Data Sheet 1_Effects of ACSM guideline–based exercise on patients with lung cancer: a syst
by Jiangxin Guo·Updated 1mo ago
278.4 KB1files
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Description
A systematic review and meta-analysis of 32 randomized controlled trials investigating the effects of exercise on lung cancer patients. The study, authored by Jiangxin Guo and uploaded in April 2026, pools standardized mean differences for outcomes like quality of life, fatigue, anxiety, depression, pain, and sleep. The analysis classifies interventions by their adherence to American College of Sports Medicine guidelines.
Use Cases
Comparing the efficacy of high vs. low adherence to ACSM exercise guidelines based on the subgroup analyses described.
Analyzing the pooled effect sizes (SMDs) for quality of life and symptom burden outcomes from the meta-analysis.
Assessing study quality and publication bias using the RoB 2 tool and Begg's/Egger's tests as described in the methods.
Strengths
Includes 32 randomized controlled trials, with 18 classified as having high adherence to ACSM guidelines.
Reports specific pooled effect sizes (SMDs) with 95% confidence intervals and p-values for multiple clinical outcomes.
Uses a systematic methodology with a registered protocol (CRD420251249211) and standard risk-of-bias assessment.
Limitations
The dataset is a 278.4 KB DOCX file; the underlying tabular meta-analysis data is not directly accessible.
Row count and column-level documentation for the extracted study data are unknown.
Freshness should be verified; the last update date is in the future (2026-04-15).
Provenance
Source
Author: Jiangxin Guo. Platform: figshare.
Collection Method
Systematic search across PubMed, Embase, Web of Science, and Cochrane Library for RCTs.
Time Range
Temporal coverage of the included studies is not specified.
Freshness
Last updated 2026-04-15 10:56:02
Geography
Spatial coverage of the included studies is not specified.
The primary file is a DOCX document summarizing the review; the underlying numerical data for the meta-analysis may not be included in a machine-readable format.