Using A RE-AIM Framework to Evaluate the Impact of Shared Medical Appointments for Diabetes Mellitus: A Systematic Review and Meta-analysis

doi:10.21203/rs.3.rs-4858860/v1

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Using A RE-AIM Framework to Evaluate the Impact of Shared Medical Appointments for Diabetes Mellitus: A Systematic Review and Meta-analysis

https://doi.org/10.21203/rs.3.rs-4858860/v1

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Background

Diabetes is a major global health concern and a leading cause of morbidity and mortality. Shared medical appointment (SMA), an integrated treatment and health management service, is effective in improving the clinical and behavioral outcomes of people with diabetes (PWD). Using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, this review aimed to evaluate the impact of SMA on PWD treatment and management to inform the feasibility, scalability, and equity of future SMA implementation.

Methods

Eight electronic databases were searched for randomized controlled trials, non-randomized grouped controlled trials, pre/post studies, and interrupted time series model studies published in English and Chinese up to February 2024. We performed meta-analyses for all RCTs using a random-effects model and presented Forrest plots and test statistics (Cochran's Q and I²) for heterogeneity analysis. Other results that did not lend themselves to quantitative analysis were summarized qualitatively.

Results

Forty-seven studies were included in the review. The SAM was a highly resource-demanded and complex intervention package. The studies were evaluated according to the RE-AIM framework, and most studies reported effectiveness well, while all other dimensions were poorly reported. Most studies demonstrated that SMA effectively improved patients’ HbA1c and SBP levels and reduced healthcare costs.

Discussion

The available evidence suggests that the SMA was beneficial in improving PWD management and health outcomes among PWD. However, its complexity would constrain its feasibility and scalability. Future research should assess the effect size of each intervention component and evaluate the implementation process, costs, and sustainability of SMA intervention, especially in resource-limited settings, to improve its scalability and equity.

Trial registration

PROSPERO CRD42019134273

people with diabetes

shared medical appointments

reach

effectiveness

adoption

implementation

and maintenance (RE-AIM)

feasibility

equity

scalability

systematic review

meta-analysis

What is already known on this topic - Current studies demonstrated that the SMA effectively improved clinical and behavioral outcomes for people with diabetes (PWD) in experimental settings, which contained adequate resources. However, there was a notable gap in the research regarding the comprehensive evaluation of the implementation outcome using the RE-AIM framework, which would constrain the scale-up of the SMA and affect equity in resource-limited communities.
What this study adds - This systematic review innovatively evaluates the impact of Shared Medical Appointments (SMA) on diabetic management by using the Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. It highlights the gap in effectiveness and implementation research, including the combination of the SMA intervention components' main and interactive effects, long-term sustainability, and equity, particularly in resource-limited settings.
How this study might affect research, practice or policy - This study demonstrated the specific impact of SMA on biochemical markers and healthcare costs, supporting the effectiveness of SMA in primary healthcare (PHC) settings, mainly reflected in improved HbA1c and SBP levels. However, the resource-intensive characteristic of the SMA would affect its scale-up and equity in various PHC settings.

People with diabetes (PWD) is a major global health concern and a leading cause of morbidity and mortality, with a rapid increase worldwide ^[1]. In 2021, there were 537 million PWD, accounting for 6.1% of the world population, which was expected to rise to 9.8% (1.31 billion) by 2050 ^[2]. PWD can lead to serious complications in multiple organs, such as blindness, heart attacks, and lower limb amputation ^[3]. According to the World Health Organization, PWD caused 1.5 million direct deaths worldwide in 2019, a 3% increase from 2010 ^[4]. The increasing prevalence, high avoidable morbidity and mortality, and huge economic burden make diabetes a significant global health challenge ^{[5, 6]}. Addressing the increasing global disease burden of PWD requires an affordable, feasible, and cost-effective comprehensive approach at the system level instead of the individual level, and one such approach is shared medical appointment (SMA) ^[7].

In SMA services, multidisciplinary medical treatment and health management providers successively provide medication consultation, physical examination, treatment, and health management services for a group of PWD with similar conditions in a consulting room ^[8]. The medical team typically includes at least one physician with prescribing rights and several trained or qualified staff, such as nurses, nutritionists, and psychologists ^{[9, 10]}. PWD can receive interactive education about their treatment options and share their personal experiences related to PWD management in a group consultation ^[11]. Several literature reviews have demonstrated that SMA is effective in improving PWD's clinical and behavioral outcomes, reflected as improved biochemical indicators, such as Hemoglobin A1c (HbA1c) and Systolic Blood Pressure (SBP), and healthier lifestyles such as diet control and exercise ^{[7, 12, 13]}. Thus, SMA represents a promising evidence-based practice (EBP) for addressing the challenges of diabetes control in other settings.

However, in the existing study, the SMA services were a resource-intensive complex intervention conducted in well-resourced settings ^{[9, 11]}. While most previous SMA evaluations focused only on the effectiveness of the interventions, leaving researchers and practitioners with little information about the generalizability of the intervention context, practitioners, conditions, and findings ^[14]. There was insufficient evidence of system and process evaluation on SMA's context, mechanisms, complexity of the SMA intervention package (i.e., various components combinations), and outcomes (e.g., SMA effectiveness, management efficiency, costs, and fidelity of research), which was essential to translate the knowledge from interventions to policy and practice ^[15], particularly for improving healthcare quality in primary healthcare (PHC) settings in low- and middle- income countries (LMICs)^[16]. These limitations will constrain further implementation to improve the feasibility, scalability, and equity of SMA implementation in PWD and people with other chronic conditions in varied PHC settings. If an effective intervention is too complex and demands too many resources for a real-world frontline setting, it cannot be scaled up ^[17]. Furthermore, this may limit the equitable access to high-quality EBP for individuals, especially in resource-limited communities ^{[18, 19]}.

The RE-AIM framework is one of the most frequently used frameworks to guide the design, implementation, and assessment of research programs to make the findings more generable and practical in real-world community settings ^[20]. RE-AIM is an acronym for the following five evaluation dimensions under two categories ^{[21, 22]}: Reach (R) and Effectiveness (E) at the individual level, and Adoption (A), Implementation (I), and Maintenance (M) at both the individual and setting level. The RE-AIM framework can improve transparency in reporting the critical components of an intervention and is particularly useful in the adoption, scaling, and maintenance of an effective intervention ^{[23, 24]}. Applying the RE-AIM framework, this systematic review and meta-analysis aimed to comprehensively summarize the effects of SMA on PWD by providing updated quantitative and qualitative evidence, which will inform the feasibility, scalability, and equity of future SMA implementation ^[25].

The systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 checklist ^[26].

A protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 28 November 2019 (registration number CRD42019134273).

Eligibility criteria

The inclusion/exclusion criteria were based on Populations, Intervention, Comparison, Outcome, and Study (PICO) design principles and research questions. Eligible study designs included randomized controlled trials (RCTs), non-randomized cluster-controlled trials, pre/post-design studies, and interrupted time-series designs. Other observational and qualitative studies on the economic costs and doctor-patient experiences were discussed in the results but not included in the statistical analysis.

We included studies on SMA interventions that satisfy the following conditions: (1) in an outpatient setting; (2) with at least two medical visits over time; (3) intervention duration of more than three weeks; (4) including at least two patients in the same clinical setting; (5) involving at least one prescribing clinician; (6) addressing each patient's unique medical needs individually. The detailed characteristics of the criteria are shown in Appendix 1.

Data sources and search strategy

We searched the following databases for literature comparing the effectiveness of SMA and usual care in PWD from January 2012 to February 2024: PubMed, EMBASE, EBSCO, CINAHL, PsycINFO, Web of Science, CNKI, and Wan Fang. This review followed the same search strategy as Edelman et al.’s previous study ^[27], including four search terms: group appointments, shared visits, interventions, and observational studies, and included papers in English and Chinese. In addition, our study population was limited to adults aged 18 years or older. See Appendix 2 for an overview of the search strategy. We looked for non-published studies in clinical trials to mitigate the risk of publication bias (available at https://www.clinicaltrials.gov/), manually searched reference lists, and tracked citations to identify additional literature.

Study quality assessment

Two reviewers assessed the methodological quality of the included studies according to the Agency for Healthcare Research and Quality's Methodological Guidelines for Review of Validity and Comparative Effectiveness ^[28]. Ratings were based on information richness reported in the literature regarding SMA for PWD. Any disagreement between the two reviewers was resolved by a third expert who was familiar with the study. This quality assessment method can help assess the risk of bias, the strength of the evidence, and the applicability of the included studies.

Data collection and extraction

After removing the duplicates, four researchers used the eligibility criteria to screen the title and abstract. The full text was reviewed in more detail based on a preliminary review. Concerning differences in full-text screening, researchers met to discuss and reach a consensus.

Four researchers conducted data extraction independently using a predesigned Excel sheet. They compared their data extraction results and discussed any disagreements or revisions until a consensus was reached. Standardized tables were used to extract data and collect documentation. The data mainly consisted of six aspects: (1) Study characteristics: author, year, research design, and research setting; (2) SMA intervention characteristics: period, number of visits, composition of SMA service team, intervention patterns, and procedures; (3) RE-AIM framework contents: reach, effectiveness, adoption, implementation, and maintenance; (4) Outcome components: biochemical indicators, adherence, symptom severity and patient quality of life at effectiveness and maintenance dimensions, patient and staff experiences of participating SMA, staff-related indicators, and economic-related indicators at adoption, implementation and maintenance dimensions ; (5) Discussion: limitations, and future directions.

Data Synthesis and Analysis

We conducted a meta-analysis on RCTs to evaluate the combined effect of intervention by comparing the means and changes in the biometrics between the intervention and control groups after the intervention. Heterogeneity between studies was assessed using Foster's plots and test statistics (Cochran's Q and I²), with a P < 0.10 or an I² > 50 indicating significant heterogeneity. In addition, we performed subgroup analyses of categorical variables using a random effects model When I² > 50 or a fixed-effects model otherwise. All statistical analyses were conducted through Review Manager 5.4. When meta-analysis was not appropriate, qualitative summaries were performed.

Search results

Among the 885 citations initially identified through a complete database search, 217 were retrieved after the title and abstract screening. After screening for full text, 31 studies met inclusion criteria and were included in our final analysis. In addition, we added three studies through a manual search of the reference lists and another 13 RCT studies in the previous systemic review. Therefore, 47 studies contributed findings to the review, including 24 RCT studies for meta-analysis ^[29–52] and 23 observational studies for qualitative analysis ^[53–75] (Fig. 1).

Figure 1 Literature flow diagram

Study characteristics

All 47 studies have compared the differences in treatment outcomes between SMA and usual one-on-one care. Most studies used Hemoglobin A1c (HbA1c) as the primary outcome, and some studies also included SBP, total cholesterol (TC), and low-density lipoprotein (LDL-c). The intervention duration was ≥ 1 year in 26 studies, between six months to 1 year in 13 studies, and < six months in 8 studies. Regarding quality, 18 were good, 20 were fair, and nine were poor (Appendix 3).

SMA intervention characteristics

Table 1 shows the characteristics of SMA intervention. Most interventions were implemented through specialty clinics in hospitals and primary healthcare centers. Thirty interventions were conducted in multiple centers. The median duration of the total intervention and each session were 12 months and 120 minutes, respectively. The median number of scheduled visits and patients per group were 7 (range: 6 to 12) and 10 (range: 8 to 13), respectively. The intervention team included a median of 4 (range: 3 to 7) members, mainly composed of clinicians (internists or endocrinologists or general practitioners) (74.5%), nurses (70.2%), and pharmacists (40.4%). Most studies' intervention frequency was once a month (40.4%).

This review also assessed the provision of health education, psychological interventions, daily management, and dietary adjustments for patients, as well as the interaction with family and friends during their treatment process. The findings indicate that a vast majority of studies (89.4%) reported the implementation of health education. Beyond health education, 70.2% of studies implemented additional services such as psychological interventions, daily management, and dietary adjustments. Furthermore, in 83.0% of the studies, patients had the opportunity to share experiences and interact with others during the intervention process. However, only 25.5% of the studies reported that patients’ family and friends were involved in the treatment process.

Table 1

SMA intervention characteristics
Characteristics	n (%) or M(IQR)
Settings
Community-based healthcare settings (i.e., PHC settings)	27(57.45)
Specialized Clinic in Government (VA, FQC) or University-affiliated clinic or Private clinic	20(42.55)
Duration of intervention (months) [M (Q_L, Q_U)]	12 (6 to 17)
Average visit duration (mins) [M (Q_L, Q_U)]	120 (90 to 120)
Times of planned visits [M (Q_L, Q_U)]	7 (6 to 12)
Number of patients per group [M (Q_L, Q_U)]	10 (8 to 13)
Intervention team size [M (Q_L, Q_U)]	4 (3 to 7)
Intervention team disciplines [n (%)]
Clinicians(endocrinologists)/General Practitioners	35 (74.5)
Nurses	33 (70.2)
Pharmacists	19 (40.4)
Psychologists (Counselors)/Behaviorists	10 (21.3)
Nutritionists	10 (21.3)
Public health physicians/diabetes educators	8(17.0)
Physiotherapists/exercise specialists	7 (14.9)
Social workers	5 (10.6)
Medical assistant	4 (8.5)
Researchers	3 (6.4)
Others ^A	4 (8.5)
Intervention team combinations [n (%)] ^B
Clinician, nurse	5 (10.6)
Clinician, nurse, public health doctor, others ^C	24 (51.1)
Clinician, public health doctor, others ^D	4 (8.5)
Clinician, others ^E	4 (8.5)
Other ^F	3 (6.4)
Not reported	7 (14.9)
Frequency of intervention [n (%)]
once /week	2 (4.3)
once/2 weeks	2 (4.3)
once/3 weeks	1 (2.1)
once/month	19 (40.4)
once/2 months	2 (4.3)
once/3 months	4 (8.5)
6 times/month	1 (2.1)
once/6 month	1(2.1)
The research interventions with uneven frequency	8 (17.0)
Not reported	7 (14.9)
Availability of health education [n (%)]
Yes	42 (89.4)
Not reported	5 (10.6)
Whether there is psychological intervention, daily management, or diet adjustment outside of education [n (%)]
Yes	33 (70.2)
Not reported	14 (29.8)
Availability of experience sharing/interaction [n (%)]
Yes	39 (83.0)
Not reported	8 (17.0)
Whether there are family/friends involved [n (%)]
Yes	12 (25.5)
Not reported	35 (74.5)
Note: PHC setting: Primary healthcare setting, including the adult primary care center^{[31, 32]}, community health centers^{[30, 36, 37, 44, 47]}, non-profit community clinic^{[29, 59]}. M: Median; IQR: Inter-Quartile Range; VA: department of veterans affairs; FQC: federally qualified center.

A: Community health center director, clerical staff, front office administrator.

B: All clinicians had rights to prescribe.

C: Other providers were pharmacists, nutritionists, social workers, secretary, behaviorists, psychologists, medical assistants, researchers, exercise specialists, physiotherapists, counselors, and/or research assistants.

D: Other providers were receptionist, psychologists, nutritionists, and/or medical assistants.

E: psychologist, pharmacist, and/or researchers.

F: Other providers were pharmacists, nurses, nutritionists, and/or physiotherapists.

SMA intervention tasks

The review summarized the implementation process of the SMA intervention, which included ① Self-introduction among patient participants and a description of their medical history, current glucose-lowering regimen, and effect of glycemic control. ② Individual counseling involves patients asking questions and getting physical examinations in turn, with medication adjustments made when necessary. ③ Collective discussion: the intervention team discusses and exchanges information with patients and their families on the key concerns individually and encourages patients to share their experience in self-glycemic control. The SMA services team will provide guidance and summarize this intervention’s unique and joint problems. ④Health education: the SMA services team will use different health education methods to explain various aspects of PWD management, including medication, diet, and exercise. Individuals providing SMA interventions are categorized into two major groups: clinical medicine and public health. Table 2 lists the specific tasks of SMA interventions provided by different personnel.

Table 2

Providers Roles of different types of health service providers in SMA
Providers	Responsibilities
Medical service providers
Clinicians(endocrinologists) / General Practitioners	1. Conduct individual/group-based consultations with patients.
	2. Review patients’ biochemical testing results.
	3. Provide medication and behavioral interventions and modifications if necessary.
Nurses	1. Conduct initial assessment of patient status and complete health education courses.
	2. Physical examination: testing weight, height, blood pressure, and waist circumference, blood sampling for blood glucose, HbA1c, and urinalysis test, as well as other tests as applicable.
	3. Medication adjustments (registered nurse only)
Pharmacists	1. Conduct comprehensive medication management for patients, including clinical assessment and development of treatment plans.
Pharmacists	2. Medication guidance.
Public health service providers
Psychologists (Counselors)/Behaviorists	Psycho-education: providing behavior education, stress coping and motivational skills training to improve self-management and self-efficacy.
Nutritionists	Healthy diet education: nutritional knowledge and skills, including carbohydrate counting and the plate methods.
Public health physicians/diabetes educators	Health education: providing healthy eating, physical activity, etc.
Physiotherapists/exercise specialists	Health education: providing exercise instruction, behavioral interventions, etc.

RE-AIM-based SMA outcomes

We used the RE-AIM framework to summarize the study results by each element (Table 3). To increase transparency in reporting, we divided the reporting into 5 phases: enrollment, baseline of trial, mid-term of trial, endline of trial, and follow-up. The results from the follow-up phase were regarded as the maintenance results of the RE-AIM framework.

Table 3

RE-AIM indicators with the number and percent of studies reporting each indicator (N = 47)
RE-AIM dimensions and components		The proportion of reports per study period (%)
RE-AIM dimensions and components		Enrolment	Baseline	Mid-term	Endline	Follow-up (Maintenance: The extent to which SMA was consistently implemented after the study ended)
Reach: Number, proportion, and representativeness of diabetic patients participating in SMA who met inclusion
The number of patients recruited		100.0	N/A	N/A	N/A	N/A
The number of patients recruited as a percentage of the target population		38.3	N/A	N/A	N/A	N/A
The number of patients who joined halfway through the project		N/A	N/A	0	N/A	0
The proportion of patients joining midway		N/A	N/A	0	N/A	0
The number of patients who dropped out in the middle of the project		N/A	N/A	44.7	N/A	0
The proportion of patients who withdrew midway		N/A	N/A	36.2	N/A	0
Effectiveness: Effectiveness of SMA interventions at the patient level
Patient biochemical indicators ^A	HbA1c	N/A	85.1	27.7	83.0	0
	FG	N/A	17.0	4.3	17.0	2.1
	Blood lipids (TC, LDL-c, HDL-c, TG)	N/A	61.7	17.0	55.3	0
	BP	N/A	53.2	14.9	55.3	4.3
	BMI	N/A	6.4	6.4	38.3	4.3
Patient physical and behavioral indicators	Patient quality of life	48.9	N/A	2.1	6.4	2.1
	Medication compliance	19.1	N/A	6.4	29.8	0
	diet	34.0	N/A	0	21.3	0
	Diabetic self-efficacy	19.1	N/A	4.3	29.8	0
	Diabetes management behavior	22.4	N/A	4.3	34.0	0
The cost of patient access to care	Cumulative number of hospitalizations, days, and costs due to diabetes exacerbations or complications during the trial period	N/A	2.1	8.5	12.8	4.3
The cost of patient access to care	Patients' treatment effect (cured, improved, ineffective) after each hospitalization	N/A	0	0	0	0
Adoption: Number, proportion, and representation of institutions and physicians participating in SMA
Indicators at the level of medical institutions	The number and reasons for participating institutions and their representation in the study	68.1	0	0	0	0
Indicators at the level of medical institutions	The number and reasons for the withdrawal or addition of institutions	N/A	0	2.1	0	0
Physician-level indicators	The number of doctors involved in the study and why, and their representation in the program	63.8	0	0	0	0
Physician-level indicators	The number of doctors who dropped out or joined in the midway	N/A	N/A	2.1	N/A	N/A
Implementation: The extent to which the implementation was completed according to the researcher's requirements (i.e., "fidelity"), the localization of SMA implementation process, and the cost of SMA implementation
Fidelity of implementation	The proportion of physicians performing and completing SMA intervention packages and reasons	N/A	0	0	0	0
	The extent to which the project is being implemented as originally planned	0	4.3	2.1	2.1	0
	Duration and frequency of intervention	100.0	N/A	N/A	N/A	N/A
Cost of implementation	The cost of money and staff time spent during SMA implementation	N/A	0	0	0	0
Note: HbA1c: HemoglobinA1c; BP: Blood pressure; FG: Fasting plasma glucose; TC: total cholesterol; LDL-c: low-density lipoprotein cholesterol; HDL-c: high-density lipoprotein cholesterol; TG: triglyceride; BMI: Body mass index

Reach

Reach refers to the number, proportion, and representativeness of diabetic patients participating in SMA who met the inclusion criteria. During the enrolment phase, all studies reported the number of patients recruited. The proportion of patients recruited at the enrollment stage was 38.3%. The number and proportion of patients who dropped out at mid-term trials were 44.7% and 36.2%, respectively.

Effectiveness

Effectiveness refers to the effectiveness of the SMA intervention at the patient level. It includes three secondary indicators: patient biochemical indicators, physical and behavioral indicators, and the cost of patient access to care.

Patient biochemical indicators

Of the 24 RCT studies, 18 focused on type 2 diabetes ^{[29–32, 34, 36–38, 40, 43, 44, 46–50]}, five investigated mixed populations ^{[33, 35, 39, 41, 45]}, and only one investigated type 1 diabetes ^[42]. All diabetic patients had been diagnosed with HbA1c or poor glycemic control. 24 RCT studies compared the SMA intervention with usual care or other strategies. Some studies also used diabetes-related risk factors (e.g., SBP, TC, and LDL-c) as secondary outcome indicators.

Among the various biochemical indicators, HbA1c was the most frequently reported at each stage: 85.1% at the trial's baseline,27.7% at the mid-term, and 83.0% at the endline. FG was less frequently reported, with a maximum reporting rate of 17.0%. Blood lipids (TC, LDL-c, HDL-c, TG) were reported by 61.7% of studies at the trial's baseline, 17.0% at mid-term, and 55.3% at the endline. BP was reported by 53.2% of studies at the baseline, 14.9% at the mid-term, and 55.3% at the endline. BMI was reported by 6.4% of studies at the baseline, 6.4% at the mid-term, and 38.3% at the endline.

The review results of the leading biochemical indicators involved in the studies are reported below (Fig. 2). First, 22 RCT studies reported HbA1c levels or changes after SMA intervention ^{[30–35, 37–46, 50, 52]}. Compared with the conventional treatment group, the SMA intervention group had reduced HbA1c levels by 0.38% (95% CI: -0.55, -0.21), a statistically significant difference. In 15 observational studies, the effects of SMA interventions were consistent with proximate RCT investigations except for two studies ^{[53–58, 60–62, 70, 71]}.

Second, 16 RCT studies included SBP levels or changes after SMA treatment as a secondary outcome ^{[29, 30, 33, 35–37, 40, 41, 44, 46–49]}. SBP levels were reduced by 3.24% (95% CI: -4.71, -1.77) in the SMA intervention group compared to the conventional treatment group. Eight observational studies reported SBP outcomes, yet with inconsistent results. In six of the eight studies ^{[53, 55, 56, 60, 65, 75]}, the pre-and post-SBP changes between the SMA intervention and conventional treatment groups were not significant (P > 0.05). The results of the other two studies were consistent with the meta-analysis and supported using SMA intervention to improve SBP ^{[61, 62]}.

Third, seven RCTs reported TC as a predictor of glycemic control after SMA treatment ^{[31, 37, 42–45, 49]}. The results showed a non-significant association between SMA and TC decline at the 95% confidence level (mean difference: -0.08 mg/dl [95% CI: -0.20, 0.03]). Four observational studies reported the results of SMA intervention on TC control, with two showing no significant effect ^{[55, 60]} and two showing a significant therapeutic effect of SMA in improving TC ^{[62, 75]}.

Finally, nine RCTs showed that SMA did not significantly improve LDL-c (95% CI: -3.99, 4.66) compared with conventional treatment modalities at the 95% confidence level ^{[53, 55, 56, 65, 75]}. Seven observational studies reported biochemical findings related to LDL-C, with five studies showing no significant effect and two studies showing a significant therapeutic effect of SMA in improving TC ^{[57, 62]}.

Figure 2 Biochemical Indicators Forest Plots: SMA Group vs. Usual Care

Heterogeneity Analysis

Heterogeneity analysis was conducted by calculating the percentage heterogeneity between studies included in the meta-analysis that was not due to chance (see Appendix 4 for the sensitivity analysis results).

First, we performed sensitivity analyses for HbA1c and SBP. For HbA1c, although there were significant differences in treatment effects between studies, heterogeneity improved after excluding three studies ^{[32, 39, 47]}, with a mean difference of -0.29% (Q: 51.83; DF: 18; P < 0.1; I²: 65); [95% CI: -0.42, -0.15]) (Appendix 4a). For SBP, sensitivity analysis showed significantly lower heterogeneity after excluding 1^[47] biased study, with a mean difference of -2.75% (Q: 14.16; DF: 11; P < 0.1; I²: 22); [95% CI: -3.37, -2.14]) (Appendix 4b). The results of the subgroup analysis confirmed that the SMA intervention contributed to the improvement of HbA1c and SBP in diabetic patients.

Second, we performed a subgroup analysis by dividing the study settings into PHC and non-PHC. For HbA1c, 15 studies were conducted in PHC and reported HbA1c as an outcome ^{[29–34, 37–39, 41, 44, 45, 47, 49, 50, 52]}. The mean reduction in HbA1c was 0.50% (95% CI: -0.79, -0.21) in the PHC setting and 0.20% (95% CI: -0.37, -0.04) in the non-PHC setting (Appendix 4c). Regarding SBP, nine studies were conducted in PHC and reported SBP as having an outcome of ^{[29, 30, 33, 36, 37, 41, 44, 47, 49]}. SBP decreased more in PHC subjects than non-PHC, with a mean reduction of -4.38 percentage points (95% CI: -6.17, -2.60) in the PHC setting (Appendix 4d).

Psych behavioral indicators

Thirty-nine studies reported five psych behavioral indicators by stage; each described as below: (1) Quality of life: enrolment (48.9%), mid-term of trial (2.1%), endline of trial (6.4%);(2) Medication compliance: enrolment (19.1%), mid-term of trial (6.4%), endline of trial (29.8%); (3) Diet: enrolment (34.0%), mid-term of trial (0), endline of trial (21.3%); (4) Diabetes self-efficacy: enrolment (19.1%), mid-term of trial (4.3%), endline of trial (29.8%); (5) Diabetes management behaviors: enrolment (22.4%), mid-term of trial (4.3%), endline of trial (34.0%).

One pre-post controlled study and two RCTs found that the SMA intervention improved patients' perceptions of PWD ^{[32, 44, 57]}, which may indirectly improve medication adherence. Five studies of patients' diets reported that the SMA intervention led to healthier eating habits ^{[36, 47, 51, 66, 75]}. Studies also showed that SMA significantly improved PWD’ diabetes self-efficacy and self-management behaviors, such as blood glucose self-monitoring ^{[36, 38, 51, 58, 66, 67, 69, 70, 73, 75]}. One study showed no effect of SMA on PWD’ exercise levels ^[47]. An RCT and an observational study showed that patients in the SMA group underwent physical examinations (e.g., foot exams, eye exams, and lipid screenings) more frequently. This can allow patients to meet other patients with the same health problems, exchange their experiences, and receive peer support, which can motivate them to adopt healthier behaviors and feel more accomplished ^{[47, 56]}.

Healthcare cost

Eight studies reported data on healthcare costs (Appendix 5), including the accumulative number of hospitalizations, days, and expenses due to diabetes exacerbations or complications during the trial period. This information was reported as baseline (2.1%), mid-term (8.5%), and endline (12.8%). However, no studies reported patients’ therapeutic outcomes after each hospitalization (cured, improved, failure).

Two studies showed no statistical difference in outpatient costs between the SMA and usual care groups (P = 0.19) ^{[45, 76]}. Another study reported higher total expenditure in the SMA group than in the usual care group (P = 0.0003) ^{[31, 35]}. However, the opposite results were reported by three other studies^{[31, 32, 46]}. They showed that the total costs were lower in the SMA intervention than in the control group. Two studies reported the cost of participating in SMA but did not report statistical differences ^{[33, 42]}.

Six studies reported the impact of SMA on hospital visits, such as admissions, emergency room visits, and primary care visits (Appendix 6). Two studies reported that PHC ensured at least one patient visit and intervention during the SMA intervention ^{[33, 45]}. Another four studies did not find differences in patient emergency room visits, hospitalization rates, or primary care visits between the SMA intervention and usual care groups ^{[35, 39, 40, 47]}.

Adoption

The adoption domain includes two indicators: medical institutions and physician-level. During the enrolment phase, the majority of studies reported: "the number and reasons for participating institutions and their representation in the study" (68.1%) and “the number of doctors involved in the study and their representation in the program” (63.8%). Only 2.1% of studies reported "the number and reasons for the withdrawal or addition of institutions" at the mid-term of the trial.

Implementation

The implementation domain includes two indicators: fidelity and cost of implementation. In terms of the fidelity to trial implementation, all studies reported the duration and frequency of the intervention during the recruitment phase. A lower proportion (0 to 4.3%) reported other dimensions and grade indicators. None of the studies reported implementation costs for institutional-level stakeholders.

Maintenance

The maintenance domain refers to how SMA was consistently implemented and patients’ health outcomes after the study ended. However, studies reporting maintenance levels of institutional indicators were minimal (0 to 4.3%) and mainly focused on patient biochemical indicators. Of the 47 included studies, the core indicators with reported maintenance levels were FG (2.1%), BP (4.3%), and BMI (4.3%). In addition, a few studies reported the cumulative number of hospitalizations, days, and costs due to worsening diabetes or complications during the project period (4.3%).

This review summarized 24 RCTs and 23 observational studies. It systematically assessed the effectiveness of SMA interventions in diabetes management according to the guidelines of the five dimensions of the RE-AIM framework: reach, effectiveness, adoption, implementation, and maintenance. However, the report of each dimension was unbalanced, each discussed below:

Effectiveness

In line with the results of previous systematic reviews ^{[77, 78]}, we found type 2 PWD who benefited more from the SMA intervention than type 1 PWD. Although a few studies have reached opposite conclusions ^{[32, 45, 46, 55]}, most studies have demonstrated that SMA improved HbA1c and SBP and reduced hospitalizations due to hyperglycemia or complications among PWD. In addition, SMA interventions can potentially improve self-management behaviors and health outcomes in PWD. However, the risk of macrovascular and microvascular complications usually increases with the duration of PWD ^[79]. Therefore, the impact of SMA on preventing complications may be underestimated in interventions targeting early patients. Qualitative interviews revealed that most patients felt that SMA was time-efficient without incurring extra costs ^[56].

As the number of PWD continues to increase, there is a growing tension between the increased need for PWD care and the limited healthcare workforce. Wu suggested promoting SMA in PHCs to avoid competition with hospital referrals and specialty providers ^[46]. Lou and Liu noted that SMA interventions in PHCs positively impacted patient attendance due to high efficiency and low cost ^{[36, 37]}. Implementing SMA in non-PHC settings may reduce patient compliance and attendance ^[59]. In contrast, PHCs are located in the local communities, which may minimize attendance time and financial costs. One study pointed out a gap between PHCs and specialty hospitals in managing PWD and that PHCs lacked sufficient medical equipment and a workforce to meet the needs of patients ^[80]. However, another study showed that PHC providers had better connections with local patients regarding daily visits and communication, thus bridging the care gap with PWD specialty hospitals ^{[44, 54]}. Furthermore, similar to other evidence-based practices implemented in PHC settings ^{[16, 81]}, SMA would be a possible way to address the equity of access to practical, evidence-based innovations for patients with PWD in communities. Therefore, it is essential to strengthen the collaboration between PWD specialty hospitals and local PHCs to provide more training to PHCs to ensure adequate disease management ^[50] and improve healthcare equity ^[16].

Although the study showed the positive effectiveness of PHCs, which are mainly responsible for patients' daily treatment and health management in LMICs, we find that most studies contained more than two service providers. Besides, the SMA services were abundant, varied, and complex. The SMA was still a highly resource-demanded intervention. However, in resource-limited settings, its feasibility and scalability would face a lot of challenges, especially in lacking of human resources. Furthermore, its complex services would require more investigation, including the cost of staff and working hours. This would raise the medical services fee and exceed the patients’ affordability, especially in rural communities in LMICs ^[82]. Furthermore, this would increase the health inequity in different resource-distribution areas, especially in vulnerable populations in LMICs ^[83].

Reach, Adoption, Implementation, and Maintenance

The included studies reported poor results on the reach, adoption, implementation, and maintenance dimensions. When researchers scale up an EBP from a highly restrictive experimental setting to real-world community settings, the implementability of the EBP will face challenges from the patient's reach and the providers' adoption ^{[84, 85]}. This study found that the included studies were conducted in resource-adequate settings. If future studies were conducted in other diverse and resource-limited settings, the external representation of the reach of patients and the adoption and complete implementation of providers would be an issue.

None of the studies reported on physicians performing and completing SMA intervention packages or the costs of institutional-level stakeholder implementation. Previous studies have evaluated the duration of SMA visits and found that SMA was malleable in terms of time costs, clinical workload, and other measurable outcomes. SMA has been shown to be a feasible and cost-effective approach compared to conventional models ^[33]. Meanwhile, if all cost information on the institutional side is explored in depth in future studies to confirm that the benefits of SMA were maximized for both patients and institutions, it would be helpful to other researchers to transfer the SMA model to other settings.

Maintenance refers to the long-term effect of EBP and the sustained implementation of SMA. A few studies have reported the long-term effects of some of the indicators of SMA ^{[29, 33, 40, 46–48, 75]}. However, there were no studies on the sustained implementation of SMA. A lack of focus on sustainability will result in a waste of investigation from research and human resources ^[86]. Future studies should focus on the sustainability of EBP, including both the long-term effect of EBP and its implementation. The assessment of EBP’s sustainability and its influencing factors are also important to improve the sustainability^[87].

In our meta-analysis, we observed considerable heterogeneity among the studies included. While we were able to attribute this heterogeneity partly to certain biased studies, the SMA, which was a complex intervention package, would be the main source of heterogeneity. Although the SMA has been shown to improve patient health outcomes overall, it remains challenging to determine which specific component(s) or combination (i.e., configurations) thereof are mainly responsible for these improvements. Given the complexity of SMAs, their implementation in resource-limited settings raises concerns. Such environments often suffer from shortages in human resources, financial constraints, and a lack of diverse healthcare specialists, which may hinder the adoption of effective SMA configurations. Consequently, future research should employ the multiphase optimization strategy (MOST)-guided optimization trial ^{[87, 88]} to dissect the component's main effects and interactive effects among the SMA components. This approach will not only enhance our understanding of each component's impact but also improve the scalability and implementability of SMAs, thereby ensuring broader applicability and effectiveness in diverse clinical settings.

Based on the RE-AIM framework, our review showed that SMA intervention effectively reduced PWD' HbA1C, SBP, and healthcare costs. The findings suggest the superiority of SMA in primary care for effectively controlling PWD' biochemical indices. However, we should notice that the SMA was a highly resource-intensive complex intervention. Its reach, adoption, implementation, and maintenance would face challenges, such as investigation constraints and human resources limitations in LMICs. The next step in SMA research for PWD should be an optimization phase that focuses on confirming its main effect and interaction effect on its different components. Empirical evidence for SMA interventions conducted in resource-limited areas would be demanded. In addition, future studies should address the shortcomings identified in the review by accurately assessing the implementation process, cost, sustainability, and equity of SMA implementation.

BP	Blood Pressure
BMI	Body Mass Index
EBP	Evidence-Based Practices
FG	Fasting Glucose
FQC	Federally Qualified Center
HbA1c	Hemoglobin A1c
HDL-c	High-Density Lipoprotein cholesterol
IQR	Inter-Quartile Range
LDL-c	Low-Density Lipoprotein cholesterol
LMICs	Low- and Middle- Income Countries
M	Median
MOST	Multi-stage Optimization Strategy
PHC	Primary Healthcare
PICOS	Populations, Intervention, Comparison, Outcome, and Study
PRISMA	Preferred Reporting Items for Systematic Reviews and Meta-Analysis
PROSPERO	Prospective Register of Systematic Reviews
PWD	People With Diabetes
RCT	Randomized Controlled Trial
RE-AIM	Reach, Effectiveness, Adoption, Implementation, and Maintenance
SBP	Systolic Blood Pressure
SMA	Shared Medical Appointment
TC	Total Cholesterol
TG	Triglyceride
VA	Department of Veterans Affairs

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

No additional data are available.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (NNSFC) [grant 72164005], and China Medical Board Open Competition Grant, grant number CMB16-260.

Authors' contributions

WY: Data extraction, verification, and manuscript writing. RM: Data extraction and verification. ZhL, ZyL, JW, RW, XL, and ML: Formulating search strategies and extracting data. YC and DRX: Guiding the writing and revision of the manuscript as mentors. All co-authors participated in the revision and approved this manuscript.

Acknowledgments

Not applicable.

Trial registration

PROSPERO CRD42019134273

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, we used [Grammarly] in order to [check the grammar of the manuscript]. After using this tool, we reviewed and edited the content as needed and took full responsibility for the content of the publication.

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Using A RE-AIM Framework to Evaluate the Impact of Shared Medical Appointments for Diabetes Mellitus: A Systematic Review and Meta-analysis

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