Developing a Multiprofessional Mobile App to Enhance Health Habits in Older Adults: User-Centered Approach
Bibliographic record
Abstract
BACKGROUND: Although comprehensive lifestyle habits are crucial for healthy aging, their adherence tends to decline as individuals grow older. Sustaining a healthy life over time poses a motivational challenge. Some digital tools, such as smartphone apps aimed at promoting healthy habits, have been used to counteract this decline. However, a more profound investigation is necessary into the diverse experiences of users, particularly when it concerns older adults or those who are unfamiliar with information and communications technologies. OBJECTIVE: We aimed to develop a mobile app focused on promoting the health of older adults based on the principles of software engineering and a user-centered design. The project respected all ethical guidelines and involved the participation of older adults at various stages of the development of the app. METHODS: This study used a mixed methods approach, combining both quantitative and qualitative methodologies for data collection. The study was conducted in Ribeirão Prêto, São Paulo, Brazil, and involved 20 older adults of both genders who were aged ≥60 years and enrolled in the Physical Education Program for the Elderly at the University of São Paulo. The research unfolded in multiple phases, encompassing the development and refinement of the app with active engagement from the participants. RESULTS: A total of 20 participants used a mobile health app with an average age of 64.8 (SD 2.7) years. Most participants had a high school education, middle-class status, and varying health literacy (mean score 73.55, SD 26.70). Overall, 90% (18/20) of the participants owned smartphones. However, 20% (4/20) of the participants faced installation challenges and 30% (6/20) struggled with web-based searches. The focus groups assessed app usability and satisfaction. Adjustments increased satisfaction scores significantly (Suitability Assessment of Materials: 34.89% to 70.65%; System Usability Scale: 71.23 to 87.14). Participant feedback emphasized font size, navigation, visual feedback, and personalization, and suggestions included health device integration, social interaction, and in-app communication support. CONCLUSIONS: This study contributes to the development of health care technologies tailored to the older adult population, considering their specific needs. It is anticipated that the resulting app will serve as a valuable tool for promoting healthy habits and enhancing the quality of life for 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".