Comparative Effectiveness of Non-Pharmacological Interventions for Postpartum Depression and Anxiety: A Network Meta-Analysis

Figures

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  Figure 1. Illustrates the process of study identification through databases and registries. The diagram shows the total number of records identified through database searching and other sources, the number of records after duplicates were removed, the number of records screened and excluded, the number of full-text articles assessed for eligibility, and the number of studies included in the final qualitative and quantitative synthesis.

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  Figure 2. Present the risk of bias assessment results for all included studies in this review. Green indicates no significant risk of bias, yellow represents insufficient information to assess the risk of bias or incomplete reporting, and red signifies a clear risk of bias in the included studies.

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  Figure 3. (A) A network graph comparing various non-pharmacological interventions for their effects on depressive symptoms in patients with PPD. (B) A network graph comparing various non-pharmacological interventions for their effects on anxiety symptoms in patients with PPD. In panels (A and B), each circle represents an intervention: ACU, EXE, PSY, ECP, MT and CON. The size of each circle is proportional to the number of studies evaluating that intervention. The lines connecting the circles represent direct comparisons between interventions, with the width of the line indicating the number of studies comparing the two interventions. Thicker lines represent a greater number of studies. The figure provides a visual representation of the network of interventions and their comparisons for both depressive and anxiety symptoms.

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  Figure 4. Trace plots and density plots for depressive trajectories and convergence diagnostics. (A and B) Trace plots of the posterior distributions for anxiety symptoms across different interventions, including acupuncture (d.CON.ACU), exercise (d.CON.EXE), music therapy (d.CON.MT), psychotherapy (d.CON.PSY), and exercise combined with psychotherapy (d.PSY.ECP). Trace of sd.d (bottom of (B) represents the trace plot for the standard deviation of the parameters (sd.d), which is used for assessing the variability in the model and ensuring convergence of the model. (C) Convergence diagnostics for the model, showing the trace plots (left) and density plots (right) of the standard deviations (sd.d) and other parameters. The red dashed lines indicate the 97.5% quantile, and the median value is shown by the solid line. Convergence is confirmed when the trace plot stabilizes and the density plot forms a clear distribution.

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  Figure 5. Trace plots and density plots for anxiety trajectories and convergence diagnostics. (A and B) Trace plots of the posterior distributions for anxiety symptoms across different interventions, including acupuncture (d.CON.ACU), exercise (d.CON.EXE), music therapy (d.CON.MT), psychotherapy (d.CON.PSY), and exercise combined with psychotherapy (d.PSY.ECP). Trace of sd.d (bottom of (B) represents the trace plot for the standard deviation of the parameters (sd.d), which is used for assessing the variability in the model and ensuring convergence of the model. (C) Convergence diagnostics for the model, showing the trace plots (left) and density plots (right) of the standard deviations (sd.d) and other parameters. The red dashed lines indicate the 97.5% quantile, and the median value is shown by the solid line. Convergence is confirmed when the trace plot stabilizes and the density plot forms a clear distribution.

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  Figure 6. Forest plot of network meta-analysis results for depressive outcomes. This forest plot illustrates the mean differences (95% credible intervals, CrI) for various interventions compared with the control group. Each intervention is represented by a horizontal line with a point estimate (hollow circle) and the 95% credible interval (horizontal line). The mean differences indicate the effect size of each intervention relative to the control group, with negative values suggesting a reduction in symptoms compared to the control. The intervals represent the uncertainty around the point estimate, and a wider interval suggests more uncertainty. The closer the interval is to zero, the less certain the effect is.

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  Figure 7. Forest plot of network meta-analysis results for anxiety outcomes. This forest plot illustrates the mean differences (95% credible intervals, CrI) for various interventions compared with the control group. Each intervention is represented by a horizontal line with a point estimate (hollow circle) and the 95% credible interval (horizontal line). The mean differences indicate the effect size of each intervention relative to the control group, with negative values suggesting a reduction in symptoms compared to the control. The intervals represent the uncertainty around the point estimate, and a wider interval suggests more uncertainty. The closer the interval is to zero, the less certain the effect is.

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  Figure 8. The ranking of each intervention in improving depressive symptoms. The horizontal axis represents each intervention, the vertical axis represents the probability of the intervention under that ranking, and the height of the bar then characterizes the probability value (0–1).

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  Figure 9. The ranking of each intervention in improving anxiety symptoms. The horizontal axis represents each intervention, the vertical axis represents the probability of the intervention under that ranking, and the height of the bar then characterizes the probability value (0–1).

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  Figure 10. Reporting bias in depressive symptoms. This funnel plot displays the relationship between the standardized mean difference (x-axis) and standard error (y-axis) for the studies included in the network meta-analysis. Each dot represents a study, with the position along the x-axis indicating the effect size (standardized mean difference) and the position along the y-axis representing the standard error. The dashed lines represent the expected distribution for studies with no publication bias, where studies with larger sample sizes are expected to cluster near the top of the plot and those with smaller sample sizes spread out at the bottom. The symmetry of the plot suggests that publication bias is unlikely, while asymmetry may indicate the presence of such bias.