Fluctuations in Daily Happiness and Nervousness Based on Depressive and Anxious Symptoms in Adolescents or Young Adults Across 3 Latin American Cities: Experience Sampling Study
Bibliographic record
Abstract
Background: Experience sampling methods (ESMs) have been used in clinical research to collect data on emotional and behavioral states in real-life contexts among different populations. Although the use of ESMs in mental health has increased, it has not been applied to larger samples of young people in disadvantaged urban settings. Objective: This study aimed to determine the extent to which mood status scores (happiness and nervousness) vary during a week, as a function of having or not having symptoms of depression or anxiety, in a sample of adolescents and young adults in the cities of Buenos Aires, Bogotá, and Lima. A secondary objective was to identify factors associated with mood scores, including sociodemographics, quality of life, and daily activities. Methods: This study was part of the Building Resilience and Resources to Reduce Mental Distress in Young People in Latin America research program, which focuses on mental health resources for young people. Participants (n=143) aged 15-24 years completed daily ESM assessments over a week using the mobile app, resulting in 5246 reports. Data were analyzed using descriptive analyses with 2-tailed t tests and chi-square tests, and multilevel linear regression was used to examine associations between depressive or anxiety symptoms, mood variability, and factors influencing mean mood. Finally, Spearman correlation assessed the relationship between happiness and nervousness. Results: The analysis revealed that depressive or anxiety symptoms were not significantly associated with increased variability in mood scores (happiness P=.40 and nervousness P=.84). However, males exhibited greater variability in happiness and nervousness scores (P<.001) than females. Additionally, young people showed higher variability in nervousness than adolescents (P=.02). Regarding average happiness scores, young adults reported higher average happiness than adolescents (β=.604; P=.003). Engaging in structured activities (eg, sports, music lessons, and dance classes) was associated with increased happiness (β=.266; P=.01). In contrast, instrumental activities (eg, cleaning, shopping, meal preparation, or taking medication; β=-.144; P=.02) and work-related tasks (β=-.205; P=.01) were linked to lower happiness and higher nervousness (β=.387; P<.001). Quality of life was positively correlated with happiness (β=.486; P<.001) and negatively correlated with nervousness (β=-.273; P=.005). Finally, as for average scores, a strong negative correlation was found between happiness and nervousness (rs=-0.92; P<.001). The simple multilevel analysis showed that for each point of happiness, nervousness decreased by 0.45 points (95% CI -0.48 to -0.42; t3=-41.7; P<.001; SE 0.01). Conclusions: Our study reveals that depressive and anxiety symptoms do not significantly affect the variability in predicted happiness and nervousness scores. However, we observed that demographic factors, such as gender and age, play a role in emotional variability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, 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".