Development of mobile phone-based dietary data collection applications in pregnant women and infants for the M-SAKHI trial.

Shilpa Bhaise, Archana Patel, Varsha Dhurde, Michelle Almeida, Tran Do, Sumithra Muthayya, Michael Dibley

Abstract readRandomized Controlled Trial

In one paragraph

Trial report in Journal of nutritional science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Modular Digital FFQ for Tibetan Highlanders in Agricultural and Pastoral Communities: Development and Rigorous Multi-Method Validation.Nutrients · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shilpa BhaiseLata Medical Research Foundation, Nagpur, Maharashtra, India.

Archana PatelLata Medical Research Foundation, Nagpur, Maharashtra, India.

Varsha DhurdeLata Medical Research Foundation, Nagpur, Maharashtra, India.

Michelle AlmeidaSydney School of Public Health, The University of Sydney, Sydney, New South Wales, Australia.

Tran DoNational Institute of Nutrition, Hanoi, Vietnam.

Sumithra MuthayyaThe Sax Institute, Ultimo, New South Wales, Australia.

Michael DibleySydney School of Public Health, The University of Sydney, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In nutritional epidemiological studies, it is imperative to collect high-quality data to ensure accurate dietary assessment. However, dietary data collection using traditional paper forms has several limitations that may compromise data quality. The aim of this study was to propose novel methods to design and develop software applications (Apps) for dietary data collection to assess the nutritional status of pregnant women and infants. This study is part of the M-SAKHI (Mobile-Solutions for Aiding Knowledge for Health Improvement) cluster randomised controlled trial (cRCT) implemented in central India. Three tablet-based software Apps were developed in this study: the ACEC (Automated Coding and Energy Calculation) App to establish a generic cooked food recipe database, the FFQ (Food Frequency Questionnaire), and the IDR (24 h Infant Dietary Recall) Apps to collect dietary data from pregnant women and their infants from rural area of Bhandara and Nagpur districts. Regional food lists, recipes, and portion resource kits were developed to support the data collection using the Apps. In conclusion, the Apps were user-friendly, required minimal prior training, had built-in validation checks for erroneous data entry and provided automated calculations. The Apps were successfully deployed in low-resource rural settings to accurately collect high-quality regional cooked food data and individual-level dietary data of pregnant women and their infants.

Indexed as

Cell PhoneMobile ApplicationsData CollectionDietFemaleHumansInfantPregnancyPregnant People24-h dietary recallDietary assessmentFood Frequency QuestionnairesMobile-based dietary application

Identifiers

PMID38155806

DOI10.1017/jns.2023.95

PMCPMC10753473

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.