Smartphone Photoplethysmography Covaries with Stress and Anxiety During a Digital Acute Social Stressor Preprint
Bibliographic record
Abstract
Heart rate is a transdiagnostic marker of affective states and the stress diathesis model of health. While most psychophysiological research has been conducted in laboratory environments, recent technological advances have provided the opportunity to index heart rate dynamics in real world environments with commercially available mobile health (mHealth) and wearable photoplethysmography (PPG) sensors that allow for improved ecologically validity of psychophysiological research. Unfortunately, adoption of wearable devices is unevenly distributed across important demographic characteristics, including race, ethnicity, socioeconomic, education, and age making it difficult to collect heart rate dynamics in diverse populations. Therefore, there is a need to democratize mHealth PPG research by harnessing more widely adopted smartphone-based PPG to both promote inclusivity and examine whether smartphone-based PPG can predict concurrent affective states. In the current preregistered study with open data and code, we examined the covariation of smartphone-based PPG and self-reported stress and anxiety during an online variant of the Trier Social Stress Test (TSST), as well as prospective relationships between PPG and future perceptions of stress and anxiety in a sample of 103 adult participants. Results demonstrated that smartphone-based PPG significantly covaries with self-reported stress and anxiety during acute digital social stressors. PPG heart rate was significantly associated with concurrent self-reported stress, but not stress at subsequent time points. These findings highlight the potential use of smartphone-based PPG as an inclusive metric to index heart rate in remote digital study designs and indicate that PPG can provide a proximal, but not subsequent, measure of stress.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".