Factors influencing older adults' participation in telehealth interventions for primary prevention and health promotion: A rapid review
Bibliographic record
Abstract
OBJECTIVE: To identify facilitators and barriers to older adults' participation in telehealth interventions for primary prevention and health promotion. METHODS: Relevant articles were searched using keywords in Embase and MEDLINE. Study characteristics, type of telehealth interventions and technology involved, as well as facilitators and barriers to their use, were extracted from selected articles. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was used to organise data. RESULTS: A total of 24 articles (pertaining to 20 studies) were included. Nine facilitators and 11 barriers influencing the participation in telehealth interventions for primary prevention and health promotion among older adults were identified. The most recurrent facilitators were related to the individual's performance expectancy and effort expectancy, as well as the presence of a social dimension associated with the intervention (i.e. having a good relationship with the other participants in the program). The two most prevalent barriers were also related to effort expectancy and performance expectancy, followed by barriers related to the inherent characteristics of the technology and older adults' health condition. Experience, age and gender were also found to moderate technology use and acceptance. CONCLUSIONS: This rapid review highlights the importance of adopting a holistic perspective when designing telehealth interventions aimed at preventive and health promotion purposes among 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".