Correlates of sedentary behaviour among individuals at risk of developing type 2 diabetes mellitus in a low resource setting

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2023

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Background: There is evidence regarding the adverse effects of prolonged sedentary behaviour (SB) on health outcomes, including the association with non-communicable diseases (NCDs) such as type 2 diabetesmellitus (T2DM). However, there is a scarcity of information regarding the correlates of SB among individuals at risk of developing T2DM in low-income settings such as in South Africa (SA). Therefore, we aimed to identify the prevalence and correlates of SB among adults at riskof developing T2DM in low-income communities in Cape Town, South Africa. Methods: This was secondary analysis of cross-sectional data from the South African Diabetes Prevention Programme (SADPP). The study population consisted of 698 participants from 16 lower socio-economic communities in Cape Town, recruited between August 2017 and March 2018. Participants classified at high-risk completed questionnaires on socio-demographic, behavioural and psychological factors, neighbourhood living conditions and medical history. Self-reported SB was measured using the Global Physical Activity Questionnaire (GPAQ) and a separate questionnaire that recorded minutesof screen time (ST) during a typical working and non- working day. Blood samples were collected forthe determination of fasting glucose, glycated haemoglobin, and lipids. A Kruskal-Wallis or one-way ANOVA was conducted depending on the distribution of the numerical variable. A chi-squared or Fisher's exact test was conducted depending on the expected frequencies of the cells. Robust regression was used to investigate the association between the exposure and outcome variable. Statistical significance was set at p<0.05. Results: Among the 698 participants, the median time (minutes/day) spent in SB and ST was 180.0 and 137.1 minutes/day, respectively. When grouped by SB or ST, most of the participants (66.0% and 77.9%) were classified as having low levels (<4h/day) of SB and ST, respectively. After adjusting for age and gender, SB was associated with type of housing, lower safety, and walking infrastructure scores, excellent self-reported sleep quality and having at least one barrier to physical activity (PA). Conclusion: SB was correlated to factors related to socioeconomic status (SES), as well as barriers to PA and self-reported sleep quality. As such interventions to decrease SB should focus on environmental factors.
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