Background:Older adults often express positive attitudes toward digital health technologies in surveys, yet adoption remains low. Self-report measures may not capture automatic affective reactions such as anxiety or distrust. Implicit paradigms such as the affect misattribution procedure (AMP) can reveal these automatic attitudes, but parameters optimized for younger adults may not be suitable for older adults because of age-related slowing and changes in visual processing. Objective:This study aimed to adapt and evaluate an influence-aware affect misattribution procedure (IA-AMP) for measuring implicit attitudes of older adults toward digital health technologies and to identify an age-appropriate prime duration that balances affect transfer strength with minimal conscious awareness. Methods:A 2-phase methodological adaptation and feasibility study was conducted among older adults (aged ≥60 y). Phase 1 (n=40) involved the development and validation of age-relevant synthetic images depicting older adults using digital health tools. The images were evaluated based on valence, arousal, thematic relevance, and low-level perceptual features. Phase 2 (n=56) implemented an IA-AMP with 3 prime durations (75, 350, and 425 ms) across 2 sequential cohorts. The first cohort (batch 2A, n=29) used the initial awareness probe, whereas the second cohort (batch 2B, n=27) used a simplified awareness interface. The core IA-AMP target judgment task remained unchanged across batches. The primary inferential outcome was the binary trial-level target judgment, coded as pleasant or unpleasant. Trial-level responses were analyzed using binomial logistic mixed-effects models with prime valence, prime duration, their interaction, and batch as fixed effects, along with random intercepts for participant and prime image. Awareness analyses were restricted to batch 2B. Results:The primary generalized linear mixed-effects model showed a significant prime valence × prime duration interaction (χ22=38.1; P<.001). Positive primes increased the odds of pleasant target judgments relative to negative primes at all durations (odds ratio [OR] 11.2, 95% CI 5.8-21.6, at 75 ms; OR 31.9, 95% CI 16.0-63.4, at 350 ms; and OR 45.6, 95% CI 22.1-94.0, at 425 ms; all Holm-adjusted P<.001). The positive-negative contrast was smaller at 75 milliseconds than at 350 and 425 milliseconds, whereas the contrasts at 350 and 425 milliseconds did not differ significantly. Batch sensitivity analyses showed stronger overall priming in batch 2B, but the prime valence × prime duration × batch interaction was not significant (χ22=0.76; P=.68). Conclusions:The IA-AMP offers a promising approach for assessing the affective responses of older adults to digital health technologies beyond self-report. A prime duration of approximately 350 milliseconds appears to be a practical calibration point for AMP studies involving older adults, producing strong affect transfer effects while avoiding the longest exposure duration. Because reported influence awareness was common, AMP effects should be interpreted alongside awareness measures rather than as awareness-free implicit attitudes. AMP-based affective measures may complement usability and adoption research by identifying emotional responses that users may not readily articulate, thereby supporting more inclusive and evidence-informed development, evaluation, and implementation of digital health technologies for aging populations.
Background The “aged” filter has gone viral on TikTok. This filter leverages AI to superimpose signs of aging, such as wrinkles, crow’s feet, and sagging skin, onto a user’s face. Notably, several dermatologists have attested to the accuracy of this filter in simulating the effects of aging. Reactions among the TikTok community have been mixed, with some utterly dismayed but others wholeheartedly embracing the sight of their aged visages. Objective This study takes a deep dive into the ways in which TikTok users react to their aged selves through a content analysis of videos featuring the aged filter. Understanding how they respond to the filter can shed light on the types of meanings commonly attributed to old age as a social construct. The following research questions form the premise of our content analysis of videos on TikTok: (1) How do individuals react to the way their possible future selves look? (2) What types of attitudes toward old age are being conveyed in these videos? (3) What do these responses say about prevailing cultural beliefs, values, and attitudes toward old age? Methods To build a comprehensive dataset, we compiled all publicly available videos (N=2682) using the following hashtags as search queries: “#AgedFilter,” “#OldFilter,” and “#AgeFilter.” Collectively, these hashtags amassed >2,068,100,000 views at the time of writing this manuscript. After applying our exclusion criteria, the dataset comprised 681 videos, which received a total of 34,382,236 likes, 1,204,741 favorites, and 211,006 comments. Both deductive and inductive approaches informed our content analysis. Results A total of 7 themes emerged from our content analysis. Half of the videos (n=342, 50%) fell under theme 1, “anxiety about aging.” The second-largest theme (n=95, 14%) focused on the “pursuit of youthfulness” (theme 2). Theme 3 was about “aging with confidence” (n=84, 12%) and theme 4 was about the “beauty of aging” (n=54, 8%). Theme 5 examined the “privilege of aging” (n=48, 7%) and theme 6 examined “generational connections” (n=43, 6%). Theme 7 covered the “inevitability of aging” (n=15, 2%). Conclusions This study marks one of the first attempts to analyze videos using the aged filter on TikTok. Our findings suggest that ageist beliefs may be prevalent among some users. Going forward, there is value in adopting a definition of beauty that transcends age. By doing so, aging can be reframed as a natural part of life to be embraced rather than as a grim reality to be dreaded.
Unlabelled:Leprosy is endemic in many low-income countries, affecting mainly the poorest communities. The disease can lead to sensorimotor functional losses that can predispose patients to reduced independence. In older adults, these losses associated with leprosy are added to those already resulting from natural aging and associated comorbidities, which can contribute to the clinical picture of frailty syndrome in these older adults. Early detection of frailty can reduce several serious consequences for these individuals, preventing loss of quality of life and financial costs to patients and the health care system. Sensors built into smartphones have been proposed as low-cost, and highly sensitive tools for detecting motor loss and could help monitor the risk of some frailty features in older people affected by leprosy.
Background:China's rapidly aging population and high burden of frailty make proactive preparation for future care increasingly urgent, yet few older adults engage in such preparation. Existing interventions often overlook the heterogeneity between prefrail and frail populations and lack precision-oriented digital strategies. Objective:Building on a series of prior empirical studies, this study aimed to develop and pilot-test a digital intervention to support preparation for future care among community-dwelling older adults with prefrailty and frailty. Methods:The "Yi Yang Plan," a WeChat mini-program, was developed through a rigorous multiphase process, including a scoping review, a convergent mixed methods study, and a structural equation model. These findings informed the design of a tailored, stage-specific intervention targeting the distinct needs of prefrail and frail older adults. An interdisciplinary team subsequently refined the intervention using an iterative design approach. The intervention modules comprised 4 processes: Awareness Enhancement, Care Resources, Care Decision-Making, and Care Planning. Pilot testing involved expert panel consultations and think-aloud tests. Expert characteristics, engagement, and authority coefficients were assessed, and feedback was synthesized using content analysis. Feasibility and acceptability among older adults and their family members were evaluated through task completion metrics, satisfaction surveys, think-aloud protocols, and semistructured interviews. Results:Seven experts participated in the consultation, with an engagement coefficient of 100% and an authority coefficient of 0.88. Recommendations were synthesized into four key themes: (1) establishing mechanisms for care plan updating and review, (2) strengthening user-centered design, (3) implementing dynamic resource management and robust data security measures, and (4) enhancing integration with community care systems and policy frameworks. A total of 20 older adults and 20 family members were recruited. All participants successfully completed the assigned tasks, with a mean completion time of 20.7 (SD 5.2) minutes. Satisfaction ratings were generally favorable. The qualitative findings indicated that the intervention was perceived as useful and professionally designed, while also identifying several challenges, including variability in preparation for future care readiness and educational levels, limited interactivity, insufficient practical content, and reliance on support from adult children. Suggested improvements included enhanced personalization, additional supportive tools, improved usability, greater involvement of adult children, and stronger integration with offline services. Conclusions:The "Yi Yang Plan" demonstrated preliminary scientific validity, feasibility, and acceptability as a multidisciplinary, collaboratively developed digital intervention to support preparation for future care among older adults with prefrailty and frailty. By translating prior empirical and theoretical findings into a differentiated, precision-oriented digital strategy, this study advances interventions beyond conventional one-size-fits-all approaches. Future iterations should focus on optimizing system architecture, user interface design, platform functionality, and implementation strategies. Further research should conduct higher-quality randomized controlled trials to evaluate the platform's effectiveness.
Background:Intergenerational communication has been recognized as a key factor in enhancing social connections among older adults by improving the quality of interactions, which is a crucial component of healthy aging. However, effective interventions to foster intergenerational communication and engagement remain scarce at this stage. Objective:This study examined the effects of a gamification intervention for intergenerational dialogue on intergenerational communication and health-related capacity across 3 age cohorts and further analyzed the differential impacts of intervention characteristics on the game experience, in terms of satisfaction and engagement. Methods:A 2-stage exploratory method was adopted with a cocreation approach to develop a theoretical framework of intergenerational dialogue for the gamification-based intervention, the Healthy Aging in Dialogue Tabletop Game, followed by a mixed methods study involving pre- and postintervention surveys and qualitative interviews to evaluate the effectiveness of the intervention in the young (aged 18-39 years), middle-aged (aged 40-64 years), and older adult (aged ≥65 years) groups. Intergenerational communication was assessed using tools measuring satisfaction and competence, whereas health-related capacity was evaluated using health literacy and patient enablement tools. Results:A total of 68 respondents completed the intervention and evaluation, with 23, 14, and 31 respondents in the young, middle-aged, and older adult groups, respectively. This intergenerational intervention yielded notable benefits in intergenerational communication satisfaction (Cohen d=0.745), communication competence (Cohen d=0.377), and health literacy (Cohen d=0.409), especially among older adults. Patient enablement was significantly higher in older adults (mean score 6.10, SD 2.18) than in young adults (mean score 4.22) (P=.04). In addition, this study highlighted the importance of leveraging gamification to enhance intergenerational communication and engagement, ultimately combating social isolation among older adults. Conclusions:The intergenerational dialogue tabletop game provided an enjoyable platform for cross-generational dialogue and enhanced each age group's communication competence, allowing participants to contribute and learn from one another. The findings provide additional evidence for aging research and shed light on approaches to foster social interaction and connection, thereby promoting health and active aging.
Background:Exergames, which combine physical exercise with interactive gameplay, are increasingly being incorporated into fall prevention programs for older adults. Gamified elements, such as real-time feedback and progress tracking, may enhance motivation, engagement, and adherence. Although several systematic reviews have examined the effects of exergaming on balance and physical function, fewer have focused specifically on clinically meaningful outcomes, such as falls and injurious falls, or on indicators that may influence real-world adoption of exergames. Objective:This study aimed to evaluate the effectiveness of exergaming interventions for preventing falls and injurious falls in people aged ≥60 years and to synthesize evidence on implementation-related outcomes, including adherence, acceptability, concerns about falling, quality of life, adverse events, and cost-effectiveness. Methods:MEDLINE, Embase, CINAHL Plus, PsycINFO, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched from inception to February 2025 for randomized controlled trials evaluating exergaming interventions in older adult populations across all settings. Outcomes included fall rate, number of fallers and injurious falls, and implementation-related secondary outcomes. Risk of bias was assessed using RoB 2.0, and certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Data were synthesized narratively and, where appropriate, pooled using meta-analysis. Results:Nine studies (N=1385) met the inclusion criteria. Comparator-specific analyses suggested that exergaming may reduce fall rates compared with active intervention comparators, although the magnitude and certainty of effect varied, and substantial heterogeneity was present across analyses. Moderate-certainty evidence also suggested that exergames reduced the number of older adults experiencing one or more falls at 12-month follow-up compared with usual care (risk ratio 0.75, 95% CI 0.61-0.92). Evidence for injurious falls, quality of life, concerns about falling, adherence, acceptability, and cost-effectiveness was limited or inconsistent. When pooled across all control groups, exergaming interventions were associated with a lower overall fall rate than comparator interventions (incidence rate ratio 0.53, 95% CI 0.41-0.68), although substantial heterogeneity was present (I²=76%). Conclusions:Low- to moderate-certainty evidence suggests that exergames may reduce fall rates, particularly in comparisons with active intervention control groups, and may reduce the number of fallers compared with usual care. These findings indicate that exergaming may offer a useful adjunct to established fall prevention strategies for older adults, particularly where sustained engagement with conventional exercise is challenging. However, substantial heterogeneity, modest sample sizes, and limited long-term follow-up reduce confidence in these estimates, and more rigorous, large-scale trials are needed before routine implementation can be recommended. This review extends previous exergaming syntheses by focusing on clinically meaningful outcomes, including falls and injurious falls, while also considering implementation-related factors relevant to real-world uptake.
Background:Rapid population aging and a worsening shortage of care workers necessitate the identification of older adults who require proactive interventions. Although machine learning (ML) has been increasingly applied in gerontology, existing studies have predominantly focused on social isolation, loneliness, depression, falls, and frailty in isolation rather than on the integrated construct of care needs. Objective:This study aimed to develop and interpret an explainable ML model that identifies care needs in community-dwelling Korean older adults. Beyond physical health indicators such as disease and functional status, this study adopted a comprehensive approach that included mental health, cognitive function, health behaviors, and socioenvironmental determinants, such as social participation, social support, and the housing environment, to present an integrated model encompassing both health and social care needs. Methods:Data were obtained from the 2023 Korea Senior Survey, a nationally representative sample of 10,078 community-dwelling adults aged 60 years and older. The data were split 70:30 into training (n=7054) and held-out test (n=3024) sets. Seven algorithms were compared (logistic regression, decision tree, support vector machine, random forest, gradient-boosted decision trees, extreme gradient boosting, and light gradient boosting machine) using stratified 5-fold cross-validation on the training set. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Model interpretability used Shapley additive explanations with bootstrap stability assessment across folds. Results:Model A (excluding activities of daily living or instrumental activities of daily living [IADL]) achieved good discrimination (AUC 0.892, 95% CI 0.866-0.918), adequate calibration (calibration slope=0.826), and positive clinical net benefit, demonstrating that upstream factors alone can identify older adults with care needs without relying on functional status. Shapley additive explanations analysis identified age, nutritional risk, employment status, depressive symptoms, self-rated health, cognitive function, household income, and home modification as the leading predictors, with high rank stability across cross-validation folds. Model B (including activities of daily living or IADL) yielded a higher AUC (0.976, 95% CI 0.962-0.989), but this reflected the near-tautological relationship between IADL and self-reported care needs rather than genuine upstream predictive value. Using an objective composite outcome yielded equivalent discrimination (AUC 0.892), supporting robustness to the outcome definition. Conclusions:Explainable ML models offer high predictive accuracy and strong interpretability for identifying care needs among older adults. Care needs in community-dwelling Korean older adults can be identified with good discrimination, calibration, and clinical net benefit using multidimensional nonfunctional factors alone. By highlighting the significant roles of health, social, and environmental factors, this study provides empirical evidence to support evidence-based decision-making for the Long-Term Care Insurance system and integrated community care policies.
Background:The swift pace of digital transformation has heightened individuals' dependence on digital technologies. This makes it imperative to explore how individual factors such as digital self-efficacy and social capital affect satisfaction with the daily life changes stemming from digital transformation. This investigation is particularly essential among middle-aged and older adults because they encounter numerous challenges and opportunities from digital transformation. Objective:This study examined the moderating effect of social capital on the relationship between digital self-efficacy and satisfaction with the daily life changes stemming from digital transformation among middle-aged and older adults. Methods:This study used data from the 2022 Digital Divide Survey conducted by the National Information Society Agency in South Korea. The sample included 4155 individuals aged 40 years or older, of whom 71.8% (n=2985) were classified as middle-aged (40-64 y) and 28.2% (n=1170) as older adults (65-96 y). Data were analyzed using multiple linear regression models with IBM SPSS (version 27.0) and the PROCESS macro. Results:Compared with the middle-aged group, the older adult group showed lower levels of digital self-efficacy, social capital, and satisfaction with the daily life changes stemming from digital transformation. The interaction term between digital self-efficacy and social capital was significant in the middle-aged group (B=-0.11, P=.002) and the older adult group (B=-0.24, P<.001). There was a positive association between digital self-efficacy and satisfaction in both groups, and this association was stronger among those with low social capital. Furthermore, this effect was more pronounced in the older adult group (B=0.71, P<.001 for low and B=0.47, P<.001 for high social capital) than in the middle-aged group (B=0.55, P<.001 for low and B=0.46, P<.001 for high social capital). Education level and digital competence were positively associated with satisfaction in both age groups; higher household income was positively associated with satisfaction only in the middle-aged group. Conclusions:Individuals with low social capital showed a stronger association between digital self-efficacy and satisfaction with the daily life changes stemming from digital transformation. Enhancing digital self-efficacy among older adults, especially those with low levels of social capital, may improve their satisfaction with the daily life changes stemming from digital transformation.
BACKGROUND:Physical and cognitive function, both of which decline with aging, are significantly interrelated. Although numerous studies have investigated the effect of physical exercise on cognitive function, relatively few have examined the impact of pure cognitive training on physical performance. OBJECTIVE:This review aimed to summarize the effects of pure cognitive training on balance and mobility in older adults. METHODS:Electronic databases (PubMed, Embase, CINAHL, and PsycInfo) were searched in February 2025. Randomized controlled trials that investigated the effect of pure cognitive training on balance and mobility in older adults were included. Pure cognitive training refers to a strictly nonphysical approach involving guided practice on a standardized set of cognitive tasks aimed at optimizing cognitive functioning. For the outcomes, balance performance focused on standing balance tests and comprehensive balance assessment scales. Mobility performance primarily focused on gait speed under a single-task condition (ie, walking only), gait speed under a dual-task condition (ie, conducting cognitive tasks while walking), and the Timed Up and Go test. The Physiotherapy Evidence Database (PEDro) scale was used to evaluate methodological quality. The level of evidence for the available outcomes was rated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Meta-analyses and sensitivity analyses of studies with high methodological quality were performed if 3 or more studies were obtained. RESULTS:Fourteen studies were eventually included. The methodological qualities of 8 (57%) studies were rated as good, while those of 6 (43%) studies were rated as fair. A meta-analysis of studies with high methodological quality showed that pure cognitive training has a significant effect on cognitive-motor dual-task gait speed (standardized mean difference [SMD] 0.39, 95% CI 0.04-0.75; n=376 participants; moderate-quality evidence) but not single-task gait speed (SMD 0.12, 95% CI -0.05 to 0.30; n=506 participants; moderate-quality evidence). No significant improvements were found in functional mobility (SMD 0.29, 95% CI -0.60 to 1.18; n=566 participants; low-quality evidence) or balance (SMD 0.18, 95% CI -0.34 to 0.69; n=139 participants; very low-quality evidence). CONCLUSIONS:Cognitive training does not improve balance or mobility under single-task conditions but does improve walking speed under cognitive-motor dual-task conditions in older adults. A cognitive training protocol targeting executive function, consisting of 40- to 60-minute sessions conducted 2 to 3 times per week over a period of 8 to 10 weeks, has been shown to be effective.
Background Artificial intelligence (AI) has the potential to improve health among older adults, yet how different stakeholders decide to develop, finance, and adopt AI innovations is not well understood. Objective This study aimed to understand the decision-making of different stakeholders regarding AI and technologies for the health care of older adults. Methods We conducted semistructured interviews with 15 older adults and care partners, 15 clinicians, 8 health system or insurance leaders, 5 investors, and 6 technology developers. Data were analyzed using thematic content analysis. Results All stakeholders considered cost, value, and usability important in adopting AI health technologies but emphasized different aspects of each concept. Older adults and care partners prioritized out-of-pocket costs and ease of use, whereas payers emphasized disease prevalence and implementation feasibility. Developers and investors focused on profitability and scalability, resulting in tension with end users’ priorities. Participant suggestions included problem-driven design, greater stakeholder engagement, public-private partnerships, and educating older adults about AI. Conclusions As with prior health-related technology innovations, aligning decisional priorities across stakeholders is critical to motivate impactful AI health technologies for older adults.
BACKGROUND:By 2050, 22% of the global population will be aged 60 years or older, with Europe experiencing rapid aging. This increase in chronic diseases and functional decline demands a shift from fragmented, hospital-centric care to continuous care models. The World Health Organization's healthy aging framework prioritizes intrinsic capacity (IC)-defined as physical and mental abilities across the locomotion, vitality, sensory, cognition, and psychology domains. Digital health solutions enable remote monitoring but face barriers, such as low digital literacy and usability challenges among older adults. OBJECTIVE:This study evaluates the impact of a multidimensional, artificial intelligence-driven digital health platform (CAREUP) on the dynamic monitoring and personalized enhancement of IC in community-dwelling older adults through the integration of real-time physiological, cognitive, and behavioral data streams. METHODS:This pilot feasibility study was conducted from September to December 2024 in Italy, Romania, and Austria. It involved 66 older adults as primary users and 17 caregivers as secondary users. The CAREUP system included Android apps (Careplan and Positive Health), smart devices, and a dashboard. Users followed a 2-month plan. We assessed usability (System Usability Scale [SUS]), user experience (User Experience Questionnaire short version [UEQ-S]), and acceptance (qualitative) at 3 time points (T0, T1, and T2), along with health and quality-of-life measures. RESULTS:Of the 66 enrolled participants, 61 primary users (37 women and 24 men; mean age 73.9 years) completed the study. The overall SUS score was 68, indicating acceptable but improvable usability, with significant variation across countries. The UEQ-S score of 1.45 indicated a good overall user experience, with high Hedonic Quality (1.66) suggesting strong emotional appeal and Pragmatic Quality (1.23) reflecting above-average usability. Most participants (49/61, 80%) rated the platform as interesting, helpful, and efficient, and 48 out of 61 (79%) were willing to recommend it to friends. Secondary outcomes demonstrated stable physical and mental health and maintained independence in daily activities. Technical connectivity issues and device complexity emerged as the primary barriers to adoption. CONCLUSIONS:CAREUP demonstrated feasibility for IC monitoring, with acceptable usability and a positive user experience. However, technical challenges and cross-country variations indicate a need for adaptations to address cultural differences and varying levels of digital literacy. Prioritizing reliability, simplifying the system, and providing user support will be essential to enhance broader adoption.
Background:Mobility limitations in older adults are associated with an increased physiological cost of walking, reduced functional independence, and elevated fall risk. Conventional wheeled walkers improve stability but lack real-time physiological monitoring and safety feedback. Digital health-enabled assistive devices integrating physiological monitoring and fall detection may enhance mobility performance and safety. Objective:This study aimed to evaluate the short-term effects of a smart wheeled walker equipped with real-time physiological monitoring and fall detection compared with a standard wheeled walker in improving walking efficiency, dynamic balance, and fear of falling among community-dwelling older adults. Methods:A single-blind randomized crossover trial was conducted in 30 community-dwelling older adults aged 65 to 80 years with mild balance impairment (Timed Up and Go >13.5 seconds). Participants completed walking trials using both a standard wheeled walker and a smart wheeled walker in a randomized order, with a 30-minute washout period. The smart walker integrated photoplethysmography-based heart rate (HR) monitoring, pulse oximetry (oxygen saturation [SpO₂]), and a tilt-based fall detection system with mobile alert functionality. The primary outcome was walking efficiency assessed using the Physiological Cost Index (PCI). Secondary outcomes included dynamic balance (Expanded Timed Up and Go [ETUG]) and fear of falling (Falls Efficacy Scale-International). Statistical analysis was performed using paired comparisons with a significance level of P<.05. Results:Thirty (100%) participants completed both intervention conditions and were included in the final analysis. Walking speed was significantly higher with the smart wheeled walker than the standard wheeled walker (mean 28.77, SD 11.94 vs mean 18.14, SD 14.55 m/min; mean difference -10.63 m/min, 95% CI -17.51 to -3.75 m/min; P<.001). The PCI was significantly lower with the smart wheeled walker (mean 0.36, SD 0.38 vs mean 0.69, SD 1.23 beats/m; mean difference 0.33 beats/m, 95% CI 0.06-0.60 beats/m; P=.02), indicating improved walking efficiency. Participants also completed the ETUG test significantly faster with the smart wheeled walker (mean 50.06, SD 24.50 vs mean 64.47, SD 23.04 s; mean difference 14.41 s, 95% CI 2.12-26.70 s; P=.048), indicating improved dynamic balance. Fear of falling did not differ significantly between walker conditions (mean 31.56, SD 9.58 vs mean 32.91, SD 10.37; mean difference 1.35, 95% CI -3.81 to 6.51; P=.62). Conclusions:A smart wheeled walker integrating real-time physiological monitoring and fall detection significantly improved walking efficiency and dynamic balance during short-term supervised testing in community-dwelling older adults. These findings support the potential clinical value of digital health-enabled mobility aids. Further longitudinal studies conducted in real-world settings are warranted to evaluate long-term mobility, fall prevention, independent use, and comprehensive device safety.
BACKGROUND:Age-related cognitive and sensorimotor declines co-occur and reinforce one another, increasing vulnerability to functional dependence and falls. Exergaming offers integrated cognitive-motor training, but most systems lack features that support personalized care for socially vulnerable, community-dwelling older adults. Low-income older adults receiving Public Community Care services bear a disproportionate burden of cognitive and sensorimotor decline, yet remain underrepresented in digital health research. As part of a broader program to develop a digital health monitoring-to-intervention framework, we evaluated a pressure sensor-based exergame designed to deliver targeted cognitive-motor training within the existing Public Community Care infrastructure. OBJECTIVE:This study evaluates the feasibility and preliminary effects of the Brain Step exergaming intervention on cognitive and sensorimotor frailty in low-income older adults living alone who were receiving Public Community Care services through a pilot study (study 1) and a randomized controlled trial (RCT; study 2). METHODS:Study 1 (n=15) was a 12-week single-arm pilot study. Study 2 was a parallel-group pilot RCT conducted in Seoul, South Korea. Thirty-one low-income older adults living alone who were receiving Public Community Care services (mean age 81.5 years; 26/31, 84%, female) were randomized to the intervention (n=16) or control (n=15) group. The 16-week intervention used custom hardware with integrated safety bars and arcade-style games that progressed from single- to dual-task challenges. Primary outcomes were cognitive function (Korean Mini-Mental State Examination [K-MMSE]), physical function (Berg Balance Scale [BBS] and Timed Up and Go [TUG]), and sarcopenia indicators (appendicular skeletal muscle mass [ASM], Appendicular Skeletal Muscle Mass Index, and grip strength). Secondary outcomes were the Korean Montreal Cognitive Assessment, physical fitness (Figure-8 Walk, 30-second Sit-to-Stand, and 2-Minute Step Test), and fall incidence. Generalized estimating equations with Benjamini-Hochberg false discovery rate (FDR) correction for multiple testing were applied. RESULTS:In the single-arm pilot study (study 1), a pre-post increase in BBS scores and a reduction in TUG time survived FDR correction among the primary outcomes (both FDR-adjusted P=.002), as did all 3 secondary physical fitness outcomes (Figure-8 Walk, FDR-adjusted P=.006; 30-second Sit-to-Stand, FDR-adjusted P=.01; and 2-Minute Step Test, FDR-adjusted P=.02). The K-MMSE Visuospatial subscale showed an uncorrected pre-post improvement (P=.02), with differential risk-stratified patterns for Recall (P=.04) and Attention (P=.006). In the pilot RCT (study 2), a nominally significant group × time interaction was observed for ASM (P=.04, FDR-adjusted P=.11), with the intervention group maintaining muscle mass while the control group declined. The risk-stratified analyses showed an uncorrected significant 3-way interaction for K-MMSE Recall (P=.01). No findings from study 2 survived FDR correction. CONCLUSIONS:These sequential studies demonstrate the feasibility of delivering a culturally adapted exergaming intervention through the existing Public Community Care infrastructure. The pilot study (study 1) showed FDR-corrected pre-post improvements in balance and mobility within an uncontrolled single-arm design. In the pilot RCT (study 2), no primary outcomes survived FDR correction. The observed preliminary signals for muscle mass preservation and, in exploratory risk-stratified analyses, cognitive improvement in the high-risk subgroups should be regarded as hypothesis-generating only. Adequately powered trials with risk-enriched samples and active comparators are needed to evaluate efficacy. TRIAL REGISTRATION:ClinicalTrials.gov NCT06664229; https://clinicaltrials.gov/study/NCT06664229.
Background:Multidomain dementia-prevention interventions delivered via apps have the potential to reach large populations. However, existing trials have tended to recruit more socioeconomically advantaged participants, raising concerns that the resulting interventions may be less usable for older adults from minority ethnic, lower educational, or lower socioeconomic backgrounds, who are at higher risk of dementia. The Tailored Intervention for Brain Health and Cognitive Enrichment (ENHANCE) app was designed to address this by prioritizing accessibility and engagement across diverse user groups. Objective:This study evaluated the usability and user experience of the ENHANCE prototype during a 1-week at-home supported-use test and explored factors influencing use and engagement among older adults. Methods:We purposively recruited adults aged 60-80 years without dementia for a 1-week mixed methods usability evaluation through community settings, including groups underrepresented in dementia-prevention trials. Participants had at least 1 of 10 prespecified dementia risk factors, attended a face-to-face onboarding session with a coach, used the app at home for 7 days with ongoing coach support, and completed a posttest interview and satisfaction survey. We analyzed quantitative data, including app usage metrics and survey responses descriptively, and used reflexive thematic analysis of qualitative data from onboarding sessions, posttest interviews, coaching calls, and in-app messages. Results:Ten participants participated in the study. The mean age was 68 (SD 6) years, and 7 were females. Participants represented a wide range of deprivation (Index of Multiple Deprivation deciles: mean 4, SD 2, range 1-8), with 6 from ethnic minority backgrounds. All met prespecified minimum-use targets (watching a module video, completing a check-in, and playing assigned games at least once). Many demonstrated additional voluntary engagement: 5 rewatched the risk factor video, 7 used the in-app messaging feature, and among the 5 participants with the hypertension module, blood pressure was logged on an average of 5 out of 7 days. Survey responses indicated high satisfaction, perceived usefulness, and ease of use; 9 participants intended to continue using the app and would recommend it to peers. Qualitative analysis identified engagement facilitators, including rewarding game design, familiar interfaces, appropriately challenging gameplay, consistent virtual rewards, trusted expert information combined with peer stories, and coach support. Barriers included unclear visual cues, insufficient accommodation of motor or sensory impairments, and visual discomfort in some games. Conclusions:Older adults found the ENHANCE prototype usable, acceptable, and engaging over 1 week. Human coaching, inclusive design, and integration of expert and peer narratives were highlighted as key engagement drivers. These findings support further feasibility testing to examine longer-term engagement and provide design insights for more inclusive digital health interventions.
Background:Many clinically relevant aspects of health are expressed through behavior, affect, and interaction, and therefore rely on observation rather than instrumental measurement; yet such information is often documented infrequently in routine care. Dementia, and particularly behavioral and psychological symptoms of dementia (BPSD), is a relevant case because symptoms fluctuate across time and context. Little is known about how structured daily symptom assessment can be integrated into routine dementia care practice. Objective:This pilot study aimed to investigate whether structured daily observation of BPSD, supported by digital documentation, can be incorporated into everyday residential dementia care. Methods:A convergent mixed methods design was applied across 4 nursing home units in southern Sweden. Feasibility was operationalized through 4 focus areas (implementation, practicality, acceptability, and integration), with adherence to the registration routine as the primary feasibility outcome. Quantitative data comprised structured registrations collected daily by care staff over 90 days for 8 residents with BPSD, analyzed descriptively, and interpreted using selected NASSS (nonadoption, abandonment, scale-up, spread, and sustainability) framework domains to contextualize variation in symptom patterns, adherence, and registration practices. Semistructured dyadic interviews were analyzed using deductive content analysis guided by the same domains, with integration occurring at the analytical and interpretive stages. Results:A total of 21,993 item-level registrations were completed: 19,123 NPI-NH (Neuropsychiatric Inventory - Nursing Home version) symptom ratings, recorded across 1623 shift registrations (1 per resident per shift), and 2870 individually selected variables. Overall adherence to the registration routine averaged 75% across shifts but varied considerably between residents (range 33%-90%) and between staff members. Mean time per shift registration decreased from 77.1 seconds in the initial 30-day period to 46.2 seconds in the final 30-day period (P=.008). Symptom prevalence varied widely across NPI domains (7.5% to 38.1% of observation days), and daily symptom counts varied both between and within residents. The routine was experienced as technically straightforward and easy to incorporate into daily work. At the same time, challenges emerged, including time constraints, inconsistent participation, varying levels of interpretive confidence, and difficulties distinguishing among overlapping symptoms. Organizational routines and team stability were central to sustaining staff engagement. Conclusions:The high number of completed registrations indicates that daily observations can be integrated into daily dementia care. The participants perceived it as meaningful and useful. Feasibility was primarily influenced by organizational and interpretive factors rather than technical constraints. Further research should explore strategies to support shared routines and sustain engagement over time.
Background:The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potentially innovative solution for aging in place. Objective:This study aims to evaluate the barriers and facilitators to the adoption of a low-cost smart home embedded within a community-based approach to health monitoring for older adults with multiple chronic conditions and who are experiencing poverty. Methods:Using a prospective, mixed methods design and iterative community co-design, 46 older adults from 7 different language groups were continuously monitored for 6 months with ambient sensors installed in their homes. Two older adults were monitored for 4 and 5 months, respectively, resulting in a total sample of 48. The system generated alerts based on movement pattern changes and escalated notifications to participants, support persons, community health workers, and nurses. Sensor data were analyzed descriptively to quantify alert patterns and response rates, while written text-based data from in-the-moment surveys, community health workers' and registered nurses' notes, and semistructured interviews underwent qualitative descriptive analysis and reflexive thematic coding. Results:The system generated 37 million sensor readings condensed into 1.2 million high-level events and 4719 novel alerts. Qualitative data comprised 34,086 words of text. Participants responded to 1.57% (74) of the initial email alerts and 7.79% (368) of the follow-up SMS text message alerts sent when no email response was received. Community health workers and registered nurses responded to 78.36% (n=3698) of the escalated alerts, resulting in 1060 contacts with participants in response to alerts. Clinical contacts resulted in 72 interventions. Three major qualitative themes emerged: (1) Alone, (2) Trust, and (3) Human Connection. Subthemes included Safety, Personalization, and Digital Distress defined as stress associated with interacting with digital health-monitoring systems. Participants rated the system highly (mean likelihood-to-recommend rating 8.68/10, SD 1.68); however, they expressed a strong preference for phone calls over automated alerts. Cultural expectations influenced adoption, particularly in multigenerational households. Conclusions:Communities can effectively engage in technology-delivered health care. Future research is needed to improve technical aspects of smart home monitoring systems, including accurate alerting using machine learning, data visualizations for older adults and health care workers, and culturally sensitive features. Additional work should address how and when to communicate automated messaging, engage older adults with their own data, and integrate sensor-based monitoring into health care workflows. Research should also explore personalization through advanced computational approaches such as machine learning and strategies to reduce digital distress.
Though exergames attract considerable research interest as tools for preserving cognitive function in older adults, a decade of meta-analytic evidence reveals a persistent gap between theoretical promise and empirical demonstration: exergames rarely outperform conventional motor-cognitive training and do not generate the sustained engagement that preventive benefit requires. These results reflect limitations that are structural: the field focused predominantly on clinical populations rather than primary prevention; training concepts borrowed from rehabilitation protocols rather than grounded in the specific neuroplastic mechanisms of the exergaming medium; and virtual environments that are digitally sophisticated but culturally and aesthetically neutral, systematically excluding the stimulation that converging evidence from neuro-aesthetics, cultural epidemiology, and narrative neuroscience identifies as a core active ingredient of brain health promotion. To address these limitations simultaneously, this Viewpoint introduces Motor-cognitive Active Gamified Immersive Cultural (MAGIC) exergames, defined as “virtual reality solutions in which motor-cognitive interactions are embedded in artistic content, cultural heritage, or historically situated narrative to exploit optimal neurobiological substrates of the intervention”. Drawing on an integrated theoretical framework, we develop mutually reinforcing mechanistic pillars: multimodal sensorimotor engagement within ecologically valid immersive environments; semantic and narrative activation of memory systems; emotional and autobiographical resonance as a dopaminergic driver of learning consolidation and sustained participation; and the re-engineering of physical effort evaluation from aversive to appetitive through aesthetic pleasure and narrative absorption. A fundamental implication is that in MAGIC exergames, motivation is not engineered through gamification mechanics — it is released by the intrinsic value of inhabiting a culturally meaningful world. We further argue MAGIC exergames correspond to a specific use that is, Cultural Snacking, which consists of the serialization of MAGIC content into brief, narratively open daily episodes of 5 to 10 minutes, designed for 3 to 5 daily consumptions across varied contexts. This dosing architecture is compatible with the motivational profile of healthy older adults who do not identify as patients or trainees. The paper opens perspectives with a design and research agenda structured around three priorities: characterizing MAGIC exergames by the type of motor-cognitive interactions they deliver rather than by the technology platform; conducting mechanistic neuroimaging investigations targeting the specific biomarkers of neuroplastic benefit that creative cultural engagement is expected to produce; and building the intersectoral innovation ecosystem that brings cultural institutions, digital health teams, and older adults together as co-designers. Without waiting for MAGIC-specific trials, we invite the digital medicine, public health, and silver economy communities to recognize that museums, heritage sites, and cultural institutions are potentially the most ecologically valid, intrinsically motivating, and cost-effective substrates for a new generation of brain health exergames that are as meaningful as they are effective. The scientific, institutional, and economic conditions for this convergence already exist.
Background:Digital technologies have the potential to support physical, cognitive, and social activity among older adults, but many small- and medium-sized enterprises (SMEs) lack the resources to conduct meaningful co-design with older adults. Toolkits derived from rigorous co-design processes may offer a scalable mechanism for translating end-user priorities into real-world product development. Objective:The Generating Older Active Lives Digitally study aimed primarily to engage older adults in a co-design process to identify priorities for digital technologies that support physical activity and reminiscence. Secondary objectives were to (1) translate these findings into a practical developer-facing toolkit, and (2) evaluate the toolkit's perceived utility and influence among digital technology SMEs. Methods:A total of 157 participants (121 older, 7 younger, and 29 staff) across 15 community and care settings in England and Scotland engaged in 106 technology interaction sessions, 22 evaluation focus groups, and 10 co-design workshops. These involved more than 20 digital technologies. Thematic analysis and structured card-ranking tasks were used to derive their priorities. Preliminary toolkits were co-designed and used by 10 UK-based SMEs who received small grants to apply the toolkit to active development projects. Their reports and follow-up interviews were analyzed thematically to identify perceived impacts on design decisions, product adaptations, and business outcomes. Results:Co-design activities generated 7 cross-cutting themes: motivation, content, barriers, design and inclusivity, suitability, acceptability, and motivations to use. These were organized into 3 toolkit sections: general design principles, online physical-activity platforms, and virtual reality. SMEs reported that the toolkits enhanced understanding of older adults' needs, validated design decisions, and inspired some new features. Perceived impacts included improved usability, expanded accessibility options, increased content variety, clearer instructional design, enhanced social components, and reduced operational costs. SMEs also reported some business benefits, including strengthened cases for investment and increased product uptake. Conclusions:Co-designed toolkits may offer a useful and cost-effective mechanism for translating older adults' priorities into digital product development. SMEs perceived this toolkit as practical, relevant, and impactful for informing design choices. This approach may complement, but does not replace, direct user involvement. It may help accelerate inclusive digital-health innovation for aging populations.
Background:As smart older adult care shifts from basic information support to more continuous algorithm-driven care, concerns have grown about whether such systems support older adults' independence or weaken their sense of autonomy. Although previous research has focused mainly on technology acceptance, less is known about how algorithmic care shapes perceived autonomy and through which psychological pathways this occurs. Objective:This study examined whether perceived algorithmic care intensity reduces older adults' perceived autonomy in community smart care settings, whether decisional substitution and perceived surveillance mediate this relationship, and whether digital literacy moderates these effects. Methods:A 3-stage design was adopted. First, a qualitative prestudy with 15 older adults was conducted to refine the focal constructs and measures. Second, a vignette-based experiment with 233 valid participants tested the effects of algorithmic care intensity on decisional substitution, perceived surveillance, and perceived autonomy. Third, a community-based survey with 298 valid participants examined the moderated mediation model in real-world smart older adult care settings. Results:In study 1, compared with the low-intensity condition, the high-intensity condition significantly increased decisional substitution and perceived surveillance, while reducing perceived autonomy. Indirect effects through decisional substitution (effect=-0.072, 95% CI -0.144 to -0.016) and perceived surveillance (effect=-0.084, 95% CI -0.161 to -0.019) were both significant. In study 2, perceived algorithmic care intensity positively predicted decisional substitution (β=.400, P<.001) and perceived surveillance (β=.440, P<.001), and negatively predicted perceived autonomy (β=-.356, P<.001). The indirect effects through decisional substitution (effect=-0.112, 95% CI -0.172 to -0.065) and perceived surveillance (effect=-0.149, 95% CI -0.215 to -0.093) were significant. Digital literacy significantly weakened the effect of algorithmic care intensity on decisional substitution (β=-.174, P=.002), but not on perceived surveillance (β=-.071, P=.18). Conclusions:Algorithmic care may undermine older adults' perceived autonomy both directly and indirectly through decisional substitution and perceived surveillance. Digital literacy appears to be a selective rather than universal buffer. These findings extend smart older adult care research beyond technology acceptance and clarify the tension between empowerment and control in algorithmic care.
Background:As people age over the coming decades, demand for in-home support and other interventions, such as home modifications, to help older adults age in place successfully, is also expected to rise. Smart home technologies have the potential to enhance aging in place by complementing traditional home modifications; however, adoption within federally funded home modification programs remains limited. Objective:This study explored grantees' perspectives on the Older Adults Home Modification Program (OAHMP) to understand current practices, perceived benefits, barriers, and strategies for integrating smart home technologies into home modification services. Methods:An exploratory qualitative study was conducted with staff, occupational therapists, and home builders from 3 OAHMP grantee organizations. A 1.5-hour virtual focus group was held and thematically analyzed using a deductive approach grounded in the discussion agenda. Results:A total of 11 participants reported early adoption of select smart devices-most commonly smart speakers, doorbell cameras, motion-activated lights, and smart plugs-to enhance home safety, communication, and independence. Barriers to broader implementation emerged at three levels: (1) older adults' digital literacy, privacy concerns, and device maintenance burden; (2) contextual constraints, such as unreliable internet in rural areas; and (3) organizational limitations, including training needs, staffing capacity, and funding challenges. Participants emphasized the importance of progressive adoption, hands-on training, and low-maintenance technology. In addition, partnerships with university educational programs were well established among selected grantees to provide technology training resources for older adults. To inform a reasonable technology selection for home modifications, criteria were proposed across 5 domains: installation, usability, accessibility, sustainability, and security and privacy. Conclusions:Integrating smart home technologies into home modification programs provides a scalable, cost-effective opportunity to improve aging in place outcomes. Policy support, workforce training, valid selection criteria, and sustainable funding models are needed to promote equitable adoption across OAHMP and similar federally supported programs.
