Published in PLOS One on 2026-05-11, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Aychew Kassa Belete, Bantie Getnet Yirsaw, Birhan Ambachew Taye
Source: PLOS One • DOI 10.1371/journal.pone.0348786
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Physical inactivity is a primary driver of global mortality, data on its specific impact in Africa remains fragmented. We conducted this study to provide the first comprehensive pooled prevalence and identify predisposing factors contributing to physical inactivity among African adults.
We searched PubMed, Scopus, Embase, and Google Scholar for studies published between March 1, 2010, and March 31, 2025. A quality assessment of the studies was performed through the Newcastle-Ottawa Scale (NOS). The random-effect (DerSimonian) model was used to calculate the aggregated rates of physical inactivity among adults and their predisposing factors. Heterogeneity was assessed using the I2 statistic, along with subgroup and sensitivity analyses. Publication bias was evaluated using Egger’s test, and all analyses were performed in STATA, version 17.
This systematic review and meta-analysis included 34 studies with a total of 41,521 participants. The pooled prevalence of physical inactivity was 45% (95% CI: 35%–55%), and there was considerable heterogeneity (I2 = 98.4%). The highest prevalence of 57% (95% CI: 0.33, 0.81) I2 = 97.7 seen in 2013 and the lowest prevalence 28% (95% CI: 0.03, 0.53) I2 = 94.3%) observed in 2011. This study identified that, being female (OR = 1.87; 95% CI: 1.55, 2.25), Adults aged ≥ 60, (OR=2.18; 95% CI:1.98,4.77), being obese (OR=3.52; 95% CI: 2.26,5.49), urban dweller (OR=1.91, 95% CI:1.24,2.95), being overweight (OR=2.03; 95% CI: 1.61,2.68), depression symptom (OR= 1.34;95% CI: 1.07,1.68) and more drinking of alcohol (OR=1.73; 95% CI: 1.18,2.64), were significant predisposing factors associated with prevalence of physical inactivity.
A high level of physical inactivity was observed amongst the African adults. Our findings indicate that female gender, older age, being overweight and obese, high levels of alcohol consumption and depression are significant predictors of physical inactivity in the African population. Promoting awareness about the positive impacts of regular exercise on health among women and an elderly age group could effectively lower the chances of hypokinetic diseases.
Introduction
Physical inactivity, defined as engaging in less than 600 Metabolic Equivalent of Task (MET) minutes per week of activity, is a major health risk factor [1]. Currently, setting is considered as the new smoking, meaning prolonged sitting and sedentary behavior have serious health consequences like smoking [2]. Despite the WHO’s advice on physical activity for adults 18 years and older, the majority of the world’s population is sedentary [3]. It is the fourth leading cause of death globally, responsible for an estimated 3.2 million deaths annually, of which 2.6 million are in developing countries (WHO, 2009, 2010) [4]. Also, physical inactivity has been approximated to cost the global healthcare system $538 billion each year [5]
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider research & evidence desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Aychew Kassa Belete, Bantie Getnet Yirsaw, Birhan Ambachew Taye. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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