Change in cardiometabolic risk factors among Asian Indian adults recruited in a mHealth-based diabetes prevention trial
Open accessAbstract
Objective: India is experiencing an increasing prevalence of type 2 diabetes and cardiovascular diseases. Mobile health technology may be a strategy to reduce the risk of cardiometabolic disorders. This paper reports on the effect of a mobile health intervention on cardiometabolic risk factors. Methods: The mobile health and diabetes intervention was a 12-week reality television-based mobile health program application delivered via videos, short message service and infographics through a smartphone application followed-up weekly by health coach calls. mobile health and diabetes was conducted in a randomized control trial mode randomized controlled trial methodology in three Indian cities (Chennai, Bengaluru and New Delhi) with participants recruited via community screening events. This paper looks at the pre–post changes in cardiometabolic risks among the participants and the place of demography in influencing these. Results: The mobile health and diabetes intervention group experienced a small reduction in waist circumference (1.8 cm) compared to the control group (0.5 cm, p < 0.05) and a greater decrease in systolic blood pressure (2.7 mmHg) compared to the control group ( p < 0.05). The improvements in cardiometabolic risk factors were more pronounced in individuals with obesity, although overall effects were very modest Conclusions: Cardiometabolic risk factors can be reduced with a mobile health application using human coaching, especially in obese individuals, but the improvements are small. To be more effective and clinically meaningful, intensive engagement with the participants is probably required.
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Muralidharan et al and Holmes et al concluded that TM, in addition to TCP, does not have a significant effect on delaying T2DM.
“For example, Muralidharan et al [51] and Holmes et al [52] concluded that TM, in addition to TCP, does not have a significant effect on delaying T2DM.”
From Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review (2025) · cites this paper as [51]
The Discussion (page unavailable) reports no change in fasting plasma glucose after 12 weeks of a multimodal mHealth intervention with human coach calls, and the Conclusions say a longer, more intensive intervention appears necessary for diabetes prevention. The paper does not test text messaging added to telephone counseling as a separate comparison or measure whether T2DM onset was delayed, so it does not support that specific conclusion. (LLM, conf=0.74)
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Cites
(2004) · The Lancet · doi:10.1016/s0140-6736(03)15268-3 · Cited by 12,717 · see citing works
Global status report on noncommunicable diseases 2010.
Alwan, A. (2011) · Cited by 8,494 · see citing works
2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2019
(2018) · Diabetes Care · doi:10.2337/dc19-s002 · Cited by 2,738 · see citing works
eHealth Literacy: Essential Skills for Consumer Health in a Networked World.
Norman, C. D., & Skinner, H. A. (2006) · Journal of Medical Internet Research · doi:10.2196/jmir.8.2.e9 · Cited by 2,643 · see citing works
Mendis, S., Davis, S. M., & Norrving, B. G. (2015) · Stroke · doi:10.1161/strokeaha.115.008097 · Cited by 1,023 · see citing works
Ramachandran, A., Snehalatha, C., Ram, J., Selvam, S., Simon, M., Nanditha, A., Shetty, A. S., Godsland, I. F., Chaturvedi, N., Majeed, A., et al. (2013) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(13)70067-6 · Cited by 330 · see citing works
Block, G., Azar, K. M., Romanelli, R. J., Block, T. J., Hopkins, D., Carpenter, H. A., Dolginsky, M. S., Hudes, M. L., Palaniappan, L. P., & Block, C. H. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4897 · Cited by 255 · see citing works
A Novel Diabetes Prevention Intervention Using a Mobile App
Fukuoka, Y., Gay, C. L., Joiner, K. L., & Vittinghoff, E. (2015) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2015.01.003 · Cited by 204 · see citing works
The Stepwise Approach to Diabetes Prevention: Results From the D-CLIP Randomized Controlled Trial
Weber, M. B., Ranjani, H., Staimez, L. R., Anjana, R. M., Ali, M. K., Narayan, K. M. V., & Mohan, V. (2016) · Diabetes Care · doi:10.2337/dc16-1241 · Cited by 189 · see citing works
Self-citationArora, S., Peters, A. L., Agy, C., & Menchine, M. D. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0252 · Cited by 147 · see citing works
Fottrell, E. F., Ahmed, N., Morrison, J., Kuddus, A., Shaha, S. K., King, C., Jennings, H. M., Akter, K., Nahar, T., Haghparast‐Bidgoli, H., et al. (2019) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(19)30001-4 · Cited by 141 · see citing works
Level of urbanization and noncommunicable disease risk factors in Tamil Nadu, India
Allender, S. E., Lacey, B., Webster, P., Rayner, M., Deepa, M., Scarborough, P., Arambepola, C., Datta, M., & Mohan, V. (2009) · Bulletin of the World Health Organization · doi:10.2471/blt.09.065847 · Cited by 134 · see citing works
Self-citationConsequences of Change in Waist Circumference on Cardiometabolic Risk Factors Over 9 Years
Balkau, B., Picard, P., Vol, S., Fezeu, L., Eschwège, É., & for the DESIR Study Group (2007) · Diabetes Care · doi:10.2337/dc06-2542 · Cited by 73 · see citing works
Face-to-Face Versus Mobile Versus Blended Weight Loss Program: Randomized Clinical Trial
Hurkmans, E. J., Matthys, C., Bogaerts, A., Scheys, L., Devloo, K., & Seghers, J. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7713 · Cited by 69 · see citing works
Mobile health technology in the prevention and management of Type 2 diabetes
Mohan, V., Muralidharan, S., Ranjani, H., Anjana, R. M., & Allender, S. E. (2017) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/ijem.ijem_407_16 · Cited by 66 · see citing works
Self-citationUse of Mobile Health Technology in the Prevention and Management of Diabetes Mellitus
Hartz, J., Yingling, L., & Powell-Wiley, T. M. (2016) · Current Cardiology Reports · doi:10.1007/s11886-016-0796-8 · Cited by 64 · see citing works
Weber, M. B., Ranjani, H., Meyers, G. C., Mohan, V., & Narayan, K. M. V. (2011) · Primary care diabetes · doi:10.1016/j.pcd.2011.04.005 · Cited by 57 · see citing works
Self-citationSadeghi, M., Golshahi, J., Ahmadzadeh, H., Mohammadifard, N., & Pourmoghaddas, A. (2015) · Advanced Biomedical Research · doi:10.4103/2277-9175.166140 · Cited by 43 · see citing works
Muralidharan, S., Mohan, V., Anjana, R. M., Jena, S., Tandon, N., Allender, S. E., & Ranjani, H. (2017) · JMIR Research Protocols · doi:10.2196/resprot.8644 · Cited by 26 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Sagastume, D., Siero, I., Mertens, E., Cottam, J., Colizzi, C., & Peñalvo, J. L. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101650 · Cited by 73 · see citing works
Castro, Ó., Mair, J. L., Salamanca-Sanabria, A., Alattas, A., Keller, R., Zheng, S., Jabir, A. I., Lin, X., Frese, B. F., Lim, C. S., et al. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1039171 · Cited by 42 · see citing works
Digital Health Solutions for Weight Loss and Obesity: A Narrative Review
Irvin, L., Madden, L. A., Marshall, P., & Vince, R. V. (2023) · Nutrients · doi:10.3390/nu15081858 · Cited by 34 · see citing works
“[49] examined the impact of usual care and one nutritionist meeting versus an app and weekly calls.”
BackgroundDigital Health Solutions for Cardiovascular Disease Prevention: Systematic Review
Qi, Y., Mohamad, E. M. W., Azlan, A. A., Zhang, C., Ma, Y., & Wu, A. (2024) · Journal of Medical Internet Research · doi:10.2196/64981 · Cited by 33 · see citing works
Sakima, A., Akagi, Y., Akasaki, Y., Fujii, T., Haze, T., Kawakami-Mori, F., Kitajima, K., Kobayashi, Y., Matayoshi, T., Sakaguchi, T., et al. (2024) · Hypertension Research · doi:10.1038/s41440-024-01792-7 · Cited by 32 · see citing works
Jeem, Y. A., Andriani, R. N., Nabila, R., Emelia, D. D., Lazuardi, L., & Josef, H. K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192013638 · Cited by 19 · see citing works
Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review
Duong, T., Olsen, Q., Menon, A., Woods, L., Wang, W., Varnfield, M., Jiang, L., & Sullivan, C. (2025) · Journal of Medical Internet Research · doi:10.2196/67507 · Cited by 14 · see citing works
Pati, S., Menon, J. C., Rehman, T., Agrawal, R., Kshatri, J. S., Palo, S. K., Janakiram, C., Mitra, S., Sreedevi, A., & Anand, T. (2025) · BMC Public Health · doi:10.1186/s12889-024-20922-x · Cited by 9 · see citing works
Castro, Ó., Mair, J. L., Zheng, S., Tan, S. Y. X., Jabir, A. I., Yan, X., Chakraborty, B., Tai, E. S., van Dam, R. M., von Wangenheim, F., et al. (2025) · Contemporary Clinical Trials · doi:10.1016/j.cct.2025.107833 · Cited by 6 · see citing works
Bravo, G. M., Paramasivam, P., Bellissimo, G. F., Jacquez, Q., Zheng, H., Amorim, F. T., Kovell, L. C., & Mota, R. I. (2025) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl31396 · Cited by 6 · see citing works
Ranjani, H., Avari, P., Nitika, S., Jagannathan, N., Oliver, N. S., Valabhji, J., Mohan, V., Chambers, J. C., & Anjana, R. M. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241310861 · Cited by 6 · see citing works
Self-citationThe Role of Digital Health in Tackling India's Diabetes Epidemic
Ranjani, H., Sharma, N., Pradeepa, R., Anjana, R. M., & Mohan, V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13244-5.00022-5 · Cited by 4 · see citing works
Self-citationChar, A., Gaudel, P., Kulathinal, S., & Kinnunen, T. I. (2022) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2022.06.001 · Cited by 4 · see citing works
Mohan, V., Kalra, S., Augustine, A., & Kober, J. (2025) · Diabetes Therapy · doi:10.1007/s13300-025-01768-x · Cited by 3 · see citing works
Self-citationPan, Z., Li, X., Yan, Q., & Zeng, N. (2025) · Journal of Medical Internet Research · doi:10.2196/78891 · Cited by 5 · see citing works
Bravo, G. M., Paramasivam, P., Bellissimo, G. F., Jacquez, Q., Zheng, H., Amorim, F. T., & Mota, R. I. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.21.608998 · Cited by 2 · see citing works
Sethi, B. K., Seshadri, K., Deshmukh, V. C., Unnikrishnan, A., Baruah, M. P., Phatak, S., Ghosal, S., Chittawar, S., Aggarwal, K., Hs, B., et al. (2024) · Cureus · doi:10.7759/cureus.73093 · Cited by 2 · see citing works
Singh, P. K., Singh, L., Borkotoky, K., Usman, M., Kumar, C., & Singh, S. (2025) · Public Health · doi:10.1016/j.puhe.2025.105814 · Cited by 1 · see citing works
Li, Q., Ikeda, A., Charvat, H., Ohira, T., Muraki, I., Okamura, T., Sato, S., Miura, M., Kamijima, J., Ikeda, S., et al. (2026) · American Journal of Hypertension · doi:10.1093/ajh/hpag013 · Cited by 1 · see citing works
Gall, S., Feigin, V. L., Chappell, K., Thrift, A. G., Kleinig, T. J., Cadilhac, D. A.-M., Bennett, D., Nelson, M. R., Purvis, T. L., Jalili‐Moghaddam, S., et al. (2026) · medRxiv · doi:10.64898/2026.03.19.26348870 · Cited by 1 · see citing works
Castro, Ó., Mair, J. L., Zheng, S., Tan, S. Y. X., Jabir, A. I., Yan, X., Chakraborty, B., Tai, E. S., van Dam, R. M., von Wangenheim, F., et al. (2024) · doi:10.31219/osf.io/qgvhs
Gall, S., Feigin, V. L., Chappell, K., Thrift, A. G., Kleinig, T. J., Cadilhac, D. A.-M., Bennett, D., Nelson, M. R., Purvis, T. L., Jalili‐Moghaddam, S., et al. (2026) · Stroke · doi:10.1161/strokeaha.125.054001 · Cited by 1 · see citing works
- Authors
- Muralidharan, S., Ranjani, H., Anjana, R. M., Gupta, Y., Ambekar, S., Koppikar, V., Jagannathan, N., Jena, S., Tandon, N., Allender, S., & Mohan, V.
- Affiliations
- Deakin University (AU)
- Madras Diabetes Research Foundation (IN)
- All India Institute of Medical Sciences (IN)
- Johnson & Johnson Vision Care (United States) (US)
- Corresponding author
- Mohan, Viswanathan
- ORCID
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2021-01
- Volume
- 7
- Pages
- 20552076211039032-20552076211039032
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC8456619
- OpenAlex
- W3197151374
- Cited by
- 22
- References
- 19
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 2 · 2022: 3
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Health (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by-nc)
- Funders
- Government of India under the Biotechnology Industry Partnership Programme of the Department of Biotechnology
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- cardiometabolic risk factors, mobile health, diabetes prevention, type 2 diabetes, mobile application, health coaching, obesity, cardiovascular disease prevention
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