{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:48:31Z","timestamp":1780638511261,"version":"3.54.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["F31HL162555"],"award-info":[{"award-number":["F31HL162555"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["F31HL162555"],"award-info":[{"award-number":["F31HL162555"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["F31HL162555"],"award-info":[{"award-number":["F31HL162555"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["F31HL162555"],"award-info":[{"award-number":["F31HL162555"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["F31HL162555"],"award-info":[{"award-number":["F31HL162555"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-024-01299-y","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T11:54:09Z","timestamp":1732881249000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A machine-learned model for predicting weight loss success using weight change features early in treatment"],"prefix":"10.1038","volume":"7","author":[{"given":"Farzad","family":"Shahabi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel L.","family":"Battalio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela Fidler","family":"Pfammatter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald","family":"Hedeker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0692-9868","authenticated-orcid":false,"given":"Bonnie","family":"Spring","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6681-7564","authenticated-orcid":false,"given":"Nabil","family":"Alshurafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"1299_CR1","unstructured":"Organization, W. H. Obesity and Overweight https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/obesity-and-overweight (2021)."},{"key":"1299_CR2","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1001\/jama.2018.13022","volume":"320","author":"SJ Curry","year":"2018","unstructured":"Curry, S. J. et al. Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: US Preventive Services Task Force recommendation statement. JAMA 320, 1163\u20131171 (2018).","journal-title":"JAMA"},{"key":"1299_CR3","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1037\/hea0000782","volume":"38","author":"B Spring","year":"2019","unstructured":"Spring, B. Sound health care economics: provide the treatment needed (not less, not more). Health Psychol. 38, 701\u2013704 (2019).","journal-title":"Health Psychol."},{"key":"1299_CR4","doi-asserted-by":"publisher","first-page":"2617","DOI":"10.1001\/jama.2012.6866","volume":"307","author":"JM Jakicic","year":"2012","unstructured":"Jakicic, J. M. et al. Effect of a stepped-care intervention approach on weight loss in adults: a randomized clinical trial. JAMA 307, 2617\u20132626 (2012).","journal-title":"JAMA"},{"key":"1299_CR5","unstructured":"Lundberg, S. & Lee, S. A Unified Approach to Interpreting Model Predictions (Curran Associates, Inc, 2017)."},{"key":"1299_CR6","doi-asserted-by":"publisher","first-page":"1090146","DOI":"10.3389\/fpubh.2023.1090146","volume":"11","author":"K Fujihara","year":"2023","unstructured":"Fujihara, K. et al. Machine learning approach to predict body weight in adults. Front. Public Health 11, 1090146 (2023).","journal-title":"Front. Public Health"},{"key":"1299_CR7","first-page":"441","volume":"12140","author":"O Babajide","year":"2020","unstructured":"Babajide, O. et al. A machine learning approach to short-term body weight prediction in a dietary intervention program. Comput. Sci. ICCS 12140, 441\u2013455 (2020).","journal-title":"Comput. Sci. ICCS"},{"key":"1299_CR8","doi-asserted-by":"publisher","first-page":"660206","DOI":"10.3389\/fdata.2021.660206","volume":"4","author":"I Kolyshkina","year":"2021","unstructured":"Kolyshkina, I. & Simoff, S. Interpretability of machine learning solutions in public healthcare: the CRISP-ML approach. Front. Big Data 4, 660206 (2021).","journal-title":"Front. Big Data"},{"key":"1299_CR9","doi-asserted-by":"publisher","first-page":"1219586","DOI":"10.3389\/fcvm.2023.1219586","volume":"10","author":"PA Moreno-S\u00e1nchez","year":"2023","unstructured":"Moreno-S\u00e1nchez, P. A. Improvement of a prediction model for heart failure survival through explainable artificial intelligence. Front. Cardiovasc. Med. 10, 1219586 (2023).","journal-title":"Front. Cardiovasc. Med."},{"key":"1299_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-0874-0","volume":"19","author":"R Elshawi","year":"2019","unstructured":"Elshawi, R., Al-Mallah, M. H. & Sakr, S. On the interpretability of machine learning-based model for predicting hypertension. BMC Med. Inf. Decis. Mak. 19, 146 (2019).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"1299_CR11","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s12529-010-9092-y","volume":"17","author":"LM Nackers","year":"2010","unstructured":"Nackers, L. M., Ross, K. M. & Perri, M. G. The association between rate of initial weight loss and long-term success in obesity treatment: does slow and steady win the race? Int. J. Behav. Med. 17, 161\u2013167 (2010).","journal-title":"Int. J. Behav. Med."},{"key":"1299_CR12","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3389\/fpubh.2020.00357","volume":"8","author":"C Iwendi","year":"2020","unstructured":"Iwendi, C. et al. COVID-19 patient health prediction using boosted random forest algorithm. Front. Public Health 8, 357 (2020).","journal-title":"Front. Public Health"},{"key":"1299_CR13","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.procs.2019.12.017","volume":"162","author":"L Zhu","year":"2019","unstructured":"Zhu, L., Qiu, D., Ergu, D., Ying, C. & Liu, K. A study on predicting loan default based on the random forest algorithm. Procedia comput. Sci. 162, 503\u2013513 (2019).","journal-title":"Procedia comput. Sci."},{"key":"1299_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-020-8252-5","volume":"20","author":"M Elliott","year":"2020","unstructured":"Elliott, M., Gillison, F. & Barnett, J. Exploring the influences on men\u2019s engagement with weight loss services: a qualitative study. BMC Public Health 20, 249 (2020).","journal-title":"BMC Public Health"},{"key":"1299_CR15","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1002\/oby.20506","volume":"22","author":"LP Svetkey","year":"2014","unstructured":"Svetkey, L. P. et al. Greater weight loss with increasing age in the weight loss maintenance trial. Obesity 22, 39\u201344 (2014).","journal-title":"Obesity"},{"key":"1299_CR16","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1056\/NEJM199106273242602","volume":"324","author":"L Lissner","year":"1991","unstructured":"Lissner, L. et al. Variability of body weight and health outcomes in the Framingham population. N. Engl. J. Med. 324, 1839\u20131844 (1991).","journal-title":"N. Engl. J. Med."},{"key":"1299_CR17","doi-asserted-by":"publisher","first-page":"3719","DOI":"10.1017\/S003329172200040X","volume":"53","author":"MJ Park","year":"2023","unstructured":"Park, M. J. et al. High body weight variability is associated with increased risk of depression: a nationwide cohort study in South Korea. Psychol. Med. 53, 3719\u20133727 (2023).","journal-title":"Psychol. Med."},{"key":"1299_CR18","doi-asserted-by":"publisher","first-page":"845","DOI":"10.3803\/EnM.2021.1098","volume":"36","author":"I Jung","year":"2021","unstructured":"Jung, I. et al. Increased risk of nonalcoholic fatty liver disease in individuals with high weight variability. Endocrinol. Metab. 36, 845\u2013854 (2021).","journal-title":"Endocrinol. Metab."},{"key":"1299_CR19","doi-asserted-by":"publisher","first-page":"e220055","DOI":"10.1001\/jamanetworkopen.2022.0055","volume":"5","author":"AD Kaze","year":"2022","unstructured":"Kaze, A. D. et al. Body weight variability and risk of cardiovascular outcomes and death in the context of weight loss intervention among patients with type 2 diabetes. JAMA Netw. Open 5, e220055 (2022).","journal-title":"JAMA Netw. Open"},{"key":"1299_CR20","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1002\/oby.21925","volume":"25","author":"EH Feig","year":"2017","unstructured":"Feig, E. H. & Lowe, M. R. Variability in weight change early in behavioral weight loss treatment: theoretical and clinical implications. Obesity 25, 1509\u20131515 (2017).","journal-title":"Obesity"},{"key":"1299_CR21","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1159\/000356147","volume":"7","author":"AL Orsama","year":"2014","unstructured":"Orsama, A. L. et al. Weight rhythms: weight increases during weekends and decreases during weekdays. Obes. Facts 7, 36\u201347 (2014).","journal-title":"Obes. Facts"},{"key":"1299_CR22","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","volume":"74","author":"WS Cleveland","year":"1979","unstructured":"Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829\u2013836 (1979).","journal-title":"J. Am. Stat. Assoc."},{"key":"1299_CR23","doi-asserted-by":"publisher","first-page":"e17977","DOI":"10.2196\/17977","volume":"8","author":"J Turicchi","year":"2020","unstructured":"Turicchi, J. et al. Data imputation and body weight variability calculation using linear and nonlinear methods in data collected from digital smart scales: simulation and validation study. JMIR Mhealth Uhealth 8, e17977 (2020).","journal-title":"JMIR Mhealth Uhealth"},{"key":"1299_CR24","first-page":"e49040","volume":"15","author":"B Binsaeed","year":"2023","unstructured":"Binsaeed, B. et al. Barriers and motivators to weight loss in people with obesity. Cureus 15, e49040 (2023).","journal-title":"Cureus"},{"key":"1299_CR25","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1093\/tbm\/ibaa140","volume":"11","author":"JG LaRose","year":"2021","unstructured":"LaRose, J. G., Lanoye, A., Ferrell, D., Lu, J. & Mosavel, M. Translating evidence-based behavioral weight loss into a multi-level, community intervention within a community-based participatory research framework: the Wellness Engagement (WE) Project. Transl. Behav. Med. 11, 1235\u20131243 (2021).","journal-title":"Transl. Behav. Med."},{"key":"1299_CR26","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1037\/a0032586","volume":"33","author":"AA Gorin","year":"2014","unstructured":"Gorin, A. A., Powers, T. A., Koestner, R., Wing, R. R. & Raynor, H. A. Autonomy support, self-regulation, and weight loss. Health Psychol. 33, 332\u2013339 (2014).","journal-title":"Health Psychol."},{"key":"1299_CR27","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.cjca.2021.09.004","volume":"38","author":"J Petch","year":"2022","unstructured":"Petch, J., Di, S. & Nelson, W. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can. J. Cardiol. 38, 204\u2013213 (2022).","journal-title":"Can. J. Cardiol."},{"key":"1299_CR28","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.cct.2019.05.007","volume":"82","author":"AF Pfammatter","year":"2019","unstructured":"Pfammatter, A. F. et al. SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management. Contemp. Clin. Trials 82, 36\u201345 (2019).","journal-title":"Contemp. Clin. Trials"},{"key":"1299_CR29","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.cct.2014.05.007","volume":"38","author":"CA Pellegrini","year":"2014","unstructured":"Pellegrini, C. A., Hoffman, S. A., Collins, L. M. & Spring, B. Optimization of remotely delivered intensive lifestyle treatment for obesity using the Multiphase Optimization Strategy: Opt-IN study protocol. Contemp. Clin. Trials 38, 251\u2013259 (2014).","journal-title":"Contemp. Clin. Trials"},{"key":"1299_CR30","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1002\/oby.21842","volume":"25","author":"B Spring","year":"2017","unstructured":"Spring, B. et al. Effects of an abbreviated obesity intervention supported by mobile technology: the ENGAGED randomized clinical trial. Obesity. 25, 1191\u20131198 (2017).","journal-title":"Obesity."},{"key":"1299_CR31","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1001\/jama.2024.0821","volume":"332","author":"B Spring","year":"2024","unstructured":"Spring, B. et al. An adaptive behavioral intervention for weight loss management: a randomized clinical trial. JAMA 332, 21\u201330 (2024).","journal-title":"JAMA"},{"key":"1299_CR32","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1093\/ajcn\/nqx056","volume":"107","author":"S Chen","year":"2018","unstructured":"Chen, S. et al. Identifying and categorizing spurious weight data in electronic medical records. Am. J. Clin. Nutr. 107, 420\u2013426 (2018).","journal-title":"Am. J. Clin. Nutr."},{"key":"1299_CR33","doi-asserted-by":"publisher","first-page":"e11","DOI":"10.5395\/rde.2019.44.e11","volume":"44","author":"HY Kim","year":"2019","unstructured":"Kim, H. Y. Statistical notes for clinical researchers: simple linear regression 3 - residual analysis. Restor. Dent. Endod. 44, e11 (2019).","journal-title":"Restor. Dent. Endod."},{"key":"1299_CR34","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1038\/s41366-018-0079-0","volume":"43","author":"GE Nam","year":"2019","unstructured":"Nam, G. E. et al. Impact of body mass index and body weight variabilities on mortality: a nationwide cohort study. Int. J. Obes. 43, 412\u2013423 (2019).","journal-title":"Int. J. Obes."},{"key":"1299_CR35","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1056\/NEJMoa1606148","volume":"376","author":"S Bangalore","year":"2017","unstructured":"Bangalore, S. et al. Body-weight fluctuations and outcomes in coronary disease. N. Engl. J. Med. 376, 1332\u20131340 (2017).","journal-title":"N. Engl. J. Med."},{"key":"1299_CR36","doi-asserted-by":"publisher","first-page":"100045","DOI":"10.1016\/j.ijchy.2020.100045","volume":"6","author":"J Turicchi","year":"2020","unstructured":"Turicchi, J. et al. Body weight variability is not associated with changes in risk factors for cardiometabolic disease. Int. J. Cardiol. Hypertens. 6, 100045 (2020).","journal-title":"Int. J. Cardiol. Hypertens."},{"key":"1299_CR37","doi-asserted-by":"publisher","first-page":"e190731","DOI":"10.1001\/jamanetworkopen.2019.0731","volume":"2","author":"J Cologne","year":"2019","unstructured":"Cologne, J. et al. Association of weight fluctuation with mortality in Japanese adults. JAMA Netw. Open 2, e190731 (2019).","journal-title":"JAMA Netw. Open"},{"key":"1299_CR38","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1038\/s41366-020-0534-6","volume":"44","author":"L Benson","year":"2020","unstructured":"Benson, L., Zhang, F., Espel-Huynh, H., Wilkinson, L. & Lowe, M. R. Weight variability during self-monitored weight loss predicts future weight loss outcome. Int. J. Obes. 44, 1360\u20131367 (2020).","journal-title":"Int. J. Obes."},{"key":"1299_CR39","unstructured":"Snoek, J., Larochelle, H. & Adams, R. P. Practical Bayesian Optimization of Machine Learning Algorithms (Curran Associates, Inc, 2012)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01299-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01299-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01299-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T12:10:15Z","timestamp":1732882215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01299-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1299"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01299-y","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"14 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2024","order":3,"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"}}],"article-number":"344"}}