Glycemic control outcome in patients with type 2 diabetes mellitus: chronic care management support of family care team in Thailand

Abstract

Purpose

We aimed to ascertain diabetic self-management predictors in the family care team chronic care model, and to analyze the factors associated with glycemic control.

Methods

A cross-sectional study was conducted with type 2 diabetes mellitus (T2DM) patients at Pak Phun Municipality Hospital, Thailand, from 2019 to 2020. The 282 participants’ compraised 16 health care providers, 128 healthy volunteers, and 138 T2DM patients. Data were collected using a questionnaire. The statistics were descriptive, association and multiple regression were tested.

Results

Of the T2DM patients, 68.1% were female, overweight (body mass index 25.8 ± 3.99 kg/m²), had diabetic periods of 8.2 ± 4.65 y, high fasting plasma glucose (FPG) (139.3 ± 44.59 mg/dL), uncontrolled A1C (7.8 ± 1.57%), and presented with diabetic nephropathy (61.6%). Diabetic self-management was at a high level (76.8%). The factors significantly associated with A1C were income (p < 0.001), low-density lipoprotein (p < 0.05) estimated glomerular filtration rate, and FPG (p < 0.001). A1C was predicted by self-efficacy (SE) (38.0%; p < 0.001), social support (SO) (40.8%; p < 0.001), health providers evaluated by the Assessment of Chronic Illness Care (ACIC) (22.8%; p < 0.001), and patients’ perception by Patient Assessment of Care for Chronic Conditions (PACIC) (17%; p < 0.01).

Conclusion

In order to reduce diabetes complications, the family care team played a critical role. Self-efficacy and social support were important factors in managing diabetes. The chronic care model begins with a procedure of self-management support and intervention by stakeholders such as caregivers in the community.

Introduction

Developing, such as Thailand, are experiencing a startling increase in diabetes [1, 2]. It was estimated to rise to 10.4% or 629 million people globally by 2040 [3]. Approximately 36.5% of Asian type 2 diabetes mellitus (T2DM) patients have adequate glycemic control. The prevalence of T2DM in Nakhon Si Thammarat (NST), Southern Thailand from 2017 to 2019 increased to 322 per population of 100,000 in 2019. Surprisingly, the population proportions of achieving A1C <7% in 2016, 2017, and 2018 were 38.06%, 43.28%, and 28.70%, respectively. The rise of T2DM has caused an increase in various complications of cardiovascular (CVD) and microvascular diseases, such as diabetic nephropathy (DN) in 20%–40%, retinopathy in 34.6%, and diabetic peripheral neuropathy, which eventually affects nearly 50% of all diabetic patients and is associated with an increased risk of morbidity and mortality [4, 5]. Evidence from meta-analysis results has shown that effective self-management training significantly reduces A1C [6, 7]. T2DM patients need to self-manage to obtain normal blood sugar levels and reduce diabetic complications [8].

Previous studies found that the chronic care model (CCM) approaches have been effective in managing diabetes [9]. CCM has been applied to deliver effective diabetes care because many patients do not achieve their goals as recommended [10]. CCM presented evidence-based guidance through a systematic approach to restructuring medical care to create partnerships between health systems and communities to improve diabetes self-management [11]. CCM includes six interrelated systems: attention to self-management support, delivery system design, decision support, information technology, community linkages, and health care organization [11]. Family care teams (FCTs) in Thailand, including primary care physicians (PCPs) and health care providers (HCPs; this included village health volunteers [VHVs],work as a community linkage for diabetic self-management (DSM) support (DSMS) [10]. Many reports found highly related factors that increased the competency of social support (HCPs and family support) in diabetic care that can effectively delay the progress of DN [11, 12]. The self-management support offered the following: 1) six weekly facilitated diabetes self-management education (DSME) sessions based on the University of Michigan DSME curriculum; 2) monthly support groups focused on foot care, healthful cooking and recipe modification, alternative treatments, and problem-solving skills; and 3) create a patient self-management toolkit.

Moreover, the systematics of facilitated weekly sessions at home comprised education programs that taught goal-setting strategies based on the empowerment approach, problem-solving skills, and behavioral change strategies. Chronic disease self-management classes taught patients behavioral goal-setting and strategies to overcome barriers [11, 13]. Being highly self-efficacious is a key factor with the construct of self-efficacy in diabetes management, particularly ‘Disease Management’ and “Lifestyle Management’, in successful DSM [14]. Thailand providers designed effective FCT care to support patient self-management to accomplish diabetic goals as suggested by the American Diabetes Association (ADA) [8, 15]. This was done through a combination of empowerment to achieve optimized outcomes, such as diabetic diet, physical activities or exercise, medication adherence, stress management, and complication prevention. Several diabetes management and control programs have been introduced in Thailand. There are still challenges in implementing the FCT policy, which makes it difficult to achieve its objectives. Many patients do not achieve diabetes-related clinical goals as recommended. The purpose of this study was to ascertain the predictors of DSM for the CCM of the FCT, and to analyze the factors associated with glycemic control. See the connection of predictors to DSM in the conceptual framework in Fig. 2.

Fig. 2.

The conceptual framework of the chronic care model and the relation of predictors to DSM

Methods

Study design

A cross-sectional study was conducted with T2DM patients in the primary care cluster in Nakhon Si Thammarat, Southern Thailand. This research was approved by the Research Ethics Committee of Walailak University in August 2019. Data were collected between September 1, 2019 and February 1, 2020. The participants completed a socio-demographic and clinical data form and instruments for assessing the outcome of diabetic care using the CCM approach.

Study population and sample

We recruit the participants from three randomly selected primary care clusters with 1989 T2DM cases. Pak Phun Municipality Hospital, were randomly selected among the five villages of responsibility. All 30 FCTs (16 HCPs and 14 CHWs) working at the PCC, and 160 VHVs. The basic characteristics of the patients with diabetes mellitus (DM) emphasized differences within the different villages, but with similarities between the samples. Approximately 822 T2DM cases were calculated to be approximately 25%, and two in five villages were selected. The G* power program was used to calculate the sample according to the probability method of the multiple regression eqs. A study by Cuningham and McCrum-Gardner (2007) showed an acceptable minimum probability of 0.20, and a test power of 0.80 and alpha of 0.05 according to Cohen (1988) [16]. The samples were calculated as 112 cases in each group, with 10% dropout or data incompletion. A total of 282 participants were recruited in the study: 16 HCPs, 128 VHVs, and 138 adults with T2DM.

The inclusion criteria were as follows: 1) diagnosis of T2DM; 2) age range of 30 to 80 y; 3) attended for at least 1 y and was supported by physicians from diabetes care centers; and 4) able to speak and read Thai. The exclusion criteria were as follows: 1) patients who requested withdrawals; 2) inability to participate due to inconvenience, becoming ill, having severe complications, moving away, and/or dying during the research duration; and 3) incomplete information (clinical health record or questionnaire). The second group comprised the VHVs, with the inclusion criteria as follows: 1) worked as a volunteer for at least 1 y; 2) in the age range of 20 to 65 y, 3) has been assigned to care for T2DM patientS; and 4) were able to speak and read Thai. The exclusion criteria were as follows: 1) requested withdrawals from the study and 2) incomplete information. The 138 T2DM and 128 VHVs participants who met the criteria were informed and provided consent. The flowchart is shown in Fig. 1.

Fig. 1.

Flowchart of sample selection

Materials and measurements

The instruments were used to collect data in four parts.

1. Assessment of Chronic Illness Care (ACIC) 3.0 was a Thai version (translated from ACIC version 3.5, copyright 2000; MacColl Institute for Healthcare Innovation, Seattle, WA, USA) and included 34 items of 1–11-point scoring about six perspectives: (1) organization of healthcare systems, (2) community linkage, (3) self-management support, (4) decision support, (5) delivery system design, and (6) clinical information systems for all health providers [17]. The scores were classified as: Between “0” and “2” = limited support for chronic illness care, Between “3” and “5” = basic support for chronic illness care, Between “6” and “8” = reasonably good support for chronic illness care, and: Between “9” and “11” = fully developed chronic illness care.

2. Patient Assessment of Care for Chronic Conditions (PACIC) (26 items PACIC+) [18] was administered to the T2DM patients about the health care they received for their diabetes over the past 6 month [9]. The answers were one of five scales: “almost never”, “generally not”, “sometimes”, “most of the time”, and “almost always”. We classified the scores after data collection.

3. DSMS was created using 33 items with rating scales by asking VHVs about what aspect they helped with in patients’ self-management: (1) diabetic diet, (2) regular exercise, (3) medication adherence, (4) stress management, and (5) complication prevention.

4. Self-efficacy (SE) and social support (SO) for diabetic control were evaluated using a 1–10-point semantic differential scale. SE was created with 13 items asking patients about the ability to control diabetic self-management in six aspects as described above, where 1 means “no confidence at all” and 10 means “a lot of confidence”. SO comprised 11 items about supporting key facilitators, such as caregivers, doctors, nurses, and health volunteers, to manage diabetic care.

Statistical Aanalysis

The data were analyzed using SPSS software (version17.0; SPSS Inc., Chicago, IL, USA). The Cronbach’s alphas of all aspects of reliability of these tools were high (ACIC, .969; PACIC, .860; DSMS, .936; Satisfaction, .949; SO, .940; SE, .920; and self-management behaviors, .936). The statistics were descriptively analyzed for personal factors, clinical health data, and classification of ACIC, PACIC, DSMS, and SE-SO. Descriptive statistics were used to calculate percentages, frequencies, means, and standard deviations (SD). Chi-square was employed to assess diabetic outcome (age, income, body mass index [BMI], DM years, low-density lipoprotein [LDL], triglyceride/high-density lipoprotein [TG/HDL], cholesterol [CHOL], microalbumin [mALB], estimated glomerular filtration rate [eGFR], fasting plasma glucose [FPG], and Cr) with their glycemic control, which was defined according to ADA guidelines and Thai Clinical practice guideline [19]. This was in association with HbA1c-evaluated glycemic control and took into account the value of the blood exam done in the last 6 month with the ADA criteria (<65 y old, A1C <7%, >65y old<7.5%), which was considered as good [20]. Significant predictors of DM were identified using multiple linear regression. The significance level was set at p < 0.05.

Results

The aim of this cross-sectional study was to analyze the factors associated with diabetes outcomes and to assess the effectiveness of the CCM of the FCT in diabetic care. The main results are as follows.

1. Characteristics of participants

   Table 1 shows the percentage of personal factors of the two groups of participants (T2D_PT n = 138, VHV n = 128). All 16 HCPs were informed of the ACIC part; most of them (14 cases) were female, Buddhist, and aged under 40 y. The majority of the patients were female, with a mean age of 60.8 ± 10.81 y, a mean BMI of 25.79 ± 3.89 kg/m2, and a mean DM duration of 8.2 ± 4.65 y. Most of the participants had low incomes and practiced Buddhism. Two-thirds of the participants had uncontrolled A1C levels of 63.0%.

2. The mean score of input, process and outcome of The CCM

   Table 2 shows the mean score of input of CCM, which included 16 HCPs and 14 CHWs (community leader of VHVs), on the organizational diabetic care delivery system. The mean scores of total ACIC and integration of CCM were high (262.3 ± 41.23 and 50.3 ± 9.26, respectively), while the sub-group was at a medium level. In the DSMS process, six aspects of the second part of Table 2 were asked. The SE-SO was also added in this part, and all factors showed a high level. In part 3, the output of the CCM was shown, and almost half of the patients’ satisfaction, PACIC, and diabetic self-management behavior was shown at a high level. In contrast, the majority of HbA1c and FPG responded at a high level, indicating that 76.8% of T2DM patients were unable to achieve glycemic targets.

3. The factors associated with the clinical outcome of T2DM patients

   Table 3 shows the key results of the chi-square test of clinical outcomes among the different parameters of uncontrolled-uncontrolled A1C. Dyslipidemia was observed in most participants with FPG (40.6%), LDL (46.4%), mALB (63.1%), and Cr (34.8%), which in turn were found to be higher in patients with poor glycemic control than in those with good glycemic control, while HDL-C (59.4%) and eGFR (34.8%) were presented. Renal function was impaired with high urine mALB, with mALB decline eGFR (<90 mL/min/1.73 m²) and high serum creatinine levels.

   The chi-square test of personal factors in T2D_PT in the controlled and uncontrolled groups showed a significant difference in income (P < .001), LDL (p < 0.05), eGFR (p < 0.001), and FPG (p < 0.001). The association between males and females was tested, and most factors in Table 3 were not different. Serum creatinine level was the only factor that was significantly different (.410, p < 0.001) (Table 3).

4. The multiple correlations and Coefficients of determinant

   The aim of this study was to assess the key factors affecting the CCM. Multiple regression analyses were used to identify the factors of the CCM (input and process in Fig. 1) and diabetic self-management behavior (Output in Fig. 1). The basic assumptions and agreement of multicollinearity were tested, and the model was fit [21]. The researchers used the individual p-values and refitted the model with only significant terms of variable selection as the enter mode. Social support, self–efficacy, ACIC, PACIC, DSMS, and T2D patient satisfaction were analyzed, and self-management related to glycemic controlled outcome. Thus, the regression equation (see Table 4) was statistically significant (R .697, F 7.298, p < 0.001). Multiple correlations were examined at the statistical significance level (R² = .485, p < 0.001). R² indicates that this model accounts for approximately 48.5% of the variance in the DSM. The coefficients of determinant interpreted by percent of the variance (b in Table 4) in the regression quotation that SO was 40.8% (b .408, p < 0.001), SE, 38.0% (b .380, p < 0.001), ACIC, 22.8% (b .228, p < 0.001), and PACIC- 17% (b .170, p < 0.01) were statistically significant (Table 4). From these findings, our factors predicted DSM with the relations generic regression equation, where Y indicates the dependent variable (DSM), a is the intercept (Constant), X is the determinant variable, and b is the regression coefficient [6]. More details are presented in Fig. 2.

Table 1.

Cases and percentage of Characteristics of respondents identify by groups of participants (n = 266)

Characteristics	Categories	T 2 DM PT			VHV
		Mean ± SD	(n 138)		(n 128)
			n	%	n	%
Sex	male	–	44	31.9	14	10.9
	female		94	68.1	114	89.1
Occupation	Not work	–	56	40.6	37	28.9
	Home Work		51	37.0	60	46.9
	Full time		31	22.5	31	24.2
Status	Single (Widow)	–	39	28.3	41	32.0
	Married		99	71.7	87	68.0
Education	High school	–	116	84.1	93	72.7
	Diploma& higher		22	15.9	35	27.3
Religious	Buddhist	–	105	76.1	94	73.4
	Muslim		33	23.9	34	26.6
Age [years]	≤40	60.8 ± 10.81	2	1.4	18	14.1
	41–60		69	50.0	105	82.0
	≥61		67	48.6	5	3.9
BMI [kg/ m2]	<25	25.79 ± 3.89	54	39.3	35	27.3
	25.0–29.9		62	44.93	39	30.5
	≥30.0		22	15.94	54	42.2
DM duration / experience of HCPs, VHVs [years]	≤5	8.2 ± 4.65	40	28.9	37	28.9
	6–10		46	33.3	65	50.8
	>10		52	37.8	26	20.3
Income [THB]	<5000	6536.52 ± 4507.07	105	76.1	68	53.1
	≥5000		33	23.9	60	46.9
HbA1c (%)	Controlled	7.8 ± 1.58	51	37.0	–	–
	Uncontrolled		87	63.0	–	–

Controlled A1C (≤65 y-o, A1C < 7%, >65y-o < 7.5%)

Table 2.

Descriptive characteristics and classification of input, process, output and outcome of CCM

Dimension of CCM	Minimum	Maximum	Mean	SD	Classified*
Input of ACIC in 14CHWs-16HCPs	203.0	352.0	262.3	41.23	High
Organization of healthcare systems	16.0	38.0	28.3	6.18	Med
Community linkage	13.0	44.0	28.6	8.07	Med
Self-management support	19.0	44.0	31.0	7.68	Med
Decision support	17.0	44.0	32.5	6.72	Med
Delivery system design	29.0	66.0	49.1	8.81	Med
Clinical information systems	29.0	55.0	42.4	7.56	Med
Integration of CCM	38.0	66.0	50.3	9.26	High
Process of DSMS in FCT-144	149.0	231.0	198.4	14.44	High
Goal setting	21.0	49.0	41.2	5.31	High
Data collection	19.0	35.0	30.0	2.57	High
Data processing and evaluation	12.0	28.0	23.5	2.48	High
The decision	24.0	42.0	36.2	3.30	High
Action	18.0	42.0	36.9	3.20	High
Self- reflection	19.0	35.0	30.5	2.69	High
PACIC in T2dm Pt-n-138	46.0	82.0	63.12	8.09	High
Self -efficacy	14.0	33.0	29.5	3.56	High
Social support	15.0	39.0	33.4	4.43	High
Output in CCM T2DM T2DM Pt-n-138
Patient’s satisfaction	30.0	60.0	47.3	7.22	High
Diabetic self-management 5 aspects	33.0	99.0	78.058	13.69	High
Outcome in CCM T2DM Pt-n-138
FPG[mg/dl] **	83.0	392.0	151.2	49.15	High
HbA1c [%]**	4.5	12.5	7.8	1.58	High

FPG fasting plasma glucose, HbA1c hemoglobin A1C, SD standard deviation, PACIC Patient Assessment of Chronic Illness Care

*Classified mean 3 level; < mean ± 2 SD is low level, mean ± 2SD is medium (Med), >mean ± 2 SD is High

**Classified by ADA criteria (2019) (≤65 y-o, A1C < 7%, FPG <130 mg/dl, >65y-o < 7.5%, FPG <150 mg/dl)

Table 3.

Factors results for Controlled and Uncontrolled patients with T2DM (n = 138)

Factors	Criteria	A1c		Chi square test
		Controlled	Uncontrolled	Pearson	Sig.
AGE [years]	<50	4(2.9)	19(13.8)	3.157	.058
	≥50	42(30.4)	73(52.9)
INCOME [THB]	<5000	41(29.7)	57(41.3)	11.001	.001**
	≥5000	5(3.6)	35(25.4)
BMI [kg/ m2]	<25.0	19(13.8)	35(25.4)	.137	.711
	≥25	27(19.6)	57(41.3)
DM period [y]	<5	11(7.9)	25(18.1)	.169	.681
	≥5	35(25.4)	67(48.6)
LDL-C [mg/dl]	Normal ≤100	22(15.9)	28(20.3)	4.015	.035*
	High>100	24(17.4)	64(46.4)
HDL-C [mg/dl]	Low ≤30	41(29.7)	82(59.4)	.001	.604
	Normal >30	5(3.6)	10(7.3)
TG [mg/dl]	Normal ≤150	32(23.2)	51(36.9)	2.554	.110
	High >150	14(10.1)	41(29.7)
TC [mg/dl]	Normal ≤250	40(28.9)	70(50.7)	2.240	.100
	High >250	6(4.4)	22(15.9)
mALB [mg]	Normal <30	4(2.9)	5(3.6)	.535	.347
	High 30–100	42(30.4)	87(63.1)
eGFR [mL/min/1.73 m2)]	Normal >90	9(6.5)	44(31.9)	10.354	.001**
	Decline <89	37(26.8)	48(34.8)
Cr [mg/dl] ***	Normal >7	9(6.5)	44(31.9)	.000	.584
	High [Male >0.7, Female >0.9]	37(26.8)	48(34.8)
FPG [mg/dl]	Normal ≤130	33(23.9)	36(26.1)	13.043	.000**
	High [<65 y-o > 130, ≥65 y-o > 150]	13(9.4)	56(40.6)

FPG fasting plasma glucose, A1C Hemoglobin A1c, TC Total Cholesterol, TG Triglyceride, LDL- c low-density lipoprotein cholesterol, HDL- C high-density lipoprotein cholesterol, mALB Microalbumin urine, Cr Serum creatinine, eGFR estimated glomerular filtration rate

*The results was the cooperation on health medical record of hospital under ethical consideration

**The classification of all parameters were defined by Thai NCD guideline

***Cr showed significant of test with sex (.410, p = .000); P.value <.05

Table 4.

Coefficients of determinant to Diabetic Self-management (DSM) (n = 138)

Variables	β	b	t	Sig.	95.0% CI for β		Collinearity Statistics
					Lower	Upper	Tolerance	VIF
(Constant)	3.560				−30.615	37.734
SO	1.555	.408	5.443	.000	.990	2.121	.700	1.428
SE	1.157	.380	5.127	.000	.711	1.604	.714	1.400
ACIC	−.442	−.228	−3.448	.001	−.695	−.188	.899	1.113
PACIC	.288	.170	2.667	.009	.074	.501	.967	1.034
DSMS	.015	.016	.249	.804	−.103	.133	.953	1.049
SA	.010	.005	.086	.932	−.228	.249	.962	1.039

P.value <.05; a. Predictors: (Constant), SE- Self -Efficacy, SO- Social Support, ACIC, PAIC, DSMS, SA- satisfaction; b. Dependent Variable: Diabetic Self-management (DSM); Multiple linear regression (R.697, R².485, p < 001); Eigenvalue (6.924), Condition Index (1.0) and Durbin-Watson (guidelines of between 2 and 2.5) with actual (1.723); the model reasonably fits well; model assumptions are met: there is no interaction between independent variables and multicollinearity problem

$$\mathrm{Y}=\mathrm{a}+\mathrm{b}1\mathrm{X}1+\mathrm{b}2\mathrm{X}2+$$ 1

The regression equation in this study can be described:

$$\mathbf{DSM}=\mathbf{3.560}+\mathbf{1.555}\left(\mathbf{SO}\right)+\mathbf{1.157}\left(\mathbf{SE}\right)-\mathbf{0.442}\left(\mathbf{ACIC}\right)+\mathbf{0.228}\left(\mathbf{PACIC}\right)$$

Discussion

The objective of this cross-sectional study was to analyze the factors associated with diabetes outcomes and to assess the predictive variables for self-management of patients with diabetes in Southern Thailand. This requires efficient management control of glycemic levels, risk management, patient-centered approach, and integration of CCM to enhance patient engagement with the target. CCM comprises six components that affect functional and clinical outcomes associated with diabetes managementt: organization of health care systems, community linkage, self-management support, decision support, delivery system design, and clinical information systems [11].

The clinical health outcome presented the diabetic nephropathy (DN)

The present study found that in the Thai middle-aged and elderly population with diabetes, A1C was uncontrolled (63.0%), in contrast to a previous study in Thailand, which showed that there were more older patients with ccontrolled (A1C <7 at 77.9%) than with uncontrolled A1C (A1C ≥7, at 21.1%) [22]. similarly, the proportion of women with uncontrolled outcomes was higher than that of men (7:4) [23]. Moreover, a finding similar to previous studies showed a higher rate of A1C >10, eAG 240 mg/dL at 7.2%, and the proportion of women to men was 2:1 [24]. The findings reported uncontrolled patients (A1C >7%, eAG >154 mg/dL) at 87cases and 63.0% (max to 12.50%, median 7.6 ± 1.6). Unexpectedly, it was found that 7.2% of 138 respondents met the higher A1C of 10.0%, which was estimated to be eAG at 240 mg/dl. Similar to a report about 33%–49% of patients did not meet general targets for glycemic control, only 14% in our study met targets [8].

The multiple signs of nephropathy were shown dramatically and clearly in this study, such as a high urine mALB level of approximately 93.5% and a decline in GFR of 61.6%. More than half of diabetic patients with poor signs of kidney failure develop in 25% of patients with T2DM [25]. Uncontrolled glycemic can lead to macro- and microvascular complications, which supports this finding [26]. It was consistent with previous studies that among our T2DM patients, 39.5% had albuminuria, 39.0% had organ damage, and 89.1% had three or more risk factors [27]. This finding is supported by approximately 40% of DM patients with an increased glomerular hyper filtration, progressive albuminuria, and declining GFR developing into diabetic kidney disease, [12, 28]. Due to potentially longer exposure to hyperglycemia before diagnosis, the older patient population (almost half of the patients) were older than 60 y and the DM period was longer than 10 y (37.8%) [29]. It was consistent that diabetes were are strongly associated with poverty, with low socioeconomic status and resided more in rural areas, especially in high- and middle-income countries as well as in this study [30]. The results showed significant differences consistently with the progression of diabetic nephropathy, which is a major course of renal failure [31]. Furthermore, greater attention should be paid to the dissemination and application of best practice support and aid patients. Nevertheless, effective team support is addressed in both clinical and community settings and is of concern for more individual risk factors for DN progression [12].

Chronic Care Model (CCM) subscales support by the family care team were high scored

The family care team presented six aspects of the chronic illness care model, which was measured with the ACIC at a high level. This was demonstrated by the concern of the FCT in caring for the archived outcomes and diabetes management program with good support in community links [9]. PACIC represented a patient’s point of view among patients that prevailed at a medium level, and the remainder demonstrated a low level. Findings indicate that people at risk of or with T2DM are aware of major self-management strategies, but fail to integrate these into their daily lives [30]. This result is consistent with HCPs’ expressed ambivalence about pushing certain patients into self-managing, preferring to retain responsibility [32]. The lack of a physician’s ability to visit homes and have general daily conversations to recommend behavioral training and behavior modification training impedes proper care. The lack of good doctor-patient communication leads to misunderstanding of the patients’ social context and expectations as treatment progresses [10].

The associated of diabetic outcome with glycemic control

The association between patient demographic characteristics and glycemic control was also assessed. HbA1c is a stronger predictor than fasting glucose for subsequent diabetes and cardiovascular events [8]. Similarly, patients with good glycemic control scored significantly higher for self-care activities [33]. DM has been shown to be an important predictor of low education and low socioeconomic status, and is associated with poor metabolic control, including dyslipidemia, hypertension, and others cardiovascular risk [34]. Early detection and treatment of dyslipidemia associated with DM may be a step in reducing CVD risk. The finding was similar to the association between HbA1c and LDL-C, but was not significantly associated with HDL-C [35]. Diabetes dyslipidemia is characterized by free fatty acids release from insulin-resistant fat cells, which promote TG production, which in turn further stimulates Apo lipoprotein and very low LDL [36]. In addition, elevated HbA1c (≥6.21%) is associated with increased odds of hyperfiltration in middle-aged and elderly individuals [25]. HbA1c was associated with hyperfiltration independent of age, sex, BMI, waist circumference, blood pressure, smoking status, alcohol consumption, hypertension status, and TC, TG, LDL-c, and HDL-c levels [25], and its presence was associated with an increased risk of albuminuria [4, 25]. Further reducing the risk of CVD in patients with T2DM can have various lipid parameter reductions. The goals of diabetic treatment are to prevent or delay complications and maintain quality of life.

Self-efficacy (SE) and social support (SO) ACIC, PAIC predicting diabetic self- management

This finding is in line with our hypothesis, as patients with higher effective health care delivery (ACIC), patients presented good views of facilities in participating taking care (PACIC), confident or high SE, and good support were expected to exhibit better self-care behavior and better glycemic control. In addition to lifestyle management, patients’ demographic factors can also influence glycemic control. This study demonstrated that a high ACIC score is a good approach to diabetic care provided by HCPs. This was confirmed by the high PACIC score, reporting that most patients had taken care of the essentials of the goal set, participated in diabetic treatment, and were provided with good educational support. Therefore, both factors are important factors in predicting DSM.

The DSM presented behaviors performed by individuals. It involved the practice of diet control, engaging with adequate physical activities, taking medications, and risk prevention. SE in patients with diabetes was assessed based on five features: healthy diet, regular exercise, stress management, medication adherence, and cooperation with HCPs. Additionally, SO was supported by HCPs, health care volunteers, and caregivers, such as family members. The perception of SE and SO in the self-care management of diabetic patients in this study were also a moderate level. The presence of diabetes negatively impacts the presence of diabetes related to social support [37]. It was confirmed that SO must be recognized as a core element in the management of patients with T2DM [38]. These results transfer to self-care behavior, affecting their glycemic outcome with the support of their significant persons. Similarly, SE is a prominent factor in the performance of self-care behaviors [38]. Consistent with diet SE, diet self-management behaviors predicted better glycemic control, whereas insulin use was a statistically significant predictor of poor glycemic control [39].

Multiple linear regression analysis revealed a significant positive correlation between predictors. Glucose management was consistently the strongest predictor of low HbA1c levels [33]. Therefore, Social support (SO), Self-efficacy (SE), Patient Assessment of Care for Chronic Conditions (PACIC), and Assessment of Care for Chronic Conditions (ACIC) collectively explain patients’ behaviors that follow along with the conceptual framework (Fig. 2). A previous study reported a significant positive correlation between the decline in GFR, albuminuria, glycosylated hemoglobin A1C, and serum cholesterol during follow-up (R²adj = 0.29, p < 0.001) [31]. Patients demonstrated good lifestyle management as a dietary control, and physical activity impacted glycemic control in patients with diabetes [40]. A regression meta-analysis showed that strategies of team changes and case management reaffirmed and produced significant incremental reductions in HbA1c values [5]. The connection of all predictors was described in Fig. 2.

Strengths and limitations

A strengths of this study that the participants were drawn from three different places and that the study looked at the relationship between self-management in both facilitators (HCPs and VHVs) and DM patients. Finally, it must be noted that diabetes outcome as A1C and its associated characteristics were found to be associated with low education and low socioeconomic status and the lifestyles management of patients with type 2 diabetes, poor metabolic control. Despite the fact that social support, self-efficacy, patient assessment of chronic care, and patient assessment of chronic disease management all predicted patients’ self-care, this is not the case for the great majority of the population. It’s important to note that there are other factors to consider. As a result, future study should incorporate assessments of more related components as well as programmed monitoring of the self-management of cultural or contextual communities.

Notwithstanding its limitations that the participants in this study may not be entirely representative of all type 2 diabetes patients, and only a small number of health practitioners, such as those in this study, were ready to engage in a study on T2DM patients’ self-management. Furthermore, in the case of the ACIC item in the chronic care model’s organization, which may have resulted in an underrepresentation of patients and influencing the CCM’s overall strategy in motivating patients to practice self-care.

Finally, when assisting patients in adopting lifestyle adjustments and adhering to self-care guidelines, our findings demonstrate that poor metabolic control and various diabetes-related factors (i.e., presence of microvascular and macrovascular problems, as well as treatment type) must be considered. Clinicians must be aware that patients’ support needs vary depending on the sort of self-care behavior at issue and the stage of disease in which they find themselves.

Recommendations

Diabetes self-care management needs strategies to enhance and promote Self-efficacy (SE), and self-management behaviors for patients are essential components of diabetes education programs. HCPs should consider social support (SO) to appropriately meet the needs of patients with diabetes.

Conclusion

In order to reduce diabetes complications, the family care team played a critical role. Providers of diabetes care should consider patients’ phases of illness as well as their personal characteristics. According to the study ideas framework, the chronic care model begins with an input of CCM components, a procedure of self-management support and intervention by stakeholders such as caregivers in the community and at home, and an output of better results in self-care behaviors. High blood creatinine levels have been linked to diabetic nephropathy, which is on the rise. Self-efficacy and social support were important factors in managing diabetes.

Author contributions

PW: The study conception, material preparation, data collection and analysis, write the first draft and edit the manuscript.

PT and SK: Data collection and commented on previous versions of the manuscript.

All authors read and approved the final manuscript and were responsible for the supervision of the survey, concept, design, and data management in their respective areas.

Funding

The research leading to these results received funding from the Research Institute for Health Sciences, Walailak University, project ID WU-IRG-62-042.

Declarations

Ethics approval

This research was labeled as “Compliance with Ethical Standards.” An appropriate ethics committee had approved research involving person data (Protocol number WU-AC-AH-2-144-62, EC number was WUEC-19-137-01 at Aug 29, 2019).

Consent to participate

Prior to beginning any study-related measures or procedures, all participants signed a written informed consent form.

Copyright: If and when the manuscript is accepted for publication, the author agree to automatic transfer of the copyright to the publisher.

Conflict of interest

All authors declared no explicit and potential conflicts of interests.

Role of the corresponding author

1. The article mentioned above has not been published or submitted for publication in any form, in any other journal. Authors declare no explicit and potential conflicts of interests associated with the publication of this article.

2. I certify that all authors have approved the manuscript before submission have reviewed final version of the manuscript and approve it for publication.

2. All communication between the journal and all co-authors during submission and proofing was to be delegated to a Contact or Submitting Author.

4. All authors agree to automatic transfer of the copyright to the publisher if manuscript is accepted for publication. The Corresponding Author is responsible for providing transparency on re-use of material.