Abstract
Objectives
Telemedicine has emerged as a promising solution for enhancing Type 2 diabetes management, which aims to reduce healthcare costs and improve patient outcomes. Despite its potential benefits for diabetes management in India, knowledge of its clinical effectiveness is scarce. This meta-analysis examines the clinical effectiveness of telemedicine versus physical consultation in managing Type 2 diabetes in India.
Methods
The meta-analysis adhered to the PRISMA-P guidelines and was registered with PROSPERO (CRD42023400562). PubMed and EMBASE were searched for eligible studies from 1st January 2012 to 30th June 2022. Included were studies on Type 2 diabetes patients in India comparing telemedicine (Intervention) with physical consultation (conventional) regarding effect on HbA1c changes. Risk of bias for randomised studies of Intervention (RoB-2) was used for study quality assessment.
Results
We found four articles that met the eligibility criteria, all reporting quantitative HbA1c change. The telemedicine interventions included video consultation, health systemcomputer-generated telephonic messages and a Video-Based Lifestyle Education Programme. Upon pooling the results from these studies, an overall analysis indicated a non-statistically significant reduction in HbA1c levels following the intervention compared to conventional treatment (pooled difference in means = -0.03, 95% CI = -0.23 to 0.17, Z = 0.30 P = 0.76). Likewise, there was no statistically significant difference in secondary outcomes (Body Mass Index, body weight and lipid profile) between both treatment groups.
Conclusion
Enhancing telemedicine approaches may hold promise in improving type 2 diabetes management in India, although the existing evidence remains inconclusive. Further research involving long-term telemedicine interventions would help to provide more substantial evidence on the clinical effectiveness of telemedicine for Type 2 diabetes management in India.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40200-024-01471-x.
Keywords: Diabetes, Telemedicine, Clinical effectiveness, HbA1c, India
This article is a meta-analysis of the clinical effectiveness of telemedicine compared with traditional clinical visits for the management of Type 2 diabetes in India.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40200-024-01471-x.
Introduction
The prevalence of diabetes poses a significant challenge to global health, as approximately 463 million individuals were living with diabetes worldwide in 2019. This figure is projected to surge to 700 million by 2045 [1]. Diabetes management requires a multifaceted approach, including lifestyle modification, medication, and regular monitoring of blood glucose levels. However, traditional healthcare delivery models may not be able to provide adequate access to care for all patients, especially those in rural or remote areas. Diabetes is a significant public health issue in India, where an estimated 77 million individuals were living with the condition in 2019. This number is projected to escalate to 134 million by 2045, emphasising the growing magnitude of the problem [1].
Telemedicine, the delivery of healthcare services through technology-enabled platforms, has emerged as a potential solution to improve diabetes management by increasing access to care, reducing healthcare costs, and improving patient outcomes [2]. Telemedicine for diabetes management can take many forms, including remote monitoring of blood glucose levels, virtual consultations with healthcare providers, and self-management education [3]. The advantages of telemedicine during the COVID-19 pandemic situation have also been well documented. Literature shows telemedicine has helped deliver care to type 2 diabetes patients during the COVID-19 lockdown, with 63.1% of patients having reported improved HbA1c [4]. In January 2021, the Indian Council of Medical Research-National Centre for Disease Informatics and Research (ICMR-NCDIR) in Bangalore, India, released telemedicine guidelines for treating cancer, diabetes, cardiovascular disease, and stroke in India [5]. These guidelines offer comprehensive recommendations and detailed instructions to healthcare providers concerningtelemedicine, prescriptions, and other related aspects. The Government of India has taken steps to make telemedicine accessible to rural communities. Since its inception, the government’s telemedicine platform, eSanjeevani, has conducted over 122 million free online consultations [6]. The Digital India program includes initiatives to create a network of telemedicine centres nationwide, providing access to doctors, diagnosis, and treatment to people in remote areas. With suitable investments and initiatives, telemedicine could revolutionise healthcare delivery and make it easier for people in remote areas to receive the care needed.
Despite the potential benefits of telemedicine in diabetes management in India, knowledge of its clinical effectiveness is scarce. Performing a meta-analysis that examines telemedicine’s clinical efficacy in managing type 2 diabetes is crucial. This type of analysis assesses telemedicine’s potential advantages and disadvantages, establishes a systematic framework for evaluating its influence on patient outcomes and healthcare provision, and supports informed decision-making regarding its implementation. Some meta-analyses have been done using global literature [7–9]. Regarding the Indian context, although studies have examined the effectiveness of telemedicine for diabetes management, there is a need for a comprehensive meta-analysis which would help inform and shape telemedicine policies and practices. Therefore, this meta-analysis examines the clinical effectiveness of telemedicine versus physical consultation for managing Type 2 diabetes in India.
Materials and methods
Protocol and registration
The meta-analysis adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) [10]. The meta-analysis protocol was also registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023400562).
Eligibility criteria
Original studies based on randomised controlled trials or quasi-experimental studies conducted among patients aged 18 years and above with Type 2 diabetes in Indian settings that compared telemedicine with physical consultation, for which outcomes were reported regarding a change in HbA1c were included. Case reports, case studies, review articles, letters and non-English literature were excluded.
Data sources and data search
PubMed and EMBASE databases were used to access published studies that met the eligibility criteria. The reference list of included papers was explored for a bibliographic search for additional articles. The review included studies were available from 1st January 2012 to 30th June 2022, given that the growth of telemedicine has gained momentum in India over the past decade with several initiatives like e-Sanjeevani, Telemedicine Practice Guidelines by the Ministry of Health and Family Welfare (MOHFW) along with National Institution for Transforming India (NITI Aayog) and National Digital Health Mission, which have contributed to creating an enabling platform for further growth of Telemedicine in India. Table 1 lists the keywords used in the search process.
Table 1.
Search terms related to telemedicine
| Main keyword | Related search terms | MeSH Terms |
|---|---|---|
| Telemedicine | “Tele-communication”, “text messages”, “video conferencing”, “m-Health app and surveys”, “E- Health”, “M-Health”,“mhealth”, “Smartphone”, | “Telemedicine” |
| Diabetes | “diabetes”, “diabetes mellitus”, “Type 2” | “Diabetes Mellitus”, “diabetes mellitus, type 1”, “diabetes mellitus, type 2”, “diabetes complications” |
| Standard care | “Clinic visits”,“physical consultations”, “hospital visit” | |
| Clinical effectiveness | “treatment outcome” “treatment” “outcome” OR “clinical” “effectiveness” “clinical effectiveness”, “ glycosylated haemoglobin”, “ glycosylated”, “ HbA1c” | “clinical effectiveness”, “treatment outcome” |
Data extraction
The data were obtained from studies that met the eligibility criteria. Two researchers independently selected the study, and a third party resolved disagreements. Data extraction followed a systematic approach. Piloted forms or templates were initially developed to ensure consistency and accuracy during extraction. These forms contained predefined fields for relevant study details, including the first author’s name, title, publication year, study setting, design, duration, and the gender proportion and mean age of participants. Furthermore, it covered the type of telemedicine intervention, mean HbA1c concentration (%) in telemedicine and physical consultation groups, and secondary outcomes, including lipid profile, Body Mass Index (BMI), and body weight. Data extraction was conducted independently by two researchers to minimise bias and errors. Each researcher reviewed the included studies and extracted data according to the predefined forms. Any discrepancies or disagreements between the researchers regarding the extracted data were resolved through discussion or consultation with a third party, such as a senior researcher or the principal investigator.
Risk of bias assessment
The methodological quality of the selected studies was evaluated by two independent investigators using the ‘Risk of bias for randomised studies of Intervention (RoB-2) tool’ [11]. RoB-2 examines various aspects of a trial such as design, execution and documentation, categorising the studies as “low risk,” “high risk,” or “unclear,”. The two investigators critically analysed various aspects, including random sequence generation, allocation concealment, blinding of patients, personnel, and assessors, adequate assessment of each outcome, avoidance of selective outcome reporting, and the inclusion of an intention-to-treat analysis.
Statistical analysis
The meta-analysis was conducted using Rev Man 5.4 software and Rayyan QCRI software to assess HbA1c levels at the final visit in the intervention group (telemedicine) and the usual care group (Physical consultation) ) in terms of difference in means accompanied by a 95% confidence interval (CI) [12, 13]. Study heterogeneity was evaluated using the Cochran Q statistic and χ2 test, and the degree of heterogeneity was quantified by I2, which indicates the percentage of total variability that is not attributable to chance. To address the high study heterogeneity, as noted in an I2 value above 50%, a random-effect model (DerSimonian–Laird method) was employed [14]. The mean difference (MD) and its corresponding 95% confidence interval (CI) were calculated using this model and presented using a forest plot. Statistical significance was determined at a p-value of < 0.05. The assessment of publication bias was not performed since, as a general guideline, tests for funnel plot asymmetry should only be applied when at least ten eligible studies are available for inclusion in the meta-analysis [15, 16]. With fewer studies, these tests lack sufficient power to distinguish chance from actual asymmetry.
Results
Study selection
A total of 733 records were identified in the electronic search from PubMed and Embase databases. The selection protocol is presented as per the PRISMA flowchart (Fig. 1). The details of search strategies and keywords used in PubMed and Embase databases are shown in the Supplementary Table at the end of the paper.
Fig. 1.
PRISMA flow diagram for selection of studies
Characteristics of the included studies
The four included studies [17–20] were conducted in a sample of 2091 patients in the intervention group and 2104 patients in the usual care [physical consultation group]. The duration of telemedicine intervention ranged from 4 to 12 months. The telemedicine interventions included video consultation, a mhealth system, computer-generated telephone messages, and a video-based lifestyle education program. The details are shown in Tables 2 and 3. The Risk of bias assessment scores are shown in Table 4. All the studies had a low risk of bias concerning the three domains: missing outcome data, measurement of the outcome and selection of the reported result. However, there were ‘some concerns’ about domains 1 and 2 since it is not possible to blind patients for the kind of treatment that they receive.
Table 2.
Summary of glycated haemoglobin (HbA1c) levels at baseline and final visit
| Authors (year) | Comparison | Device | Duration of follow-up, months | Number of patients | Male/female (%) |
Mean age∗ | HbA1c (%)∗ | |
|---|---|---|---|---|---|---|---|---|
| Baseline | Final visit | |||||||
| Dutta et al., (2021) | Intervention | Video consultation | ≤ 6 months | 48 | 60.41/39.58 | 54.7 ± 12.2 | 8.7 ± 1.8 | 6.9 ± 1.1 |
| Usual care | 48 | 47.91/52.08 | 56.4 ± 15.2 | 8.6 ± 2.1 | 7 ± 1 | |||
| Gupta et al., (2020) | Intervention | Video-based lifestyle education program (VBLEP) | 4 months | 40 | 45/55 | 50.1 ± 9.4 | 8.51 ± 0.74 | 7.86 ± 1.04 |
| Usual care | 41 | 58.5/41.5 | 50.2 ± 8.6 | 8.39 ± 0.65 | 8.38 ± 1.37 | |||
| Sadanshiv et al., (2020) | Intervention | Computer-generated telephonic message | 6 months | 161 | 57.8/42.2 | 48.7 (7.4) | 7.86(1.48) | 7.28(1.14) |
| Usual care | 159 | 52.2/47.8 | 47.9 (8.0) | 7.37(1.23) | 7.31(1.28) | |||
| Prabhakaran et al., (2019) | Intervention | mWellcare | 12 Months | 1842 | NA | 55.8(11.0) | 9.5 (2.2) | 7.6 (2.3) |
| Usual care | 1856 | 55.2/ 42.9 | 54.5(10.9) | 9.3 (2.4) | 7.5 (2.4) | |||
*Mean ± S, D
Table 3.
Summary of total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, BMI, fasting glucose and weight levels at baseline and final visit
| Authors (year) |
Comparison | Total cholesterol, (mg/dL) * | HDL Cholesterol (mg/dL) * | LDL Cholesterol (mg/dL) * | Triglycerides (mg/dL) * | BMI, kg/m2* | Fasting glucose (mg/dL)* | Weight (Kg)* | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Baseline | Final visit | Baseline | Final visit | Baseline | Final visit | Baseline | Final visit | Baseline | Final visit | Baseline | Final visit | Baseline | Final visit | ||
|
Dutta et al., (2021) |
Intervention | NA | NA | 42.5 ± 12.7 | 41.1 ± 11.5 | 95.3 ± 35.6 | 88.7 ± 33.7 | 196 ± 111.4 | 151.2 ± 41.1 | 29.2 ± 6.6 | NA | 184.1 ± 69 | 120.3 ± 20.8 | 81.1 ± 19.5 | 82.6 ± 18.3 |
| Usual care | NA | NA | 45.8 ± 11.1 | 34 ± 9.8 | 104.6 ± 39.7 | 77.3 ± 14.8 | 151.7 ± 58.1 | 117.4 ± 30.6 | 28.4 ± 5.2 | NA | 184.9 ± 73.1 | 118.6 ± 29.3 | 74.6 ± 16.5 | 76.2 ± 14.7 | |
|
Gupta et al., (2020) |
Intervention | 79.2 ± 18 | 79.2 ± 18 | 19.8 ± 3.6 | 18 ± 3.6 | 45 ± 16.2 | 45 ± 16.2 | 32.4 (27-43.2) | 28.8(23.4–41.4) | 28.4 ± 5.4 | 28.4 ± 5.0 | 158.4 ± 52.2 | 149.4 ± 39.6 | 72.7 ± 14.1 | 72.2 ± 13.6 |
| Usual care | 77.4 ± 18 | 75.6 ± 18 | 19.8 ± 5.4 | 18 ± 5.4 | 43.2 ± 12.6 | 43.2 ± 14.4 | 27 (21.6–36) | 27(19.8–36) | 27.1 ± 4.1 | 27.0 ± 4.0 | 151.2 ± 27 | 153 ± 50.4 | 70.4 ± 11.8 | 70.0 ± 11.7 | |
| Sadashiv et al., (2020) | Intervention | NA | NA | NA | NA | NA | NA | NA | NA | 26.53 (3.49) | 25.90 (3.40) | NA | NA | 69.05(10.26) | 67.28(9.82) |
| Usual care | 25.82 (3.15) | 25.76 (3.09) | NA | NA | 66.52(9.74) | 66.35(9.66) | |||||||||
| Prabhakaran et al., (2019) | Intervention | 194.5 (45.0) | 193.8 (45.2) | NA | NA | NA | NA | NA | NA | 26.0 (4.7) | 26.2 (4.6) | 185.9 (60.5) | 150.9 (66.8) | 64.6 (13.1) | 65.1 (12.2) |
| Usual care | 191.8 (44.8) | 194.7 (45.2) | NA | NA | NA | NA | NA | NA | 25.8 (4.6) | 26.0 (4.6) | 197.7 (67.0) | 148.7 (67.9) | 65.4 (12.3) | 65.6(11.9) | |
*Mean ± S.D
Table 4.
Risk of bias scoring for included studies
| Dutta et al., (2021) | Gupta et al., (2020) | Sadashiv et al., (2020) | Prabhakaran et al., (2019) | ||
|---|---|---|---|---|---|
| Domain 1 | Risk of bias arising from the randomisation process | Some concerns | Some concerns | Some concerns | Some concerns |
| Domain 2 | Domain 2: Risk of bias due to deviations from the intended interventions (effect of assignment to intervention) | Some concerns | Some concerns | Some concerns | Some concerns |
| Domain 3 | Missing outcome data | Low | Low | Low | Low |
| Domain 4 | Risk of bias in measurement of the outcome | Low | Low | Low | Low |
| Domain 5 | Risk of bias in selection of the reported result | Low | Low | Low | Low |
Results of the meta-analysis
Primary results (effect on HbA1c)
A random-effects model was employed to analyse the mean difference in HbA1c values between the telemedicine and usual care group at the final visit due to the moderate heterogeneity observed in the studies (I2 = 44%). The overall results demonstrated a change in HbA1c values of -0.03 [-0.23, 0.17], which was not statistically significant (Fig. 2).
Fig. 2.
Forest plot on the meta-analysis results regarding HbA1c difference (primary outcome)
Secondary results
Impact on fasting blood glucose
Three studies (13, 14, 15) provided data fasting blood glucose (FBG) at the final visit. The intervention group comprised 1930 cases, while the control group comprised 1945 cases. The duration of the interventions ranged from 4 to 12 months. The meta-analysis revealed no statistically significant reduction in FBG levels between the intervention and usual care groups (MD: 1.90, 95% CI [-2.02, 5.81], p = 0.34; I2 = 0%), as illustrated in Fig. 3.
Fig. 3.
Forest plot on the meta-analysis results regarding (a) fasting blood glucose (FBG) change, (b) BMI change and (c) Body weight
Effect on Body Mass Index (BMI)
Three studies (13, 15, 16) provided data on Body Mass Index (BMI). The intervention group had 2043 cases, while the control group had 2056 cases. The duration of the interventions ranged from 4 to 12 months. The meta-analysis indicated no statistically significant difference in BMI levels at the final visit between the intervention and usual care groups (MD: 0.21, 95% CI [-0.06, 0.49], p = 0.12; I2 = 0%), as depicted in Fig. 3.
Effect on body weight
The results of the effect on body weight were reported in all four studies. The meta-analysis revealed no statistically significant difference in body weight at the final visit between the intervention and usual care groups (MD: 0.721, 95% CI [-1.18, 2.62], p = 0.46; I2 = 51%), as depicted in Fig. 3.
Lipid profile
Two studies examined the effects on the lipid profile In a sample of 1882 patients in the intervention group and 1897 patients in the usual care group, (13, 15). The results of the meta-analysis showed no significant difference ibetween the intervention and usual care group fortriglycerides (MD: 17, 95% CI [-14.32,48.32], p < 0.29 I2 = 94%]) and High Density Lipoprotein or HDL (MD: 3.29, 95% CI [-3.65,10.23], p = 0.35; I2 = 89%]) at the final visit. Also, no significant difference was noted between the total and LDL cholesterol levels in the telemedicine and physical consultation group (Fig. 4).
Fig. 4.
Forest plot on the meta-analysis results regarding lipid profile, (a) Effect on total cholesterol, (b) Effect on LDL cholesterol, (c) Effect on HDL cholesterol & (d) Effect on triglycerides
Discussion
The present meta-analysis included four studies from India that compared the clinical effectiveness of diabetes management through telemedicine and clinic visits among patients with type 2 diabetes regarding the effect on HbAIc, fasting glucose and other outcomes such as body mass index and lipid profile. While two studies [18–19], used video-based telemedicine consultations, the others used computer-generated telephonic messages [20] and mHealth-Based Electronic Decision Support System [17].
The results reported from this meta-analysis showed no improvement in glycated haemoglobin levels among telemedicine users despite the moderate heterogeneity, contrary to the meta-analysis of global studies on telemedicine’s clinical effectiveness for managing diabetes [7–9]. One of the reasons the present meta-analysis could not demonstrate any significant change could be that out of four studies, the maximum duration of telemedicine intervention was six months in three studies. The impact of telemedicine on glycemic control is higher for programmes lasting for more than six months in other meta-analyses. In their meta-analysis, Zhang A. et al. (2022) conducted a subgroup analysis based on intervention duration, wherein it was observed that the intervention group exhibited a reduction in HbA1c levels compared to the conventional group. Still, this effect was only evident with a 6-month intervention period [8]. Conversely, no substantial enhancement in glycosylated haemoglobin was noted during interventions in shorter-term (3 months) or longer-term (12 months). Likewise, other studies also show that short-term telemedicine interventions fail to implement effective glycemic control [21, 22]. Gupta et al., (2020) and Prabhakaran et al., (2019) also analysed the effect of education as a predictor for a favourable outcome for HbA1c reduction but could not demonstrate any significant effect.
Further, the effect of treatment for type 2 diabetes takes time to reflect on the change of HbA1c, which indicates the glucose level over an average duration of three months [21]. This gives rise to the need for long-standing longitudinal studies comparing both treatment groups to generate more accurate treatment efficacy results. However, as demonstrated by Zhang A, et al., the reduction in efficacy after 12 months of intervention could be associated with how patients respond to the treatment, aligning with findings from similar studies. This indicates that one of the limitations of telemedicine is the loss of patient compliance to treatment due to a lack of in-person communication. Consistent monitoring and personalised reminders play a crucial role in sustaining the ongoing nature of telemedicine intervention.
Our meta-analysis did not reveal any statistically significant reduction in fasting blood glucose levels between the intervention and control groups, contrasting the results and findings of Zhang A et al. Similar to our study, Zhang A et al., in their meta-analysis of 32 studies, did not observe any beneficial impact on lipid metabolism and weight reduction [8] []. The control of the lipid profile in a diabetic patient is contingent upon lifestyle modification and drug therapy for diabetes [22].
Since this meta-analysis could not effectively demonstrate the clinical effectiveness of telemedicine intervention, there remains a gap in our understanding of whether telemedicine is an advantage for improved disease control compared to clinic visits. Four studies fulfilled the eligibility criteria for the meta-analysis and assessed the change in HbA1c over a given period. However, there was no uniformity in assessing secondary outcomes such as lipid profile, body mass index and body weight across the four studies. Some of the typical limitations of telemedicine include possible deterioration of the rapport between healthcare provider and patient, challenges of a technical and structural nature, absence of integration between diabetes telemedicine platforms and hospital electronic medical records and insufficient data encryption and security measures to guarantee comprehensive patient privacy. In India, the tradition of patient physical visits is widely acceptable, and telemedicine interventions may take time to gain mass acceptance. Factors such as inadequate and poor infrastructure, low technological literacy, lack of rapport with the healthcare provider and concerns with data security have been identified as potential challenges in delivering effective telemedicine interventions in India.
Nevertheless, telemedicine is a potential solution for ensuring improved access to care for those living in difficult-to-reach geographic areas and those with limited mobility. While telemedicine is gaining an increasing momentum in India, it should not be limited to improving access to carebut it must be strengthened as an intervention that ensures better metabolic control. Diabetes is a complex disease, and a wide range of lifestyle and psychosocial factors influence its treatment outcomes. Although studies from India and other parts of the world have addressed clinical effectiveness, there is a need for further understanding of operational challenges in the effective uptake of telemedicine interventions in the country’s settings and solutions that focus on improvement in treatment adherence and patient empowerment. With the expanding availability of national telemedicine services under e-Sanjeevani, efforts must be geared towards sustainable technological infrastructure and technological literacy, culminating in a reduction in diabetes-related morbidity and high patient satisfaction.
Limitations
The limited number of studies included in this analysis may have introduced publication bias; that is, studies that had reported non-significant effects are less likely to have been published [23]. The different telemedicine applications and methods used in the selected studies could have contributed to heterogeneity. Further, there was a notable variation in the sample size of selected studies, wherein as many as 88.15% of the participants were from a single study by Prabhakaran et al. This could have contributed to heterogeneity. Due to the limited number of studies, conducting a sub-group analysis was not feasible to verify which method and duration of use could be more effective. None of the included studies blinded its participants, although it is not possible to blind the study participants in such kind of interventions [24]. However, this could give rise to a bias in the study results, given that patients who were allocated into either intervention or control groups adopted particular preventive measures/lifestyle modifications that could have influenced study outcomes [25].
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors acknowledge the support of Dr Prashant Mathur, Director, ICMR-NCDIR, in the conduct of this work.
Author contributions
Concept and design: Anita Nath. Acquisition of data: Stany Mathew, Apourv Pant, Yamini Thadi. Analysis and interpretation of data: Stany Mathew, Apourv Pant. Drafting of the manuscript: Anita Nath, Stany Mathew, Apourv Pant. Critical revision of the paper for important intellectual content: Anita Nath, Stany Mathew, Apourv Pant, Yamini Thadi.
Funding
This work was funded by the Health Technology Assessment in India (HTAIn), Department of Health Research, Ministry of Health and Family Welfare, Govt of India.
Data availability
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Declarations
Ethical approval
The study was approved by the IEC committee of ICMR-NCDIR [NCDIR/IEC/3067/2023].
Conflict of interest
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data sharing is not applicable to this article as no new data were created or analysed in this study.