European Journal of Cardiovascular Medicine

Mobile phones are playing an increasingly important role in healthcare service delivery and self-care support for patients, a field known as mobile health (mHealth). ¹mHealth technologies encompass a variety of communication channels, including text messaging, video messaging, “live” voice calling, interactive voice response calls, and Internet-enabled smartphone apps. ²Worldwide mobile health (mHealth) research and development is increasing. ³A number of studies have examined the impact of mHealth technologies on communicable diseases and maternal and child health.⁴

Still, most of the data on the use of mobile technology in the area of non-communicable diseases (NCDs) and risk factor management have been restricted to developed countries, highlighting the need for more rigorous research in Low- and Middle-Income Countries. ⁵Developing countries are witnessing epidemiological transition towards DM. ⁶

Since mHealth has wider application concerning economic and social constraints in health care delivery, it has become imperative to assess the potential fit of mHealth solution in local settings towards Type II DM. Hence the study was carried out to know the mobile phone use among patients with Type II DM in urban slum dwelling of Kalaburagi.

A review of published mHealth interventions was conducted. Relevant studies were identified through a systematic literature search in databases such as PubMed, Google Scholar, and Cochrane Library. Criteria for inclusion were interventions that were published within the last 5 years, aimed at improving public health outcomes, and had measurable effectiveness metrics.

Descriptive statistics were used to summarize mobile phone usage rates for both Type II DM and non-DM groups. Comparisons between the two groups were performed using independent t-tests or Mann-Whitney U tests, depending on the distribution of the data. Chi-square tests were used to compare categorical variables. Multiple linear regression analysis was conducted to identify socio-demographic factors that significantly influenced mobile phone usage rates among Type II DM participants. Variables included in the model were age, gender, education level, income, employment status, and residence location. A qualitative synthesis of the mHealth interventions was performed. Effectiveness was evaluated based on reported outcomes such as improvements in health behaviors, adherence to treatment, and overall public health impact. The quality of evidence was assessed using established criteria for evaluating intervention studies.

The study was approved by the institutional review board (IRB) of Mahadevappa Rampure Medical College. All participants provided informed consent prior to participation. Confidentiality was maintained by anonymizing participant data and securely storing all information.

A systematic review was conducted to evaluate the effectiveness of mobile health (mHealth) interventions for patients with Type II Diabetes Mellitus (DM) in the urban area of Kalaburagi, India. The review aimed to assess how these interventions impact patient outcomes and contribute to strengthening public health.

Studies that focused on mHealth interventions for Type II DM patients. Research conducted in urban areas of India, specifically Kalaburagi or similar settings. Publications from the past 5 years to ensure relevance. Studies with measurable outcomes related to patient health improvements or public health impacts. Studies not involving mHealth interventions, research conducted outside the specified geographical and temporal scope, articles not written in English or lacking sufficient methodological details were excluded A comprehensive search strategy was employed to identify relevant studies. Databases searched included PubMed, Google Scholar, Cochrane Library, and Indian databases such as IndMed and ICMR."mHealth interventions", "Type II Diabetes Mellitus", "urban health India", "public health strengthening", "mobile health India"The search was conducted from January 2020 to July 2024. Boolean operators (AND, OR) were used to refine the search results.

Titles and abstracts of retrieved articles were screened for relevance. Full-text articles were reviewed to confirm eligibility based on inclusion and exclusion criteria, A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram was used to document the selection process, Key information was extracted from each study, including study design, population characteristics, mHealth intervention types, outcomes measured, and key findings, Data extraction was performed independently by two reviewers to ensure accuracy and minimize bias. A qualitative synthesis was performed to summarize the evidence on the effectiveness of mHealth interventions.

Studies were categorized based on the type of mHealth intervention, outcomes reported, and impact on public health.Common themes and patterns were identified across studies. The quality of included studies was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for evidence-based practice. Studies were evaluated for methodological rigor, sample size, and relevance to the research questions. The overall impact of mHealth interventions on patient health outcomes and public health was assessed. Key metrics included improvements in glycemic control, medication adherence, patient education, and overall health behavior changes. Ethical approval was not required for this systematic review as it involved secondary data analysis of published studies. All data were extracted from publicly accessible sources, ensuring compliance with ethical standards in research.

PRISMA Flowchart

Identification: Records identified through database searching (n = 350): Total number of articles identified from the initial database search. Additional records identified through other sources (n = 20): Articles identified from other sources such as references or grey literature.

1. Screening:
• Records screened (n = 320): Number of records reviewed after removing duplicates.
• Records excluded (n = 170): Articles excluded based on title and abstract screening for relevance.
2. Eligibility:
• Full-text articles assessed for eligibility (n = 150): Articles reviewed in detail for inclusion criteria.
• Full-text articles excluded with reasons (n = 120): Articles excluded based on specific reasons such as not meeting inclusion criteria or being irrelevant.
3. Included:
• Studies included in qualitative synthesis (n = 30): Final number of studies included in the review after the full-text assessment.

Study Selection

A total of 350 articles were initially identified through database searches. After removing duplicates and screening titles and abstracts, 150 articles were selected for full-text review. Of these, 30 studies met the inclusion criteria and were included in the final review.

Table 1: Characteristics of Included Studies

Table 2: Effectiveness of mHealth Interventions on HbA1c Levels

Table 3: Impact on Medication Adherence

Table 4: Patient Education and Self-Management Improvements

Table 5: Cost-Effectiveness of mHealth Interventions

Overview of Findings

This systematic review evaluated the effectiveness of mobile health (mHealth) interventions for patients with Type II Diabetes Mellitus (DM) in the urban area of Kalaburagi, India. The included studies varied in their design, intervention types, and outcomes. Overall, mHealth interventions demonstrated significant potential in improving diabetes management and public health.

1. Glycemic Control

The review found that mobile apps, SMS reminders, teleconsultation, and wearable devices all contributed to improvements in glycemic control. Mobile apps showed the largest reduction in HbA1c levels (1.2%), which aligns with findings from previous studies indicating that comprehensive self-management apps can significantly enhance diabetes care [1, 2]. SMS reminders also had a meaningful impact, although to a lesser extent, which is consistent with research suggesting that simple, automated reminders can reinforce medication adherence and lifestyle changes [3, 4]. Teleconsultation services were effective in improving glycemic control, which underscores the value of remote consultations in managing chronic conditions [5, 6]. Wearable devices also contributed to improved HbA1c levels, highlighting their role in continuous glucose monitoring and management [7, 8].

2. Medication Adherence

Improvement in medication adherence was observed across all intervention types. Mobile apps and teleconsultation services showed particularly strong effects, enhancing adherence rates by 25% and 22%, respectively. This finding is in line with studies demonstrating that digital tools and remote support can lead to better adherence through enhanced patient engagement and feedback [9, 10]. SMS reminders were effective as well, though they had a somewhat lower impact compared to apps and teleconsultation. This suggests that while SMS can be a cost-effective tool, more interactive and engaging methods may be more effective [11, 12].

3. Patient Education and Self-Management

Patient education and self-management skills improved significantly with mobile apps and teleconsultation services. These interventions provided valuable information and support, leading to enhanced self-management practices. This is consistent with evidence that educational components and interactive features in mHealth tools can foster better disease management [13, 14]. Wearable devices also had a positive effect, though less pronounced, suggesting that while they are useful for monitoring, additional educational support may be needed to maximize their impact [15, 16].

4. Public Health Impact

mHealth interventions demonstrated a positive impact on public health by reducing emergency visits and improving the overall quality of life for patients. The cost-effectiveness of these interventions varied, with mobile apps and SMS reminders being particularly cost-effective due to their low implementation costs and significant health benefits. Teleconsultation and wearable devices showed potential for cost savings in the long term, although they involved higher initial costs. This reflects broader trends in digital health, where upfront investments in technology can lead to substantial long-term savings and health improvements [17, 18].

Several limitations were identified in the studies reviewed. The quality of evidence was mixed, with some studies having small sample sizes or short follow-up periods. Additionally, there was variability in the effectiveness of different mHealth interventions, which may be influenced by contextual factors such as local healthcare infrastructure and patient engagement levels [19, 20]. The generalizability of the findings may also be limited to the specific urban setting of Kalaburagi.

Future Research

Future research should focus on larger-scale studies with longer follow-up periods to better assess the long-term impacts of mHealth interventions. Additionally, exploring the integration of multiple mHealth tools and their combined effects on diabetes management could provide insights into optimizing intervention strategies [21, 22]. Research should also consider the adaptability of mHealth solutions to diverse populations and settings to enhance their applicability and effectiveness [23, 24]. 

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