Variability in Depression Symptoms Across Levels of Alexithymia: An Ecological Momentary Assessment Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Alexithymia, characterized by difficulties in identifying and describing one’s own emotions, is associated with various psychological conditions, particularly depression. Although depressive symptoms often fluctuate over short periods, traditional depression scales may not adequately capture this symptom variability due to recall bias. Ecological momentary assessment (EMA) can act as an effective method for capturing mood fluctuations and emotional states, enabling a deeper understanding of emotional regulation mechanisms. OBJECTIVE This study aims to examine the relationship between alexithymia and variability in depression symptoms variability using EMA data collected over a 28-day period. Specifically, we investigate whether individuals with high levels of alexithymia exhibit greater symptom variability compared to those with low levels of alexithymia METHODS A total of 469 participants were classified into two groups based on their total scores of the Toronto Alexithymia Scale (TAS-20K): a low alexithymia group (n=246) and a high alexithymia group (n=223). Participants completed daily self-reports via a mobile EMA app over 28 consecutive days, assessing their depressive symptoms including mood, appetite, sleep, general condition, and concentration, experienced during the past 24 hours. Independent t-tests were used to compare group differences in symptom variability, quantified by coefficient of variation (CV, %). RESULTS Significant differences in depressive symptom variability were observed between the two alexithymia groups. Compared to participants in the low alexithymia group, those in the high alexithymia group exhibited significantly greater symptom variability in mood (mean 15.95, SD 10.93 vs. mean 20.22, SD 12.40; t(444.944)=3.96; P<.001), appetite (mean 17.08, SD 15.81 vs. mean 21.51, SD 12.21; t(467)=3.37; P<.001), sleep (mean 21.30, SD 10.76 vs. mean 25.48, SD 12.30; t(443.517)=3.92; P<.001), general condition (mean 16.53, SD 10.32 vs. mean 19.98, SD 11.93; t(467)=3.44; P<.001), and concentration (mean 17.80, SD 11.10 vs. mean 22.35, SD 12.36; t(448.163)=4.19; P<.001). CONCLUSIONS The findings suggest that alexithymia is associated with greater variability in depression symptoms, including mood, appetite, sleep, general condition, and concentration. These results highlight the importance of considering alexithymia in clinical assessments of emotional and psychological functioning and suggest that individuals with high levels of alexithymia may benefit from tailored interventions targeting difficulties in emotional regulation. CLINICALTRIAL N/A
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 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 | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".