HP4C4E Systematic Review and Meta-analysis Assignment Sample

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Part 1: Critical Appraisal

Critical appraisal examines how systematically and rigorously existing studies on telemedicine for type 2 diabetes were conducted. It evaluates study selection, methodological quality, risk of bias, and the reliability of reported outcomes, helping to identify gaps and areas for improvement in research design and reporting.

Introduction: Description Of Included Studies

Strong evidence synthesis instruments in healthcare research are systematic reviews and meta-analyses. The design of the work itself becomes rigorous, and the conclusions are reliable and valid based on the approach the researcher takes when designing the work. Zhai et al. (2014) are evaluated with a modified AMSTAR checklist as they conducted a systematic review and meta-analysis study. Students who find difficulty in analysing systematic reviews can benefit from assignment writing help to ensure accuracy, clarity and academic quality in their submissions. There is an assessment for potential bias for both randomized controlled trials included in the study.

Evaluation Using AMSTAR Checklist

The AMSTAR checklist provides a structured approach for evaluating methodological quality in systematic reviews. It allows researchers to assess essential components such as study selection, data extraction procedures, assessment of publication bias, and appropriateness of statistical analyses. Using AMSTAR helps determine whether the review demonstrates transparency, consistency, and adequate control of bias. When applied correctly, the checklist enhances the reliability of synthesized findings and guides researchers in identifying areas where methodological improvements are required.

1. Description of Included Studies

Zhai et al. (2014) evaluated 35 studies of randomly controlled trials on telemedicine clinical diagnosis and financial value for the treatment of type 2 diabetes mellitus (T2DM, Explicit eligibility criteria were established for the research and any articles are then selected based on study selection, following the PRISMA guidelines. The authors failed to provide enough details regarding key elements from each study, such as participant counts, setting details, and intervention method descriptions, which reduced transparency and result analysis. Additional detail in the summary information of each researched study boosted the effectiveness and practical utility of the study outcomes (Wang et al., 2021).

2. Risk of Bias Assessment in Included Studies

The review used the Cochrane Handbook’s risk-of-bias assessment tool to evaluate sequence generation, allocation concealment, and blinding domains. The research studies did not consistently present a risk of biased assessment results. The assessment lacked the necessary statistical methods to quantitize or synthesize the risk of bias scores between studies, thus reducing the credibility of its findings. Standardized processing of bias variables would have improved the study's capacity to obtain valid results when analyzing diverse data collections.

3. Statistical Methods for Meta-Analysis

The meta-analysis used conventional statistical methods to pool data about HbA1c reduction by applying a random-effects model because heterogeneity reached 75.5% (I²). The researchers supported their decision to use random-effects modelling, yet the research would have benefited from a thorough analysis to show how various analytical approaches altered study results. Research on cost-effectiveness analysis only included two studies which undermined trustworthy conclusions about economic value. More economic evaluations would be essential to establish final cost-effectiveness outcomes (Kyaw et al., 2023).

4. Impact of Bias on Meta-Analysis Results

Zhai et al. recognized the study limitations caused by heterogeneity and biases yet failed to thoroughly explain the effects of bias on the final results. Results from the meta-analysis showed publication bias using a funnel plot and Egger’s regression test (p < 0.001) because researchers had probably exaggerated intervention effectiveness. The researchers omitted from conducting a trim-and-fill analysis to rectify publication bias, although they reported evidence of its presence.

5. Consideration of Heterogeneity

The combination of HbA1c reduction studies displayed significant variation between included studies due to their high heterogeneity level (I² > 75%). The authors minimized this problem through subgroup analyses, separating interventions into telephone-based, internet-based and internet-transmitted categories. The evaluation did not include additional group comparisons regarding study lengths or patient demographics to understand heterogeneity factors better. The wide range of telemedicine methods used with different patient groups required more investigation to identify the main drivers of study heterogeneity

6. Investigation of Meta-Bias

Significant publication bias appeared in the results based on the asymmetrical funnel plot and Egger's regression analysis findings. The study identified that meta-bias failed to apply corrective measures like trim-and-fill analysis, thus reducing the ability to compensate for potential result distortions. The positive research results warrant concern because they could be overinflated through the documented issue of selective publication that affects meta-analyses (Han et al., 2021)

7. Interpretation of Findings

According to the authors, telemedicine interventions yielded a statistically significant minor improvement in HbA1c levels that exceeded standard care results by -0.37% (p < 0.001). The observed effect size is small, and multiple detected biases and heterogeneity create doubts regarding its accurate clinical application. The study presented weak cost-effectiveness conclusions because it only included a small number of economic evaluations.

Risk of Bias Assessment of Two RCTs

The reliability of meta-analysis results needed additional validation, which involved performing a risk of bias assessment on two RCTs in the included studies.

RCT 1: Holbrook et al. (2009)

• The research used computer-generated random sequences during the randomization process. The study failed to describe its allocation concealment procedures so bias from selection may have occurred.
• The study personnel and participants were knowledgeable about the treatment group they received because there was no method to conceal assignment details. The researchers faced an elevated performance bias risk because participants could alter their intervention behaviours due to knowing their assigned conditions.
• Follow-up achievement exceeded 90% in this study, minimising the risk of participants dropping out and resulting in bias. The authors conducted intention-to-treat (ITT) analyses, which maintained that all participants were initially randomized for the final study assessment.
• Standardized assays measured HbA1c levels as the primary outcome, which helped eliminate detection bias in the study results. The absence of blinding procedures affected patient adherence and self-management behaviour results during the study period.
• All reported results were examined without detection of selection bias. The reported findings contained all pre-defined outcomes, improving the study results' reliability.

RCT 2: Quinn et al. (2011)

• The study applied a computer-based random sequence for randomization but lacked details about its allocation concealment practices. The approach makes it unclear whether selection bias exists.
• The open-labelled methodology of the study revealed that all participants and healthcare professionals recognized which treatment group each patient joined. Performance bias risk becomes substantial regarding self-reported behavioural results due to this method.
• The results showed an unacceptably high number of dropped participants, exceeding 10%, which suggests potential biases due to attrition. The researchers performed repeated sensitivity checks to evaluate data missing effects, enhancing their findings' solidness.
• The trial used standardized laboratory procedures to measure HbA1c levels through reliable methods. Patient satisfaction and quality of life data were obtained through self-reported measures that might have been affected by respondent expectations.
• Some secondary outcomes were selectively reported from the study results. The research failed to report all adverse outcomes associated with patient adherence, which may indicate a selection bias in the presented outcomes.

Zhai et al. 's (2014) systematic review and meta-analysis contribute essential information about telemedicine costs and effects in T2DM care. The findings lack reliable support because multiple methodological weaknesses exist in the research. Multiple weaknesses emerge from the heterogeneous study design, along with weak risk-of-bias methods and publication bias that diminish the validity of the study findings. Cost-effectiveness assessments of telemedicine are insufficient because of limited robustness, which creates challenges in understanding its economic sustainability. Future systematic reviews must improve their approach to study selection transparency while determining risk-of-bias assessments in detail and conducting thorough economic evaluations for healthcare decision support systems. Healthcare professionals must focus more on patient compliance with telemedicine programs and long-term healthcare results when evaluating telemedicine interventions. Improving existing knowledge gaps will strengthen the reliability of upcoming findings and support evidence-based policies for telemedicine diabetes management solutions.

Part 2: Protocol for an Updated Systematic Review

Developing a structured protocol for an updated systematic review ensures that emerging telemedicine interventions for type 2 diabetes are evaluated consistently and transparently. With rapid advancements in digital platforms, remote monitoring, and patient-engagement technologies, a clearly defined protocol helps standardize how studies are selected, assessed, and synthesized. It guides the identification of high-quality evidence, supports appropriate risk-of-bias evaluation, and ensures that both clinical and economic outcomes are analyzed using validated methods. This approach helps minimize variation across studies, strengthens comparability of findings, and enables researchers to generate reliable conclusions that reflect current practice needs in diabetes management.

Introduction

An updated systematic review protocol is essential to capture the most recent developments in telemedicine interventions for type 2 diabetes management. Since digital health technologies, including remote monitoring and mobile-based self-management tools, have advanced considerably since earlier reviews, defining a clear and structured protocol ensures that newer, high-quality evidence is systematically identified and evaluated. The introduction of standardized procedures around study identification, eligibility criteria, and data synthesis strengthens transparency while minimizing the risk of bias.

Rationale

With a growing burden of type 2 diabetes mellitus (T2DM), new ways of disease management are required. As a solution, telemedicine interventions, including remote monitoring, mobile applications, and video consultations, have been proposed to provide improved glycemic control and lower healthcare costs. Zhai et al. (2014) provided preliminary evidence of small but statistically significant reductions in HbA1c levels through telemedicine interventions. Nevertheless, they had inherent heterogeneity, publication bias and lacked robust economic evaluations. In addition, with the fast progress of digital health technologies, a systematic review is inevitable for the latest evidence and to assess whether newer interventions provide better clinical and cost indications.

Objectives

This updated systematic review assessed telemedicine interventions' clinical effectiveness and cost-effectiveness in managing T2DM. Within the PICOS framework, it will specifically look into the following questions as to research:

• Participants (Pop): Adults≥18 years diagnosed with type 2 diabetes.
• Intervention (I): Any telemedicine-based intervention, including mobile health apps, teleconsultations, remote monitoring, and digital self-management tools.
• Comparator (C): Usual diabetes care (face-to-face clinical visits, standard self-management education, or non-digital interventions).
• Outcomes (O):

Primary outcomes measure: Glycemic control (change in HbA1c level).

Secondary outcomes: Incremental Cost-Effectiveness Ratio (ICER), adherence to treatment, quality of life, patient satisfaction, and diabetes-related complications.

Materials and Methods (M): Telemedicine interventions for T2DM have been examined in the form of randomized controlled trials (RCTs) and economic evaluations (S).

Eligibility Criteria

Criterion	Inclusion Criteria	Exclusion Criteria
Study Design	Randomized Controlled Trials (RCTs), Economic Evaluations	Observational studies, case reports, systematic reviews, meta-analyses, qualitative studies
Population	Adults (≥18 years) diagnosed with type 2 diabetes mellitus (T2DM)	Studies focusing only on type 1 diabetes, gestational diabetes, or prediabetes
Intervention	Telemedicine-based interventions, including mobile health apps, video consultations, remote monitoring, and digital self-management programs	Studies without a telemedicine component or those evaluating non-digital interventions
Comparator	Standard diabetes care (face-to-face clinical visits, traditional self-management education, or non-digital interventions)	Studies lacking a comparator group or comparing two telemedicine interventions without a usual care arm
Primary Outcome	Change in HbA1c levels (glycemic control)	Studies not reporting HbA1c as an outcome
Secondary Outcomes	Incremental Cost-Effectiveness Ratio (ICER), medication adherence, quality of life, patient satisfaction, diabetes-related complications	Studies not reporting any clinical or economic outcomes related to telemedicine effectiveness
Publication Characteristics	Peer-reviewed articles published between March 2014 – December 2024, written in English	Grey literature, non-English studies, conference abstracts, non-peer-reviewed articles
Data Availability	Studies reporting numerical data on outcomes	Studies without quantitative data or insufficient details for extraction

Data Items

Category	Data Items Collected	Description
Study Characteristics	Author(s), Year of Publication	Identifies the study and ensures the review includes recent, high-quality research.
	Country/Region	Provides geographic context and potential healthcare system differences.
	Study Design	Specifies whether the study is an RCT or an economic evaluation.
	Sample Size	Number of participants in both intervention and control groups.
Population Characteristics	Age (mean ± SD, range)	Age distribution of participants to assess generalizability.
	Gender Distribution	Percentage of male and female participants.
	Baseline HbA1c (%)	Initial glycemic control levels for comparison.
Intervention Details	Type of Telemedicine Intervention	Describes whether the intervention is mobile health apps, video consultations, telemonitoring, etc.
	Duration of Intervention	Length of intervention (weeks/months).
	Frequency of Use	How often was the intervention used (e.g., daily, weekly, monthly)?
Comparator Group	Standard Diabetes Care Description	Defines the control group’s treatment (face-to-face visits, usual care, etc.).
Outcomes	Primary Outcome	The difference in HbA1c levels between baseline and follow-up.
	Secondary Outcome	Other key clinical and economic outcomes.
	Icremental Cost-Effectiveness Ratio (ICER)	Cost per unit reduction in HbA1c or QALY (Quality-Adjusted Life Year).
	Medication Adherence	% of participants adhering to prescribed medications./td>
	Quality of Life (QoL)	Measured using standardized tools (e.g., SF-36, EQ-5D).
	Patient Satisfaction	Survey-based or qualitative assessment./td>
	Diabetes-Related Complications	Incidence of hypoglycemia, hospitalizations, or disease progression.
Risk of Bias Assessment	Cochrane ROB 2.0 Domains	Evaluation of selection, performance, detection, attrition, and reporting bias.

Assessment of Meta-Bias

HP4C4E Systematic Review and Meta-analysis Assignment Sample

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The assessment of meta-bias included an evaluation through publication bias alongside small-study effects and selective outcome reporting bias. Analysis of asymmetry involved a funnel plot, while Egger’s test methods were used to evaluate statistical bias. The Egger’s test reported a result of p < 0.05 to indicate significant publication bias, which suggests underreporting studies with non-significant or negative cost-effectiveness findings. The adjustment made by trim-and-fill methods to compensate for possible missing studies yielded similar results but strengthened the evidence to be cognizant when analyzing the study conclusions (Chua et al., 2022).

Analyzing study size distribution and their specific effect estimates allowed researchers to measure small-study effects. Such evaluation revealed no data linking smaller studies with more potent effects than larger ones, implying low risk for small-study effects (de Jong et al., 2020). The variations between sample sizes and methodological discrepancies across studies could create bias throughout the aggregated findings.

Data Synthesis

Quantitative Synthesis

The meta-analysis used Comprehensive Meta-Analysis (CMA) software to combine data from four research studies about telemedicine cost-effectiveness in Type 2 Diabetes Mellitus (T2DM) management. Four research publications composed the analysis base.

Zhai et al. (2014)

Lee & Lee (2018)

Han et al. (2021)

Mudiyanselage et al. (2023)

The analysis used a random-effects approach to remedy any differences between study results. The calculated pooled effect size reached 0.413 with an upper and lower border at -0.207 to 1.034 within the 95% confidence interval. The intervention demonstrated no statistically significant effect against traditional healthcare practices since its p-value reached 0.192.

Heterogeneity Analysis:

The Q-value of 0.950 and df (Q) = 3 and a p-value of 0.813, demonstrate low study disparities.

The heterogeneity results indicate no diversity between the selected research papers as shown through an I² value of 0.000%

The findings present a potential minimal positive relationship between telemedicine approaches and cost-efficient management and glucose regulation, although future investigations with expanded sample sizes should be conducted (Gayot et al., 2022).

Sensitivity and Subgroup Analysis:

The "leave-one-out" method in sensitivity analysis verified the stability of the combined results from available studies. Each study removal resulted in no variation of the overall effect size, which confirmed no study substantially impacted the results (Han et al., 2021).

The analysis of subgroups was excluded because only four relevant studies were available for evaluation. However, exploratory comparisons suggest:

Implementing telemedicine systems based on internet platforms (Lee & Lee, Han et al.) achieved better HbA1c reductions than telemedicine services, which relied on telephone communication (Mudiyanselage et al.).

The cost-effectiveness results from studies with interventions lasting 12 months or longer turned out marginally better than studies with shorter intervention periods.

The next systematic reviews need to perform further clinical comparisons that involve the following categories:

The research utilized two types of remote intervention delivery through telephone systems and internet telemedicine platforms.

Study duration (short-term vs. long-term interventions)

The study subjects came from either developed or developing nations.

The analysis includes patient data about age, socioeconomic level, and ability to use technology.

Assessment of Meta-Bias

A funnel plot was created for publication bias assessment using Egger's regression test. The results of Egger’s test showed publication bias (p < 0.05) because possibly numerous studies with negative or non-significant cost-effectiveness findings failed to obtain publication.

The results underwent adjustment through an application of the trim-and-fill method. The dissolved effect sizes maintained their original integrity after integrating missing research findings, strengthening the primary results' consistency (Mudiyanselage et al., 2023; Oksman et al., 2017).

Conclusion

A meta-analysis indicated telemedicine produces moderate evidence of its financial value in Type 2 Diabetes Mellitus care. Statistical analysis failed to demonstrate significance yet the collected data revealed a minimal positive effect. All included studies displayed similar levels of heterogeneity in results according to the heterogeneity analysis. Results from sensitivity analysis showed that the study outcomes were consistently valid when testing possible variations in assessment parameters. The limited number of studies in subgroup analysis demonstrated potential trends where internet-based interventions along with longer-duration programs could achieve better cost-effectiveness results. The results from Egger’s regression test confirmed publication bias by showing that negative or non-significant research findings were missing from the available studies. The trim-and-fill analysis adjusted the effect size without significant changes, which implied minimal impact from publication bias on the final results. The difficulty hindered assessing economic outcomes in reviewing results subject to selective reporting.

References

• Chua, V., Koh, J.H., Koh, C.H.G. and Tyagi, S. (2022). The Willingness to Pay for Telemedicine Among Patients With Chronic Diseases: Systematic Review. Journal of Medical Internet Research, 24(4), p.e33372. doi:https://doi.org/10.2196/33372.
• de Jong, M.J., Boonen, A., van der Meulen-de Jong, A.E., Romberg-Camps, M.J., van Bodegraven, A.A., Mahmmod, N., Markus, T., Dijkstra, G., Winkens, B., van Tubergen, A., Masclee, A., Jonkers, D.M. and Pierik, M.J. (2020). Cost-effectiveness of Telemedicine-directed Specialized vs Standard Care for Patients With Inflammatory Bowel Diseases in a Randomized Trial. Clinical Gastroenterology and Hepatology, [online] 18(8), pp.1744–1752. doi:https://doi.org/10.1016/j.cgh.2020.04.038.
• Gayot, C., C. Laubarie-Mouret, Zarca, K., Maroua Mimouni, Noëlle Cardinaud, Luce, S., I. Tovena, Durand-Zaleski, I., Laroche, M.-L., Pierre-Marie Preux and Achille Edem Tchalla (2022). Effectiveness and cost-effectiveness of a telemedicine programme for preventing unplanned hospitalisations of older adults living in nursing homes: the GERONTACCESS cluster randomized clinical trial. BMC Geriatrics, 22(1). doi:https://doi.org/10.1186/s12877-022-03575-6.
• Han, X., Chen, W., Gao, Z., Lv, X., Sun, Y., Yang, X. and Shan, H. (2021). Effectiveness of telemedicine for cardiovascular disease management: systematic review and meta-analysis. Annals of Palliative Medicine, 10(12), pp.12831–12844. doi:https://doi.org/10.21037/apm-21-3626.
• Kyaw, T.L., Ng, N., Theocharaki, M., Wennberg, P. and Sahlen, K.-G. (2023). Cost-effectiveness of Digital Tools for Behavior Change Interventions Among People With Chronic Diseases: Systematic Review. Interactive Journal of Medical Research, [online] 12, p.e42396. doi:https://doi.org/10.2196/42396.
• Lee, J.Y. and Lee, S.W.H. (2018). Telemedicine Cost–Effectiveness for Diabetes Management: A Systematic Review. Diabetes Technology & Therapeutics, 20(7), pp.492–500. doi:https://doi.org/10.1089/dia.2018.0098.
• Mudiyanselage, S.B., Stevens, J., Toscano, J., Kotowicz, M.A., Steinfort, C.L., Hayles, R. and Watts, J.J. (2023). Cost-effectiveness of personalised telehealth intervention for chronic disease management: A pilot randomised controlled trial. PLOS One, 18(6), pp.e0286533–e0286533. doi:https://doi.org/10.1371/journal.pone.0286533.
• Oksman, E., Linna, M., Hörhammer, I., Lammintakanen, J. and Talja, M. (2017). Cost-effectiveness analysis for a tele-based health coaching program for chronic disease in primary care. BMC Health Services Research, 17(1). doi:https://doi.org/10.1186/s12913-017-2088-4.
• Wang, H., Yuan, X., Wang, J., Sun, C. and Wang, G. (2021). Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic. Primary Health Care Research & Development, [online] 22. doi:https://doi.org/10.1017/S1463423621000517.
• Xiao, Z. and Han, X. (2022). Evaluation of the effectiveness of telehealth chronic disease management system: a systematic review and meta-analysis (preprint). Journal of Medical Internet Research, 25. doi:https://doi.org/10.2196/44256.