This paper is in the following e-collection/theme issue:

Nursing and Care for Patients with Infectious or Chronic Conditions (29) Apps, Mobile, Wearables for Diabetes (337) Diabetes Education and Elearning for Health Professionals (15) Diabetes Technology Around the World (26) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (642) Cost-Effectiveness and Economics (297) Web-based and Mobile Health Interventions (3680)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72230, first published .
Effect of Digital Tools on the Knowledge and Performance of Frontline Health Workers For Diabetes Control in Myanmar: Cost-Effective Analysis and Quasi Experimental Study

Effect of Digital Tools on the Knowledge and Performance of Frontline Health Workers For Diabetes Control in Myanmar: Cost-Effective Analysis and Quasi Experimental Study

Effect of Digital Tools on the Knowledge and Performance of Frontline Health Workers For Diabetes Control in Myanmar: Cost-Effective Analysis and Quasi Experimental Study

Authors of this article:

Kyi Thar1 Author Orcid Image ;   Sathirakorn Pongpanich1 Author Orcid Image ;   Min Nwe Tun2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1College of Public Health Sciences, Chulalongkorn University, Sabbasastravicaya Building (10th - 12th Floor), Soi Chulalongkorn 62, Phyathai Rd, Pathumwan, Bangkok, Thailand

    2Diabetes Foundation Myanmar, Yangon, Myanmar

    Corresponding Author:

    Sathirakorn Pongpanich, PhD


    Background: Diabetes has become a significant global health issue, particularly imposing a deep economic burden on low-income countries. Innovative and integrated digital solutions can reduce the impact of diabetes and enhance the quality of care. However, digital solutions have not been utilized before in Myanmar.

    Objective: This study aimed to demonstrate the novel integrated effect of diabetes knowledge and registry tools on the performance of front-line health workers in primary health care settings.

    Methods: A quasi-experimental study with an intervention and a control group was conducted in two townships from October 2022 to April 2023. For the first time, researchers trained the intervention group to use digital tools for diabetes control and performed monthly follow-ups. The study employed multiple linear regression models to explore the novel impact of digital tools on knowledge and performance scores, their correlations, and their association with covariates. Additionally, it assessed the cost-effectiveness of the intervention by using self-administered questionnaires as measurement tools formulated based on the National Diabetes Guidelines.

    Results: A total of 96 participants were enrolled in the study, divided evenly into the two groups. The intervention group exhibited a significant increase in the mean knowledge scores from 85.81 to 99.25 (P<.001) and performance scores from 71.22 to 107.16 (P<.001). The intervention accounted for 43.2% of the variance in knowledge scores and 62.5% in performance scores (P<.001). A positive correlation was found between knowledge and performance scores (r=0.45, P<.001). The intervention was also cost-effective, with a cost-effectiveness analysis value of 0.711 and an incremental cost-effectiveness ratio of 10127.04 Kyats (US$ 4.83).

    Conclusions: As the new integrated intervention yields significant economic gains and positive effects, researchers suggest policy makers replicate this intervention as a nationwide program and recommend scaling up the use of digital tools to improve knowledge and performance for diabetes control in frontline health workers.

    JMIR Nursing 2025;8:e72230

    doi:10.2196/72230

    Keywords

    diabetes mellitus; digital tools; cost-effectiveness analysis; health personnel; Myanmar 


    Global Situation Regarding Diabetes

    Diabetes is a life-threatening chronic disease that requires effective and sustainable care and treatment. In 2021, it was responsible for 6.7 million deaths worldwide, and the number of people affected was estimated at 537 million, projected to rise to 783 million by 2045 [1]. Despite the increasing burden of diabetes, there is a shortfall of 5.9 million health care professionals required to provide quality care for people living with diabetes [2]. Moreover, many low-income countries face challenges in translating evidence-based knowledge, cost-effective guidelines, and electronic records into actionable solutions to enhance the ability of frontline health workers to deliver quality diabetes services [3].

    Health IT has great potential for enhancing diabetes management by saving time and costs involved in data interpretation. Digital knowledge tools can serve as an effective resource for bridging knowledge gaps among health care providers; however, the integrated effect of knowledge tools and diabetes registry on provider performance is not well known [4]. The World Health Organization (WHO) indicates that only 50% of countries use electronic diabetes registries and expects national data to be standardized when registry coverage exceeds 75% [5]. Many low-income countries still struggle to provide comprehensive digital tools for knowledge and registries for frontline health workers. Myanmar should seize these opportunities to improve community-level diabetes care management.

    Cost-effective interventions are urgently needed to address the diabetes burden, which cost US$ 966 billion globally in 2021. Southeast Asia’s expenditure was significantly lower at US$ 10.1 billion, compared to North America’s US$ 414.5 billion [6]. Therefore, it is vital to implement high-impact and affordable solutions in low-income countries. A meta-analysis shows that digital tools for diabetes knowledge can be cost-effective [7], yet there is limited evidence in low-income settings [8]. Therefore, it is essential to perform integrated and interdisciplinary research on digital tools for the effective implementation of diabetes control programs in the region.

    Myanmar Context for Diabetes

    Myanmar is one of the countries in Southeast Asia with a high burden of diabetes, with a prevalence among 10.5% of the population, which is comparatively higher than that in other countries in the region [9]. A diabetes prevalence survey conducted in 2014 revealed that the burden of the disease had doubled over a decade, and there were no effective strategies or guidelines implemented to raise awareness about diabetes management [10]. Additionally, health workers need to enhance their knowledge, and further research is necessary to improve the quality of diabetes control services at the primary health care level [11].

    The rapid growth of mobile technology in Myanmar has created new opportunities for digital health. In 2014, the Ministry of Health established an electronic health management information system and a real-time District Health Information System for all townships. They also distributed 26,000 tablets with essential guidelines for frontline health workers [12]. However, the digital health information system is still in its early stages, and no specific digital application for the diabetes control program exists.

    Objectives

    Unlike other studies, this study aimed to evaluate the novel integrated effects of digital tools on diabetes knowledge and registry in relation to diabetes control performance among frontline health care workers in Myanmar. The primary outcomes of the study were the knowledge and performance levels of the health workers, while the secondary outcome was the cost-effectiveness of the intervention.

    Conceptual Frameworks

    Researchers designed the study based on two main theories: attribution theory, which examines how knowledge affects diabetes management [13]; and an economic principle assessing the cost-effectiveness of digital health technology [14]. The research hypothesized that integrated digital tools would enhance frontline health workers’ knowledge and performance by reducing diabetes program costs.


    Study Design

    A quasi-experimental study was conducted in Naypyitaw, the capital of Myanmar, between October 2022 and April 2023 (spanning 6 months). Two townships were selected for the intervention and control groups based on matched population characteristics, geographical conditions, and access to essential diabetes control packages offered by the diabetes control program. The selection criteria for the study areas included a high unknown prevalence of diabetes among the population and inadequate knowledge among health workers [15]. Baseline and endline assessments were carried out for both the groups.

    Participants

    Frontline health staff, including midwives, lady health visitors, and public health supervisors, were selected for the study based on specific inclusion criteria: involvement in the diabetes control program, ability to use digital tools, and willingness to participate. Exclusion criteria included those absent for over 1 month, nearing retirement, deemed unfit for intervention, or not approved by supervisors. Using G*Power software (version 3.1.9.2; Heinrich-Heine-Universitat Dusseldorf), the sample size was calculated for multiple linear regression with a 95% CI and power. The reference minimum effect size of the intervention on diabetes control was F4,75=.25 [16]. The minimum sample required was 86 participants, and 96 were recruited to account for dropouts, finally assigning 48 participants to each group.

    Interventions

    The new intervention involved integrating the two new digital tools developed by the authors, Myanmar Diabetes Guides and Digital Registry. This is unique and significant because other studies measure the silo effects [17]. Researchers installed digital tools and provided 3 days of intensive training on how to use the tools. Additionally, researchers conducted monthly follow-ups and provided reorientation sessions to the intervention group.

    Myanmar Diabetes Guide

    This is a new comprehensive bilingual knowledge tool developed by researchers in collaboration with the National Diabetes Control Program. According to the WHO and National program guidelines, the tool addresses the risks and promotes the health, screening, diagnosis, care and treatment, and complication referrals. It includes interactive patient dialogues, video demonstrations, and diabetes-related wikis. The tool is open to access in both online and offline settings and can be used on mobile tablets [18].

    Diabetes Registry Tool

    The electronic diabetes registry tool was designed using Kobo Toolbox, an open-source tool for field data collection in humanitarian response [19]. This tool collects vital patient information for diabetes management programs and replaces paper-based reporting. The tools enable health workers to trace risk factors, analyze data, calculate prevalence, identify complications, and estimate the requirement of diabetes-related commodities. The application is available for online and offline use and is compatible with both tablets and computers with a user password to protect data privacy [19].

    Training for Utilization of the Application

    Researchers conducted training on the orientation for using the digital tools that consisted of three components with lectures, demonstration, and practice sessions on (i) health promotion, identifying high-risk individuals, and establishing volunteer networks; (ii) training for the diabetes knowledge tools; and (iii) training for the diabetes registry tool.

    Measurement Instruments

    The researchers developed self-administered questionnaires for data collection. The questionnaire was created in English and subsequently translated into the Burmese language. It included a scoring system for the primary outcomes, which assessed the knowledge and performance in five key domains of diabetes management: (i) health promotion, (ii) diabetes screening, (iii) care and treatment, (iv) referral, and (v) reporting, all referencing the WHO and National Diabetes Guidelines. The researchers established an expert panel to review the questionnaire to ensure content validity. This panel included a diabetes program manager, an expert clinician, and township health officers. The validity index for the questionnaire was scored at 0.6. Additionally, the reliability of the questionnaire was pretested by two different observers, with an interrater kappa value of 0.68 between the two observers.

    The WHO-CHOICE (WHO’s Choosing Interventions that are Cost-Effective) Analysis tool for noncommunicable diseases was used to measure costing data [20]. This costing data included direct costs (intervention costs, program costs, and treatment costs) and indirect costs (communication, consulting, value of time, and work). The cost-effectiveness analysis aimed to demonstrate the economic benefits of the intervention for future investments.

    Data Collection and Statistical Analysis

    The principal investigator and two research assistants collected baseline and endline data. Before data collection began, the study’s purpose was explained to authorities and participants. Primary cost data were obtained from participants, while secondary data on treatment costs came from the township hospital and program costs from the township health departments. The team checked the accuracy of the questionnaires, addressed any missing responses, and cross-verified participant data with the secondary data. Health assistants from the townships were followed-up monthly on using the tools.

    Data analysis was conducted using SPSS software (version 22.0; IBM Corp). Knowledge and performance scores followed the National Diabetes Control Guideline criteria. Sociodemographic data differences were assessed with the χ2 test, mean outcome data with the t test, and outcome correlations with the Pearson correlation test. The impact of the intervention was evaluated using multiple logistic regression analysis. All tests were statistically significant at a 95% CI. Cost-effectiveness was assessed through the cost-effectiveness ratio and incremental cost-effectiveness ratio.

    Ethical Considerations

    The study received approval from the Chulalongkorn University Ethics Review Committee (090.2/64, COA No. 177/2022). The Ministry of Health, Naypyitaw Department of Health, granted permission for data collection (NPT/NCD/007-2021/5925). Participation in the research was voluntary; informed consent was obtained, and data confidentiality was ensured. During the registration, an internet package (US$ 2) and a 50 pcs box of surgical masks were provided to participants as compansation for participating the research.


    Patient Inclusion

    Ninety-six participants enrolled in the study, and 1 patient dropped out. Figure 1 shows the flow chart of the quasi-experimental study design.

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    Figure 1. Flow chart for quasi-experimental design.

    Participant Sociodemographic Characteristics

    The study used the χ2 test to examine participant characteristics in relation to the sociodemographic variables, work-related variables, and variables related to diabetes control (Table 1). Significant differences between the two groups were observed based on gender (P=.02), job designation (P=.02), distance from the township (P=.001), and duration of internet use (P=.001). Independent t tests analyzed differences in mean knowledge and performance scores. The mean knowledge scores were statistically associated with gender (P=.001), job designation (P<.001), and diabetes control training (P=.02). The mean performance scores were statistically associated with gender (P=.04). job designation (P=.009), diabetes control training (P=.02), diabetes registry training (P=.02), number of postings (P=.03), level of facilities (P=.02), and experiences with diabetes campaigns (P=.007).

    Table 1. Sociodemographic variables of the participants (n=96).
    Sociodemographic variablesTotal (n=96)Intervention group (n=48)Control group (n=48)P value
    Age (years), mean (SD)32.71 (9.52)31.75 (9.18)33.67 (9.55).33a
    Gender, n (%).02b,c
    Male14 (14)3 (6.3)11 (22.9)
    Female82 (82)45 (93.7)37 (77.1)
    Marital status, n (%).33b
    Single31 (32.3)14 (29.2)17 (35.4)
    Married65 (67.7)34 (70.8)31 (64.6)
    Educational status, n (%).58b
    High school24 (25)11 (22.9)13 (27.1)
    Graduate72 (75)37 (77.1)35 (72.9)
    Designations, n (%).02b,c
    Lady health visitors8 (8.3)4 (8.3)4 (8.3)
    Midwives55 (57.3)34 (70.9)21(43.8)
    Public health supervisor33 (34.4)10 (20.8)23 (47.9)
    Number of postings, n (%).73b
    1st posting55 (57.3)27 (56.3)28 (58.3)
    2nd-5th posting30 (31.3)16 (33.3)14 (29.2)
    >5th posting11 (11.4)5 (10.4)6 (12.5)
    Level of facilities, n (%).74b
    Township19 (19.8)11 (22.9)8 (16.7)
    Rural health center21 (21.9)10 (20.8)11 (22.9)
    Subcenter56 (58.3)27 (56.3)29 (60.4)
    Distance from township, n (%).001b,c
    ≤10 miles55 (57.3)19 (39.6)36 (75)
    >10 miles41 (42.7)29 (60.4)12 (25)
    DMd control training, n (%).50b
    Received before67 (69.8)33 (68.8)34 (70.8)
    Never received29 (30.2)15 (31.2)14 (29.2)
    DM registry training, n (%).21b
    Received before47 (49)26 (54.2)21 (43.8)
    Never received49 (51)22 (45.8)27 (56.2)
    DM campaign experiences, n (%).11b
    Received before57 (59.4)25 (52.1)32 (66.7)
    Never received39 (40.6)23 (47.9)16 (33.3)
    Duration of internet usage, n (%).001b,c
    ≤5 years49 (51)35 (72.9)14 (29.2)
    >5years47 (49)13 (27.1)34 (70.8)

    aindependent t-test.

    bχ2 test.

    cstatistically significant P<.05.

    dDM: diabetes mellitus.

    Effect of Intervention on Knowledge

    An average of 94.2% of the intervention group (45/48) regularly utilized diabetes knowledge tools. During the baseline assessment, no significant difference in the mean knowledge scores was noted between the two groups (P=.20). However, a significant difference emerged between the two groups at the endline (P<.001). The mean (SD) knowledge score in the intervention group rose significantly from 85.04 (9.73) to 99.25 (5.33; P<.001). In contrast, the mean (SD) knowledge score in the control group slightly declined from 83.58 (11.17) to 80.47 (16.99) (Figure 2); however, this change was not significant (P=.21; Table 2). The multiple linear regression model, after adjusting for potential confounding factors, showed a significant effect, with an adjusted r² of 0.43, an unstandardized β coefficient of 17.769, and a standardized β coefficient of 0.569 (P<.001; Table 3).

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    Figure 2. Comparison of mean outcomes between the intervention and control: (A) mean knowledge scoring, (B) mean performance scoring.
    Table 2. Comparison of mean outcome variables between the intervention and control groups.
    Outcome variableTotal
    Mean (SD)
    N Ba=96
    N Eb=95    		Intervention group
    Mean (SD)
    N B=48
    N E=48    		Control group
    Mean (SD)
    N B=48
    N E=47    		P valuec
    Knowledge Scoring
    Total knowledge scoring B85.04 (9.73)85.81 (8.20)83.58 (11.17).20
    Total knowledge scoring E87.25 (21.67)99.25 (5.33)80.47 (16.99)<.001d
      Health promotion B20.01 (2.51)20.00 (2.37)19.95 (2.71).76
      Health promotion E20.22 (4.83)22.35 (1.53)19.31 (3.96)<.001d
      Screening and diagnosis B19.23 (3.52)19.25 (3.55)19 (3.44).55
      Screening and diagnosis E19.93 (5.34)22.68 (2.41)18.41 (4.46)<.001d
      Care and treatment B16.82 (3.12)17.68 (2.69)15.97 (3.29).08
      Care and treatment E18.15 (5.46)21.54 (1.67)15.72 (4.55)<.001d
      Referral of severe cases B14.34 (2.44)14.43 (1.85)14.10 (2.91).47
      Referral of severe cases E14.63 (3.94)16.46 (1.43)13.50 (3.42)<.001d
      Reporting and registry B14.52 (2.24)14.43 (2.21)14.54 (2.33).89
      Reporting and registry E14.45 (3.86)16.21 (1.50)13.52 (3.19)<.001d
    Performance scoring
    Total performance scoring B72.33 (9.73)71.22 (28.35)73.29 (35.66).75
    Total performance scoring E88.50 (33.50)107.16 (15.62)70.04 (29.72)<.001d
      Health promotion B9.23 (4.22)9.54 (3.99)8.91 (4.23).46
      Health promotion E10.59 (3.8)12.42 (2.15)8.77 (3.44)<.001 d
      Screening and diagnosis B9.41 (4.32)9.38 (4.28)9.42 (4.26).97
      Screening and diagnosis E10.37 (4.1)12.56 (2.89)8.17 (3.13)<.001d
      Care and treatment B38.93 (20.83)37.17 (19.57)40.54 (22.06).43
      Care and treatment E49.01(21.6)59.08 (12.21)38.95 (21.17)<.001 d
      Referral of severe cases B4.24 (4.14)3.89 (4.21)4.73 (4.21).34
      Referral of severe cases E5.37 (5.3)7.13 (6.24)3.60 (3.49).001d
      Reporting and registry B10.42 (6.23)11.23 (5.68)9.56 (6.58).19
      Reporting and registry E13.24 (6.5)15.95 (4.20)10.54 (6.78)<.001d

    aB=baseline.

    bE=endline.

    cindependent t-test.

    dstatistically significant P<.05.

    Table 3. Effect of intervention on total knowledge scoring after adjusting for all possible confounding variables (full model multiple linear regression analysis).
    VariablesTotal knowledge score at endline (n=95)
    βSE95% CIStandardized β coefficientt test (df)P value
    Intervention township17.7672.94211.92 to 23.610.5696.040 (7)<.001a
    Baseline knowledge score.4970.1370.23 to 0.770.3113.630 (7)<.001a
    Gender−1.8763.932−9.69 to 5.93−0.042−0.477 (7).63
    Designation of work2.7262.168−2.69 to 7.960.1051.258 (7).21
    Distance from township2.6332.697−3.50 to 7.290.0830.983 (7).33
    Duration of internet usage.0420.423−0.79 to 0.880.0090.100 (7).92
    Diabetes control training6.0042.7450.549 to 11.460.1772.187 (7).03a

    asignificant at P<.05.

    br2=0.472.

    cAdjusted r2=0.430.

    dFitness Sample Corrected Akaike’s Information Criterion (AICC)=758.765.

    Effect of Intervention on Performance

    A total of 91.2% (44/48) of the intervention group regularly used the registry tool and registered 1747 diabetes patients within 6 months. The mean (SD) performance score in the intervention group significantly increased from 71.22 (28.35) to 107.16 (15.62; P<.001). In contrast, the control group experienced a decrease in the mean (SD) performance scores from 73.29 (35.66) to 70.04 (29.72; P=.22). At baseline, the two groups had no significant difference in the mean performance scores (P=.75). At the endline, a significant difference was noted between the two groups (P<.001; Table 1). A multiple linear regression model, adjusted for potential confounding factors, indicated a significant effect, with an adjusted r² of 0.642, an unstandardized β coefficient of 33.143, and a standardized β coefficient of 0.554 (P<.001; Table 4).

    Table 4. Effect of intervention on the total performance score after adjusting for all possible confounding variables (full model multiple linear regression analysis).
    VariablesTotal performance score at endline (n=95)
    βSE95% CIStandardized β coefficientt test (df)P value
    Intervention township33.1434.52024.15 to 42.130.5547.332 (11)<.001a
    Baseline performance score.4990.670.36 to ‐0.630.5327.483 (11)<.001a
    Gender8.7066.134−3.49 to 20.900.1031.419 (11).16
    Designation of work3.0544.103−5.11 to 11.210.0610.744 (11).46
    Distance from township7.4194.247−1.03 to 15.860.1231.747 (11).08
    Duration of internet usage−.6700.668−1.99 to 0.66−0.71−1.003 (11).32
    Diabetes control training−.3395.795−11.86 to 11.18−0.005−0.058 (11).95
    Diabetes registry training1.8865.210−8.47 to 12.250.310.362 (11).72
    Diabetes campaign experience−.1704.319−8.76 to 8.42−0.003−0.039 (11).97
    Level of facility2.9062.552−2.17 to 7.980.0771.138 (11).26
    Number of postings−.7732.210−5.167 to 3.62−0.028−0.350 (11).73

    asignificant at P<.05.

    br2=0.684.

    cAdjusted r2=0.642.

    dFitness Sample Corrected Akaike’s Information Criterion (AICC)= 845.994.

    Correlation Between Knowledge and Performance

    The study showed a significant positive correlation between total knowledge and performance scores, with a correlation coefficient of r=0.45 (P<.001) at the endline (Figure 3). A significant correlation was found in the intervention group compared to the control group, with r=0.34 (P=.02). However, no significant correlation was observed in the control group, with r=0.02 (P=.89)

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    Figure 3. Correlation between mean knowledge and performance scoring (n=95). (A) Total scoring, (B) health promotion, (C) screening and diagnosis, (D) care and treatment, (E) referral, (F) registry and reporting.

    Cost-Effectiveness of the Intervention

    Researchers categorized the cost data into three main categories: (1) intervention costs, which included web application development, training, internet usage, and stationery; (2) program implementation costs, covering travel expenses, costs for consultation and communication, loss of income due to diabetes-related work, and other miscellaneous costs; and (3) treatment costs for complications. The total cost for the intervention group was 22,213,000 Kyats (US$ 10,586.71), while the control group incurred a total cost of 22,779,000 Kyats (US$ 10,856,4714) (Table 5). The intervention was deemed cost-effective, with a cost-effectiveness ratio of 0.711, and it was considered cost-effective when the cost-effectiveness ratio was less than 1. The study also analyzed the incremental cost-effectiveness ratio for comparative investment. The incremental cost-effectiveness ratio indicated saving 10,127.04 Kyats (US$ 4.83) for both outcomes, 30,154.50 Kyats (US$ 14.40) for knowledge, and 15,247.84 Kyats (US$ 7.27) in performance. The visibility of cost-effectiveness was demonstrated by plotting bootstrap results against outcomes and expenses (Figure 4).

    Table 5. Summary of costing and outcome for cost-effective analysis.
    Overall and detailed costsIntervention groupControl group
    Intervention cost (Kyats)a
    Training and software cost2,000,000 (US$ 953.20)0 (US$ 0)
    Internet cost4,662,000 (US$ 221.91)1,878,000 (US$ 895.05)
    Stationary cost949,000 (US$ 452.29)1,262,000 (US$ 601.47)
     Sub total7,611,000 (US$ 3627.40)3,140,000 (US$ 1496.52)
    Hospital expense (Kyats)
    Care and treatment cost due to diabetes complications4,600,000 (US$ 2192.36)6,000,000 (US$ 2859.6)
     Sub total4,600,000 (US$ 2192.36)6,000,000 (US$ 2859.6)
    Staff expense (Kyats)
    Travel cost2,460,000 (US$ 1172.44)3,484,000 (US$ 1660.47)
    Consultation cost for diabetes1,825,000 (US$ 869.79)2,524,000 (US$ 1202.94)
    Communication cost1,716,000 (US$ 817.85)1,891,000 (US$ 901.25)
    Loss of income due to extra workload1,280,000 (US$ 610.05)2,807,000 (US$ 1337.82)
    Miscellaneous cost2,721,000 (US$ 1296.83)2,933,000 (US$ 1397.87)
     Sub total10,002,000 (US$ 4766.95)13,639,000 (US$ 6500.35)
    Total cost (Kyats)22,213,000 (US$ 10,586.72)22,779,000 (US$ 10,856.47)
    Outcomes
    Outcome scoring  
    Mean knowledge scoring99.2580.48
    Mean performance scoring107.1670.04
    Total outcome score206.41150.52

    aA currency exchange rate of Kyat 1=US $0.72 is applicable.

    ‎
    Figure 4. Cost-effectiveness between the intervention and control.

    Principal Findings

    The intervention of new integrated digital tools saved costs and yielded significant positive outcomes in knowledge and performance of diabetes control among frontline health workers. Although the study is quasi-experimental, the researcher minimized confounding by matching selection criteria and adjusting covariates through multiple linear logistic regression. Consequently, the study achieved its goals through a consistent design and reliable analytical methods, resulting in valid outcomes. The overall results align with other research on digital solutions to enhance diabetes control [21].

    Comparison With Previous Studies

    Digital Knowledge Solution for Diabetes Control

    This study significantly observed that using integrated digital tools could enhance five key domains of knowledge and performance related to health promotion, screening, care and treatment, referral, and reporting diabetes in primary health care settings. Integrating digital knowledge tools and a registry tool is an effective intervention for diabetes control among frontline health workers. A systematic review of evidence-based medicine found that digital knowledge tools can improve diabetes control knowledge among primary health care staff [22]. Digital tools can improve the screening process, as supported by other meta-analyses regarding the performance of diabetes screening [23]. Additionally, positive effects of digital tools on providers’ performance were observed in areas such as reminders, clinical care decisions, glycemic control, and web-based training and education programs [24].

    While this study concentrated solely on the provider side, digital tools can offer numerous patient benefits, such as increased awareness, improved understanding, and enhanced self-management skills within the community [25]. However, a meta-analysis conducted in Southeast Asia found that patients’ knowledge of diabetes was inadequate, especially among women with low education levels and poor diabetes control [26]. Therefore, further research and tailored training in digital interventions are recommended to improve knowledge and awareness among patients, their families, and the community.

    Diabetes Registry for Electronic Health Records

    A meta-analysis using electronic health records for diabetes across 12 countries showed positive outcomes [27]. A diabetes registry can enhance the quality of patient care in rural areas, both in high-income countries like the United States of America [28] and in various low-income countries [29]. Unlike other studies, this research connected the positive results of using a digital diabetes registry and knowledge tools in routine diabetes program reporting, especially for resource-limited settings. The findings showed that reporting performance was significantly improved, and several patients were registered correctly. Therefore, the authors recommend adopting an open-resource, low-cost digital diabetes registry as a nationwide program for diabetes control in Myanmar and other low-income countries.

    Cost-Effectiveness on Diabetes Interventions

    This study is significant because it measures the direct and indirect costs associated with diabetes management using the WHO-CHOICE formula. Furthermore, the cost analysis was conducted on both knowledge and performance outcomes. Digital monitoring for diabetes has gained popularity alongside increased access to high-speed internet. This advancement has helped reduce costs, lower the number of hospital visits, save time, and improve the quality of life for those managing diabetes [30]. However, some studies suggest that the cost categories related to diabetes are too complex to provide accurate data [31,32]. Additionally, other research indicates that cost analyses may be inadequate due to factors, such as underlying socioeconomic conditions, underreporting, the severity of complications, and the long-term effects of diabetes [32].

    Generally, an intervention is considered cost-effective when the cost-effectiveness ratio is less than 1. This study demonstrated cost-effectiveness with a cost-effectiveness ratio of 0.711. Similar evidence supporting cost-effectiveness has been observed in consumer-based solutions, digital tools for blood glucose, and diabetes self-management education in the United Kingdom [33]. This study explored the incremental cost-effectiveness ratio to assess the additional investment needed to enhance knowledge and performance scores for diabetes control. In contrast, another study conducted in Sweden examined incremental cost-effectiveness ratio results related to diabetes control through patients’ quality-adjusted life years [31]. Nevertheless, this study urges policy makers to consider further investments in digital tools, even though a sophisticated cost-effectiveness framework has not yet been developed.

    Limitations

    According to this study, despite several benefits, the rollout and sustainability of the digital diabetes registry encountered some limitations. Initially, the studies intended to measure baseline, midterm, and endline assessments. However, the authority approved only two measurements based on the country’s political situation and the expectation of no significant variation in the midterm. Although the initial goal was to collect real-time data, health staff could only upload information monthly due to being overburdened with competing priorities. The study focused on Naypyitaw, which limits generalizability to the entire country, and only has a 6-month duration, so it cannot evaluate the long-term impacts. The study population focused solely on public providers, excluding private providers, patients, and the wider community. Furthermore, the digital tools were not interoperable with the District Health Information System.

    Conclusions

    The intervention used a multidisciplinary approach for frontline health personnel at the grassroots level, significantly improving knowledge and performance and reducing program costs. Unlike other studies, this research demonstrated the integrated and correlated effects of digital knowledge and reporting tools. Given these strengths and limitations, researchers recommend that policy makers replicate the intervention nationwide, develop clear standard operating procedures, establish a reporting schedule, and provide an internet data package to enhance the use of digital tools. Furthermore, the diabetes registry operates in isolation, necessitating the creation of an interoperable system to connect with the District Health Information System. Additionally, extensive studies on long-term population research and economic evaluations are essential to evaluate the sustainability of digital tools. We suggest engaging outstanding community nurses as champions to share their best practices of digital applications, and these measures could ensure data quality and sustainability of digital tools to enhance diabetes control in Myanmar and other low-income countries.

    Acknowledgments

    The authors acknowledge the National Diabetes Control Programme of Myanmar and the College of Public Health Sciences, Chulalongkorn University, for their guidance and support. Novo Nordisk Myanmar is especially recognized for providing the necessary technical support. The authors acknowledge the technical expert group that validates measurement tools. The authors highly appreciate the support of the township health authorities and the contribution of participants from Leway and Tatkone townships to the study.

    This article is submitted to accomplish the PhD thesis requirements for Chulalongkorn University Phase II (Intervention Phase).

    Data Availability

    The datasets for this study will be available in Chulalongkorn University’s i-Thesis system and can also be obtained from the corresponding author upon reasonable request.

    Authors' Contributions

    KT contributed to the conceptualization, design, methodology, development of measurement tools, data collection and analysis, provision of training, follow-up, visualization, and the writing of the original paper. SP contributed to the conceptualization, supervision, and provision of overall advice for structuring the original paper. MNT contributed to developing measurement tools, supporting data collection and analysis, and reviewing the paper. All authors take responsibility for data integrity, and all authors have read and approved the final version of the manuscript.

    Conflicts of Interest

    None declared.

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    ‎
    WHO: World Health Organization
    WHO-CHOICE: WHO’s Choosing Interventions that are Cost-Effective

    Edited by Elizabeth Borycki; submitted 06.02.25; peer-reviewed by Olalekan Kehinde, Olumide Ibikunle, Pahurat Kongmuang Taisuwan; final revised version received 02.04.25; accepted 15.04.25; published 16.06.25.

    Copyright

    ©Kyi Thar, Sathirakorn Pongpanich, Min Nwe Tun. Originally published in JMIR Nursing (https://nursing.jmir.org), 16.6.2025.

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