Cocreating a Mobile Health App Providing Physical Activity Recommendations for Older People Living With Parkinson Disease or Dementia: User-Centered Pilot Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».