.png){kind=icon}
.png){kind=icon}
Return
Psychological distress; digital behavioral risks; and physical activity as predictors of subjective wellbeing: a machine learning study in a large sample of community-dwelling adults
.png){kind=icon}
OA
AI
M
N
W
F
H
L
N
M
R
I
DOI：10.3389/fnbeh.2026.1876257.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
BackgroundRegular physical activity is a key determinant of psychological wellbeing; yet its interaction with emerging digital behavioral risks remains insufficiently understood. Compulsive internet use and nomophobia have been linked to psychological distress; which negatively affects life satisfaction and happiness. However; few studies have examined these factors simultaneously. Machine learning offers a promising approach for improving predictive accuracy and clarifying the relative contributions of these variables to subjective wellbeing.AimThis study examined the combined predictive roles of physical activity; psychological distress; compulsive internet use; and nomophobia in subjective wellbeing (life satisfaction and happiness) and identified the optimal machine learning models for each outcome.MethodsA cross-sectional study was conducted among 1; 479 community-dwelling adults (51.05% males) recruited between September and December 2024. Participants completed validated Arabic versions of the IPAQ-SF; DASS-21; CIUS; NMP-Q; SWLS; and SHS. Six machine learning algorithms (linear regression; random forests; support vector machines; XGBoost; k-nearest neighbors; and LASSO) were trained using an 80/20 train–test split with 5-fold cross-validation. Performance was evaluated using R2; RMSE; and MAE.ResultsPsychological distress was the strongest negative predictor of wellbeing; showing the largest associations with happiness (r = −0.40) and life satisfaction (r = −0.37). Compulsive internet use was moderately associated with distress (r = 0.48); whereas nomophobia showed negligible relationships with both outcomes. For subjective happiness; random forest achieved the best performance (R2 = 0.154; RMSE = 0.869); slightly outperforming linear regression and LASSO (R2 = 0.153). For life satisfaction; linear regression and LASSO performed best (R2 = 0.129; RMSE = 1.317); while support vector machines showed the lowest accuracy. Higher physical activity levels; particularly vigorous activity; were consistently associated with more favorable psychological profiles.ConclusionPrediction of subjective wellbeing is outcome-specific; with different machine learning models performing optimally for happiness and life satisfaction. Psychological distress emerged as the strongest negative predictor; whereas physical activity demonstrated a consistent protective association. Longitudinal studies are needed to clarify causal pathways linking digital behavioral risks to wellbeing.
Keywords:
machine learning
physical activity
psychological distress
subjective wellbeing
nomophobia
compulsive internet use
Journal
IF:
2.9
Papers:
385
Citations:
1.1W
Organization
Cited Papers