An App-Based Ecological Momentary Assessment of Undergraduate Mental Health During the COVID-19 pandemic in Canada (Smart Healthy Campus Version 2.0): Longitudinal Study
Bibliographic record
Abstract
Abstract This paper presents results from the Smart Healthy Campus 2.0 study/smartphone app, developed and used to collect mental health-related lifestyle data from 86 Canadian undergraduates January – August 2021. This was a longitudinal repeat measures study conducted over 40 weeks. A 9-item mental health questionnaire was accessible once daily in the app. Two variants of this mental health questionnaire existed; the first was a weekly variant, available each Monday or until a participant responded during the week. The second was a daily variant available after the weekly variant. Mixed models were fit for responses to the two variants and 12 phone digital measures (e.g. GPS, step counts). A second round of models was fit based on backward elimination to determine associations between the 12 digital measures and the variants. 6518 digital measure samples and 1722 questionnaire responses were collected. The daily questionnaire had positive associations with floors walked, installed apps, and campus proximity, while having negative associations with uptime, and daily calendar events. Daily depression had a positive association with uptime. Daily resilience appeared to have a slight positive association with campus proximity. The weekly questionnaire variant had positive associations with device idling and installed apps, and negative associations with floors walked, calendar events, and campus proximity. Physical activity, weekly, had a negative association with uptime, and a positive association with calendar events and device idling. SHC 2.0, via phone digital measures, identified indicators of lifestyle that appeared to be associated with measures of mental health in undergraduates during COVID-19. Author Summary This paper analyzes the associations between digital measures from smartphones (such as GPS and step counts) and a broad mental health questionnaire (covering items like depression and anxiety). This data was collected from students at a relatively large, urban university in Canada during the COVID-19 pandemic. We conducted this study because smartphone-based studies observing aspects of mental health in students as they go about their daily lives are uncommon in Canada. Additionally, mental health concerns, such as depression and anxiety, can be common on university campuses, although it isn’t always clear what is associated with those concerns, especially when only looking at them with smartphone data. We were also interested in how an overview of student mental health would relate to digital measures coming from smartphones during the pandemic, as this information would be relevant to inform future pandemics. In general, these relationships might potentially inform ways to improve student mental health, or potentially predict aspects of it, based on data coming in from smartphones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".