Boosting Digital Health Engagement Among Older Adults in Hong Kong: Pilot Pre-Post Study of the Generations Connect Project
Bibliographic record
Abstract
Background: Older adults' utilization of digital health care remains low despite a high demand for regular health services. Easily accessible eHealth interventions designed for older adults are needed. Objective: This study aimed to examine the feasibility and effectiveness of an intergenerational, home-based eHealth literacy intervention package on older adults in Hong Kong. Methods: In this study, 101 older adults (n=64, 63.4% female) with a median age of 80 (IQR 77-85) years received an intergenerational, home-based eHealth literacy intervention package, delivered by trained university student interventionists. The intervention (median 60, IQR 40.8-70 minutes) included personalized guidance on using mobile health apps, QR code scanners and instant messaging, and access to online health information, along with recommendations for physical and mental well-being. Following the intervention, a daily health-coaching message was sent to older adults via WhatsApp for 14 days. eHealth literacy, health, and lifestyle were assessed at baseline and at a 2-week follow-up using paired t tests. Results: Retention rate for the 2-week follow-up was 70.3% (71/101). Compared to baseline, eHealth literacy scores increased by 2.39 points (P=.11; Cohen d=0.20), and daily smartphone use rose by 0.45 hours (P=.07; Cohen d=0.05). Participants self-reported increased physical activity (50/71, 70%), more frequent viewing of health videos (43/70, 61%), and improved handwashing practices (39/71, 55%). The intervention achieved a high satisfaction rating of 4.32 out of 5. Conclusions: The intergenerational, home-based eHealth literacy intervention package was feasible and acceptable, showing promise for increasing older adults' engagement with digital health care resources and promoting healthy behaviors. Future studies should explore longer-term effects and ways to further improve the intervention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".