Cocreating a Mobile Health App Providing Physical Activity Recommendations for Older People Living With Parkinson Disease or Dementia: User-Centered Pilot Study
Bibliographic record
Abstract
Abstract Background The project “Personalized Integrated Care Promoting Quality of Life for Older People” aimed to develop an integrated care system based on information and communication technology to support older people living with Parkinson disease or dementia disease. One module focuses on physical activity (PA) recommendations. Objective The objective of the study is to describe the development process of the PA recommendation system from the behavior-change and technical perspective, followed by its content and satisfaction evaluation. Methods This study describes the development of the PA recommendations based on the Health Action Process Approach (HAPA). A first pilot assessed the feasibility of the overall PROCare4Life system (previously reported). In a second pilot, users evaluated the content of the PA recommendations during 40 intervention days. In a third pilot, users evaluated their satisfaction with a mobile health satisfaction questionnaire. Results The PA recommendations focused on different aspects of an adapted version of the HAPA model, while they simultaneously approached 3 activation factors: skills, knowledge, and motivation. The content was generally well-received, with most users rating key sections as excellent or good, particularly “benefits and consequences of PA” (34/43, 79%) and “five golden rules of PA” (34/41, 83%). However, less than a third gave high ratings to “PA guidelines of the WHO” (9/36, 25%) and “practical tips for PA” (10/35, 29%). Regarding satisfaction, at least half of the 237 participants found it easy and good to use, with acceptable time spent and clear instructions. Compared with agreement or neutral evaluations, most disagreed with negative statements about it being time-consuming (111/237, 47%) or boring (99/237, 42%). While 41% (97/237) recommended it and 44% (104/237) felt it helped them understand lifestyle benefits, fewer agreed the recommender system helped them set personal goals (78/237, 33%) or motivated change (88/237, 37%). Users found the recommendations understandable, engaging, and practical, though some aspects, such as motivation and goal setting, received criticism. Challenges in pilot 2, particularly related to setup difficulties and limited participation, led to system modifications in pilot 3 that improved usability and data collection. Conclusions It has been confirmed that cocreating and iteratively testing the contents on HAPA and approaching the activation factors contributed to increasing the acceptance of the PA. The intervention development was based on user needs and used comparable methodology across user profiles and pilot phases. All in all, users were positive about the content. The research team has identified that digital systems, that provide monitoring functions of the mobile health app and Fitbit wristband are considered advantageous by participants in the cocreation process. Addressing the activation factors can be recommended for researchers and technical developers of other projects. Future adjustments to the design should focus on personalization to encourage the adoption of a healthier lifestyle.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".