Ecological Momentary Assessment Using Smartphones in Patients With Depression: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Ecological momentary assessment (EMA) is a promising tool in the management of psychiatric disorders and particularly depression. It allows for a real-time evaluation of symptoms and an earlier detection of relapse or treatment efficacy. The generalization of the smartphone in the modern world offers a new, large-scale support for EMA. OBJECTIVE: The main objective of this study was twofold: (1) to assess patients' compliance with an EMA smartphone app defined by the number of EMAs completed, and (2) to estimate the external validity of the EMA using a correlation between self-esteem/guilt/mood variables and Hamilton Depression Rating Scale (HDRS) score. METHODS: Eleven patients at the Pitié-Salpêtrière Hospital, Paris, France, were monitored for 28 days by means of a smartphone app. Every patient enrolled in the study had two types of assessment: (1) three outpatient consultations with a psychiatrist at three different time points (days 1, 15, and 28), and (2) real-time data collection using an EMA smartphone app with a single, fixed notification per day at 3 pm for 28 days. The results of the real-time data collected were reviewed during the three outpatient consultations by a psychiatrist using a dashboard that aggregated all of the patients' data into a user-friendly format. RESULTS: Of the 11 patients in the study, 6 patients attended the 3 outpatient consultations with the psychiatrist and completed the HDRS at each consultation. We found a positive correlation between the HDRS score and the variables of self-esteem, guilt, and mood (Spearman correlation coefficient 0.57). Seven patients completed the daily EMAs for 28 days or longer, with an average response rate to the EMAs of 62.5% (175/280). Furthermore, we observed a positive correlation between the number of responses to EMAs and the duration of follow-up (Spearman correlation coefficient 0.63). CONCLUSIONS: This preliminary study with a prolonged follow-up demonstrates significant patient compliance with the smartphone app. In addition, the self-assessments performed by patients seemed faithful to the standardized measurements performed by the psychiatrist. The results also suggest that for some patients it is more convenient to use the smartphone app than to attend outpatient consultations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".