Web-Based Interventions to Promote Healthy Lifestyles for Older Adults: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND With the aging of the population and rising rates of chronic diseases, older adults need support if they are to adopt healthy lifestyles. Web-based interventions should be considered for this purpose, since they are easily accessed and can foster healthy lifestyles among older adults. However, the literature on such interventions discusses a variety of components and effects and provides only 2 syntheses of knowledge on web-based interventions with older adults. These studies focus on populations aged 50 years and older, whereas the components and effects of interventions for a population of older adults (ie, 65 years and older) may differ. In addition, these 2 syntheses examined only quantitative studies, although other types of studies (ie, qualitative) are available and could help advance knowledge in this field. A scoping review is therefore relevant in order to explore the extent of the literature on this subject. OBJECTIVE The purpose of the study described by this protocol is to explore the extent of the literature (experimental, quasi-experimental, qualitative, systematic reviews, and grey literature) on the components and effects of web-based interventions as a way to promote healthy lifestyles among older adults. METHODS The databases MEDLINE, CINAHL, PsycInfo, Web of Science, Cochrane Database of Systematic Review and Joanna Briggs Library will be searched, in addition to the grey literature using Google Scholar and OpenGrey. Studies will be selected for the review by 2 researchers, working independently. The data will be synthesized based on the conceptualization of web-based interventions (ie, behavior change techniques, dispensation modes, and theories). A thematic analysis will be performed to summarize the components of the interventions studied. RESULTS The database search will begin in August 2020 and be completed in October 2020. CONCLUSIONS This scoping review should highlight web-based interventions designed to promote healthy lifestyles, as well as their components and effects, among people aged 65 years and older. These results could provide important guidance for intervention developers and designers in identifying the components of web-based interventions relevant to older adults and lead to further studies on this topic. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/23207
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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 | 0.070 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.133 | 0.022 |
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".