Designing a Mobile e-Coaching App for Immigrant Informal Caregivers: Qualitative Study Using the Persuasive System Design Model
Notice bibliographique
Résumé
BACKGROUND: Informal caregivers are vital in caring for their family and friends at home who may have illnesses or disabilities. In particular, the demands for caregiving can be even more challenging for those with limited resources, support systems, and language barriers, such as immigrant informal caregivers. They face complex challenges in providing care for their relatives. These challenges can be related to sociocultural diversity, language barriers, and health care system navigation. Acknowledging the global context of the increasing number of immigrants is essential in designing inclusive mobile health apps. OBJECTIVE: This study aims to investigate the needs of immigrant informal caregivers in Sweden and discuss the application of the Persuasive System Design Model (PSDM) to develop an e-coaching prototype. By addressing the unique challenges faced by immigrant informal caregivers, this study will contribute to the development of more effective and inclusive mobile health apps. METHODS: The participants were considered immigrants and included in the study if they and their parents were born outside of Sweden. Through various channels, such as the National Association of Relatives, rehabilitation departments at municipalities, and immigrant groups, we recruited 13 immigrant informal caregivers. These immigrant informal caregivers were primarily women aged 18 to 40 years. Most participants belonged to the Middle Eastern region whereas some were from North Africa. However, all of them spoke Arabic. We used semistructured interviews to gather data from the participants in Arabic, which were translated into English. Data were analyzed using thematic analysis and discussed in relation to the extended PSDM. The needs of the caregivers were compared with the description of persuasive design principles, and a design principle was chosen based on the match. The PSDM was extended if the need description did not match any principles. Several brainstorming and prototyping sessions were conducted to design the mobile e-coaching app. RESULTS: Immigrant informal caregivers have various needs in their caregiving role. They reported a need for training on the illness and future caregiving needs, assistance with understanding the Swedish language and culture, and help with accessing internet-based information and services. They also required recognition and appreciation for their efforts, additional informal support, and easy access to health care services, which can be important for their mental health. The PSDM was adapted to the informal caregiving context by adding "facilitating conditions" and "verbal encouragement" as additional persuasive design principles. This study also presents the subsequent mobile e-coaching app for immigrant informal caregivers in Sweden. CONCLUSIONS: This study revealed important immigrant informal caregivers' needs based on which design suggestions for a mobile e-coaching app were presented. We also proposed an adapted PSDM, for the informal caregiving context. The adapted PSDM can be further used to design digital interventions for caregiving.
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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 | 0,011 | 0,013 |
| 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 | 0,008 | 0,004 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».