Older Adults Engage With Personalized Digital Coaching Programs at Rates That Exceed Those of Younger Adults

Insights Into the Use of a Digital Healthy Aging Coach (AGATHA) for Older Adults From Malaysia: App Engagement, Usability, and Impact Study

BACKGROUND

Digital inclusion is considered a pivotal social determinant of health, particularly for older adults who may face significant barriers to digital access due to physical, sensory, and social limitations. Avatar for Global Access to Technology for Healthy Aging (AGATHA) is a virtual healthy aging coach developed by the World Health Organization to address these challenges. Designed as a comprehensive virtual coach, AGATHA comprises a gamified platform that covers multiple health-related topics and modules aimed at fostering user engagement and promoting healthy aging.

OBJECTIVE

The aim of this study was to explore the perception and user experience of Malaysian older adults in their interactions with the AGATHA app and its avatar. The focus of this study was to examine the engagement, usability, and educational impact of the app on health literacy and digital skills.

METHODS

We performed a qualitative study among adults 60 years and older from suburban and rural communities across six states in Malaysia. Participants were purposefully recruited to ensure representation across various socioeconomic and cultural backgrounds. Each participant attended a 1-hour training session to familiarize themselves with the interface and functionalities of AGATHA. Subsequently, all participants were required to engage with the AGATHA app two to three times per week for up to 2 weeks. Upon completion of this trial phase, an in-depth interview session was conducted to gather detailed feedback on their experiences.

RESULTS

Overall, the participants found AGATHA to be highly accessible and engaging. The content was reported to have a comprehensive structure and was delivered in an easily understandable and informative manner. Moreover, the participants found the app to be beneficial in enhancing their understanding pertaining to health-related issues in aging. Some key feedback gathered highlighted the need for increased interactive features that would allow for interaction with peers, better personalization of content tailored to the individual's health condition, and improvement in the user-experience design to accommodate older users' specific needs. Furthermore, enhancements in decision-support features within the app were suggested to better assist users in making health decisions.

CONCLUSIONS

The prototype digital health coaching program AGATHA was well received as a user-friendly tool suitable for beginners, and was also perceived to be useful to enhance older adults' digital literacy and confidence. The findings of this study offer important insights for designing other digital health tools and interventions targeting older adults, highlighting the importance of a user-centered design and personalization to improve the adoption of digital health solutions among older adults. This study also serves as a useful starting point for further development and refinement of digital health programs aimed at fostering an inclusive, supportive digital environment for older adults.

Depiction of conversational agents as health professionals: a scoping review

OBJECTIVE

The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care.

INTRODUCTION

The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings.

INCLUSION CRITERIA

This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors.

METHODS

This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables.

RESULTS

A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents.

CONCLUSIONS

The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.

Type 2 Diabetes Mellitus and Sarcopenia as Comorbid Chronic Diseases in Older Adults: Established and Emerging Treatments and Therapies

Type 2 diabetes mellitus (T2DM) and sarcopenia (low skeletal muscle mass and function) share a bidirectional relationship. The prevalence of these diseases increases with age and they share common risk factors. Skeletal muscle fat infiltration, commonly referred to as myosteatosis, may be a major contributor to both T2DM and sarcopenia in older adults via independent effects on insulin resistance and muscle health. Many strategies to manage T2DM result in energy restriction and subsequent weight loss, and this can lead to significant declines in muscle mass in the absence of resistance exercise, which is also a first-line treatment for sarcopenia. In this review, we highlight recent evidence on established treatments and emerging therapies targeting weight loss and muscle mass and function improvements in older adults with, or at risk of, T2DM and/or sarcopenia. This includes dietary, physical activity and exercise interventions, new generation incretin-based agonists and myostatin-based antagonists, and endoscopic bariatric therapies. We also highlight how digital health technologies and health literacy interventions can increase uptake of, and adherence to, established and emerging treatments and therapies in older adults with T2DM and/or sarcopenia.

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention

OBJECTIVE

Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes.

METHODS

We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics.

RESULTS

Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals.

CONCLUSIONS

Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

Predictors of program interest in a digital health pilot study for heart health

Digital health programs can play a key role in supporting lifestyle changes to prevent and reduce cardiovascular disease (CVD) risk. A key concern for new programs is understanding who is interested in participating. Thus, the primary objective of this study was to utilize electronic health records (EHR) to predict interest in a digital health app called Lark Heart Health. Because prior studies indicate that males are less likely to utilize prevention-focused digital health programs, secondary analyses assessed sex differences in recruitment and enrollment. Data were drawn from an ongoing pilot study of the Heart Health program, which provides digital health behavior coaching and surveys for CVD prevention. EHR data were used to predict whether potential program participants who received a study recruitment email showed interest in the program by "clicking through" on the email to learn more. Primary objective analyses used backward elimination regression and eXtreme Gradient Boost modeling. Recruitment emails were sent to 8,649 patients with available EHR data; 1,092 showed interest (i.e., clicked through) and 345 chose to participate in the study. EHR variables that predicted higher odds of showing interest were higher body mass index (BMI), fewer elevated lab values, lower HbA1c, non-smoking status, and identifying as White. Secondary objective analyses showed that, males and females showed similar program interest and were equally represented throughout recruitment and enrollment. In summary, BMI, elevated lab values, HbA1c, smoking status, and race emerged as key predictors of program interest; conversely, sex, age, CVD history, history of chronic health issues, and medication use did not predict program interest. We also found no sex differences in the recruitment and enrollment process for this program. These insights can aid in refining digital health tools to best serve those interested, as well as highlight groups who may benefit from behavioral intervention tools promoted by additional recruitment efforts tailored to their interest.

Older Adult Engagement With Symptom Reporting in a COVID-19 Citizen Science Application

The current study examined the frequency and predictors of older adults' engagement with symptom reporting in COVIDWATCHER, a mobile health (mHealth) citizen science application. Citizen science is a type of participatory research that leverages information provided by community members. There were 1,028 COVIDWATCHER participants who engaged with symptom reporting between April 2020 and January 2021. Approximately 13.5% (n = 139) were adults aged ≥65 years. We used a Wilcoxon test to compare the mean frequency of engagement with symptom reporting by older adults (i.e., aged ≥65 years) to younger adults (i.e., aged ≤64 years) and multivariable linear regression to explore the predictors of engagement with symptom reporting. There was a significant difference in engagement with symptom reporting between adults aged ≥65 years compared to those aged ≤64 years (p < 0.001). In our final model, age (β = 26.0; 95% confidence interval [14.8, 34.2]) was a significant predictor for engagement with symptom reporting. These results help further our understanding of older adult engagement with mHealth-enabled citizen science for symptom reporting. [Journal of Gerontological Nursing, 49(4), 6-11.].

Determinants of and Willingness to Use and Pay for Digital Health Technologies Among the Urban Elderly in Hangzhou, China

Purpose

In recent years, little research has been conducted on the willingness to use and pay for digital health technologies for elderly people. Thus, this study investigates the willingness to use and pay for digital health technologies and their influential factors among urban elderly in Hangzhou, China.

Methods

A structured questionnaire was completed by 639 older adults from 12 communities in Hangzhou. This paper presents a descriptive statistics analysis and performs a multivariate regression to identify the determinants of willingness to use and pay for digital health technologies among the elderly.

Results

The result shows the percentage of participants who chose very willing (3.6%) and partly willing (10%) to use was lower than those who were less unwilling (26.4%) and not willing (27.1%) to use. The percentage of participants who are unwilling (less unwilling,30.5%; not willing, 39.7%) to pay for digital health technology is even higher. The regression results show that age, working status, exercise and physical activity, medical insurance, income, life satisfaction and history of illness were significantly associated with willingness to use digital health technology among urban elderly. On the other hand, age, exercise status, income and history of illness were significantly associated with willingness to pay for digital health technologies among older adults.

Conclusion

The overall willingness to use and pay for digital health technologies are low among urban older people living in Hangzhou. Our results have important implications for digital health policy making. Practitioner and regulator should develop strategies to improve the supply of digital health technology services to meet different demands of the elderly with different age, working status, exercise and physical activity, medical insurance, income, life satisfaction and history of illness. And medical insurance would be an important instrument to facilitate digital health development.

Acceptance of Social Networking Sites by Older People before and after COVID-19 Confinement: A Repeated Cross-Sectional Study in Chile, Using the Theory of Planned Behaviour (TPB)

This study aims to examine the capacity of the Theory of Planned Behaviour (TPB) to explain the intention to use social networking sites by older people in two time periods, before and after confinement due to the COVID-19 epidemic, as well as the evolution of effects (paths) over time of TPB's determinants. Based on interviews from samples of 384 and 383 elderly Chilean adults collected before and after confinement, the evolution of the effects (paths) was analysed using the TPB model applying the PLS-SEM technique. The intention to use social networks and its association with three factors were evaluated: attitude toward the behaviour, subjective norms, and perceived control over the behaviour. The model explains the intention to use social networks by 27% before confinement, increasing its magnitude to 50% after confinement. After the period of confinement, their attitudes become more significant, their perceptions of control become less important, and social pressures remain permanent in predicting the behaviour. In conclusion, better access and greater use of social networks by older people during the lockdown period increased the predictive strength of the attitude towards these technologies.

Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence

Objective

The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC).

Methods

We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n  =  191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n  =  223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members.

Results

CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M  =  5.3%, SE  =  .8 vs. M  =  3.3%, SE  =  .8; p  =  .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE  =  .1), with 35% achieving ≥5%. Male sex (β = .11; P  =  .009), weeks with ≥2 weigh-ins (β = .68; P < .0001), and days with an AI-powered coaching exchange (β = .43; P < .0001) were associated with a greater likelihood of achieving ≥5% weight loss.

Conclusions

An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.

Mobile survey engagement by older adults is high during multiple phases of the COVID-19 pandemic and is predicted by baseline and structural factors

Digital surveys, such as mobile phone ecological momentary assessment (EMA), bear the potential to assess and target individual wellbeing in a personalized, real-time approach and allow for interaction in situations when in-person contact is not possible, such as during the coronavirus pandemic. While the use of digital technology might especially benefit research in older adults who find themselves in circumstances of reduced mobility, little is known about their barriers to adherence. We investigated baseline and structural factors that predict study withdrawal and adherence from daily smartphone EMA self-report surveys in the StayWELL Study. The StayWELL study is a longitudinal, observational study on the relationship between social restrictions during the coronavirus pandemic and mental well-being in 95 community-dwelling older aged adults (67–87 years) who were participants in a randomized clinical trial using EMA. Withdrawal was associated with less research staff changes and less likely in participants that reached the study mid-point. No baseline characteristics predicted withdrawal. Main reasons for withdrawal were communication issues, i.e. staff not being able to contact participants. We found an adherence rate of 82% and no fatigue effects. Adherence was predicted by education status, study participation duration, reaching the study midpoint and time between study start and enrollment. COVID infections or supporting people in the household was not related to adherence. To conclude, it is feasible to conduct an EMA study in older people without impacting engagement during a pandemic. Furthermore, personal characteristics and smartphone operating system (Android vs. iOS) used did not relate to engagement, allowing for a broad distribution of digital health technologies. Our study adds information on single predictive variables relevant for adherence and withdrawal from EMA smartphone surveys in older people that can inform the design of future digital EMA research to maximize engagement and reliability of study results.

The Effects of a Digital Well-Being Intervention in Older Adults: An Analysis of Real-World User Data (Preprint)

Background

Digital interventions have been shown to be effective for a variety of mental health disorders and problems. However, few studies have examined the effects of digital interventions in older adults; therefore, little is known about how older adults engage with or benefit from these interventions. Given that adoption rates for technology among people aged ≥65 years remain substantially lower than in the general population and that approximately 20% of older adults are affected by mental health disorders, research exploring whether older adults will use and benefit from digital interventions is needed.

Objective

This study aimed to examine the extent to which older adults engaged with a digital well-being intervention (Happify) and whether engaging with this program led to improvements in both subjective well-being and anxiety symptoms.

Methods

In this retrospective analysis, we analyzed data from 375 real-world Happify users aged ≥65 years who signed up for the platform between January 1, 2019, and December 23, 2021. Changes in well-being and anxiety symptoms across 42 to 182 days were assessed using responses to the in-app assessment, which users were prompted to take every 2 weeks, and were compared among users who engaged with the program at the recommended level (ie, 2 or more activities per week) or below the recommended level.

Results

In all, 30% (113/375) of the sample engaged with the platform at the recommended level (ie, completed an average of 2 or more activities per week), and overall, users completed an average of 43.35 (SD 87.80) activities, ranging from 1 to 786, between their first and last assessment. Users were also active on the platform for an average of 19.36 (SD 27.16) days, ranging from 1 to 152 days. Moreover, older adults who engaged at the recommended level experienced significantly greater improvements in subjective well-being (P=.002) and anxiety symptoms (P<.001) relative to those who completed fewer activities.

Conclusions

These data provide preliminary evidence that older adults engage with and benefit from digital well-being interventions. We believe that these findings highlight the importance of considering older adult populations in digital health research. More research is needed to understand potential barriers to using digital interventions among older adults and whether digital interventions should be modified to account for this population’s particular needs (eg, ensuring that the intervention is accessible using a variety of devices). However, these results are an important step in demonstrating the feasibility of such interventions in a population that is assumed to be less inclined toward digital approaches.

Engagement in Digital Health App-Based Prevention Programs Is Associated With Weight Loss Among Adults Age 65+

Background

Digital health programs have been shown to be feasible and effective for the prevention of chronic diseases such as diabetes. Contrary to expectations, findings also suggest that older adults have higher levels of engagement with digital health programs than younger adults. However, there is a paucity of research examining outcomes among older adults in digital health programs and whether higher engagement is related to better outcomes.

Methods

We examined weight loss outcomes for 538 users aged 65 and older participating in one of two app-based prevention programs called the Diabetes Prevention Program and the Prevention Program, respectively. Both programs were available on a single artificial intelligence (AI)-powered digital health platform and shared a common goal of weight loss. We also examined the relationship between key engagement metrics (i.e., conversing with the AI-powered coach, weigh-ins, and initiating educational lessons early in the program) and weight loss outcomes.

Results

The average weight loss of all enrollees having a weight measurement after after the 9th week was 4.51%, and the average weight loss of the Diabetes Prevention Program enrollees meeting a minimum engagement level was 8.56%. Greater weight loss was associated with a greater number of days with AI-powered coaching conversations (p = 0.03), more weigh-ins (p = 0.00), and early educational lesson initiation (p = 0.02).

Conclusions

Digital health programs powered by AI offer a promising solution for health management among older adults. The results show positive health outcomes using app-based prevention programs, and all three engagement metrics were independently associated with weight loss.

Human Support in App-Based Cognitive Behavioral Therapies for Emotional Disorders: Scoping Review

BACKGROUND

Smartphone app-based therapies offer clear promise for reducing the gap in available mental health care for people at risk for or people with mental illness. To this end, as smartphone ownership has become widespread, app-based therapies have become increasingly common. However, the research on app-based therapies is lagging behind. In particular, although experts suggest that human support may be critical for increasing engagement and effectiveness, we have little systematic knowledge about the role that human support plays in app-based therapy. It is critical to address these open questions to optimally design and scale these interventions.

OBJECTIVE

The purpose of this study is to provide a scoping review of the use of human support or coaching in app-based cognitive behavioral therapy for emotional disorders, identify critical knowledge gaps, and offer recommendations for future research. Cognitive behavioral therapy is the most well-researched treatment for a wide range of concerns and is understood to be particularly well suited to digital implementations, given its structured, skill-based approach.

METHODS

We conducted systematic searches of 3 databases (PubMed, PsycINFO, and Embase). Broadly, eligible articles described a cognitive behavioral intervention delivered via smartphone app whose primary target was an emotional disorder or problem and included some level of human involvement or support (coaching). All records were reviewed by 2 authors. Information regarding the qualifications and training of coaches, stated purpose and content of the coaching, method and frequency of communication with users, and relationship between coaching and outcomes was recorded.

RESULTS

Of the 2940 titles returned by the searches, 64 (2.18%) were eligible for inclusion. This review found significant heterogeneity across all of the dimensions of coaching considered as well as considerable missing information in the published articles. Moreover, few studies had qualitatively or quantitatively evaluated how the level of coaching impacts treatment engagement or outcomes. Although users tend to self-report that coaching improves their engagement and outcomes, there is limited and mixed supporting quantitative evidence at present.

CONCLUSIONS

Digital mental health is a young but rapidly expanding field with great potential to improve the reach of evidence-based care. Researchers across the reviewed articles offered numerous approaches to encouraging and guiding users. However, with the relative infancy of these treatment approaches, this review found that the field has yet to develop standards or consensus for implementing coaching protocols, let alone those for measuring and reporting on the impact. We conclude that coaching remains a significant hole in the growing digital mental health literature and lay out recommendations for future data collection, reporting, experimentation, and analysis.

Predicting Uptake of the COVID Coach App Among US Military Veterans: Funnel Analysis Using a Probability-Based Panel

BACKGROUND

Although the COVID-19 pandemic has not led to a uniform increase of mental health concerns among older adults, there is evidence to suggest that some older veterans did experience an exacerbation of preexisting mental health conditions, and that mental health difficulties were associated with a lack of social support and increasing numbers of pandemic-related stressors. Mobile mental health apps are scalable, may be a helpful resource for managing stress during the pandemic and beyond, and could potentially provide services that are not accessible due to the pandemic. However, overall comfort with mobile devices and factors influencing the uptake and usage of mobile apps during the pandemic among older veterans are not well known. COVID Coach is a free, evidence-informed mobile app designed for pandemic-related stress. Public usage data have been evaluated; however, the uptake and usage of the app among older veterans have not been explored.

OBJECTIVE

The purpose of this study was to characterize smartphone ownership rates among US veterans, identify veteran characteristics associated with downloading and use of COVID Coach, and characterize key content usage within the app.

METHODS

Data were analyzed from the 2019-2020 National Health and Resilience in Veterans Study (NHRVS), which surveyed a nationally representative, prospective cohort of 3078 US military veterans before and 1 year into the pandemic. The NHRVS sample was drawn from KnowledgePanel, a research panel of more than 50,000 households maintained by Ipsos, Inc. The median time to complete the survey was nearly 32 minutes. The research version of COVID Coach was offered to all veterans who completed the peripandemic follow-up assessment on a mobile device (n=814; weighted 34.2% of total sample). App usage data from all respondents who downloaded the app (n=34; weighted 3.3% of the mobile completers sample) were collected between November 14, 2020, and November 7, 2021.

RESULTS

We found that most US veterans (81.5%) own smartphones, and that veterans with higher education, greater number of adverse childhood experiences, higher extraversion, and greater severity of pandemic-related posttraumatic stress disorder symptoms were more likely to download COVID Coach. Although uptake and usage of COVID Coach were relatively low (3.3% of eligible participants, n=34), 50% of the participants returned to the app for more than 1 day of use. The interactive tools for managing stress were used most frequently.

CONCLUSIONS

The COVID-19 pandemic has increased the need for and creation of digital mental health tools. However, these resources may require tailoring for older veteran populations. Future research is needed to better understand how to optimize digital mental health tools such as apps to ensure uptake and usage among older adults, particularly those who have experienced traumas across the lifespan.

Reach of a Fully Digital Diabetes Prevention Program in Health Professional Shortage Areas

The National Diabetes Prevention Program (NDPP) offers lifestyle change education to adults at risk for diabetes across the United States, but its reach is curbed due, in part, to limitations of traditional in-person programs. Diabetes Prevention Programs (DPPs) that are fully digital may increase reach by overcoming these barriers. The aim of this research was to examine the reach of Lark's DPP, a fully digital artificial-intelligence-powered DPP. This study assessed geographic features and demographic characteristics of a sample of Lark DPP commercial health plan members with complete data (N = 16,327) and compared several demographic features with a large composite sample of members from DPPs across the nation (NDPP; N = 143,489) and a National Health Interview Survey (NHIS) sample of prediabetic adults in the United States (NHIS; N = 2118). Examination of the Lark DPP sample revealed that 24.4% of members lived in rural areas, 30.8% lived in whole county health professional shortage areas, and only 7.6% of members lived in a zip code with an in-person DPP. When comparing the Lark sample with the NDPP and NHIS samples, Lark DPP enrollees tended to be younger and have a higher body mass index (BMI) (p's < 0.001). Lark provides convenient access to a DPP for individuals living in hard-to-reach areas who may face barriers to participating in in-person or telephonic DPPs or who prefer a digital program. Compared with the NDPP sample, Lark is also reaching younger and higher BMI users, who are traditionally difficult to enroll and have a high need for intervention.

Generational Perspectives on Technology's Role in Mental Health Care: A Survey of Adults With Lived Mental Health Experience

Introduction

Personal technology (e.g., smartphones, wearable health devices) has been leveraged extensively for mental health purposes, with upwards of 20,000 mobile applications on the market today and has been considered an important implementation strategy to overcome barriers many people face in accessing mental health care. The main question yet to be addressed is the role consumers feel technology should play in their care. One underserved demographic often ignored in this discussion are people over the age of 60. The population of adults 60 and older is predicted to double by 2,050 signaling a need to address how older adults view technology for their mental health care.

Objective

The objective of this study is to better understand why digital mental health tools are not as broadly adopted as predicted, what role people with lived mental health experience feel technology should play in their care and how those results compare across age groups.

Method

In a mixed-methods approach, we analyzed results from a one-time cross-sectional survey that included 998 adults aged 18–83 with lived experience of mental health concerns recruited from Prolific, an online research platform. We surveyed participant's use of technology including their perspectives on using technology in conjunction with their mental health care. We asked participants about their previous use of digital mental health tools, their treatment preferences for mental health care, and the role technology should play in their mental health care.

Results

Across all age groups, respondents had favorable views of using digital mental health for managing mental health care. However, older adults rated their acceptability of digital mental health tools lower than middle-aged and younger adults. When asked what role technology should play in mental health care in an open-ended response, most participants responded that technology should play a complementary role in mental health care (723/954, 75.8%).

Conclusion

Digital mental health is seen as a valuable care management tool across all age groups, but preferences for its role in care remain largely administrative and supportive. Future development of digital mental health should reflect these preferences.

Social Robots for Evaluating Attention State in Older Adults

Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances. The indices from the modified SART suggested a well-documented conservative response strategy endorsed by older adults, which were represented by slower responses and increased omission errors. Moreover, the slower responses and increased omissions were found to be associated with less self-reported mind-wandering, thus showing consistency with their higher subjective ratings of attentional control. Overall, this study demonstrates the potential of constructing age-related cognitive profiles with attention evaluation instruction based on a social companion robot for older adults at home.