Evaluating Individual Differences in Emotion Regulation in Response to Sadness Using Digital Phenotyping
Bibliographic record
Abstract
BACKGROUND The majority of research on emotion regulation processes has been restricted to controlled laboratory settings that use experimental paradigms to investigate short-term outcomes. A true understanding of emotion regulation requires an unobtrusive, ecologically valid assessment of the construct as it naturally unfolds in the environment. Digital phenotyping, or the moment-by-moment quantification of individual-level human behavior using data from smartphone sensors, is a novel method for evaluating human behavior in naturalistic settings. To our knowledge, this is the first study to implement digital phenotyping in the investigation of emotion regulation. OBJECTIVE This study aimed to evaluate whether smartphone-based digital phenotyping data predicts individual differences in emotion regulation in both in-lab and naturalistic settings. METHODS During an in-lab session, unselected university student participants (N = 69) completed self-report questionnaires measuring trait emotion regulation as well as state emotion regulation and state affect following a baseline period, a negative mood induction, and a recovery period. Smartphone-based digital phenotyping data were then collected over the course of a 7-day follow-up period using the Beiwe Research Platform,1 an open-access mobile- and cloud-based research tool for collecting digital data via smartphones. Specifically, variation in global positioning system (GPS) distance and mobile power state (i.e., smartphone battery) level were examined as predictors of longitudinal variation in negative affect, emotion regulation, and depression. RESULTS Results showed that variation in GPS distance was significantly associated with variation in state cognitive reappraisal (b = -0.0004, SE = 0.0002, p = .02) and negative state affect (b = 0. 005, SE = 0.002, p = .01) over time. Variation in power state level was also significantly associated with variation in cognitive reappraisal over time (b = -4.98, SE = 1.72, p = .005) and marginally associated with variation in negative state affect (b = -29.58, SE = 16.73, p = .08) over time. Cluster and classification analyses showed both power state level and GPS distance accurately classified two trait emotion regulation clusters with high sensitivity (.95 and .96 respectively) and specificity (.86 and .97 respectively). However, variation in power state level and GPS distance together with trait and state emotion regulation did not predict current depressive symptoms (ps > .05). CONCLUSIONS Overall, the findings provide initial data on the use of digital phenotyping data in predicting individual differences in state and trait emotion regulation in both in-lab and naturalistic settings. The results suggest that operationalizations of digital phenotyping data and modeling approaches are particularly important factors to consider when implementing digital phenotyping methodology in the study of mental health processes such as emotion regulation.
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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 | 0.001 |
| 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) | 0.002 | 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".