{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:20:08Z","timestamp":1762165208929,"version":"build-2065373602"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Obes"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The primary aim was to develop and assess the performance and applicability of different models utilizing sensor data to determine dietary adherence, specifically within the context of intermittent fasting. Our approach utilized time-series data from two completed human trials, which included continuous glucose monitoring, acceleration data, and food diaries, and a synthetic data set. Machine learning models achieved an average F1-score of 0.88 in distinguishing between fasting and non-fasting times, indicating a high level of reliability in classifying fasting states. The Hutchison Heuristic statistical method, while more moderate in performance, proved to be robust across different cohorts, including individuals with and without type 1 diabetes. A dashboard was developed to visualize results efficiently and in a user-friendly manner. The findings highlight the effectiveness of using sensor data, combined with advanced statistical and machine learning approaches, to passively evaluate dietary adherence in an intermittent fasting context.<\/jats:p>","DOI":"10.1038\/s41366-025-01889-0","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:39:03Z","timestamp":1755862743000},"page":"2378-2382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sensor-based evaluation of intermittent fasting regimes: a machine learning and statistical approach"],"prefix":"10.1038","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0245-2046","authenticated-orcid":false,"given":"Nico","family":"Steckhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanja","family":"Manlik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tillmann","family":"Int-Veen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beeke","family":"Peters","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina Laetitia","family":"Pappe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3367-3327","authenticated-orcid":false,"given":"Daniela A.","family":"Koppold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8380-7667","authenticated-orcid":false,"given":"Bert","family":"Arnrich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Michalsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henrik","family":"Dommisch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Schwarz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3583-8546","authenticated-orcid":false,"given":"Olga","family":"Pivovarova-Ramich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"1889_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/00002030-199501000-00001","volume":"9","author":"CL Besch","year":"1995","unstructured":"Besch CL. Compliance in clinical trials. AIDS. 1995;9:1\u201310.","journal-title":"AIDS"},{"key":"1889_CR2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1124\/mi.11.2.8","volume":"11","author":"P Czobor","year":"2011","unstructured":"Czobor P, Skolnick P. The secrets of a successful clinical trial: compliance, compliance, and compliance. Mol Interv. 2011;11:107\u201310.","journal-title":"Mol Interv"},{"key":"1889_CR3","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1016\/j.cmet.2018.04.010","volume":"27","author":"EF Sutton","year":"2018","unstructured":"Sutton EF, Beyl R, Early KS, Cefalu WT, Ravussin E, Peterson CM. Early time-restricted feeding improves insulin sensitivity, blood pressure, and oxidative stress even without weight loss in men with prediabetes. Cell Metab. 2018;27:1212\u201321.e3.","journal-title":"Cell Metab"},{"key":"1889_CR4","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cmet.2019.11.004","volume":"31","author":"MJ Wilkinson","year":"2020","unstructured":"Wilkinson MJ, Manoogian ENC, Zadourian A, Lo H, Fakhouri S, Shoghi A, et al. Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Cell Metab. 2020;31:92\u2013104.e5.","journal-title":"Cell Metab"},{"key":"1889_CR5","doi-asserted-by":"publisher","unstructured":"Sahoo D, Hao W, Ke S, Xiongwei W, Le H, Achananuparp P, et al. FoodAI: food image recognition via deep learning for smart food logging. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. Anchorage AK USA: ACM; 2019. pp. 2260\u20138. https:\/\/doi.org\/10.1145\/3292500.3330734","DOI":"10.1145\/3292500.3330734"},{"key":"1889_CR6","doi-asserted-by":"publisher","first-page":"e25138","DOI":"10.2196\/25138","volume":"9","author":"C Rodriguez-Le\u00f3n","year":"2021","unstructured":"Rodriguez-Le\u00f3n C, Villalonga C, Munoz-Torres M, Ruiz JR, Banos O. Mobile and wearable technology for the monitoring of diabetes-related parameters: systematic review. JMIR MHealth UHealth. 2021;9:e25138 .","journal-title":"JMIR MHealth UHealth"},{"key":"1889_CR7","doi-asserted-by":"publisher","first-page":"e16405","DOI":"10.2196\/16405","volume":"8","author":"SM Dimitratos","year":"2020","unstructured":"Dimitratos SM, German JB, Schaefer SE. Wearable technology to quantify the nutritional intake of adults: validation study. JMIR MHealth UHealth. 2020;8:e16405.","journal-title":"JMIR MHealth UHealth"},{"key":"1889_CR8","doi-asserted-by":"publisher","first-page":"105968","DOI":"10.1016\/j.cmpb.2021.105968","volume":"202","author":"MT Garc\u00eda-Ord\u00e1s","year":"2021","unstructured":"Garc\u00eda-Ord\u00e1s MT, Benavides C, Ben\u00edtez-Andrades JA, Alaiz-Moret\u00f3n H, Garc\u00eda-Rodr\u00edguez I. Diabetes detection using deep learning techniques with oversampling and feature augmentation. Comput Methods Prog Biomed. 2021;202:105968.","journal-title":"Comput Methods Prog Biomed"},{"key":"1889_CR9","doi-asserted-by":"crossref","unstructured":"Tripathi G, Kumar R. Early prediction of diabetes mellitus using machine learning. In: 2020 8th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO). Amity University, Noida, India. 2020. pp. 1009\u201314.","DOI":"10.1109\/ICRITO48877.2020.9197832"},{"key":"1889_CR10","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s40200-020-00520-5","volume":"19","author":"H Naz","year":"2020","unstructured":"Naz H, Ahuja S. Deep learning approach for diabetes prediction using PIMA Indian dataset. J Diab Metab Disord. 2020;19:391\u2013403.","journal-title":"J Diab Metab Disord"},{"key":"1889_CR11","doi-asserted-by":"publisher","first-page":"765543","DOI":"10.3389\/fnut.2021.765543","volume":"8","author":"B Peters","year":"2021","unstructured":"Peters B, Koppold-Liebscher DA, Schuppelius B, Steckhan N, Pfeiffer AFH, Kramer A, et al. Effects of early vs. late time-restricted eating on cardiometabolic health, inflammation, and sleep in overweight and obese women: a study protocol for the ChronoFast trial. Front Nutr. 2021;8:765543.","journal-title":"Front Nutr"},{"key":"1889_CR12","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.3390\/nu16162663","volume":"16","author":"B Peters","year":"2024","unstructured":"Peters B, Pappe CL, Koppold DA, Schipp K, Arnrich B, Michalsen A, et al. Twenty-four hour glucose profiles and glycemic variability during intermittent religious dry fasting and time-restricted eating in subjects without diabetes: a preliminary study. Nutrients. 2024;16:2663.","journal-title":"Nutrients"},{"key":"1889_CR13","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.3390\/nu14183697","volume":"14","author":"I Baum Martinez","year":"2022","unstructured":"Baum Martinez I, Peters B, Schwarz J, Schuppelius B, Steckhan N, Koppold-Liebscher DA, et al. Validation of a smartphone application for the assessment of dietary compliance in an intermittent fasting trial. Nutrients. 2022;14:3697.","journal-title":"Nutrients"},{"key":"1889_CR14","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1509\/jmkr.47.4.627","volume":"47","author":"JW Hutchinson","year":"2010","unstructured":"Hutchinson JW, Alba JW, Eisenstein EM. Heuristics and biases in data-based decision making: effects of experience, training, and graphical data displays. J Mark Res. 2010;47:627\u201342.","journal-title":"J Mark Res"},{"key":"1889_CR15","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1177\/1932296813514502","volume":"8","author":"CD Man","year":"2014","unstructured":"Man CD, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C. The UVA\/PADOVA type 1 diabetes simulator: new features. J Diab Sci Technol. 2014;8:26\u201334.","journal-title":"J Diab Sci Technol"},{"key":"1889_CR16","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/OJEMB.2021.3105816","volume":"2","author":"B Bent","year":"2021","unstructured":"Bent B, Henriquez M, Dunn J. Cgmquantify: Python and R Software packages for comprehensive analysis of interstitial glucose and glycemic variability from continuous glucose monitor data. IEEE Open J Eng Med Biol. 2021;2:263\u20136.","journal-title":"IEEE Open J Eng Med Biol"},{"key":"1889_CR17","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.3390\/s20082200","volume":"20","author":"D Garcia-Gonzalez","year":"2020","unstructured":"Garcia-Gonzalez D, Rivero D, Fernandez-Blanco E, Luaces MR. A public domain dataset for real-life human activity recognition using smartphone sensors. Sensors. 2020;20:2200.","journal-title":"Sensors"},{"key":"1889_CR18","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261\u201372.","journal-title":"Nat Methods"},{"key":"1889_CR19","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"1889_CR20","first-page":"1","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre G, Nogueira F, Aridas CK. Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res. 2017;18:1\u20135.","journal-title":"J Mach Learn Res"},{"key":"1889_CR21","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GEAPA Batista","year":"2004","unstructured":"Batista GEAPA, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl. 2004;6:20\u20139.","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"1889_CR22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"1889_CR23","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.3390\/nu16121849","volume":"16","author":"F Grundler","year":"2024","unstructured":"Grundler F, Mesnage R, Ruppert PMM, Kouretas D, Wilhelmi De Toledo F. Long-term fasting-induced ketosis in 1610 subjects: metabolic regulation and safety. Nutrients. 2024;16:1849.","journal-title":"Nutrients"},{"key":"1889_CR24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.clinbiochem.2017.11.007","volume":"52","author":"LJ Cartier","year":"2018","unstructured":"Cartier LJ, Collins C, Lagac\u00e9 M, Douville P. Comparison of fasting and non-fasting lipid profiles in a large cohort of patients presenting at a community hospital. Clin Biochem. 2018;52:61\u20136.","journal-title":"Clin Biochem"},{"key":"1889_CR25","doi-asserted-by":"publisher","first-page":"e2005143","DOI":"10.1371\/journal.pbio.2005143","volume":"16","author":"H Hall","year":"2018","unstructured":"Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018;16:e2005143.","journal-title":"PLoS Biol"},{"key":"1889_CR26","doi-asserted-by":"publisher","first-page":"e0253125","DOI":"10.1371\/journal.pone.0253125","volume":"16","author":"WPTM Van Doorn","year":"2021","unstructured":"Van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, Van Der Kallen CJH, et al. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: the Maastricht Study. PLoS ONE. 2021;16:e0253125.","journal-title":"PLoS ONE"},{"key":"1889_CR27","doi-asserted-by":"publisher","first-page":"e18","DOI":"10.2196\/mhealth.7030","volume":"5","author":"EE Bain","year":"2017","unstructured":"Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J, et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR MHealth UHealth. 2017;5:e18.","journal-title":"JMIR MHealth UHealth"}],"container-title":["International Journal of Obesity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01889-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01889-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01889-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:16:33Z","timestamp":1762164993000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41366-025-01889-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,22]]},"references-count":27,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1889"],"URL":"https:\/\/doi.org\/10.1038\/s41366-025-01889-0","relation":{},"ISSN":["0307-0565","1476-5497"],"issn-type":[{"type":"print","value":"0307-0565"},{"type":"electronic","value":"1476-5497"}],"subject":[],"published":{"date-parts":[[2025,8,22]]},"assertion":[{"value":"16 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}