ArticleAppetite2021
Association of number of bites and eating speed with energy intake: Wearable technology results under free-living conditions.
Article in Appetite, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.Nature communications · 2025Review
- Wearable sensing in eating episode monitoring: an updated systematic review protocol.BMJ open · 2025Article
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- Evidence-Based Eating Patterns and Behavior Changes to Limit Excessive Gestational Weight Gain: A Scoping Review.International journal of environmental research and public health · 2023Article
- Experience: Barriers and Opportunities of Wearables for Eating Research.Extended abstracts on Human factors in computing systems. CHI Conference · 2023Article
- Article
- Body mass index and variability in meal duration and association with rate of eating.Frontiers in nutrition · 2022Article
- The energy density of meals and snacks consumed by young Australian adults (18-30 years old) are influenced by preparation location but not screen use nor social interactions: findings from the MYMeals wearable camera study.Journal of nutritional science · 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
6 authors at 1 institution in 2 countries.
Funding
Abstract
Personalized weight management strategies are gaining interest. However, knowledge is limited regarding eating habits and association with energy intake, and current technologies limit assessment in free-living situations. We assessed associations between eating behavior and time of day with energy intake using a wearable camera under free-living conditions and explored if obesity modifies the associations. Sixteen participants (50% with obesity) recorded free-living eating behaviors using a wearable fish-eye camera for 14 days. Videos were viewed by trained annotators who confirmed number of bites, eating speed, and time of day for each eating episode. Energy intake was determined by a trained dietitian performing 24-h diet recalls. Greater number of bites, reduced eating speed, and increased BMI significantly predicted higher energy intake among all participants (P < 0.05, each). There were no significant interactions between obesity and number of bites, eating speed, or time of day (p > 0.05). Greater number of bites and reduced eating speed were significantly associated with higher energy intake in participants without obesity. Results show that under free-living conditions, more bites and slower eating speed predicted higher energy intake when examining consumption of foods with beverages. Obesity did not modify these associations. Findings highlight how eating behaviors can impact energy balance and can inform weight management interventions using wearable technology.
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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.