University of California, San Diego (UCSD), and Wadonda Consult. The project spanned the period of November 2016 to June 2017 and yielded a unique dataset which includes a measure of physical activity of 415 adults (>15 years old) sampled from 215 rural households across 20 enumeration areas in the two districts (Pratt et al., 2020; Friedman et al., 2023).

The physical activity tracking data collection was spread over the period of February-April 2017 and was piggybacked onto the weekly visits that were paid to these 215 households for collecting weekly individual-disaggregated data on plot-level household agricultural labor inputs throughout the 2016/17 rainy season. The field staff were trained intensively on the activity tracking component prior to the start of the fieldwork. This training was led by a team of technical specialists on physical activity tracking from the Active Living Research laboratory at UCSD, who also monitored the incoming physical activity data daily and provided feedback to the field staff on the quality of the incoming data.

During fieldwork, each adult household member that was engaged in agriculture wore a researchgrade ActiGraph physical activity tracker for a two-week period during non-sleeping hours of each day. The enumerators visited the sampled households three times during the 17 days when the accelerometers were with the participants, typically days 1 (deployment), 9 (check in) and 17 (pick up). On day 1, the enumerators explained the study objectives to the participants, obtained their consent and demonstrated how to wear the accelerometers (with a belt around their waist). Day 9 was a checkin visit, during which data were downloaded and enumerators checked on battery status and for any unusual patterns in the data (which could signal that the devices were not worn correctly). On day 17 the remaining data were downloaded, and the accelerometers returned for reassignment to subsequent participants.

The participants in the study wore an accelerometer around the waist. Physical activity can be detected using accelerometers worn on various parts of the body, such as waist (hip), ankle, or wrist (Doherty et al., 2017; Menai et al., 2017; Willetts et al., 2018). Waist and wrist are the most frequently used body locations, and both have demonstrated to be valid locations for measuring and classifying physical activity intensity and sedentary time (Romazini et al., 2012; Hildebrand et al., 2014; Chandler et al., 2015). There are a few studies about how the physical activity detection using wrist and waistworn accelerometers correlate or differ. Kamada et al. (2016) and Dieu et al. (2016) report moderateto-high correlations between acceleration values from either wrist or waist. Hildebrand et al. (2014) find that wrist-worn accelerometers produce higher output as compared to waist-worn accelerometers. Shiroma et al. (2016) conclude that the waist-worn and the wrist-worn monitors may be employed for different behaviors during the day. Waist-worn devices may measure the whole-body movements while wrist-worn devices measure most of the whole-body movements in addition to the movements being specific to wrist or upper body. Zhang et al. (2012) used a wrist-worn GENEA with a waist-worn GENEA to compare classification accuracy for physical activity into walking, running, household, or sedentary activities. They report that the results from both devices are comparable. It becomes clear from these studies that while wrist worn accelerometers may offer higher accuracy for some activities, waist-worn devices offer a suitable alternative at low cost and with low profile to collect physical activity data in low-income contexts.

Complementing the activity tracking data, each participant also responded to a 24-hour recall time use module at the second and third visit (Days 9 and 17, respectively), which was modeled after the time use module of the Women’s Empowerment in Agriculture Index (WEIA). The module intended to capture each respondent’s main and secondary activities in 15-minute intervals during the reference day, relying on a list of 26 activities (Appendix A). One of the activities ‘Plan the household’s finances or bills’ was never reported. Only 25 activities are thus used during the analyses. The time use activities broadly describe how Malawian people allocate time in their daily lives, such as ‘Farming’ and ‘Cleaning house’. The time use activities can therefore be considered as broad group activities. **Figure 1** shows the relative subdivision of the 25 activities that is derived from the number of reported minutes in the time use survey.

4