Supporting Mental Health During the COVID-19 Pandemic Using a Digital Behavior Change Intervention: An Open-Label, Single-Arm, Pre-Post Intervention Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is taking a toll on people's mental health, particularly as people are advised to adhere to social distancing, self-isolation measures, and government-imposed national lockdowns. Digital health technologies have an important role to play in keeping people connected and in supporting their mental health and well-being. Even before the COVID-19 pandemic, mental health and social services were already strained. OBJECTIVE: Our objective was to evaluate the 12-week outcomes of the digitally delivered Gro Health intervention, a holistic digital behavior change app designed for self-management of mental well-being, sleep, activity, and nutrition. METHODS: The study used a quasi-experimental research design consisting of an open-label, single-arm, pre-post intervention engagement using a convenience sample. Adults who had joined the Gro Health app (intervention) and had a complete baseline dataset (ie, 7-item Generalized Anxiety Disorder scale, Perceived Stress Scale, and 9-item Patient Health Questionnaire) were followed up at 12 weeks (n=273), including 33 (12.1%) app users who reported a positive COVID-19 diagnosis during the study period. User engagement with the Gro Health platform was tracked by measuring total minutes of app engagement. Paired t tests were used to compare pre-post intervention scores. Linear regression analysis was performed to assess the relationship between minutes of active engagement with the Gro Health app and changes in scores across the different mental health measures. RESULTS: =0.479; P<.001). CONCLUSIONS: A digital behavior change platform that provides remote mental well-being support can be effective in managing depression, anxiety, and perceived stress during times of crisis such as the current COVID-19 pandemic. The outcomes of this study may also support the implementation of remote digital health apps supporting behavior change and providing support for low levels of mental health within the community.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".