Older Adults' and Healthcare Professionals' Perspectives on Web-Based Interventions to Promote Healthy Lifestyle Habits
Bibliographic record
Abstract
The use of Web-based interventions has expanded considerably in recent years, particularly during the COVID-19 pandemic, due to the increased need for remote care, especially among older adults. These interventions could support healthy lifestyle habits, but little is known about the perspectives of potential users. The aim of this study was to investigate the perspectives of older adults and healthcare professionals on Web-based interventions for promoting healthy lifestyles. A qualitative survey was conducted with 20 older adults and 22 healthcare professionals using online questionnaires. Among them, 13 participants (seven older adults and six healthcare professionals) took part in semistructured interviews. The data were analyzed using descriptive statistics and thematic analysis. The results show that older adults are less convinced of the relevance of Web-based interventions, citing technological barriers and a preference for face-to-face care. In contrast, healthcare professionals recognize the potential of Web-based interventions to support healthy habits. Both groups agreed on the importance of specific content, such as examples of healthy habits, but differed in their preferences regarding mode of delivery. Older adults preferred stand-alone interventions, whereas healthcare professionals favored integrating professional support. These findings can guide researchers designing new interventions that address the perspectives of both older adults and healthcare professionals to promote healthy lifestyle habits.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".