Ecological momentary assessment of mood and movement with bipolar disorder over time: Participant recruitment and efficacy of study methods
Bibliographic record
Abstract
OBJECTIVES: Mobile technology and ambulatory research tools enable the study of human experience in vivo, when and where it occurs. This includes cognitive processes that cannot be directly measured or observed (e.g., emotion) but can be reported in the moment when prompted. METHODS: For the Bipolar Affective Disorder and older Adults (BADAS) Study, 50 participants were randomly prompted twice daily to complete brief smartphone questionnaires. This included the Bipolar Disorder Symptom Scale which was developed to briefly measure symptoms of both depression (cognitive and somatic) and hypo/mania (affrontive symptoms and elation/loss of insight). Participants could also submit voluntary or unsolicited app responses anytime; all were time- and GPS-stamped. Herein, we describe BADAS study methods that enabled effective recruitment, adherence and retention. RESULTS: We collected 9600 app responses over 2 year, for an average response rate of 1.4×/day. Over an average of 145 consecutive days (range 2-435 days), BADAS participants reported depression and hypo/mania symptom levels (a.m. and p.m.), sleep quality (a.m.), medication adherence (a.m.) and any significant events of the day (p.m.). They received $1/day for the first 90 days after submitting both a.m. and p.m. questionnaires. CONCLUSION: BADAS study methods demonstrates the utility of ecological momentary assessment in longitudinal psychiatric research.
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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 | 0.053 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".