Sustainable effects of a hybrid self-care education program on diet quality and cardiovascular risk in patients with chronic conditions: A randomized controlled trial

Abstract

Background

Inadequately managed chronic diseases heighten cardiovascular risk. Improved dietary habits are crucial for risk reduction and enhanced patient health. This study evaluated the sustained impact of a hybrid self-care education program on diet quality and cardiovascular risk in patients with chronic conditions.

Methods

In this 2023 randomized controlled trial in Saravan, Iran, 150 patients with chronic conditions were enrolled. Participants were randomly allocated to an intervention group (n = 75) or a control group (n = 75). Data were collected using a self-care nutrition questionnaire and the Mini-EAT. The intervention group received a one-month hybrid self-care education program integrating the Teach-Back method with digital education, followed by monthly follow-ups. Data were analyzed using SPSS version 26 with repeated measures ANOVA, one-way ANOVA, independent t-tests, and chi-square tests (statistical significance: p < 0.05). Follow-up assessments occurred at 3 and 12 months post-intervention.

Results

Post-intervention, nutritional self-care scores in the intervention group increased significantly from baseline (11.90) to 3 months (24.12) and remained stable at 12 months (23.74; p < 0.001). No comparable change occurred in controls. Diet quality improved markedly: the proportion with unhealthy diets decreased from 98.67 % to 49.34 %, while adherence to healthy diets rose from 0 % to 13.33 % (p < 0.001). At the 12-month follow-up, statistically significant reductions were observed across all key cardiovascular risk metrics: systolic blood pressure decreased from 142.21 mmHg to 132.22 mmHg, diastolic blood pressure from 104.70 mmHg to 92.16 mmHg, fasting blood glucose from 212.66 mg/dL to 151.48 mg/dL, and BMI from 27.91 kg/m² to 25.32 kg/m² (all p < 0.001).

Conclusion

The integrated Teach-Back and digital education intervention produced sustained improvements in nutritional self-care, diet quality, and cardiovascular risk factors among patients with chronic conditions. Effect durability at 12 months underscores the value of ongoing follow-up in educational strategies. These findings support integrating digital education into health promotion programs to reinforce self-care behaviors and improve clinical outcomes.

1. Introduction

Global shifts in health patterns driven by aging populations and rapid urbanization-related lifestyle changes have fostered widespread adoption of unhealthy behaviors. Consequently, non-communicable diseases (NCDs)—particularly cardiovascular diseases (CVDs)—now represent a critical global health challenge [1]. According to the latest WHO report, CVDs account for 32 % of global mortality (approximately 17.9 million annual deaths) [2]. In Iran, CVDs constitute the leading cause of death, responsible for 46 % of total mortality [3]. Chronic diseases collectively contribute to 71 % of annual deaths worldwide [4].

Hypertension, a major risk factor for CVDs, directly damages vascular function, which can lead to heart failure, stroke, and chronic kidney disease [5,6]. Similarly, obesity exacerbates lipid deposition in blood vessels, doubling the risk of CVD-related mortality [7,8]. Furthermore, type 2 diabetes, characterized by insulin resistance, in conjunction with hyperlipidemia and hypertension, increases CVD risk by 2- to 8-fold [9,10]. Studies indicate that several CVD-related risk factors, such as obesity, diabetes, and hypertension, are directly influenced by dietary habits [11,12]. Unhealthy diets—marked by excessive intake of saturated fats, trans fats, cholesterol, and sodium, coupled with insufficient consumption of fruits, vegetables, and fish—have a direct correlation with heightened CVD risk [13].

Domestic studies reveal insufficient awareness and inadequate dietary behaviors among Iranian households when selecting appropriate nutritional patterns [14,15]. In this regard, nutritional self-care—as an integral component of chronic disease management—can facilitate risk factor control by enhancing dietary knowledge and fostering behavioral change [16]. Patient empowerment through educational interventions is recognized as an effective approach to improving diet quality and self-care adherence [14]. Previous research underscores the Mediterranean diet as a prominent strategy for managing chronic conditions, such as diabetes, hypertension, and obesity [17].However, existing studies exploring the long-term effects of self-care interventions on dietary quality—especially in regions with distinct sociocultural characteristics—remain limited [18]. Additionally, traditional educational interventions in self-care have often demonstrated limited success in fostering sustainable behavioral changes [19,20].

In response to these evidence gaps, we implemented a hybrid self-care education program combining the Teach-Back method with digital delivery as a promising approach to enhance nutritional self-care among chronic disease patients. Given diet's established role in CVD prevention and the challenge of sustaining dietary adherence, this study evaluated the sustained effectiveness of this intervention on diet quality and CVD risk reduction among patients with hypertension, obesity, or diabetes in Saravan, Iran (2023). Our findings may inform evidence-based interventions to improve dietary behaviors and public health outcomes in similar populations.

2. Methodology

This single-blind, parallel-group randomized controlled trial (Registration: IRCT20230612058460N1) was conducted in 2023 in Saravan, southeastern Iran. Participants were blinded to group allocation, while researchers and implementers were unblinded. The study population included adults aged >18 years with hypertension, diabetes, or overweight/obesity registered at Saravan's urban comprehensive health centers.

2.1. Sample size calculation

Sample size was determined based on HbA1c outcomes from the randomized controlled trial by Celli et al. [21], which evaluated lifestyle interventions in older adults with type 2 diabetes. Assuming an expected between-group difference of 0.6 % in mean HbA1c (SD = 1.0 %), an effect size of 0.6 was derived. Using the formula for comparing two independent means with α = 0.05 and 80 % power, and accounting for an estimated 40 % attrition rate, the calculated sample size was approximately 74 participants per group $n_{\text{adjusted}}=\frac{44}{1-0.4}\approx 74$. A final sample of 75 participants per group was enrolled.

$$n=\frac{2{\left(Z_{1-\alpha /2}+Z_{1-\beta }\right)}^2{\alpha }^2}{d^2}$$

Given the extended duration of this intervention and heightened attrition risk among patients with chronic conditions (e.g., type 2 diabetes, hypertension) due to physical/psychological burdens or treatment discontinuation, we anticipated substantial participant loss—a risk further compounded by the study population's socio-cultural context, including limited mobility, familial obligations, and financial constraints, which collectively undermined engagement consistency. Consequently, a 40 % attrition rate was incorporated a priori to maintain statistical power, aligning with a recent meta-analysis reporting 43 % pooled attrition in chronic disease interventions [22] and regional evidence from Sistan and Baluchestan where socioeconomic barriers elevate dropout likelihood.

2.2. Sampling and randomization

From the four urban comprehensive health service centers in Saravan, one center was randomly selected using simple random sampling. Among eligible participants including 542 hypertensive patients, 481 diabetic patients, and 611 overweight/obese individuals - we performed random sampling to select 50 hypertensive patients, 50 diabetic patients, and 50 overweight/obese individuals. These selected participants were then randomly allocated to either the intervention or control group. to mitigate comorbidity-related confounding, each study group was intentionally composed of: 25 hypertensive patients, 25 diabetic patients, and 25 obese patients.

The study enrolled adults aged >18 years who were literate, capable of using mobile applications, and residing in Saravan with active medical records at comprehensive health service centers. For participants with hypertension or diabetes, a minimum one-year period since initial diagnosis was required. Exclusion criteria consisted of failure to attend educational sessions, pre-existing psychological conditions that could impair participation, or relocation from the study area during the trial period.

2.3. Data collection tools

Three instruments collected data [1]: Demographic form [2]; 12-item Nutritional Self-Care Questionnaire (score range 0–36; CVI = 0.89/CVR = 0.81; Cronbach's α = 0.85) with frequency-based responses (Never = 0 to Always = 3) [3]; Mini-EAT for dietary quality. All were self-administered.

2.4. Diet quality assessment

Diet quality was evaluated using the 9-item Mini-EAT, a validated rapid screener of weekly dietary patterns assessing nine components: fruits, vegetables, whole grains, refined grains, fish/seafood, legumes/nuts/seeds, low-fat dairy, high-fat dairy, and sweets. It shows strong convergent validity with food frequency questionnaires (r = 0.71) [23]. Diet quality classification is determined based on the total score, with individuals scoring below 61 classified as having an unhealthy diet, those scoring between 61 and 69 categorized as intermediate, and scores above 69 representing a healthy diet [24]. The validity and reliability of the Persian Mini-EAT were assessed through a standardized forward-backward translation process conducted by two independent translators. The final version was refined through expert review, involving a specialized committee comprising nutritionists, health education specialists, and translators. Content validity was confirmed through a panel of 10 experts, including four nutritionists, two physicians, and four health education specialists, with a Content Validity Index (CVI) of 0.78 and a Content Validity Ratio (CVR) of 0.86. Instrument reliability was established via Cronbach's alpha coefficient (α = 0.83), indicating strong internal consistency. These findings suggest that the Persian Mini-EAT possesses acceptable psychometric properties for use among Persian-speaking populations.

2.5. Blood pressure measurement

Trained technician measured blood pressure (BP) using a calibrated mercury sphygmomanometer (Riester) according to standardized protocols [25], with measurements conducted in participants' homes between 08:00–10:00 a.m. following ≥5 min of seated rest and adherence to the following preconditions: abstention from vigorous physical activity, smoking, coffee consumption, or heavy meals within 30 min prior; ≤11 h fasting duration; and post-measurement administration of antihypertensive medications. The technique incorporated appropriately sized cuff placement on the right arm positioned at heart level, alongside research specialist monitoring to ensure procedural accuracy throughout.

Hypertension classification followed: Normal (SBP <120 mmHg and DBP <80 mmHg), Elevated (SBP 120–129 mmHg and DBP <80 mmHg), Grade 1 (SBP 130–139 mmHg or DBP 80–89 mmHg), Grade 2 (SBP ≥140 mmHg or DBP ≥90 mmHg), and Crisis (SBP ≥180 mmHg or DBP ≥120 mmHg) [26].

Fasting blood glucose (FBG) was measured after 8–12 h of fasting using capillary blood samples collected via Accu-Chek glucometers between 07:00–09:00 a.m. per American Diabetes Association standards [27], with methodological accuracy ensured through parallel validation against laboratory-analyzed venous samples using high-precision biochemical analyzers and control serum references. Type 2 diabetes (T2D) diagnosis required meeting ≥1 criterion: fasting plasma glucose (FPG) ≥126 mg/dL (≥7.0 mmol/L); 2-h plasma glucose ≥200 mg/dL (≥11.1 mmol/L) during 75-g OGTT; HbA1c ≥ 6.5 % (48 mmol/mol; NGSP-certified); or random plasma glucose ≥200 mg/dL with classic symptoms (polyuria, polydipsia, unexplained weight loss), where asymptomatic cases required confirmatory testing. T2D was distinguished from Type 1 by preserved C-peptide levels and absence of autoimmune markers (GAD-65/IA-2 antibodies), with universal screening recommended for adults ≥35 years and annual re-screening for prediabetes (HbA1c 5.7–6.4 % [39–46 mmol/mol] or FPG 100–125 mg/dL [5.6–6.9 mmol/L]) [27].

3. Body weight and BMI measurement

Participants underwent anthropometric assessment using calibrated instruments: body weight was measured to the nearest 0.1 kg using a Beurer PS 240 digital scale (Germany) after bladder emptying, with participants standing without shoes in minimal clothing; height was recorded to the nearest 0.1 cm via wall-mounted stadiometer in the Frankfort plane position. Body Mass Index (BMI) was calculated as weight (kg)/height² (m²) [28]. Classification followed standard categories: Underweight (BMI <18.5 kg/m²), Healthy Weight (18.5–24.9 kg/m²), Overweight (25.0–29.9 kg/m²), Obesity Class 1 (30.0–34.9 kg/m²), Class 2 (35.0–39.9 kg/m²), and Class 3 (≥40.0 kg/m²) [29].

3.1. Implementation of the educational intervention

Following participant needs assessment, we developed educational content on nutritional self-care for chronic conditions (diabetes, hypertension, obesity) aligned with Ministry of Health guidelines, covering core principles: vegetable/fruit diversity; whole grains; reduced simple sugars; low-fat dairy; minimized processed foods; healthy proteins (legumes/fish/lean meats); nutritional balance; and portion control. Simplified scientific materials were delivered through domestic digital platforms (Soroush, Eitaa, Bale) using: five 10–15 min structured videos (introduction-instruction-summary); reinforcing infographics/texts; and interactive exercises (e.g., healthy food selection)—all co-developed with nutrition/health education specialists for cultural relevance and elementary literacy alignment. Pre-implementation validation employed a researcher-adapted SAM(Suitability Assessment of Materials) framework assessed by 10 representative patients via 5-point Likert scale; mean scores ≥4/5 across comprehensibility, applicability, and cultural appropriateness domains necessitated only minor enhancements like incorporating local salt-reduction examples.

3.2. Educational sessions implementation

The educational intervention was implemented over one month through three integrated components [1]: Conceptual education disseminated scientific yet accessible content on diet quality and nutritional self-care principles via domestic digital platforms [2]; Practical exercises incorporated daily meal logging and concept reinforcement using the Teach-Back technique; and [3] Feedback/monitoring provided weekly individualized feedback during the initial month, transitioning to monthly assessments through one-year follow-up, with progress updates and adherence reinforcement reminders delivered through Soroush, Eitaa, and Bale.

3.3. Techniques used to enhance nutritional self-care

Five complementary techniques optimized nutritional self-care [1]: Teach-Back required participants to restate key concepts post-session, enabling real-time misconception clarification [2]; Multimedia Education delivered 10–15-min videos, infographics, and interactive assessments (e.g., food selection exercises) via mobile applications [3]; Problem-Based Learning presented weekly scenarios (e.g., designing low-sodium meals) for applied skill development [4]; Digital Communities utilized domestic platforms (Soroush/Eitaa/Bale) for daily educational content sharing and peer support [5]; Continuous Evaluation administered monthly Mini-EAT assessments with personalized feedback to track dietary improvements.

Questionnaires were administered to both groups at baseline, 3-month, and 12-month post-intervention intervals with 100 % completion. The intervention group received the educational program, while controls continued routine healthcare consisting of basic education on diabetes/BP/weight management, standard nutritional counseling, physical activity recommendations, and periodic follow-ups by community health center staff.

3.4. Ethical considerations

This study received ethical approval from Zahedan University of Medical Sciences (ZAUMS), Iran (IR.ZAUMS.REC.1402.099). All procedures strictly complied with ethical principles. Following comprehensive explanation of study objectives and confidentiality protections, participants provided written informed consent.

3.5. Data analysis

We analyzed data in SPSS v26.0. Quantitative variables' normality was confirmed via Kolmogorov-Smirnov testing. Descriptive statistics (frequencies, means ± SDs) summarized baseline characteristics. For longitudinal within-group comparisons across three timepoints (baseline, 3-month, 12-month), Repeated Measures ANOVA analyzed normally distributed parameters (nutritional self-care, diet quality, FBG, BP, BMI), while Friedman tests handled non-parametric equivalents. Between-group differences (intervention vs. control) were assessed using independent t-tests for continuous variables and χ² tests for categorical measures, with statistical significance set at p < 0.05.

4. Results

At baseline, 150 participants were randomized to intervention (n = 75) or control (n = 75). Attrition totaled nine participants at 3 months (intervention = 4; control = 5) due to death (n = 3), migration (n = 2), or withdrawal (n = 4). Final 12-month analysis included 141 participants (intervention = 71; control = 70) (Fig. 1).

Fig. 1.

Flow of participants through each stage of the programme HTN= Hypertension, DM = Diabetes Mellitus, OW= Overweight.

Baseline demographic and clinical characteristics showed no significant intergroup differences: age (46.81 ± 13.64 vs. 48.36 ± 13.64 years; p = 0.668), gender (85.33 % vs. 81.33 % female; p = 0.511), BMI (27.91 ± 4.41 vs. 26.92 ± 4.32 kg/m²; p = 0.730), and other key variables (all p > 0.05), confirming successful randomization (Table 1). All patients with diabetes or hypertension (n = 50 per group) received medication for disease management, with comparable treatment regimens between groups at baseline (see Table 1).

Table 1.

Comparison of baseline characteristics in the intervention and control groups before implementing the educational intervention.

Variable		Intervention group	Control group	ρ
		Mean ± SD	Mean ± SD
Age(year)		46.81 ± 13.64	48.36 ± 13.64	0.668∗
BMI		27.91 ± 4.41	26.92 ± 4.32	0.730∗
Variable Levels		n(%)	n(%)
Gender	Male	11(14.67)	14(18.67)	0.511∗∗
	Female	64(85.33)	61(81.33)
Level of education	Primary	45(60)	46(61.34)	0.896∗∗
	High school	18(24)	19(25.33)
	College	12(16)	10(13.33)
Monthly income	Less than enough	34(45.34)	37(49.34)	0.854∗∗
	Enough	37(49.33)	33(44)
	More than enough	4(5.33)	5(6.66)
Job	Unemployed	3(4)	4(5.34)	0.213∗∗
	Housewife	58(77.34)	52(69.34)
	Employee	4(5.34)	1(1.33)
	Retired	5(6.66)	13(17.33)
	Self-employment	5(6.66)	5(6.66)
Marital status	Single	3(4)	1(1.33)	0.741∗∗
	Married	64(85.34)	64(85.34)
	Divorced	1(1.33)	1(1.33)
	Widow	7(9.33)	9(12)
Underlying disease	Hypertension	25(33.33)	25(33.33)	1.000∗∗
	Diabetes	25(33.33)	25(33.33)
	Overweight & obesity	25(33.33)	25(33.33)

P∗Independent Samples Test, P∗∗Chi-square Test.

Compared to controls receiving conventional care, the intervention group (Teach-Back/digital education with continuous follow-up) demonstrated statistically significant improvements in nutritional self-care scores and diet quality at both 3- and 12-month follow-ups (p < 0.05), with meaningful 12-month reductions in systolic/diastolic blood pressure, fasting blood glucose, and body mass index (all p < 0.05).(see Table 2) ( see Fig. 2)

Table 2.

Comparison of baseline and post-intervention health outcomes between intervention and control groups following educational programs.

Variable	Groups		Pre-intervention	Post-intervention (3 months)	Post-intervention (12 months)	P∗
			Mean ± SD	Mean ± SD	Mean ± SD
Nutritional self-care	Intervention		11.90 ± 4.47	24.12 ± 3.82	23.74 ± 3.73	0.001∗∗
	Control		11.53 ± 4.20	11.12 ± 3.72	10.84 ± 3.52	0.133∗∗
	P∗		0.599∗	0.001∗	0.001∗
Diet quality	Intervention		45.49 ± 6.58	60.78 ± 7.02	59.71 ± 6.44	0.001∗∗
	Control		45.78 ± 5.77	46.02 ± 4.99	46.84 ± 5.52	0.209∗∗
	P∗		0.113∗	0.001∗	0.001∗
Diet quality Levels	Intervention	Unhealthy	74(98.67)	36(48)	37(49.34)
		Intermidiate	1(1.33)	29(38.67)	28(37.33)	0.001∗∗∗
		Healthy	0(0)	10(13.33)	10(13.33)
	Control	Unhealthy	73(97.34)	74(98.67)	74(98.67)
		Intermidiate	2(2.66)	1(1.33)	1(1.33)	0.560∗∗∗
		Healthy	0(0)	0(0)	0(0)
	∗∗∗∗P		0.560∗∗∗∗	0.001∗∗∗∗	0.001∗∗∗∗
BP	Intervention	Systolic blood pressure	142 ± 21.84	131.12 ± 19.15	132.22 ± 18.13	0.001∗∗
	Control	Systolic blood pressure	143.60 ± 20.33	143.96 ± 21.08	144.25 ± 20.45	0.282∗∗
	P∗		0.790∗	0.001∗	0.001∗
	Intervention	Diastolic blood pressure	104.70 ± 20.40	91.24 ± 20.74	92.16 ± 21.32	0.001∗∗
	Control	Diastolic blood pressure	102.02 ± 21.13	101.89 ± 20.95	101.78 ± 21.19	0.879∗∗
	P∗		0.680∗	0.001∗	0.001∗
FBS	Intervention		212 ± 66.18	146 ± 52.35	151 ± 48.93	0.001∗∗
	Control		213 ± 93.23	212 ± 88.14	214 ± 94.12	0.990∗∗
	P∗		0.285∗	0.001∗	0.001∗
BMI	Intervention		27.91 ± 4.41	25.02 ± 351	25.32 ± 3.15	0.001∗∗
	Control		26.92 ± 4.32	26.84 ± 4.02	27.05 ± 3.92	0.198∗∗
	P∗		0.230∗	0.274∗	0.018∗

P∗Independent Samples Test, P∗∗ Repeated Measure, P∗∗∗Friedman, P∗∗∗∗Chi-square Test, BP: Blood Pressure,FBS: Fasting Blood Sugar, BMI:Body Mass Index.

Fig. 2.

Sustained improvements in nutritional self-care, diet quality, and chronic disease markers following educational intervention (baseline to 12 Months post-intervention) FBS: Fasting blood sugar, BMI:Body mass index.

5. Discussion

This study evaluated the long-term effectiveness of a self-care education program combining the teach-back method with a digital application, focusing on dietary quality and chronic disease risk factors (blood pressure, diabetes, and body mass index) in patients with chronic conditions. Results demonstrated significant improvements in self-care behaviors, dietary quality, and these risk markers. Critically, these benefits were sustained not only at 3 months but also at 12 months post-intervention.

Results revealed that the mean nutritional self-care score increased significantly in the intervention group at 3 months and, despite slight attenuation, remained substantially elevated above baseline at 12 months. Conversely, the control group showed no significant changes. This demonstrates successful long-term maintenance of improved nutritional self-care behaviors following the intervention.

These findings align with Khorashadizadeh et al. [30], who reported sustained improvements in self-care behaviors (including diabetic-specific diet, physical activity, and medication adherence) for 12 weeks following a combined in-person/telephone education program with weekly follow-ups in pregnant women with diabetes. Similar to our results, the authors attributed this sustainability to continuous support and regular reminders. Support also comes from Kermansaravi et al. [31], demonstrating that follow-up care models positively modified self-care in post-Myocardial Infarction patients. Further corroboration is provided by Shaw et al. [32], whose research in heart failure patients showed significantly better self-care in groups receiving structured follow-up within 5–10 days post-discharge compared to controls.

Similarly, in a randomized clinical trial, Rosero et al. [33] found that an educational intervention combining in-person training with telephone follow-ups significantly improved knowledge and self-care among COPD patients at six months, attributing this enhancement to the reinforcing effect of phone calls on disease management skills.

Conversely, some studies report negative outcomes. For example, a computer-based multimedia intervention delivered to diabetic patients in waiting rooms found no significant effect on blood glucose control, self-efficacy, or diabetes self-management behaviors [34]. Similarly, a one-month mobile-based intervention did not yield significant improvements in key diabetes management outcomes (including blood glucose, nutritional self-care, physical activity, or BMI) at follow-up [35]. Variations in study results may stem from factors such as inadequate post-intervention follow-up duration, varying technology acceptance, individual participant differences, and the lack of a continuous feedback mechanism.

Within the intervention group, dietary quality improvements remained statistically significant at 3 months post-intervention and stabilized by month 12. Dramatic shifts were observed in dietary patterns: the proportion of individuals with unhealthy diets halved, decreasing from 98.7 % to 49.3 %, while adherence to a healthy diet rose from 0 % to 13.3 %. These results underscore the intervention's effectiveness and demonstrate its sustained impact on dietary behavior. Conversely, the control group showed no significant changes, reinforcing that continuous education and follow-up are essential for durable behavioral change.

Aligned with our findings, Fresan et al. [36] assessed the feasibility of a digital intervention promoting sustainable dietary habits. Their digital strategy—featuring text messages and individualized online feedback over one year—significantly increased diet quality: 92 % of participants boosted daily fruit/vegetable intake, while 58 % reduced red meat and processed food consumption. The researchers attributed sustained effects to continuous engagement and personalized feedback.

However, Kelly et al. [37] found remote nutrition education (via phone/SMS) for kidney failure patients safe but ineffective for improving overall diet quality or blood pressure, despite yielding some benefits in vegetable/fiber intake and weight reduction. This contrasts with our results, possibly due to higher baseline diet quality in their cohort limiting measurable impact, compounded by a follow-up duration too short to capture longitudinal effects.

Critically, our intervention group—unlike the control group—achieved significant, sustained reductions in body mass index (BMI), blood pressure, and fasting blood glucose at both 3 and 12 months post-intervention. This demonstrates the importance of continuous educational support for driving lasting lifestyle changes. These outcomes mirror Rasoul et al. [38], where web-based self-management education significantly lowered fasting blood glucose, BMI, and systolic/diastolic blood pressure in their intervention cohort.

Foppa et al. [39] demonstrated that year-long nurse-led interventions (combining telephone and in-person counseling) significantly improved glycemic control, self-care adherence, and knowledge in type 1 diabetes patients. While self-management is essential for all patient groups, both intervention intensity and educational methodology critically influence outcomes. Appropriately designed educational strategies enhance disease control, reduce complications, and empower patients' ability to make informed decisions—ultimately elevating quality of life [40]. Mobile technologies (e.g., smartphones, texting)—now ubiquitous—offer promising pathways for self-care enhancement [41], with applications enabling immediate delivery of concise content and asynchronous access (allowing user-determined engagement) [42]. Consequently, research increasingly focuses on leveraging these tools to optimize patient care quality [43].

Ardestani et al. [44] identified success, satisfaction, accessibility, effectiveness, readability, and engagement as pivotal factors for e-health education adoption—with success emerging as the most influential. Furthermore, e-learning offers distinct advantages: broad accessibility, enhanced interaction, flexibility, knowledge management, and cost efficiency. These attributes position it as a powerful tool for improving clinical skills, medical knowledge, and developing effective educational programs—particularly in resource-limited or remote settings.

The predominance of female participants likely reflects documented sociocultural trends where women demonstrate greater health consciousness and participation in preventive healthcare. Structural facilitators—including easier healthcare access and motivation from family caregiving roles—may further explain this disparity. Conversely, men's persistent underrepresentation despite targeted outreach aligns with established literature (e.g., Deeks et al.) [45], highlighting the critical need for gender-specific recruitment strategies in community health research.

A key finding was the exceptionally high adherence of housewives to the digital intervention compared to other occupational groups—a disparity primarily attributable to structural advantages. Housewives benefited from greater schedule flexibility, enabling engagement during off-peak hours without employment constraints. Lower cognitive load from reduced work-related stressors likely facilitated consistent platform use. Conversely, employed participants faced systematic barriers: limited device/internet access during work hours, occupational fatigue, and time constraints. This occupational stratification underscores the necessity of context-sensitive design in digital health implementations for heterogeneous populations. Echoing these findings, WHO emphasizes digital technologies' role in enhancing women's self-efficacy through accessible, private health channels—specifically noting housewives' structural advantages in leveraging such tools [46].

5.1. Strengths and limitations of the current study

The current study has several limitations that must be considered when interpreting the results. Primarily, The study population consisted of individuals with access to technology and basic digital literacy. Therefore, the generalizability of the findings may be limited for populations with restricted access to digital resources or lower levels of digital literacy. This limitation may affect the broader applicability of the results across diverse settings. Another major limitations of this study was the gender imbalance among participants, with over 80 % being female. This discrepancy largely stemmed from voluntary participation patterns, where women were more willing to engage in health-related research. Despite targeted recruitment strategies aimed at men (e.g., workplace outreach, flexible scheduling, and emphasizing hypertension awareness), male enrollment remained below 20 %. As a result, the generalizability of the findings to male populations is limited, and future gender-balanced studies are recommended. Additionally, reliance on self-reported data, particularly for dietary information, introduces possible recall bias. The combined Teach-Back and digital app intervention, while innovative, requires substantial human and time resources that may challenge large-scale implementation in real-world healthcare settings. Although the 12-month follow-up period exceeds many similar studies, it may still be insufficient to fully evaluate the sustainability of complex lifestyle behavior changes. These limitations highlight the need for future research with more diverse samples, objective measurement methods, and extended follow-up periods. Another important limitation of this study is the potential for performance bias, as the research team was aware of the group allocations. This awareness may have unintentionally influenced researchers’ behavior, their interactions with participants, or the manner in which recommendations and follow-ups were delivered—factors that could have impacted the study outcomes. Although efforts were made to maintain uniform implementation standards across groups, this issue should be taken into account when interpreting the findings. It is also recommended that future studies incorporate blinding methods into their design to minimize the risk of such biases.

Despite these constraints, the study offers significant strengths including its evaluation of both short-term (3-month) and long-term (12-month) intervention effects, innovative combined methodology, use of objective clinical measures alongside self-reported data, systematic comparison with previous research, and rigorous randomized controlled trial design. These features, combined with an adequate sample size and extended follow-up, establish this work as a valuable contribution to chronic disease self-management literature.

6. Recommendations for future studies

Future research should prioritize mixed-methods designs to capture patient experiences and expand inclusion criteria to encompass broader age ranges, different jobs, male sex, diverse digital literacy levels, and varied chronic conditions; conduct component-analysis studies comparing the combined intervention against standalone teach-back or digital app approaches; incorporate objective monitoring technologies (e.g., wearables, biochemical testing) and extend follow-up beyond 12 months to assess long-term sustainability; and perform cost-effectiveness analyses to generate policy-relevant evidence. These steps would address current methodological limitations while advancing the intervention's real-world applicability and scalability within chronic disease management paradigms.

7. Conclusion

Findings indicate that the integrated Teach-Back and digital application intervention effectively promotes sustained behavioral changes. Nevertheless, outcome heterogeneity across studies underscores the critical importance of adequate follow-up duration, technology adoption, individual patient characteristics, and robust feedback mechanisms. Integrated digital education platforms incorporating structured follow-up protocols demonstrate significant promise for chronic disease management, particularly within resource-constrained settings. Mobile health (mHealth) technologies—including applications and SMS-based systems—offer substantial potential to enhance care quality while reducing associated costs. Consequently, a synergistic approach combining digital education with supportive methodologies, continuous monitoring, and personalized engagement represents a viable, evidence-based strategy for advancing patient self-management and public health outcomes. Implementation research is therefore warranted to rigorously evaluate the real-world effectiveness and scalability of this approach across diverse healthcare systems and patient populations.

CRediT authorship contribution statement

Hossein Izadirad: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. Zahra Jangizahi: Writing – review & editing, Writing – original draft, Project administration, Methodology.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Funding

The study did not receive any funding.

Declaration of competing interest

The authors declare that there are no financial affiliations, investor dependencies, employment, consultancy, or potential conflicts of interest related to this research. This study was conducted independently, and no entity influenced its results.

Handling editor: D Levy