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Techniques and Methods Validated Methods

Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error

Abstract

Objectives

Dietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard.

Subjects/Methods

Thirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences.

Results

Sensor-derived EI from regression modeling (R2= 0.331) showed the closest agreement with EI from WFR, followed by diet diary estimates. EI from image analysis differed significantly from that by WFR. Bland–Altman analysis showed wide limits of agreement for all three test methods with WFR, with the sensor method overestimating at lower and underestimating at higher EI. Nutritionist error in portion size estimation and irreconcilable differences in portion size between food and nutrient databases used for WFR and image analyses were the greatest contributors to image analysis and WFR differences (44.4% and 44.8% of WFR EI, respectively).

Conclusions

Estimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

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Fig. 1: Overview of the AIM-2.
Fig. 2: Comparison of methods for assessing daily energy intake (EI).
Fig. 3: Bland–Altman plots showing mean differences and 2 SD limits of agreement.

Data availability

Data may be made available upon request to the corresponding authors pending IRB approval.

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Funding

Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number R01DK100796 and by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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JAH, MAM, and ES conceived and/or designed the work that led to the submission, and agree to be accountable for all aspects of the work ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved; AD, TG, DH, and TM acquired data; AD drafted the manuscript; JAH, MAM, and ES revised the manuscript; all authors played an important role in interpreting the results and approved the final version.

Corresponding authors

Correspondence to Megan A. McCrory or Edward Sazonov.

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Doulah, A., Ghosh, T., Hossain, D. et al. Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error. Int J Obes 46, 2050–2057 (2022). https://doi.org/10.1038/s41366-022-01225-w

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