MIB, other from FPT, other from AbbVie, outside the submitted work. J. Muscedere is Scientific Director for the Canadian Frailty Network, a non-for profit network funded by the Government of Canada. S. Perera has received Travel expenses to the International Conference on Frailty and Sarcopenia Task Force meeting in February 2019 in Miami Beach, FL paid by Alliance for Aging Research. J.Y. Reginster reports grants and personal fees from IBSA-GENEVRIER, grants and personal fees from MYLAN, grants and personal fees from RADIUS HEALTH, personal fees from PIERRE FABRE, grants from CNIEL, personal fees from DAIRY RESEARCH COUNCIL (DRC), outside the submitted work. B. Vellas reports grants from Nestle, Nutricia, Novartis outside the submitted work.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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