constitutes too large a bucket including several, diverse activities. We found that some consecutively performed activities, such as fetching water, cooking, eating, and cleaning are often misclassified with each other. Since the motion patterns belonging to these activities should not be similar, the most logical conclusion is that the reported activities are overlapping in time. A key finding here is that misclassifications do not always behave symmetrically. Eating, for example, is often confused with cooking, but the opposite (cooking being confused with eating) is less common. This can be indicative of a systematic (mis)timing offset in one direction.

We evaluated potential differences by age and gender as well. In terms of predictions, we found that there are no significant differences by age or gender, after correcting for any size dependence intrinsic to the data. This suggests that the model is unbiased with respect to gender or age. When applying the model on the whole dataset, we see we pick up noticeable gendered differences in time allocation, which align well with the observed time use data.

One final interesting discovery is that background information, such as participant’s age and gender and information on the time of day and the day of week, plays a role in improving model predictions. By running multiple models with different subsets of the data as input, that is, pure sensor signal data, pure background data, and the complete data we were able to disentangle the impact of background information on model predictions. The analysis shows evidence of a fixed boost in model performance when background information is used in combination with the physical activity sensor signal data.

We conclude that it is feasible to predict time use data with high accuracy and that obtainable improvements in the quality of both time use and physical activity data collection hold promise of significantly elevating the predictive capabilities of the underlying models. Based on these results, we offer a few recommendations for the design of future data collection campaigns and suggest some priorities for future research to advance the agenda toward scalable adoption of these techniques for the integrated use of sensor data and time use survey data.

First, to reduce the possibility of misclassification it will be important to revisit the activity lists in time use modules such that the activities with broad definitions (‘Social, religious’ activities for instance) are split into multiple subcategories. Not only would this avoid ambiguity in reporting social and religious activities, thereby reducing misreporting of activities, but it would also improve the prediction of other activities. Inclusion of more disaggregated activity lists in recall-based time use modules are relatively costless exercises - since the interviewers aim to elicit respondent narratives and classify the activities in accordance with the narrative, rather than asking whether each respondent performed each potential activity in a list for a given time interval. The development of a more disaggregated activity list, however, may involve an initial investment and subsequent refinements, based on qualitative work and additional piloting. For instance, the current International Classification of Activities for TimeUse Statistics (ICATUS) is updated as of 2016 - comprising 165 activity groups, classified into 56 divisions and 9 major divisions – and still represents a manageable number of categories (UN Women, 2021). By comparison, the activity list used in this study included only 26 activity groups.

Second, a key source of misclassification, respondents’ recall, can be averted only by stepping away from recall-based time use surveys. Mobile phone apps that allow respondents to log activities in real time, particularly those with activity illustrations that are fit for low-literacy settings, offer promise to reduce recall bias (Daum et al., 2019; Kilic et al., 2024). If implemented well, mobile phone-based time use diaries, coupled with higher-frequency physical activity tracking data collection (i.e., at sub-minute data collection frequency, without additional data collection cost), can potentially improve the predictive power of machine learning models.

Future research in this area should document the predictive accuracy gains, if any, associated with using mobile phone-based time use diaries and sub-minute-level physical activity tracking data collection vis-à-vis relying on traditional, recall-based time use diaries and minute-level sensor data collection as in the case of our current paper. Fortunately, a follow-up study implemented in 2022/23 in Malawi (Kilic et al., 2024) - whose design has been informed by the analysis presented here - will 18