Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women’s Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda

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Study Justification:
The study aimed to validate the use of automated wearable cameras (AWCs) with an image-assisted recall (IAR) method and the traditional 24-hour recall (24HR) method for assessing women’s time allocation in a nutritionally vulnerable population in rural Uganda. Accurate data on time use are crucial for understanding the relationship between maternal time-use patterns and nutritional outcomes. The study sought to determine if AWC-IAR could reduce recall bias and provide more accurate data compared to the 24HR method.
Highlights:
– The study compared women’s time allocations estimated via AWC-IAR and 24HR methods with direct observation as the criterion method.
– Systematic bias varied for different activities, ranging from 1 minute to 226 minutes for 24HR and 1 minute to 109 minutes for AWC-IAR.
– The limits of agreement (LOA) were within 2 hours for employment, own production, and self-care for both 24HR and AWC-IAR, but exceeded 11 hours for caregiving and socializing.
– The LOA for concurrent activities were within four activities for both 24HR and AWC-IAR.
– Cronbach’s alpha for time allocation ranged from 0.1728 to 0.8056 for 24HR and 0.2270 to 0.7938 for AWC-IAR.
– The study concluded that the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores, but poor for caregiving and socializing.
– The results suggest the need to revisit previous research on the associations between women’s time allocations and nutrition outcomes.
Recommendations:
Based on the study findings, the following recommendations can be made:
1. Researchers and policymakers should consider using AWC-IAR or 24HR methods for assessing women’s time allocation in nutrition-related studies, particularly for employment, own production, and domestic chores.
2. Further research is needed to explore alternative methods or improvements to accurately capture time allocations for caregiving and socializing activities.
3. Future studies should investigate the associations between women’s time allocations and nutrition outcomes using the validated methods.
Key Role Players:
To address the recommendations, the following key role players may be needed:
1. Researchers: To conduct further research and validate alternative methods for assessing time allocations.
2. Policymakers: To incorporate the validated methods into nutrition-related policies and programs.
3. Community-based facilitators: To assist in data collection and community sensitization.
4. Enumerators: To administer questionnaires and collect data.
5. Study participants: Women in the rural Ugandan population who can provide accurate information on their time allocations.
Cost Items for Planning Recommendations:
While the actual cost may vary, the following cost items should be considered in planning the recommendations:
1. Research funding: To support the research activities, including data collection, analysis, and publication.
2. Personnel costs: Including salaries or stipends for researchers, community-based facilitators, and enumerators.
3. Equipment and technology: Such as wearable cameras, memory cards, tablets, and software for data collection and analysis.
4. Training and capacity building: To ensure researchers and enumerators are proficient in using the methods and tools.
5. Community sensitization and engagement: To inform and involve the community in the research process.
6. Travel and logistics: Including transportation and accommodation for researchers and enumerators during data collection.
Please note that the above cost items are general considerations and may vary depending on the specific context and requirements of the study.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study used a sample size of 211 women and compared the automated wearable camera-based image-assisted recall (AWC-IAR) method and the 24-hour recall (24HR) method with direct observation as the criterion method. The Bland-Altman limits of agreement (LOA) method and Cronbach’s coefficient alpha were used to assess the validity and reliability of the methods. The study found that the AWC-IAR and 24HR methods were accurate and reliable for employment, own production, and domestic chores, but poor for caregiving and socializing. The study suggests the need to revisit previously published research on the associations between women’s time allocations and nutrition outcomes. To improve the evidence, future studies could consider increasing the sample size to enhance generalizability and conducting a longitudinal study to assess the stability of the methods over time.

Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women’s time use in rural Eastern Ugandan. Women’s (n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland–Altman limits of agreement (LOA) method of analysis and Cronbach’s coefficient alpha (time allocation) or Cohen’s κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (−1.1 to 3.7) and AWC-IAR (−3.2 to 3.2). Cronbach’s alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women’s time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women’s time allocations and nutrition outcomes.

This study was nested within a cross-sectional study of women with a child aged between 12 and 23 months inclusive (n = 211), to examine the impact of a labor-saving technology on women’s time for childcare, food preparation, and dietary practices. The study was conducted between January and February 2018 in Bugiri and Kamuli Districts, Eastern Region, Uganda. In our study, women’s time allocation was assessed, for the same day, using three concurrent methods: (1) direct continuous observation (15 h), (2) 24HR, and (3) IAR using photos captured via an AWC. An IAR is a method using photographs, either automatically generated from a wearable camera or taken by the participant, as an autobiographical memory cue (recall trigger) to help respondents reconstruct key details from their previous day [100,101,102,103]. Data were collected over five consecutive days, following one of two possible patterns, as presented in Supplementary Table S1. Specifically, for both patterns, on day 1, eligibility was confirmed, a structured questionnaire was administered, and anthropometric data were collected for all participants. For half of the study participants, on day 2, time allocation data were collected using direct observation and recorded on the AWC attached to the respondent. On day 3, a 24HR was administered, followed by an IAR using photos captured on day 2 by the AWC. On day 4, time allocation data were again recorded via AWC only (i.e., no observation). On day 5, an IAR was administered using photos captured on day 4 by the AWC. The other half of the study participants began with the AWC only (i.e., days 2 and 4 were switched) and ended with all three methods (i.e., days 3 and 5 were switched). For all participants, on the 5th day, a final structured questionnaire was also administered. Time allocation data collection was distributed across all days of the week at the population level to account for a day-of-the-week effect, and for each respondent, the enumerator assigned to conduct the direct observation was different from the enumerator assigned to administer the 24HR and IAR. Ethical approval was obtained from the [location masked for blind review] (A24ES), [location masked for blind review] Research Ethics Committee (Project ID: 1420), and [location masked for blind review] Ethics Committee (Project ID: B0501). Following community sensitization, verbal explanation of the study, and demonstration of the AWC, written consent (signature or thumb print) was obtained from all respondents who participated in our study. Twenty-two villages were purposefully selected, for this study, of which eleven had access to labor-saving technology and eleven did not. These villages participated in the Sasakawa Global 2000 Uganda (SG2000 Uganda) country program (the local implementing partner for the parent study). The sample size calculation (n = 264; 22 communities, 12 households per community) was based on requirements of the main study within which this current study was nested. This sample size was deemed sufficient for the current validation study, using the Bland–Altman (BA) method of analyses [106,107,108]. The sampling frame, for each village, was a household listing of all women with a child born between 1 January 2017 and 1 May 2017 (children aged 12 to 23 months at the time of data collection). These lists were generated by the SG2000 community-based facilitators. Twelve mother–child dyads in each village were randomly selected to participate in the study. Substitutions were made, as needed, until 12 mother–child dyads who met the inclusion/exclusion criteria were recruited. Mother–child dyads were excluded if the child was less than 12 months or greater than 23 months of age, was not yet eating solid foods on a regular basis, or was a multiple-birth child; the mother was unable to communicate in Lusoga, Luganda, or English; either the mother or child had a severe disability; the mother was not the biological mother of the child; the mother was a co-wife with a selected mother; or either the mother or child was not available for the duration of the study. The enumerators administered two structured questionnaires to the respondent. The first questionnaire collected information on household socio-demographics and assets, and factors related to women’s empowerment. The second questionnaire collected information on household mobile phone access and ownership, and perceptions of their experiences with each of the three time allocation data collection methods. For the criterion time allocation assessment method (i.e., direct observation), enumerators recorded all activities undertaken by the respondent in 15 min intervals (“timeslots”) from approximately 06:00 to 21:00, using a structured instrument comprising 44 activities. On the day after the observation day, a multiple-pass 24HR was administered to the respondent to collect information on all activities undertaken by the respondent on the previous day. In the first pass, the respondent was asked to list everything she did the previous day; in the second pass, additional details about each activity and any concurrent activities were recorded. The time and duration of each activity were recorded in 15 min increments. In the third pass, the enumerator confirmed with the respondent that each activity was recorded accurately. The 24HR protocol was based on a module developed for the WEAI, which was itself based on the Lesotho Time Budget Study [65,109]. On the observation day, a small, lightweight AWC (iON SnapCam Lite, dimensions 42 × 42 × 13 mm3) was attached to a t-shirt worn by the respondent at approximately 06:00 and removed at approximately 21:00. Participants were instructed to wear the AWC while continuing their usual activities, covering or removing the camera as needed for privacy. The AWC automatically recorded a picture every 30 s, storing all photos (approximately 1800) on a memory card. The following day, an enumerator first reviewed the photos captured by the AWC on a tablet and annotated the activities she thought—based on the photos—were undertaken by the respondent, i.e., the enumerator image interpretation (EII). Based on her interpretation of the photos, the enumerator demarcated the series of activities for review later that day with the respondent. Upon meeting with the respondent, the enumerator first administered the 24HR. The enumerator then administered the IAR by first reviewing the AWC photos with the respondent on the tablet (16GB Samsung Galaxy Tab 3 with a 10” screen, using Simple Gallery software for image display). During this interview, the enumerator used “verbal probing” to elicit from the participant additional relevant information about the activities performed, for example, to elaborate on what she was doing, who she was with, where she was going, and why [110,111]. The enumerator revised her original annotations (i.e., the EII) of activities undertaken by the respondent, as needed, based on the respondent’s feedback. The IAR protocol was adapted from one described by Kelly et al. (2015). The protocol followed ethical guidelines for AWC research to ensure privacy of the participants was maintained [112]. All protocols were pilot tested and refined prior to the start of the study. The number of minutes allocated to each of 44 activities recorded over the fifteen-hour period was calculated for each respondent and for each of the 3 data collection methods, in 15 min intervals (“timeslots”). The discrete activities were categorized into the nine mutually exclusive ICATUS-2016 major divisions (“activity groups”): (1) employment and related activities (“employment”), (2) production of goods for own final use (“own production”), (3) unpaid domestic services for household and family members (“domestic chores”), (4) unpaid caregiving services for household and family members (“caregiving”), (5) unpaid volunteer, trainee, and other unpaid work, (6) learning, (7) socializing and communication, community participation, and religious practice (“socializing”), (8) culture, leisure, mass media, and sports practices (“leisure”), and (9) self-care and maintenance (“self-care”), as presented in Supplementary Table S2 [70]. When individuals were observed performing more than one activity concurrently, the activities were given equal weight such that no activity was deemed “primary” or “secondary”. Of the 44 activities tracked, four were considered to be “simultaneous”, i.e., they could be performed while also performing other activities: care of the index child, care of other children or adults, chatting with friends or family, and watching TV or listening to the radio. When just one activity was performed in a timeslot, the activity performed counted for the entire 15 min. The simultaneous activities were always credited the full 15 min. However, for all other activities, when more than one activity was performed per timeslot, the 15 min were evenly distributed across the activities performed. For example, if in a 15 min timeslot, the participant was snacking (self-care) and then started preparing food (domestic chores) while feeding the index child (caregiving), caregiving—a simultaneous activity—was credited 15 min and self-care and domestic chores were each credited 7.5 min. The proportion of the study population living below USD 1.25/day was calculated using the Uganda 2012 Poverty Probability Index (PPI) with data collected via the respondents’ questionnaires [113]. The primary outcome variables analyzed were the total minutes allocated to each of the nine ICATUS-2016 activity groups and the median number of concurrent activities performed across all 15 min timeslots. Data were analyzed using Stata/SE version 15.1. p-values less than 0.05 were considered significant for all tests. Cases with incomplete data for any of the three methods (observation, 24HR, or IAR) were eliminated from analysis. Key socio-demographic characteristics for participating and missing households were compared using the Mann–Whitney U two-sample statistic for continuous data, and the Fisher exact test for categorical data. Due to inter-participant differences in actual observation start and end times and technical challenges with insufficient light in the early morning and evening, the analyses were limited to the 12 h period from 8 a.m. to 8 p.m. to retain as many cases as possible with complete data. The Wilcoxon signed rank sum test was used to compare the distributions of time allocation obtained via the criterion method (observation) versus the 24HR, IAR, or EII. The median time allocated for only those women partaking in each activity was also calculated and compared using the Wilcoxon signed rank sum test. Women’s time allocations estimated via EII and IAR were also calculated for the non-observation day and compared to the corresponding estimates for the observation day using the Wilcoxon signed rank sum test. The Wilcoxon signed rank sum test was also used to compare the distributions of the median number of concurrent activities obtained via the criterion method (observation) versus the 24HR or IAR. The inter-tool agreement between the criterion method (observation) and 24HR or IAR was assessed using the Bland–Altman limits of agreement (LOA) method for each ICATUS-2016 major division (minutes/d) [106]. Specifically, for each individual, the differences between the methods (the criterion measure of time allocation minus the time allocation estimated using either 24HR or IAR) versus the mean of the two methods were plotted; the bias and the 95% LOA (mean difference ± 2 SD of the differences) were estimated. The numbers of participants for whom the differences between the two methods were greater or less than zero were also calculated. The Bland–Altman LOA approach was used to assess inter-method agreement for estimating the median number of concurrent activities. Time allocations estimated via the 24HR and IAR methods against the reference method were compared using Cronbach’s (reliability) coefficient alpha. Cronbach’s coefficient alpha was interpreted as follows: 0.70 acceptable; >0.80 moderate; 0.90–0.95 high; >0.95 suspect [114]. The inter-method reliability (24HR and IAR methods against the criterion method) for estimating the median number of concurrent activities was compared using the weighted Cohen’s κ coefficient. Cohen’s κ coefficient was interpreted as follows: <0·00 poor agreement; 0·00–0·20 slight agreement; 0·21–0·40 fair agreement; 0·41–0·60 moderate agreement; 0·61–0·80 substantial agreement; 0·81–1·00 almost perfect agreement [115,116].

The study titled “Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women’s Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda” explores the use of innovative methods to collect accurate data on women’s time allocation in rural Eastern Uganda. The study compares the traditional 24-hour recall (24HR) method with the use of automated wearable cameras (AWCs) and image-assisted recall (IAR) to reduce recall bias.

The study found that the AWC-IAR and 24HR methods were accurate and reliable for assessing women’s time allocations for employment, own production, and domestic chores. However, these methods were less accurate for caregiving and socializing activities. The study suggests the need to revisit previous research on the associations between women’s time allocations and nutrition outcomes.

The study was conducted as part of a larger cross-sectional study of women with children aged between 12 and 23 months in Bugiri and Kamuli Districts, Eastern Region, Uganda. The data collection involved three concurrent methods: direct continuous observation, 24HR, and IAR using photos captured by the AWC. The study collected data over five consecutive days, with different activities and methods implemented on each day.

Ethical approval was obtained, and written consent was obtained from all participants. The study included villages with and without access to labor-saving technology, and the sample size was determined based on the requirements of the larger study.

Data analysis was conducted using Stata/SE version 15.1, and statistical tests were used to compare the distributions of time allocation obtained from different methods. The Bland-Altman limits of agreement method was used to assess inter-method agreement, and Cronbach’s coefficient alpha and Cohen’s kappa coefficient were used to assess reliability.

Overall, the study highlights the potential of using innovative methods such as AWCs and IAR to improve the accuracy and reliability of data collection on women’s time allocation. These methods can provide valuable insights for understanding the relationship between time use patterns and nutritional outcomes in vulnerable populations.
AI Innovations Description
The recommendation from the study is to use an automated wearable camera-based image-assisted recall (AWC-IAR) method in combination with the 24-hour recall (24HR) method to assess women’s time allocation in rural Uganda. This method aims to reduce recall bias and provide accurate data for investigating the relationship between maternal time-use patterns and nutritional outcomes.

The study found that the AWC-IAR and 24HR methods were accurate and reliable for assessing women’s time allocations in employment, own production, and domestic chores. However, they were less accurate for caregiving and socializing activities. The study suggests revisiting previous research on the associations between women’s time allocations and nutrition outcomes based on these findings.

The AWC-IAR method involves wearing a small, lightweight camera that automatically takes pictures every 30 seconds. The images captured by the camera serve as memory cues to help respondents reconstruct their activities from the previous day. The 24HR method involves administering a multiple-pass questionnaire to collect information on all activities undertaken by the respondent on the previous day.

The study was conducted in rural Eastern Uganda, specifically in Bugiri and Kamuli Districts, among women with a child aged between 12 and 23 months. Data collection was done over five consecutive days using direct observation, 24HR, and AWC-IAR methods. The time allocations for different activities were recorded in 15-minute intervals.

Ethical approval was obtained for the study, and informed consent was obtained from all participants. The study sample included 211 women from 22 purposefully selected villages, with half of the villages having access to labor-saving technology and half not.

Overall, the recommendation is to use the AWC-IAR and 24HR methods together to accurately assess women’s time allocations and improve understanding of the relationship between time use and nutrition outcomes in maternal health.
AI Innovations Methodology
The study mentioned in the description aims to validate the use of an automated wearable camera-based image-assisted recall (AWC-IAR) method and the 24-hour recall (24HR) method for assessing women’s time allocation in a nutritionally vulnerable population in rural Uganda. The goal is to collect accurate data on maternal time-use patterns and their relationship with nutritional outcomes.

The methodology used in the study involves comparing the time allocations estimated through the AWC-IAR and 24HR methods with direct observation (considered the criterion method). The Bland-Altman limits of agreement (LOA) method of analysis and Cronbach’s coefficient alpha (for time allocation) or Cohen’s kappa (for concurrent activities) are used to evaluate the concurrent criterion validity of the AWC-IAR and 24HR methods.

The study was conducted over five consecutive days, with data collected from women with a child aged between 12 and 23 months. Three concurrent methods were used to assess women’s time allocation: direct continuous observation, 24HR, and IAR using photos captured by the AWC. The AWC automatically recorded a picture every 30 seconds, and the photos were reviewed and annotated by an enumerator. The 24HR and IAR methods were administered on different days, and the data collected were compared with the direct observation data.

The time allocations estimated through the AWC-IAR and 24HR methods were compared with the direct observation data using statistical tests such as the Wilcoxon signed rank sum test and the Bland-Altman LOA method. Cronbach’s coefficient alpha and Cohen’s kappa were used to assess the reliability and agreement between the methods.

The results of the study showed that the AWC-IAR and 24HR methods were accurate and reliable for assessing women’s time allocations for employment, own production, and domestic chores. However, they were less accurate for caregiving and socializing. The study suggests the need to revisit previous research on the associations between women’s time allocations and nutrition outcomes.

In summary, the methodology used in the study involved comparing the AWC-IAR and 24HR methods with direct observation to assess women’s time allocation. Statistical tests and reliability measures were used to evaluate the validity and reliability of the methods. The study provides insights into the use of innovative methods for collecting accurate data on maternal time-use patterns and their impact on nutrition outcomes.

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