Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana
(1) Background: Spatial energy expenditure patterns, driven by physical activity, particularly among females, remain underexplored in Ghana. This study, therefore, investigates spatial energy expenditure clustering or dispersion patterns using metabolic equivalents of task (METs) values among Ghanai...
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MDPI AG
2025-05-01
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| author | Sally Sonia Simmons |
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| description | (1) Background: Spatial energy expenditure patterns, driven by physical activity, particularly among females, remain underexplored in Ghana. This study, therefore, investigates spatial energy expenditure clustering or dispersion patterns using metabolic equivalents of task (METs) values among Ghanaian females across rural and urban areas. (2) Methods: Using 13,799 data from the 2022 Ghana Demographic and Health Survey, METs values were assigned to self-reported occupation categories as proxies for physical activity. Global and local spatial autocorrelation metrics (Queen contiguity and Moran’s I) were employed to assess spatial clustering or dispersion of METs values across the 16 administrative regions. (3) Results: Rural females reported higher METs (mean = 3.35 ± 1.627) and lower BMI (23.476 ± 3.888) than urban females (METs: mean = 2.42 ± 1.208, BMI: 25.313 ± 4.854). There was a significant but weak global spatial autocorrelation (Moran’s I = 0.003, <i>p</i>-value = 0.001), with stronger clustering observed in rural (Moran’s I = 0.004, <i>p</i>-value = 0.001) than in urban areas (Moran’s I = 0.002, <i>p</i>-value = 0.002). Also, High–High clusters were prevalent in the Northern, Savannah and Northeast regions particularly due to the lingering labour-intensive occupations as compared to Low–Low clusters in the Eastern and Greater Accra regions where jobs are often desk-based and sedentary. (4) Conclusions: Given the revealed geographic heterogeneity (High–High and Low–Low clustering) of female energy expenditure in Ghana, there is a need for regionally tailored health policies targeting physical inactivity and its associated risks. |
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| spelling | doaj-art-0a92d2997f014272b4e6e47b1b5462da2025-08-20T02:21:52ZengMDPI AGObesities2673-41682025-05-01523310.3390/obesities5020033Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural GhanaSally Sonia Simmons0Department of Social Policy, London School of Economics and Political Science, London, WC2A 2AE, UK(1) Background: Spatial energy expenditure patterns, driven by physical activity, particularly among females, remain underexplored in Ghana. This study, therefore, investigates spatial energy expenditure clustering or dispersion patterns using metabolic equivalents of task (METs) values among Ghanaian females across rural and urban areas. (2) Methods: Using 13,799 data from the 2022 Ghana Demographic and Health Survey, METs values were assigned to self-reported occupation categories as proxies for physical activity. Global and local spatial autocorrelation metrics (Queen contiguity and Moran’s I) were employed to assess spatial clustering or dispersion of METs values across the 16 administrative regions. (3) Results: Rural females reported higher METs (mean = 3.35 ± 1.627) and lower BMI (23.476 ± 3.888) than urban females (METs: mean = 2.42 ± 1.208, BMI: 25.313 ± 4.854). There was a significant but weak global spatial autocorrelation (Moran’s I = 0.003, <i>p</i>-value = 0.001), with stronger clustering observed in rural (Moran’s I = 0.004, <i>p</i>-value = 0.001) than in urban areas (Moran’s I = 0.002, <i>p</i>-value = 0.002). Also, High–High clusters were prevalent in the Northern, Savannah and Northeast regions particularly due to the lingering labour-intensive occupations as compared to Low–Low clusters in the Eastern and Greater Accra regions where jobs are often desk-based and sedentary. (4) Conclusions: Given the revealed geographic heterogeneity (High–High and Low–Low clustering) of female energy expenditure in Ghana, there is a need for regionally tailored health policies targeting physical inactivity and its associated risks.https://www.mdpi.com/2673-4168/5/2/33metabolic equivalents of task (METs)physical activityspatial autocorrelationMoran’s Iruralurban |
| spellingShingle | Sally Sonia Simmons Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana Obesities metabolic equivalents of task (METs) physical activity spatial autocorrelation Moran’s I rural urban |
| title | Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana |
| title_full | Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana |
| title_fullStr | Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana |
| title_full_unstemmed | Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana |
| title_short | Spatial Analysis of Metabolic Equivalents of Task Among Females in Urban and Rural Ghana |
| title_sort | spatial analysis of metabolic equivalents of task among females in urban and rural ghana |
| topic | metabolic equivalents of task (METs) physical activity spatial autocorrelation Moran’s I rural urban |
| url | https://www.mdpi.com/2673-4168/5/2/33 |
| work_keys_str_mv | AT sallysoniasimmons spatialanalysisofmetabolicequivalentsoftaskamongfemalesinurbanandruralghana |