Effects of a Technology-Assisted Integrated Diabetes Care Program on Cardiometabolic Risk Factors Among Patients With Type 2 Diabetes in the Asia-Pacific Region: The JADE Program Randomized Clinical Trial
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Importance: Many health care systems lack the efficiency, preparedness, or resources needed to address the increasing number of patients with type 2 diabetes, especially in low- and middle-income countries. Objective: To examine the effects of a quality improvement intervention comprising information and communications technology and contact with nonphysician personnel on the care and cardiometabolic risk factors of patients with type 2 diabetes in 8 Asia-Pacific countries. Design, Setting, and Participants: This 12-month multinational open-label randomized clinical trial was conducted from June 28, 2012, to April 28, 2016, at 50 primary care or hospital-based diabetes centers in 8 Asia-Pacific countries (India, Indonesia, Malaysia, the Philippines, Singapore, Taiwan, Thailand, and Vietnam). Six countries were low and middle income, and 2 countries were high income. The study was conducted in 2 phases; phase 1 enrolled 7537 participants, and phase 2 enrolled 13 297 participants. Participants in both phases were randomized on a 1:1 ratio to intervention or control groups. Data were analyzed by intention to treat and per protocol from July 3, 2019, to July 21, 2020. Interventions: In both phases, the intervention group received 3 care components: a nurse-led Joint Asia Diabetes Evaluation (JADE) technology-guided structured evaluation, automated personalized reports to encourage patient empowerment, and 2 or more telephone or face-to-face contacts by nurses to increase patient engagement. In phase 1, the control group received the JADE technology-guided structured evaluation and automated personalized reports. In phase 2, the control group received the JADE technology-guided structured evaluation only. Main Outcomes and Measures: The primary outcome was the incidence of a composite of diabetes-associated end points, including cardiovascular disease, chronic kidney disease, visual impairment or eye surgery, lower extremity amputation or foot ulcers requiring hospitalization, all-site cancers, and death. The secondary outcomes were the attainment of 2 or more primary diabetes-associated targets (glycated hemoglobin A1c <7.0%, blood pressure <130/80 mm Hg, and low-density lipoprotein cholesterol <100 mg/dL) and/or 2 or more key performance indices (reduction in glycated hemoglobin A1c≥0.5%, reduction in systolic blood pressure ≥5 mm Hg, reduction in low-density lipoprotein cholesterol ≥19 mg/dL, and reduction in body weight ≥3.0%). Results: A total of 20 834 patients with type 2 diabetes were randomized in phases 1 and 2. In phase 1, 7537 participants (mean [SD] age, 60.0 [11.3] years; 3914 men [51.9%]; 4855 patients [64.4%] from low- and middle-income countries) were randomized, with 3732 patients allocated to the intervention group and 3805 patients allocated to the control group. In phase 2, 13 297 participants (mean [SD] age, 54.0 [11.1] years; 7754 men [58.3%]; 13 297 patients [100%] from low- and middle-income countries) were randomized, with 6645 patients allocated to the intervention group and 6652 patients allocated to the control group. In phase 1, compared with the control group, the intervention group had a similar risk of experiencing any of the primary outcomes (odds ratio [OR], 0.94; 95% CI, 0.74-1.21) but had an increased likelihood of attaining 2 or more primary targets (OR, 1.34; 95% CI, 1.21-1.49) and 2 or more key performance indices (OR, 1.18; 95% CI, 1.04-1.34). In phase 2, the intervention group also had a similar risk of experiencing any of the primary outcomes (OR, 1.02; 95% CI, 0.83-1.25) and had a greater likelihood of attaining 2 or more primary targets (OR, 1.25; 95% CI, 1.14-1.37) and 2 or more key performance indices (OR, 1.50; 95% CI, 1.33-1.68) compared with the control group. For attainment of 2 or more primary targets, larger effects were observed among patients in low- and middle-income countries (OR, 1.50; 95% CI, 1.29-1.74) compared with high-income countries (OR, 1.20; 95% CI, 1.03-1.39) (P = .04). Conclusions and Relevance: In this 12-month clinical trial, the use of information and communications technology and nurses to empower and engage patients did not change the number of clinical events but did reduce cardiometabolic risk factors among patients with type 2 diabetes, especially those in low- and middle-income countries in the Asia-Pacific region. Trial Registration: ClinicalTrials.gov Identifier: NCT01631084.
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Among the nine technology-assisted studies, physical activity was the most commonly targeted behavior (67%, 6/9 studies), followed by medication adherence (44%, 4/9), dietary behaviors (33%, 3/9), and self-monitoring of blood glucose (22%, 2/9).
“Among the nine technology-assisted studies, physical activity was the most commonly targeted behavior (67%, 6/9 studies), followed by medication adherence (44%, 4/9), dietary behaviors (33%, 3/9), and self-monitoring of blood glucose (SMBG) (22%, 2/9) [10,20,23], [25,26,30,31,34,37].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [34]
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Cites
<b>mice</b>: Multivariate Imputation by Chained Equations in<i>R</i>
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870 · see citing works
International Diabetes Federation 2017
Carracher, A. M., Marathe, P. H., & Close, K. L. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12644 · Cited by 13,369 · see citing works
(2010) · University of Twente Research Information · Cited by 6,037 · see citing works
Effect of a multifactorial intervention on mortality in type 2 diabetes
Gæde, P., Lund-Andersen, H., Parving, H.-H., & Pedersen, O. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0706245 · Cited by 3,489 · see citing works
Global trends in diabetes complications: a review of current evidence
Harding, J. L., Pavkov, M. E., Magliano, D. J., Shaw, J. E., & Gregg, E. W. (2018) · Diabetologia · doi:10.1007/s00125-018-4711-2 · Cited by 1,674 · see citing works
Cardiovascular outcomes with ertugliflozin in type 2 diabetes
Cannon, C. P., Pratley, R., Dagogo-Jack, S., Mancuso, J., Huyck, S., Masiukiewicz, U., Charbonnel, B., Frederich, R., Gallo, S., Cosentino, F., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2004967 · Cited by 1,514 · see citing works
Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes
Rawshani, A., Rawshani, A., Franzén, S., Sattar, N., Eliasson, B., Svensson, A.-M., Zethelius, B., Miftaraj, M., McGuire, D. K., Rosengren, A., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1800256 · Cited by 1,475 · see citing works
Association of cardiometabolic multimorbidity with mortality
Di Angelantonio, E., Kaptoge, S., Wormser, D., Willeit, P., Butterworth, A. S., Bansal, N., O’Keeffe, L. M., Gao, P., Wood, A. M., Burgess, S., et al. (2015) · JAMA · doi:10.1001/jama.2015.7008 · Cited by 1,155 · see citing works
Fullman, N., Yearwood, J., Abay, S. M., Abbafati, C., Abd-Allah, F., Abdela, J., Abdelalim, A., Abebe, Z., Abebo, T. A., Aboyans, V., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30994-2 · Cited by 1,013 · see citing works
Self-citationThe Lancet Commission on diabetes: using data to transform diabetes care and patient lives
Chan, J. C. N., Lim, L.-L., Wareham, N. J., Shaw, J. E., Orchard, T. J., Zhang, P., Lau, E. S. H., Eliasson, B., Kong, A. P. S., Ezzati, M., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)32374-6 · Cited by 793 · see citing works
Self-citationZelniker, T. A., Wiviott, S. D., Raz, I., Im, K., Goodrich, E. L., Furtado, R. H. M., Bonaca, M. P., Mosenzon, O., Kato, E. T., Cahn, A., et al. (2019) · Circulation · doi:10.1161/circulationaha.118.038868 · Cited by 781 · see citing works
The business case for investing in physician well-being
Shanafelt, T., Goh, J., & Sinsky, C. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.4340 · Cited by 714 · see citing works
The Lancet Commission on diabetes: using data to transform diabetes care and patient lives
Chan, J. C. N., Lim, L., Wareham, N. J., Shaw, J. E., Orchard, T. J., Zhang, P., Lau, E. S. H., Eliasson, B., Kong, A. P. S., Ezzati, M., et al. (2020) · Figshare · Cited by 337 · see citing works
Self-citationManne‐Goehler, J., Geldsetzer, P., Agoudavi, K., Andall‐Brereton, G., Aryal, K. K., Bicaba, B. W., Bovet, P., Brian, G. R., Dorobanţu, M., Gathecha, G. K., et al. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002751 · Cited by 325 · see citing works
Ueki, K., Sasako, T., Okazaki, Y., Kato, M., Okahata, S., Katsuyama, H., Haraguchi, M., Morita, A., Ohashi, K., Hara, K., et al. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30327-3 · Cited by 313 · see citing works
Yeung, R. O., Zhang, Y., Luk, A., Yang, W., Sobrepena, L., Yoon, K.-H., Aravind, S. R., Sheu, W., Nguyen, T. K., Ozaki, R., et al. (2014) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(14)70137-8 · Cited by 296 · see citing works
Self-citationChan, J. C. N., Sui, Y., Oldenburg, B. F., Zhang, Y., Chung, H. H. Y., Goggins, W. B., Au, S., Brown, N., Ozaki, R., Wong, R. Y. M., et al. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2014.655 · Cited by 150 · see citing works
Self-citationEffects of Structured Versus Usual Care on Renal Endpoint in Type 2 Diabetes: The SURE Study
Chan, J. C. N., So, W., Yeung, C., Ko, G. T. C., Lau, I. T., Tsang, M.-W., Lau, K.-P., Siu, S.-C., Li, J. K., Yeung, M. W., et al. (2009) · Diabetes Care · doi:10.2337/dc08-1908 · Cited by 145 · see citing works
Self-citationLim, L., Lau, E. S. H., Kong, A. P. S., Davies, M. J., Levitt, N. S., Eliasson, B., Aguilar-Salinas, C. A., Ning, G., Seino, Y., So, W. Y., et al. (2018) · Diabetes Care · doi:10.2337/dc17-2010 · Cited by 143 · see citing works
Self-citationFrom Hong Kong Diabetes Register to JADE Program to RAMP-DM for Data-Driven Actions
Chan, J. C. N., Lim, L., Luk, A. O. Y., Ozaki, R., Kong, A. P. S., Ronald, C. W., So, W., & Lo, S. (2019) · Diabetes Care · doi:10.2337/dci19-0003 · Cited by 133 · see citing works
Self-citationWu, H., Lau, E. S. H., Ronald, C. W., Kong, A. P. S., Wild, S. H., Goggins, W. B., Chow, E., So, W., Chan, J. C. N., & Luk, A. O. Y. (2020) · Diabetologia · doi:10.1007/s00125-019-05074-7 · Cited by 119 · see citing works
Self-citationWan, E., Fung, C., Jiao, F., Yu, E., Chin, W. Y., Fong, D., Wong, C., Chan, A. K. L., Chan, K. H., Kwok, R., et al. (2017) · Diabetes Care · doi:10.2337/dc17-0426 · Cited by 101 · see citing works
Luk, A. O. Y., Hui, E. M., Sin, M.-C., Yeung, C., Chow, W.-S., Ho, A. Y. Y., Hung, H.-F., Kan, E. C. Y., Ng, C., So, W. Y., et al. (2017) · Diabetes Care · doi:10.2337/dc16-2354 · Cited by 96 · see citing works
Self-citationMinding the Gap: Factors Associated With Primary Care Coordination of Adults in 11 Countries
Penm, J. H.-D., MacKinnon, N. J., Strakowski, S. M., Ying, J., & Doty, M. M. (2017) · The Annals of Family Medicine · doi:10.1370/afm.2028 · Cited by 65 · see citing works
Fan, W., Song, Y., Inzucchi, S. E., Sperling, L. S., Cannon, C. P., Arnold, S. V., Kosiborod, M., & Wong, N. D. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13625 · Cited by 54 · see citing works
Kong, A. P. S., Yang, X., Ko, G. T. C., So, W., Chan, W. B., Ronald, C. W., Ng, V., Chow, C., Cockram, C. S., Tong, P. C. Y., et al. (2007) · Diabetes Care · doi:10.2337/dc06-2443 · Cited by 50 · see citing works
Self-citationLuk, A. O. Y., Li, X., Zhang, Y., Guo, X., Jia, W., Li, W., Weng, J., Yang, W., Chan, W. B., Ozaki, R., et al. (2015) · Diabetic Medicine · doi:10.1111/dme.13014 · Cited by 40 · see citing works
Self-citationLim, L., Lau, E. S. H., Ozaki, R., Chung, H. H. Y., Fu, A. W. C., Chan, W., Kong, A. P. S., Ronald, C. W., So, W., Chow, E., et al. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003367 · Cited by 37 · see citing works
Self-citationTutino, G., Yang, W., Li, X., Li, W. H., Zhang, Y. Y., Guo, X. H., Luk, A. O. Y., Yeung, R. O., Yin, J., Ozaki, R., et al. (2016) · Diabetic Medicine · doi:10.1111/dme.13164 · Cited by 29 · see citing works
Self-citationWhat can we learn from the recent blood glucose lowering megatrials?
Chan, J. C. N. (2010) · Journal of Diabetes Investigation · doi:10.1111/j.2040-1124.2010.00063.x · Cited by 16 · see citing works
Self-citationYin, J., Luk, A. O. Y., Wong, R. Y. M., Chung, H. H. Y., Kong, A. P. S., Ozaki, R., Ronald, C. W., Chow, C., So, W.-Y., & Chan, J. C. N. (2017) · Journal of Diabetes · doi:10.1111/1753-0407.12527 · Cited by 12 · see citing works
Self-citationSonthon, P., Promthet, S. S., Changsirikulchai, S., Rangsin, R., Thinkhamrop, B., Rattanamongkolgul, S., & Hurst, C. P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180977 · Cited by 12 · see citing works
Zuo, H., Wang, W., Deng, L., et al. (2017) · Journal of the American Society of Hypertension · doi:10.1016/j.jash.2017.12.006 · Cited by 3 · see citing works
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Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review
Khandoker, F., & Grigsby, T. J. (2025) · Applied Sciences · doi:10.3390/app151910567 · Cited by 2 · see citing works
- Authors
- Lim, L.-L., Lau, E. S. H., Fu, A. W. C., Ray, S., Hung, Y.-J., Tan, A. T. B., Chamnan, P., Sheu, W. H. H., Chawla, M. S., Chia, Y.-C., Chuang, L.-M., Nguyen, D.-C., Sosale, A., Saboo, B. D., Phadke, U., Kesavadev, J., Goh, S.-Y., Gera, N., Huyen Vu, T. T., Ma, R. C. W., Lau, V., Luk, A. O. Y., Kong, A. P. S., Chan, J. C. N., & Asia-Pacific JADE Study Group
- Affiliations
- Chinese University of Hong Kong (HK)
- Asia Diabetes Foundation (CN)
- Relief Polyclinic, Hoogly, India
- Tri-Service General Hospital (TW)
- University of Malaya (MY)
- Sappasithiprasong Hospital (TH)
- Taichung Veterans General Hospital (TW)
- Lina Diabetes Care Centre, Mumbai, India
- National University of Singapore (SG)
- National Taiwan University Hospital (TW)
- Thong Nhat Hospital (VN)
- Diacon Hospital (IN)
- Dia Care–Diabetes Care and Hormone Clinic, Gujarat, India
- Instride, Pune, India
- Jothydev's Diabetes and Research Center (IN)
- Singapore General Hospital (SG)
- Max Healthcare (IN)
- Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong Special Administrative Region, China
- Corresponding author
- Chan, Juliana C. N.
- ORCID
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2021-04-30
- Volume
- 4
- Issue
- 4
- Pages
- e217557
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC8087959
- OpenAlex
- W3157371898
- Cited by
- 37
- References
- 33
- Citations by year
- 2026: 9 · 2025: 4 · 2024: 8 · 2023: 6 · 2022: 10
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 2 diabetes, integrated diabetes care, information and communication technologies, nurse-led care, cardiometabolic risk factors, HbA1c, blood pressure, LDL cholesterol, cardiovascular disease, chronic kidney disease, diabetes complications, patient empowerment, low- and middle-income countries, Asia-Pacific region, quality improvement intervention, primary care
- MeSH terms
- Self-Management, Treatment Adherence and Compliance, Cardiometabolic Risk Factors, Aged, Amputation, Surgical, Asia, Southeastern, Blood Pressure, Cardiovascular Diseases, Developing Countries, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Diabetic Retinopathy, Female, Glycated Hemoglobin, Humans, India, Cholesterol, LDL, Male, Middle Aged, Mortality, Neoplasms, Patient Participation, Taiwan, Technology, Diabetic Foot, Risk Assessment, Evidence-Based Medicine, Disease Management, Decision Support Systems, Clinical, Renal Insufficiency, Chronic, Quality Improvement
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