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A. Piau1,2, Z. Steinmeyer1, M. Cesari3, J. Kornfeld4, Z. Beattie4, J. Kaye4, B. Vellas1,2, F. Nourhashemi1,2

1. Gerontopole, Toulouse University Hospital, 31059 Toulouse, France; 2. UPS/INSERM, UMR1027, F-31073 Toulouse, France; 3. Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; 4. Oregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland, USA.
Corresponding author: Antoine Piau, La Cité de la Santé, Bâtiment Ex-Biochimie, Hôpital La Grave, Place Lange, TSA 60033, 31059 Toulouse Cedex 9, France, E-mail address: piau.a@chu-toulouse.fr, Phone number: +335 61 32 30 10, Fax number: +335 61 77 64 75

J Frailty Aging 2020;in press
Published online October 5, 2020, http://dx.doi.org/10.14283/jfa.2020.51



The WHO action plan on aging expects to change current clinical practices by promoting a more personalized model of medicine. To widely promote this initiative and achieve this goal, healthcare professionals need innovative monitoring tools. Use of conventional biomarkers (clinical, biological or imaging) provides a health status assessment at a given time once a capacity has declined. As a complement, continuous monitoring thanks to digital biomarkers makes it possible to remotely collect and analyze real life, ecologically valid, and continuous health related data. A seamless assessment of the patient’s health status potentially enables early diagnosis of IC decline (e.g. sub-clinical or transient events not detectable by episodic evaluations) and investigation of its probable causes. This narrative review aims to develop the concept of digital biomarkers and its implementation in IC monitoring.

Key words: ICOPE program, digital biomarkers, technology, remote monitoring, intrinsic capacity.


The ICOPE program and the necessary but difficult monitoring of intrinsic capacity over time

The WHO defines Intrinsic Capacity (IC) as a “composite of all physical and mental capacities that an individual can draw on”, and functional ability as “health-related attributes that enable people to be and to do what they have reason to value” determined by the interaction of a person’s IC with their environment (1). The ‘Integrated care for older people’ (ICOPE) report translates this strategy into clinical recommendations (WHO ICOPE), from assessment of individual needs and preferences to the development of a comprehensive care plan and coordinated services. ICOPE defines older adult’s health status with five domains of intrinsic capacity: locomotor, vitality, sensory, cognition and psychological capacity. WHO ICOPE recommended an overall clinical approach from large-scale screening to the proposal of an individualized intervention if IC declines.
IC evaluation should be based on longitudinal multiple observations of an individual’s trajectory over time (1), but real-life implementation raises serious issues. Previous primary care initiatives have shown the possibility to screen and assess older people presenting a high risk of IC impairment and to implement a personalized care plan as necessary (2, 3). Ideally, this close follow-up would occur in a person’s home and community setting with minimal intrusion. However, promotion of this outpatient care follow-up remains sparse. Despite being challenging, continuous monitoring of health-related real-life data (not reliant on clinician-mediation), persists as among the most critical issues faced by modern medicine (4, 5) for a number of reasons:
(i) Increasing numbers of patients with limited health resources, constrains the possibility of in-person assessment (self-management is increasingly necessary);
(ii) For implementation of proactive or preventive personalized interventions, early negative trends in IC must be detected in order to investigate underlying causes;
(iii) Continuous monitoring of IC dimensions is needed to measure an individual’s longitudinal trajectories over time after an intervention;
(iv) Functional abilities assessment – explained by the interaction of a person’s IC with its environment – must be unobtrusively measured in one’s own environment (achieving ecological validity).

In this narrative review, we propose to illustrate, through examples from basic research and clinical initiatives, the potential contribution of the so-called «digital biomarker» to this need for intrinsic capacity monitoring.

Why Digital Medicine can be useful for ICOPE implementation

How is a digital biomarker different from a ‘conventional’ biomarker?

Early detection of health transitions by physicians is often difficult (4, 5). Thus, identifying sensitive and clinically relevant biomarkers to detect subtle but meaningful changes is a challenge. Most conventional biomarkers (6) do not fit a long-term monitoring strategy. Indeed, clinical biomarkers such as questionnaires are time consuming, performed at a given point in time, which affects sensitivity, and are based on patients’ recall of prior events (7). Biological biomarkers are often invasive and do not lend themselves to high throughput collection. Finally, imaging biomarkers are costly, require specialized facilities and personnel, and inappropriate for a continuous data collection model (5).
Relatively new terms and concepts have been developed in Information and Communication Technologies (ICT) in particular with regard to innovative clinical endpoints emerging from digital medicine (8-12). We have chosen to use the widespread term ‘digital biomarkers’ (DB). The proposed definition of ‘digital biomarkers’ is : objective, quantifiable, physiological and behavioral data that are collected and measured by means of digital devices such as embedded environmental sensors or wearables, which opens up opportunities for remote data collection and processing of large amounts of ecologically valid, real-life, continuous and long-term health-related data. Table 1. illustrates the difference between conventional compared to digital biomarkers.

Table 1
Key points underling the uniqueness of digital biomarkers and opportunities for ICOPE

*IC, Intrinsic Capacity.


What do digital biomarkers have to offer for ICOPE?

The use of conventional biomarkers proposes an in-depth clinical, biological or imaging assessment when a decline in capacity is diagnosed. Conversely, continuous monitoring with digital biomarkers make it possible to prospectively detect transitory or short-term variation over time or sub-clinical decline in IC (see Figure 1 for practical examples). For example, the evolution of ultradian (e.g. variation in body temperature) or circadian (e.g. sleep-wake cycle) rhythms is poorly explored and exploited in clinical practice due to the lack of simple and acceptable measurement tools. DB can capture short-term intra-individual fluctuations in IC and functional abilities. Intra-individual variability in performance (e.g. day-to-day gait speed variability) may also be among the earliest indicators of a negative trend in one’s IC (13-18) (figure 2). DBs enable the differentiation of short-term fluctuations (i.e. a good day vs. a bad day) from more persistent, long-term changes and provides a means to objectify the speed of recovery of an individual after an acute event (e.g. fall). Lastly, DB could measure subclinical events, which are associated with poor prognosis that are not detectable by episodic assessments. For example, ‘silent falls’ are falls which are unnoticed by healthcare professionals but lead nevertheless to impaired walking ability and an increased the risk of loss of autonomy in the future (19).
DB can thus provide health care professionals with information about negative trends in IC by timely checking the patient’s status and investigating the underlying causes. This measurement of variations in IC gives us access to information that was previously inaccessible and allows us to understand why and when a person remains stable, recovers quickly after an event or continues to lose autonomy despite interventions.

Figure 1
Monitoring the activity dimension of intrinsic capacity through digital biomarkers

Note: The monitoring of physical activity through digital biomarkers (e.g. gait speed as measured by wearables or embedded sensors) could detect: (i) early negative trends or subclinical declines not detected by episodic clinical assessments due to lack of sensitivity ( scenario A); (ii) a transient decline between two face-to-face visits ( scenario B); (iii) a change in intra-individual variability that may precede a decline or herald recovery ( scenario C).


Figure 2
Intra-individual variability in performance over time

Note: one hypothesis, supported by several authors (15, 40), is an increased variability of health-related parameter in a situation of early pre-symptomatic impairment followed by a decreased variability before a clinically perceptible impairment occur (scenario A). If we stick to that hypothesis, an increased variability in performance (physiological reaction) and then a return to a normal variability could announce an early recovery in IC and thus a favorable prognosis (scenario B).


How can we design a digital health program for ICOPE

The implementation of ICOPE is divided into several steps: a remote first line screening of the domains of intrinsic capacity as defined by WHO (STEP 1); follow-up over time and, if necessary, an in-depth assessment carried out at home or in a health care facility (STEP 2). The latter would occur if a decline is detected. Once the underlying causes are investigated, an individualized intervention program can be proposed (STEPS 3-5). Digital biomarkers can be implemented in the first step (STEP 1) and carried through to later steps for long-term patient monitoring.

First-line Screening (STEP 1)

It is possible to propose different digital tools depending on the healthcare context, specific use case and end-users. Large-scale evaluation (e.g. text messaging) may be implemented by the use of existing technologies such as smartphones which are low-cost and widely available tools. For specific scenarios, other tools may be developed and integrated (e. g. dedicated smartphone apps, embedded sensors). Examples of tools are proposed in Table 2: from the simplest use of online surveys to complex multi-domain, sensor-based measurements. A personalized and increasing level of digital assistance can be offered to the user. The level of assistance required and its evolution over time may by itself indicate the user’s capabilities.
Digital technologies offer us numerous possibilities of remote screening and assessment, as shown in Table 2, However, by definition, digital biomarkers are more related to continuous monitoring or high frequency collection, and not to the occasional use of a digital tool for one-time screening.. Ideally, DBs allow us to uncover (figure 1):
– a transient decline in an IC during a minor clinical event;
– a negative and sustained subclinical trend in one or several IC subdomains;
– an abnormal intra-individual fluctuation in IC and/or in functional abilities.

This will also be used to objectify the recovery rate during the proposed intervention.

Table 2
Examples of digital solution for STEP 1 implementation

*Abbreviations: SPPB, Short Physical Performance Battery; GP, General Practitioner; MOCA, Montreal Cognitive Assessment; GDS, Geriatric depression Scale; MNA-SF, Mini Nutritional Assessment Short-Form; § Note. Most of the examples of simple digital solutions (STEP 1, column 3) are already implemented in the ICOPE-CARE program, whereas the example of advanced digital solutions (STEP 1, column 4) are ideas for future improvements.


DB for remote continuous follow-up of IC

Several research teams have confirmed both the relationship between real life sensor-based DB and IC dimensions (5, 10, 11, 20, 21) and the possibility of tracking subclinical events (19). Sensor-based real life in-home walking speeds (15), activity patterns (18), routine driving (22), variability in medication taking (17), sleep activity (23), and daily computer use (14) have been correlated to functional autonomy and cognitive status. Continuous monitoring of IC-related parameters provides relevant information under free-living conditions. Given the importance of acceptability and adherence issues in this population, use of passive unobtrusive solutions are preferred (e.g. smart home environments) rather than wearables that require more active engagement (24). However, wearables (and their related apps) rely on self-management and thus can actively involve patients in their care.
Remote follow-up may be performed in different ways:
– End-user centered active solutions (self-assessment) providing feedback to the patient in order to drive him/her to a healthier lifestyle without any professional support.
– A solution focused on healthcare professionals through regular teleconsultations with connected objects (e.g. actimeter) to provide continuous objective data.

In between, the intervention of a third party, will be required in many cases. The range of solutions is wide, from a family care partner to a health professional’s support (e.g. nurse), at home or in a dedicated place (e.g. pharmacy, public office). The tool chosen and the complexity of the assessment must obviously be adapted to the patient’s socio-economic context (e.g. highly educated urban dwellers vs low income, rural, socially isolated). Many variables must be taken into account before designing a monitoring solution (Table 3). Using tools that are familiar to the target audience (e.g. email, text messaging) allows a better implementation at a lower cost. One simple solution for continuous follow-up of IC is to repeat ‘STEP 1’ over time through a text-messaging questionnaire coupled to connected objects or sensors.

Table 3
Different variables illustrating the possible monitoring modalities depending on available resources


Moving forward, the digital part of the INSPIRE project

To better illustrate how DB could be implemented in daily clinical practice, here is the practical example of the Inspire project, which includes a digital part focusing on IC monitoring.

The INSPIRE-T cohort

A vast research program dedicated to healthy aging was launched in the Occitania region (France), called the INSPIRE initiative (25-27). The INSPIRE Human Research Translational cohort (called INSPIRE-T) is part of this program. The specific aim is to explore and identify biomarkers of aging and IC evolution through annual biological, clinical, and digital data over 10 years, and therefore through both so-called conventional biomarkers and digital biomarkers. 1000 subjects ages 20 to 100+ years old are being enrolled. An important part of the project is to better understand the evolution of IC over time, followed by a web-application for IC self-monitoring, the ICOPE MONITOR Smartphone-app. This application can be easily used by the population and, beyond this research initiative, will be implemented on a large scale through the INSPIRE ICOPE-CARE program (28-34).
This free, open-access application allows the patient to self-assess quickly every 4 months (Step 1, about 6 minutes) through simple, validated tests. For example, he is asked to perform a self-timed chair-raising test. A team of nurses receives the results remotely on a dashboard. When a decline is detected between two self-assessments, the patient is offered a face-to-face evaluation (Step 2). If the time gap between two self-assessments is relatively long (4 months) and if the measurement is not yet sensor-mediated, this type of organization paves the way for the wider use of digital biomarkers for IC monitoring (e.g. automatic and unobtrusive measurement of transfers from sitting to standing by an accelerometer throughout the day). It is with this in mind that we launched the ancillary study CART France to anticipate this transition.

The CART-France digital biomarkers cohort

To further explore the field of IC digital biomarkers, we launched the CART-France cohort (an ancillary study of INSPIRE-T). It consists of a subgroup of 100 INSPIRE-T participants monitored by ambient and wearable sensors at home (e.g. infrared sensors on the ceiling, bed mat) over a long period of time. This will allow remote and continuous monitoring of IC trajectories and detect subtle changes that are not readily detected with conventional methods. For this purpose, INSPIRE has initiated a partnership with the ORCATECH team at Oregon Health & Science University in Portland (OHSU, OR, USA), a leading center in the field of smart homes and digital biomarkers in the field of advanced age and the coordinating center of the Collaborative Aging (in Place) Research Using Technology (CART) research project. The ORCATECH CART program supports an infrastructure for rapid and effective conduct of research utilizing technology (35). As such, we will be the first center outside the USA and Canada to join this research network. The collaboration between INSPIRE and CART is an important step in facilitating a more global view of digital health technology methodology and data sharing. These digital biomarkers will be correlated with clinical data but also biological and imaging -hallmarks of aging gathered through the INSPIRE-T cohort (36). This will be the most original part of the project, which will allow a better understanding of the complementary aspects of each type of biomarker. If we take the example of the ‘locomotion’ domain of intrinsic capacity (figure 1 and 2), continuous real-life measurements such as: room transitions, walking speed and its variability over time, number of steps, activity time and sleep time, etc. will be correlated with the annual biological, imaging and clinical evaluations. They are all digital biomarker candidates to be confirmed or discovered.


Challenges and opportunities

Integration of digital health in a pre-existing care network and a given socio-economic context is challenging. Beyond technical and regulatory requirements (e.g. health data security, interoperability with the existing IT ecosystem), raw collected data needs to be analyzed before it can be used in decision-making in clinical practice (e.g. establishing thresholds, critical scores). A clinician’s involvement in technical solutions specification is necessary to develop tools which are easily implemented in routine clinical practice. Up until now, most of the literature dealing with digital monitoring has been limited to small samples assessed in specially designed test-homes or bioengineering laboratories (21). These latter results are difficult to extrapolate to real life settings, and less selected, wider populations. Furthermore, as with any change in clinical practice, we need a combination of specific initial and continuing training in addition to political and public awareness to achieve widespread implementation of digital innovations in this area.
As fully discussed elsewhere, technological tools, as a medical intervention, comprises potential risks and thus security, ethical and regulatory concerns (37). As an example, both health data security and regulatory policy issues (e.g. medical device regulation) remain unsettled areas, highly dependent on national regulations despite the effort to develop international health data security standards (e.g. HIPAA, GDPR). Another point of concern is access to care. Paradoxically, new technologies have the potential to both increase and decrease health inequalities. On one hand, digital devices may increase social inequalities due to limited access. For example, an older age, lower income, lower education, living alone, and living in rural areas were found to be associated with lower eHealth use (38); On the other hand, certain studies suggest that digital technologies may reduce disparity in healthcare access regardless of social or economic status and geographical considerations (39, 40).
The clinical validation of DBs also remains a fundamental issue (8-15). It can be done through comparison to clinical gold standard. For example, walking speed as measured by infrared sensors versus walking speed as measured by a stopwatch. However, this example illustrates the paradoxical aspect of such an approach since the precision of these measurements are in no way comparable. The use of a handheld chronometer only gives a measurement that is far from the desired accuracy, which can approach 0.1cm/sec (41). It is also possible to validate these measurements by direct correlations with clinical events (41), when possible and appropriate. Finally, although the first large-scale continuous monitoring initiatives are now more than 10 years old, and have demonstrated valid associations with clinical evaluations using gold standards, there is little cross-reference of these DB with biological or imaging biomarkers. The INSPIRE-T cohort aligns with this need (26, 28).


Conclusion and perspectives

Digital technologies offer opportunities to support new models of healthcare through telehealth initiatives enriched by digital biomarkers data. The ICOPE initiative provides a framework to capture the potential contribution of these technologies to the field of aging. DB provide the opportunity to enhance ‘conventional’ biomarkers by adding continuous data flow to discrete and focused in depth analysis. They do not substitute but complement each other. Moreover, DBs uniquely assess a person’s IC interaction with their own environment and thus their functional abilities. Implementing digital medicine in routine clinical practice has the great advantage of integrating overall medical care from screening to treatment. ICTs may also promote other recommendations such as self-management encouragement and remote support for a healthy lifestyle. (http://www.who.int/ageing/health-systems/mAgeing/en/).


Funding: The Inspire Program was supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175), the European Regional Development Fund (ERDF) (Project number: MP0022856), and the Inspire Chairs of Excellence funded by: Alzheimer Prevention in Occitania and Catalonia (APOC), EDENIS, KORIAN, Pfizer, Pierre-Fabre, Fondation Avenir Cogfrail Grant.
Acknowledgements: The authors thank all the health professionals participating in the INSPIRE ICOPE CARE Program. We would also like to thank Zachary Beattie and Judith Kornfeld (OHSU, OR, Portland) for their careful rereading of the first drafts of the article.
Potential Conflicts of Interest: All authors declare to have no support from any organization for the submitted work (except the French Ministry of Health which supported the study by a grant), no financial relationships with any organisations that might have an interest in the submitted work, no other relationships or activities that could appear to have influenced the submitted work.
Ethical Standards: There were no patients included in this study, therefore we did not obtain consent and were not reviewed by an ethics committee.



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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



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.



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.



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).



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.



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.



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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