Evaluation of acceptability, functionality, and validity of a passive image-based dietary intake assessment method in adults and children of Ghanaian and Kenyan origin living in London, UK

BACKGROUND: Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs. METHODS: Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records. RESULTS: The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. CONCLUSIONS: Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records

Methodology for objective, passive, image- and sensor-based assessment of dietary intake, meal-timing, and food-related activity in Ghana and Kenya (P13-028-19).

Objectives: Herein we describe a new system we have developed for assessment of dietary intake, meal timing, and food-related activities, adapted for use in low- and middle-income countries. Methods: System components include one or more wearable cameras (the Automatic Ingestion Monitor-2 (AIM), an eyeglasses-mounted wearable chewing sensor and micro-camera; ear-worn camera; the eButton, a camera attached to clothes; and eHat, a camera attached to a visor worn by the mother when feeding infants and toddlers), and custom software for evaluation of dietary intake from food-based images and sensor-detected food intake. General protocol: The primary caregiver of the family uses one or more wearable cameras during all waking hours. The cameras aim directly in front of the participant and capture images every few seconds, thereby providing multiple images of all food-related activities throughout the day. The camera may be temporarily removed for short periods to preserve privacy, such as during bathing and personal care. For analysis, images and sensor signals are processed by the study team in custom software. The images are time-stamped, arranged in chronological order, and linked with sensor-detected eating occasions. The software also incorporates food composition databases of choice such as the West African Foods Database, a Kenyan Foods Database, and the USDA Food Composition Database, allowing for image-based dietary assessment by trained nutritionists. Images can be linked with nutritional analysis and tagged with an activity label (e.g., food shopping, child feeding, cooking, eating). Assessment of food-related activities such as food-shopping, food gathering from gardens, cooking, and feeding of other family members by the primary caregiver can help provide context for dietary intake and additional information to increase accuracy of dietary assessment and analysis of eating behavior. Examples of the latter include assessment of specific ingredients in prepared dishes, the source of these ingredients, cooking method, and how, where, and when food is consumed. Results: N/A. Conclusions: Pilot- and feasibility-testing is underway. The system will be tested for accuracy of dietary intake assessment versus weighed food intake in urban and rural settings around Accra, Ghana and Nairobi, Kenya. Funding Sources: [Funded by the Bill & Melinda Gates Foundation]

Modeling and Forecasting Infections, Fatalities and Recoveries From COVID-19 Pandemic in SSA: a Case of the 10 Hotspot in Sub-Saharan Africa

This paper aims to model COVID 19 infections and fatalities and how the growth paradigm of the continent will shift overtime. The study used epidemiological data of countries and two mathematical models, i.e., Logistic model growth model and modified growth model to predict number of patients (infections), number of deaths and number of recoveries from COVID-19 in the continent. In this study, it is showed that with the current state of the spread of the COVID19, it is projected that the number of infections will reach 141,733 in the next 30 days and 986,059 patients in the next 60 days. Also, 12,972 will die in the next 30 days and 151,190 in the next 60 days and 73,590 will recover in the next 30 days and 490,547 in the next 60 days. These estimates will help countries to strengthen their policies towards COVID-19 such as lockdown, social distancing, wearing of face masks, washing of hands with water and soap and using hand sanitizers

Development and validation of objective, passive dietary assessment Method for estimating food and nutrient intake in households in Low and Middle-Income Countries (LMICs): a study protocol

Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460)