Evaluating the Impact of Insurer-Sponsored Health Coaching on Hospitalization and Costs in India: A Retrospective Cohort Study.

Introduction

India is witnessing a rapid rise in non-communicable diseases (NCDs), which now account for more than 60% of total deaths. Conditions such as cardiovascular disease, diabetes, and hypertension are placing an immense burden on the healthcare system. This growing prevalence of chronic illnesses is closely linked to rapid urbanization, sedentary lifestyles, poor dietary habits, and an aging population. Data show that the proportion of cardiovascular disease-related deaths nearly doubled from 15% in 1990 to 28% in 2016, marking an 87% relative increase [ 1 ]. Much of this escalation can be attributed to preventable complications arising from inadequate disease management, delayed interventions, and lifestyle-related risk factors. India’s constrained public health ecosystem and limited follow-up care further worsen the situation, contributing to higher hospitalization rates - the proportion of users who experienced at least one hospitalization during a specified observation period - and increased healthcare expenditure [ 2 ].

Because a significant portion of healthcare expenses in India are paid out-of-pocket, many households experience financial strain or fall into poverty when dealing with chronic illnesses [ 3 ]. This financial vulnerability has prompted insurers such as Aditya Birla Health Insurance (ABHI) to explore proactive healthcare strategies aimed at improving patient outcomes while containing costs. Instead of relying solely on reactive medical care, insurers are increasingly turning toward preventive interventions as a means to reduce claims and enhance policyholder well-being.

Despite the growing burden of chronic disease in India, health insurance frameworks have historically adopted a reactive, curative stance, primarily covering hospitalization and treatment costs rather than prevention or disease management. While global evidence demonstrates that health coaching interventions improve clinical outcomes in diabetes, hypertension, and related conditions (reducing HbA1c, blood pressure, and cholesterol levels) while lowering healthcare utilization and costs, such proactive, integrated disease management models remain underexplored within India's insurance landscape. Recent systematic reviews highlight that health coaching significantly enhances quality of life and self-efficacy in chronic illness care, and telemedicine-enabled coaching strategies have proven equally or more effective than in-person consultations in reducing HbA1c by 0.31-0.44% compared to standard care alone. However, most published evidence emanates from developed health systems with different socioeconomic, cultural, and operational contexts. The Indian health insurance sector, despite schemes like Ayushman Bharat and earlier programs such as Rashtriya Swasthya Bima Yojana, has focused primarily on financial protection and hospitalization coverage, with limited empirical evidence on the effectiveness of structured health coaching programs tailored to high-risk, insured populations. This gap is particularly significant given India's escalating diabetes and hypertension prevalence and the cost-containment imperatives facing private insurers.

In recent years, progressive health insurers like ABHI have introduced wellness programs designed to encourage healthy behavior and improve overall health outcomes. However, most initiatives have traditionally centered around physical activity tracking, health screenings, and reward-based incentives [ 4 ]. Health coaching - although not uniformly defined in literature - has emerged as a patient-centered approach grounded in established behavior change models such as the Transtheoretical Model, Social Cognitive Theory, and Motivational Interviewing [ 5 ]. Typically, health coaching incorporates structured goal setting, personalized guidance, self-monitoring, and accountability, all aimed at sustaining long-term improvements in health behaviors [ 6 ].

While the benefits of insurer-sponsored health coaching programs have been documented in Western countries, including reductions in weight, improvements in glycemic control, and smoking cessation [ 7 ], their effectiveness in the Indian context remains underexplored. Early evidence from lifestyle interventions in India suggests promising potential for improving chronic disease management [ 8 ], yet systematic evaluations of insurer-led health coaching and its influence on hospitalization rates and claims remain limited.

ABHI is among the first insurers in India to launch a structured risk and disease management program targeting high-risk policyholders, initiated around 2020 as part of the broader “Health-First” model and scaled substantially by FY25. Engagement in this program refers to a policyholder’s active participation in health coaching, defined as successful completion of onboarding and, where applicable, continued interaction through scheduled care calls and chat communication, while onboarding denotes the user’s status with respect to informed consent and initiation of the health coaching program, operationalized as successful completion of the initial onboarding call. Through the Chronic Care Management Program and a broader wellness ecosystem, including the WellBeing Score™, HealthReturns™, Healthy Heart Score™, and Active Dayz™, policyholders with chronic conditions such as diabetes, hypertension, hyperlipidemia, and asthma received personalized health coaching from day 1, along with coverage for outpatient diagnostics, consultations, and pharmacy expenses. By FY25, more than one lakh customers had received health coaching services, and another 1.6 lakh had benefited from additional clinical interventions, with 81% of engaged customers showing measurable improvements in key health indicators such as HbA1c, cholesterol, blood pressure, and blood sugar levels. These outcomes illustrate ABHI’s transition from a reactive insurance model to one that proactively manages disease burden and incentivizes long-term healthy behavior, further reinforced by ABHI’s partnership with Zyla Health in 2022 to scale health coaching for specific cohorts of high-risk members.

The present research, conducted using real-world evidence provided by ABHI in collaboration with Zyla Health, seeks to generate actionable insights into how insurers can optimize the design and delivery of health coaching programs. The findings have the potential to inform strategic decisions regarding investments in preventive health initiatives and support a broader shift toward proactive disease management in India’s insurance landscape. Moreover, as India continues to strengthen its public health infrastructure, the insights from this study may guide policymakers in conceptualizing scalable interventions to curb the rising burden of NCDs [ 9 ].

The primary objective of this study is to evaluate the impact of an insurer-sponsored health coaching program on total claims paid among high-risk policyholders. Specifically, the analysis will examine how demographic factors such as age and gender influence program adherence and claim outcomes. We expect that higher levels of engagement with coaching sessions will be associated with improved health outcomes and lower hospitalization claims. Additionally, we anticipate that improvements in symptoms may mediate the relationship between coaching engagement and healthcare costs. By exploring these questions, this study aims to contribute evidence-based guidance for insurers, healthcare providers, and policymakers seeking to design more effective preventive health strategies within India’s evolving healthcare ecosystem.

Research questions

This study examines the effect of insurer-led health coaching on healthcare use and costs among high-risk policyholders and addresses the following questions:

What is the impact of insurer-sponsored health coaching on hospitalization rates and claims paid among high-risk policyholders in India?

How do demographic factors (age, gender) influence program adherence and hospitalization outcomes?

Is higher engagement with coaching sessions (measured by number of calls) associated with improved health outcomes and lower hospitalization rates?

Do symptom improvements mediate the relationship between coaching engagement and healthcare costs?

How does gender moderate the relationship between coaching call frequency and total claims paid?

How does symptom resolution (both complete and partial) moderate the relationship between coaching calls and healthcare expenditure?

Materials and methods

ABHI offers a comprehensive health coaching program designed to support high-risk policyholders who are living with chronic conditions such as dyslipidemia, hypertension, type 2 diabetes, and obesity. The program seeks to promote sustained health behavior change and reduce the likelihood of complications that frequently lead to hospitalization. To ensure broad awareness, ABHI promotes the program through its agent network as well as its digital platforms, allowing beneficiaries across demographic groups to learn about and access the service. The initiative is jointly developed and overseen by ABHI and Zyla Health, enabling the integration of clinical expertise with technology-driven engagement to deliver personalized and structured interventions. High-risk individuals are identified using underwriting data, historical claims information, and findings from annual health check-ups. Once flagged, trained representatives reach out by phone to explain the program and onboard eligible members.

After enrollment, patients enter a structured nine-month coaching journey consisting of six scheduled sessions with a qualified clinical nutritionist. Reminder notifications are sent one day before and on the day of each appointment to promote adherence. The first coaching session, which is conducted in the patient’s regional language, acts as a detailed intake assessment. During this call, the health coach captures information on the patient’s medications, presenting symptoms, coexisting conditions, laboratory results, previous surgeries, family health history, lifestyle habits, and daily routines. All details are systematically documented in ABHI’s in-house electronic medical record (EMR) system to ensure consistency and continuity in follow-up interactions. Drawing from this assessment, the coach sets individualized health goals with the patient and formulates a customized nutrition plan that reflects the patient’s medical needs, demographic profile, and food preferences. Follow-up coaching sessions take place approximately every six weeks and are focused on reviewing adherence, monitoring changes in symptoms, and reinforcing motivation to maintain lifestyle improvements. Medical questions raised during these sessions are addressed by a general physician, while issues such as body pain are escalated to a physiotherapist for further guidance.

Alongside these scheduled sessions, the program incorporates personalized digital engagement delivered through ABHI’s omni-channel platforms. Patients receive targeted nudges and reminders to track specific health parameters, along with curated educational content tailored to their primary chronic condition. Additional support is available through ABHI’s integrated mobile application, where patients can submit queries at any time, ensuring continuous guidance beyond formal coaching sessions. This combination of structured coaching, medical oversight, and technology-based reinforcement supports long-term engagement and strengthens disease management outcomes.

To evaluate the effectiveness of this intervention, we conducted a retrospective analysis of individuals enrolled in the ABHI-Zyla Health Coaching Program between April 2023 and March 2024. The study draws from two primary data sources. The first includes information supplied by ABHI that is available at the time of underwriting, along with claims data, results from annual health assessments, and any newly declared health information submitted during the policy period. This information is securely transferred to the coaching team through system integrations and data-sharing mechanisms that comply with applicable security regulations. The second source consists of health-related data collected during the coaching journey itself, gathered through regular coach interactions and digital touchpoints within ABHI’s app. All data are coded using standardized medical classifications such as ICD-10 and LOINC to ensure accuracy, consistency, and analytical reliability.

Participants were eligible for inclusion in the study if they were registered members of ABHI who had been identified as high-risk for health complications associated with chronic conditions. Only individuals who had at least one diagnosis of obesity, type 2 diabetes, dyslipidemia, or hypertension were considered, and they must have been enrolled in the ABHI-Zyla Health Coaching Program between April 2023 and March 2024. Participants were excluded if they had incomplete or missing baseline data required for analysis, were diagnosed primarily with acute or non-chronic conditions beyond the scope of the coaching program, withdrew consent for the use of their data, or had records that could not be reliably linked across the ABHI and Zyla data systems. The final analytical sample comprised 29,463 patients who had at least one diagnosis of obesity, type 2 diabetes, dyslipidemia, or hypertension.

Symptoms were recorded using the International Classification of Diseases, Tenth Revision (ICD-10) framework (Appendix). At enrollment, symptoms were identified through a structured telephonic interaction conducted by a trained health coach and coded into ICD-10 based on patient-reported complaints and clinician-approved mapping protocols embedded within Zyla Health’s electronic medical record system. During each follow-up interaction, the health coach systematically assessed the status of previously recorded symptoms using a standardized questionnaire. Symptom status was determined based on patient self-report during the phone consultation and categorized into one of four predefined outcome states: same as before, better than before, completely resolved, or worse than before. The categorization criteria were as follows: “completely resolved” indicated that the patient reported complete absence of the symptom since the prior interaction; “better than before” indicated a clear subjective improvement in symptom frequency, intensity, or functional impact, but with persistence of the symptom to some degree; “same as before” indicated no meaningful change in the symptom as perceived by the patient; and “worse than before” indicated an increase in symptom severity, frequency, or negative impact on daily activities compared to the prior assessment. Symptom resolution and improvement status were therefore based on patient self-report rather than direct clinical examination. Health coaches were trained to use consistent probing questions and documentation standards to minimize inter-observer variability, and symptom status was recorded in the electronic medical record.

Our measurement strategy focused on capturing the key elements of program engagement and health progression. Onboarded status was recorded as a binary indicator reflecting whether a patient consented to participate in the coaching program and actively began using the service. Engagement was further assessed through the number of successful calls completed with the health coach following onboarding. Health status at baseline was captured through the total number of symptoms reported during the initial assessment call. Measures of improvement included the number of symptoms that had fully resolved and the number that had improved but not completely resolved, both documented in the EMR system. Demographic attributes such as age and gender were included to account for their potential influence on engagement and outcomes.

The study’s dependent variables focused on healthcare utilization following the intervention. The total number of preventable claims post-intervention captured claims linked to complications from chronic conditions that could potentially be avoided with better disease management, excluding unavoidable medical events such as those involving trauma or maternity care. The total claim paid amount for such preventable claims captured the financial impact on the insurer. When examined together, these variables offered a comprehensive understanding of how program participation and symptom changes relate to healthcare use and cost.

The analysis aimed to address three main objectives. First, we examined correlations among the independent variables, demographic characteristics, and the two key dependent outcomes. Second, we evaluated the direct effects of age, gender, onboarding status, engagement (measured through number of calls), and symptom-related indicators on the total claim paid amount for preventable conditions after the intervention. Finally, given that the number of coaching calls represents the core mechanism through which patients receive support, we tested whether its influence on claim costs was moderated by key variables such as gender and symptom improvements, including both complete resolution and partial improvement. Together, these analyses provide deeper insights into the pathways through which health coaching may influence patient outcomes and healthcare expenditures.

Results

Prior to analysis, data completeness was assessed across all variables. Of the 38,250 eligible participants, 8,787 (22.9%) were excluded due to missing outcome data (total claims post-intervention) or incomplete baseline demographic information. The final analytical sample comprised 29,463 individuals (77.1%). Missing data patterns were examined; data were found to be missing completely at random (MCAR) as assessed using Little's MCAR test (χ² = 124.5, p = 0.053). Given the large sample size and assumption of MCAR, complete-case analysis was deemed appropriate without employing multiple imputation.

Descriptive statistics (means, standard deviations, and frequency distributions) for all variables were computed. Pearson correlations were calculated to assess bivariate associations among continuous variables and point-biserial correlations for categorical-continuous relationships. Normality of the dependent variable (total claim paid amount) was assessed using Q-Q plots and the Shapiro-Wilk test. Given significant departures from normality (W = 0.823, p < 0.001), we log-transformed the outcome variable to satisfy regression assumptions, while unstandardized estimates were back-transformed for interpretation.

The following three hierarchical ordinary least squares (OLS) regression models were fitted with total claim paid amount for preventable claims (post-intervention) as the dependent variable:

Model 1 (baseline): age, gender, and onboarded status as predictors

Model 2: added engagement (number of calls) and symptom-related variables

Model 3: tested moderation effects by including interaction terms (number of calls × gender; number of calls × symptoms resolved; number of calls × symptoms better)

Analyses were conducted in R (version 4.3.1) using the car, lmtest, and interactions packages. All statistical tests were two-tailed with α = 0.05.

Table 1 presents the mean values, standard deviations, and correlations (unadjusted) among the study variables for 29,463 patients (77.1%). The average age of patients in the sample was 42.1 years, and 10,607 (36%) of the participants were female. Age demonstrated a positive and significant correlation with all other variables in the study, indicating that older patients tended to engage more with the program and reported a greater number of symptoms, as well as more symptoms that had improved or resolved. Consistent with this pattern, we found that the average age of onboarded patients (43.4 years; N = 15,077) was significantly higher than that of patients who did not onboard (40.8 years; N = 14,386), a difference that was statistically significant (p < 0.001).

Gender was also significantly associated with several program engagement and health outcome indicators. Female participants reported a higher number of symptoms compared to male participants but made fewer calls to the health coach. The proportion of females in the not-onboarded group (N=11,638; 39.5%) was significantly higher than in the onboarded group (N=9,664; 32.8%), suggesting lower levels of uptake among women (p < 0.001). Onboarding status itself was found to be higher among male participants and was positively correlated with the total claims paid amount. This association is likely driven by selection bias, as individuals with more severe health conditions - who naturally incur higher claim amounts - are more likely to opt into the coaching program. Importantly, the number of coaching calls made by patients was positively correlated with the number of symptoms reported initially, as well as the number of symptoms that improved or resolved during the course of the program, indicating that higher engagement is associated with better health progress.

In terms of overall program engagement, 15,085 (51.2%) of patients with at least one chronic diagnosis onboarded into the program by providing consent during the outreach call. Among those who onboarded, only 4,963 (32.9%) were female, echoing earlier gender-based differences in participation. Nearly half of the full sample (7,497; 49.7%) made at least one call with the health coach. For onboarded members, the average number of calls was 2.2, with a median of two calls, suggesting moderate engagement levels across the cohort. The mean number of claims post-enrollment was 1.02 in engaged users versus 1.48 in non-engaged users (p<0.05), representing a 31% reduction in healthcare utilization events among those actively participating in the coaching program.

Table 2 summarizes the regression analyses (adjusted) conducted to identify factors influencing the total claim paid amount for preventable claims following the intervention. We also tested moderation effects of gender, number of symptoms resolved, and number of symptoms improved on the relationship between the number of calls and total claims paid. Age emerged as a significant predictor of claim amounts, with older patients incurring higher total claims compared to younger individuals (b = 0.03; p < 0.01). After controlling for age and gender, the number of calls showed a significant negative association with total preventable claim amounts (b = -0.08; p < 0.01), indicating that increased engagement through coaching calls is linked to reduced healthcare costs.

Further insights from the unstandardized regression coefficients in Table 3 reveal that each additional coaching call is associated with a reduction of INR 667.99 in total claim amounts, holding all other variables constant. When accounting for the average cost of delivering the intervention, we found that every rupee invested in coaching calls yielded savings of approximately 1.72 rupees in claim payments. The analysis was conducted on a sample of 29,463 individuals with at least one of the four chronic conditions under focus, among whom 155 preventable hospitalization claims were recorded in the post-intervention period.

Gender significantly moderated the relationship between the number of calls and the total claim paid amount (b = -0.02; p < 0.05). Specifically, the negative association between coaching calls and claim amounts was stronger for female patients. Conditional effects computed at high (+1 SD) and low (-1 SD) levels of gender, along with simple slope tests [ 10 ], revealed that increases in the number of calls resulted in a significantly greater reduction in claim amounts for women compared to men, as illustrated in Figure 1 .

The number of symptoms resolved also moderated the relationship between call frequency and claim amounts (b = 0.01; p < 0.05). Patients who experienced a higher number of resolved symptoms benefited more from increased engagement, showing a stronger negative relationship between calls and claim amounts. As shown in Figure 2 , individuals with a greater number of resolved symptoms incurred lower claim amounts across both low and high levels of call frequency. Among the 4,272 unique users who showed symptom improvement or resolution, a total of 7,626 symptoms were documented as improved or resolved.

Finally, the number of symptoms reported as "better" (but not fully resolved) significantly moderated the relationship between the number of calls and total claims (b = 0.02; p < 0.05). As illustrated in Figure 3 , patients with fewer partially improved symptoms demonstrated a steeper reduction in claim amounts as call frequency increased. Nonetheless, individuals with a higher number of improved symptoms consistently reported lower claim amounts regardless of whether they made relatively few or many calls.

Discussion

The findings of this study provide valuable insights into the association between ABHI-sponsored health coaching programs and healthcare costs among high-risk policyholders in India. The results highlight the observed relationship between patient engagement in such programs and health outcomes, with a notable correlation between the number of coaching calls and reported improvements in health status. These findings are consistent with existing literature suggesting a role for structured health coaching in supporting patient self-management and reducing healthcare utilization [ 5 ].

One notable finding is that older patients demonstrated higher program engagement, with greater frequency of coaching calls and higher reported rates of symptom resolution. This pattern aligns with research indicating that older adults tend to engage more actively in health interventions, potentially due to increased health concerns and disease management needs [ 11 ]. Additionally, age showed a positive association with total claims paid, consistent with the well-established observation that older individuals typically incur higher healthcare costs [ 12 ]. These patterns suggest that while health coaching may benefit individuals across age groups, tailored strategies could potentially enhance engagement among younger policyholders, who may perceive a lower immediate need for such interventions. Age was found to be positively correlated with number of calls, number of symptoms reported as better, and number of symptoms reported as resolved. Older patients made more coaching calls and reported higher rates of symptom resolution, while also demonstrating higher overall healthcare costs. These associations may reflect heightened health awareness and greater chronic disease burdens commonly observed in older populations.

Gender differences in engagement patterns and reported outcomes also emerged as a notable theme. While female participants reported a higher number of symptoms, they made fewer calls than male counterparts. This pattern may reflect potential barriers to participation, such as competing household responsibilities or differential health-seeking behaviors. Notably, female participants showed a stronger negative association between the number of calls and total claims paid, suggesting that increased engagement may correspond to greater cost reductions for women. This finding underscores the potential value of gender-informed health coaching strategies that address identified barriers and support participation among women. In the Indian social context, these patterns may reflect broader structural factors, including differential household responsibilities and variable access to medical attention between genders.

The study also reveals an association between symptom resolution and claims reduction. The number of calls made by health coaches showed a positive correlation with both symptom resolution and symptom improvement. Furthermore, symptom resolution appeared to strengthen the association between coaching calls and total claims paid, suggesting that patients reporting symptom relief also demonstrated lower healthcare costs. This pattern is consistent with prior findings documenting associations between improved symptom management and reduced hospitalization rates and overall medical expenditures [ 13 ]. These results suggest that greater patient engagement in health coaching programs may correspond with economic benefits, building a case for further exploration of such initiatives by insurers.

An important observation concerns onboarding status, which was positively correlated with total claims paid, likely reflecting selection bias wherein individuals with more severe health conditions are preferentially enrolled in coaching programs and consequently file more claims. This finding is consistent with prior research indicating that high-risk patients are more likely to participate in health interventions due to perceived need for medical support [ 14 ]. However, the data also suggest that sustained engagement through multiple coaching sessions corresponds with cost reductions, indicating potential long-term financial and health associations with continued participation.

A particularly notable finding is the negative association between the number of coaching calls and total claims paid for preventable conditions. This association appeared stronger for patients who reported resolution of a greater number of symptoms. This pattern suggests that structured, personalized health coaching interventions may be associated with reduced financial burden related to preventable complications from chronic conditions such as diabetes, hypertension, and cardiovascular diseases. Given the substantial out-of-pocket healthcare expenditure in India, these findings are potentially relevant for both insurers and policymakers. They suggest that expanded access to such preventive programs warrants further investigation for potential public health and economic benefits [ 15 , 16 ].

The findings of this study suggest a potential shift in India's insurance-led health delivery paradigm: structured health coaching programs may be associated with measurable improvements in health status among high-risk, insured populations, bridging traditional distinctions between insurance coverage and health outcomes. The observed finding that 81% of engaged customers reported improvements in HbA1c, cholesterol, and blood pressure is consistent with patterns documented in global meta-analyses and extends this literature to India's market-driven, private insurance context - an area where evidence remains limited. The mechanisms underlying these observed associations likely involve behavioral pathways described in health coaching theory: personalized goal-setting, continuous monitoring, motivational support, and accountability may support behavioral change and treatment adherence, which, in turn, could be associated with improvements in glycemic and cardiometabolic control. Notably, the engagement definition employed in this study (successful onboarding plus continued interaction) captures an often-unmeasured dimension in effectiveness research: real-world engagement patterns that reflect actual intervention uptake. By operationalizing engagement and examining its association with reported clinical improvement, this study contributes evidence suggesting that coaching-based disease management approaches may be both clinically relevant and operationally feasible at scale in the Indian context. Future research should examine cost-effectiveness outcomes and long-term sustainability trajectories, particularly as India considers scaling such models across its expanding insurance base to address chronic disease burden disparities across income and geographic distribution.

Limitations and future research directions

Despite these promising results, this study has certain limitations. First, the reliance on self-reported symptoms may introduce bias, as patients' perceptions of improvement might not always align with objective clinical assessments. Future research should incorporate biometric and medical test data to validate self-reported outcomes. Second, while the study controlled for key demographic variables such as age and gender, additional factors such as socioeconomic status, educational background, and occupation could influence program engagement and health outcomes. Future studies should explore these aspects to provide a more comprehensive understanding of health coaching effectiveness. Third, the study primarily focused on short-term claims data, and a longitudinal analysis would be necessary to assess the sustained impact of health coaching on long-term healthcare costs and chronic disease progression.

Looking ahead, future research and policy implications should consider integrating health coaching with broader public health initiatives. Given the rapid expansion of India's digital health ecosystem, leveraging mobile health (mHealth) technologies and AI-driven personalized coaching could enhance program scalability and effectiveness. Additionally, insurers could explore incentive structures, such as reduced premiums for active participants, to further encourage policyholder engagement.

Conclusions

This study provides strong evidence supporting the effectiveness of insurer-led health coaching programs in India. Higher engagement in coaching sessions is associated with improved symptom resolution and reduced healthcare costs, particularly for females and older individuals. These findings highlight the need for targeted strategies to enhance engagement, reduce barriers to participation, and optimize program delivery. By shifting from a reactive to a preventive healthcare model, insurers and policymakers can play a crucial role in mitigating the growing burden of NCDs in India.

Appendices

Disclosures

Human subjects: Informed consent for treatment and open access publication was obtained or waived by all participants in this study.

Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following:

Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work.

Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.

Other relationships: This work was conducted jointly by Aditya Birla Health Insurance and Zyla Health, who have a business (client–vendor) relationship. One of the authors, Dr Kapil Verma, PhD, provided consulting services to Zyla Health for this study. The authors declare no other conflicts of interest.

Author Contributions

Acquisition, analysis, or interpretation of data: Kapil Verma, Khushboo Aggarwal

Drafting of the manuscript: Kapil Verma, Khushboo Aggarwal

Critical review of the manuscript for important intellectual content: Kapil Verma, Gouri Karajgi, Rajesh Patidar

Concept and design: Gouri Karajgi, Rajesh Patidar, Khushboo Aggarwal

References

1. Cardiovascular diseases in India: current epidemiology and future directions. Prabhakaran D, Jeemon P, Roy A. Circulation. 2016;133:1605–1620. doi: 10.1161/CIRCULATIONAHA.114.008729.

2. Cost of hospitalisation for non-communicable diseases in India: are we pro-poor? Tripathy JP, Prasad BM, Shewade HD, et al. https://www.researchgate.net/profile/Jaya-Tripathy/publication/313215851_Prioritised_Research_Agenda_for_NCDs/links/5892be4e458515aeac946479/Prioritised-Research-Agenda-for-NCDs.pdf . Trop Med Int Health. 2016;21:1019–1028. doi: 10.1111/tmi.12732.

3. A study of catastrophic health expenditures in India-evidence from nationally representative survey data: 2014-2018. Sriram S, Albadrani M. F1000Res. 2022;11:141. doi: 10.12688/f1000research.75808.1.

4. Wellness programs offered by the health insurance companies. Rangayyan M, Kapadia D. Int J Prev Med. 2024;4:6–11.

5. A systematic review of the literature on health and wellness coaching: defining a key behavioral intervention in healthcare. Wolever RQ, Simmons LA, Sforzo GA, et al. Glob Adv Health Med. 2013;2:38–57. doi: 10.7453/gahmj.2013.042.

6. Supporting sustainable health behavior change: the whole is greater than the sum of its parts. Matthews JA, Matthews S, Faries MD, Wolever RQ. Mayo Clin Proc Innov Qual Outcomes. 2024;8:263–275. doi: 10.1016/j.mayocpiqo.2023.10.002.

7. Impacts of chronic disease prevention programs implemented by private health insurers: a systematic review. Wanni Arachchige Dona S, Angeles MR, Hall N, Watts JJ, Peeters A, Hensher M. BMC Health Serv Res. 2021;21:1222. doi: 10.1186/s12913-021-07212-7.

8. Cost-effectiveness of a lifestyle intervention in high-risk individuals for diabetes in a low- and middle-income setting: trial-based analysis of the Kerala Diabetes Prevention Program. Sathish T, Oldenburg B, Thankappan KR, et al. BMC Med. 2020;18:251. doi: 10.1186/s12916-020-01704-9.

9. The Ayushman Bharat Digital Mission of India: an assessment. Mishra US, Yadav S, Joe W. Health Syst Reform. 2024;10:2392290. doi: 10.1080/23288604.2024.2392290.

10. Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Preacher KJ, Curran PJ, Bauer DJ. J Educ Behav Stat. 2006;31:437–448.

11. Transforming mental health systems globally: principles and policy recommendations. Patel V, Saxena S, Lund C, et al. Lancet. 2023;402:656–666. doi: 10.1016/S0140-6736(23)00918-2.

12. Factors predicting engagement of older adults with a coach-supported eHealth intervention promoting lifestyle change and associations between engagement and changes in cardiovascular and dementia risk: secondary analysis of an 18-month multinational randomized controlled trial. Coley N, Andre L, Hoevenaar-Blom MP, et al. J Med Internet Res. 2022;24:0. doi: 10.2196/32006.

13. Catastrophic health spending among older adults in India: role of multiple deprivation. Panda BK, Mohanty SK. Aging Health Res. 2022;2:100100.

14. Heard and valued: the development of a model to meaningfully engage marginalized populations in health services planning. Snow ME, Tweedie K, Pederson A. BMC Health Serv Res. 2018;18:181. doi: 10.1186/s12913-018-2969-1.

15. Factors that influence older patients' participation in clinical communication within developed country hospitals and GP clinics: a systematic review of current literature. Gaffney HJ, Hamiduzzaman M. PLoS One. 2022;17:0. doi: 10.1371/journal.pone.0269840.

16. Consequences of India's population aging to its healthcare financing and provision. Sahoo PM, Rout HS, Jakovljevic M. J Med Econ. 2023;26:308–315. doi: 10.1080/13696998.2023.2178164.