Greater within- and between-day instability is associated with worse anxiety and depression symptoms
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety affect hundreds of millions of people worldwide, and their prevalence increased during the COVID-19 pandemic as social schedules were disrupted. This study explores the associations between anxiety and depression and within- and between-day instability of affective, somatic, and cognitive symptoms during the early pandemic stages. METHODS: Participants (n = 153, ages 18-77, 72 % female) reported daily levels of affective (anxiety/sadness), somatic (appetite/sleepiness), and cognitive (concentration/energy) symptoms for 14-44 days at five timepoints: 0, 3, 6, 9, and 12 h after awakening. At the end of the study, participants completed validated scales for anxiety (GAD-7) and depression (PHQ-9). Symptom instability was assessed using the Absolute Real Variability (ARV) index. Regression models examined within-day instability (WD-I) and between-day instability (BD-I) with GAD-7 and PHQ-9 scores as outcomes. RESULTS: Greater instability (both WD-I and BD-I) of affective symptoms correlated with elevated GAD-7 and PHQ-9 scores. For somatic and cognitive symptoms, greater BD-I was associated with higher scores. LIMITATIONS: The study used retrospective daily data, which could benefit from real-time assessments for improved accuracy. CONCLUSIONS: This study provides empirical evidence of a connection between greater anxiety and depression severity and increased instability in daily mood and physiological symptoms. The findings underscore the importance of consistent symptom monitoring to understand overall mental health trajectories. Additionally, it highlights the role of daily routines in stabilizing the circadian system, potentially regulating physiological and psychological processes and reducing symptom instability.
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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 | 0.002 |
| 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 | 0.001 |
| 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".