Automated meal detection from continuous glucose monitor data through simulation and explanation

Zheng, Min; Ni, Baohua; Kleinberg, Samantha

doi:10.1093/jamia/ocz159

Cited by 33 publications

(31 citation statements)

References 34 publications

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“…However, meal detection models using CGM have developed from being purely computer-based simulations to now showing promise when fitted to real-world data. The mean delay in detecting the start of a meal has reduced from 45 to 25 min 62 . CGM could therefore be another method for the passive, objective detection of meal timings in future.…”

Section: When We Eatmentioning

confidence: 99%

Future directions for integrative objective assessment of eating using wearable sensing technology

Skinner¹,

Toumpakari²,

Stone³

et al. 2020

Preprint

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Established methods for nutritional assessment suffer from a number of important limitations. Diaries are burdensome to complete, food frequency questionnaires only capture average food intake, and both suffer from difficulties in self estimation of portion size and biases resulting from misreporting. Online and app versions of these methods have been developed, but issues with misreporting and portion size estimation remain. New methods utilising passive data capture are required that address reporting bias, extend timescales for data collection, and transform what is possible for measuring habitual intakes. Digital and sensing technologies are enabling the development of innovative and transformative new methods in this area that will provide a better understanding of eating behaviour and associations with health. In this article we describe how wrist-worn wearables, on-body cameras, and body-mounted biosensors can be used to capture data about when, what and how much people eat and drink. We illustrate how these new techniques can be integrated to provide complete solutions for the passive, objective assessment of a wide range of traditional dietary factors, as well as novel measures of eating architecture, within person variation in intakes, and food/nutrient combinations within meals. We also discuss some of the challenges these new approaches will bring.

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Section: When We Eatmentioning

confidence: 99%

Future directions for integrative objective assessment of eating using wearable sensing technology

Skinner¹,

Toumpakari²,

Stone³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, meal detection models using CGM have developed from being purely computer-based simulations to now showing promise when fitted to real-world data. The mean delay in detecting the start of a meal has reduced from 45 to 25 min ( 66 ). CGM could therefore be another method for the passive, objective detection of meal timings in future, although further research, particularly in populations without diabetes, is required.…”

Section: When We Eatmentioning

confidence: 99%

Future Directions for Integrative Objective Assessment of Eating Using Wearable Sensing Technology

et al. 2020

View full text Add to dashboard Cite

Established methods for nutritional assessment suffer from a number of important limitations. Diaries are burdensome to complete, food frequency questionnaires only capture average food intake, and both suffer from difficulties in self estimation of portion size and biases resulting from misreporting. Online and app versions of these methods have been developed, but issues with misreporting and portion size estimation remain. New methods utilizing passive data capture are required that address reporting bias, extend timescales for data collection, and transform what is possible for measuring habitual intakes. Digital and sensing technologies are enabling the development of innovative and transformative new methods in this area that will provide a better understanding of eating behavior and associations with health. In this article we describe how wrist-worn wearables, on-body cameras, and body-mounted biosensors can be used to capture data about when, what, and how much people eat and drink. We illustrate how these new techniques can be integrated to provide complete solutions for the passive, objective assessment of a wide range of traditional dietary factors, as well as novel measures of eating architecture, within person variation in intakes, and food/nutrient combinations within meals. We also discuss some of the challenges these new approaches will bring.

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“…2) Meal Size: To calculate the appropriate amount of bolus insulin needed to compensate for an expected increase in BG stemming from consumption of food/beverages, insulin pump users need to "announce meals" by estimating the food content [26]- [28]. Given this, insulin pump data includes self-reported estimates of meal sizes.…”

Section: Quantifying Behavioral Factorsmentioning

confidence: 99%

Data-Driven Insights on Behavioral Factors that Affect Diabetes Management

Morton

Dibbo

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The prevalence of personal health data from wearable devices enables new opportunities to understand the impact of behavioral factors on health. Unlike consumer devices that are often auxiliary, such as Fitbit and Garmin, wearable medical devices like continuous glucose monitoring (CGM) devices and insulin pumps are becoming critical in diabetes care to minimize the occurrence of adverse glycemic events. Joint analysis of CGM and insulin pump data can provide unparalleled insights on how to modify treatment regimen to improve diabetes management outcomes. In this paper, we employ a data-driven approach to study the relationship between key behavioral factors and proximal diabetic management indicators. Our dataset includes an average of 161 days of timematched CGM and insulin pump data from 34 subjects with Type 1 Diabetes (T1D). By employing hypothesis testing and association mining, we observe that smaller meals and insulin doses are associated with better glycemic outcomes compared to larger meals and insulin doses. Meanwhile, the occurrence of interrupted sleep is associated with poorer glycemic outcomes. This paper introduces a method for inferring disrupted sleep from wearable diabetes-device data and provides a baseline for future research on sleep quality and diabetes. This work also provides insights for development of decision-support tools for improving short-and long-term outcomes in diabetes care.

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Automated meal detection from continuous glucose monitor data through simulation and explanation

Cited by 33 publications

References 34 publications

Future directions for integrative objective assessment of eating using wearable sensing technology

Future directions for integrative objective assessment of eating using wearable sensing technology

Future Directions for Integrative Objective Assessment of Eating Using Wearable Sensing Technology

Data-Driven Insights on Behavioral Factors that Affect Diabetes Management

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