ASSOCIATION OF FINGER TAPPING MOVEMENTS WITH FRAILTY STATUS IN OLDER JAPANESE ADULTS: A CROSS-SECTIONAL STUDY

 

R. Shi^1,2,*, W. Hao^1,*, W. Zhao¹, T. Kimura¹, T. Mizuguchi³, S. Ukawa⁴, K. Kondo^5,6, A. Tamakoshi¹

 

1. Department of Public Health, Hokkaido University Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan; 2. Haidian Maternal and Child Health Hospital, Beijing, China; 3. Maxell, Ltd. Yokohama, Kanagawa, Japan; 4. Department of Social Welfare Science and Clinical Psychology, Osaka Metropolitan University Graduate School of Human Life and Ecology, Osaka, Osaka, Japan; 5. Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, Chiba, Chiba, Japan;
6. Department of Gerontological Evaluation, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan; * These authors contributed equally to this work

Corresponding Author: Prof Akiko Tamakoshi MD, PhD, Department of Public Health, Hokkaido University Graduate School of Medicine, N15W7, Kita-ku, Sapporo 060-0812, Japan, Tel: +81 11 7065068; Fax: +81 11 7065068, E-mail: tamaa@med.hokudai.ac.jp

J Frailty Aging 2024;in press
Published online April 10, 2024, http://dx.doi.org/10.14283/jfa.2024.34

 

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Abstract

BACKGROUND: Finger tapping impairment and frailty share overlapping pathophysiology and symptoms in older adults, however, the relationship between each other has not been previously studied.
OBJECTIVES: To investigate how finger tapping movements correlate with frail status in older Japanese adults.
DESIGN, SETTING, AND PARTICIPANTS: Data were from a cross-sectional study called the Cognition and Activity in Rural Environment of Hokkaido Senior Survey 2018. A total of 244 community-dwelling older adults (mean age 75.3 years) were included.
MEASUREMENTS: Participants underwent physical examinations, gait and finger tapping tests, and completed self-administered questionnaires. Frailty was assessed using Fried’s frailty phenotype, and factor analysis was conducted to extract relevant finger tapping factors. Multinomial logistic regression was employed to analyze associations, generating adjusted odds ratios.
RESULTS: Of the participants, 18 were frail, and 145 pre-frail. Analysis identified three distinct finger tapping patterns: “Range of Motion – Nondominant Hand,” “Variability – Dominant Hand – Anti,” and “Variability – Nondominant Hand – Anti.” These patterns showed significant associations with aspects of Fried’s frailty phenotype, particularly low physical activity (P = 0.002), weakness (P = 0.003), and slowness (P = 0.004). A larger range of motion in the nondominant hand correlated with a lower frailty risk (Odds Ratio: 0.09, 95% CI: 0.02-0.46), while higher variability in the same hand increased the risk of pre-frailty (Odds Ratio: 2.19, 95% CI: 1.09-4.39).
CONCLUSION: Finger tapping movements are significantly associated with frailty status as determined by Fried’s phenotype. The findings underscore the importance of further longitudinal studies to understand the relationship between motor function and frailty.

Key words: Finger tapping, frailty, aged.

Abbreviations: PF: physical frailty; GS: grip strength; FFP: Fried’s Frailty Phenotype; CARE-DO: Cognition and Activity in the Rural Environment of Hokkaido Senior; JAGES: Japan Gerontological Evaluation Study; IADL: Instrumental Activities of Daily Living; BMI: body mass index; OR: odds ratio; CI: confidence interval; ROM: range of motion; NH: non-dominant hand; DH: dominant hand.

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Introduction

Frailty, a common geriatric syndrome characterized by a reduction in physiological reserve and resistance to stressors among older adults, is associated with a higher risk of disability, falls, hospitalization, and death (1). In ultra-aging societies like Japan, the prevalence of pre-frailty and frailty, along with the healthcare and formal long-term care costs related to frailty prevention and treatment, are expected to rise significantly in the future years (2). However, early detection and timely interventions can effectively address and potentially reverse frail conditions, making the early identification of frailty crucial for healthcare and extending healthy life expectancy (3, 4).
The underlying pathophysiology of frailty has not been fully understood, likely due to its complex and multifactorial nature. This clinical condition of frailty may manifest in various phenotypes, including sensorial, physical, social, cognitive, psychological/depressive, and nutritional aspects (5). Within all, physical frailty (PF) is the most studied dimension and is known to predict the onset of other forms of frailty (6). PF results from the cumulative deterioration of multiple functions, including muscle mass loss, diminished motor skills, and weakened cognitive function. Studies have shown that older adults with PF often exhibit motor impairments that requiring multifunctional coordination, such as a slow gait speed (7, 8), decreased grip strength (GS) (9), and limitations in daily activities (10).
Repetitive and coordinated finger tapping is a common test of fine motor function. Normal finger tapping relies on the functional integrity of the corticospinal tract, cerebellar motor circuitry, and proprioceptive pathways, offering a quantitative method for evaluating motor skills (11). Finger tapping movements has been proved as an easy yet sensitive tool of assessing motor neuron lesion, sarcopenia, and a variety of diseases related to motor and cognitive functions (12–14). Notably, a reduction in the speed and regularity of finger tapping is linked to decreased muscle strength, impaired finger joints, and, specifically, brain abnormalities and related neuro deficits in both cross-sectional and longitudinal studies (15, 16). While these conditions are often causative factors or prodromes of PF (17–19). These findings indicate that finger tapping movements could be related to PF and may serve as an early indicator of a pre-PF status. However, the internal relationship between these two factors has not yet been explored so far.
In the current cross-sectional study, we adopted a well-validated magnetic sensing device that can collect multi-dimensional finger-tapping data. Our aim is to explore the comprehensive association with both pre-PF and PF status, as defined by Fried’s Frailty Phenotype (FFP), among community-dwelling older adults. We hypothesize that finger-tapping movements are highly correlated with both pre-PF and PF and could provide deeper insights into the underlying neurophysiological pathways associated with frailty.

 

Material and methods

This study obtained data from the Cognition and Activity in the Rural Environment of HokkaiDO Senior (CARE-DO) study 2018. The CARE-DO study was conducted after the Japan Gerontological Evaluation Study (JAGES) 2016, which is a large panel study that aimed to understand the association of health with social, and behavioral factors among older adults in Japan. The detail of JAGES has been described elsewhere (20). The baseline CARE-DO study, targeting 5,103 individuals from the 2016 JAGES survey aged 69 to 78 years in six Hokkaido towns, received responses from 569 individuals who agreed to participate in the winter 2017 survey following a mail invitation. The following year, these participants were re-invited to the CARE-DO summer survey 2018. Of these, 262 did not respond, and two requested their partners to participate instead. Ultimately, 309 individuals participated in the 2018 CARE-DO summer survey. From the eligible participants, those who self-reported Parkinson’s disease or cognitive impairment (n=3), were left-handed (n=14), did not finish the finger tapping test (n=24), or could not identify their PF status (n=24) were excluded. Finally, a total of 244 participants were enrolled (Figure 1).

 

During the study, participants first underwent a comprehensive health examination conducted by trained investigators. This examination encompassed a range of assessments including blood pressure, height, weight, body composition (Inbody 430, Japan), sensor-measured gait, GS, finger tapping test, and cognitive evaluation. Cognitive function was assessed through a face-to-face interview using the Japanese version of Montreal Cognitive Assessment (21). At the end of the interview, years of education were recorded. Body mass index was calculated as weight in kilogram divided by height in meters squared. Following these examinations, they underwent a physical activity assessment using the wearable accelerometer (Active Style Pro HJA-350T, Omron Healthcare Co., Ltd.) (22), and completed an at-home self-administered questionnaire that included the information on age, sex, smoking and drinking status (current, past, never), the presence of medical histories (hypertension, heart disease, diabetes, musculoskeletal disease, injuries (fall/fracture) and cancer), and the instrumental activities of daily living (IADL) (23). Participants were instructed to return the questionnaires and accelerometers to the researchers after a period of two weeks.
All participants provided written informed consent for data collection and analysis. The study design was approved by the Institutional Review Committee of the Hokkaido University Graduate School of Medicine for Ethical Issues (No. 18-025).

Finger tapping measurements

The finger tapping performance was assessed using a magnetic-sensing finger tapping device (UB-2, Maxell, Tokyo, Japan), details of which have been described elsewhere (13, 24). As shown in Figure 2, the magnetic sensors were worn on the tips of the thumb and index finger of both hands. Before testing, the handedness of each participant was recorded. The finger tapping test had two tasks in sequence: in-phase and antiphase tasks. Each task involved a short practice session, and the formal test lasted 15 seconds. During the in-phase and antiphase, participants were asked to tap their thumb tips on the index fingertips simultaneously and commutatively in a 4-centimeter opening distance for both hands, the action should be performed as quick as possible while keeping a steady rhythm and without resting their arms on the table.
The sensors were connected to a laptop, and all finger tapping data were exported by the testing application. Forty unimanual parameters (right and left hands) were measured (Supplementary Table 1) and comprised: 7 parameters for the distance domain, 15 for the velocity domain, 10 for the acceleration domain, and 8 for the tapping-interval domain. And four bimanual parameters were measured. In total, (40 left-hand parameters + 40 right hand parameters + 4 bimanual parameters) × 2 phases, that is, 168 variables were recorded for each participant.

(a) Magnetic sensing finger tapping device. (b) In-phase, (c) Anti-phase

 

PF evaluation

PF status was assessed using the FFP (1) as follows: 1) Shrinking, determined by self-reported weight loss: “Have you lost 3 kg in the past 6 months?”, with a ‘yes’ response meeting the criteria. 2) Exhaustion, based on the Geriatric Depression Scale question: “Do you feel full of energy?”, where a ‘no’ response indicated exhaustion. 3) Low physical activity, measured by weekly calories expended as recorded by a wearable accelerometer (Active Style Pro HJA-350T, Omron Healthcare Co., Ltd.). Participants wore the accelerometer for two weeks, except during bathing and sleeping. Only data from devices worn at least 10 hours daily for more than four days were used. The lowest 20% in calorie consumption, stratified by sex (men <3,166 kcal/week; women <2,782 kcal/week), met the low activity criterion. 4) Weakness, gauged by the average grip strength (GS) of the dominant hand using a JAMAR Plus+ digital hand dynamometer (Sammons Preston). The weakest 20%, stratified by sex and BMI, were classified as weak (details in Supplementary Table 2). 5) Slowness, ascertained from average walking speed over a 6-meter course, recorded with Physilog® (GaitUp). The slowest 20%, stratified by sex and height, were categorized as slow (details in Supplementary Table 3) (25).
The Physical Frailty (PF) score was computed based on the number of criteria met, with a total possible score of 5 points. Participants scoring 3 or higher were classified as PF, those with a score of 1 or 2 as pre-PF, and those with a score of 0 as robust. In cases where a participant missed one criterion, the PF status was determined based on the available PF scores. Records with more than two missing criteria were excluded from the analysis.

Statistical analysis

To avoid over-calculation in later statistical analyses, 13 finger tapping parameters were initially extracted (see Supplementary Table 1). We then conducted an exploratory factor analysis on the remaining 116 finger tapping parameters to identify distinct finger tapping pattern factors. The suitability of factor analysis was assessed using Bartlett’s test and the Kaiser–Meyer–Olkin measure. Parameters with rotated factor loadings of 0.6 or higher were deemed significant contributors to the identified finger tapping pattern factors.
Finally, multinomial logistic regression was conducted to calculate odds ratio (OR) and 95% confidence interval (CI) of each finger tapping pattern factor by tertile, adjusted for sex, age, and the IADL score. Cochran–Armitage trend was calculated using the Wald statistics with finger tapping pattern tertile treated as continuous variables. Statistical significance was set at p < 0.05. All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

 

Results

Of the 244 participants, 123 (50.4%) were women, and the average age was 75.5 (±2.8) years. Eighteen (7.4%) participants were frail, and 145 (59.4%) were pre-frail.
Factor analysis yielded three factors that accounted for 63.3% of variance (Supplementary Table 1). The first factor explained 35.0% of total variance with greater factor loadings of nondominant hand parameters and was named “range of motion (ROM)-non-dominant hand (NH).” The second factor explained 16.5% of total variance with greater factor loadings and was named “Variability-dominant hand (DH)-anti.” The third factor explained 13.2% of total variance with greater factor loadings and was named “Variability-NH-anti.” Based on the parameters included and their vectors, the higher the value for the first pattern and the lower the value for the last two patterns, the better the finger tapping performance.
The basic characteristics and finger tapping patterns by tertile are presented in Supplementary Table 4. Age and IADL scores were similar across tertiles. Men were more likely to be in the higher tertile of the ‘ROM-NH’ pattern.
Table 1 shows the relationship between finger tapping factors and PF criteria in the robust group. Higher tertiles of ‘ROM-NH’ were associated with significantly lower OR for low physical activity, weakness, and slowness. Conversely, higher tertiles of ‘Variability-NH-anti’ were linked to increased OR for weakness and slowness.

Note: N, number; OR, odds ratio; CI, confidence interval; NH, non-dominant hand; DH, dominant hand; Multinomial logistic regression was conducted to calculate the ORs and 95%CI; *P<0.05

 

Table 2 details the relationship between finger tapping patterns and pre-PF/PF status, compared to the robust group. A higher ‘ROM-NH’ tertile significantly reduced PF risk (OR: 0.09, 95% CI: 0.02–0.46), with a significant decreasing trend in PF risk as ‘ROM-NH’ scores increased (P for trend: 0.001). The ‘Variability-DH-anti’ pattern showed no significant association with pre-PF/PF. The ‘Variability-NH-anti’ pattern was potentially related to pre-PF, but no significant differences were observed in the middle or highest tertiles compared to the lowest.

Note: N, number; OR: odds ratio; CI: confidence interval; NH: non-dominant hand; DH: dominant hand; Multinomial logistic regression was conducted to calculate the ORs and 95%CI; *P<0.05

 

Discussion

The present study discerned three finger tapping patterns: “ROM-NH,” “Variability-DH-anti,” and “Variability-NH-anti”. We found that a better performance of “ROM-NH” pattern was significantly related to lower PF risk, and the higher “Variability-NH-anti” was significantly related to a higher pre-PF risk.
“ROM-NH” denotes a finger tapping pattern predominantly characterized by the tapping range of the non-dominant hand. Older adults exhibiting a superior “ROM-NH” pattern demonstrated longer tap distances in both in-phase and antiphase activities with their non-dominant hand. Significantly, these individuals had a lower prevalence of PF compared to those with shorter tap distances. Based on the strong correlation between the weakness criterion of PF and “ROM-NH” pattern, although no previous study has evaluated PF and finger tapping parameters directly before, the relationship between poor GS/hand muscle strength and fine-motor skill has been demonstrated previously. For instance, using a motor performance series work panel, the GS can predict hand dexterity, including the aimed tapping ability and numbers (26); GS and pinch strength values correlated with the Duruöz and Dreiser indices, which are self-reported scales for assessing hand function (27). Based on this evidence, which could support the current results, we hypothesized that the poor GS of frail older adults might affect their fine-motor skills, and thereby cause lower tapping ROM in the finger tapping test. The difference between frail and robust older adults regarding the “ROM-NH” pattern indicates the possibility of early PF detection.
In contrast, poor GS and poor upper limb strength are common in frailty. Prior research indicated that pre-frailty and frailty in individuals aged 55 years and older were associated with diminished upper limb dexterity and lower power, as measured by the box-and-block test (28). Additionally, older adults with stronger arm-curl strength and better manual coordination demonstrated improved performance in hand function tests (29). In our study, the finger tapping test required participants to keep their arms raised off the table, suggesting that for robust participants, greater upper limb strength could facilitate more effective completion of the test.
Several mechanisms could underlie the link between frailty and reduced finger tapping ROM. This diminished ROM might be affected by hand muscle strength and fatigue. Common PF symptoms, such as sarcopenia and increased fatigability, are significant physical factors contributing to PF (30, 31). Furthermore, neurological, endocrine, and immune dysfunctions associated with PF can disrupt muscle homeostasis, leading to loss of muscle mass and strength (31, 32). A decline in maximal voluntary contractions is often attributed to muscle contractile failure (33). The finger tapping test in our study required participants to tap as quickly as possible while maintaining a specific ROM. Therefore, weaker hand muscle strength may have limited the tapping ROM in frail older adults. The ‘Variability-NH-anti’ pattern, reflecting intraindividual variability during the nondominant hand’s antiphase finger tapping, indicated that individuals with higher variability could not maintain stable tapping speed, suggesting a higher prevalence of pre-PF. The exact mechanism linking individual variability with PF is not fully understood. However, based on previous research, it may be influenced by neurogenic impairment in older adults. This could involve the diminished tactile input and efficiency of motor functional neurobiological substrates with aging, along with a loss of tactile sensory feedback (34–36).
In our study, the ‘ROM-NH’ pattern appeared to reflect impairment in hand muscle strength, while ‘Variability-NH-anti’ seemed to indicate neuronal impairment in pre-PF. These findings provide initial evidence that PF affects finger tapping intraindividual variability, shedding light on the complex interplay between physical and neurological factors in aging.
As mentioned above, PF is defined as a biological syndrome of decreased reserve and resistance to stressors during aging (1). We considered that aging may interact in the association of frailty with finger tapping performance. However, we did not observe a difference between age and each finger tapping pattern in the current study. One reason for this might be the small age range of our participants’, as increasing evidence has demonstrated an association of aging with fine-motor skill impairment. Compared with young adults aged 18–25 years, older adults aged 65–77 years have a significantly slower tapping pace (37). Finger tapping frequency decreases with age (38). Older adults relied more heavily on executive control functions for sequential tapping (39). Hand tactile discrimination decreases and slower finger tapping reaction times are related to aging (40, 41). As the participants in our study were relatively healthy, and their age distribution was within 10 years, we might have underestimated age-related fine-motor decline.
This study had a few limitations. Firstly, as this was a cross-sectional study, the causal relationship between finger tapping and PF remains unclear. Secondly, the response rate for the current Hokkaido-DO study from the original JAGES participants was quite low (less than 10%). This low response rate suggests that the participants in this study may be relatively younger and healthier compared to the broader population. Consequently, this could lead to an underestimation of the prevalence of PF in the study’s findings. Additionally, in this study, FFP was utilized to assess frailty, with a focus primarily on motor functions. While FFP is validated for PF, it lacks comprehensive coverage of other frailty determinants, such as social, cognitive, or psychological aspects. This limitation suggests that despite multi-pathophysiology being considered in the relationship between finger movements and frailty, the observed significance in this study may still be predominantly due to overlapping motor mechanisms. Nevertheless, other studies have reported a strong correlation between finger movements, frailty, and multidimensional deficits. This underscores the need for future research to explore finger movements in conjunction with other aspects of frailty, aiming to achieve a more comprehensive understanding of the underlying mechanisms.

 

Conclusion

The study’s findings distinctly link comprehensive finger movements with both pre-PF and PF. This significant association points to the potential of finger motor movement as an effective tool for early frailty assessment. In the future, longitudinal cohort research with larger sample size and assessing multi-dimensional frailty are needed to elucidate the internal causality and pathophysiology underlying these associations.

 

Acknowledgements: We would like to thank all participants and investigators for their collaboration in this study. And we would like to thank Editage (www.editage.com) for English language editing.

Formatting of funding sources: The study was supported by the Japan Society for the Promotion of Science’s Grants-in-Aid for Scientific Research (B): Influence of Indoor Temperature Distribution on Health of elderly in Cold Climate [17H04129] and Challenging Research (Exploratory): Research on the effect of gait parameters and modifiers on cognitive function of older adults by using wearable devices [18H05389].

Conflicts of Interest: Dr. Tamakoshi reports non-financial support from Maxell, Tokyo, during the conduct of the study.

Ethical standard: All participants provided written informed consent for data collection and analysis. The study design was approved by the Institutional Review Committee of the Hokkaido University Graduate School of Medicine for Ethical Issues (No. 18-025).

 

SUPPLEMENTARY MATERIAL

 

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