Reliability and Structure of Diabetes Diet Adherence Scale (D-DAS): A Follow-up Study among Type 2 Diabetes Patients of India

This study validates the reliability and structure of the Diabetes Diet Adherence Scale (D-DAS) for Indian type 2 diabetes patients, finding it to be a robust measurement tool while revealing that only 68.3% of participants adhered to their prescribed diet plans.

The Gist

Imagine you are trying to lose weight or manage a health condition like diabetes. Your doctor gives you a specific "recipe for life"—a diet plan. But here's the tricky part: knowing the recipe is easy; actually cooking it every day is hard.

This research paper is like a detective story about figuring out how well people are actually following that recipe, and building a better "report card" to measure it.

Here is the story of the study, broken down into simple terms:

1. The Problem: The Missing Ruler

In India, millions of people have Type 2 Diabetes. Doctors tell them what to eat (less sugar, more veggies, specific portion sizes). But how do doctors know if the patient is actually doing it?

• The Issue: There was no good "ruler" or "thermometer" to measure diet adherence specifically for Indian patients. Existing tools were either in English, meant for other countries, or just didn't fit the local culture.
• The Goal: The researchers wanted to build and test a new tool called the D-DAS (Diabetes Diet Adherence Scale). Think of this as creating a custom-made "fitness tracker" specifically for diet habits in India.

2. The Experiment: The 4-Month Check-In

The researchers gathered 120 patients from a hospital in Chandigarh.

• The Setup: They gave these patients a specific diet plan (based on Indian Council of Medical Research guidelines) that was affordable and easy to find in local markets.
• The Support: For four months, the team didn't just sit back. They called the patients every month and sent text messages twice a month to cheer them on and answer questions. It was like having a supportive coach in your pocket.
• The Test: At the end of the four months, they asked the patients to fill out the new D-DAS questionnaire.

3. The "Magic" Math: Cleaning the Tool

The researchers had to make sure their new "ruler" was accurate. They used some fancy statistical tools (like Multiple Correspondence Analysis and Confirmatory Factor Analysis) which you can think of as sifting sand through a sieve.

• The Sieve: The original scale had 10 questions. The math showed that 3 of those questions were "loose sand"—they didn't fit well with the others or didn't measure what they were supposed to.
• The Result: They kept the 7 best questions and tossed the other 3. These 7 questions formed a strong, solid core that accurately measured whether someone was sticking to their diet or slipping up.
  • Analogy: Imagine you have a team of 10 players. After watching them play, you realize 3 of them aren't really playing the same game. You bench them and keep the 7 who are working together perfectly.

4. The Findings: The "Report Card"

Once they had their clean, 7-question scale, they used it to grade the patients.

• The Score: The results showed that 68% of the patients were doing a good job sticking to their diet.
• The Gap: However, 32% were struggling. This means nearly one in three people found it very hard to keep up with the healthy eating plan, even with the support calls and texts.
• Reliability: The scale itself was proven to be very reliable. If you used it twice, it would give you the same answer. It's a sturdy tool.

5. Why This Matters: The "Why" Behind the "What"

The study found that while the scale works, the real-world challenge is huge.

• The Barrier: It's not just about willpower. It's about money, culture, and habit. Changing decades of eating habits is like trying to change the course of a massive river; it takes a lot of effort and the right tools.
• The Solution: The researchers suggest that doctors should use this new scale (D-DAS) in their clinics.
  • Analogy: Instead of guessing if a patient is eating well, the doctor can hand them this "Diet Report Card." If the score is low, the doctor knows, "Ah, this person is struggling with practical barriers (like cost or time), not just laziness." This helps the doctor give better, more specific help.

The Bottom Line

This paper is about building a better compass for navigating the difficult journey of diabetes management.

1. They built a new tool (D-DAS) to measure diet habits.
2. They tested it and proved it works well in India.
3. They found that while most people try hard, many still struggle to stick to the plan.
4. The Takeaway: We need to stop guessing and start measuring. If we know exactly where people are struggling, we can build better support systems, policies, and food options to help them succeed.

In short: It's a step toward making sure that when a doctor says "eat healthy," the patient actually has the tools and support to do it, and the doctor has a way to check if it's working.

Technical Summary

1. Problem Statement

• Clinical Gap: While Medical Nutrition Therapy (MNT) is a cornerstone of Type 2 Diabetes Mellitus (T2DM) management, adherence to prescribed diet plans remains a significant challenge. Non-adherence rates vary widely (2.2% to 87.5%), yet there is a lack of validated tools to measure dietary adherence specifically within the Indian population.
• Measurement Limitation: Existing scales (e.g., PDAQ, ADQ) are either not validated for India, focus on medication adherence, or assess general diet quality rather than adherence to a prescribed MNT plan.
• Objective: The study aims to translate, validate, and determine the psychometric structure (reliability and construct validity) of the Diabetes Diet Adherence Scale (D-DAS) for the Indian context and to identify underlying dimensions of adherence.

2. Methodology

• Study Design: A four-month, single-group, non-randomized follow-up study conducted at the Endocrine Department of PGIMER, Chandigarh, India.
• Participants:
  • Sample: 120 T2DM patients recruited at baseline; 104 completed the study (14% attrition).
  • Inclusion: Patients attending the outpatient clinic with a prescribed diet plan based on Indian Council of Medical Research (ICMR) guidelines.
  • Data Collection: Demographics, anthropometrics (BMI), Fasting Blood Glucose (FBG), and HbA1c were collected.
• Intervention: Patients received a customized diet plan. Adherence was monitored via monthly phone calls and bi-monthly text messages over the 4-month period.
• Instrument: The D-DAS (originally developed by Mohammed et al., 2019) was translated into Hindi following standard cross-cultural adaptation protocols (forward/back-translation, expert panel review, pilot testing). The scale consists of 10 items.
• Statistical Analysis:
  • Normality: Mardia's test indicated non-normal distribution, necessitating robust estimators.
  • Dimensionality: Multiple Correspondence Analysis (MCA) was used to identify underlying dimensions.
  • Item Response Theory (IRT): Two-Parameter Logistic (2PL) model to assess item discrimination and difficulty.
  • Construct Validation: Confirmatory Factor Analysis (CFA) using Weighted Least Squares Mean and Variance Adjusted (WLSMV) estimator.
  • Reliability: McDonald's Omega ($\omega$) and Guttman's $\lambda_2$ were prioritized over Cronbach's $\alpha$ due to the violation of tau-equivalence assumptions.
  • Classification: Latent Class Analysis (LCA) was performed to determine adherence cutoffs and profile distinct adherence groups.

3. Key Contributions

• Cultural Adaptation: First validation of the D-DAS specifically for the Indian population, including a rigorous Hindi translation process.
• Advanced Psychometric Approach: The study moves beyond traditional Cronbach's alpha by employing:
  • MCA to map categorical adherence behaviors.
  • 2PL IRT to evaluate item-level discrimination.
  • WLSMV-CFA to handle non-normal data and categorical variables.
  • LCA to derive data-driven cutoffs for adherence classification.
• Scale Refinement: The study identified that the original 10-item scale could be refined. Items D8–D10 showed poor discrimination and weak factor loadings, suggesting the scale performs best as a 7-item instrument (D1–D7) focusing on "implementation lapses" regarding the prescribed plan.

4. Key Results

• Demographics: The cohort was predominantly male (64.17%), with a median age of ~60 years. 60% were vegetarians.
• Dimensionality (MCA):
  • Four dimensions explained 68.4% of the total variance.
  • Dimension 1 was the primary axis, clearly separating "non-adherence behaviors" (Yes responses to D1–D7) from "adherence" (No responses).
  • Items D1–D7 showed high contribution and representation quality on Dimension 1.
• Construct Validity (CFA):
  • Model Fit: The single-factor model (D1–D7) showed good fit (CFI = 0.981, TLI = 0.975, RMSEA = 0.056).
  • Note: SRMR was slightly elevated (0.144), suggesting potential local misfit or secondary dimensions, but the 2-factor model did not improve this significantly.
  • Factor Loadings: D2 had the highest loading (0.962). D1, D3, D4, D5, D6, and D7 showed moderate-to-strong loadings. D8–D10 had weak loadings.
  • Convergent Validity: Average Variance Extracted (AVE) = 0.565; Composite Reliability (CR) = 0.900.
• Reliability:
  • McDonald's $\omega$ = 0.836.
  • Guttman's $\lambda_2$ = 0.836.
  • Cronbach's $\alpha$ = 0.817 (lower than $\omega$ due to non-tau-equivalence).
• Item Response Theory (IRT):
  • The 7-item model (D1–D7) converged successfully with excellent discrimination parameters ($a_i > 2.0$ for D2, D3, D4, D7).
  • The full 10-item model failed to converge; D8–D10 showed poor discrimination ($a_i < 1.0$).
• Adherence Outcomes:
  • LCA Results: Identified two distinct classes: High Adherence and Low Adherence.
  • Prevalence: 68.3% of participants exhibited high adherence to the prescribed diet, while 31.7% demonstrated non-adherence.
  • Demographic Correlation: No significant association was found between adherence and gender, smoking, alcohol consumption, or BMI.

5. Significance and Implications

• Clinical Utility: The validated D-DAS (specifically the 7-item version) provides clinicians in India with a reliable, self-administered tool to screen for dietary non-adherence early in the treatment course.
• Targeted Interventions: The scale distinguishes between "implementation lapses" (D1–D7) and "behavioral inconsistencies" (D8–D10). High scores on the former suggest practical barriers (access, cost), while the latter suggests motivational issues, allowing for tailored interventions.
• Policy and Public Health: The finding that ~32% of patients are non-adherent despite support highlights the need for systemic changes, including better food security, affordable healthy food options, and Knowledge-Attitude-Practice (KAP) based interventions.
• Future Directions: The authors recommend larger, multi-center studies to further validate the LCA cutoffs and address the limitation of self-reported data. They also suggest integrating the scale into digital health platforms (QR codes, tablets with voice-over) for routine clinical use.

Conclusion: The study successfully establishes the D-DAS as a psychometrically robust instrument for the Indian population, confirming that while a majority of patients adhere to diet plans, a significant minority requires targeted support to achieve optimal glycemic control.