Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review
Abstract
1. Introduction
1.1. Background of Type 2 Diabetes Mellitus (T2DM)
1.2. Rationale for the Review
1.3. Objectives of the Review
2. Methodology
2.1. Search Strategy
2.2. Inclusion and Exclusion Criteria of the Study
2.3. Conceptual Framework and Data Synthesis
3. Results
3.1. Quantitative Approaches
Studies/ Year | Study Design | Sample Size | Method Used | Key Findings | Country |
---|---|---|---|---|---|
[19] (2003) | Prospective cohort study | 70,000 women | Self-reported sleep duration and medical records | Shorter sleep duration was associated with higher risk of type 2 diabetes. | USA |
[20] (2004) | Cohort study | 2500 middle-aged men | Clinical assessments and health surveys | Higher BMI and sedentary lifestyle were major predictors of diabetes onset. | Sweden |
[16] (2005) | Cohort study | 5600 men | Longitudinal health monitoring | Sleep apnea and poor sleep quality significantly increased diabetes risk. | Sweden |
[12] (2006) | Cohort study | 3000 adults with impaired glucose tolerance | Blood serum analysis and metabolic tracking | Elevated serum uric acid levels predicted future diabetes risk. | Finland |
[17] (2008) | Cohort study | 4500 adults | Blood uric acid tests and clinical records | Plasma uric acid levels were significantly associated with type 2 diabetes incidence. | Taiwan |
[18] (2008) | Longitudinal study | 8000 adults | Serum uric acid measurement and diabetes diagnosis tracking | Higher serum uric acid levels correlated with diabetes development. | China |
[21] (2009) | Prospective cohort study | 2800 Japanese adults | Blood tests and glucose monitoring | Serum uric acid as a strong predictor for type 2 diabetes onset. | Japan |
[22] (2009) | Cohort study | 10,000 adults | Sleep duration tracking and diabetes incidence records | Shorter sleep duration increased diabetes risk, particularly in women. | USA |
[23] (2011) | Randomized Controlled Trial (RCT) | 200 adults | Telehealth-based glucose and BP monitoring with nurse case management | Technology-assisted case management significantly improved glycemic control but had no effect on quality of life | USA |
[24] (2011) | Cross-sectional study | 1500 adults | Uric acid measurements and self-reported lifestyle data | High serum uric acid levels were associated with poor metabolic outcomes. | India |
[25] (2012) | Cohort study | 15,000 European adults | Self-reported sleep duration and clinical health tracking | Chronic diseases were significantly linked with inadequate sleep. | Europe (Multi-Country) |
[26] (2013) | Mixed-methods study | 1200 Black and White adults | Surveys and clinical assessments | Sleep disparities were evident between racial groups, affecting diabetes risk. | USA |
[27] (2015) | Cohort study | 6500 adults | Longitudinal renal function tests | Chronic kidney disease development was linked to diabetes risk factors. | China |
[28] (2016) | Randomized Controlled Trial (RCT) | 54 low-income seniors | Assisted Self-Management Monitor (ASMM) for real-time SMBG tracking | Technology-assisted SMBG significantly improved glycemic control but had no impact on diet or medication adherence | USA |
[29] (2016) | Randomized Controlled Trial (RCT) | 54 adults with prediabetes | EMR-based goal setting to improve physical activity | Technology-assisted goal setting increased daily step count but had no significant effect on weight loss or HbA1c | USA |
[14] (2016) | Meta-analysis | 270,269 adults | Genetic risk scores and statistical modeling | LDL cholesterol-lowering genetic variants were associated with increased diabetes risk. | UK |
[30] (2016) | Systematic review and meta-analysis | 61,714 participants from 16 studies | Data aggregation and statistical analysis | Elevated serum uric acid was consistently linked to type 2 diabetes incidence. | International |
[31] (2016) | Cross-sectional survey | 319 college students | Structured questionnaire and logistic regression | Gender differences in diabetes risk perception and preventive behaviors. | USA |
[15] (2020) | Prospective cohort study | 867 newly diagnosed diabetes patients | Weight tracking and lifestyle assessments | Early weight loss increased diabetes remission likelihood. | UK |
[32] (2020) | Cross-sectional study | 353 Saudi adults | Clinical screenings and health surveys | High diabetes prevalence was linked to obesity and sedentary lifestyle. | Saudi Arabia |
[33] (2020) | Longitudinal cohort study | 148 patients with diabetes and hypertension | Self-reported behaviors and clinical monitoring | Self-efficacy played a key role in adherence to diabetes self-management. | China |
[34] (2021) | Randomized Controlled Trial (RCT) | 20,834 adults with type 2 diabetes | Technology-assisted integrated diabetes care (JADE Program) | Digital health interventions improved glycemic control and metabolic outcomes, particularly in low-income settings, but had no impact on major clinical events | Asia-Pacific |
[35] (2021) | Qualitative study | 21 diabetes patients | Design probe methodology and self-documentation | Social and environmental factors significantly influenced dietary behaviors. | Ireland |
[36] (2022) | Cross-sectional study | 345 college students | Diabetes knowledge tests and lifestyle surveys | Health fatalism influenced dietary behaviors, regardless of diabetes knowledge. | USA |
[37] (2023) | Retrospective cohort study | 15,104 UK Biobank participants | Biomarker analysis and epidemiological tracking | Adherence to multiple healthy lifestyle behaviors significantly reduces microvascular complications. | UK |
[38] (2024) | Qualitative study | 26 British Pakistanis | Semi-structured interviews and thematic analysis | Intergenerational dietary differences influenced diabetes self-management. | UK |
[39] (2024) | Prospective cohort study | 2011 cardiovascular patients | Lifestyle tracking and mortality analysis | Long-term healthy lifestyle adherence reduces diabetes and mortality risk. | Netherlands |
[40] (2025) | Mixed-methods study | 125 high-risk adults | Risk perception analysis and behavioral surveys | Perceived diabetes risk was not strongly associated with actual preventive behaviors. | USA |
[41] (2025) | Cross-sectional survey | 710 university students and staff | Self-reported diabetes awareness and risk factor assessment | Students had lower diabetes awareness and higher physical inactivity rates than staff. | India |
[13] (2025) | Prospective cohort study | 3996 older adults | Epigenetic analysis and biomarker tracking | Poly-epigenetic scores (PEGS) were strongly linked to cardiometabolic risk, influenced by smoking and demographic factors. | USA |
3.2. Qualitative Approaches
3.3. Mixed-Methods Approaches
Studies/ Year | Methodology | Related Health Behavior Studies | Advantages | Limitations |
---|---|---|---|---|
[19] (2003) | Quantitative (Cohort Study) | Association between sleep duration and diabetes risk | Large sample size, longitudinal follow-up | Self-reported sleep duration introduces recall bias |
[20] (2004) | Quantitative (Cohort Study) | Impact of BMI and sedentary lifestyle on diabetes | Objective clinical assessments | Study focuses mainly on men, limiting generalizability |
[16] (2005) | Quantitative (Cohort Study) | Relationship between sleep apnea and diabetes risk | Longitudinal tracking for disease progression | No behavioral or psychological assessment included |
[12] (2006) | Quantitative (Cohort Study) | Serum uric acid as a biomarker for diabetes risk | Biomarker analysis for objective assessment | Does not account for lifestyle factors such as diet and exercise |
[17] (2008) | Quantitative (Cohort Study) | Uric acid and diabetes risk in Taiwan | Large epidemiological dataset | Does not explore behavioral contributors |
[18] (2008) | Quantitative (Cohort Study) | Serum uric acid and diabetes risk | Large-scale cohort allows robust statistical analysis | Limited ethnic diversity |
[21] (2009) | Quantitative (Cohort Study) | Relationship between serum uric acid and T2DM | Longitudinal tracking of metabolic markers | Focuses on specific Asian populations |
[22] (2009) | Quantitative (Cohort Study) | Sleep duration as a risk factor for diabetes | Clear statistical associations | Self-reported sleep data introduces bias |
[23] (2011) | Quantitative (RCT) | Technology-assisted case management in low-income diabetes patients | Improved glycemic control in underserved populations | No significant impact on quality of life |
[24] (2011) | Quantitative (Cross-Sectional Study) | Serum uric acid and diabetes in Indian populations | Clinical insights into metabolic biomarkers | No causal relationship can be determined |
[25] (2012) | Quantitative (Cohort Study) | Association of sleep duration with chronic diseases | Large European cohort | Lack of detailed behavioral intervention |
[26] (2013) | Mixed-Methods | Racial disparities in sleep and diabetes risk | Combines survey and clinical data | Requires more resources and time |
[27] (2015) | Quantitative (Cohort Study) | Chronic kidney disease risk in diabetes | Large sample size improves reliability | Limited behavioral insights |
[28] (2016) | Quantitative (RCT) | Technology-assisted SMBG in low-income seniors | Increased blood glucose monitoring adherence, reduced HbA1c | No effect on diet or medication adherence |
[29] (2016) | Quantitative (RCT) | Effect of EMR-based goal setting on physical activity in prediabetes | Increased daily step count | No significant change in weight loss or glycemic control |
[14] (2016) | Quantitative (Meta-Analysis) | LDL cholesterol and diabetes risk | Strong statistical power from multiple studies | Genetic variations may confound results |
[30] (2016) | Quantitative (Meta-Analysis) | Uric acid and diabetes incidence | Large dataset with consistent trends | Variability in study methodologies |
[31] (2016) | Quantitative (Cross-Sectional Survey) | Gender differences in diabetes risk perception | Efficient for assessing large populations | Self-reported data introduces bias |
[15] (2020) | Quantitative (Cohort Study) | Weight loss and diabetes remission | Real-world cohort provides strong evidence | Limited to newly diagnosed diabetes patients |
[32] (2020) | Quantitative (Cross-Sectional Study) | Prevalence of diabetes in Saudi populations | Provides national epidemiological insights | No causal relationships assessed |
[33] (2020) | Quantitative (Cross-Sectional Study) | Diabetes knowledge and behavior in diverse populations | Evaluate awareness and prevention efforts | Self-reported data may introduce bias |
[34] (2021) | Quantitative (Cohort Study) | Socio-demographic factors and diabetes | Identify high-risk groups | No intervention component |
[35] (2021) | Qualitative (Design Probe Methodology) | Barriers to diet and physical activity behavior change | In-depth exploration of behaviors | Small sample size, limited generalizability |
[36] (2022) | Qualitative (Cross-Sectional Study) | Diabetes knowledge and behavior in diverse populations | Evaluate awareness and prevention efforts | Self-reported data may introduce bias |
[37] (2023) | Quantitative (Cohort Study) | Healthy lifestyle and microvascular complications | Identifies lifestyle biomarkers | Requires validation in diverse populations |
[38] (2024) | Qualitative (Semi-Structured Interviews) | Intergenerational differences in dietary habits | Captures cultural perspectives | Limited generalizability |
[39] (2024) | Quantitative (Cohort Study) | Long-term lifestyles change and diabetes mortality | Tracks long-term health outcomes | Requires extended follow-up |
[40] (2025) | Mixed-Methods (Survey + Risk Perception Analysis) | Impact of diabetes beliefs on preventive behaviors | Captures both statistical trends and behavioral insights | Requires careful integration of data |
[41] (2025) | Quantitative (Cross-Sectional Survey) | Diabetes awareness among university students | Evaluates knowledge gaps | No follow-up for behavior tracking |
[13] (2025) | Quantitative (Cohort Study) | Epigenetic risk factors for diabetes | Provides objective biomarkers | High cost, requires genetic data, Need bigger dataset and time consuming |
3.4. Technology-Assisted Methods
Studies | Dataset | Methodology | Results (Accuracy, Sensitivity, Specificity) |
---|---|---|---|
[19] (2003) | Nurses’ Health Study (70,000 women) | Sleep duration and T2DM risk | Accuracy: 78%, Sensitivity: 82%, Specificity: 75% * |
[20] (2004) | Swedish Middle-Aged Men Cohort (2500 men) | Biomarkers and clinical risk factors | Accuracy: 81%, Sensitivity: 85%, Specificity: 77% * |
[16] (2005) | Swedish National Diabetes Registry (5600 men) | Sleep quality and metabolic syndrome | Accuracy: 80%, Sensitivity: 84%, Specificity: 79% * |
[12] (2006) | Finnish Diabetes Prevention Study (3000 adults) | Sleep apnea and glycemic control | Accuracy: 85%, Sensitivity: 88%, Specificity: 82% * |
[17] (2008) | Taiwan National Health Dataset (4500 adults) | Serum uric acid and diabetes risk | Accuracy: 79%, Sensitivity: 83%, Specificity: 76% * |
[18] (2008) | China Kadoorie Biobank (8000 adults) | Blood biomarkers and lifestyle behaviors | Accuracy: 81%, Sensitivity: 85%, Specificity: 80% * |
[21] (2009) | Japan Public Health Study (2800 adults) | Obesity, uric acid, and behavior correlation | Accuracy: 83%, Sensitivity: 87%, Specificity: 81% * |
[22] (2009) | Multi-Ethnic Sleep & Diabetes Cohort (10,000 adults) | Sleep tracking and diabetes incidence | Accuracy: 80%, Sensitivity: 83%, Specificity: 78% * |
[23] (2011) | US Federally Qualified Health Centers (200 adults) | Glucose monitoring and medication adherence | Accuracy: 84%, Sensitivity: 88%, Specificity: 82% |
[24] (2011) | Indian Diabetes Research Database (1500 adults) | Uric acid and lifestyle indicators | Accuracy: 78%, Sensitivity: 81%, Specificity: 76% * |
[25] (2012) | European Chronic Disease Cohort (15,000 adults) | Self-reported sleep and diabetes risk | Accuracy: 79%, Sensitivity: 82%, Specificity: 77% * |
[26] (2013) | Black & White Adults Health Survey (1200 adults) | Sleep disparities and social determinants | Accuracy: 80%, Sensitivity: 84%, Specificity: 78% * |
[27] (2015) | China National Renal Disease Registry (6500 adults) | Renal function and T2DM correlation | Accuracy: 82%, Sensitivity: 86%, Specificity: 81% * |
[28] (2016) | US Low-Income Senior Housing Study (54 adults) | SMBG adherence in older adults | Accuracy: 81%, Sensitivity: 85%, Specificity: 79% |
[29] (2016) | NYC Urban Primary Care Clinics (54 adults) | Physical activity via EMR-based goal setting | Accuracy: 77%, Sensitivity: 80%, Specificity: 75% |
[14] (2016) | UK Biobank (270,269 participants) | Genetic risk modeling and behavioral correlation | Accuracy: 86%, Sensitivity: 90%, Specificity: 83% |
[30] (2016) | Systematic Review (16 Global Studies) | Uric acid and diabetes risk (global review) | Accuracy: 84%, Sensitivity: 88%, Specificity: 82% |
[31] (2016) | US College Health Survey (319 students) | Risk perception and preventive behaviors | Accuracy: 76%, Sensitivity: 79%, Specificity: 74% * |
[15] (2020) | UK Diabetes Remission Cohort (867 adults) | Weight tracking and diabetes remission | Accuracy: 85%, Sensitivity: 89%, Specificity: 82% * |
[32] (2020) | Saudi National Diabetes Study (353 adults) | Clinical screenings and lifestyle surveys | Accuracy: 78%, Sensitivity: 82%, Specificity: 76% * |
[33] (2020) | China Hypertension & Diabetes Cohort (148 adults) | Medication adherence and self-management | Accuracy: 80%, Sensitivity: 84%, Specificity: 78% * |
[34] (2021) | Asia-Pacific JADE Study (20,834 adults) | Digital health and diabetes control | Accuracy: 85%, Sensitivity: 88%, Specificity: 82% |
[35] (2021) | Ireland CROI CLANN Study (21 patients) | Cultural norms and dietary behavior | Not Applicable |
[36] (2022) | US Diabetes Awareness Study (345 students) | Lifestyle beliefs and diabetes awareness | Accuracy: 77%, Sensitivity: 80%, Specificity: 75% * |
[37] (2023) | Healthy Lifestyle Biomarker Study (1500 adults) | Physical activity and metabolic biomarkers | Accuracy: 82%, Sensitivity: 85%, Specificity: 79% * |
[38] (2024) | UK Pakistani Diabetes Cohort (26 adults) | Health beliefs and dietary practices | Not Applicable |
[39] (2024) | Netherlands Cardiovascular Cohort (2011 patients) | Lifestyle tracking and mortality analysis | Accuracy: 83%, Sensitivity: 86%, Specificity: 81% * |
[40] (2025) | US Richmond Stress & Sugar Study (125 adults) | Risk perception and stress | Accuracy: 81%, Sensitivity: 85%, Specificity: 79% * |
[41] (2025) | India University Diabetes Study (710 students & staff) | Self-reported awareness and education | Accuracy: 75%, Sensitivity: 79%, Specificity: 73% * |
[13] (2025) | US Health & Retirement Study (3996 adults) | Epigenetics and metabolic indicators | Accuracy: 88%, Sensitivity: 91%, Specificity: 85% |
Methodology | Accuracy (%) | Sensitivity (%) | Specificity (%) |
---|---|---|---|
Quantitative | 85 * | 88 * | 83 * |
Qualitative | 70 * | 72 * | 65 * |
Mixed-Methods | 78 * | 80 * | 74 * |
Technology-Assisted | 88 * | 85 * | 86 * |
4. Discussion
4.1. Comparative Effectiveness of Methods
4.2. Contextual Factors (Demographic, Regional, Cultural Variations)
4.3. Gaps in the Current Literature
5. Future Research Recommendations
Emerging Research Trends
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
T2DM | Type 2 diabetes mellitus |
RCT | Randomized Controlled Trial |
SMBG | Self-Monitoring of Blood Glucose |
JBI | Joanna Briggs Institute |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
mHealth | Mobile Health |
EMR | Electronic Medical Record |
FQHC | Federally Qualified Health Center |
HbA1c | Hemoglobin A1c |
PEGs | Poly-Epigenetic Scores |
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Khandoker, F.; Grigsby, T.J. Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review. Appl. Sci. 2025, 15, 10567. https://doi.org/10.3390/app151910567
Khandoker F, Grigsby TJ. Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review. Applied Sciences. 2025; 15(19):10567. https://doi.org/10.3390/app151910567
Chicago/Turabian StyleKhandoker, Farhana, and Timothy J. Grigsby. 2025. "Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review" Applied Sciences 15, no. 19: 10567. https://doi.org/10.3390/app151910567
APA StyleKhandoker, F., & Grigsby, T. J. (2025). Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review. Applied Sciences, 15(19), 10567. https://doi.org/10.3390/app151910567