Document Type

Article

Publication Date

2-1-2018

College/Unit

School of Public Health

Department/Program/Center

Biostatistics

Abstract

Objective—Using the traditional random-effects model, a recently reported standardized effect size (g) reduction of −0.42 (95% CI, −0.58 to −0.27) was observed as a result of communitydeliverable exercise in adults with arthritis and other rheumatic diseases (AORD). However, a recently proposed alternative model (IVhet) has been shown to have superior coverage probability to the random-effects model. The purpose of this brief report was to compare these previous random-effects results with the IVhet model. Methods—Based on a previous meta-analysis of 35 g’s representing 2,449 participants, results were pooled using the IVhet model. Influence analysis, number needed-to-treat (NNT), percentile improvement, and gross estimates of the number of inactive adults with arthritis who could benefit from exercise were also calculated. Results—The IVhet model yielded statistically significant reductions in depressive symptoms (g = −0.30, 95% CI, −0.49 to −0.11), a difference that was −0.12 (28.7%) smaller than the randomeffects model. With each study deleted from the model once, results remained statistically significant, ranging from −0.28 to −0.34. The percentile improvement, NNT, and estimated number of people with arthritis in the United States who could improve their depressive symptoms by participating in a regular exercise program was, respectively, 11.8% (95% CI, 4.5% to 18.8%), 8 (95% CI, 5 to 23) and 2.7 million (95% CI, 1.0 to 4.4 million). Conclusions—These findings provide more conservative and accurate evidence that community-deliverable exercise improves depressive symptoms in adults with AORD. Future meta-analyses may want to consider using the IVhet versus traditional random-effects model.

Source Citation

Kelley GA, Kelley KS. Community-deliverable exercise and depression in adults with arthritis: Confirmatory evidence of a meta-analysis using the IVhet model. Journal of Evidence-Based Medicine. 2017;11(1):51-55. doi:10.1111/jebm.12229

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