Content and Feature Preferences for a Physical Activity App for Adults with Physical Disabilities: Focus Group Findings (Preprint)
Notice bibliographique
Résumé
BACKGROUND: Hundreds of thousands of mobile phone apps intended to improve health and fitness are available for download across platforms and operating systems; however, few have been designed with people with physical disabilities in mind, ignoring a large population that may benefit from an effective tool to increase physical activity. OBJECTIVE: This study represents the first phase in the development process of a fitness tracking app for people with physical disabilities interested in nontraditional sport. The aim of this research was to explore user preferences for content, appearance, and operational features of a proposed physical activity app for people with physical disabilities to inform the design of a mobile phone app for increasing physical activity. METHODS: Four focus groups were conducted with 15 adults with physical disabilities who currently participate in nontraditional, non-Paralympic sport. Data collected from the focus group sessions centered on content, functionality, and appearance of apps currently used by participants as well as preferences for a future app. RESULTS: Participants (mean age 35.7, SD 9.2 years) were mostly white (13/15, 87%), and all were currently participating in CrossFit and at least one other sport. Five main themes were identified. Themes included preferences for (1) workout-specific features that were tailored or searchable by disability, (2) user experience that was intuitive and accessible, (3) profile personalization options, (4) gamification features that allowed for competition with self and other users, and (5) social features that allowed increased interaction among users. Participants expressed a primary interest in having a fitness app that was designed for people with physical disabilities such that the features present in other fitness tracking apps were relevant to them and their community of adaptive athletes. CONCLUSIONS: The results showed that features related to user experience, social engagement, and gamification are considered important to people with physical disabilities. Features highlighted by participants as most desired, from a consumer perspective, were in line with research identifying attributes of quality apps that use behavior change techniques to influence positive physical activity behavior change. Such insights should inform the development of any fitness app designed to integrate users with disabilities as a primary user base.
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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 | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».