Opportunities and Challenges of a Self-Management App to Support People With Spinal Cord Injury in the Prevention of Pressure Injuries: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Mobile health applications can offer tailored self-management support to individuals living with chronic health conditions. However, there are several challenges to the adoption of these technologies in practice. Co-design is a promising approach to overcoming some of these challenges by enabling the development of solutions that meet the actual needs and preferences of the relevant stakeholder groups. OBJECTIVE: Taking spinal cord injury as a case in point, the overall objectives of this study were to identify the perceived benefits of a co-designed self-management app that could promote its uptake and to explore the factors that may impede adoption. METHODS: We adopted a qualitative research approach guided by the Technology Acceptance Model. Data were collected through semistructured interviews with individuals with spinal cord injury (n=15) and two focus groups with health care professionals specialized in spinal cord injury (n=7, n=5). Prior to the interviews and focus groups, study participants were given time to explore the app prototype. All interviews were transcribed verbatim and analyzed using inductive thematic analysis. RESULTS: Findings of our analysis indicate that study participants perceived the app prototype as potentially useful for supporting individuals with spinal cord injury in preventing pressure injuries. In particular, we identified three concrete use cases highlighting the benefits of the app for different audiences: (1) a companion for newly injured individuals, (2) an emergency kit and motivational support, and 3) a guide for informal caregivers and family members. We also uncovered several challenges that might impede the adoption of the self-management app in practice, including (1) challenges in motivating individuals to use the app, (2) concerns about the misuse and abuse of the app, and (3) organizational and maintenance challenges. CONCLUSIONS: This study adds to a growing body of research that investigates individuals' adoption and nonadoption behavior regarding mobile health solutions. Building on earlier work, we make recommendations on how to address the barriers to the adoption of mobile health solutions identified by this study. In particular, there is a need to foster trust in mobile health among prospective users, including both patients and health care professionals. Moreover, increasing personal relevance of mobile health solutions through personalization may be a promising approach to promote uptake. Last but not least, organizational support also plays an instrumental role in mobile health adoption. We conclude that even though co-design is promoted as a promising approach to develop self-management tools, co-design does not guarantee adoption. More research is needed to identify the most promising strategies to promote the adoption of evidence-based mobile health solutions in practice.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,011 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,006 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».