Observations of positive mental health indicators in undergraduates using specialized mobile apps during the COVID-19 pandemic
Notice bibliographique
Résumé
This dissertation presents findings from three longitudinal studies examining indicators of positive undergraduate mental health primarily during COVID-19 pandemic conditions at a large, residential, urban university in Canada (Western University) using apps with personal sensing data collection capabilities (e.g. GPS, step counts) over March 2019-August 2021. The apps featured mental health-related questionnaires – responses were representative of psychosocial outcomes for participants. Personal sensing data were representative of participant behavior/lifestyle. Questionnaires and personal sensing data were collected at the same time; personal sensing data was also collected hourly in the background for the final two studies. Associations between psychosocial outcomes and behavior/lifestyle found via mixed linear modelling revealed indicators of positive mental health. The first study, Smart Healthy Campus 1.0, began March 2019, concluding with limited data when COVID-19 was declared a pandemic; possible associations were identified. Initial plans were to improve SHC 1.0 with an upgraded SHC 2.0 study, but the Student Pandemic Experience (SPE) study was launched first, with questionnaires more fixated on pandemic conditions. Following this, SHC 2.0 was also launched shortly after, still relevant, although using shorter generic questionnaires. SPE completed with 315 participants who were primarily female (76%) iOS users (85.3%). Data collected for 40 weeks (11/2020 – 09/2021) included 4851 questionnaire responses and 25985 sensor samples with up to 68 individual values per sample. Mixed linear models were fit relating 15 mobile device (phone or tablet) sensors (e.g. step count) to 12 mental health-related questionnaire scores. SHC 2.0 ran with 94 participants were who primarily male (76.6%) iOS users (86%). Data collected for 30 weeks (01/2021 – 08/2021) included 1722 questionnaire responses and 6518 sensor samples with up to 68 individual values per sample. Mixed linear models were fit relating 12 mobile device (phone or tablet) sensors (e.g. step count) to the SHC 2.0 questionnaires. From these studies, it was found that device sensors had statistically significant associations with the selected mental health-related questionnaires for undergraduates during a major pandemic. These findings suggest directions for mental health-related programs (e.g. apps or physical activity) and interventions during a pandemic for a comparable group and setting.
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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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».