e-Health Interventions for Promoting Physical Activity in Aging Adults: A Scoping Review
Bibliographic record
Abstract
Background: The use of e-health interventions to promote physical activity (PA) among older adults has significantly increased in recent years. This review aims to comprehensively summarize the various e-health modalities and strategies used to encourage PA in aging adults. Methods: A systematic search of Medline, Embase, CINAHL, AMED, and PubMed databases was conducted to identify studies on e-health interventions targeting PA promotion in individuals aged 50 and older, published between 2012 and 2023. Information pertaining to study characteristics and e-health intervention specificities was extracted using a standardized data collection form. A narrative synthesis approach was employed to synthesize the data collected from the included studies. Results: Of 4,915 studies initially retrieved, 81 met the eligibility criteria. The findings reveal a diverse array of methods and interaction modes utilized to stimulate PA in aging adults, regardless of their medical conditions. Asynchronous methods such as web-based programs, mobile apps, and activity monitors were used in 71.6% of the studies and were most frequently employed for initiating behavior change components. Synchronous interaction modes mainly included videoconferencing and were predominantly featured in studies where real-time supervision and demonstration of exercises were integral to PA programs. There was a lack of information to guide the selection of the most effective e-health intervention format for motivating older adults to engage in regular exercise. Conclusion: This review underscores the versatility of e-health interventions, showcasing a wide spectrum of methods and interaction modalities. Future studies should compare these different modalities and methods while also identifying their barriers and facilitators. This will help in selecting the most suitable interventions for older adults.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".