A Technology-Based Intervention to Help Health Care Provider Parents Manage Stress During the COVID-19 Pandemic: Findings From a Pilot Microrandomized Trial
Bibliographic record
Abstract
Background The COVID-19 pandemic has increased the stress levels of parents, especially health care workers and other COVID-19 frontline workers. Nonetheless, little is known about stress management for this population. Objective This pilot study tested the impact of a mobile app apt.mind in reducing stress in health care provider parents by delivering a 30-day microrandomized intervention. Methods Participants included 102 parents who work in health care and their coparenting partners. They were given smartwatches and access to a mindfulness app. Each day, all parents were randomly assigned to (1) brief stress reduction messages, (2) meditation audio activities via the app, or (3) no intervention. Stress was evaluated using a self-reported COVID-19 Family Stressor Screener (10 items; 5-point Likert scale) to rate levels of stress regarding food security, job stability, family conflict, mental health, and social isolation. Dosage was measured by the percentage of parents who received any of the activities (app or messages; mean 66%, SD 9.8%), and parents were divided into 3 groups by dosage level: low (below 60%), middle (61%-70%), and high (above 71%). Results Using a pre-post test, this study assessed changes in mental health symptoms and parenting by individuals’ dosage levels. Participants who received high levels of intervention reported significant decreases in COVID-19–related family stress (t=–2.50; P=.02) and a significant increase in parenting efficacy (t=2.39; P=.03), while those who received low or middle levels of the intervention did not show those changes. Conclusions This study supports the feasibility and efficacy of technology-based tools to reduce stress and the need to examine just-in-time interventions. Future studies can improve by focusing on microchanges in the parents’ stress level on a daily basis and including physiological data such as heart rate variability to include objective stress data. Conflicts of Interest None declared.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".