To systematically review the role of digital phenotyping (DP) in predicting and identifying peripartum depression (PPD) symptoms, emphasizing the need for innovative approaches in clinical settings.
Key Findings:
Passive DP data related to sleep and circadian rhythms frequently associated with depressive symptoms.
Findings for physical activity as a predictor of PPD were inconsistent, indicating a need for further investigation.
Active DP data, including language features from text entries and social media behavior, were informative when combined with personal history or self-reports.
Considerable variation in study designs and validation strategies limits direct comparison and causal interpretation.
Interpretation:
The evidence on digital phenotyping for predicting PPD is largely exploratory, necessitating cautious interpretation pending further validation, particularly in clinical applications.
Limitations:
Variation across study designs and data sources complicates comparisons.
Lack of consistent biomarkers for PPD limits diagnostic capabilities.
Many cases of PPD remain undetected due to limitations in conventional screening methods, including biases in self-reported data.
Conclusion:
Digital phenotyping holds promise for enhancing early identification and prediction of PPD, but further rigorous validation is needed, particularly in diverse populations and clinical settings.
