Abstract
Introduction:
mHealth technology has the potential to deliver personalized health care; however, data on cardiometabolic risk factors are limited. This study aims to assess the effectiveness of mobile health applications (apps) on cardiometabolic risk factor reduction in adults aged 25 to 60 years in urban and rural India.
Methods:
The study design was a pilot randomized controlled trial conducted in Tamil Nadu, India. Smartphone users (25-60 years) with basic literacy and at high risk of developing diabetes (Indian Diabetes Risk Score ≥30 and/or fasting blood sugar [FBS] 100-125 mg/dL) were recruited. Four mobile apps (two commercially available, two novel) for cardiometabolic risk reduction were evaluated. Primary outcome (weight loss) was analyzed using intention-to-treat analysis with post hoc analysis and logistic regression models adjusted for confounders.
Results:
A total of 5264 participants were screened, and 610 were recruited into the study. Participants (7%) dropped out largely due to the COVID-19 pandemic. Data from 567 participants were used for the final analysis. In the intention-to-treat analysis, a significant reduction in body weight was observed in the intervention group as compared with control, more so in the urban (−2.40 kg, 95% confidence interval [CI] = [−3.10, −1.69], P < .001) compared with rural population (−1.19 kg, 95% CI = [−1.55, −0.82], P < .001). Intervention group participants showed significant reductions in body mass index, waist circumference, blood pressure, FBS, total serum cholesterol, and a positive effect on dietary and physical activity behaviors compared with controls.
Conclusions:
mHealth interventions can reduce diabetes risk, improve cardiometabolic health, and improve lifestyle behaviors in South Asian populations.
Trial Registration:
The trial is registered with the Central Trials Registry, India (CTRI/2020/03/024327).
Keywords: app, diabetes, digital health, mHealth, weight reduction
Introduction
The increased prevalence of type 2 diabetes (T2D) and obesity poses major public health concerns, with the need for immediate action in terms of prevention and control. The prevalence of diabetes is expected to increase to 700 million people by 2045, 1 with cardiovascular disease (CVD) and diabetes accounting for 17.9 million and 1.5 million deaths, respectively, every year. 2 India reports 101 million individuals with diabetes, 136 million with prediabetes; 315 million with hypertension, 213 million with hypercholesterolemia, 254 with generalized obesity, and 351 million people with abdominal obesity. 3 These disorders may be preventable through lifestyle changes.
Intensive lifestyle modification has been shown to reduce the incidence of T2D by 32% in individuals with prediabetes 4 and improve CVD risk profile. 5 However, most lifestyle intervention programs in India are not scalable, costly, time-consuming, require trained personnel, and are limited to urban settings. Various socioeconomic and behavioral barriers (eg, work/family commitments, distance from intervention center, lack of time, family support and finances) can hinder effective uptake.6,7
Mobile phone user penetration in India is projected to reach 96% by 2040, up from 0.64% in 2012. 8 Between 2015 and 2021, rural Internet penetration increased by 200%, whereas urban areas saw a 158% rise. 9 Hence, leveraging digital mobile health (mHealth) technology provides a scalable and convenient method to deliver personalized health care to the community at large. 10 mHealth studies have largely shown the reduction in weight by supporting positive lifestyle changes.11,12 However, there are no extensive data on changes in other cardiometabolic risk factors. 13
The aim of this study was to assess the effectiveness of mobile health applications (apps) on cardiometabolic risk factor reduction in adults aged 25 to 60 years in urban and rural India.
Methodology
Study Design
This was a pilot randomized controlled trial (RCT) conducted in urban and rural areas of Tamil Nadu, one of the largest states in southern India. Urban 14 and rural areas 15 in the study were defined using standard government consensus guidelines. The study was approved by the Institutional Ethics Committee and registered with the Central Trials Registry, India (CTRI/2020/03/024327).
Participants
Inclusion Criteria
Participants included were smartphone users aged 25 to 60 years with high risk of developing diabetes (Indian Diabetes Risk Score [IDRS] ≥30 and/or fasting blood sugar [FBS] 100-125 mg/dL). Participants were required to have basic literacy (able to read and understand basic English [for urban population] and Tamil [for rural population]) (Figures 1 and 2).
Figure 1.
Recruitment flowchart for urban study.
Figure 2.
Recruitment flowchart for rural study.
Exclusion Criteria
Participants were excluded if they had (1) a diagnosis of diabetes, (2) any conditions preventing lifestyle adherence, (3) uncontrolled hypertension, (4) significant ischemic heart disease within six months of enrollment, (5) recent cancer treatment, (6) renal insufficiency, (7) severe liver dysfunction, (8) alcohol abuse, (9) significant arthritis, (10) psychiatric issues, (11) pregnancy, or (12) other systemic illnesses.
Recruitment
Participants in the digital study were identified through other surveillance programs, namely, the Global Health Research Unit (GHRU) surveillance program 16 (CTRI/2018/08/015536) in urban areas and the Telemedicine Project for Screening Diabetes and Its Complications in Rural Tamil Nadu (TREND) study 17 (CTRI/2017/09/009909) in rural areas. Participants recruited from these programs were administered a one-page screening questionnaire to assess their eligibility for the digital study (Figure 1 for urban and Figure 2 for rural recruitment).
In addition, targeted screening approaches included urban residential colonies (Figure 1) around Chennai and three selected rural villages in Tamil Nadu (Figure 2). Cluster randomization was used in rural areas to avoid cross-contamination as communities are very close-knit. All eligible participants were approached for further written informed consent. During the COVID-19 pandemic, in line with government and national policies, participant recruitment was suspended to prevent COVID-19 spread and ensure participant safety.
Procedures
Interventions: Mobile Health Apps
We tested four mobile apps for cardiometabolic risk reduction. Two commercial apps (Google Fit and HealthifyMe) were selected based on their high scores in a systematic review. 18 The other two, BUD.D (a chatbot) and Sweet Hack (a gaming app), were novel apps developed in-house. These apps were designed to be culturally specific and were translated into the local language (Tamil) for rural use (Supplementary Figure 1).
BUD.D and Sweet Hack were beta tested by 30 participants of diverse backgrounds. Their feedback on onboarding, navigation, functionality, features, infographics, and reading material was incorporated into the final app versions.
Each intervention app has been briefly described below. BUD.D incorporates an interactive chatbot with a decision tree matrix (Supplementary Figure 1) and personalized cardiometabolic education based on individual risk profiles and usage patterns. It also deploys nudges such as reminders, quizzes, and messages to support engagement and behavior change.
Sweet Hack uses gamification to deliver guideline-based educational content, and behavioral nudges with gameplay experience, through puzzles, quests, and challenges. The game includes 100 gaming levels for urban users and 80 levels for rural users, addressing lifestyle behaviors such as diet, physical activity, stress, sleep, smoking, and alcohol (Supplementary Figure 1).
Google Fit records physical fitness activities, estimates calories burned, tracks weight loss, exercise duration, heart rate, and sleep (through third-party applications) and offers personalized goal settings, customized tips, and actionable coaching.
HealthifyMe promotes weight loss, fitness, and diabetes prevention through lifestyle changes, sending reminders to track weight, activity, food, and water. It also provides personalized reminders for walking and workouts, and the premium version provides personalized coaching.
The two commercial apps could not be tested in the rural settings due to language limitations. Hence, four apps were tested in the urban setting and two apps in the rural setting.
Randomization
Urban
In urban areas, participants were randomly assigned to one of the four intervention groups or the control group, using a web-based randomization grid, randomizing in blocks of 100. 19 The site coordinator sent the participant identification numbers (ID) to the study manager weekly, who completed the randomization and returned the grid back to the field teams to inform participants.
Rural
In rural areas, each of the three villages was randomized to either of the two intervention or control groups using an online lottery method tool. 19
Intervention Group
Participants in the intervention group were assisted by the field team to download the relevant app, using a play store link, and were supported to set up their user profiles. The research team make weekly 3-5 minutes calls (up to 16 weeks in urban areas and 10 weeks in rural areas) to encourage the app engagement and address any issues.
Control Group
Participants randomized to the control group received a single 20- to 30-minute brief lifestyle counseling session by a diabetes educator at the diabetes center. During the COVID pandemic, this was a telephone call.
Data Measures
All participants were followed up for 12 to 16 weeks in the urban areas and 6 to 10 weeks in the rural areas. Enrolment and follow-up visits included physical measurements (blood pressure [BP], weight, waist circumference [WC]), fasting blood samples for glucose and total serum cholesterol, and questionnaires on demographics, medical history, lifestyle, quality of life, smartphone usage, sleep, and diet (fruit and vegetable intake during a typical week). Physical activity data were collected using the Madras Diabetes Research Foundation (MDRF) physical activity (MPAQ) questionnaire.
Anthropometric measurements were obtained using standardized techniques: height (Stadiometer, SECA Model 213, SecaGmbh Co, Hamburg, Germany); weight (Tanita BC-601); and BP (OMRON, Tokyo, Japan). Blood pressure was obtained twice, taken 5 minutes apart, and the mean used. Waist circumference was measured with non-stretchable tape.
Capillary blood samples were collected for fasting blood glucose and total cholesterol using the Aina blood monitoring system (Siemens) and validated by a trained laboratory technician.
Outcomes
The primary outcome of the study was weight loss post-intervention, and the secondary outcomes were reduction in metabolic parameters (body mass index [BMI], WC, BP, FBS, and total serum cholesterol) and improvement in lifestyle behavior changes such as consumption of fruits and vegetables and moderate to vigorous physical activity (MVPA).
Sample Size Calculation
The minimum sample size required for pilot studies ranges from 20 to 70 per group, as reported in the literature. These guidelines, tailored for two-group studies, can be adjusted for single-group or multi-group studies by multiplying the recommended pilot study sample size with an appropriate adjustment factor. In our study, with eight groups (across urban and rural settings), the total sample size needed was 80 to 280. Our final sample size exceeds these thresholds, with 80 participants across five groups (comprising four intervention groups and one control group) in urban areas, and 70 participants across three groups (comprising two intervention groups and one control group) in rural areas.20,21 This sample size provided 80% power to study the effect of the digital interventions on weight loss with a total sample size of 610 participants.
Statistical Analysis
The primary outcome, weight loss, was analyzed using the intention-to-treat (ITT) protocol. Data were not combined for the urban and rural settings. Missing data (n = 43) for the primary outcome variable (body weight) due to follow-up losses (n = 40 urban; n = 3 rural) were imputed using regression-based Multiple Imputation Generalized Estimating Equations (MI-GEE). SPSS statistical software package was used, and P < .05 considered statistically significant. Data normality was confirmed with histograms.
Baseline characteristics were described using means and standard deviation (SD) for numerical data and percentages for categorical data. A paired t-test evaluated pre-intervention and post-intervention changes in anthropometric, clinical, and biochemical variables. Analysis of variance (ANOVA) with a difference-in-difference approach compared weight reduction between intervention and control groups. Post hoc analysis assessed changes in body weight, BMI, WC, BP, FBS, and total serum cholesterol.
To evaluate the digital intervention in reducing body weight, the event rate was determined by mean weight loss in both the intervention and control groups. Absolute risk reduction (ARR) was reported with a 95% confidence interval and P-value. Logistic regression models were adjusted for confounders (age and education) at baseline in both urban and rural areas.
Results
Characteristics of Study Participants
Overall, 5264 participants were screened (4778 in urban and 486 in rural areas) from November 12, 2020 until February 3, 2022. Based on the eligibility criteria, 610 participants were recruited into the study; 400 in the urban and 210 in the rural areas. Forty-three participants (7%) dropped out of the study. As the study was held in part during the COVID-19 pandemic, one of the main causes for study dropouts was participants moving to other places after the lockdown. In total, data from 360 and 207 urban and rural participants, respectively, were used for the final analysis (Figure 3).
Figure 3.
Consort flowchart.
Baseline demographics are outlined in Table 1. In the urban cohort, 58.3% (n = 233) were male, with mean [standard deviation (SD)] age of 38.4 ± 8.9 years. The mean baseline weight was 72.4 ± 14.3 kg, with systolic and diastolic BP of 127 ± 17 mmHg and 87 ± 11mm/Hg respectively. Within the rural cohort, 45.7% (n = 96) of participants were male and age 37.1 ± 8.9 years. Baseline weight was 64.8 ± 13.1 kg, with systolic and diastolic BP was 121 ± 14mm/Hg and 82 ± 10mm/Hg respectively. More participants in urban areas (n = 293, 73.3%) had completed a college/university degree compared to rural areas (n = 40, 19%) (most had completed school). In both urban (n = 260, 65%) and rural (n = 102, 48.6%) areas most participants were employed in private organizations. In the urban and rural cohorts, the mean FBS and total cholesterol was 112.5 ± 16.9 mg/dL, 107.6 ± 13.0 mg/dL and 152.3 ± 32.4 mg/dL, 151.2 ± 30.4 mg/dL respectively. A higher percentage of urban participants (n = 134, 33.5%) consumed 3-5 servings of fruits and vegetables daily compared to rural participants (n = 58, 27.6%). Additionally, 50.7% of urban (n = 203) and 19.0% of rural participants (n = 40) engaged in 150 minutes per week of MVPA.
Table 1.
Baseline Characteristics of Participants for Urban and Rural Populations.
| URBAN (n = 400) |
RURAL (n = 210) |
|||||||
|---|---|---|---|---|---|---|---|---|
| Characteristics | BUD.D (n = 80) | Sweet Hack (n = 80) | Google Fit (n = 80) | HealthifyMe (n = 80) | Control (n = 80) | BUD.D (n = 70) | Sweet Hack (n = 70) | Control (n = 70) |
| Age a | 39.1 ± 8.6 | 37.7 ± 8.8 | 38.4 ± 8.6 | 36.0 ± 9.1 | 41.0 ± 8.4 | 36.5 ± 9.6 | 38.9 ± 9.5 | 36.1 ± 7.7 |
| Gender, male | 50 (62.5) | 43 (53.8) | 47 (58.8) | 44 (55.0) | 49 (61.3) | 32 (45.7) | 30 (42.3) | 34 (48.6) |
| Educational qualification b | ||||||||
| No schooling | 1 (1.3) | 1 (1.3) | 0 | 3 (3.8) | 4 (5.0) | 6 (8.6) | 17 (25.3) | 5 (7.1) |
| Completed school | 20 (25.0) | 18 (22.5) | 19 (23.8) | 12 (15.0) | 29 (36.3) | 46 (65.7) | 42 (59.2) | 54 (77.1) |
| Completed college/university | 59 (73.8) | 61 (76.3) | 61 (76.3) | 65 (81.3) | 47 (58.8) | 18 (25.7) | 11 (15.5) | 11 (15.7) |
| Occupation | ||||||||
| Government employee | 1 (1.3) | 2 (2.5) | 1 (1.3) | 3 (3.8) | 5 (6.3) | 9 (12.9) | 2 (2.8) | 2 (2.9) |
| Private employed | 55 (68.8) | 50 (62.5) | 54 (67.5) | 55 (68.8) | 46 (57.5) | 31 (44.3) | 36 (50.7) | 35 (50.0) |
| Home maker | 21 (26.3) | 17 (21.3) | 19 (23.8) | 17 (21.3) | 26 (32.5) | 21 (30.0) | 29 (40.8) | 29 (41.4) |
| Unemployed | 3 (3.8) | 11 (13.8) | 6 (7.5) | 5 (6.3) | 3 (3.8) | 9 (12.9) | 4 (5.6) | 4 (5.7) |
| Anthropometric and clinical parameters | ||||||||
| Weight (kg) | 72.5 ± 14.3 | 72.2 ± 14.5 | 73.7 ± 14.7 | 72.9 ± 14.3 | 71.0 ± 13.9 | 65.7 ± 13.9 | 61.5 ± 12.2 | 67.3 ± 12.5 |
| BMI (kg/m2) | 27.2 ± 5.0 | 27.1 ± 5.8 | 27.1 ± 4.6 | 27.5 ± 4.5 | 26.8 ± 5.0 | 25.9 ± 4.4 | 24.7 ± 3.5 | 26.0 ± 4.4 |
| WC (cm) | ||||||||
| Male | 94.1 ± 1.5 | 91.5 ± 1.8 | 97.2 ± 1.8 | 94.1 ± 1.5 | 94.9 ± 1.6 | 93.2 ± 1.9 | 89.6 ± 2.0 | 91.5 ± 1.6 |
| Female | 91.6 ± 2.2 | 91.7 ± 2.0 | 93.7 ± 2.1 | 91.1 ± 1.7 | 87.6 ± 1.9 | 85.8 ± 1.5 | 82.7 ± 1.2 | 87.9 ± 1.9 |
| Systolic blood pressure (mm Hg) | 128 ± 16 | 127 ± 15 | 125 ± 17 | 128 ± 17 | 129 ± 18 | 121 ± 12 | 119 ± 13 | 125 ± 16 |
| Diastolic blood pressure (mm Hg) | 87 ± 12 | 88 ± 11 | 85 ± 11 | 87 ± 9 | 86 ± 12 | 84 ± 10 | 82 ± 9 | 81 ± 10 |
| Biochemical parameters | ||||||||
| Fasting blood sugar (mg/dL) | 112.0 ± 12.3 | 113.1 ± 15.4 | 113.6 ± 13.3 | 111.3 ± 15.5 | 112.1 ± 25.0 | 107.7 ± 12.7 | 105.3 ± 12.4 | 110.0 ± 13.5 |
| Total serum cholesterol (mg/dL) | 155.0 ± 24.6 | 153.7 ± 33.9 | 155.5 ± 32.0 | 156.0 ± 37.0 | 141.7 ± 31.5 | 155.8 ± 34.6 | 150.1 ± 25.6 | 147.8 ± 30.2 |
| Lifestyle parameters, n (%) | ||||||||
| Fruits and vegetables (three to five servings/day) | 32 (40.0) | 22 (27.5) | 29 (36.3) | 30 (37.5) | 21 (26.3) | 21 (30.0) | 18 (25.7) | 19 (27.1) |
| MVPA (≥150 minutes/week) | 36 (45.0) | 41 (51.2) | 45 (56.3) | 42 (52.5) | 39 (48.8) | 13 (18.6) | 12 (17.1) | 15 (21.4) |
Data presented as n (%) or mean (SD).
Abbreviations: WC, waist circumference; MVPA, moderate to vigorous physical activity; cm = centimeter; mg/dL = milligram per deciliter.
Significant for only urban (P < .05).
Significant for both urban and rural (P < .05).
Primary Endpoint: Change in Body Weight
In the urban participants, there was an overall weight reduction post-intervention across groups from baseline 72.8 ± 14.4 kg to endpoint 71.6 ± 14.4 (−1.2 ± 0.1) kg, P < .001. Each of the mHealth apps showed significant reductions in weight compared with baseline: BUD.D −1.3 ± 0.1 kg; Sweet Hack −1.2 ± 0.2 kg; Google Fit −0.7 ± 0.2 kg, and HealthifyMe −1.8 ± 0.2 kg (P < .05 for each digital app; Table 2). However, there were no significant differences between the mHealth apps.
Table 2.
Effect of Digital Intervention on Primary Outcome (Body Weight).
| Intention-to-treat (ITT) analysis | ||||||||
|---|---|---|---|---|---|---|---|---|
| Site | Group | Baseline mean ± SD | Post-intervention mean ± SD | Difference mean ± SD | Intervention vs control difference in difference (95% CI) P** | Event rate | RR a | ARR b |
| % | % (CI) | % (CI) | ||||||
| Urban (n = 400) | Intervention (n = 320) | 72.8 ± 14.5 | 71.6 ± 14.4 | −1.2 ± 0.1* | −2.40 (−3.10, −1.69) P < .001 |
74.7 | 2.2 (1.6, 3.1) |
42.0 (30.8, 53.5) |
| Control (n = 80) | 71.0 ± 13.9 | 72.1 ± 13.5 | 1.1 ± 0.5* | 32.5 | ||||
| Rural (n = 210) | Intervention (n = 141) | 63.6 ± 13.2 | 63.1 ± 13.2 | −0.5 ± 0.1* | −1.19 (−1.55, −0.82) P < .001 |
66.7 | 1.8 (1.3, 2.5) |
30.0 (16.6, 44.1) |
| Control (n = 69) | 67.3 ± 12.5 | 67.9 ± 12.9 | 0.6 ± 0.1* | 36.1 | ||||
| Urban (n = 400) | BUD.D (n = 80) | 72.5 ± 14.3 | 71.2 ± 14.3 | −1.3 ± 0.1* | −2.42 (−3.55, −1.29) P < .001 |
83.8 | 2.5 (1.8, 3.5) |
51.0 (38.1, 64.3) |
| Sweet Hack (n = 80) | 72.2 ± 14.5 | 71.0 ± 14.5 | −1.2 ± 0.2* | −1.14 (−1.74, −0.55) P < .001 |
71.3 | 2.1 (1.5, 3.0) |
38.7 (24.4, 53.0) |
|
| Google Fit (n = 80) | 73.7 ± 14.7 | 73.0 ± 14.6 | −0.7 ± 0.2* | −0.60 (−1.01, −0.20) P =.004 |
55.0 | 1.6 (1.1, 2.0) |
22.5 (7.5, 37.4) |
|
| HealthifyMe (n = 80) | 72.9 ± 14.3 | 71.1 ± 14.4 | −1.8 ± 0.2* | −0.76 (−1.06, −0.47) P < .001 |
55.0 | 2.6 (1.9, 3.7) |
55.0 (42.4, 67.5) |
|
| Control (n = 80) | 71.0 ± 13.9 | 72.1 ± 13.5 | 1.1 ± 0.5* | .. | 32.5 | .. | .. | |
| Rural (n = 210) | BUD.D (n = 71) | 65.7 ± 13.9 | 65.1 ± 13.9 | −0.6 ± 0.1* | −1.27 (−1.73, −0.80) P < .001 |
66.2 | 1.8 (1.2, 2.6) |
30.0 (14.1, 45.7) |
| Sweet Hack (n = 70) | 61.5 ± 12.2 | 61.0 ± 12.1 | −0.5 ± 0.1* | −0.55 (−0.76, −0.34) P < .001 |
67.1 | 1.8 (1.3, 2.6) |
30.9 (15.1, 46.7) |
|
| Control (n = 69) | 67.3 ± 12.5 | 67.9 ± 12.9 | 0.6 ± 0.1* | 36.1 | ||||
Abbreviation: CI, confidence interval.
Risk reduction.
Absolute risk reduction.
Denotes significant difference from baseline to post-intervention.
Denotes significance between intervention and control group (difference in differences).
In the rural participants, similar differences were observed with statistically significant weight reductions in the digital intervention arms compared with control (BUD.D −0.6 ± 0.1 kg; Sweet Hack −0.5 ± 0.1 vs control +0.6 ± 0.1; P < .001) (Table 2), but not among each other.
Secondary Endpoints: Other Measures of Cardiometabolic Risk
For secondary outcomes (BMI, WC, BP, FBS, and total serum cholesterol), intervention groups in both urban and rural areas showed significant reductions compared with control groups (P < .001) (Figures 4 and 5 and Supplementary Table 1). However, no significant differences were found between the digital apps themselves. Table 3 shows that these reductions remained significant even after adjusting for confounders like age and education.
Figure 4.
Mean change in anthropometric, clinical, and biochemical parameters with the use of mobile apps in urban settings.
Figure 5.
Mean change in anthropometric, clinical, and biochemical parameters with the use of mobile apps in rural settings.
Table 3.
Regression Models Showing Association of Each Digital Intervention With Clinical and Metabolic Parameters.
| Group | Urban n = 360 (adjusted for age and education) | |||||||
|---|---|---|---|---|---|---|---|---|
| Variables | Weight (kg) | Waist circumference (cm) male | Waist circumference (cm) female | Systolic BP (mm Hg) | Diastolic BP (mm Hg) | FBS (mg/dL) | Total serum cholesterol (mg/dL) | |
| Control | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| BUD.D (n = 71) | Unadjusted | 0.46 (0.38, 0.55) | 0.71 (0.61, 0.83) | 0.85 (0.75, 0.99) | 0.86 (0.82, 0.90) | 0.88 (0.84, 0.91) | 0.93 (0.90, 0.95) | 0.94 (0.92, 0.96) |
| Adjusted a | 0.45 (0.37, 0.55) | 0.73 (0.61, 0.84) | 0.84 (0.73, 0.95) | 0.84 (0.82, 0.90) | 0.86 (0.83, 0.91) | 0.91 (0.90, 0.95) | 0.91 (0.88, 0.96) | |
| Sweet Hack (n = 69) | Unadjusted | 0.48 (0.39, 0.57) | 0.73 (0.62, 0.85) | 0.86 (0.76, 0.97) | 0.86 (0.83, 0.91) | 0.89 (0.86, 0.93) | 0.91 (0.88, 0.94) | 0.95 (0.92, 0.97) |
| Adjusted a | 0.47 (0.39, 0.57) | 0.75 (0.63, 0.87) | 0.83 (0.73, 0.94) | 0.88 (0.82, 0.91) | 0.86 (0.85, 0.93) | 0.91 (0.84, 0.95) | 0.94 (0.91, 0.96) | |
| Google Fit (n = 73) | Unadjusted | 0.57 (0.48, 0.68) | 0.76 (0.65, 0.87) | 0.90 (0.79, 1.01) | 0.92 (0.89, 0.96) | 0.93 (0.90, 0.96) | 0.94 (0.91, 0.96) | 0.96 (0.94, 0.98) |
| Adjusted a | 0.56 (0.47, 0.67) | 0.76 (0.65, 0.90) | 0.86 (0.76, 0.98) | 0.92 (0.87, 0.95) | 0.92 (0.89, 0.96) | 0.93 (0.90, 0.98) | 0.95 (0.93, 0.99) | |
| HealthifyMe (n = 71) | Unadjusted | 0.42 (0.35, 0.51) | 0.82 (0.71, 0.95) | 0.81 (0.71, 0.92) | 0.89 (0.85, 0.93) | 0.92 (0.89, 0.96) | 0.93 (0.90, 0.95) | 0.94 (0.92, 0.97) |
| Adjusted a | 0.40 (0.34, 0.51) | 0.85 (0.72, 0.99) | 0.78 (0.69, 0.90) | 0.87 (0.85, 0.93) | 0.90 (0.88, 0.95) | 0.91 (0.90, 0.98) | 0.96 (0.82, 0.97) | |
| RURAL n = 207 (adjusted for education) | ||||||||
| Control | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| BUD.D (n = 70) | Unadjusted | 0.58 (0.47, 0.72) | 0.87 (0.65, 0.92) | 0.81 (0.63, 0.95) | 0.90 (0.86, 0.94) | 0.94 (0.90, 0.97) | 0.91 (0.88, 0.94) | 0.95 (0.93, 0.97) |
| Adjusted a | 0.54 (0.47, 0.72) | 0.89 (0.67, 0.93) | 0.83 (0.61, 0.93) | 0.92 (0.86, 0.97) | 0.90 (0.84, 0.97) | 0.92 (0.85, 0.97) | 0.97 (0.91, 0.98) | |
| Sweet Hack (n = 70) | Unadjusted | 0.60 (0.49, 0.74) | 0.78 (0.59, 0.83) | 0.67 (0.53, 0.90) | 0.86 (0.82, 0.91) | 0.92 (0.88, 0.96) | 0.91 (0.87, 0.94) | 0.96 (0.94, 0.98) |
| Adjusted a | 0.62 (0.49, 0.75) | 0.75 (0.58, 0.85) | 0.71 (0.54, 0.93) | 0.84 (0.80, 0.91) | 0.92 (0.87, 0.97) | 0.90 (0.87, 0.94) | 0.93 (0.84, 0.98) | |
Abbreviations: WC, waist circumference; BP, blood pressure; FBS, fasting blood sugar.
Adjusted for age and education for urban setting and adjusted for education for rural setting. Results displayed as odds ratio (confidence intervals).
Effect of Engagement Calls on the Primary Outcome Body Weight
Table 4 shows the relationship between engagement calls and body weight. The table indicates that a higher number of calls in urban and rural areas are associated with a greater reduction in body weight. Despite variations in the number of calls, all apps show a significant reduction in body weight in both settings.
Table 4.
Effect of Engagement Calls on Body Weight.
| Average no of calls (CI) | Body weight difference | P a | |
|---|---|---|---|
| Mean ± SD | |||
| Urban only | 9 (7, 10) | −1.34 ± 0.2 | < .001 |
| Rural only | 5 (3, 6) | −0.60 ± 0.1 | < .001 |
| App wise (urban) | |||
| BUD.D | 9 (7, 11) | −1.43 ± 0.1 | < 001 |
| Sweet Hack | 8 (6, 10) | −1.30 ± 0.2 | < .001 |
| Google Fit | 7 (5, 9) | −0.60 ± 0.3 | < .001 |
| HealthifyMe | 9 (7, 11) | −2.01 ± 0.2 | < .001 |
| App wise (rural) | |||
| BUD.D | 7 (6, 9) | −0.466 ± 0.2 | < .001 |
| Sweet Hack | 8 (7, 9) | −0.51 ± 0.1 | < .001 |
Significant difference from baseline to post-intervention.
Lifestyle Parameters
Unadjusted and adjusted multilinear regression models that associate each digital intervention group with lifestyle parameters in urban and rural populations are shown in Table 5. Post-intervention, across the intervention groups in the urban area, participants consumed on an average 2.1% more fruits and vegetables (3-5 servings/day) and increased MVPA by 2.7%. Whereas, in the rural area, the increases were 1.8% and 1.5% respectively. Female participants showed a significant increase in fruit and vegetable intake in both areas and in MVPA in rural areas compared with men (Supplementary Figures 2 and 3).
Table 5.
Regression Models Showing Association of Each Digital Intervention With Diet and Physical Activity.
| Group | URBAN n = 360 (adjusted for age and education) | ||
|---|---|---|---|
| Variables | Fruits and vegetables (three to five servings/day) | MVPA (≥150 minutes/week) | |
| Control | 1 | 1 | |
| BUD.D (n = 71) | Unadjusted | 2.41 (1.59, 3.67) | 2.98 (1.52, 5.83) |
| Adjusted a | 2.49 (1.23, 5.06) | 2.61 (1.31, 5.18) | |
| Sweet Hack (n = 69) | Unadjusted | 2.06 (1.35, 3.14) | 3.03 (1.54, 5.98) |
| Adjusted a | 1.41 (0.67, 2.94) | 2.96 (1.44, 6.06) | |
| Google Fit (n = 73) | Unadjusted | 2.05 (1.37, 3.11) | 4.29 (2.15, 8.57) |
| Adjusted a | 1.89 (0.92, 3.85) | 2.63 (1.33, 5.20) | |
| HealthifyMe (n = 71) | Unadjusted | 3.00 (1.99, 4.64) | 4.43 (2.02, 8.93) |
| Adjusted a | 2.64 (1.23, 5.06) | 2.51 (1.26, 4.98) | |
| RURAL n = 207 (adjusted for education) | |||
| Control | 1 | 1 | |
| BUD.D (n = 70) | Unadjusted | 2.65 (1.17, 4.11) | 1.94 (1.26, 3.00) |
| Adjusted a | 2.63 (1.70, 4.09) | 1.96 (1.27, 3.02) | |
| Sweet Hack (n = 70) | Unadjusted | 1.27 (0.57, 2.79) | 1.46 (0.70, 3.03) |
| Adjusted a | 1.02 (1.01, 1.23) | 1.02 (1.01, 1.36) | |
Abbreviations: MVPA, moderate-to-vigorous physical activity; C, control.
Adjusted for age and education for urban setting and adjusted for education for rural setting. Results displayed as odds ratio (confidence intervals).
Discussion
In this randomized controlled pilot study, participants using mHealth apps demonstrated significant reductions in body weight and other health parameters compared with the control group participants across urban and rural settings. To our knowledge, this is one of the few mHealth studies to report on mHealth effectiveness on cardiometabolic risk factors and behavioral changes in India.
In the current study, intervention group participants lost between 1 and 2 kg of weight across apps. Furthermore, participants in the intervention group showed significant reductions in BMI (0.2%-0.8%), WC (male: 0.4%-1.3% and female: 2.0%-3.4%), systolic BP (4%-10%), diastolic BP (3%-7%), FBS (5.5%-9.7%), total cholesterol (5.6-11.7%) as well as positive changes in dietary and physical activity behaviors compared with control group participants in urban settings.
These findings are in keeping to other studies, making mHealth a potential scalable option for improving patient health, especially for weight reduction. 22 Although these results demonstrate modest levels of weight loss, it is important to note that participants did not gain weight, as observed in the control group. Interventions achieving such weight reductions can have meaningful impacts on various health metrics, and statistically significant improvements were observed across several parameters (BP, fasting blood glucose, total cholesterol, and lifestyle behaviors), which are well-documented risk factors to the progression of type 2 diabetes. Research supports that reductions in body weight, waist circumference, and BMI, even at low levels of 2% to 5%23,24 can lead to various health benefits, challenging the conventional threshold for effective weight loss. 24
An intensive digital RCT demonstrated that a 12-week intervention with texts, an email, coach calls, and access to resources via a website, 25 the intervention group lost 2.2 kg weight. Another 12-week mobile app intervention with weekly coach calls led to a significant 1 kg weight loss. 13 However, a similar 12-week intervention using texts, emails, and apps found no significant changes in body weight. 26 Early digital trials in South Indian people with prediabetes showed text message (SMS)-based lifestyle advice resulted in a relative risk reduction of 28.5% in type 2 diabetes at three years. 27 A Pan-India study also showed the intervention group participants receiving 56 text messages over six months improved in a health behavior composite score. 28 Other studies highlighted mHealth as a potential tool to improve the BMI, 29 waist circumference, 30 BP, 31 and total cholesterol. 32
Using digital health applications compared with in-person programs can be a scalable and sustainable way to deliver disease prevention, particularly in a country like India, to reduce cardiovascular risk. The Government of India has recently integrated over 100 health programs and apps with Ayushman Bharat Digital Mission (ABDM), 33 aiming to create a unified digital health ecosystem. Examples of its programs include the Health ID, Healthcare Professionals Registry (HPR), Health Facility Registry (HFR), and Ayushman Bharat Digital Health Application. These components aim to enable seamless access and exchange of health records, strengthen the health care delivery system, and empower individuals with their health information. The ABDM integrates various stakeholders like patients, providers, payers, and regulators to create an interoperable digital health infrastructure. 34 As a way forward, the integration of a diabetes digital prevention program under ABDM has the potential to address India’s growing burden of non-communicable diseases.
The engagement calls in this study lasted only 2 to 5 minutes to support app usage, and staff did not require any specific training or prior diabetes knowledge. Despite this, our pilot results demonstrate that engagement calls had a statistically significant impact on body weight in the urban intervention group. A randomized control trial in India (mDiab) involved high-risk T2D smartphone users, 12 using a mobile app, weekly coach calls, and T2D prevention videos. The intervention group experienced a significant 1 kg weight loss, with 15% meeting the 5% weight loss target. 12 Similarly, a recent meta-analysis of 2478 participants from 14 studies reported that the use of mobile apps combined with patient-centered health coaching can lead to significant weight loss of up to 2.2 kg in countries such as the United States, Australia, Belgium, Korea, and Japan. Most of the coaching sessions were delivered individually and 57.1% of the interventionists were dieticians. 35 Importantly, the provision of a weekly coach call/session (either individualized or as a group) has implications on scalability, requirement of trained staff, and long-term cost-effectiveness. However, it is likely the combination of digital tools and personal telephonic support can create a more comprehensive and well-rounded health care experience, increasing user acceptance of the overall program.
Worldwide, urban health care systems consume the majority of resources and facilities, often neglecting rural health. 36 mHealth can play a precedent-setting role to bridge this gap, but the studies rural areas are scarce. In our study, two health apps helped reduce body weight, BMI, waist circumference, BP, FBS, and total serum cholesterol. No change in physical activity was observed in the rural cohort, likely due to the higher baseline physical activity. With 70% of India still living in rural areas, even small lifestyle changes can significantly impact public health.
Strengths of the study include (1) an RCT design that tested two in-house and two commercially available mHealth apps for the prevention of diabetes and obesity and (2) mHealth apps were used across urban and rural settings.
Limitations of digital interventions can include exclusion of people who do not have an appropriate device, digital literacy of users, and service availability for data transfer, which may be limited by financial or infrastructure constraints. In this pilot study, the COVID-19 pandemic (Supplementary Table 2) led to dropouts, as participants relocated to other parts of the country. This also led to the research team having to contact the participants for a welfare check weekly, thus making this a digital study design aided with telephonic support. Furthermore, iOS (Apple) users were not included in the study due to the in-house apps only being available on Android, although it is important to note that 80% of the mobile market in India is held by Android users primarily due to cost. As the trial was short term, we were unable to determine whether behavioral changes brought about by the mHealth apps were maintained. Finally, convenience (snowball) sampling was conducted in the rural areas to leverage resources from another ongoing rural study, which could have created a bias. Further research with extended follow-up following digital interventions is required to evaluate cost-effectiveness, progression to diabetes, as well as long-term impact on complications.
Conclusions
The present pilot study demonstrates that mHealth interventions can be an effective and feasible method for improving cardiometabolic health, reducing diabetes risk, and improving lifestyle behaviors in South Asian populations. Further large-scale intervention studies are required to demonstrate efficacy, cost-effectiveness, scalability, and sustainability of using mHealth apps for CVD risk reduction with or without human support via brief telephonic calls.
Supplemental Material
Supplemental material, sj-docx-1-dst-10.1177_19322968241310861 for Effectiveness of Mobile Health Applications for Cardiometabolic Risk Reduction in Urban and Rural India: A Pilot, Randomized Controlled Study by Harish Ranjani, Parizad Avari, Sharma Nitika, Narayanaswamy Jagannathan, Nick Oliver, Jonathan Valabhji, Viswanathan Mohan, John Campbell Chambers and Ranjit Mohan Anjana in Journal of Diabetes Science and Technology
Acknowledgments
The authors thank Mr D. Pandiyan and Mr D. Sathish Kumar for leading the urban and rural field teams, respectively. The authors also acknowledge Ms K. Yuvarani and D. Vinothini’s contributions toward engagement calls. The authors gratefully acknowledge the participants who agreed to participate in this study.
Footnotes
Abbreviations: AI, artificial intelligence; Apps, applications; BMI, body mass index; BP, blood pressure; cm, centimeter; COVID-19, coronavirus disease; CVD, cardiovascular disease; FBS, fasting blood sugar; GHRU, Global Health Research Unit; ID, identification number; IDRS, Indian Diabetes Risk Score; Kg, kilogram; mg/dL, milligram per deciliter; mHealth, mobile health; mm Hg, millimeter of mercury; MVPA, moderate-to-vigorous physical activity; NCD, non-communicable disease; NIHR, National Institute of Health Research; RCT, randomized controlled trial; SD, standard deviation; SPSS, Statistical Package for Social Sciences; T2D, type 2 diabetes; TREND, telemedicine project for screening diabetes and its complications in rural Tamil Nadu.
Author Contributions: Conceptualization was done by HR and RMA, and writing the initial draft was done by HR. Study methodology by RMA, HR, PA, JCC, JV, and NO; data curation by SN and NJ; and review and editing by all authors. SN, NJ, RMA, and HR have directly accessed and verified the underlying data reported in the manuscript. All authors have read and approved the final version of the article and agree with the order of presentation of the authors. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JV was National Clinical Director for Diabetes and Obesity at NHS England from April 2013 to September 2023. During this time, he established and led the NHS Diabetes Prevention Programme, which has included digital interventions. NO has received honoraria for speaking and advisory board participation from Abbott Diabetes, Dexcom, Medtronic Diabetes, Tandem Diabetes, Sanofi, and Roche Diabetes and has received research funding from the National Institute of Health Research (NIHR), Diabetes UK, Medtronic Diabetes, and Dexcom.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the NIHR 16/136/68 & 132960, using UK aid from the UK Government to support global health research. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK government. Infrastructure support in the United Kingdom was provided by the NIHR Imperial Biomedical Research Centre, the NIHR North West London Applied Research Collaboration, and the NIHR Imperial Clinical Research Facility. Infrastructure support in India was provided by Dr Mohan’s Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu.
ORCID iDs: Harish Ranjani 
https://orcid.org/0000-0002-1521-7398
Parizad Avari
https://orcid.org/0000-0001-9047-3589
Sharma Nitika
https://orcid.org/0000-0001-9052-7719
Narayanaswamy Jagannathan
https://orcid.org/0000-0003-3804-5557
Nick Oliver
https://orcid.org/0000-0003-3525-3633
Jonathan Valabhji
https://orcid.org/0000-0001-9756-4061
Viswanathan Mohan
https://orcid.org/0000-0001-5038-6210
John Campbell Chambers
https://orcid.org/0000-0003-0209-4541
Ranjit Mohan Anjana
https://orcid.org/0000-0002-4843-1374
Data Availability: The de-identified data sets analyzed during this study are available from the corresponding author on reasonable request.
Supplemental Material: Supplemental material for this article is available online.
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Associated Data
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Supplementary Materials
Supplemental material, sj-docx-1-dst-10.1177_19322968241310861 for Effectiveness of Mobile Health Applications for Cardiometabolic Risk Reduction in Urban and Rural India: A Pilot, Randomized Controlled Study by Harish Ranjani, Parizad Avari, Sharma Nitika, Narayanaswamy Jagannathan, Nick Oliver, Jonathan Valabhji, Viswanathan Mohan, John Campbell Chambers and Ranjit Mohan Anjana in Journal of Diabetes Science and Technology