Background Dietary data gaps limit the effectiveness of food policies and programmes in low-and middle-income countries (LMICs). Automated mobile phone-based tools could fill data gaps at reduced time and cost compared with face-to-face methods, especially for high-frequency dietary quality monitoring in resource-constrained environments.

Objective This study assessed the validity of automated participant-recorded Interactive Voice Response (IVR) 24-hour dietary recalls to assess dietary quality amongst marginalised rural women in a sub-Sahara African context, against same-day gold standard observed weighed food records (WFR).

Methods Automated IVR with push-button response on basic mobile phones collected semi-quantitative list-based 24-hour dietary recalls from 156 randomly selected women in rural Northern Uganda during the wet season. Inter-method agreement was assessed by comparing mean women’s dietary diversity scores (WDDS), the percentage achieving minimum DDS for women (MDD-W), and consumption of unhealthy foods and beverages.

Results Most women (74.4%) completed the IVR. Compared with the WFR, agreement for the IVR was moderate for MDD-W (21.6% vs. 15.5%; kappa=0.52; area under the curve=0.80), mean WDDS (3.3 vs. 3.5; weighted kappa=0.41), and unhealthy food (34.5% vs 23.3%; kappa=0.44) and beverage consumption (32.8% vs 31.9%; kappa=0.43).

Conclusion This is the first study to validate the use of IVR via basic mobile phones to collect dietary data to estimate population-level MDD-W, WDDS and percentage consuming unhealthy foods and beverages amongst marginalised rural women in sub-Saharan Africa. With provision of short participant training, results indicate this innovative automated method can be used in place of enumerator-administered methods for monitoring key dietary quality indicators widely used in LMICs with low-literate, rural women in Uganda.

Highlights

This is the first study to validate the use of Interactive Voice Response (IVR) to collect dietary data from digitally marginalised rural women in a sub-Saharan African context, for estimating key international dietary quality indicators widely used in LMICs - MDD-W, WDDS, and the percentage consuming unhealthy foods and beverages

This innovative method can be used in place of conventional enumerator-administered methods, after contextualisation and training participants on its use

Such methods can help fill critical data gaps on dietary quality with low-literate women in resource-scarce settings

Graphical abstract

The authors have declared no competing interest.

This study was financially supported by an Innovative Methods and Metrics for Agriculture and Nutrition Actions (IMMANA) grant, funded by UK AID (IMMANA 3.01). LO is a PhD candidate supported by Research England through the Food and Nutrition Security Initiative (50.18-E3) at the University of Greenwich. The funders had no influence on the research findings or reporting of this study.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was obtained from the University of Greenwich Faculty of Engineering and Science Ethics Committee (21.3.8.i.e), Uganda National Council for Science and Technology (A24ES), Clarke International University in Uganda (CIUREC/0066), and London School of Hygiene and Tropical Medicine (26645) IRB. Approval for digital data collection platforms (engageSPARK and SurveyCTO) was granted in line with UK data protection laws. Written prior informed consent or thumbprint was obtained from all participants. Modest compensation in kind was given to all participants for their time.

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Funding This study was financially supported by an Innovative Methods and Metrics for Agriculture and Nutrition Actions (IMMANA) grant, funded by UK AID (IMMANA 3.01). LO is a PhD candidate supported by Research England through the Food and Nutrition Security Initiative (50.18-E3) at the University of Greenwich. The funders had no influence on the research findings or reporting of this study.

Disclosure The authors have no conflicts of interest to declare.

Data is available at Harvard Dataverse: “Replication Data for: validation of Interactive Voice Response (IVR) for collecting dietary data among low-literate women in Northern Uganda 2022”. R script for all analysis is available on github: https://github.com/l-c-omeara/ivr-validation.git.