Exploring the Potential of a Digital Intervention to Enhance Couple Relationships (the Paired App): Mixed Methods Evaluation
Notice bibliographique
Résumé
BACKGROUND: Despite the effects of poor relationship quality on individuals', couples', and families' well-being, help seeking often does not occur until problems arise. Digital interventions may lower barriers to engagement with preventive relationship care. The Paired app, launched in October 2020, aims to strengthen and enhance couple relationships. It provides daily questions, quizzes, tips, and detailed content and facilitates in-app sharing of question and quiz responses and tagged content between partners. OBJECTIVE: To explore the potential of mobile health to benefit couple relationships and how it may do this, we examined (1) Paired's impact on relationship quality and (2) its mechanisms of action. METHODS: This mixed methods evaluation invited Paired subscribers to complete (1) brief longitudinal surveys over 3 months (n=440), (2) a 30-item web-based survey (n=745), and (3) in-depth interviews (n=20). For objective 1, survey results were triangulated to determine associations between relationship quality measures and the duration and frequency of Paired use, and qualitative data were integrated to provide explanatory depth. For objective 2, mechanisms of action were explored using a dominant qualitative approach. RESULTS: Relationship quality improved with increasing duration and frequency of Paired use. Web-based survey data indicate that the Multidimensional Quality of Relationship Scale score (representing relationship quality on a 0-10 scale) was 35.5% higher (95% CI 31.1%-43.7%; P=.002), at 7.03, among people who had used Paired for >3 months compared to 5.19 among new users (≤1 wk use of Paired), a trend supported by the longitudinal data. Of those who had used Paired for >1 month, 64.3% (330/513) agreed that their relationship felt stronger since using the app (95% CI 60.2%-68.4%), with no or minimal demographic differences. Regarding the app's mechanisms of action, interview accounts demonstrated how it prompted and habituated meaningful communication between partners, both within and outside the app. Couples made regular times in their day to discuss the topics Paired raised. Daily questions were sometimes lighthearted and sometimes concerned topics that couples might find challenging to discuss (eg, money management). Interviewees valued the combination of fun and seriousness. It was easier to discuss challenging topics when they were raised by the "neutral" app, rather than during stressful circumstances or when broached by 1 partner. Engagement seemed to be enhanced by users' experience of relationship benefits and by the app's design. CONCLUSIONS: This study demonstrates proof of concept, showing that Paired may have the potential to improve relationship quality over a relatively short time frame. Positive relationship practices became embedded within couples' daily routines, suggesting that relationship quality improvements might be sustained. Digital interventions can play an important role in the relationship care ecosystem. The mixed methods design enabled triangulation and integration, strengthening our findings. However, app users were self-selecting, and methodological choices impact our findings' generalizability.
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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 | 0,098 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».