Digital Assessment of Depression, Acute Stress, and Socioeconomic Disparities Using Wearable and Smartphone Devices Across the Lifespan
Bibliographic record
Abstract
Traditional assessment of affective and behavioral functioning relies almost entirely on questionnaires, self-report interviews, and laboratory-based measurements. Although each of these approaches has important strengths, they are also subject to limitations. Recently, technological advances in mobile computing have allowed for the widespread adoption of consumer mobile technologies that may ameliorate many methodological limitations of traditional assessment methods as these devices contain a multitude of sensors enabling the scalable, unobtrusive, and ecologically valid collection of biobehavioral variables. Despite many review articles delineating the promise of these devices, research has largely been limited to single symptom profiles and homogenous populations. This symposium will address these gaps by presenting novel findings that utilize multimethod approaches (e.g., actigraphy, GPS, photoplethysmography, camera and light sensors) to examine how intensively longitudinal study designs leveraging consumer smartphone and wearable technology can be used to index mental health profiles, acute stress, and socioeconomic disparities across the lifespan and in diverse populations. First, Dr. Nelson will present a preregistered assessment of multiple clinical profiles using a computational psychiatry machine learning approach with large scale wearable data collection in a large nationally representative sample of adolescents. Second, Dr. Lockwood, will present a large-scale longitudinal study using newly-validated smartphone-based optic sensor to assess socioeconomic disparities. Third, Vega will present on how 6 months of smartphone sensor data during COVID-19 predicts weekly levels of depression and anxiety. Lastly, Harvie will present on how smartphone-based measures of photoplethysmography using a consumer wearable device tracks self-reported increases in perceived stress in children and adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".