Observations of positive mental health indicators in undergraduates using specialized mobile apps during the COVID-19 pandemic
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".