A Novel Experience Sampling Method Tool Integrating Momentary Assessments of Cognitive Biases: Two Compliance, Usability, and Measurement Reactivity Studies
Notice bibliographique
Résumé
BACKGROUND: Experience sampling methods (ESMs) are increasingly being used to study ecological emotion dynamics in daily functioning through repeated assessments taken over several days. However, most of these ESM approaches are only based on self-report assessments, and therefore, studies on the ecological trajectories of their underlying mechanisms are scarce (ie, cognitive biases) and require evaluation through experimental tasks. We developed a novel ESM tool that integrates self-report measures of emotion and emotion regulation with a previously validated app-based cognitive task that allows for the assessment of underlying mechanisms during daily functioning. OBJECTIVE: The objective of the study is to test this new tool and study its usability and the possible factors related to compliance with it in terms of latency and missing responses. Among the compliance predictors, we considered psychological and time-related variables, as well as usability, measurement reactivity, and participants' satisfaction with the tool. METHODS: We conducted 2 extensive ESM studies-study 1 (N=84; a total of 3 assessments per day for 5 days) and study 2 (N=135; a total of 3 assessments per day for 10 days). RESULTS: In both studies, participants found the tool highly usable (average usability score >81). By using mixed regression models, we found both common and specific results for the compliance predictors. In both study 1 and study 2, latency was significantly predicted by the day (P<.001 and P=.003, respectively). Participants showed slower responses to the notification as the days of the study progressed. In study 2 but not in study 1, latency was further predicted by individual differences in overload with the use of the app, and missing responses were accounted for by individual differences in stress reactivity to notifications (P=.04). Thus, by using a more extensive design, participants who experienced higher overload during the study were characterized by slower responses to notifications (P=.01), whereas those who experienced higher stress reactivity to the notification system were characterized by higher missing responses. CONCLUSIONS: The new tool had high levels of usability. Furthermore, the study of compliance is of enormous importance when implementing novel ESM methods, including app-based cognitive tasks. The main predictors of latency and missing responses found across studies, specifically when using extensive ESM protocols (study 2), are methodology-related variables. Future research that integrates cognitive tasks in ESM designs should take these results into consideration by performing accurate estimations of participants' response rates to facilitate the optimal quality of novel eHealth approaches, as in this study.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,017 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».