ArticleJMIR mHealth and uHealth2020
Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06429124 (Time-Restricted Eating in Alzheimer's Disease), which is not on this map. Cited by 12 papers.
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.
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.
Time-Restricted Eating in Alzheimer's Disease : The T.R.E.A.D Trial
Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- A case series evaluation of standardized exercise dosage using a cycle ergometer on body weight and patient-reported outcome measures among overweight female patients with knee osteoarthritis.Journal of physical therapy science · 2026Article
- The promise and challenges of exposomics in child health research.Pediatric research · 2025Review
- Time-restricted eating in Alzheimer's disease: TREAD pilot trial design.Contemporary clinical trials communications · 2025Article
- Sensor-based evaluation of intermittent fasting regimes: a machine learning and statistical approach.International journal of obesity (2005) · 2025Article
- Wearable Technology and Its Potential Role in Cardiovascular Health Monitoring and Disease Management.Health science reports · 2025Article
- Personalized Nutrition in the Era of Digital Health: A New Frontier for Managing Diabetes and Obesity.Food science & nutrition · 2025Review
- Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.European journal of clinical investigation · 2025Review
- Chrononutrition: Potential, Challenges, and Application in Managing Obesity.International journal of molecular sciences · 2025Review
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- Utility and applicability of a novel online dietary balance survey system: comparison with weighed dietary records.Journal of nutritional science · 2025Article
- Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.JMIR public health and surveillance · 2024Article
- An Open-Source Privacy-Preserving Large-Scale Mobile Framework for Cardiovascular Health Monitoring and Intervention Planning With an Urban African American Population of Young Adults: User-Centered Design Approach.JMIR formative research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWearable and mobile sensor technologies can be useful tools in precision nutrition research and practice, but few are reliable for obtaining accurate and precise measurements of diet and nutrition.
objectiveThis study aimed to assess the ability of wearable technology to monitor the nutritional intake of adult participants. This paper describes the development of a reference method to validate the wristband's estimation of daily nutritional intake of 25 free-living study participants and to evaluate the accuracy (kcal/day) and practical utility of the technology.
methodsParticipants were asked to use a nutrition tracking wristband and an accompanying mobile app consistently for two 14-day test periods. A reference method was developed to validate the estimation of daily nutritional intake of participants by the wristband. The research team collaborated with a university dining facility to prepare and serve calibrated study meals and record the energy and macronutrient intake of each participant. A continuous glucose monitoring system was used to measure adherence with dietary reporting protocols, but these findings are not reported. Bland-Altman tests were used to compare the reference and test method outputs (kcal/day).
resultsA total of 304 input cases were collected of daily dietary intake of participants (kcal/day) measured by both reference and test methods. The Bland-Altman analysis had a mean bias of -105 kcal/day (SD 660), with 95% limits of agreement between -1400 and 1189. The regression equation of the plot was Y=-0.3401X+1963, which was significant (P<.001), indicating a tendency for the wristband to overestimate for lower calorie intake and underestimate for higher intake. Researchers observed transient signal loss from the sensor technology of the wristband to be a major source of error in computing dietary intake among participants.
conclusionsThis study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake, highlighting the need for reliable, effective measurement tools to facilitate accurate, precision-based technologies for personal dietary guidance and intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.