{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T11:32:38Z","timestamp":1793619158593,"version":"4.5.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"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":["Hypertens Res"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Hypertension is a leading risk factor for cardiovascular diseases, thereby necessitating effective management through regular blood pressure monitoring. Although home monitoring is beneficial for managing hypertension, maintaining consistent measurement frequency remains challenging. This study aimed to develop a model to predict measurement inactivity and to identify clinically relevant risk factors for declining adherence using machine learning, thereby allowing for targeted interventions. Using a large-scale dataset (&gt;199 million measurement records) from 295,758 health app users, we employed a LightGBM (Light Gradient Boosting Machine) model to predict future inactivity according to 2-week measurement patterns and users\u2019 demographics. The model demonstrated high predictive accuracy, with areas under the receiver operating characteristic curve of 0.930 and 0.851 for 28- and 56-day predictions, respectively. SHAP (SHapley Additive exPlanations) analysis revealed elevated dropout risks among both younger and older participants, women, and users who did not report sex information. The maximum systolic blood pressure (SBP) recorded during the 2-week period was also identified as a significant predictor of dropout, showing a U-shaped association wherein both low and high extremes increased the risk. This maximum SBP value, which is rarely used in routine clinical assessments, offered unique insights into dropout behavior, further supported by descriptive statistics. Additionally, a reduction in weekday measurement frequency showed to be a major predictor of future discontinuation. Therefore, our model can identify dropout factors that are difficult to detect by conventional methods, and through accurate prediction, it supports early clinical interventions to improve monitoring adherence and blood pressure control.<\/jats:p>","DOI":"10.1038\/s41440-025-02444-0","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T08:16:30Z","timestamp":1762503390000},"page":"852-862","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting measurement continuity in home blood pressure monitoring using machine learning"],"prefix":"10.1038","volume":"49","author":[{"given":"Asami","family":"Matsumoto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohei","family":"Mineharu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirohiko","family":"Kohjitani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Koshimizu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Okuno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"2444_CR1","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/S0140-6736(21)01330-1","volume":"398","author":"NCD Risk Factor Collaboration (NCD-RisC)","year":"2021","unstructured":"NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398:957\u201380.","journal-title":"Lancet"},{"key":"2444_CR2","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.1093\/eurheartj\/ehab559","volume":"42","author":"K Kario","year":"2021","unstructured":"Kario K, Nomura A, Harada N, Okura A, Nakagawa K, Tanigawa T, et al. Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial. Eur Heart J. 2021;42:4111\u201322.","journal-title":"Eur Heart J"},{"key":"2444_CR3","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1161\/HYPERTENSIONAHA.120.15492","volume":"76","author":"KL Margolis","year":"2020","unstructured":"Margolis KL, Dehmer SP, Sperl-Hillen J, O\u2019Connor PJ, Asche SE, Bergdall AR, et al. Cardiovascular events and costs with home blood pressure telemonitoring and pharmacist management for uncontrolled hypertension. Hypertension. 2020;76:1097\u2013103.","journal-title":"Hypertension"},{"key":"2444_CR4","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1089\/tmj.2019.0066","volume":"26","author":"FACde Farias","year":"2020","unstructured":"Farias FACde, Dagostini CM, Bicca YdeA, Falavigna VF, Falavigna A. Remote patient monitoring: a systematic review. Telemed J E Health. 2020;26:576\u201383.","journal-title":"Telemed J E Health"},{"key":"2444_CR5","first-page":"e13","volume":"71","author":"PK Whelton","year":"2018","unstructured":"Whelton PK, Carey RM, Aronow WS, Casey DE Jr, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC\/AHA\/AAPA\/ABC\/ACPM\/AGS\/APhA\/ASH\/ASPC\/NMA\/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: A Report of the American College of Cardiology\/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71:e13\u2013115.","journal-title":"Hypertension"},{"key":"2444_CR6","doi-asserted-by":"publisher","first-page":"e187","DOI":"10.2196\/jmir.7479","volume":"19","author":"SS Lie","year":"2017","unstructured":"Lie SS, Karlsen B, Oord ER, Graue M, Oftedal B. Dropout from an eHealth intervention for adults with type 2 diabetes: a qualitative study. J Med Internet Res. 2017;19:e187.","journal-title":"J Med Internet Res"},{"key":"2444_CR7","doi-asserted-by":"publisher","first-page":"e101","DOI":"10.2196\/mhealth.4924","volume":"3","author":"P Krebs","year":"2015","unstructured":"Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR mHealth uHealth. 2015;3:e101.","journal-title":"JMIR mHealth uHealth"},{"key":"2444_CR8","doi-asserted-by":"publisher","first-page":"e11","DOI":"10.2196\/jmir.7.1.e11","volume":"7","author":"G Eysenbach","year":"2005","unstructured":"Eysenbach G. The law of attrition. J Med Internet Res. 2005;7:e11.","journal-title":"J Med Internet Res"},{"key":"2444_CR9","doi-asserted-by":"publisher","first-page":"439","DOI":"10.2165\/00044011-200626080-00002","volume":"26","author":"S Ewald","year":"2006","unstructured":"Ewald S, vor dem Esche J, Uen S, Neikes F, Vetter H, Mengden T. Relationship between the frequency of blood pressure self-measurement and blood pressure reduction with antihypertensive therapy : results of the OLMETEL (OLMEsartan TELemonitoring blood pressure) study. Clin Drug Investig. 2006;26:439\u201346.","journal-title":"Clin Drug Investig"},{"key":"2444_CR10","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1038\/s41440-023-01434-4","volume":"47","author":"F Hisaki","year":"2024","unstructured":"Hisaki F, Aga M, Tomitani N, Okawara Y, Harada N, Kario K. Daily self-reported behavioural efficacy records on hypertension digital therapeutics as digital metrics associated with the reduction in morning home blood pressure: post-hoc analysis of HERB-DH1 trial. Hypertens Res. 2024;47:120\u20137.","journal-title":"Hypertens Res"},{"key":"2444_CR11","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s13142-017-0508-y","volume":"7","author":"KJ Serrano","year":"2017","unstructured":"Serrano KJ, Coa KI, Yu M, Wolff-Hughes DL, Atienza AA. Characterizing user engagement with health app data: a data mining approach. Transl Behav Med. 2017;7:277\u201385.","journal-title":"Transl Behav Med"},{"key":"2444_CR12","doi-asserted-by":"publisher","first-page":"e0201166","DOI":"10.1371\/journal.pone.0201166","volume":"13","author":"K Lee","year":"2018","unstructured":"Lee K, Kwon H, Lee B, Lee G, Lee JH, Park YR, et al. Effect of self-monitoring on long-term patient engagement with mobile health applications. PLoS ONE. 2018;13:e0201166.","journal-title":"PLoS ONE"},{"key":"2444_CR13","doi-asserted-by":"crossref","unstructured":"Pedersen DH, Mansourvar M, Sorts\u00f8 C, Schmidt T. Predicting dropouts from an electronic health platform for lifestyle interventions: analysis of methods and predictors. J Med Internet Res. 2019;21:e13617.","DOI":"10.2196\/13617"},{"key":"2444_CR14","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/s41746-020-0224-8","volume":"3","author":"A Pratap","year":"2020","unstructured":"Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med. 2020;3:21.","journal-title":"NPJ Digit Med"},{"key":"2444_CR15","unstructured":"Ke G, Meng Q, Finely T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems 30 (NIP 2017). 2017. https:\/\/www.microsoft.com\/en-us\/research\/publication\/lightgbm-a-highly-efficient-gradient-boosting-decision-tree\/."},{"key":"2444_CR16","unstructured":"Lundberg S, Lee S-I. A unified approach to interpreting model predictions. arXiv: 1705.07874. 2017. Available from: https:\/\/arxiv.org\/abs\/1705.07874."},{"key":"2444_CR17","unstructured":"OMRON connect Official Site General https:\/\/www.omronconnect.com\/general\/. Accessed 13 Aug 2024."},{"key":"2444_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"2444_CR19","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. New York, NY, USA: ACM; 2016. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"2444_CR20","doi-asserted-by":"publisher","first-page":"e25630","DOI":"10.2196\/25630","volume":"23","author":"D Wu","year":"2021","unstructured":"Wu D, An J, Yu P, Lin H, Ma L, Duan H, et al. Patterns for patient engagement with the hypertension management and effects of electronic health care provider follow-up on these patterns: cluster analysis. J Med Internet Res. 2021;23:e25630.","journal-title":"J Med Internet Res"},{"key":"2444_CR21","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.amepre.2016.06.015","volume":"51","author":"L Yardley","year":"2016","unstructured":"Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med. 2016;51:833\u201342.","journal-title":"Am J Prev Med"},{"key":"2444_CR22","doi-asserted-by":"crossref","unstructured":"Thompson TL, Haskard-Zolnierek K. Adherence and communication. In: Oxford research encyclopedia of communication. Oxford University Press; 2016.","DOI":"10.1093\/acrefore\/9780190228613.013.23"},{"key":"2444_CR23","doi-asserted-by":"publisher","first-page":"e0000008","DOI":"10.1371\/journal.pdig.0000008","volume":"1","author":"BY Miao","year":"2022","unstructured":"Miao BY, Arneson D, Wang M, Butte AJ. Open challenges in developing digital therapeutics in the United States. PLoS Digit Health. 2022;1:e0000008.","journal-title":"PLoS Digit Health"},{"key":"2444_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106319","volume":"209","author":"JS Hong","year":"2021","unstructured":"Hong JS, Wasden C, Han DH. Introduction of digital therapeutics. Comput Methods Prog Biomed. 2021;209:106319.","journal-title":"Comput Methods Prog Biomed"},{"key":"2444_CR25","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s41746-024-01182-w","volume":"7","author":"SY Tan","year":"2024","unstructured":"Tan SY, Sumner J, Wang Y, Wenjun Yip A. A systematic review of the impacts of remote patient monitoring (RPM) interventions on safety, adherence, quality-of-life and cost-related outcomes. NPJ Digit Med. 2024;7:192.","journal-title":"NPJ Digit Med"},{"key":"2444_CR26","doi-asserted-by":"publisher","first-page":"e147","DOI":"10.2196\/jmir.7463","volume":"19","author":"F Muench","year":"2017","unstructured":"Muench F, Baumel A. More than a text message: dismantling digital triggers to curate behavior change in patient-centered health interventions. J Med Internet Res. 2017;19:e147.","journal-title":"J Med Internet Res"},{"key":"2444_CR27","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1509\/jmr.16.0163","volume":"55","author":"E Ascarza","year":"2018","unstructured":"Ascarza E. Retention futility: targeting high-risk customers might be ineffective. J Mark Res. 2018;55:80\u201398.","journal-title":"J Mark Res"},{"key":"2444_CR28","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1093\/ajh\/hpad021","volume":"36","author":"P Natale","year":"2023","unstructured":"Natale P, Ni JY, Martinez-Martin D, Kelly A, Chow CK, Thiagalingam A, et al. Perspectives and experiences of self-monitoring of blood pressure among patients with hypertension: a systematic review of qualitative studies. Am J Hypertens. 2023;36:372\u201384.","journal-title":"Am J Hypertens"},{"key":"2444_CR29","doi-asserted-by":"publisher","first-page":"e13","DOI":"10.2196\/jmir.1194","volume":"11","author":"H Christensen","year":"2009","unstructured":"Christensen H, Griffiths KM, Farrer L. Adherence in internet interventions for anxiety and depression. J Med Internet Res. 2009;11:e13.","journal-title":"J Med Internet Res"}],"container-title":["Hypertension Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41440-025-02444-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41440-025-02444-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41440-025-02444-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T23:01:53Z","timestamp":1772665313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41440-025-02444-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":29,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2444"],"URL":"https:\/\/doi.org\/10.1038\/s41440-025-02444-0","relation":{},"ISSN":["0916-9636","1348-4214"],"issn-type":[{"value":"0916-9636","type":"print"},{"value":"1348-4214","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"30 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Employment: AM, HirosKos (Omron Healthcare Co., Ltd). Research fund: YO (Omron Healthcare Co., Ltd). Endowed departments by commercial entities: The Department of Artificial Intelligence in Healthcare and Medicine (Omron Healthcare Co., Ltd).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}