Browsing by Author "Higgins, Janine A."

Now showing 1 - 8 of 8
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    Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol
    (Oxford University Press, 2020) Jobarteh, Modou L.; McCrory, Megan A.; Lo, Benny; Sun, Mingui; Sazonov, Edward; Anderson, Alex K.; Jia, Wenyan; Maitland, Kathryn; Qiu, Jianing; Steiner-Asiedu, Matilda; Higgins, Janine A.; Baranowski, Tom; Olupot-Olupot, Peter; Frost, Gary; Imperial College London; Boston University; University of Pittsburgh; University of Alabama Tuscaloosa; University of Georgia; University of Ghana; University of Colorado Anschutz Medical Campus; Baylor College of Medicine; United States Department of Agriculture (USDA)
    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).
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    Energy intake estimation from counts of chews and swallows
    (Elsevier, 2015) Fontana, Juan M.; Higgins, Janine A.; Schuckers, Stephanie C.; Bellisle, France; Pan, Zhaoxing; Melanson, Edward L.; Neuman, Michael R.; Sazonov, Edward; University of Alabama Tuscaloosa; University of Colorado Anschutz Medical Campus; Clarkson University; INRAE; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite Paris 13; heSam Universite; Conservatoire National Arts & Metiers (CNAM); Children's Hospital Colorado; Michigan Technological University
    Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, timeconsuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal). Energy intake was estimated by four different methods: weighed food records (gold standard), diet diaries, photographic food records, and CCS models. Counts of chews and swallows were measured using wearable sensors and video analysis. Results for the training meals demonstrated that CCS models presented the lowest reporting bias and a lower error as compared to diet diaries. For the validation meal, CCS models showed reporting errors that were not different from the diary or the photographic method. The increase in error for the validation meal may be attributed to differences in the physical properties of foods consumed during training and validation meals. However, this may be potentially compensated for by including correction factors into the models. This study suggests that estimation of energy intake from CCS may offer a promising alternative to overcome limitations of self-report. (C) 2014 Elsevier Ltd. All rights reserved.
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    Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices
    (Frontiers, 2022) Pan, Zhaoxing; Forjan, Dan; Marden, Tyson; Padia, Jonathan; Ghosh, Tonmoy; Hossain, Delwar; Thomas, J. Graham; McCrory, Megan A.; Sazonov, Edward; Higgins, Janine A.; University of Colorado Anschutz Medical Campus; University of Alabama Tuscaloosa; Brown University; Boston University
    Objective: To describe best practices for manual nutritional analyses of data from passive capture wearable devices in free-living conditions. Method: 18 participants (10 female) with a mean age of 45 +/- 10 years and mean BMI of 34.2 +/- 4.6 kg/m(2) consumed usual diet for 3 days in a free-living environment while wearing an automated passive capture device. This wearable device facilitates capture of images without manual input from the user. Data from the first nine participants were used by two trained nutritionists to identify sources contributing to inter-nutritionist variance in nutritional analyses. The nutritionists implemented best practices to mitigate these sources of variance in the next nine participants. The three best practices to reduce variance in analysis of energy intake (EI) estimation were: (1) a priori standardized food selection, (2) standardized nutrient database selection, and (3) increased number of images captured around eating episodes. Results: Inter-rater repeatability for EI, using intraclass correlation coefficient (ICC), improved by 0.39 from pre-best practices to post-best practices (0.14 vs 0.85, 95% CI, respectively), Bland-Altman analysis indicated strongly improved agreement between nutritionists for limits of agreement (LOA) post-best practices. Conclusion: Significant improvement of ICC and LOA for estimation of EI following implementation of best practices demonstrates that these practices improve the reproducibility of dietary analysis from passive capture device images in free-living environments.
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    Meal Microstructure Characterization from Sensor-Based Food Intake Detection
    (Frontiers Media, 2017-07-17) Doulah, Abul; Farooq, Muhammad; Yang, Xin; Parton, Jason; McCrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado System; University of Colorado Anschutz Medical Campus; University of Colorado Denver
    To avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure. This paper aims to estimate the time resolution needed to accurately represent the microstructure of meals such as duration of eating episode, the duration of actual ingestion, and number of eating events. Twelve participants wore the automatic ingestion monitor (AIM) and kept a standard diet diary to report their food intake in free-living conditions for 24 h. As a reference, participants were also asked to mark food intake with a push button sampled every 0.1 s. The duration of eating episodes, duration of ingestion, and number of eating events were computed from the food diary, AIM, and the push button resampled at different time resolutions (0.1-30s). ANOVA and multiple comparison tests showed that the duration of eating episodes estimated from the diary differed significantly from that estimated by the AIM and the push button (p-value <0.001). There were no significant differences in the number of eating events for push button resolutions of 0.1, 1, and 5 s, but there were significant differences in resolutions of 10-30s (p-value <0.05). The results suggest that the desired time resolution of sensor-based food intake detection should be <= 5 s to accurately detect meal microstructure. Furthermore, the AIM provides more accurate measurement of the eating episode duration than the diet diary.
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    Reproducibility of Dietary Intake Measurement From Diet Diaries, Photographic Food Records, and a Novel Sensor Method
    (Frontiers, 2020) Fontana, Juan M.; Pan, Zhaoxing; Sazonov, Edward S.; McCrory, Megan A.; Graham Thomas, J.; McGrane, Kelli S.; Marden, Tyson; Higgins, Janine A.; Universidad Nacional Rio Cuarto; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University of Colorado Anschutz Medical Campus; Colorado School of Public Health; University of Alabama Tuscaloosa; Boston University; Brown University
    Objective:No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake. Method:This was a retrospective analysis of data from a previous study, in which 30 participants (15 female), aged 29 +/- 12 y and having a BMI of 27.9 +/- 5.5, consumed three identical meals on different days. Four different methods were used to estimate total mass and energy intake on each day: (1) weighed food record; (2) photographic food record; (3) diet diary; and (4) novel mathematical model based on counts of chews and swallows (CCS models) obtained via the use of electronic sensors and video monitoring system. The study staff conducted weighed food records for all meals, took pre- and post-meal photographs, and ensured that diet diaries were completed by participants at the end of each meal. All methods were compared against the weighed food record, which was used as the reference method. Results:Reproducibility was significantly different between the diet diary and photographic food record for total energy intake (p= 0.004). The photographic record had greater reproducibility vs. the diet diary for all parameters measured. For total energy intake, the novel sensor method exhibited good reproducibility (repeatability coefficient (RC) of 59.9 (45.9, 70.4), which was better than that for the diet diary [RC = 79.6 (55.5, 103.3)] but not as repeatable as the photographic method [RC = 43.4 (32.1, 53.9)]. Conclusion:Photographic food records offer superior precision to the diet diary and, therefore, would be valuable for longitudinal studies with repeated measures of dietary intake. A novel electronic sensor also shows promise for the collection of longitudinal dietary intake data.
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    Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
    (Nature Portfolio, 2019) Yang, Xin; Doulah, Abul; Farooq, Muhammad; Parton, Jason; McCrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Anschutz Medical Campus; University of Colorado Denver
    Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean +/- standard deviation) absolute percentage errors of 25.2% +/- 18.9% and 30.1% +/- 33.8%, respectively, and mean +/- standard deviation estimation errors of -17.7 +/- 226.9 g and -6.1 +/- 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.
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    A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake
    (IEEE, 2019) Doulah, Abul; Mccrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Denver
    Accurate measurement of energy intake (EI) is important for estimation of energy balance, and, correspondingly, body weight dynamics. Traditional measurements of EI rely on self-report, which may be inaccurate and underestimate EI. The imperfections in traditional methodologies such as 24-hour dietary recall, dietary record, and food frequency questionnaire stipulate development of technology-driven methods that rely on wearable sensors and imaging devices to achieve an objective and accurate assessment of EI. The aim of this research was to systematically review and examine peer-reviewed papers that cover the estimation of EI in humans, with the focus on emerging technology-driven methodologies. Five major electronic databases were searched for articles published from January 2005 to August 2017: Pubmed, Science Direct, IEEE Xplore, ACM library, and Google Scholar. Twenty-six eligible studies were retrieved that met the inclusion criteria. The review identified that while the current methods of estimating EI show promise, accurate estimation of EI in free-living individuals presents many challenges and opportunities. The most accurate result identified for EI (kcal) estimation had an average accuracy of 94%. However, collectively, the results were obtained from a limited number of food items (i.e., 19), small sample sizes (i.e., 45 meal images), and primarily controlled conditions. Therefore, new methods that accurately estimate EI over long time periods in free-living conditions are needed.
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    Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment
    (MDPI, 2019) Farooq, Muhammad; Doulah, Abul; Parton, Jason; McCrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Anschutz Medical Campus
    Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants' food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (+/- standard deviation (STD)) kappa value of 0.74 (+/- 0.02) and for food intake annotation the average kappa (Light's kappa) of 0.82 (+/- 0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (+/- 0.10) and 0.78 (+/- 0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters' annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.