Development of an eHealth Intervention Including Self-Management for Reducing Sedentary Time in the Transition to Retirement: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: Having a great amount of sedentary time is common among older adults and increases with age. There is a strong need for tools to reduce sedentary time and promote adherence to reduced sedentary time, for which eHealth interventions have the potential to be useful. Interventions for reducing sedentary time in older adults have been found to be more effective when elements of self-management are included. When creating new eHealth interventions, accessibility and effectiveness can be increased by including end users as co-designers in the development process. OBJECTIVE: The aim was to explore the desired features of an eHealth intervention including self-management for reducing sedentary time and promoting adherence to reduced sedentary time in older adults transitioning from working life to retirement. Further, the aim was to develop a digital prototype of such an eHealth intervention. METHODS: The study used the participatory design approach to include end users, researchers, and a web designer as equal partners. Three workshops were conducted with 6 older adults transitioning to retirement, 2 researchers, and 1 web designer. Thematic analysis was used to analyze the data from the workshops. RESULTS: Participants expressed a desire for an easy-to-use eHealth intervention, which could be accessed from mobile phones, tablets, and computers, and could be individualized to the user. The most important features for reducing sedentary time were those involving finding joyful activities, setting goals, and getting information regarding reduced sedentary time. Participants expressed that the eHealth intervention would need to first provide the user with knowledge regarding sedentary time, then offer features for measuring sedentary time and for setting goals, and lastly provide support in finding joyful activities to perform in order to avoid being sedentary. According to the participants, an eHealth intervention including self-management for reducing sedentary time in older adults in the transition to retirement should be concise, accessible, and enjoyable. A digital prototype of such an eHealth intervention was developed. CONCLUSIONS: The developed eHealth intervention including self-management for reducing sedentary time in older adults transitioning to retirement is intended to facilitate behavior change by encouraging the user to participate in autonomously motivated activities. It uses several behavior change techniques, such as goal setting and action planning through mental contrasting and implementation intention, as well as shaping knowledge. Its active components for reducing sedentary time can be explained using the integrated behavior change model. Further research is needed to evaluate the feasibility and effectiveness of the eHealth intervention.
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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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".