Evidence mapPaperPMID 36364728Full record

ArticleNutrients2022

Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.

Willem J van den Brink, Tim J van den Broek, Salvator Palmisano, Suzan Wopereis, Iris M de Hoogh

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
7.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Willem J van den BrinkNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands.ORCID 0000-0002-4493-2382
Tim J van den BroekNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands.
Salvator PalmisanoNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands.ORCID 0000-0001-5120-4239
Suzan WopereisNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands.ORCID 0000-0001-9612-657X
Iris M de HooghNetherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands.ORCID 0000-0002-1952-4774
Netherlands Organisation for Applied Scientific Research · NL

Funding

Dutch Top Sector Agri & Food TKI-AF-15262
6 · The paper itself

Abstract

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.

Indexed as

Diabetes Mellitus, Type 2Wearable Electronic DevicesBiomarkersBlood GlucoseBlood Glucose Self-MonitoringGlucoseHumansBiomarkersBlood GlucoseGlucosecontinuous glucose monitor (CGM)digital biomarkersmeal detectionpersonalized nutritionwearables

Identifiers

PMID36364728
PMCPMC9654068
OpenAlexW4307273017

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.