{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T21:35:20Z","timestamp":1777671320779,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore MOH Health Innovation (MHI) fund","award":["MH 110:12\/12-30"],"award-info":[{"award-number":["MH 110:12\/12-30"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background\/Objective: Accurate resting energy expenditure (REE) estimation is paramount for the nutritional management of older Asian adults with multimorbidity. However, standard predictive equations (PEs) lack precision for this cohort. This study aimed to recalibrate PEs using BMI-stratified, slope-only regression to enhance bedside accuracy. Methods: REE was measured via indirect calorimetry in 400 hospitalized patients (age \u2265 65). Sensitivity analyses identified significant proportional bias in existing models. Models were recalibrated and validated using 1000-iteration bootstrap resampling. Results: Standard PEs exhibited significant bias, particularly overpredicting requirements for 68% of underweight patients. The new Singapore Older Adults Resting energy expenditure (SOAR) PE 1 (963.67 + 8.56 \u00d7 weight \u2212 5.6 \u00d7 age) eliminated weight-dependent systematic errors. The recalibrated models utilizing actual body weight achieved accuracy rates of up to 64% in obese cohorts, comparable to complex adjusted-weight protocols. Conclusions: Population-specific recalibration is essential to mitigate the bidirectional risks of malnutrition and overfeeding in geriatric rehabilitation. The BMI-stratified multipliers provided offer a robust, clinically efficient framework for individualized nutritional care.<\/jats:p>","DOI":"10.3390\/nu18091345","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T12:29:11Z","timestamp":1777033751000},"page":"1345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recalibrating Resting Energy Expenditure Prediction Equations in Asian Older Adults with Multimorbidity"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1064-4741","authenticated-orcid":false,"given":"Pei San","family":"Kua","sequence":"first","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Musfirah","family":"Albakri","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su Mei","family":"Tay","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phoebe Si-En","family":"Thong","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivia Jiawen","family":"Xia","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0677-7584","authenticated-orcid":false,"given":"Wendelynn Hui Ping","family":"Chua","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Chong","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas Wei Kiat","family":"Tan","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4172-8972","authenticated-orcid":false,"given":"Xin Hui","family":"Loh","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia Hui","family":"Tan","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian Leng","family":"Low","sequence":"additional","affiliation":[{"name":"SingHealth Community Hospitals, 10 Hospital Blvd, Singapore 168582, Singapore"},{"name":"SingHealth Centre for Population Health Research and Implementation, 10 Hospital Boulevard, #19-01 SingHealth Tower, Singapore 168582, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic and Social Affairs (2023). World Social Report 2023: Leaving No One Behind in an Ageing World, United Nations."},{"key":"ref_2","unstructured":"National Population and Talent Division, Strategy Group, Prime Minister\u2019s Office, Singapore (2023). Population in Brief 2023, National Population and Talent Division."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1093\/gerona\/glad167","article-title":"The Health, Aging, and Body Composition (Health ABC) Study\u2014Ground-Breaking Science for 25 Years and Counting","volume":"78","author":"Newman","year":"2023","journal-title":"J. Gerontol. Ser. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"68","DOI":"10.7762\/cnr.2017.6.2.68","article-title":"Measurement methods for physical activity and energy expenditure: A review","volume":"6","author":"Ndahimana","year":"2017","journal-title":"Clin. Nutr. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s00421-017-3641-x","article-title":"Doubly labelled water assessment of energy expenditure: Principle, practice, and promise","volume":"117","author":"Westerterp","year":"2017","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.clnu.2020.06.024","article-title":"Indirect calorimetry: The 6 main issues","volume":"40","author":"Achamrah","year":"2021","journal-title":"Clin. Nutr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ocagli, H., Lanera, C., Azzolina, D., Piras, G., Soltanmohammadi, R., Gallipoli, S., Gafare, C.E., Cavion, M., Roccon, D., and Vedovelli, L. (2021). Resting energy expenditure in the elderly: Systematic review and comparison of equations in an experimental population. Nutrients, 13.","DOI":"10.3390\/nu13020458"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kua, P.S., Albakri, M., Tay, S.M., Thong, P.S.-E., Xia, O.J., Chua, W.H.P., Chong, K., Tan, N.W.K., Loh, X.H., and Tan, J.H. (2025). Novel Resting Energy Expenditure Prediction Equations for Multi-Ethnic Asian Older Adults with Multimorbidity. Nutrients, 17.","DOI":"10.3390\/nu17132144"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1080\/21551197.2022.2070320","article-title":"Validation of Resting Energy Expenditure Equations in Older Adults with Obesity","volume":"41","author":"Griffith","year":"2022","journal-title":"J. Nutr. Gerontol. Geriatr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1073\/pnas.4.12.370","article-title":"A biometric study of human basal metabolism","volume":"4","author":"Harris","year":"1918","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1093\/ajcn\/51.2.241","article-title":"A new predictive equation for resting energy expenditure in healthy individuals","volume":"51","author":"Mifflin","year":"1990","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_12","first-page":"5","article-title":"Predicting basal metabolic rate, new standards and review of previous work","volume":"39","author":"Schofield","year":"1985","journal-title":"Hum. Nutr. Clin. Nutr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.clnu.2017.06.025","article-title":"ESPEN guidelines on nutritional support for polymorbid internal medicine patients","volume":"37","author":"Gomes","year":"2018","journal-title":"Clin. Nutr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112188","DOI":"10.1016\/j.nut.2023.112188","article-title":"New prediction equations for resting energy expenditure in older hospitalized patients: Development and validation","volume":"115","author":"Kawase","year":"2023","journal-title":"Nutrition"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1016\/j.clnu.2022.01.024","article-title":"ESPEN practical guideline: Clinical nutrition and hydration in geriatrics","volume":"41","author":"Volkert","year":"2022","journal-title":"Clin. Nutr."},{"key":"ref_16","first-page":"14","article-title":"Sample size calculation in medical studies","volume":"6","author":"Pourhoseingholi","year":"2013","journal-title":"Gastroenterol. Hepatol. Bed Bench"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s12603-010-0005-1","article-title":"Nutritional assessment in an Asian nursing home and its association with mortality","volume":"14","author":"Chan","year":"2010","journal-title":"J. Nutr. Health Aging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1186\/s13690-025-01727-9","article-title":"Validity of RMR equations in underweight, normal weight, overweight, and obese Emirati female young adults","volume":"83","author":"Haroun","year":"2025","journal-title":"Arch. Public Health"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pavlidou, E., Papadopoulou, S.K., Seroglou, K., and Giaginis, C. (2023). Revised Harris\u2013Benedict equation: New human resting metabolic rate equation. Metabolites, 13.","DOI":"10.3390\/metabo13020189"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1016\/j.ajcnut.2023.04.010","article-title":"Development and validation of new predictive equations for the resting metabolic rate of older adults aged \u226565 y","volume":"117","author":"Porter","year":"2023","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_21","first-page":"e869","article-title":"Data distribution analysis\u2013a preliminary approach to quantitative data in biomedical research","volume":"92","author":"Guzik","year":"2023","journal-title":"J. Med. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1093\/jamia\/ocz228","article-title":"A tutorial on calibration measurements and calibration models for clinical prediction models","volume":"27","author":"Huang","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e074820","DOI":"10.1136\/bmj-2023-074820","article-title":"Evaluation of clinical prediction models (part 2): How to undertake an external validation study","volume":"384","author":"Riley","year":"2024","journal-title":"BMJ"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Binuya, M.A., Engelhardt, E.G., Schats, W., Schmidt, M.K., and Steyerberg, E.W. (2022). Methodological guidance for the evaluation and updating of clinical prediction models: A systematic review. BMC Med. Res. Methodol., 22.","DOI":"10.1186\/s12874-022-01801-8"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1186\/s41043-025-01117-5","article-title":"From past to future: A review of methods for assessing physical activity energy expenditure","volume":"44","author":"Zhang","year":"2025","journal-title":"J. Health Popul. Nutr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1016\/j.apmr.2022.03.020","article-title":"First systematic review and meta-analysis of the validity and test-retest reliability of physical activity monitors for estimating energy expenditure during walking in individuals with stroke","volume":"103","author":"Cabot","year":"2022","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_27","first-page":"55","article-title":"Normality testing in statistics: What clinician\u2013researchers should know","volume":"2","author":"Paramasivam","year":"2024","journal-title":"Heart Fail. J. India"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.3346\/jkms.2024.39.e35","article-title":"Data distribution: Normal or abnormal?","volume":"39","author":"Habibzadeh","year":"2024","journal-title":"J. Korean Med. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4097\/kjae.2016.69.1.8","article-title":"Nonparametric statistical tests for the continuous data: The basic concept and the practical use","volume":"69","author":"Nahm","year":"2016","journal-title":"Korean J. Anesthesiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e220030228","DOI":"10.1002\/bimj.202200302","article-title":"Stability of clinical prediction models developed using statistical or machine learning methods","volume":"65","author":"Riley","year":"2023","journal-title":"Biom. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"de-Medeiros, L., San Martin, R., Junqueira, G., de Oliveira, J., Trunce-Morales, S.T., Nogueira-de-Almeida, C., and Marchini, J.S. (2025). Energy metabolism and obesity stratified by BMI: Impact on lipid oxidation, a cross-sectional observational study. Front. Nutr., 12.","DOI":"10.3389\/fnut.2025.1701686"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20552076251367081","DOI":"10.1177\/20552076251367081","article-title":"Smartphone accelerometer-measured physical activity and adiposity indicators among female university students","volume":"11","author":"Alhajri","year":"2025","journal-title":"Digit. Health"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gerke, O. (2020). Reporting standards for a Bland\u2013Altman agreement analysis: A review of methodological reviews. Diagnostics, 10.","DOI":"10.3390\/diagnostics10050334"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1002\/oby.23469","article-title":"Predictive equations for energy expenditure in adult humans: From resting to free-living conditions","volume":"30","author":"Galgani","year":"2022","journal-title":"Obesity"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Abawi, O., Koster, E.C., Welling, M.S., Boeters, S.C.M., van Rossum, E.F.C., van Haelst, M.M., van der Voorn, B., de Groot, C.J., and van den Akker, E.L.T. (2022). Resting energy expenditure and body composition in children and adolescents with genetic, hypothalamic, medication-induced or multifactorial severe obesity. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.862817"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1186\/s12986-016-0145-3","article-title":"Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients","volume":"13","author":"Kruizenga","year":"2016","journal-title":"Nutr. Metab."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00394-024-03571-z","article-title":"Does physical activity level and total energy expenditure relate to food intake, appetite, and body composition in healthy older adults? A cross-sectional study","volume":"64","author":"Dericioglu","year":"2025","journal-title":"Eur. J. Nutr."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Verdi, A.M.O.H., Soares, J.M., Carneiro, J.F., Felez, I.O., Schiessel, D.L., Vieira, D.G., K\u00fchl, A.M., Gon\u00e7alves, D.C., and Melhem, A.R.D.F. (2025). Estimated Energy Requirement: Comparison Between the 2005 and 2023 Dietary Reference Intakes in Sedentary Adults and Older Adults\u2014A Retrospective Cross-Sectional Study. Obesities, 5.","DOI":"10.3390\/obesities5010015"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cehan, V.D., Cehan, A.R., Pui, M.C., and Lazar, A. (2025). A New Perspective on Overfeeding in the Intensive Care Unit (ICU): Challenges, Dangers and Prevention Methods. Life, 15.","DOI":"10.3390\/life15050828"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.jss.2024.07.098","article-title":"Predictive Energy Equations Inaccurately Estimate Metabolic Demands of Older Adult Trauma Patients","volume":"302","author":"Haines","year":"2024","journal-title":"J. Surg. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.clnesp.2025.02.009","article-title":"The accuracy of estimating equations for total resting energy expenditure in hospitalized patients","volume":"66","author":"Pape","year":"2025","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1159\/000528872","article-title":"Total Energy Expenditure in Healthy Ambulatory Older Adults Aged \u226580 Years: A Doubly Labelled Water Study","volume":"79","author":"Nguo","year":"2023","journal-title":"Ann. Nutr. Metab."},{"key":"ref_43","first-page":"439","article-title":"Muscle preservation during hospitalization: Energy balance, protein intake, and habitual physical activity","volume":"28","author":"Fuchs","year":"2025","journal-title":"Curr. Opin. Clin. Nutr. Metab. Care"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"20220044","DOI":"10.2490\/prm.20220044","article-title":"Rehabilitation of Older Adults with Sarcopenia: From Cell to Functioning","volume":"7","author":"Frontera","year":"2022","journal-title":"Prog. Rehabil. Med."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Olufson, H.T., Ellick, J., McCoy, S., Barrimore, S.E., Knowlman, T., and Young, A.M. (2025). An Overlooked Challenge: A Retrospective Audit of Overnutrition in Hospital Rehabilitation Wards. Healthcare, 13.","DOI":"10.3390\/healthcare13020188"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"afae201","DOI":"10.1093\/ageing\/afae201","article-title":"New horizons in prediction modelling using machine learning in older people\u2019s healthcare research","volume":"53","author":"Stahl","year":"2024","journal-title":"Age Ageing"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.clnu.2023.02.003","article-title":"How do guideline recommended energy targets compare with measured energy expenditure in critically ill adults with obesity: A systematic literature review","volume":"42","author":"Lambell","year":"2023","journal-title":"Clin. Nutr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"ezaf142","DOI":"10.1093\/ejcts\/ezaf142","article-title":"Statistical primer: Sample size considerations for developing and validating clinical prediction models","volume":"67","author":"Martin","year":"2025","journal-title":"Eur. J. Cardiothorac. Surg."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e074819","DOI":"10.1136\/bmj-2023-074819","article-title":"Evaluation of clinical prediction models (part 1): From development to external validation","volume":"384","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cai, Z., You, B., Yu, S., Fan, Y., Tian, H., Ainsworth, B.E., and Chen, P. (2026). Prediction Equations to Estimate Resting Metabolic Rate in Healthy, Community-Dwelling Chinese Older Adults. Nutrients, 18.","DOI":"10.3390\/nu18020344"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/S0002-8223(95)00369-X","article-title":"Predictive equations for basal metabolic rate in Chinese adults: A cross-validation study","volume":"95","author":"Liu","year":"1995","journal-title":"J. Am. Diet. Assoc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"E539","DOI":"10.1152\/ajpendo.2000.279.3.E539","article-title":"Resting energy expenditure-fat-free mass relationship: New insights provided by body composition modeling","volume":"279","author":"Wang","year":"2000","journal-title":"Am. J. Physiol. Endocrinol. Metab."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xue, J., Li, S., Zhang, Y., and Hong, P. (2019). Accuracy of predictive resting-metabolic-rate equations in Chinese Mainland adults. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16152747"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1607247","DOI":"10.3389\/ijph.2024.1607247","article-title":"Challenges and solutions in recruiting older vulnerable adults in research","volume":"69","author":"Sourial","year":"2024","journal-title":"Int. J. Public Health"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1111\/jgs.18704","article-title":"Around the EQUATOR with Clin-STAR: Prediction modeling opportunities and challenges in aging research","volume":"72","author":"Deardorff","year":"2024","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1007\/s40520-022-02263-0","article-title":"The challenge of recruiting multimorbid older patients identified in a hospital database to a randomised controlled trial","volume":"34","author":"Biegus","year":"2022","journal-title":"Aging Clin. Exp. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"afag058","DOI":"10.1093\/ageing\/afag058","article-title":"Exploring cost trajectories of patients admitted to geriatric rehabilitation in the Netherlands","volume":"55","author":"Preitschopf","year":"2026","journal-title":"Age Ageing"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Panayotova, G.G. (2025). Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research. Healthcare, 13.","DOI":"10.3390\/healthcare13202579"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Huang, L., Huhulea, E.N., Abraham, E., Bienenstock, R., Aifuwa, E., Hirani, R., Schulhof, A., Tiwari, R.K., and Etienne, M. (2025). The role of artificial intelligence in obesity risk prediction and management: Approaches, insights, and recommendations. Medicina, 61.","DOI":"10.3390\/medicina61020358"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e705","DOI":"10.1002\/osp4.705","article-title":"Automated extraction of weight, height, and obesity in electronic medical records are highly valid","volume":"10","author":"Sandhu","year":"2023","journal-title":"Obes. Sci. Pract."}],"container-title":["Nutrients"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/9\/1345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:00:11Z","timestamp":1777449611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/9\/1345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":60,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["nu18091345"],"URL":"https:\/\/doi.org\/10.3390\/nu18091345","relation":{},"ISSN":["2072-6643"],"issn-type":[{"value":"2072-6643","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]}}}