Feasibility and Acceptability of a Health App Platform Providing Individuals With a Budget to Purchase Preselected Apps to Work on Their Health and Well-Being: Quantitative Evaluation Study
Notice bibliographique
Résumé
BACKGROUND: The potential of health apps for health promotion and disease prevention is widely recognized. However, uptake is limited due to barriers individuals face in finding suitable and trustworthy apps, such as the overwhelming amount of available health apps. Therefore, the health app platform "FitKnip" was developed, enabling individuals to purchase preselected, trustworthy health apps with a budget of 100 euros (a currency exchange rate of EUR €1=US $1.0831 is applicable). The platform aimed to empower individuals to improve their health and vitality, ultimately supporting a more healthy society. OBJECTIVE: The primary aim of this study was to evaluate the health app platform in terms of feasibility and acceptability. Potential effects on health empowerment and health outcomes were secondarily explored. METHODS: This quantitative study was part of a mixed methods study with a prospective pre-post interventional design. We collected web-based user data, and self-reported web-based questionnaires were collected over 5 measurements over an 8-month period. Use statistics were tracked on the platform, including the number of purchased apps and euros spent per user registered within the health app platform. We measured the user-friendliness of the health app platform using the System Usability Scale (SUS) and satisfaction using the Client Satisfaction Questionnaire-8 (CSQ-8) and several 10-point Likert items. We asked participants to indicate, on a scale from 1 (not at all) to 10 (completely), how much the health app platform contributed to various areas related to health empowerment. We assessed health-related quality of life by the 12-item Short-Form Health Survey (SF-12) and one's perceived level of stress by the 10-item Perceived Stress Scale (PSS-10). RESULTS: A total of 1650 participants were included, of whom 42% (685/1650) bought at least 1 app. The majority of those purchased one app (244/685, 35.6%). The health app platform was rated as user-friendly (SUS mean 66.5, SD 20.7; range 66.5-70.0), and the acceptability of the health app platform was moderate (CSQ-8 mean 20.0, SD 1.5; range 19.6-20.0). Results furthermore showed that participants were generally satisfied to highly satisfied with the ease of the payment system to purchase apps on the platform (median 8, IQR 7-10), the look and feel of the platform (median 7, IQR 6-8), as well as the provided budget of 100 euros (median 9, IQR 7-10). Participants were less satisfied with the amount (median 6, IQR 4-7) and diversity (median 6, IQR 4-7) of apps offered on the platform. CONCLUSIONS: A health app platform is a promising initiative to enhance public health. Feasibility and acceptability are critical for success, as they ensure that such a platform is accessible, user-friendly, and meets end users' needs and preferences. This can help to increase uptake, engagement, and ultimately the platform's adoption and effectiveness.
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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 | 0,042 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».