Abstract Background While telecare has considerable potential to improve home care services, benefit older adults, and support informal caregivers, its adoption remains limited. There is also a notable gap in empirical research on the factors that influence informal caregivers’ behavioral intention to use telecare. In particular, existing theoretical acceptance models are insufficiently comprehensive, as they do not specifically address informal caregivers of older adults and have largely overlooked the psychosocial outcomes of engaging with telecare. Objective This study examined the behavioral intention of Slovenian informal caregivers of older adults to use telecare, focusing on the preuse stage of technology acceptance, by integrating the perceived psychosocial outcomes of telecare engagement (peace of mind, reduced anxiety, sense of safety, and perceived connectedness with the care recipient) into the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods The study applied nested ordinary least squares regression analysis to survey data collected in 2021 in Slovenia from informal caregivers of older adults, all of whom were aged 40 years or older. Of 612 respondents in the dataset, 558 were retained after excluding current or former users of care-related assistive technologies; the regression analyses were based on the 526 participants with complete data on all model variables. Results The regression analysis used 3 nested models to examine predictors of the intention of informal caregivers of older adults to use telecare. The first model, which included only background variables (burden of care, financial coping, and caregiver sociodemographic characteristics), had limited explanatory power ( R ²=0.03), with financial coping as the only significant predictor (β=−0.14, P =.005). Adding UTAUT predictors significantly improved the model’s fit ( R ²=0.49). Performance expectancy (β=0.52, P <.001), facilitating conditions (β=0.11, P =.005), and social influence (β=0.21, P <.001) emerged as additional significant predictors. Incorporating perceived psychosocial outcomes further improved the model’s fit and increased the explained variance ( R ²=0.54); this additional factor also showed the strongest association (β=0.41, P <.001). Patterns for the other predictors remained consistent across models, but including perceived psychosocial outcomes substantially reduced the coefficient for performance expectancy. Conclusions In this cross-sectional sample, perceived psychosocial outcomes were the predictors most strongly associated with behavioral intention in the extended UTAUT model, indicating that they may be important factors in understanding telecare acceptability among informal caregivers. The findings suggest that developers, policymakers, and health and social care providers should consider not only functional aspects of telecare but also the psychosocial needs of informal caregivers. As the study measured self-reported intention at the preuse stage rather than actual use, longitudinal and behavioral research is needed to establish whether these associations translate into telecare adoption.
Abstract BackgroundHealth care systems increasingly offer services to citizens through online portals, which have been found to be associated with patient satisfaction and better management of health concerns. Few studies have compared self-reported use with objectively documented portal use, especially among older adults. ObjectiveThis study aimed to examine differences between documented and self-reported health portal use, and their associations with background variables and digital health literacy (DHL) among older adults. MethodsA cross-sectional telephone survey was conducted among 600 participants aged 60 years and older, who were sampled from a registry of members of a national health maintenance organization (HMO). The participants reported their use of the HMO’s portal, their DHL, and demographic and background variables. Participants’ use of the portal was retrospectively retrieved from the HMO’s administrative database. ResultsA large gap between self-reported use and documented use of the health portal was found: for example, while only 8.1% (46/566) reported not using the portal, the HMO’s records indicated that 62% (372/600) did not make use of the portal. However, a positive correlation was found between self-reported use and documented use (rPrPrPPPrP=P ConclusionsThe findings indicate that users tend to overestimate their health portal use. Interpretations vary from measurement issues, such as social desirability bias regarding digital functioning or the perception that assisted use also constitutes usage, to signaling their identity as engaged digital health consumers. Future studies on digital health use should also include measures of electronically documented use and not only self-reported use.
Abstract Background The older adult population is increasing rapidly and experiences a multitude of chronic conditions affecting quality of life. Physical activity and reduced sedentary behavior reduce adverse health conditions and help preserve functional independence and cognitive abilities, but the older adult age group has much lower levels of physical activity than recommended compared with other population demographics. Objective The study aimed to understand older adults’ motivations toward health and physical activity and the role of technology in achieving their health and physical activity goals and to explore the attitudes toward the use of generative AI in health apps. Methods This study was qualitative and based on hour-long interviews with 20 older adults living in the United States. To elicit specific design preferences, participants were presented with visual probes of AI-generated avatars. The interviews were analyzed using thematic analysis, and the coding process drew on reflexive and grounded theory techniques. Results Older adults primarily wished to maintain functional independence, both physical and cognitive, prioritizing baseline maintenance rather than continuous optimization. They reported satisfaction with levels of physical activity, adjusting them to chronic conditions and injuries. They were active through everyday functions of living as well as structured exercise programs with many participants using wearable devices and smartphones to help monitor their activity. They reported being motivated by social accountability and gamification, although some found the latter condescending or anxiety-inducing. Accessibility issues were mentioned frequently, including problems with touchscreens, limited viewing, difficulty navigating interfaces with clutter, complex multitouch processes for undertaking actions, and steep learning curves. Computers were preferred for certain tasks over mobile phones due to easier viewability, keyboard access, and greater security features. Personalization was highly desired in health apps. There was a willingness to use more health technology, including generative AI, and they were willing to consider AI in both motivational and informational roles in apps as long as it was personalized. AI embodiment preferences varied widely but with an emphasis on relatability. However, there was marked skepticism toward data privacy, concerns about the loss of independence and self-efficacy, and doubts about AI’s accuracy. Conclusions Older adults are primarily motivated by sustaining functional independence, although accessibility barriers in current health apps remain. Participants were open to AI for physical activity support, but expectations varied. They viewed AI as a passive “tool,” a directive “coach,” or a relational “companion.” Considerable variation in preferences for gamification and visual design suggested that future apps require greater modularity. As skepticism over data privacy and AI accuracy remains high, greater transparency and explainability might be needed to engender trust.
Abstract BackgroundOlder adults with chronic joint pain often exhibit decreased mobility. Daily walking measures are being increasingly used in aging research as objective indicators of mobility. ObjectiveIn this study, we aimed to examine the effects of a 12-week hybrid exercise-based program on daily-life walking characteristics. MethodsA total of 59 community-dwelling older adults (aged ≥65 years) with mild to moderate knee and/or low back pain were randomized to either a hybrid exercise group or an educational control group. Daily-life walking was monitored using an ankle sensor. Daily observation data were collected within 2 planned 7-day windows. Primary parameters included activity volume and gait characteristics. Statistical analyses were conducted based on a modified intention-to-treat principle using linear mixed-effects models, with a per-protocol set used for sensitivity analyses. ResultsThe mixed-effects analysis based on the modified intention-to-treat principle (n=52 participants; 696 observations) suggested an effect of the intervention on improving walking quality, particularly step length (β=2.39 cm; PgPgPgPg ConclusionsA 12-week hybrid exercise program may improve the quality of daily-life walking, with the clearest effect observed on step length. Given the attenuation after multiplicity-aware interpretation and in sensitivity analyses, these findings should be interpreted as suggestive rather than definitive.
Abstract BackgroundSubjective cognitive decline (SCD) typically refers to self- or informant-reported decline in cognition despite the absence of objective impairment on standardized testing. Older adults with documented normal cognitive test performance provide a pragmatic anchor cohort for electronic health record (EHR)–based SCD phenotyping. However, cognitive concerns are primarily recorded in unstructured notes and are inconsistently documented, making it unclear how often and in whom concerns are captured in routine care. ObjectiveThis study aims to operationalize EHR-based SCD phenotyping in an objectively normal-testing cohort using a large language model (LLM)–based natural language processing approach and to examine the frequency and clinical and sociodemographic correlates of cognitive concern documentation. MethodsWe conducted an EHR-based observational study of patients aged 65 years or older with a first normal cognitive test recorded in EHR flowsheets between January 2019 and April 2024 in a large health care system. We developed and iteratively refined a 2-stage LLM-based natural language processing pipeline to identify documented cognitive concerns in unstructured notes during the 12 months prior to the index date, and evaluated performance against manual review. We quantified the frequency of documented concerns within this normal-testing cohort and used multivariable logistic regression to assess associations with sociodemographic factors (age, marital status, insurance, and neighborhood Area Deprivation Index), sequentially adjusting for clinical comorbidities and relevant medications. ResultsAmong 15,750 older adults with normal cognitive test scores, 13.8% (n=2175) had at least 1 documented cognitive concern in the prior year, captured in 1.2% (7394/605,177) of notes. On manual validation, the 2-stage pipeline (Med42-v2-8B screening followed by GPT-4o confirmation) achieved a sensitivity of 0.957, positive predictive value of 0.935, specificity of 0.985, and F1 ConclusionsA 2-stage LLM pipeline enabled accurate identification of documented cognitive concerns consistent with SCD among older adults with normal cognitive testing. Documentation was uncommon and selectively captured by clinical and sociodemographic factors, with implications for equity and the validity of EHR-based phenotypes.
