Participants and procedures
The study was conducted in two districts purposely selected, Moshi municipal and Moshi rural district, of Kilimanjaro region in the Northern part of Tanzania. Two primary schools (1 private and 1 government) from each district were conveniently selected. The study was approved by the National Institute for Medical Research (NIMR), certificate number: IX/2735 and the Kilimanjaro Christian Medical University College Ethics Committee (KCMUCO) certificate number: 2225. School permission was obtained from the regional medical officer, district education officers and school authorities.
Eighty children were randomly selected, i.e. 20 from each school, and their parents were contacted for a detailed explanation of the study aims and procedures. Thereafter children were sent home with the information sheet and consent form for parents to sign. Data were collected from May to July 2018 from primary school children aged 9 – 11 years.
Questionnaire adaptation process
For this study, questionnaires from the International Study on Childhood Obesity, Lifestyle and Environment (ISCOLE) were adapted and modified (13). The ISCOLE physical activity questions were reviewed to check for the appropriateness of the cultural context and applicability for use with primary school children in Tanzania. These questionnaires had been used in several high-income countries, and only in one African country (Kenya). The focus during modification was to retain those questions which were descriptive enough for children to understand, and that related to durations and participation in different activities. Modifications were made to account for the relevant usual activity types and the structuring of questions. These involved rewording of some questions, and removing questions that were not appropriate for the Tanzania school children, (e.g., the question asking “How much time did you spend outside before school, or before bedtime?” was removed because it did not necessarily imply physical activity). Questions asking about attitudes and personal reasons for making someone active and sleep information were also removed as they were not under study aim (e.g., “I can ask my parent or other adult to do physically active things with me”, “I find exercise a pleasure activity”).
The modified questionnaire draft was shared with the region’s school health coordinator for review and advice and then piloted with 15 school children to check for comprehension and relevance of questions used. Children were asked to indicate activities during typical days in their lives, stratified by school days and weekend days.
Physical activity measurement from self-reports
The final questionnaire was designed to collect information on multiple dimensions of physical activities including types, frequency and duration. Therefore, in this questionnaire, some questions included the duration of different activities, whilst others asked whether or not a child participated in these activities. Questions involved were walking to school, being physically active for 60 minutes a day, exercise during school breaks, after school activities and sedentary behaviours such as television viewing and playing with electronic games.
Physical activity measurement from accelerometry
Children were instructed how to wear the triaxial accelerometers (ActiGraph, wGT3X-BT Pensacola, FL) for 7 consecutive days. Instructions were also given to teachers and parents in order to assist their children with accelerometer attachment. Accelerometers were attached with an elastic band on children’s right hip. Children were instructed to remove the accelerometers when bathing or swimming. Accelerometers were set to collect data from 06:00 AM to 09:00 PM (bedtime) except for the initiation day when accelerometers were commenced from 09:00 AM. When returned, data from each accelerometer were uploaded to the computer using Actigraph software.
Accelerometer data reduction and scoring
The raw activity data were reduced into 15-s epochs data for analysis, scored then converted to “. agd” files and imported into “CSV” and Excel sheets using Actigraph software. Evenson’s cut points for children were used to categorize sedentary, light, moderate and vigorous activities (14-16). Total moderate and vigorous activity (Total MVPA) was also estimated.
To avoid bias with the children who did not wear the device for the set time and days, we applied filters to define time blocks of activities from the accelerometers to match with the activities from self-reports (Additional file 1). For each block, we allowed two minutes after breaks assuming children will be reorganizing themselves for starting break or next classroom session.
An example of accelerometer captured patterns of activities in three spatial dimensions X, Y and Z and varied by blocks is indicated in Fig. 1. The graph was taken from one child in one day of the week. The period with no bars means the child was either not active or the device was not worn at all.
Self-report data were entered into Excel and accelerometer data were exported to Excel; both were then imported into STATA for analysis. Descriptive summaries of the study population were done using frequencies, percentages and plots for demographic characteristics, physical activity from self-reports and accelerometer.
The distributions of data were checked using Shapiro Wilk test. For data that were normally distributed, mean and standard deviation were presented; for skewed data, median and interquartile ranges were presented.
Self-reports: Questions with information on time spent on participating in certain activities were included, and total time calculated. Total weekday MVPA was defined as the sum of minutes for walking to school for five days (since this question had categorical responses, we calculated the midpoint for example: a response of 15 – 30 minutes of walking to school was considered as 22.5 minutes) and reported being physically active for at least 60 minutes for each day (for example: if the child reported being active for 3 days, we multiplied by 60 minutes to get 180 active minutes. The average minutes of MVPA was calculated by dividing the total time of MVPA by the number of days of the week recorded.
Total weekday sedentary time was defined as the sum of minutes spent on leisure activities which were watching television, using a computer or playing video games. The average minutes of sedentary time was estimated by adding all sedentary activities dividing by five days of the week.
Accelerometry: Total time spent in moderate and vigorous physical activity (Total MVPA) and total sedentary time were estimated. All children were included in the analysis if they had sufficient and valid accelerometry data with minimum of 3 weekdays and at least 1 weekend day.
To examine validity, we included only school days as we expected the child to be more active in school, as they spend most of their times in school, while for the weekend they might be engaged in unstructured activities which might be difficult to remember. We used scatter plots and Spearman rank test to check for correlation between overall weekly activities (MVPA) from self-reports and accelerometry. Bland Altman plots were used to assess the level of agreement between average weekday self-report MVPA and accelerometry based MVPA.
The mean weekday accelerometry and self-reports MVPA (minutes per week) were calculated. Wald test was used to compare the mean weekday MVPA across sex, age, school location, school type, school location, walking to school, exercise during breaks, after school activities and participation in physical education sessions, taking into account for clustering effect of children within schools. Box plots were constructed to check for variations between self-reported (walking to school, exercise during break/ lunch and after school activities) and accelerometry MVPA for blocks of activities and across days of the week.
For understanding the associations between weekday accelerometry MVPA and different child level variables (sex, age, school type, school location and walking to school) a simple linear regression was done accounting for repeated measures and the clustering effects. A child was regarded as a cluster due to repeated measurements of accelerometry data on different weekdays. Regression coefficients from the linear regression, 95% confidence intervals (95% CI) and intra-class correlations were presented.