Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies
Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the po...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Nutrients |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6643/14/21/4465 |
| _version_ | 1797466967316103168 |
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| author | Willem J. van den Brink Tim J. van den Broek Salvator Palmisano Suzan Wopereis Iris M. de Hoogh |
| author_facet | Willem J. van den Brink Tim J. van den Broek Salvator Palmisano Suzan Wopereis Iris M. de Hoogh |
| author_sort | Willem J. van den Brink |
| collection | DOAJ |
| description | Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2–96%) and 76.8% (74.3–81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/−0.04) mmol/L for the training data and 0.62 (+/−0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations. |
| first_indexed | 2024-03-09T18:47:22Z |
| format | Article |
| id | doaj.art-c649e22e4e094bac80831f5e98c1be51 |
| institution | Directory Open Access Journal |
| issn | 2072-6643 |
| language | English |
| last_indexed | 2024-03-09T18:47:22Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nutrients |
| spelling | doaj.art-c649e22e4e094bac80831f5e98c1be512023-11-24T06:11:55ZengMDPI AGNutrients2072-66432022-10-011421446510.3390/nu14214465Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable TechnologiesWillem J. van den Brink0Tim J. van den Broek1Salvator Palmisano2Suzan Wopereis3Iris M. de Hoogh4Netherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The NetherlandsNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The NetherlandsNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The NetherlandsNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The NetherlandsNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The NetherlandsDigital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2–96%) and 76.8% (74.3–81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/−0.04) mmol/L for the training data and 0.62 (+/−0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.https://www.mdpi.com/2072-6643/14/21/4465digital biomarkerspersonalized nutritioncontinuous glucose monitor (CGM)wearablesmeal detection |
| spellingShingle | Willem J. van den Brink Tim J. van den Broek Salvator Palmisano Suzan Wopereis Iris M. de Hoogh Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies Nutrients digital biomarkers personalized nutrition continuous glucose monitor (CGM) wearables meal detection |
| title | Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies |
| title_full | Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies |
| title_fullStr | Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies |
| title_full_unstemmed | Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies |
| title_short | Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies |
| title_sort | digital biomarkers for personalized nutrition predicting meal moments and interstitial glucose with non invasive wearable technologies |
| topic | digital biomarkers personalized nutrition continuous glucose monitor (CGM) wearables meal detection |
| url | https://www.mdpi.com/2072-6643/14/21/4465 |
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