Prevalence and socio-demographic correlates of accelerometer measured physical activity levels of school-going children in Kampala city, Uganda

PLoS ONE, Volume 15, No. 7 July, Year 2020

Interpretation

Background The current international physical activity guidelines for health recommend children to engage in at least 60 minutes of moderate-to-vigorous physical activity (MVPA) daily. Yet, accurate prevalence estimates of physical activity levels of children are unavailable in many African countries due to the dearth of accelerometer-measured physical activity data. The aim of this study was to describe the prevalence and examine the socio-demographic correlates of accelerometer-measured physical activity among school-going children in Kampala city, Uganda. Methods A cross-sectional study design was used to recruit a sample of 10-12 years old schoolgoing children (n = 256) from 7 primary schools (3 public schools and 4 private schools) in Kampala city, Uganda. Sedentary time, light-intensity physical activity (LPA), moderateintensity physical activity (MPA) and vigorous-intensity physical activity (VPA) were measured by accelerometers (ActiGraph GT3X+ [Pensacola, Florida, USA]) over a seven-day period. Socio-demographic factors were assessed by a parent/guardian questionnaire. Weight status was generated from objectively measured height and weight and computed as body mass index (BMI). Multi-level logistic regressions identified socio-demographic factors that were associated with meeting physical activity guidelines. Results Children’s sedentary time was 9.8±2.1 hours/day and MVPA was 56±25.7 minutes/day. Only 36.3% of the children (38.9% boys, 34.3% girls) met the physical activity guidelines. Boys, thin/normal weight and public school children had significantly higher mean daily MVPA levels. Socio-demographic factors associated with odds of meeting physical activity guidelines were younger age (OR = 0.68; 95% CI = 0.55-0.84), thin/normal weight status (OR = 4.08; 95% CI = 1.42-11.76), and socioeconomic status (SES) indicators such as lower maternal level of education (OR = 2.43; 95% CI = 1.84-3.21) and no family car (OR = 0.31; 95% CI = 0.17-0.55). This was a cross-sectional study of a representative sample of school-going children aged 10–12 years old in Kampala city, Uganda. As children aged 10 to 12 years old are transiting from childhood to adolescence, they gain some autonomy in decision making which may be critical to declines in their physical activity [46,47]. Kampala city is the capital and largest city in Uganda covering an area of 182 km2 with population of 1,516,210 residents from diverse ethnic groups and SES [48]. Kampala comprises of five administrative divisions, that is Nakawa, Makindye, Rubaga, Central and Kawempe [49]. Participants were selected using a multistage random sampling method. In stage one, we randomly selected two out of the five divisions (Central and Nakawa); from which 7 primary schools (3 public schools and 4 private schools) were randomly selected. One classroom from any one grade (5th through 7th) was randomly selected and all children from the selected classroom, except those who had physical and health conditions that limited their participation in physical activity were invited to participate in this study. Ethical approval to conduct the study was obtained from the Uganda National Council of Science and Technology (SS4340) and Kenyatta University Ethical Review Board (PKU/619/1703). Permission to access schools was granted by the Directorate of Education and Social Services, Kampala Capital City Authority (KCCA). The respective school head teachers, approved the school’s participation in the study. A parent/guardian provided written informed consent for themselves and their child in addition to written assent from the child. Data were collected during school sessions from May 2017 through August 2018 Children wore a tri-axial ActiGraph GT3X+ (Pensacola, Florida, USA) accelerometer on the right hip using an elastic belt for 7 consecutive days including 2 weekend days. A 24-hour wear protocol was employed to increase compliance [28,50]; and as such children were requested to wear the monitor all the time except when engaging in water-based activities like swimming and bathing. ActiGraph accelerometers are reliable and valid measures of children’s physical activity [21,26]. Using Actilife software (version 6.13.3) (ActiGraph, Pensacola, Florida, USA) the fully charged accelerometers were initialized to collect second to second movement counts at midnight following the first day that the children received the accelerometers; at a samplings rate of 80 HZ. Data were downloaded using ActiLife v6.13.3 (ActiGraph, Pensacola, Florida, USA) in raw format as GT3+ files and AGD files with 1 second epoch. The 24-hour protocol required sleep time to be identified and accounted for before evaluating wake wear time and generating physical activity variables of interest [51,52]. We used the Sadeh algorithm, which is in built into the sleep scoring function in ActiLife software to identify individualised daily sleep on set and offset time for each valid day for each child [53]; this is a valid method for removal of sleep [54]. Daily sleep on set and offset time was used to create time filters in CSV files (Excel Microsoft co-operation, 2016). Time filtered files for the wake period were created and used to identify non wear time and wear time. We defined non-wear as 20 minutes of consecutive 0 counts. Sufficient wear time was determined as 4 days including 1 weekend day with ≥ 10 hours/day. The time spent in different levels of movement intensity were generated basing on the Evenson cut points as: Sedentary time (≤ 25 counts/15 s), LPA (26–573 counts/15 s), MPA (574–1002 counts/15 s) and VPA (≥1003 counts/15 s) [55]. These cut-offs have been recommended as the most accurate for classifying children’s physical activity levels [56]. Time spent in MVPA was calculated as the sum of MPA and VPA. Children were classified as meeting the physical activity guidelines (sufficiently active) if their mean amount of time spent in MVPA/day was ≥60 minutes in accordance to the WHO, 2010 physical activity recommendations [4]. Each child had their height (to the nearest 0.1 cm) and body weight (to the nearest 0.1Kg) measured without shoes and with minimal clothing, using a portable stadiometer (Seca 213 portable stadiometer, Hamburg, Germany) and a digital weighing scale (Seca 869 portable electronic digital weighing scale, Hamburg, Germany) respectively following a standardised procedure. Weight status was calculated as BMI (kilograms per meter squared) and children were categorised as thin/normal weight and overweight/obese using the WHO, 2007 age and gender specific BMI percentiles [57]. A validated questionnaire assessing children and parents’ socio-demographics and neighbourhood built environment [58] was completed by parents/guardians. In this paper, questions that captured children and parents’ socio-demographic factors were analysed. Parents reported their children’s date of birth (from which the child’s actual age at the time of the study was generated) and sex. The questionnaire also captured information about parents’ age, sex, marital status, level of education; number of cars at home and the number of children and youth aged 6 to 17years in their homes. Using the Daniel (1999) formula [59], and an expected prevalence of 21.4% obtained from a previous study by Millstein and colleagues [60] a sample size of 254 was generated. However, because the children were to be sampled in clusters by divisions and schools, the above sample size was multiplied by a design effect of 2 [61] which produced a required sample size of 500 children. To further allow for children who may fail to provide valid and/or incomplete data the enrolment target was set to 600 children. A sample of 600 children received a study package that contained an introduction letter, parent informed consent form, child assent form and a parent/guardian questionnaire to take home to their parents/guardians. Of the 600 children who were invited to participate, 400 (66.7%) had parents/guardians who completed the questionnaire and 328 (54.6%) parental/guardian consented for their children to participate in accelerometry and anthropometric assessment. Of the 328 children who obtained parental consent to wear devices, 256 had valid accelerometry data and were therefore retained for analysis. The response rate was 42.7%. We further assessed demographic characteristics of children who had valid accelerometry results (n = 256) and compared them to those who had complete questionnaire data (n = 400) and found no differences. Continuous data such as accelerometer counts were summarised as means and standard deviations while categorical data such as sex were presented as frequencies and percentages. To test for statistical differences between physical activity intensity levels and children’s socio-demographic factors, Student’s t-tests with unequal variance for factors with two levels and analysis of variance (ANOVA) for factors with more than two levels were used. The two tests were run after testing for assumptions such as equality of variance using the variance ratio test and the Bartlett’s test for the t-test and ANOVA respectively. A multi-level mixed effect logistic regression model adjusted for clustering at division and school level was used to examine associations between compliance with physical activity guidelines and each of the socio-demographic variables. We used a backward model fitting technique and set the inclusion into the multivariable model at a p<0.2 and also included other factors highlighted in literature such as age and sex. Statistical significance was set at p<0.05 and all data were analysed using STATA statistical software Version 14.2.

AI Digest

Study Justification:

This study aimed to address the lack of accurate prevalence estimates of physical activity levels among school-going children in Kampala city, Uganda. By using accelerometer-measured physical activity data, the study aimed to provide valuable insights into the prevalence and socio-demographic correlates of physical activity levels in this population. This information is important for informing public health policies and interventions aimed at promoting physical activity among children.

Study Highlights:

– The study included a representative sample of 256 school-going children aged 10-12 years in Kampala city, Uganda.
– Sedentary time among the children was found to be 9.8±2.1 hours/day, while moderate-to-vigorous physical activity (MVPA) was 56±25.7 minutes/day.
– Only 36.3% of the children met the recommended physical activity guidelines of at least 60 minutes of MVPA per day.
– Boys, thin/normal weight children, and those attending public schools had higher levels of MVPA.
– Socio-demographic factors associated with meeting physical activity guidelines included younger age, thin/normal weight status, lower maternal level of education, and no family car.

Recommendations:

Based on the findings of the study, the following recommendations can be made:

1. Develop and implement interventions targeting school-going children to increase their physical activity levels, particularly among girls, overweight/obese children, and those from lower socioeconomic backgrounds.
2. Promote physical activity within schools by incorporating physical education classes, active recess periods, and extracurricular sports activities.
3. Raise awareness among parents and caregivers about the importance of physical activity for children’s health and well-being, and provide resources and support to encourage active lifestyles.
4. Improve the built environment in Kampala city to provide safe and accessible spaces for children to engage in physical activity, such as parks, playgrounds, and walking/cycling paths.

Key Role Players:

1. Ministry of Health: Responsible for developing and implementing policies and programs related to physical activity promotion among children.
2. Ministry of Education and Sports: Collaborate with the Ministry of Health to integrate physical activity initiatives into the school curriculum and promote active school environments.
3. Local government authorities: Involved in the planning and development of infrastructure and facilities that support physical activity, such as parks and recreational areas.
4. Schools: Play a crucial role in implementing physical activity programs and creating supportive environments for children to be active.
5. Parents and caregivers: Responsible for encouraging and facilitating physical activity among their children, both at home and in the community.

Cost Items for Planning Recommendations:

1. Program development and implementation: Funding is needed to develop and implement interventions targeting school-going children, including curriculum modifications, teacher training, and provision of resources.
2. Infrastructure development: Investment is required to improve the built environment, such as constructing and maintaining parks, playgrounds, and walking/cycling paths.
3. Awareness campaigns: Resources are needed to raise awareness among parents and caregivers about the importance of physical activity and provide educational materials and workshops.
4. Monitoring and evaluation: Funding is necessary to monitor the effectiveness of interventions and evaluate the impact on children’s physical activity levels.
5. Research and data collection: Continued research and data collection are essential to monitor trends in physical activity levels and inform future interventions.
Please note that the above cost items are general categories and the actual costs will vary depending on the specific context and implementation strategies.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, methods, and results. However, it lacks information on the representativeness of the sample and the generalizability of the findings. To improve the evidence, the abstract could include information on the sampling method used to select the participants and the demographic characteristics of the sample. Additionally, it would be helpful to provide information on the limitations of the study and potential implications of the findings.

Abstract

This was a cross-sectional study of a representative sample of school-going children aged 10–12 years old in Kampala city, Uganda. As children aged 10 to 12 years old are transiting from childhood to adolescence, they gain some autonomy in decision making which may be critical to declines in their physical activity [46,47]. Kampala city is the capital and largest city in Uganda covering an area of 182 km2 with population of 1,516,210 residents from diverse ethnic groups and SES [48]. Kampala comprises of five administrative divisions, that is Nakawa, Makindye, Rubaga, Central and Kawempe [49]. Participants were selected using a multistage random sampling method. In stage one, we randomly selected two out of the five divisions (Central and Nakawa); from which 7 primary schools (3 public schools and 4 private schools) were randomly selected. One classroom from any one grade (5th through 7th) was randomly selected and all children from the selected classroom, except those who had physical and health conditions that limited their participation in physical activity were invited to participate in this study. Ethical approval to conduct the study was obtained from the Uganda National Council of Science and Technology (SS4340) and Kenyatta University Ethical Review Board (PKU/619/1703). Permission to access schools was granted by the Directorate of Education and Social Services, Kampala Capital City Authority (KCCA). The respective school head teachers, approved the school’s participation in the study. A parent/guardian provided written informed consent for themselves and their child in addition to written assent from the child. Data were collected during school sessions from May 2017 through August 2018 Children wore a tri-axial ActiGraph GT3X+ (Pensacola, Florida, USA) accelerometer on the right hip using an elastic belt for 7 consecutive days including 2 weekend days. A 24-hour wear protocol was employed to increase compliance [28,50]; and as such children were requested to wear the monitor all the time except when engaging in water-based activities like swimming and bathing. ActiGraph accelerometers are reliable and valid measures of children’s physical activity [21,26]. Using Actilife software (version 6.13.3) (ActiGraph, Pensacola, Florida, USA) the fully charged accelerometers were initialized to collect second to second movement counts at midnight following the first day that the children received the accelerometers; at a samplings rate of 80 HZ. Data were downloaded using ActiLife v6.13.3 (ActiGraph, Pensacola, Florida, USA) in raw format as GT3+ files and AGD files with 1 second epoch. The 24-hour protocol required sleep time to be identified and accounted for before evaluating wake wear time and generating physical activity variables of interest [51,52]. We used the Sadeh algorithm, which is in built into the sleep scoring function in ActiLife software to identify individualised daily sleep on set and offset time for each valid day for each child [53]; this is a valid method for removal of sleep [54]. Daily sleep on set and offset time was used to create time filters in CSV files (Excel Microsoft co-operation, 2016). Time filtered files for the wake period were created and used to identify non wear time and wear time. We defined non-wear as 20 minutes of consecutive 0 counts. Sufficient wear time was determined as 4 days including 1 weekend day with ≥ 10 hours/day. The time spent in different levels of movement intensity were generated basing on the Evenson cut points as: Sedentary time (≤ 25 counts/15 s), LPA (26–573 counts/15 s), MPA (574–1002 counts/15 s) and VPA (≥1003 counts/15 s) [55]. These cut-offs have been recommended as the most accurate for classifying children’s physical activity levels [56]. Time spent in MVPA was calculated as the sum of MPA and VPA. Children were classified as meeting the physical activity guidelines (sufficiently active) if their mean amount of time spent in MVPA/day was ≥60 minutes in accordance to the WHO, 2010 physical activity recommendations [4]. Each child had their height (to the nearest 0.1 cm) and body weight (to the nearest 0.1Kg) measured without shoes and with minimal clothing, using a portable stadiometer (Seca 213 portable stadiometer, Hamburg, Germany) and a digital weighing scale (Seca 869 portable electronic digital weighing scale, Hamburg, Germany) respectively following a standardised procedure. Weight status was calculated as BMI (kilograms per meter squared) and children were categorised as thin/normal weight and overweight/obese using the WHO, 2007 age and gender specific BMI percentiles [57]. A validated questionnaire assessing children and parents’ socio-demographics and neighbourhood built environment [58] was completed by parents/guardians. In this paper, questions that captured children and parents’ socio-demographic factors were analysed. Parents reported their children’s date of birth (from which the child’s actual age at the time of the study was generated) and sex. The questionnaire also captured information about parents’ age, sex, marital status, level of education; number of cars at home and the number of children and youth aged 6 to 17years in their homes. Using the Daniel (1999) formula [59], and an expected prevalence of 21.4% obtained from a previous study by Millstein and colleagues [60] a sample size of 254 was generated. However, because the children were to be sampled in clusters by divisions and schools, the above sample size was multiplied by a design effect of 2 [61] which produced a required sample size of 500 children. To further allow for children who may fail to provide valid and/or incomplete data the enrolment target was set to 600 children. A sample of 600 children received a study package that contained an introduction letter, parent informed consent form, child assent form and a parent/guardian questionnaire to take home to their parents/guardians. Of the 600 children who were invited to participate, 400 (66.7%) had parents/guardians who completed the questionnaire and 328 (54.6%) parental/guardian consented for their children to participate in accelerometry and anthropometric assessment. Of the 328 children who obtained parental consent to wear devices, 256 had valid accelerometry data and were therefore retained for analysis. The response rate was 42.7%. We further assessed demographic characteristics of children who had valid accelerometry results (n = 256) and compared them to those who had complete questionnaire data (n = 400) and found no differences. Continuous data such as accelerometer counts were summarised as means and standard deviations while categorical data such as sex were presented as frequencies and percentages. To test for statistical differences between physical activity intensity levels and children’s socio-demographic factors, Student’s t-tests with unequal variance for factors with two levels and analysis of variance (ANOVA) for factors with more than two levels were used. The two tests were run after testing for assumptions such as equality of variance using the variance ratio test and the Bartlett’s test for the t-test and ANOVA respectively. A multi-level mixed effect logistic regression model adjusted for clustering at division and school level was used to examine associations between compliance with physical activity guidelines and each of the socio-demographic variables. We used a backward model fitting technique and set the inclusion into the multivariable model at a p<0.2 and also included other factors highlighted in literature such as age and sex. Statistical significance was set at p<0.05 and all data were analysed using STATA statistical software Version 14.2.

Materials

Innovations

Based on the provided information, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information and resources related to maternal health. These apps can provide educational content, appointment reminders, and allow women to track their health and receive personalized recommendations.

2. Telemedicine: Implement telemedicine services that allow pregnant women to consult with healthcare providers remotely. This can help overcome geographical barriers and provide access to medical advice and prenatal care, especially in rural areas where healthcare facilities may be limited.

3. Community Health Workers: Train and deploy community health workers who can provide maternal health education, support, and basic healthcare services to pregnant women in underserved areas. These workers can act as a bridge between the community and healthcare facilities, improving access to care.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services. These vouchers can cover the cost of prenatal care, delivery, and postnatal care, ensuring that women can afford the necessary healthcare.

5. Transportation Solutions: Develop transportation solutions, such as mobile clinics or ambulance services, to help pregnant women in remote areas reach healthcare facilities for prenatal care, delivery, and emergency obstetric care.

6. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, counseling, and referrals to pregnant women. This can help address their concerns and provide guidance on accessing appropriate care.

7. Maternal Health Education Programs: Implement comprehensive maternal health education programs in schools, community centers, and workplaces to raise awareness about the importance of prenatal care, nutrition, and healthy behaviors during pregnancy.

8. Public-Private Partnerships: Foster collaborations between government agencies, healthcare providers, and private sector organizations to improve access to maternal health services. These partnerships can leverage resources and expertise to expand healthcare infrastructure and services.

It’s important to note that the specific context and needs of the target population should be considered when implementing these innovations.

AI Innovations Description

The study described in the provided text focuses on the prevalence and socio-demographic factors associated with accelerometer-measured physical activity levels among school-going children in Kampala city, Uganda. The aim of the study was to gather data on children’s physical activity levels and identify factors that may influence their adherence to physical activity guidelines.

The study used a cross-sectional design and recruited a sample of 256 children aged 10-12 years from 7 primary schools in Kampala city. The children wore accelerometers on their hips for 7 consecutive days to measure sedentary time, light-intensity physical activity, moderate-intensity physical activity, and vigorous-intensity physical activity. Socio-demographic factors, such as age, weight status, maternal level of education, and family car ownership, were assessed through a parent/guardian questionnaire.

The results of the study showed that children in Kampala city had an average sedentary time of 9.8 hours per day and engaged in an average of 56 minutes of moderate-to-vigorous physical activity (MVPA) per day. Only 36.3% of the children met the physical activity guidelines of at least 60 minutes of MVPA daily. Boys, thin/normal weight children, and those attending public schools had higher levels of MVPA. Socio-demographic factors associated with meeting physical activity guidelines were younger age, thin/normal weight status, lower maternal level of education, and no family car ownership.

In summary, this study provides valuable information on the physical activity levels of school-going children in Kampala city, Uganda, and identifies socio-demographic factors that may influence their adherence to physical activity guidelines. These findings can be used to inform the development of interventions and innovations aimed at improving access to maternal health. For example, interventions could focus on promoting physical activity among children by targeting specific socio-demographic groups, such as older children, overweight/obese children, and children from families with lower levels of education. Additionally, interventions could aim to improve access to physical activity opportunities, such as through the provision of safe and accessible recreational spaces or the promotion of active transportation options.

AI Innovations Methodology

The study described in the provided text focuses on the prevalence and socio-demographic correlates of accelerometer-measured physical activity levels among school-going children in Kampala city, Uganda. The aim of the study is to understand the physical activity patterns of children in this population and identify factors that may influence their activity levels.

To improve access to maternal health, it is important to consider innovations that can address barriers and challenges faced by pregnant women in accessing healthcare services. Here are a few potential recommendations:

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with information, reminders, and access to healthcare services. These apps can offer prenatal care guidance, appointment reminders, nutrition advice, and emergency contact information.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women to consult with healthcare providers remotely. This can be particularly beneficial for women in remote areas who may have limited access to healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and pregnant women, especially in underserved areas.

4. Transportation Support: Develop transportation programs or partnerships to provide pregnant women with affordable and reliable transportation to healthcare facilities. This can help overcome transportation barriers that often prevent women from accessing maternal health services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific group of pregnant women who would benefit from the innovations. Consider factors such as geographic location, socio-economic status, and availability of healthcare facilities.

2. Collect baseline data: Gather information on the current access to maternal health services in the target population. This can include data on the number of women receiving prenatal care, the distance to healthcare facilities, and any existing barriers to access.

3. Develop a simulation model: Create a simulation model that incorporates the recommended innovations and their potential impact on improving access to maternal health. This model should consider factors such as the number of women reached, the increase in prenatal care utilization, and the reduction in barriers to access.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the innovations. Vary the parameters such as the coverage of the innovations, the population size, and the effectiveness of the interventions.

5. Analyze results: Analyze the simulation results to determine the potential impact of the innovations on improving access to maternal health. Assess the changes in key indicators such as the number of women receiving prenatal care, the reduction in barriers, and the overall improvement in access.

6. Refine and validate the model: Refine the simulation model based on the analysis and feedback from experts in the field. Validate the model by comparing the simulated results with real-world data, if available.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of innovations on improving access to maternal health. This information can guide decision-making and resource allocation to effectively address the challenges faced by pregnant women in accessing healthcare services.

Author

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.1371/journal.pone.0235211

Research Areas:

Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Technology and Innovations

Study Countries:

Uganda

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female