Effect of Brief Biofeedback via a Smartphone App on Stress Recovery: Randomized Experimental Study
Bibliographic record
Abstract
BACKGROUND: Smartphones are often vilified for negatively influencing well-being and contributing to stress. However, these devices may, in fact, be useful in times of stress and, in particular, aid in stress recovery. Mobile apps that deliver evidence-based techniques for stress reduction, such as heart rate variability biofeedback (HRVB) training, hold promise as convenient, accessible, and effective stress-reducing tools. Numerous mobile health apps that may potentially aid in stress recovery are available, but very few have demonstrated that they can influence health-related physiological stress parameters (eg, salivary biomarkers of stress). The ability to recover swiftly from stress and reduce physiological arousal is particularly important for long-term health, and thus, it is imperative that evidence is provided to demonstrate the effectiveness of stress-reducing mobile health apps in this context. OBJECTIVE: The purpose of this research was to investigate the physiological and psychological effects of using a smartphone app for HRVB training following a stressful experience. The efficacy of the gamified Breather component of the Happify mobile health app was examined in an experimental setting. METHODS: In this study, participants (N=140) underwent a laboratory stressor and were randomly assigned to recover in one of three ways: with no phone present, with a phone present, with the HRBV game. Those in the no phone condition had no access to their phone. Those in the phone present condition had their phone but did not use it. Those in the HRVB game condition used the serious game Breather on the Happify app. Stress recovery was assessed via repeated measures of salivary alpha amylase, cortisol, and self-reported acute stress (on a 1-100 scale). RESULTS: =3.78, P=.03). There were no significant differences among the conditions during recovery for salivary cortisol levels or self-reported stress. CONCLUSIONS: These results show that engaging in a brief HRVB training session on a smartphone reduces levels of salivary alpha amylase following a stressful experience, providing preliminary evidence for the effectiveness of Breather in improving physiological stress recovery. Given the known ties between stress recovery and future well-being, this study provides a possible mechanism by which gamified biofeedback apps may lead to better health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".