Ethics statement

The study was approved by the Health Ministry Screening Committee of the Government of India; ethics committees of the Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCT/IEC-333/May 2011), Trivandrum, India; Monash University (CF11/0457-2011000194); and The University of Melbourne (1441736) in Australia. Written informed consent was obtained from all study participants.

Study design

This study is reported in accordance with the Consolidated Standards of Reporting Trials guidelines for cluster RCTs (S1 Checklist) [19]. The details of the K-DPP study design have been described in detail elsewhere [18], and the protocol is available at https://link.springer.com/article/10.1186/1471-2458-13-1035. Briefly, K-DPP was a cluster RCT conducted in 60 polling areas (clusters) of Neyyattinkara taluk (subdistrict) in Trivandrum district, Kerala state. Polling areas are well-defined and identifiable locations demarcated with landmarks such as hills, rivers, roads, streets, etc. by the Election Commission of India [20]. A cluster design was chosen for the study, as the risk of contamination would otherwise be high among individuals from the same community.

Randomization and masking

At the time of study enrolment, Neyyattinkara taluk had 603 polling areas across four legislative assembly constituencies (LACs). Although there were maps available to locate polling areas in each of the four LACs, there was no single map connecting the four LACs, which could show the contiguous polling areas across the borders of LACs. To reduce the risk of selecting contiguous polling areas, we removed 244 polling areas that were located along the borders of the four LACs and selected at random 60 of the remaining 359 polling areas. The 60 polling areas were then randomly assigned in a 1:1 ratio to the control group (received an education booklet on general lifestyle advice) or the intervention group (peer-support lifestyle intervention) by an independent statistician using a computer-generated randomization sequence. After randomization, in both the intervention and control groups, there were two contiguous polling areas. Therefore, one polling area from each pair was replaced with a nearby polling area, which were at least two kilometers apart. Participants were masked to group assignment until the completion of the baseline assessment. Field staff members administering questionnaires (no group-specific questions were included in the questionnaires) and undertaking measurements at baseline and follow-ups, along with laboratory technicians and investigators, were masked. Field staff members administering the process evaluation questionnaire to intervention participants were not masked.

Participants

From the electoral roll of each of the 60 polling areas, 80 individuals (age 30 to 60 years) were selected randomly and were approached through home visits. Eligibility criteria included no history of diabetes or other chronic illness that might affect their participation in the trial, being literate in the local language (Malayalam), not being pregnant, and not taking medications known to affect glucose tolerance (glucocorticoids, antiretroviral drugs and antipsychotics). Those satisfying the eligibility criteria were screened using the Indian Diabetes Risk Score (IDRS), which comprises four simple parameters: age, family history of diabetes, physical activity (regular exercise or strenuous work), and waist circumference [21]. At the time of study enrolment, the IDRS was the only risk score from India that had been previously evaluated in a cohort study, with a score of ≥60 being a strong predictor of incident diabetes in Asian Indians [22]. Therefore, we chose the IDRS to screen and recruit our trial participants. Those with an IDRS score ≥60 were invited to attend a community-based clinic to undergo a 75-gm OGTT. Clinics were conducted in local neighborhoods in community buildings (e.g., schools, library halls, church halls). The OGTT was performed according to the World Health Organization (WHO) guidelines [23]. A venous blood sample was taken after an overnight fast for at least eight hours, and a second blood sample was collected two hours after oral ingestion of 75-g glucose dissolved in 250–300 ml of water. Those with fasting plasma glucose (FPG) ≥7.0 mmol/l or 2-hr plasma glucose (2-hr PG) ≥11.1 mmol/l or both were diagnosed to have diabetes based on the American Diabetes Association (ADA) criteria [12]. They were then referred to a healthcare facility and were excluded from the study. The remaining individuals were enrolled in the trial irrespective of their baseline glucose tolerance. If the participant had not fasted for the recommended time, they were asked to attend another clinic in a nearby neighborhood on a different day.

Interventions

Detailed information on the development and cultural adaptation of the intervention program have been reported elsewhere [24,25]. Briefly, the main theory underpinning the intervention program was the Health Action Process Approach model [26] with more emphasis given to collectivistic rather than individualistic strategies during the intervention design phase. The intervention program was adapted from the Finnish Good Ageing in Lahti Region (GOAL) program [27] and the Australian Greater Green Triangle (GGT) Diabetes Prevention Project [28] through situational analysis, needs assessment, and cultural translation [24,25]. This adaptation process was guided by the Intervention Mapping Approach [29]. The program utilized the core functions of peer support identified in the US Peers for Progress Program [16] and incorporated behavior change strategies that were identified from the needs assessment study [25]. The intervention model and program were tested and further refined following piloting with two groups in 2012–2013 [18].

The 12-month peer-support program consisted of 15 group sessions: an introductory session delivered by the K-DPP team; two education sessions conducted by local experts; and 12 sessions delivered by trained lay peer leaders. An introductory session was planned to introduce the group participants to the program and its mentoring style. The needs assessment study showed that at least one peer group session per month would be optimal and feasible to deliver the intervention [25], and thus 12 peer group sessions were planned. The needs assessment study also emphasized the importance of including sessions on diabetes prevention and management by local experts, as the knowledge on these among people with prediabetes in Kerala was low [25]. Furthermore, during the pilot phase [18], peer leaders were selected from within the groups, and their level of diabetes-specific knowledge was also limited. Therefore, we decided that information would be delivered by experts through two education sessions, and the peer leaders’ role would be to help participants to translate the information into their daily lives. To minimize resources, the education sessions were delivered to participants from two to three neighborhoods within close proximity. Participants were encouraged to bring family members along to further extend the reach of the education.

Procedures

Participants were assessed at baseline, 12 months, and 24 months. During each assessment, field staff members administered standardized and validated questionnaires to collect measures of sociodemographic characteristics, lifestyle behaviors, medical history, and health-related quality of life (HRQoL). Self-reported levels of physical activity were measured using the Global Physical Activity Questionnaire [31]. Intake of fruits and vegetables were assessed using a food frequency questionnaire [32]. HRQoL was assessed using the 36-item Short-Form (SF-36) health survey [33]. The SF-36 is divided into eight scales (physical functioning, role limitation—physical, role limitation—emotional, bodily pain, general health, mental health, social functioning, and vitality) and two domains (physical component summary and mental component summary). Scores for each of the scales and domains range from 0 to 100, with higher scores indicating better quality of life. The SF-36 data were converted into a six-dimensional health state called the Short Form 6 Dimension (SF-6D), whose score ranges between 0.29 (worse health) and 1.00 (full health). Following the 12-month intervention period, a process evaluation questionnaire was administered to intervention participants by field staff members who were different from those administering main questionnaires. During each assessment, anthropometry (height, weight, fat percent, muscle mass, waist circumference, and hip circumference) and blood pressure were measured, and blood samples were taken for the OGTT, HbA1c, and lipids according to standard protocols [34]. Individuals diagnosed with diabetes on the OGTT at 12-month follow-up were referred to healthcare facilities for treatment and care. However, they were still followed-up, although an OGTT was not performed at 24 months; instead, FPG alone was measured. Blood samples were centrifuged within 30 minutes of collection and transported in boxes with dry ice to a nationally accredited laboratory. Plasma glucose was measured using the hexokinase method on a COBAS 6000 analyzer, with kits supplied from Roche Diagnostics, Switzerland. HbA1c was measured using the high-performance liquid chromatography method on a D-10 BIORAD analyzer and lipids by enzymatic methods on a COBAS 6000 analyzer, using kits supplied by Roche Diagnostics, Switzerland. Low-density lipoprotein (LDL) cholesterol was estimated using the Friedewald equation [35] for participants with triglycerides ≤4.52 mmol/l, and for the rest, values obtained from the direct method were used in the analysis.

Outcomes

The primary outcome was the incidence of diabetes at 24 months, diagnosed by an annual OGTT, according to the ADA criteria (FPG ≥7.0 mmol/l and/or 2-hr PG ≥11.1 mmol/l) [12]. Participants who were diagnosed with diabetes by a physician and taking antidiabetic medications (‘clinical diagnosis’) subsequent to entry in the trial were also included in the primary outcome. Secondary outcomes included weight, waist circumference, waist-to-hip ratio, fat percent, muscle mass, systolic and diastolic blood pressure, FPG, 2-hr PG, HbA1c, lipid profile, IDRS score, ≥5 servings of fruit and vegetables intake per day, physical activity, tobacco use, alcohol use, and HRQoL.

Statistical analysis

Assuming an annual incidence of diabetes of 18.3% in the control group [4], an intracluster correlation coefficient (ICC) of 0.02 for plasma glucose [36], an average of 17 participants per group, at 5% significance with 80% power, allowing a loss to follow-up of 10%, the numbers of participants and polling areas per study group required were 510 and 30 respectively, to detect a relative risk reduction (RRR) of 30% at 24 months [18]. Funding dictated that the primary outcome be measured at 24 months’ follow-up. Since ICC values for diabetes incidence were not available from published studies, ICC for plasma glucose from a previous study [36] was used. However, a positive ICC for diabetes incidence was not observed in the trial. Hence, the 32% inflation of sample size for a design effect was redundant.

Baseline characteristics of clusters and participants are summarized using mean and standard deviation (SD) or median and interquartile range (IQR) for continuous variables and frequency and percentage for categorical variables. The analyses observed intention-to-treat, i.e., participants and clusters were analyzed according to the group to which they were allocated. There were a few changes to the analysis plan specified in the study protocol [18]. For the primary outcome analysis, instead of logistic regression and Cox-proportional hazards regression, to estimate the relative risk (RR) (and 95% confidence interval [CI] and P value) at 24 months, we used log binomial models estimated by generalized estimating equations (GEE) with an exchangeable working correlation structure and robust standard errors to account for clustering by polling areas. This approach gave improved interpretability of the intervention effect (as increased cumulative incidence (RR) rather than increased odds) and accorded with the use of RR in the protocol’s sample size. As diabetes was only observed systematically at 12- and 24-month time points a discrete time proportional hazards model was considered appropriate in place of the Cox model but this model provides little extra information above and beyond the log-binomial model for diabetes incidence at 12 and 24 months. We also conducted post hoc subgroup analyses by baseline glucose tolerance: normal glucose tolerance (NGT), isolated impaired fasting glucose (IFG), and IGT defined by the ADA criteria [12] and the WHO criteria [23]. To examine the heterogeneity of intervention effect by subgroup, an interaction term between the intervention assignment and subgroup was included in the GEE models, and its significance was tested using the Wald test. The subgroup analyses were done because the current literature on diabetes prevention programs has been largely limited to people with IGT [37], yet the target population in the real world is much broader.

For the analysis of continuous secondary outcomes, mixed-effects linear regression models were used and included outcomes at baseline, 12 months and 24 months, and included all participants with outcome data available at one or more of these timepoints. Skewed variables were log-transformed before analysis. Study group (intervention vs. control), timepoint (follow-up vs. baseline) and a study group-by-timepoint interaction were specified as fixed effects. Random effects were specified for polling areas, to account for the clustered study design, and for participants, to account for correlation between the repeated measurements on the same individual. The P value of the study-group-by-timepoint interaction was used to test the difference in change between study groups. For categorical secondary outcomes, the log binomial model was used.

We assessed the sensitivity of the primary outcome analysis to missing outcome data using multilevel multiple imputation (MMI), accounting for clustering [38]. We performed 10 imputations using GEE to fit log binomial imputation models for missing outcomes and with study group and the following baseline covariates included as auxiliary variables: age, sex, education, occupation, monthly household expenditure, current tobacco use, current alcohol use, fruit and vegetable intake (in servings/day), leisure time physical activity, family history of diabetes, body mass index, waist-to-hip ratio, fat percent, muscle mass, systolic blood pressure, diastolic blood pressure, FPG, 2-hr PG, HbA1c, LDL cholesterol, and triglycerides. The log RR (and its standard error) was computed on each multiply imputed dataset, and the results were combined to obtain the multiple imputation estimate using Rubin’s rule [39]. MMI was performed using the R Jomo package [40].

The costs associated with delivering the peer-support intervention over 12 months were estimated across five major categories (training sessions for peer leaders and LRPs, group sessions, resource materials, administrative costs, and community activities). The K-DPP personnel (intervention manager and intervention assistant) were interviewed to estimate the amount of time they spent on various intervention activities. Personnel costs were calculated based on the actual salary (or remuneration) paid to the intervention manager, intervention assistant, local experts, and LRPs. Nonpersonnel costs (travel, food and logistics, rent for venues, phone calls, designing and printing charges for resource materials, and administrative costs) were estimated based on the actual expenditure. The cost figures were obtained from the finance registers. The cost estimates in Indian Rupees (INR) were converted to US dollars using an exchange rate of INR58.6 = US$1 for the year 2013 [41].

A two-sided P value <0.05 was considered statistically significant for all analyses. Analyses were performed using Stata version 14.2 (StataCorp LP, College Station, Texas, USA), R 3.4.3, or Microsoft Excel 2016 (Microsoft corporation, Redmond, Washington, USA).

Participant flow and characteristics

Participants from the 60 polling areas were recruited between January 20, 2013, and October 27, 2013. Fig 1 shows the trial profile. A total of 3,689 individuals were contacted through home visits, of whom 137 (3.7%) did not satisfy the age criteria and were therefore excluded. Of the remaining 3,552 individuals, 131 (3.7%) declined participation, and 3421 were assessed for eligibility, of whom 835 (24.4%) were not eligible. Of 2,586 eligible individuals screened with the IDRS, 1,529 (59.1%) had a score ≥60, of whom 1,209 (79.1%) attended community-based clinics and underwent an OGTT. After excluding 202 (16.7%) individuals with diabetes, 1,007 (507 in the control group and 500 in the intervention group) were enrolled in the trial. Baseline characteristics of clusters and participants were similar between the study groups (Table 1). Participants’ mean age was 46.0 years, and the majority were male (52.8%), educated up to secondary school (75.6%), and employed (72.3%). According to the ADA criteria [12], 11.5% had IGT, 57.5% had isolated IFG, and 31.0% had NGT. The corresponding figures for the WHO criteria [23] were 11.5%, 22.5%, and 66.0%, respectively. The prevalence of several cardiovascular risk factors was high at baseline, as reported previously [42]. All clusters and 95.7% (964/1007) of participants were followed-up at 24 months (95.1% in the control group; 96.4% in the intervention group).

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Fig 1. Trial profile.

IDRS, Indian Diabetes Risk Score; OGTT, oral glucose tolerance test.

https://doi.org/10.1371/journal.pmed.1002575.g001

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Table 1. Baseline characteristics of clusters and participants by study group.

https://doi.org/10.1371/journal.pmed.1002575.t001

Process evaluation

Of the 15 total program sessions, participants attended a median of 9 (IQR 3 to 13) sessions; 10.8% attended all 15 sessions, 62.4% attended seven or more sessions, and 89.2% attended at least one of these sessions. Twenty-nine out of 30 groups delivered all of their 12 peer group sessions, according to the intervention protocol. Nearly two-thirds (61.8%, 309/500) of intervention participants reported that they had regular contact with peer leaders outside the formal group sessions during the 12-month program, with a mean number of contacts being 11.3 (SD 8.1). More than half (57.2%, 286/500) reported participation in community activities, including yoga sessions, kitchen gardening, and walking groups. Among those who did not attend formal program sessions (n = 54), one-third (33.3%) still reported that they had regular contact with their peer leaders outside the group sessions, and 16.7% also reported participating in community activities. Overall, only 27 participants (5.4%) did not have any exposure to the program.

Incidence of diabetes

After a median follow-up of 24 months, overall, diabetes developed in 147 participants (144 were diagnosed on the OGTT and 3 were clinically diagnosed): 17.1% (79/463) of participants in the control group and 14.9% (68/456) of participants in the intervention group. The RR was 0.88 (95% CI 0.66–1.16), p = 0.36. The RR did not change appreciably after excluding those with baseline HbA1c ≥6.5% (n = 52) (ADA [12] and International Expert Committee [43] cutoff value for diabetes) (RR 0.93, 95% CI 0.66–1.31, p = 0.66) and was similar to the results obtained using MMI (RR 0.86, 95% CI 0.61–1.13), p = 0.29.

The RR in the IGT subgroup (ADA and WHO criteria: 0.66, 95% CI 0.45–0.98, p = 0.038) was lower than that in the isolated IFG (ADA criteria: 0.95, 95% CI 0.68–1.33, p = 0.77; WHO criteria: 0.98, 95% CI 0.68–1.42, p = 0.92) or NGT (ADA criteria: 1.23, 95% CI 0.36–4.26; WHO criteria: 1.15, 95% CI 0.66–2.01, p = 0.63) subgroups. However, there was no evidence in favor of an interaction between baseline glucose tolerance and study group on diabetes incidence (ADA criteria: p = 0.24; WHO criteria: p = 0.11).

Secondary outcomes

Table 2 shows the changes in clinical and biochemical characteristics by study group at 24 months. The IDRS score reduced in both study groups, but the reduction was greater in the intervention group by 1.50 points (p = 0.022). Table 3 shows the changes in behavioral characteristics by study group at 24 months. Compared with the control participants, intervention participants were more likely to consume ≥5 servings of fruit and vegetables per day by 83% (p = 0.008). Intervention participants were 23% less likely to consume alcohol compared with the control participants (p = 0.018), and the amount of alcohol consumed was lower among intervention participants (p = 0.030). Table 4 shows the changes in HRQoL variables by study group at 24 months. Compared with the control group, the intervention participants had a greater increase in physical functioning score of the HRQoL scale by 3.9 points (p = 0.016). The 12-month changes in secondary outcomes by study group are given in S2, S3 and S4 Tables.

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Table 2. Changes in clinical and biochemical characteristics at 24 months by study group.

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Table 3. Changes in behavioral characteristics at 24 months by study group.

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Table 4. Changes in health-related quality of life variables at 24 months by study group.

https://doi.org/10.1371/journal.pmed.1002575.t004

Intervention costs

Table 5 shows the costs associated with delivering the peer-support intervention over 12 months. The total intervention costs amounted to US$11,225 (US$22.5 per participant). The group sessions were the largest cost contributor (52.8% of total costs), followed by designing and printing charges for resource materials (21.7%), administrative costs (13.8%), and training of peer leaders and LRPs (11.7%). Personnel costs accounted for 26.7% of the total costs. Community activities incurred no program costs.

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Table 5. Costs of the peer-support lifestyle intervention over 12 months.

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Adverse events

We recorded no adverse events related to the intervention.

Summary of principal findings

To our knowledge, K-DPP is the first RCT from a LMIC to evaluate the effectiveness of a peer-support lifestyle intervention delivered mainly by lay people in a community setting. This study showed that the intervention resulted in a non-significant reduction in diabetes incidence at 24 months in a high-risk population identified on the basis of a diabetes risk score. However, there were significant improvements in some cardiovascular risk factors, including IDRS score, fruit and vegetable intake, and alcohol use, and physical functioning score of the HRQoL scale.

Comparison with other studies

The trial was powered for a 30% RRR for diabetes incidence at 24 months and observed a 12% RRR (nonsignificant), which was lower than that reported in other effectiveness trials. A meta-analysis by Ashra and colleagues assessing the effectiveness of 13 pragmatic lifestyle interventions implemented in routine clinical practice showed that the pooled estimate of RRR was 26% [44]. In the Study on Lifestyle intervention and Impaired glucose tolerance Maastricht (SLIM) study, the RRR was 58% at three years [45]. In the Joetsu Diabetes Prevention Trial, the RRR varied from 27% (nonhospitalization with diabetes education and support) to 42% (short-term hospitalization with diabetes education and support) at three years [46]. In the Spanish Diabetes in Europe—Prevention using Lifestyle, Physical Activity and Nutritional Intervention (DE-PLAN) project, which was implemented in primary care settings, the RRR was 36% at four years [47]. In a mobile phone effectiveness study conducted in India, the RRR was 36% at two years [48]. In the Diabetes Community Lifestyle Improvement (D-CLIP) translational trial conducted in India, the RRR was 32% at three years [49]. The lower effect in our study could be attributed to the following reasons. In previous studies, most (if not all) participants had IGT, while in K-DPP, participants were identified on the basis of a risk score, and the majority had isolated IFG or NGT, albeit with a high burden of cardiovascular risk factors [42]. So far, from the limited recent literature available [49–51], there is no proven intervention to reduce diabetes incidence among those with isolated IFG. Furthermore, 24 months’ follow-up may not have been long enough to allow for an intervention effect to be observable, and thus a longer-term follow-up has been planned.

In the control group, there was a decline in fruit and vegetable intake and the reported level of physical activity at 24 months. These are consistent with findings from other recent longitudinal studies conducted in Kerala, showing that the proportion of people meeting the recommended intake of fruits and vegetables and level of physical activity is continuing to decrease over time in the absence of any intervention [52,53]. There was a greater increase in physical functioning score of the HRQoL scale in the intervention group at 24 months. Previous studies have shown that improvement in HRQoL is likely to be mediated by improved physical activity and weight loss [54].

In our study, the cost of delivering the peer-support lifestyle intervention over 12 months was US$22.5 per participant, a large percentage (52.8%) of which was accounted for by the group sessions, the main mode of formal program delivery. This is less than one-third of the intervention costs incurred in IDPP (US$75 per participant per year) [6]. The lower cost could be mainly attributed to the fact that, in IDPP, health professionals (physicians, dieticians, and social workers) were involved in delivering the intervention, while in K-DPP, the intervention was delivered mainly by lay peer leaders. In IDPP, the personnel cost was US$36 per participant, whereas in K-DPP, this cost was only US$6. However, the effect size in IDPP (28.5%) was higher than that in K-DPP (12%). It is possible that resource-intensive lifestyle interventions are more effective than low-resource interventions in reducing diabetes progression, at least in the IGT population (S5 Table and S1 Fig). If the K-DPP intervention were implemented as a real-world program, the unit costs for each individual would be much lower. This is because the one-off costs (e.g., training of peer leaders and LRPs and printing charges of resource materials) would be distributed over a much larger number of individuals, and the relative travel and administrative costs would also be lower. In K-DPP, travel and administrative costs accounted for one-third (33.3%) of the total costs. This is because the K-DPP personnel and local experts had to travel to the field, spending around three hours for every return trip. However, these costs would be relatively lower in a program setting and if the program were delivered at scale. Moreover, the very important K-DPP community activities incurred no program costs, as they were led by peer leaders with assistance from LRPs.

Strengths and limitations

The K-DPP trial has a number of strengths. The study was conducted in the Indian state of Kerala, which has a high prevalence of diabetes (approximately 20%) and several other cardiovascular risk factors [52,55,56]. The state is in the most advanced stage of epidemiological transition compared to other Indian states [57], and it is also the harbinger of the future for the rest of India in relation to the burden of chronic diseases [55,56]. Therefore, Kerala provides an ideal setting for the implementation and evaluation of a diabetes prevention program in a community setting in India. As far as we are aware, K-DPP is the first diabetes prevention trial to deliver a peer-support lifestyle intervention program mainly by trained lay people in a low- and middle-income setting. In the D-CLIP trial from Chennai, India, although peer support was provided by community volunteers, it involved a team of health coaches and fitness trainers in the delivery of intervention. Also, metformin (500 mg twice a day) was added to those at highest risk of developing diabetes [49]. Although metformin was found to be equally effective as lifestyle intervention in previous studies [2,4] and is cheap, the current evidence base supports its use only in combination with lifestyle interventions [58]. Other strengths of our trial include a very high follow-up rate at 24 months (97.5%), use of a rigorous study design, and a much better representation of women (nearly half the participants were women) compared to previous diabetes prevention trials in India [4,48,49,59]. However, there are also some study limitations. In subgroup analyses, balance of potentially confounding characteristics between the subgroups compared is not guaranteed, and the power may have been insufficient for such analyses. Data on behavioral risk factors (tobacco use, alcohol use, physical activity, and fruit and vegetable intake) were collected using questionnaires that were not validated by objective measures and are likely to be subject to response bias. It is possible that social desirability and acquiescence biases associated with the intervention may have resulted in the small differences observed in some of the behavioral outcomes at 24 months. Furthermore, we did not adjust for multiple comparison, and given the likelihood of type 1 errors [60], changes in secondary outcomes observed should be interpreted cautiously.

Implications for policy and future research

In efficacy trials of behavioral or social interventions, recruitment of highly selected individuals, resource-intensive interventions, and close monitoring to ensure compliance will almost always overestimate the outcomes that will actually be achievable under ‘real-world’ conditions [61]. However, given that the efficacy of lifestyle interventions to prevent diabetes among high-risk individuals, particularly among those with IGT, has been quite well established, it is now important to determine their effectiveness in real-world settings, in which the target population is likely to be much broader if program participants are not recruited on the basis of clinical testing [62]. Our study findings have some important implications for policy and future research with regards to diabetes prevention in India (and perhaps also in other LMICs). First, using a risk score rather than the OGTT to identify high-risk individuals was part of our strategy to develop a low-cost diabetes prevention program. While the IDRS with a score of ≥60 identified individuals with a high burden of cardiovascular risk factors [42], the majority had isolated IFG or NGT and not IGT. The results of our subgroup analyses suggest a trend towards greater reduction in diabetes incidence among those with IGT compared to those with isolated IFG or NGT. As mentioned previously, so far, lifestyle interventions have not been shown to be effective in reducing diabetes risk among those with isolated IFG [49–51]. Further research is required to determine the optimal cutoff for the IDRS to identify those at highest risk of developing diabetes. Alternatively, risk scores that are better at picking up people with IGT could be developed. Second, given the high burden of cardiovascular risk factors in the trial population [42] and improvements observed in some of these at 24 months, it is important to evaluate the potential longer-term benefits of the intervention on both diabetes incidence and cardiovascular risk. Finally, although the K-DPP intervention was low-cost and delivered mainly by lay people in community neighborhoods with support from local self-government bodies, it is important to do more research on how to increase program adherence and engagement, possibly by using more flexible modes of program delivery, e.g., at worksites and by text messaging. This research should also consider how continued program implementation beyond the current 12-month program for group participants can be supported by developing partnerships with other kinds of community organizations or partnerships that could deliver the intervention at scale in Kerala and elsewhere in India in the future.

Conclusions

In this low- and middle-income setting, a low-cost peer-support lifestyle intervention resulted in a nonsignificant reduction in diabetes incidence at 24 months in a high-risk population identified on the basis of a risk score. However, there were significant improvements in some cardiovascular risk factors and physical functioning score of the HRQoL scale.