Social Gaming to Decrease Loneliness in Older Adults: Recruitment Challenges and Attrition Analysis in a Digital Mixed Methods Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Digital mental health interventions could sustainably and scalably prevent and reduce loneliness in older adults. We designed an app containing 29 text-based games and a questionnaire-administering chatbot to stimulate intergenerational contact. OBJECTIVE: This study aims to evaluate the feasibility of a social gaming app in reducing loneliness among older adults by evaluating recruitment strategies, data collection procedures, and gameplay activity. METHODS: This mixed methods study recruited participants via newsletters, articles, and a social media campaign. We used semistructured interviews and descriptive analysis of questionnaire answers and game data to assess feasibility. Key measures included recruitment reach and efficiency, participant demographics, in-app activity, and app usability and engagement feedback. RESULTS: The social media campaign reached 192,641 potential participants, resulting in 1363 game downloads. A total of 155 participants (aged 65 years and older: n=34, 21.9% and aged less than 65 years: n=121, 78.1%) provided informed consent, yielding a conversion rate of 0.08%. The recruitment campaign focusing on distanced playful interaction had a significantly (P<.001) higher click-through rate (1.98%) than a campaign focusing on research participation (click-through rate=0.51%; P<.001). The overall conversion rate from advertisement exposure to research participation was 0.08%. Participants had a mean age of 48 (SD 16) years. The 65 years and older group averaged 70 (SD 5) years, while the less 65 years group averaged 42 (SD 12) years. Additionally, 45.2% (57/126) reported at least moderate levels of loneliness at baseline. Of the initial 554 players, 91 (16.4%) remained active after the first week, and 32 (5.8%) remained active for more than 90 days. Active participants tended to interact with those within their own age group, as indicated by a Pearson correlation of r=0.31 between the ages of the message sender and receiver. Interviews with 12 (48%) participants (aged 65-79 years; female: n=12, 83%) revealed barriers such as excessive chatbot questions and a mismatch between the target group and app design focus. Questionnaire completion rates dropped from 46% at baseline to 10% at follow-up. CONCLUSIONS: These findings underscore the challenges of recruitment and retention for older adults in a fully digital social gaming intervention. Effective recruitment strategies and targeted app design are crucial for engagement. Based on these insights, future interventions should focus on simplified interfaces, clear guidance for gameplay, and addressing the specific needs and preferences of older adults, thereby enhancing the effectiveness of digital mental health interventions.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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