Effects of Choice Set Sizes and Moderations of Anxiety and State Emotions on Mental Health Self-Care Uptake, Engagement, and User Experience: Experimental Study
Notice bibliographique
Résumé
Background: Digital mental health platforms often consist of many different forms of self-care exercises. To our knowledge, whether the number of choices presented to the users affects their uptake and experiences and poses negative consequences (ie, not choosing any exercises, choice dissatisfaction) for users, especially those experiencing anxiety and depressive symptoms or unpleasant state emotions, has not been empirically investigated. Objective: This study investigated the impact of choice set size on practice decisions, completion, satisfaction, and subjective experiences, as well as potential moderators including depression and anxiety symptoms, state emotions, and motivational and decisional attributes on these choice outcomes. Methods: Participants were recruited through university mass email and social media, and 652 participants were included in our analyses. Participants completed questions regarding anxiety and depressive symptoms, state emotions, and other psychological attributes. Then, they were randomly assigned to 1-choice, 4-choice, and 16-choice conditions, in which they may choose a self-care activity to practice or decide not to practice. Finally, they completed questions regarding completion, satisfaction, engagement, attitude, and perceived improvement in psychological state. Results: Presenting multiple choices resulted in a higher likelihood of practice (odds ratio 3.12, 95% CI 2.08 to 4.67 and 3.83, 95% CI 2.55 to 5.76; P<.001) and better decision satisfaction (16-choice vs 1-choice: d=0.36, 95% CI 0.17 to 0.56, P<.001; 4-choice vs 1-choice: d=0.24, 95% CI 0.05 to 0.43, P=.03) compared with presenting with a single choice. Tentative evidence indicates anxiety symptoms and state emotions were meaningful moderators. Specifically, for individuals with more anxiety symptoms and intense negative emotions, presenting a larger choice set (16 choices) resulted in more positive chosen exercise satisfaction, better attitudes toward chosen activity, and higher perceived improvement in mental health state after the activity, when compared with presenting with smaller choice sets (anxiety: β=-0.38, 95% CI -0.69 to -0.06 to -0.51, 95% CI -0.84 to -0.18; state emotions: β=-0.31, 95% CI -0.66 to 0.03 to -0.60, 95% CI -0.92 to -0.28). No evidence was found for the moderating effect of motivational and decisional attributes. Conclusions: The moderation results were contradictory to prior research and our expectation that a larger choice set may result in worse outcomes than a smaller choice set for people who were experiencing higher levels of psychological distress. We speculated that a possible reason for these findings may be that people with more anxiety symptoms and unpleasant emotions may have a stronger need to reduce these uncomfortable symptoms and emotions, and when presented with more choices on self-care activities, there may be a higher possibility that these self-care activities can address their distress.
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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 | 0,013 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 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 ».