Systematic Review of Exercise Interventions for Depression in Children and Adolescents

A 2024 systematic review and Bayesian network meta-analysis synthesizing evidence from 26 randomized controlled trials involving 3,507 participants. The research, authored by Jinping Hu and published on figshare, compares the efficacy of different exercise modalities and doses on depressive symptoms in children and adolescents. The analysis identifies optimal exercise types and a therapeutic dose window.

Use Cases

• Compare the efficacy of Group Training versus Aerobic Exercise for depression based on the reported Standardized Mean Differences.
• Model the non-linear dose-response relationship between exercise volume (METs-min/week) and symptom reduction based on the described curvilinear pattern.
• Identify the therapeutic window for exercise prescription (360–980 METs-min/week) based on the dose-response modeling results.
• Assess the inconclusive evidence for Mixed Training or Mind–Body Exercise modalities highlighted in the review.

Strengths

• Analysis is based on 26 randomized controlled trials, providing a structured evidence base.
• Includes a specific, quantified therapeutic dose window (360–980 METs-min/week) derived from modeling.
• Employs multiple analytical methods: pairwise, network, and dose–response meta-analyses.
• Risk of bias was assessed using the Cochrane tool, indicating methodological rigor.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• The authors note heterogeneity among included studies and advise caution in interpreting findings.

Provenance

Source

Systematic review authored by Jinping Hu, published on figshare.

Collection Method

Systematic search of PubMed, Web of Science, PsycINFO, and Scopus databases up to October 2024.

Time Range

Review includes studies up to October 2024.

Freshness

Last updated 2026-05-25 04:34:38; freshness should be verified.