Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review

[37] integrated behavioral surveys with risk perception analysis to assess the gap between perceived and actual preventive behaviors.

“For instance, [23] employed a combination of surveys and clinical assessments to investigate racial disparities in sleep and diabetes risk, while [37] integrated behavioral surveys with risk perception analysis to assess the gap between perceived and actual preventive behaviors.”

• cites [37] Geng, T., Zhu, K., Lu, Q., Wan, Z., Chen, X., Liu, L., Pan, A., and Liu, G. (2023). Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study. PLoS Med., 20. sourcePartly supported

  The Methods section (‘Measurements of lifestyle behaviors’; page unavailable) describes questionnaire-derived and self-reported lifestyle data, supporting the behavioral-survey part. It does not describe risk-perception analysis or a comparison between perceived and actual preventive behaviors; the Results instead examine lifestyle behaviors and microvascular complications. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Cohort studies and large-scale meta-analyses exemplify designs expected to demonstrate high sensitivity in identifying behavioral trends.

“Cohort studies and large-scale meta-analyses [11,12] exemplify designs expected to demonstrate high sensitivity in identifying behavioral trends.”

• cites [11] Thomas, J., and Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med. Res. Methodol., 8. sourceCould not verify

  The Methods (Searching) section discusses how statistical meta-analyses differ from qualitative syntheses, while the Discussion (Study quality and sensitivity analyses) uses “sensitivity analyses” to mean testing the effect of including studies of differing quality. Neither passage addresses whether cohort studies or large-scale meta-analyses have high sensitivity for identifying behavioral trends; searches for those terms found no supporting passage. (LLM, conf=0.76)

  Experimental — accuracy not yet measured
• cites [12] Niskanen, 2006, Serum Uric Acid as a Harbinger of Metabolic Outcome in Subjects With Impaired Glucose Tolerance The Finnish Diabetes Prevention Study, Diabetes Care sourceNot checked — no full text of the source

[23] employed a combination of surveys and clinical assessments to investigate racial disparities in sleep and diabetes risk.

“For instance, [23] employed a combination of surveys and clinical assessments to investigate racial disparities in sleep and diabetes risk, while [37] integrated behavioral surveys with risk perception analysis to assess the gap between perceived and actual preventive behaviors.”

• cites [23] Egede, 2011, Effectiveness of technology-assisted case management in low income adults with type 2 diabetes (TACM-DM): Study protocol for a randomized controlled trial, Trials sourcePartly supported

  The Methods (Study Instruments and Data Collection Schedule; Data Analysis; pages unavailable) propose questionnaires and clinical measurements, with race/ethnicity considered in exploratory analyses. But this is a trial protocol for adults with type 2 diabetes, not a completed investigation of racial disparities in sleep or diabetes risk; the searched passages do not identify a sleep measure. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Studies that used objective tools such as biomarkers, wearable devices, or continuous glucose monitoring were conceptually associated with higher accuracy than those that relied on self-reported measures.

“Studies that used objective tools such as biomarkers, wearable devices, or continuous glucose monitoring, including those [9,10], were conceptually associated with higher accuracy than those that relied on self-reported measures.”

• cites [9] Page, 2021, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, BMJ sourceCould not verify

  The Abstract and Discussion describe PRISMA as a guideline for reporting systematic reviews, not a study comparing measurement methods. Searches for biomarkers, wearable devices, continuous glucose monitoring, and self-reported measures found no passage addressing whether objective tools yield higher accuracy. (LLM, conf=0.78)

  Experimental — accuracy not yet measured
• cites [10] Asif, 2025, The Impact of Digital Health Technologies on Chronic Disease Management, Telehealth Med. Today sourceNot checked — no full text of the source

Qualitative approaches helped uncover nuanced factors influencing diabetes-related health behaviors that cannot be easily captured through statistical models alone.

“Nevertheless, these qualitative approaches helped uncover nuanced factors influencing diabetes-related health behaviors that cannot be easily captured through statistical models alone [39].”

• cites [39] Gao, 2024, Lifestyle change in patients with cardiovascular disease: Never too late to adopt a healthy lifestyle, Eur. J. Prev. Cardiol. sourcePartly supported

  The discussion of lifestyle adherence (labeled Abstract in our copy; no page given) says that multiple factors may facilitate or hinder healthy behaviors. It does not report using qualitative approaches, show that they uncovered those factors in people with diabetes, or compare their insights with statistical models. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Mixed-methods designs were particularly useful in understanding motivations behind complex behaviors such as diet and exercise.

“They achieved 78% accuracy, 80% sensitivity, and 74% specificity, and were particularly useful in understanding the motivations behind complex behaviors such as diet and exercise [23,37].”

• cites [23] Egede, 2011, Effectiveness of technology-assisted case management in low income adults with type 2 diabetes (TACM-DM): Study protocol for a randomized controlled trial, Trials sourceCould not verify

  The Methods sections describe a randomized trial that will measure motivation and diabetes self-care activities and analyze them with statistical models. They do not address whether mixed-methods designs are particularly useful for understanding motivations behind diet or exercise; searches for mixed-methods and those behaviors found no supporting passage. No page numbers were available in our copy. (LLM, conf=0.76)

  Experimental — accuracy not yet measured
• cites [37] Geng, T., Zhu, K., Lu, Q., Wan, Z., Chen, X., Liu, L., Pan, A., and Liu, G. (2023). Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study. PLoS Med., 20. sourceCould not verify

  The Methods (Measurements of lifestyle behaviors) describe questionnaire-based activity measures and a dietary score, while the Statistical analysis section describes quantitative biomarker analyses. Searches for mixed-methods designs, motivations behind diet and exercise, and the cited accuracy, sensitivity, and specificity figures found no passage addressing the claim. (LLM, conf=0.75)

  Experimental — accuracy not yet measured

[32,35] examined how generational dietary norms, family dynamics, and food beliefs shaped self-management in specific communities.

“For example, [32,35] examined how generational dietary norms, family dynamics, and food beliefs shaped selfmanagement in specific communities.”

• cites [32] Al Mansour, M.A. (2020). The prevalence and risk factors of type 2 diabetes mellitus (DMT2) in a semi-urban Saudi population. Int. J. Environ. Res. Public Health, 17. sourceCould not verify

  The Abstract describes a cross-sectional study of diabetes prevalence and risk factors in a semi-urban Saudi population. The Discussion mentions daily fatty-food consumption, but does not examine generational dietary norms, family dynamics, food beliefs, or their effects on self-management. Searches for those concepts found no supporting passage in our copy. (LLM, conf=0.75)

  Experimental — accuracy not yet measured
• cites [35] Cradock, K.A., Quinlan, L.R., Finucane, F.M., Gainforth, H.L., Martin Ginis, K.A., Barros, A.C., Sanders, E.B., and ÓLaighin, G. (2021). Identifying barriers and facilitators to diet and physical activity behaviour change in type 2 diabetes using a design probe methodology. J. Pers. Med., 11. sourcePartly supported

  The Results (Social Support; page unavailable) examine how family and friends influence healthy eating, supporting the family-dynamics portion of the claim. The Discussion (page unavailable) says cultural issues were not reported as a theme in this study; our copy does not establish that it examined generational dietary norms or food beliefs in specific communities. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Cross-sectional data and short-term interventions are insufficient for evaluating the long-term impact of behavioral strategies on glycemic control or health outcomes.

“Most studies relied on cross-sectional data or short-term interventions [28,33], which are insufficient for evaluating the long-term impact of behavioral strategies on glycemic control or health outcomes [4,12].”

• cites [4] Caballero-María, P., Caballero-Villarraso, J., Arenas-Montes, J., Díaz-Cáceres, A., Castañeda-Nieto, S., Alcalá-Díaz, J.F., Delgado-Lista, J., Rodríguez-Cantalejo, F., Pérez-Martínez, P., and López-Miranda, J. (2025). Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles. Appl. Sci., 15. sourcePartly supported

  The Discussion (page unavailable) says longitudinal follow-up would improve the model’s ability to study how changes in patient factors relate to the development of type 2 diabetes. That supports the need for follow-up, but the paper does not specifically say that cross-sectional data or short-term behavioral interventions are insufficient to evaluate long-term glycemic control or health outcomes. (LLM, conf=0.82)

  Experimental — accuracy not yet measured
• cites [12] Niskanen, 2006, Serum Uric Acid as a Harbinger of Metabolic Outcome in Subjects With Impaired Glucose Tolerance The Finnish Diabetes Prevention Study, Diabetes Care sourceNot checked — no full text of the source

Few reviewed studies measured the economic impact or long-term feasibility of behavioral interventions within public health systems.

“few studies measured economic impact or long-term feasibility within public health systems [3,44].”

• cites [3] Brzan, 2016, Mobile Applications for Control and Self Management of Diabetes: A Systematic Review, J. Med. Syst. sourceNot checked — no full text of the source
• cites [44] Alam, M.M., and Latifi, S. (2025). A Systematic Review of Techniques for Early-Stage Alzheimer’s Disease Diagnosis Using Machine Learning and Deep Learning. J. Data Sci. Intell. Syst. sourceCould not verify

  The Methodology and Discussion and Conclusion sections review machine-learning approaches to Alzheimer’s diagnosis and discuss computational costs and clinical applicability. They do not say whether reviewed studies measured the economic impact or long-term feasibility of behavioral interventions in public health systems (searched for those terms and cost-effectiveness). (LLM, conf=0.76)

  Experimental — accuracy not yet measured

Studies such as [25,31] leveraged tools like mobile health apps, telemonitoring systems, and wearable fitness trackers to enhance real-time behavioral tracking and adherence.

“Studies such as [25,31] leveraged tools like mobile health (mHealth) apps, telemonitoring systems, and wearable fitness trackers to enhance real-time behavioral tracking and adherence.”

• cites [25] von Ruesten, A., Weikert, C., Fietze, I., and Boeing, H. (2012). Association of sleep duration with chronic diseases in the european prospective investigation into cancer and nutrition (epic)-potsdam study. PLoS ONE, 7. sourceCould not verify

  The Study population section describes baseline interviews and questionnaires, with mailed follow-up questionnaires every 2–3 years. The Strengths and limitations section says sleep duration was self-reported and measured only at recruitment. Searches for mHealth apps, telemonitoring, wearables, real-time tracking, and adherence found no passage showing that this study used those tools. (LLM, conf=0.76)

  Experimental — accuracy not yet measured
• cites [31] Amuta, 2016, Gender Differences in Type 2 Diabetes Risk Perception, Attitude, and Protective Health Behaviors: A Study of Overweight and Obese College Students, Am. J. Health Educ. sourceNot checked — no full text of the source

Most reviewed studies relied on cross-sectional data or short-term interventions.

“Most studies relied on cross-sectional data or short-term interventions [28,33], which are insufficient for evaluating the long-term impact of behavioral strategies on glycemic control or health outcomes [4,12].”

• cites [28] Levine, 2016, Randomized trial of technology-assisted self-monitoring of blood glucose by low-income seniors: Improved glycemic control in type 2 diabetes mellitus, J. Behav. Med. sourceNot checked — no full text of the source
• cites [33] Xie, Z., Liu, K., Or, C., Chen, J., Yan, M., and Wang, H. (2020). An examination of the socio-demographic correlates of patient adherence to self-management behaviors and the mediating roles of health attitudes and self-efficacy among patients with coexisting type 2 diabetes and hypertension. BMC Public Health, 20. sourcePartly supported

  Methods > Data source (page unavailable) says this paper analyzed baseline data from a 24-week trial, so it provides an example of a study using cross-sectional data. It does not review the designs of multiple studies or establish that most relied on cross-sectional data or short-term interventions. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Quantitative approaches excelled in detecting broader behavioral patterns such as dietary intake and physical activity.

“These approaches excelled in detecting broader behavioral patterns such as dietary intake and physical activity and demonstrated the highest sensitivity (88%) and strong specificity (83%), as seen in large-scale cohort and randomized controlled trials [9,11].”

• cites [9] Page, 2021, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, BMJ sourceCould not verify

  The Scope of the guideline describes PRISMA 2020 as a reporting guideline for systematic reviews; it does not assess quantitative approaches for detecting dietary or physical-activity patterns. Searches for those behaviors and for the cited 88% sensitivity and 83% specificity found no relevant passage in our copy. (LLM, conf=0.78)

  Experimental — accuracy not yet measured
• cites [11] Thomas, J., and Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med. Res. Methodol., 8. sourceCould not verify

  The Background section says quantitative studies were used to test hypotheses about the effectiveness of healthy-eating interventions, while the Results mention interventions combining healthy eating and physical activity. Neither passage says quantitative approaches excel at detecting broader behavioral patterns or reports the cited 88% sensitivity and 83% specificity (searched for those concepts and for cohort and randomized trials). (LLM, conf=0.76)

  Experimental — accuracy not yet measured

Cultural values and family roles played an important part in behavior.

“Cultural values and family roles played an important part in behavior, as shown in qualitative studies by [32,35].”

• cites [32] Al Mansour, M.A. (2020). The prevalence and risk factors of type 2 diabetes mellitus (DMT2) in a semi-urban Saudi population. Int. J. Environ. Res. Public Health, 17. sourcePartly supported

  The Discussion (page unavailable) says marriage affects lifestyle and that couples may eat more and become less active after marriage, offering limited support for a family-related influence on behavior. It does not examine cultural values or family roles, and the Patients and Methods section (page unavailable) describes a cross-sectional study, not a qualitative study. (LLM, conf=0.82)

  Experimental — accuracy not yet measured
• cites [35] Cradock, K.A., Quinlan, L.R., Finucane, F.M., Gainforth, H.L., Martin Ginis, K.A., Barros, A.C., Sanders, E.B., and ÓLaighin, G. (2021). Identifying barriers and facilitators to diet and physical activity behaviour change in type 2 diabetes using a design probe methodology. J. Pers. Med., 11. sourcePartly supported

  The Results (Social Support; page unavailable) identify family support and interactions as influences on healthy behaviours, supporting the family-related part of the claim. The Discussion (page unavailable) says cultural issues were not reported in this study, so it does not support the claim that cultural values played an important part. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Qualitative methods were critical for capturing cultural nuance and behavioral motivations often missed by quantitative methods.

“Nevertheless, they were critical for capturing cultural nuance and behavioral motivations often missed by quantitative methods [39].”

• cites [39] Gao, 2024, Lifestyle change in patients with cardiovascular disease: Never too late to adopt a healthy lifestyle, Eur. J. Prev. Cardiol. sourceCould not verify

  The paper’s Abstract discusses lifestyle adherence, behavioral preferences, and limitations of binary lifestyle measures, but does not discuss qualitative methods or cultural nuance. Searches for qualitative research, interviews, and comparisons with quantitative methods found no passage addressing the attributed claim. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

Few reviewed studies considered how digital literacy, age, or socioeconomic status may hinder access and engagement with mHealth interventions.

“Few studies considered how digital literacy, age, or socioeconomic status may hinder access and engagement with mHealth interventions [42,43].”

• cites [42] Hennink, 2017, How are qualitative methods used in diabetes research? A 30-year systematic review, Glob. Public. Health sourceNot checked — no full text of the source
• cites [43] Alam, M.M., and Latifi, S. (2025). Early Detection of Alzheimer’s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging. Algorithms, 18. sourceCould not verify

  Section 4.2 reviews generative models for Alzheimer’s diagnosis using neuroimaging, while the Discussion and Conclusion addresses limitations such as generalizability and clinical validation. Searches for digital literacy, socioeconomic barriers, and mHealth access or engagement found no passage addressing the attributed claim. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

[30] found that self-efficacy and health beliefs mediated the relationship between demographic factors and self-care behavior in patients with both T2DM and hypertension.

“For example, [30] found that selfefficacy and health beliefs mediated the relationship between demographic factors and self-care behavior in patients with both T2DM and hypertension.”

• cites [30] Xu, 2016, Elevation of serum uric acid and incidence of type 2 diabetes: A systematic review and meta-analysis, Chronic Dis. Transl. Med. sourceCould not verify

  The Results and Discussion examine serum uric acid and the incidence of T2DM, with hypertension discussed as a potential confounder. Searches for self-efficacy, health beliefs, self-care behavior, and mediation found no passage addressing the attributed finding in patients with both T2DM and hypertension. (LLM, conf=0.78)

  Experimental — accuracy not yet measured

When SMBG was included in reviewed studies, it was often measured through self-report rather than device-generated data.

“when included, it was often measured through self-report rather than device-generated data [1,25].”

• cites [1] World Health Organization (2025, May 31). WHO Discussion Group for People Living with Diabetes. Available online: https://iris.who.int/bitstream/handle/10665/374810/9789240081451-eng.pdf?sequence=1.Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites [25] von Ruesten, A., Weikert, C., Fietze, I., and Boeing, H. (2012). Association of sleep duration with chronic diseases in the european prospective investigation into cancer and nutrition (epic)-potsdam study. PLoS ONE, 7. sourceCould not verify

  The Strengths and limitations section (page not provided) says sleep duration was self-reported, but it does not discuss self-monitoring of blood glucose (SMBG). Searches for SMBG and for blood-glucose monitoring measured by self-report versus device data found no passage addressing the claim. (LLM, conf=0.78)

  Experimental — accuracy not yet measured

Technology-assisted methods showed promise in improving behavioral tracking and real-time feedback.

“while technology-assisted methods showed promise in improving behavioral tracking and real-time feedback [26,31]”

• cites [26] Jackson, 2013, Association between sleep duration and diabetes in black and white adults, Diabetes Care sourceCould not verify

  The National Health Interview Survey section describes computer-assisted interviews, and the Measures section describes self-reported sleep and health behaviors. Neither section reports that technology-assisted methods improved behavioral tracking or provided real-time feedback; searches for those concepts found no supporting passage. Page numbers were not available in our copy. (LLM, conf=0.76)

  Experimental — accuracy not yet measured
• cites [31] Amuta, 2016, Gender Differences in Type 2 Diabetes Risk Perception, Attitude, and Protective Health Behaviors: A Study of Overweight and Obese College Students, Am. J. Health Educ. sourceNot checked — no full text of the source

Incorporating cultural beliefs, gender roles, and social structures in behavioral assessments is valuable for intervention design and interpretation.

“Research by [35] and [32] highlighted the importance of incorporating cultural beliefs, gender roles, and social structures in behavioral assessments, emphasizing the value of cultural context in intervention design and interpretation.”

• cites [35] Cradock, K.A., Quinlan, L.R., Finucane, F.M., Gainforth, H.L., Martin Ginis, K.A., Barros, A.C., Sanders, E.B., and ÓLaighin, G. (2021). Identifying barriers and facilitators to diet and physical activity behaviour change in type 2 diabetes using a design probe methodology. J. Pers. Med., 11. sourcePartly supported

  The Methods (§2.4) and Discussion (§4.2) describe assessing participants’ lived context to inform intervention design, and the Results identify family and friends as influences on behaviour. But the Discussion says cultural issues and gender-specific factors were not reported as themes in this study, so it does not support the claim’s emphasis on cultural beliefs and gender roles. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [32] Al Mansour, M.A. (2020). The prevalence and risk factors of type 2 diabetes mellitus (DMT2) in a semi-urban Saudi population. Int. J. Environ. Res. Public Health, 17. sourcePartly supported

  The Patients and Methods section records social and lifestyle history, and the Discussion examines differences by sex, occupation, and marital status and calls for further community-level social studies. The paper does not discuss incorporating cultural beliefs or gender roles into behavioral assessments, or explain how cultural context should guide intervention design and interpretation. Page numbers were not available in our copy. (LLM, conf=0.79)

  Experimental — accuracy not yet measured

Combining statistical data with participant narratives provided a more comprehensive understanding of behavior change, especially in diet and exercise interventions.

“For example, [37] and [23] demonstrated how combining statistical data with participant narratives provided a more comprehensive understanding of behavior change, especially in diet and exercise interventions.”

• cites [37] Geng, T., Zhu, K., Lu, Q., Wan, Z., Chen, X., Liu, L., Pan, A., and Liu, G. (2023). Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study. PLoS Med., 20. sourceCould not verify

  The Methods sections describe questionnaire-derived lifestyle measures and statistical analyses, and the Discussion describes the study as a cohort analysis. Searches for participant narratives, qualitative interviews, and mixed methods found no passage saying that this study combined narratives with statistical data or used that combination to understand behavior change in diet and exercise interventions. (LLM, conf=0.76)

  Experimental — accuracy not yet measured
• cites [23] Egede, 2011, Effectiveness of technology-assisted case management in low income adults with type 2 diabetes (TACM-DM): Study protocol for a randomized controlled trial, Trials sourceCould not verify

  The Methods > Data Analysis section (page unavailable) describes planned statistical analyses, not an analysis combining statistical data with participant narratives. A search for qualitative interviews and diet-and-exercise narratives found no passage showing that the protocol demonstrated the claimed benefit for understanding behavior change. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

A small but increasing number of studies integrate biometric markers such as serum uric acid, BMI, and HbA1c as outcome variables alongside behavioral data.

“Additionally, a small but increasing number of studies are beginning to integrate biometric markers such as serum uric acid levels, BMI, and HbA1c as outcome variables alongside behavioral data [9,20].”

• cites [9] Page, 2021, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, BMJ sourceCould not verify

  The Scope of the guideline section defines outcomes generally, while the Discussion addresses reporting practices. Neither discusses a trend in studies combining serum uric acid, BMI, or HbA1c with behavioral data; searches for those markers and for biometric measures found no relevant passage in our copy. (LLM, conf=0.77)

  Experimental — accuracy not yet measured
• cites [20] Nilsson, 2004, Incidence of Diabetes in Middle-Aged Men Is Related to Sleep Disturbances, Diabetes Care sourceNot checked — no full text of the source

Quantitative methodologies were employed in 77% (n = 23) of the reviewed studies.

“Quantitative methodologies were employed in 77% of the reviewed studies (n = 23), confirming their dominance in research on T2DM related health behaviors.”

• Also stated in: § 4.1. Comparative Effectiveness of Methods · plain

  “Quantitative methods were the most widely used, employed in 77% (n = 23) of the reviewed studies.”

Most studies lacked culturally adapted tools.

“Despite the variety of approaches, most studies used cross-sectional designs and lacked culturally adapted tools.”

Most studies used cross-sectional designs.

“Despite the variety of approaches, most studies used cross-sectional designs and lacked culturally adapted tools.”

The review analyzed 30 peer-reviewed studies and 10 comparative studies published between 2003 and 2025.

“A total of 30 peer-reviewed studies and 10 comparative studies published between 2003 and 2025 were analyzed.”

The review identified four dominant methodological categories: quantitative, qualitative, mixed-methods, and technology-assisted designs.

“The review identified four dominant methodological categories: quantitative, qualitative, mixed-methods, and technology-assisted designs.”

• AssumptionRests on an assumption: The four methodological categories are mutually exclusive and exhaustive for the included studies.

Approximately 65% of the quantitative studies relied on self-reported data.

“However, approximately 65% of the quantitative studies relied on self-reported data, introducing potential for recall bias and social desirability effects.”

• Also stated in: § 4.1. Comparative Effectiveness of Methods · plain

  “However, approximately 65% of these studies relied on self-reported data, raising concerns about recall bias and social desirability, especially when assessing sensitive behaviors like medication adherence [30,33].”

Technology-assisted methods including mobile apps and wearable devices enabled real-time monitoring and behavior tracking.

“Technology-assisted methods including mobile apps and wearable devices enabled real-time monitoring and behavior tracking, improving objectivity while raising concerns about privacy.”

Mixed-methods designs were applied in approximately 6% of the reviewed studies (n = 2).

“Mixed-methods designs were applied in approximately 6% of the reviewed studies (n=2), combining the strengths of both quantitative and qualitative methodologies.”

Technology-assisted approaches were employed in 30% of the reviewed studies (n = 9 out of 30).

“Technology-assisted approaches were employed in 30% of the reviewed studies (n = 9 out of 30), highlighting the increasing integration of digital tools in T2DM behavioral research.”

Qualitative methods were applied in approximately 6% of the reviewed studies (n = 2).

“Qualitative methods were applied in approximately 6% of the reviewed studies (n = 2).”

Qualitative studies explored cultural and interpersonal factors influencing self-management behaviors among individuals with T2DM.

“These studies explored cultural and interpersonal factors influencing self-management behaviors among individuals with T2DM.”

The aggregated accuracy, sensitivity, and specificity of quantitative approaches were 85%, 88%, and 83%, respectively.

“According to Table 4, the aggregated accuracy, sensitivity, and specificity of quantitative approaches were 85%, 88%, and 83%, respectively.”

• AssumptionRests on an assumption: The synthesized accuracy, sensitivity, and specificity values are comparable across studies and methodological categories.

Technology-assisted interventions often paired digital tools with behavioral strategies to improve adherence, such as EMR-based goal setting or integrated care platforms.

“Notably, technology-assisted interventions often paired digital tools with behavioral strategies to improve adherence, such as EMR-based goal setting or integrated care platforms.”

Quantitative studies utilized a range of instruments, including structured questionnaires (e.g., SDSCA, MMAS-8), clinical screenings, and objective biomarkers such as HbA1c, BMI, and serum uric acid levels.

“These studies utilized a range of instruments, including structured questionnaires (e.g., SDSCA, MMAS-8), clinical screenings, and objective biomarkers such as HbA1c, BMI, and serum uric acid levels.”

[17] used a design probe methodology and self-documentation to examine how social and environmental contexts shaped dietary behaviors in Ireland.

“For instance, [17] used a design probe methodology and self-documentation to examine how social and environmental contexts shaped dietary behaviors in Ireland.”

• cites [17] Chien, 2008, Plasma uric acid and the risk of type 2 diabetes in a Chinese community, Clin. Chem. sourceNot checked — no full text of the source

Recent T2DM behavioral research shows increased adoption of technology-assisted methods such as mHealth apps, wearable fitness trackers, and continuous glucose monitoring systems.

“One of the most notable trends is the increased adoption of technology-assisted methods, such as mobile health, mHealth apps, wearable fitness trackers, and continuous glucose monitoring systems.”

The reviewed T2DM behavioral literature shows a notable absence of longitudinal designs.

“First, a notable absence of longitudinal designs limits the understanding of sustained behavior change over time.”

These factors influenced participants' comfort and openness in responding to surveys and digital tools.

“Moreover, these factors influenced participants' comfort and openness in responding to surveys and digital tools.”

Limited access to smartphones and low digital literacy in some regions constrained full participation.

“However, limited access to smartphones and low digital literacy in some regions constrained full participation.”

Cost-effectiveness and sustainability of behavioral interventions are rarely evaluated in the reviewed literature.

“cost-effectiveness and sustainability of behavioral interventions are rarely evaluated.”

The majority of quantitative designs in the reviewed literature failed to tailor methodologies to the specific needs of diverse populations.

“the majority of quantitative designs failed to tailor methodologies to the specific needs of diverse populations.”

Digital inequities were rarely addressed in the reviewed studies.

“digital inequities were rarely addressed.”

Self-monitoring of blood glucose (SMBG) remains underexplored relative to other behaviors in T2DM behavioral research.

“Second, self-monitoring of blood glucose (SMBG) remains underexplored relative to other behaviors.”

• Also stated in: § 5. Future Research Recommendations · plain

  “Self-monitoring of blood glucose remains critical but underexplored behavior in current research.”

Culturally tailored methodologies are receiving growing attention, particularly in qualitative and community-based studies.

“There is also growing attention to culturally tailored methodologies, particularly in qualitative and community-based studies.”

Figure 2 evaluated four methodological approaches (Quantitative, Qualitative, Mixed-Methods, Technology-Assisted) across accuracy, sensitivity, and specificity based on patterns synthesized from 30 reviewed studies.

“Each method was evaluated across three core criteria accuracy, sensitivity, and specificity based on patterns synthesized from 30 reviewed studies.”

• AssumptionRests on an assumption: The 30 reviewed studies are representative of the broader T2DM behavioral research literature.
• AssumptionRests on an assumption: Accuracy, sensitivity, and specificity are meaningful criteria for comparing methodological approaches, including qualitative ones.

Technology-assisted approaches demonstrated growing promise in delivering real-time, objective data to support self-management and intervention tracking.

“Technologyassisted approaches demonstrated growing promise in delivering real-time, objective data to support self-management and intervention tracking.”

Quantitative methods provided scale and generalizability in the reviewed T2DM behavioral research.

“Quantitative methods provided scale and generalizability.”

Real-time and objective data collection through digital tools and wearable technologies has advantages.

“These results highlight the advantages of real-time and objective data collection through digital tools and wearable technologies.”

• AssumptionRests on an assumption: Accuracy, sensitivity, and specificity are meaningful criteria for comparing methodological approaches, including qualitative ones.

Mixed-methods combined analytical rigor with contextual insight in the reviewed T2DM behavioral research.

“Mixed-methods combined analytical rigor with contextual insight.”

Qualitative studies offered depth and cultural perspective in the reviewed T2DM behavioral research.

“Qualitative studies offered depth and cultural perspective.”

Mixed-methods approaches posed challenges in execution.

“Mixed-methods approaches integrated statistical and narrative depth but posed challenges in execution.”

Technology-assisted methods improved objectivity.

“Technology-assisted methods including mobile apps and wearable devices enabled real-time monitoring and behavior tracking, improving objectivity while raising concerns about privacy.”

Mixed-methods approaches integrated statistical and narrative depth.

“Mixed-methods approaches integrated statistical and narrative depth but posed challenges in execution.”

Technology-assisted methods raised concerns about privacy.

“Technology-assisted methods including mobile apps and wearable devices enabled real-time monitoring and behavior tracking, improving objectivity while raising concerns about privacy.”

Qualitative studies had limited scalability.

“Qualitative studies provided rich contextual understanding of psychosocial and cultural factors but had limited scalability.”

Qualitative studies provided rich contextual understanding of psychosocial and cultural factors.

“Qualitative studies provided rich contextual understanding of psychosocial and cultural factors but had limited scalability.”

A standardized quality appraisal tool such as the Joanna Briggs Institute (JBI) checklist was not used in this review.

“A standardized quality appraisal tool such as the Joanna Briggs Institute (JBI) checklist was not used in this review.”

For studies that did not explicitly report these metrics, approximate values were derived based on methodological characteristics.

“For studies that did not explicitly report these metrics, approximate values were derived based on methodological characteristics, such as the use of objective tools (e.g., biomarkers, wearable devices), type of study design (e.g., cohort vs. cross-sectional), and data collection method (e.g., self-report vs. clinical assessment).”

• AssumptionRests on an assumption: The synthesized accuracy, sensitivity, and specificity values are comparable across studies and methodological categories.

Mixed-methods approaches enabled researchers to collect numerical data while simultaneously exploring contextual or psychosocial dimensions that influence health behaviors.

“These approaches enabled researchers to collect numerical data while simultaneously exploring contextual or psychosocial dimensions that influence health behaviors.”

Thirty studies met all inclusion criteria and were included.

“Of these, 30 met all inclusion criteria and were included.”

Technology-assisted methods allowed for greater objectivity and precision through automated data collection, minimizing biases associated with self-reported outcomes.

“These methods allowed for greater objectivity and precision through automated data collection, minimizing the biases associated with self-reported outcomes.”

Qualitative findings were limited in terms of scalability and broader generalizability.

“Although the findings provided valuable cultural and contextual understanding, they were limited in terms of scalability and broader generalizability.”

Self-reported data introduced potential for recall bias and social desirability effects.

“However, approximately 65% of the quantitative studies relied on self-reported data, introducing potential for recall bias and social desirability effects.”

Because included studies varied substantially in design, scope, and data collection strategies, a conceptual framework allowed for more appropriate cross-method analysis.

“Because the included studies varied substantially in their design, scope, and data collection strategies, a conceptual framework allowed for more appropriate cross-method analysis.”

These results emphasize the strength of digital health tools in capturing reliable and scalable behavioral data in T2DM research.

“These results emphasize the strength of digital health tools in capturing reliable and scalable behavioral data in T2DM research.”

Mixed-methods integration provided a more holistic understanding of factors affecting diabetes-related outcomes.

“Such integration provided a more holistic understanding of factors affecting diabetes-related outcomes.”

Qualitative methods' smaller purposive samples limited generalizability.

“While qualitative methods achieved 70% accuracy, 72% sensitivity, and 65% specificity, their smaller purposive samples limited generalizability.”

The effectiveness and appropriateness of each methodological approach depended on the behavioral domain, target population, and study objective.

“The effectiveness and appropriateness of each methodological approach depended on the behavioral domain, target population, and study objective.”

Studies focusing on physical activity and diet often benefited from the scalability of quantitative or technology-assisted methods.

“For instance, studies focusing on physical activity and diet often benefited from the scalability of quantitative or technology-assisted methods, while research on motivation and cultural beliefs required qualitative depth.”

Qualitative methods provided rich insights into sociocultural and emotional influences on self-management behaviors.

“Qualitative methods were used in 6.7% (n = 2) of studies and provided rich insights into sociocultural and emotional influences on self-management behaviors.”

Research on motivation and cultural beliefs required qualitative depth.

“For instance, studies focusing on physical activity and diet often benefited from the scalability of quantitative or technology-assisted methods, while research on motivation and cultural beliefs required qualitative depth.”

No single methodology is superior in all aspects.

“Further, the results underscore that no single methodology is superior in all aspects.”

• Also stated in: § 6. Conclusions · boosted

  “Further, the results underscore that no single methodology is superior in all aspects.”

• AssumptionRests on an assumption: Accuracy, sensitivity, and specificity are meaningful criteria for comparing methodological approaches, including qualitative ones.

The absence of longitudinal designs limits the understanding of sustained behavior change over time.

“First, a notable absence of longitudinal designs limits the understanding of sustained behavior change over time.”

T2DM behavioral research is shifting from static, self-report-based methodologies toward dynamic, patient-centered, and technology-enhanced research models.

“These patterns suggest a shift from static, self-report-based methodologies toward dynamic, patient-centered, and technology-enhanced research models.”