A Multidimensional Digital Health Platform to Support Intrinsic Capacity and Healthy Aging: Usability, Acceptance, and Impact in a Pilot Study (Preprint)
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".