Everyday Digital Support to Promote Health and Literacy Among Older Adults: 14-Week Randomized Digital Pilot Trial by Engagement Level
Bibliographic record
Abstract
Background: While digital health solutions are becoming increasingly sophisticated, simple forms of everyday digital support may offer underexplored opportunities to promote health among older adults. However, evidence remains scarce on whether such teleassistance-based approaches can effectively enhance health literacy and daily self-care, particularly among populations facing socioeconomic and educational disparities. Objective: This study examined whether a 14-week mobile teleassistance intervention could support daily health promotion and improve health literacy and quality of life among older adults, and whether different levels of user engagement were associated with differences in outcomes. Methods: This randomized digital pilot study involved 21 older adults (aged ≥60 years) from Ribeirão Preto, Brazil. All participants were assigned to the intervention arm and subsequently categorized into high-engagement (n=11) and low-engagement (n=10) subgroups according to platform-use metrics. The intervention combined weekly teleconsultations, gamified educational quizzes, and guided health-related activities delivered through a mobile app. Outcomes included health literacy (Health Literacy Questionnaire), quality of life (36-Item Short-Form Health Survey), physical activity, and sedentary behavior, assessed at baseline and postintervention. Analyses appropriate for small samples were applied, including frequentist and Bayesian models. Results: Participants in the high-engagement subgroup showed greater improvements in health literacy compared with those in the low-engagement subgroup (mean change +9.5 vs +9.1 points; time × group: P<.001; Bayes Factors [BF₁₀]=15). Significant interactions also favored higher engagement for selected quality-of-life domains: vitality (P≤.001), functional capacity (P=.02), and general health (P=.02). A group effect was observed for the mental component (P<.001). Physical activity (F2,38=0.95; P=.39; BF_incl=0.68) and sedentary behavior (F1,19=1.12; P=.32; BF_incl=0.53) did not differ significantly between subgroups. Engagement analytics confirmed higher overall platform use in the high-engagement subgroup (mean 6483.8, SD 807.0 vs mean 3345.3, SD 742.7; t19=6.238; P<.001; d=2.73) and more weekly health-activity minutes (mean 5124.3, SD 757.9 vs mean 3120.7, SD 704.3; t19=6.256; P<.001; d=2.73). Conclusions: This 14-week randomized digital pilot trial suggests that everyday digital teleassistance may enhance health literacy and specific quality-of-life domains among older adults when engagement is high. However, such support alone appears insufficient to modify physical activity or sedentary behavior in the short term. Larger and longer trials are needed to assess sustainability, scalability, and strategies to address structural inequalities in digital health adoption.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".