User Perceptions of eHealth and mHealth Services Promoting Physical Activity and Healthy Diets: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Physical activity and a diet that follows general recommendations can help to prevent noncommunicable diseases. However, most adults do not meet current recommended guidelines, and support for behavior change needs to be strengthened. There is growing evidence that shows the benefits of eHealth and mobile health (mHealth) services in promoting healthy habits; however, their long-term effectiveness is uncertain because of nonadherence. OBJECTIVE: We aimed to explore users' perceptions of acceptability, engagement, and usability of eHealth and mHealth services that promote physical activity, healthy diets, or both in the primary or secondary prevention of noncommunicable diseases. METHODS: We conducted a systematic review with a narrative synthesis. We performed the literature search in PubMed, PsycINFO, and CINAHL electronic databases in February 2021 and July 2021. The search was limited to papers published in English between 2016 and 2021. Papers on qualitative and mixed method studies that encompassed eHealth and mHealth services for adults with a focus on physical activity, healthy diet, or both in the primary or secondary prevention of noncommunicable diseases were included. Three authors screened the studies independently, and 2 of the authors separately performed thematic analysis of qualitative data. RESULTS: With an initial finding of 6308 articles and the removal of 427 duplicates, 23 articles were deemed eligible for inclusion in the review. Based on users' preferences, an overarching theme-eHealth and mHealth services provide value but need to be tailored to individual needs-and 5 subthemes-interactive and integrated; varying and multifunctional; easy, pedagogic, and attractive; individualized and customizable; and reliable-emerged. CONCLUSIONS: New evidence on the optimization of digital services that promote physical activity and healthy diets has been synthesized. The findings represent users' perceptions of acceptability, engagement, and usability of eHealth and mHealth services and show that services should be personalized, dynamic, easily manageable, and reliable. These findings can help improve adherence to digital health-promoting services.
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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 | 0.021 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".