Against this background, this paper reports on a proof-of-concept study that aims to evaluate whether machine learning and accelerometer technology can be leveraged to accurately predict time use patterns from physical activity sensor output collected in household surveys in low-income contexts. We derive our insights from a distinctive survey of agricultural families in rural Malawi. This survey combined twice-repeated 24-hour recall-based time use data collection with concurrent sensor-driven physical activity monitoring. Our paper sits at the intersection of the research streams on time use measurement and physical activity tracking in household surveys, and the main contributions are threefold. First, we build and validate a machine learning model within our sample, by pairing the highfrequency physical activity tracking data with time use survey data for the same individuals. Specifically, we train the model for a subset of the individuals and infer the time use in the rest of the sample and compare the predicted versus observed time use outcomes. If successful and crossculturally validated, this framework offers survey practitioners the possibility of increasing accuracy in time use data while managing respondent burden by combining data from low-cost, waist-worn, inconspicuous accelerometers and time use data collection from a subsample of survey respondents. Machine learning-based imputed estimates of time allocation to specific activities can then be obtained for the part of the sample that is not part of survey data collection on time use.

Second, by combining time use and sensor data, we identify issues in recall-based time use data collection that provide motivation for improving the activity lists used in surveys and assessing the accuracy of 24-hour recall. This can potentially motivate pairing real-time physical activity tracking with real-time self-administered time use diaries, inspired by the successful experience with smartphonebased time use data collection (henceforth referred to as mobile time diaries) elsewhere in Africa (Daum et al., 2019; Kilic et al., 2024).

Third, the paper brings in evidence from a rural, low-income context regarding the feasibility and utility of physical activity tracking data collection for time use measurement – a topic that has not been the subject of previous research – based on a significantly larger sample of human subjects vis-à-vis earlier work in low-income countries. While accelerometers<sup>3</sup> are widely used to document physical activity levels for specific population groups (see Troiano et al., 2008 and Colley et al., 2011, and the studies cited therein), the vast majority of accelerometer data collection campaigns has been implemented in high-income contexts. The uses of accelerometers in low-income contexts, and specifically as part of socioeconomic surveys and development research, have been rare events, except for a handful of small-scale studies (Benefice et al., 2001; Assah et al., 2011; Christensen et al., 2012; Zanello et al., 2017; Picchioni et al., 2020; Srinivasan et al., 2020).

The paper is organized as follows. Section 2 describes the data collection steps and provides descriptive statistics of the data. Section 3 presents the methodology of data preprocessing, and the machine learning models. Section 4 discusses the results, focusing on model outcomes, model and data validation, and insights into activity patterns of various population groups. Section 5 applies the trained model on actual data to obtain the time allocation of Malawian people. Section 6 concludes and draws implications for future work.

# **2 Data**

# **2.1 The datasets**

The data used in this paper were collected in the Ntcheu and Zomba districts of Malawi in 2017 as part of a larger methodological survey experiment on measurement of agricultural labor, managed by the World Bank Living Standards Measurement Study (LSMS). This was in collaboration with the World Bank’s Gender Group, Development Research Group, the Active Living Research (ALR) team at the

> 3 See Chen et al. (2012) for a scientific review of devices and technologies from the point of view of physical activity researchers.

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