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The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: Accurate measurement of dietary intake is vital for providing nutrition interventions and understanding the complex role of diet in health. Traditional dietary assessment methods are very resource intensive and burdensome to participants. Technology may help mitigate these limitations and improve dietary data capture., Objective: Our objective was to evaluate the accuracy of a novel mobile application (PIQNIQ) in capturing dietary intake by self-report. Our secondary objective was to assess whether food capture using PIQNIQ was comparable with an interviewer-assisted 24-h recall (24HR)., Methods: This study was a single-center randomized clinical trial enrolling 132 adults aged 18 to 65 y from the general population. Under a provided-food protocol with 3 menus designed to include a variety of foods, participants were randomly assigned to 1 of 3 food capture methods: simultaneous entry using PIQNIQ, photo-assisted recall using PIQNIQ, and 24HR. Primary outcomes were energy and nutrient content (calories, total fat, carbohydrates, protein, added sugars, calcium, dietary fiber, folate, iron, magnesium, potassium, saturated fat, sodium, and vitamins A, C, D, and E) captured by the 3 methods., Results: The majority of nutrients reported were within 30% of consumed intake in all 3 food capture methods (n = 129 completers). Reported intake was highly (>30%) overestimated for added sugars in both PIQNIQ groups and underestimated for calcium in the photo-assisted recall group only (P < 0.001 for all). However, in general, both PIQNIQ methods had similar levels of accuracy and were comparable to the 24HR except in their overestimation (>30%) of added sugars and total fat (P < 0.001 for both)., Conclusions: Our results suggest that intuitive, technology-based methods of dietary data capture are well suited to modern users and, with proper execution, can provide data that are comparable to data obtained with traditional methods. This trial was registered at clinicaltrials.gov as NCT03578458., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background Lifestyle interventions are the first-line treatment for obesity, but participant weight loss is typically low. Objectives We evaluated the efficacy of an alternative lifestyle intervention [Healthy Weight for Living (HWL)] compared with a modified Diabetes Prevention Program (m-DPP). HWL was based on a revised health behavior change model emphasizing hunger management and the development of healthy food preferences. m-DPP was a standard Diabetes Prevention Program implemented with counselor time matched to HWL. Participants were adult dependents of military personnel and had overweight or obesity. Methods Participants were randomly assigned to HWL (n  = 121) or m-DPP (n  = 117), delivered primarily by group videoconference with additional midweek emails. The primary outcome was 12-mo weight change. Secondary outcomes included 6-mo changes in cardiometabolic risk factors and diet. Intention-to-treat (ITT) and complete case (CC) analyses were performed using linear mixed models. Results Retention did not differ between groups (72% and 66% for HWL and m-DPP at 12 mo, respectively; P  = 0.30). Mean ± SE adjusted 12-mo weight loss in the ITT cohort was 7.46 ± 0.85 kg for HWL and 7.32 ± 0.87 kg for m-DPP (P  = 0.91); in the CC cohort, it was 7.83 ± 0.82 kg for HWL and 6.86 ± 0.88 kg for m-DPP (P  = 0.43). Thirty-eight percent of HWL and 30% of m-DPP completers achieved ≥10% weight loss (P  = 0.32). Improvements in systolic blood pressure, LDL cholesterol, triglycerides, fasting glucose, general health, sleep, and mood were similar across groups; improvements in diastolic blood pressure were greater in m-DPP. Adjusted group mean reductions in energy intake were not significantly different between groups, but HWL participants were more adherent to their dietary prescription for lower glycemic index and high fiber and protein (P  = 0.05 to <0.001 for ITT). Conclusions HWL and m-DPP showed equivalent and clinically impactful mean weight loss with cardiometabolic benefits. These results identify an alternative approach for behavioral treatment of overweight and obesity. This trial was registered at clinicaltrials.gov as NCT02348853. [ABSTRACT FROM AUTHOR]