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Nursing and Health Sciences (2015), 17, 362–369
Research Article
Effectiveness of a self-management support program for Thais with type 2 diabetes: Evaluation according to the RE-AIM framework Jamabhorn Jaipakdee, MPH, PhD (Cand), Wiroj Jiamjarasrangsi, MD, PhD, Vitool Lohsoonthorn, MD, PhD and Somrat Lertmaharit, MSc M Med Stat Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
Abstract
Delivering diabetes self-management support is an enormous challenge for healthcare providers with limited human resources. We conducted a cluster randomized controlled trial to assess the effectiveness of a DSMS program incorporating the computer-assisted instruction. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework was applied to evaluate the DSMS program. Ten Public Health Centers in Bangkok, Thailand were randomized into either DSMS program or usual care. Forty eligible patients with type 2 diabetes in each Public Health Center were randomly selected. Totally, 403 patients (200 controls and 203 interventions) participated. About 93.8% participants completed the six-month follow-up. Over six months, adjusted mean changes of hemoglobin A1c (−0.14%, 95% confidence interval = −0.02 to −0.26, fasting plasma glucose (−6.37 mg/dL, −1.95 to −10.78), health behaviors (3.31 score, 2.27 to 4.34), and quality of life (1.41 score, 0.69 to 2.12) were significantly improved in intervention compared to control group. In conclusion, the DSMS program facilitates Public Health Centers to accomplish their support for people with diabetes.
Key words
cluster randomized trial, diabetes self-management support, RE-AIM framework, type 2 diabetes, Thailand.
INTRODUCTION Diabetes is a major public health problem and produces healthcare burdens worldwide according to its morbidity, mortality, complications, and health expenditure (van Susan et al., 2010; WHO, 2011). In Thailand, the fourth nationwide survey reported that prevalence of impaired fasting glucose (IFG), diagnosed diabetes, and uncontrolled diabetes was 10.6%, 7.5%, and 37%, respectively (Aekplakorn et al., 2011). Around 31% of people with diabetes had complications while 65% had comorbidity (Chatterjee et al., 2011). In Thailand, health expenditure on diabetes per person was about US$144, accounting for 11% of the total healthcare cost (Zhang et al., 2010). People with diabetes require continuing medical care and ongoing self-management support (SMS) in order to improve disease outcomes, prevent acute complications, and reduce long-term complications (American Diabetes Association, 2013). Diabetes education is considered an important part in supporting the patients for day-to-day control of their diabetes (Funnell et al., 2007). Some Correspondence address: Wiroj Jiamjarasrangsi, Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Rama 4 Road, Bangkok 10330, Thailand. Email: [email protected] Received 30 January 2014; revision received 11 November 2014; accepted 15 November 2014
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meta-analyses indicated that diabetes self-management support (DSMS) had a significant effect on hemoglobin A1c (HbA1c), lifestyle, and psychosocial outcomes, diabetes knowledge,self-management skills,self-efficacy,and quality of life (QOL), as well as reduced healthcare cost (Chodosh et al., 2005; Boren et al., 2009; Pimouguet et al., 2011; Steinsbekk et al., 2012). Since diabetes management is complex for health systems at every level, embedding the DSMS program into routine practice is quite difficult.This is especially the case for health facilities with a large patient volume and limited personnel resources. In Thailand, barriers to continuing a DSMS program service include low public awareness,high healthcare worker turnover and workloads in rural areas, insufficient resource, and staff shortages (Deerochanawong & Ferrario, 2013). Therefore, the evaluation of DSMS programs is important in order to assess their overall impact as well as potential application into “real world” practice. However, most randomized controlled trials (RCTs) of DSMS intervention have emphasized only on efficacy of outcomes rather than overall impacts (Eldridge et al., 2008). In this study, we evaluate the potential utility of a DSMS program equipped with a computer-assisted instruction (CAI) in a Thailand healthcare context. The program approaches were based on five major steps (the 5C intervention) of behavior change support (Peyrot & Rubin, 2007). The CAI was newly created by the research team in order to doi: 10.1111/nhs.12198
Diabetes self-management support
facilitate diabetes education. We conducted a cluster randomized controlled trial (CRT) incorporating the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework in order to determine the effectiveness of the DSMS program. This framework provided guidance for evaluating the potential public health impact, decision making, and translation of research into practice in “real-world” settings. In addition, it has also been used to understand the strengths and weaknesses of interventions (Belza et al., 2007; Bakken & Ruland, 2009; Glasgow et al., 2011). The results of this study may provide evidencebased for implementation and maintenance of a DSMS program into routine care for people with diabetes in Thailand, as well as in other developing countries.
Purpose of the study This study aimed to compare the HbA1c level, health behavior, depression, and QOL between people with diabetes receiving a DSMS program with CAI and those receiving the routine care.
MATERIALS AND METHODS Participant recruitment We carried out this CRT in 2012. We invited Public Health Centers (PHCs) of Bangkok Metropolitan Administration (BMA) to cooperate in the study. Ten PHCs were randomly assigned into five intervention or five control groups. Participants who met the following inclusion criteria were recruited: (i) aged ≥ 20 years; (ii) diagnosed with type 2 diabetes and treated in a PHC; (iii) fasting plasma glucose (FPG) level ≥ 130 mg/dL or HbA1c ≥ 7% (53 mmol/mol) within two months before launching the program; (iv) were willing to participate; and (v) were able to speak, read, and write in Thai. Patients with severe complications, which made them unable to participate in the study, were excluded. In each PHC, 40 eligible patients were randomly selected by using their patient identification numbers. A total of 403 people met criteria and agreed to participate in this study after giving written informed consent.
Ethical considerations The study was approved by the Ethics Committee of the Faculty of Medicine, Chulalongkorn University and Bangkok Metropolitan Administration Ethics Committee for Human Research (BMAEC).
Sample size We used HbA1c for primary outcome calculation to detect mean difference of 0.4%. An Intra-class correlation coefficient (ICC) of 0.05 has been assumed (Littenberg & MacLean, 2006). Forty people were recruited from each PHC. Consequently, the design effect was calculated as 2.95. For an α of 5% and a power of 80%, allowing a loss to
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follow-up of 10%, the number of participants and PHCs per arm required 200 and five respectively.
Intervention The DSMS program provided by trained nurses and healthcare staff was adapted from three strategies, including diabetes education, behavior change support, and emotional support (Funnell et al., 2007; Peyrot & Rubin, 2007). This program comprised of two components: (I) diabetic educational section to help patients understand the disease process; and (II) proper skill learning to manage their condition and change their lifestyle. In Component I, we developed a CAI for using in the educational sessions. The CAI facilitates the DSMS delivery lesson by lesson according to the pre-set steps, while lesson repetition is also possible. Its video component included lessons on diabetes and pre- and post-tests (10-questions with three choice answers of each lesson). The lessons consisted of: (i) knowledge of diabetes; (ii) foods for diabetes; (iii) physical activity; (iv) foot care; (v) medication used to control diabetes; (vi) reducing complications and stress management; (vii) self-monitoring of clinical indicators and goals of diabetes control. The lessons were designed in various forms such as stories, graphics, animated images, interviews, and demonstrations to stimulate learners’ interest and enjoyment. In Component II, the step-by-step approach for behavior change and psychological support was in accordance with the 5C intervention and consisted of: (i) constructing a problem definition; (ii) collaborative goal setting; (iii) collaborative problem solving; (iv) contracting for change; (v) continuing support (Peyrot & Rubin, 2007). The nurse supporters performed as facilitators to help participants define their problem in a potentially useful way, set goals, identify barriers, and solve problems in achieving those goals. Participants were followed-up for their behavior changes that were engaged in the previous session, such as dietary habits, exercise, foot care, medication, and blood glucose monitoring. Three nurses in each PHC of the intervention group were trained as the supporters in a two-day intensive course. The training course included explanations, demonstrations, and practices. The supporters delivered the six-monthly sessions of 3 h each for the intervention group. The supporters were observed and advised by the research team. The developed DSMS program and its accompanying guidebook for supporters were both approved by physicians, academic nurses, pharmacologists, and nutritionists. Forms used in baseline assessment, goal setting, progress monitoring, and psychological evaluation of the participants were also included in the guidebook.The tools were tried out in a PHC that was not participating in the study. At the end of the study, the CAI and guidebook were also provided for the PHCs in the control group.
Usual care group Patients in the control group received usual health care, including physical examination, monitoring of blood sugar © 2015 Wiley Publishing Asia Pty Ltd.
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levels, individual health education and consultation from a registered nurse and/or other healthcare provider.
Data collection
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mean difference of each outcome at three and six months follow-up. Generalized estimating equation (GEE) to account for the correlated data over six months was used to estimate the average treatment effects. These analyses also adjusted for age, duration of diabetes, complication, marital status, and baseline values of each outcome. A P < 0.05 was considered statistically significant.All data analyses were performed with STATA software, Version 11.0 (Stata, College Station, TX). Data from the focus group were analyzed by: (i) the research team reviewing each transcript separately, searching for meaningful relations to research questions; (ii) discussing the findings with each other in order to establish coding categories through consensus; (iii) grouping data according to their meaning.
Data were gathered through standard questionnaire, anthropometry measurements (weight, height, blood pressure, and waist circumference), and blood test (HbA1c, FPG). Patients provided information about their demographics, personal and family medical histories, health behavior, and physical activity. The internal consistency (Cronbach’s α coefficient) of health behavior was 0.78 and QOL was 0.67. Depression was assessed by the Thai version of the Patient Health Questionnaire (PHQ-9), Cronbach’s α was 0.79, sensitivity was 0.53, and specificity was 0.98 (Lotrakul et al., 2008). Health behavior included 13 items concerning diet, eight items concerning foot care, and 10 items concerning general self-care. Diabetes QOL was assessed by the 15 items of brief clinical inventory category (Burroughs et al., 2004). Each item of the PHQ-9 ranges from 0 (not at all) to 3 (nearly every day). The QOL ranges from 1 (very dissatisfied/all the time) to 5 (very satisfied/ never). Patients’ satisfaction ranges from 1 (very dissatisfied) to 5 (very satisfied). Health behavior score ranges from 1 (never) to 5 (everyday). Five milliliters (mL) venous blood sample was collected from each patient after 8-h overnight fast. All blood tests were performed in the same laboratory at the BMA Health Department. The laboratory quality control was assessed by the Thai Medical Science Center. All data were collected at baseline, three- and sixmonth follow-up in both groups. After finishing the program session, we invited 10 nurse supporters to participate in the focus group on April 2012. The aim was to assess the benefits, flexibility, barriers, satisfaction, and impact of the DSMS program. The moderator facilitated the discussion while the assistant moderator kept detailed notes and audio recorders.
A total of 403 participants (203 interventions and 200 controls) were recruited from 10 PHCs. Most of them were women (76.7%), and the mean age was 61.3 ± 9.6 years. Duration of diabetes was on average 9.2 ± 7.1 years. Twenty patients were current smokers (4.9%) and alcohol drinkers (6.9%). Complications were common, as 247 (61.2%) had hypertension and 146 (36.2%) had dyslipidemia. Characteristics of both groups were similar, except marital status and systolic blood pressure (SBP). The mean SBP was slightly higher in the intervention group (P = 0.012) (Table 1). Overall, 378 participants (194 interventions vs. 184 controls) completed the 6-month follow-up. Of 25 participants lost to follow-up, 12 moved to another province, six transferred to the other hospitals, five could not be contacted, and two had died (not related to the research) (Fig. 1). Characteristics of 25 patients lost to follow up did not differ from the rest of the sample.
The RE-AIM framework
Reach
The RE-AIM framework includes:(i) Reach, defined as the percentage of eligible patients’ willingness to participate; (ii) Effectiveness, assessed as the potential impact of intervention on HbA1c, FPG, body weight, health behavior, depression, and QOL; (iii) Adoption, a number of PHCs agree to deliver the DSMS program into the healthcare service and participants’ satisfaction with the program; (iv) Implementation, concerned with the extent to which intervention is delivered as intended, barriers to implementation, and attendance rate; (v) Maintenance, the extent to which individual participants maintain health behavior and HbA1c (individual level), and the PHCs intend to sustain the program (setting level) in the long term.
Of 68 PHCs, we gained the cooperation of 30 PHCs. Fifteen out of the contacted PHCs were willing to participate. Among the five PHCs in the intervention group, we invited 300 eligible patients to attend the program. About 235 (78.3%) of those were willing to participate (Fig. 1).The main reasons for refusal were not having enough time (n = 36), tied up with work (n = 11), inconvenient to travel (n = 10), and taking care of child/older person in their family (n = 8); however, these reasons are common in daily life. No significant differences in characteristics (gender, age, education, and marital status) were found between participants and people who refused to participate.
RESULTS Baseline characteristics of participants
Effectiveness Data analysis Group differences were assessed by independent t-test, Mann–Whitney U test, or χ2-test, whichever was appropriate. Analysis of covariance (ancova) was used to assess the © 2015 Wiley Publishing Asia Pty Ltd.
The adjusted mean differences at three- and six-month follow-up are shown in Table 2. Participants in both groups slightly improved outcomes. At six months, significant decreases in FPG (−13.08, 95% confidence interval
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Table 1. Baseline characteristics of participants
Characteristics Age group (years); range Mean ± SD 20–49 50–60 > 60 Gender Men Women Marital status Single Married Widowed/separated Household income (baht/month) (n = 371) Median (IQR) < 10 000 10 000–20 000 > 20 000 Regular physical activity No Yes Smoking status Never smoker Former smoker Current smoker Alcohol drinking Never drinker Former drinker Current drinker Complication No Yes Hypertension (HT) No Yes Dyslipidemia No Yes Duration of diabetes (years)† Body mass index (kg/m2)‡ Waist circumference (cm)‡ Systolic blood pressure (mmHg)‡ Diastolic blood pressure (mmHg)‡ Fasting plasma glucose (mg/dL)‡ Hemoglobin A1c (%)‡
DSMS program (n = 203) Number (%)
Usual care (n = 200) Number (%)
33–79 61.1 ± 9.6
26–80 61.5 ± 9.7
P-value
0.682§ 0.929
22 77 104
(10.9) (37.9) (51.2)
23 72 105
(11.5) (36.0) (52.5)
48 155
(23.6) (76.4)
46 154
(23.0) (77.0)
0.878
23 141 39
(11.3) (69.5) (19.2)
27 110 63
(13.5) (55.0) (31.5)
0.008
8 500 (5 000–15 500) 97 (52.7) 61 (33.2) 26 (14.1)
10 000 (6 000–20 000) 85 (45.5) 76 (40.6) 26 (13.9)
0.221¶ 0.300
74 129
(36.5) (63.5)
62 138
(31.0) (69.0)
0.260
181 13 9
(89.2) (6.4) (4.4)
172 17 11
(86.0) (8.5) (5.5)
0.458
176 16 11
(86.7) (7.9) (5.4)
160 23 17
(80.0) (11.5) (8.5)
0.119
44 159
(21.7) (78.3)
45 155
(22.5) (77.5)
0.883
79 124
(38.9) (61.1)
77 123
(38.5) (61.5)
0.950
132 71
(65.0) (35.0)
125 75
(62.5) (37.5)
0.651
7 (4–10) 27.4 ± 4.8 90.6 ± 10.9 140.1 ± 20.1 78.4 ± 12.8 165.7 ± 48.9 8.2 ± 1.5
8 (5–13) 26.7 ± 4.6 91.5 ± 12.3 135.3 ± 18.0 79.2 ± 13.5 163.1 ± 43.2 8.5 ± 1.6
0.258¶ 0.105§ 0.415§ 0.012§ 0.551§ 0.583§ 0.160§
†Median (IQR). ‡Mean ± SD; χ2 test for categorical variables. §Independent t-test. ¶Mann-Whitney U test.
(CI) = −21.07 to −5.1) and body weight (−2.28 kg, −3.61 to −0.94) and increases in health behavior score (5.49, 3.54 to 7.44) and QOL (2.54, 1.37 to 3.71) were observed. The findings of the GEE model showed that all outcome estimates were small. Adjusted mean differences of HbA1c, FPG, body weight, QOL, and health behavior were slightly, but significantly, improved in the intervention group (Table 3).
Adoption Four out of five PHCs in the intervention group adopted the DSMS program into their routine healthcare services. One site did not adopt because of staff shortage, however, it does plan to apply the program when it has a sufficient healthcare team. Overall, 90.3%of the participants who had received the DSMS program were satisfied or very satisfied with it. About
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Figure 1. Flow chart illustrating recruitment and follow-up of the PHCs and participants.
Table 2.
Comparison of adjusted mean differences between DSMS program and usual care participants
Outcomes Hemoglobin A1c (%) Baseline 3 months 6 months FPG, mg/dl Baseline 3 months 6 months Health behavior score† Baseline 3 months 6 months Body weight (kg) Baseline 3 months 6 months Depression score Baseline 3 months 6 months Quality of life score Baseline 3 months 6 months
Mean (SD) DSMS program Usual care
Unadjusted Mean difference‡ (95% CI)
Adjusted mean Difference§ (95% CI)
P-value
8.2 7.9 7.8
(1.5) (1.5) (1.4)
8.5 8.4 8.2
(1.6) (1.7) (1.8)
−0.21 (−0.51, 0.08) −0.48 (−0.80, −0.16) −0.33 (−0.65, −0.01)
– −0.29 (−0.48, −0.09) −0.12 (−0.37, 0.13)
– 0.004 0.334
165.7 157.5 149.5
(48.9) (48.7) (32.4)
163.1 162.7 162.4
(43.2) (49.9) (50.9)
2.53 (−6.52, 11.58) −5.15 (−14.95, 4.65) −12.91 (−21.59, −4.23)
– −6.16 (−14.31, 1.99) −13.08 (−21.07, −5.10)
– 0.138 0.001
124.4 130.7 135.9
(12.5) (9.8) (10.5)
125.1 126.9 130.5
(10.9) (10.9) (10.2)
−0.54 (−1.11, 1.0) 1.88 (0.85, 2.92) 1.78 (0.86, 2.69)
66.6 64.5 64.7
(12.2) (12.6) (12.7)
64.7 64.8 65.2
(12.3) (12.6) (12.9)
1.87 (−0.54, 4.28) −0.15 (−2.67, 2.36) −0.53 (−3.11, 2.05)
4.3 4.4 3.5
(4.1) (3.3) (3.2)
5.1 3.8 3.4
(4.2) (3.5) (3.9)
58.4 62.1 64.1
(6.2) (6.1) (5.8)
60.1 61.8 62.4
(6.2) (14.2) (6.8)
– 4.09 (2.24, 5.93) 5.49 (3.54, 7.44)
– < 0.001 < 0.001
– −1.79 (−3.02, −0.56) −2.28 (−3.61, −0.94)
– 0.005 0.001
−0.83 (−1.64, −0.001) 0.57 (−0.11, 1.25) 0.04 (−0.69, 0.77)
– 0.88 (0.33, 1.43) 0.24 (−0.45, 0.92)
– 0.002 0.495
−1.73 (−2.95, −0.50) 0.29 (−1.88, 2.45) 1.70 (0.41, 2.99)
– 1.25 (−0.87, 3.38) 2.54 (1.37, 3.71)
– 0.247 < 0.001
†Health behavior consists of dietary, foot care, medication, and general self-care. ‡Independent t tests. §ancova was used to analyze mean difference adjusted for age, duration of diabetes, complication, marital status, and baseline values of each outcome.
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Table 3. Adjusted mean differences over six months by GEE analysis
Outcomes HbA1c (%) FPG (mg/dL) Health behavior score Body weight (kg) Depression score Quality of life score
Unadjusted Mean difference (95% CI) −0.34 −5.08 2.77 0.52 −0.10 0.10
(−0.62, −0.06) (−12.42, 2.26) (1.09, 4.45) (−1.87, 2.90) (− 0.69, 0.49) (−1.05, 1.24)
Adjusted† Mean difference (95% CI) −0.14 −6.37 3.31 −1.35 0.32 1.41
(− 0.26, −0.02) (−10.78, −1.95) (2.27, 4.34) (−2.20, −0.51) (0.00, 0.64) (0.69, 2.12)
P-value 0.025 0.005 < 0.001 0.002 0.050 < 0.001
†Adjusted for age, duration of diabetes, marital status, complication, and baseline values of each outcome.
59.0% were willing to retain the program, and 64.1% intended to persuade other patients to attend the program.
Implementation of the DSMS program Of four adopting PHCs, two applied the DSMS program on-site and launched the program at community settings by integration with community care visits. Around 190 (93.6%) patients completed the program sessions. Findings from the focus group revealed that the DSMS program helped the PHCs to achieve their primary mission and all supporters appreciated the program process. The video and guidebook greatly facilitated the supporter team’s DSMS program delivery. Moreover, group participation and lunch meetings enhanced patients’ acquaintance. The main enabling factors contributing to the program’s success were the PHC administrator’s policy and encouragement, sufficient budget and equipment support, consideration for patients’ well-being, good relationship between healthcare provider and patients, and teamwork. The main barriers to implementation were inadequate staff and information technology, and lack of tools and budgets.
Maintenance At each follow-up, the means of health behavior score were significantly improved in both groups (P < 0.05), especially in the intervention group. Improvements in HbA1c, FPG, health behavior, and QOL were significantly sustained in the long term (Table 3). The proportion of patients who achieved optimal HbA1c < 7% (53 mmol/mol) was higher in the intervention group, although not statistically significant (18.5% vs. 15.5%, P = 0.435). Having finished the study, four out of five PHCs continued providing the program within their healthcare services. However, some adaptations were also made to suit their contexts, including shorter duration and fewer numbers of participants in each session, and higher frequency of foot examination practice.
DISCUSSION Based on the RE-AIM framework, this study showed that the DSMS program with CAI was effective and applicable within routine care in PHCs in Bangkok, Thailand. Significant
improvements in glycemic control, health behavior, body weight and QOL were observed in intervention group. Four PHCs adopted the program within their diabetes care services after the research period. The supporters and participants very satisfied with the program. About 78.3% of invited patients were willing to participate in the program. Although the period of recruitment was limited (one month before launching the study), it was long enough to reach sufficient eligible patients. Most patients were willing to participate in a program that was integrated with their primary care visit (Glasgow et al., 2006). The most reported barriers to participation were similar to those in some recent studies. Those barriers more likely depend on patient-level factors, such as patients not concerned about the DSMS (Schäfer et al., 2013), distance to work (Dubuy et al., 2013), literacy, transportation, and reimbursement (Balamurugan et al., 2006). At six months, 194 (95.6%) in intervention group and 184 (92.0%) in the control group completed the follow-up, with no significant difference in participation rate (P = 0.134). Sustained improvements in the FPG, health behavior, and QOL were also demonstrated. The findings seem to comply with the theory that behavior change and improved psychosocial outcomes result in increased glycemic control. Ongoing reinforcement of health behavior, diabetes education, and patient self-efficacy are also needed for successful long-term interventions (Sperl-Hillen et al., 2013). The optimal strategies are that the supporters deliver the DSMS to improve selfefficacy, provide peer and cognitive behavioral support, and launch automatic telephone reminders, while patients are mostly responsible for their own diabetes management (Jones et al., 2013). Some recent studies revealed beneficial effects of DSMS programs on HbA1c, FPG, psychosocial outcomes, knowledge, QOL, and self-management behavior (Schillinger et al., 2009; Pimouguet et al., 2011; Steinsbekk et al., 2012). Diabetes education class and individual home visit methods were able to encourage and empower participants to be more confident in their abilities to deal with their diabetes (Wattana et al., 2007). Our findings also confirm the effects of the DSMS program on the positive change in all outcomes, but only in the short term. Although the adjusted mean change of the HbA1c was not significantly different at six months, the percentage of patients with the optimal HbA1c < 7% © 2015 Wiley Publishing Asia Pty Ltd.
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(53 mmol/mol), as recommended by the American Diabetes Association (2013) was, however, higher in the intervention than in the control groups. A study revealed that 1% reduction of mean HbA1c was associated with a 21% reduction in deaths related to diabetes, and 14% for myocardial infarction (Stratton et al., 2000). Accordingly, 0.3–0.4% reduction of mean HbA1c in this study may be clinically important. Some studies also found no significant effects on patient outcomes for long-term conditions (Kennedy et al., 2013; Sperl-Hillen et al., 2013). This might be due to short training interventions, which are ineffective for enhancing the SMS in routine primary care (Kennedy et al., 2013). In this study, most healthcare staff indicated that the main barriers to implementation and adoption of the DSMS program were the lack of staff, tools, and budgets, and inadequate information technology. These reasons are consistent with previous reports (Bodenheimer & Laing, 2007; Battersby et al., 2010). The challenge for busy healthcare settings is how to conduct and apply a DSMS program in an efficient way that does not require additional staff within the team (Glasgow, 2010). The flexibility and acceptance of the program contributes to its adoption and continuous delivery in usual care practice. Adoption of the DSMS into routine care or maintenance in the long term may require considerable additional incentives to encourage supporters to engage with a self-management agenda (Kennedy et al., 2013). Consequently, we developed the CAI to facilitate the supporters in part of the education sessions and to resolve work-load issues. We hope that the CAI can be routinely used in the PHCs’ services since all diabetes patients should have continuing diabetes knowledge and self-management support. However, long-term impact and cost effectiveness of the DSMS program need further evaluation. Strengths of the study include the large number of participants with a relatively high participation rate (93.8%). Participants’ characteristics at the baseline in both groups were similar. The CRT design of the study also simplified data collection and reduced contamination. However, the results should be interpreted cautiously because of some limitations. First, CRTs are more complex because recruitment and follow-up processes differ from those in individually randomized trials. Such process is susceptible to recruitment bias. Participants were differentially recruited among clusters. (Giraudeau & Ravaud, 2009). Second, we were not able to separate the effect of CAI from that of nurse support. Third, health behavior scores and QOL were based on selfreport and such measures may differ from other studies. Finally, the data concerning patients’ hypoglycemic drug and other medications used, and their knowledge and skills for diabetic management, were incomplete. Some factors could thereby confound the effects of the intervention.
CONCLUSION Our findings indicated that the DSMS program with CAI was clearly effective in terms of RE-AIM evaluation. Participant rate (78.3%) for reaching the program and attendance rate (93.8%) for the training sessions were generally high. Over six months, the program improved HbA1c, FPG, health © 2015 Wiley Publishing Asia Pty Ltd.
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behavior, and QOL, and had higher achievement in optimal HbA1c level in the intervention group. Although the estimates of the effects were quite small, there was trend towards improvement compared with the baseline. Four out of five PHCs adopted the program into their routine healthcare services.The program facilitates nurse supporters in accomplishing their practices, and can be applied to other PHCs.
ACKNOWLEDGMENTS This study was supported by the National Health Security Office (NHSO) of Thailand, Region 13 Bangkok, via grant no. GUC7/2551/23-04-2551; and the 90th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund). We would like to thank the executives and healthcare staff in the PHCs of the Bangkok Metropolitan Administration for their cordial cooperation with the provision of the program performance and data collection.
CONTRIBUTIONS Study Design: WJ, JJ. Data Collection and Analysis: JJ, VL, SL. Manuscript Writing: JJ, WJ, VL, SL.
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Nursing and Health Sciences (2015), 17, 362–369
Research Article
Effectiveness of a self-management support program for Thais with type 2 diabetes: Evaluation according to the RE-AIM framework Jamabhorn Jaipakdee, MPH, PhD (Cand), Wiroj Jiamjarasrangsi, MD, PhD, Vitool Lohsoonthorn, MD, PhD and Somrat Lertmaharit, MSc M Med Stat Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
Abstract
Delivering diabetes self-management support is an enormous challenge for healthcare providers with limited human resources. We conducted a cluster randomized controlled trial to assess the effectiveness of a DSMS program incorporating the computer-assisted instruction. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework was applied to evaluate the DSMS program. Ten Public Health Centers in Bangkok, Thailand were randomized into either DSMS program or usual care. Forty eligible patients with type 2 diabetes in each Public Health Center were randomly selected. Totally, 403 patients (200 controls and 203 interventions) participated. About 93.8% participants completed the six-month follow-up. Over six months, adjusted mean changes of hemoglobin A1c (−0.14%, 95% confidence interval = −0.02 to −0.26, fasting plasma glucose (−6.37 mg/dL, −1.95 to −10.78), health behaviors (3.31 score, 2.27 to 4.34), and quality of life (1.41 score, 0.69 to 2.12) were significantly improved in intervention compared to control group. In conclusion, the DSMS program facilitates Public Health Centers to accomplish their support for people with diabetes.
Key words
cluster randomized trial, diabetes self-management support, RE-AIM framework, type 2 diabetes, Thailand.
INTRODUCTION Diabetes is a major public health problem and produces healthcare burdens worldwide according to its morbidity, mortality, complications, and health expenditure (van Susan et al., 2010; WHO, 2011). In Thailand, the fourth nationwide survey reported that prevalence of impaired fasting glucose (IFG), diagnosed diabetes, and uncontrolled diabetes was 10.6%, 7.5%, and 37%, respectively (Aekplakorn et al., 2011). Around 31% of people with diabetes had complications while 65% had comorbidity (Chatterjee et al., 2011). In Thailand, health expenditure on diabetes per person was about US$144, accounting for 11% of the total healthcare cost (Zhang et al., 2010). People with diabetes require continuing medical care and ongoing self-management support (SMS) in order to improve disease outcomes, prevent acute complications, and reduce long-term complications (American Diabetes Association, 2013). Diabetes education is considered an important part in supporting the patients for day-to-day control of their diabetes (Funnell et al., 2007). Some Correspondence address: Wiroj Jiamjarasrangsi, Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Rama 4 Road, Bangkok 10330, Thailand. Email: [email protected] Received 30 January 2014; revision received 11 November 2014; accepted 15 November 2014
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meta-analyses indicated that diabetes self-management support (DSMS) had a significant effect on hemoglobin A1c (HbA1c), lifestyle, and psychosocial outcomes, diabetes knowledge,self-management skills,self-efficacy,and quality of life (QOL), as well as reduced healthcare cost (Chodosh et al., 2005; Boren et al., 2009; Pimouguet et al., 2011; Steinsbekk et al., 2012). Since diabetes management is complex for health systems at every level, embedding the DSMS program into routine practice is quite difficult.This is especially the case for health facilities with a large patient volume and limited personnel resources. In Thailand, barriers to continuing a DSMS program service include low public awareness,high healthcare worker turnover and workloads in rural areas, insufficient resource, and staff shortages (Deerochanawong & Ferrario, 2013). Therefore, the evaluation of DSMS programs is important in order to assess their overall impact as well as potential application into “real world” practice. However, most randomized controlled trials (RCTs) of DSMS intervention have emphasized only on efficacy of outcomes rather than overall impacts (Eldridge et al., 2008). In this study, we evaluate the potential utility of a DSMS program equipped with a computer-assisted instruction (CAI) in a Thailand healthcare context. The program approaches were based on five major steps (the 5C intervention) of behavior change support (Peyrot & Rubin, 2007). The CAI was newly created by the research team in order to doi: 10.1111/nhs.12198
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facilitate diabetes education. We conducted a cluster randomized controlled trial (CRT) incorporating the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework in order to determine the effectiveness of the DSMS program. This framework provided guidance for evaluating the potential public health impact, decision making, and translation of research into practice in “real-world” settings. In addition, it has also been used to understand the strengths and weaknesses of interventions (Belza et al., 2007; Bakken & Ruland, 2009; Glasgow et al., 2011). The results of this study may provide evidencebased for implementation and maintenance of a DSMS program into routine care for people with diabetes in Thailand, as well as in other developing countries.
Purpose of the study This study aimed to compare the HbA1c level, health behavior, depression, and QOL between people with diabetes receiving a DSMS program with CAI and those receiving the routine care.
MATERIALS AND METHODS Participant recruitment We carried out this CRT in 2012. We invited Public Health Centers (PHCs) of Bangkok Metropolitan Administration (BMA) to cooperate in the study. Ten PHCs were randomly assigned into five intervention or five control groups. Participants who met the following inclusion criteria were recruited: (i) aged ≥ 20 years; (ii) diagnosed with type 2 diabetes and treated in a PHC; (iii) fasting plasma glucose (FPG) level ≥ 130 mg/dL or HbA1c ≥ 7% (53 mmol/mol) within two months before launching the program; (iv) were willing to participate; and (v) were able to speak, read, and write in Thai. Patients with severe complications, which made them unable to participate in the study, were excluded. In each PHC, 40 eligible patients were randomly selected by using their patient identification numbers. A total of 403 people met criteria and agreed to participate in this study after giving written informed consent.
Ethical considerations The study was approved by the Ethics Committee of the Faculty of Medicine, Chulalongkorn University and Bangkok Metropolitan Administration Ethics Committee for Human Research (BMAEC).
Sample size We used HbA1c for primary outcome calculation to detect mean difference of 0.4%. An Intra-class correlation coefficient (ICC) of 0.05 has been assumed (Littenberg & MacLean, 2006). Forty people were recruited from each PHC. Consequently, the design effect was calculated as 2.95. For an α of 5% and a power of 80%, allowing a loss to
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follow-up of 10%, the number of participants and PHCs per arm required 200 and five respectively.
Intervention The DSMS program provided by trained nurses and healthcare staff was adapted from three strategies, including diabetes education, behavior change support, and emotional support (Funnell et al., 2007; Peyrot & Rubin, 2007). This program comprised of two components: (I) diabetic educational section to help patients understand the disease process; and (II) proper skill learning to manage their condition and change their lifestyle. In Component I, we developed a CAI for using in the educational sessions. The CAI facilitates the DSMS delivery lesson by lesson according to the pre-set steps, while lesson repetition is also possible. Its video component included lessons on diabetes and pre- and post-tests (10-questions with three choice answers of each lesson). The lessons consisted of: (i) knowledge of diabetes; (ii) foods for diabetes; (iii) physical activity; (iv) foot care; (v) medication used to control diabetes; (vi) reducing complications and stress management; (vii) self-monitoring of clinical indicators and goals of diabetes control. The lessons were designed in various forms such as stories, graphics, animated images, interviews, and demonstrations to stimulate learners’ interest and enjoyment. In Component II, the step-by-step approach for behavior change and psychological support was in accordance with the 5C intervention and consisted of: (i) constructing a problem definition; (ii) collaborative goal setting; (iii) collaborative problem solving; (iv) contracting for change; (v) continuing support (Peyrot & Rubin, 2007). The nurse supporters performed as facilitators to help participants define their problem in a potentially useful way, set goals, identify barriers, and solve problems in achieving those goals. Participants were followed-up for their behavior changes that were engaged in the previous session, such as dietary habits, exercise, foot care, medication, and blood glucose monitoring. Three nurses in each PHC of the intervention group were trained as the supporters in a two-day intensive course. The training course included explanations, demonstrations, and practices. The supporters delivered the six-monthly sessions of 3 h each for the intervention group. The supporters were observed and advised by the research team. The developed DSMS program and its accompanying guidebook for supporters were both approved by physicians, academic nurses, pharmacologists, and nutritionists. Forms used in baseline assessment, goal setting, progress monitoring, and psychological evaluation of the participants were also included in the guidebook.The tools were tried out in a PHC that was not participating in the study. At the end of the study, the CAI and guidebook were also provided for the PHCs in the control group.
Usual care group Patients in the control group received usual health care, including physical examination, monitoring of blood sugar © 2015 Wiley Publishing Asia Pty Ltd.
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levels, individual health education and consultation from a registered nurse and/or other healthcare provider.
Data collection
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mean difference of each outcome at three and six months follow-up. Generalized estimating equation (GEE) to account for the correlated data over six months was used to estimate the average treatment effects. These analyses also adjusted for age, duration of diabetes, complication, marital status, and baseline values of each outcome. A P < 0.05 was considered statistically significant.All data analyses were performed with STATA software, Version 11.0 (Stata, College Station, TX). Data from the focus group were analyzed by: (i) the research team reviewing each transcript separately, searching for meaningful relations to research questions; (ii) discussing the findings with each other in order to establish coding categories through consensus; (iii) grouping data according to their meaning.
Data were gathered through standard questionnaire, anthropometry measurements (weight, height, blood pressure, and waist circumference), and blood test (HbA1c, FPG). Patients provided information about their demographics, personal and family medical histories, health behavior, and physical activity. The internal consistency (Cronbach’s α coefficient) of health behavior was 0.78 and QOL was 0.67. Depression was assessed by the Thai version of the Patient Health Questionnaire (PHQ-9), Cronbach’s α was 0.79, sensitivity was 0.53, and specificity was 0.98 (Lotrakul et al., 2008). Health behavior included 13 items concerning diet, eight items concerning foot care, and 10 items concerning general self-care. Diabetes QOL was assessed by the 15 items of brief clinical inventory category (Burroughs et al., 2004). Each item of the PHQ-9 ranges from 0 (not at all) to 3 (nearly every day). The QOL ranges from 1 (very dissatisfied/all the time) to 5 (very satisfied/ never). Patients’ satisfaction ranges from 1 (very dissatisfied) to 5 (very satisfied). Health behavior score ranges from 1 (never) to 5 (everyday). Five milliliters (mL) venous blood sample was collected from each patient after 8-h overnight fast. All blood tests were performed in the same laboratory at the BMA Health Department. The laboratory quality control was assessed by the Thai Medical Science Center. All data were collected at baseline, three- and sixmonth follow-up in both groups. After finishing the program session, we invited 10 nurse supporters to participate in the focus group on April 2012. The aim was to assess the benefits, flexibility, barriers, satisfaction, and impact of the DSMS program. The moderator facilitated the discussion while the assistant moderator kept detailed notes and audio recorders.
A total of 403 participants (203 interventions and 200 controls) were recruited from 10 PHCs. Most of them were women (76.7%), and the mean age was 61.3 ± 9.6 years. Duration of diabetes was on average 9.2 ± 7.1 years. Twenty patients were current smokers (4.9%) and alcohol drinkers (6.9%). Complications were common, as 247 (61.2%) had hypertension and 146 (36.2%) had dyslipidemia. Characteristics of both groups were similar, except marital status and systolic blood pressure (SBP). The mean SBP was slightly higher in the intervention group (P = 0.012) (Table 1). Overall, 378 participants (194 interventions vs. 184 controls) completed the 6-month follow-up. Of 25 participants lost to follow-up, 12 moved to another province, six transferred to the other hospitals, five could not be contacted, and two had died (not related to the research) (Fig. 1). Characteristics of 25 patients lost to follow up did not differ from the rest of the sample.
The RE-AIM framework
Reach
The RE-AIM framework includes:(i) Reach, defined as the percentage of eligible patients’ willingness to participate; (ii) Effectiveness, assessed as the potential impact of intervention on HbA1c, FPG, body weight, health behavior, depression, and QOL; (iii) Adoption, a number of PHCs agree to deliver the DSMS program into the healthcare service and participants’ satisfaction with the program; (iv) Implementation, concerned with the extent to which intervention is delivered as intended, barriers to implementation, and attendance rate; (v) Maintenance, the extent to which individual participants maintain health behavior and HbA1c (individual level), and the PHCs intend to sustain the program (setting level) in the long term.
Of 68 PHCs, we gained the cooperation of 30 PHCs. Fifteen out of the contacted PHCs were willing to participate. Among the five PHCs in the intervention group, we invited 300 eligible patients to attend the program. About 235 (78.3%) of those were willing to participate (Fig. 1).The main reasons for refusal were not having enough time (n = 36), tied up with work (n = 11), inconvenient to travel (n = 10), and taking care of child/older person in their family (n = 8); however, these reasons are common in daily life. No significant differences in characteristics (gender, age, education, and marital status) were found between participants and people who refused to participate.
RESULTS Baseline characteristics of participants
Effectiveness Data analysis Group differences were assessed by independent t-test, Mann–Whitney U test, or χ2-test, whichever was appropriate. Analysis of covariance (ancova) was used to assess the © 2015 Wiley Publishing Asia Pty Ltd.
The adjusted mean differences at three- and six-month follow-up are shown in Table 2. Participants in both groups slightly improved outcomes. At six months, significant decreases in FPG (−13.08, 95% confidence interval
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Table 1. Baseline characteristics of participants
Characteristics Age group (years); range Mean ± SD 20–49 50–60 > 60 Gender Men Women Marital status Single Married Widowed/separated Household income (baht/month) (n = 371) Median (IQR) < 10 000 10 000–20 000 > 20 000 Regular physical activity No Yes Smoking status Never smoker Former smoker Current smoker Alcohol drinking Never drinker Former drinker Current drinker Complication No Yes Hypertension (HT) No Yes Dyslipidemia No Yes Duration of diabetes (years)† Body mass index (kg/m2)‡ Waist circumference (cm)‡ Systolic blood pressure (mmHg)‡ Diastolic blood pressure (mmHg)‡ Fasting plasma glucose (mg/dL)‡ Hemoglobin A1c (%)‡
DSMS program (n = 203) Number (%)
Usual care (n = 200) Number (%)
33–79 61.1 ± 9.6
26–80 61.5 ± 9.7
P-value
0.682§ 0.929
22 77 104
(10.9) (37.9) (51.2)
23 72 105
(11.5) (36.0) (52.5)
48 155
(23.6) (76.4)
46 154
(23.0) (77.0)
0.878
23 141 39
(11.3) (69.5) (19.2)
27 110 63
(13.5) (55.0) (31.5)
0.008
8 500 (5 000–15 500) 97 (52.7) 61 (33.2) 26 (14.1)
10 000 (6 000–20 000) 85 (45.5) 76 (40.6) 26 (13.9)
0.221¶ 0.300
74 129
(36.5) (63.5)
62 138
(31.0) (69.0)
0.260
181 13 9
(89.2) (6.4) (4.4)
172 17 11
(86.0) (8.5) (5.5)
0.458
176 16 11
(86.7) (7.9) (5.4)
160 23 17
(80.0) (11.5) (8.5)
0.119
44 159
(21.7) (78.3)
45 155
(22.5) (77.5)
0.883
79 124
(38.9) (61.1)
77 123
(38.5) (61.5)
0.950
132 71
(65.0) (35.0)
125 75
(62.5) (37.5)
0.651
7 (4–10) 27.4 ± 4.8 90.6 ± 10.9 140.1 ± 20.1 78.4 ± 12.8 165.7 ± 48.9 8.2 ± 1.5
8 (5–13) 26.7 ± 4.6 91.5 ± 12.3 135.3 ± 18.0 79.2 ± 13.5 163.1 ± 43.2 8.5 ± 1.6
0.258¶ 0.105§ 0.415§ 0.012§ 0.551§ 0.583§ 0.160§
†Median (IQR). ‡Mean ± SD; χ2 test for categorical variables. §Independent t-test. ¶Mann-Whitney U test.
(CI) = −21.07 to −5.1) and body weight (−2.28 kg, −3.61 to −0.94) and increases in health behavior score (5.49, 3.54 to 7.44) and QOL (2.54, 1.37 to 3.71) were observed. The findings of the GEE model showed that all outcome estimates were small. Adjusted mean differences of HbA1c, FPG, body weight, QOL, and health behavior were slightly, but significantly, improved in the intervention group (Table 3).
Adoption Four out of five PHCs in the intervention group adopted the DSMS program into their routine healthcare services. One site did not adopt because of staff shortage, however, it does plan to apply the program when it has a sufficient healthcare team. Overall, 90.3%of the participants who had received the DSMS program were satisfied or very satisfied with it. About
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Figure 1. Flow chart illustrating recruitment and follow-up of the PHCs and participants.
Table 2.
Comparison of adjusted mean differences between DSMS program and usual care participants
Outcomes Hemoglobin A1c (%) Baseline 3 months 6 months FPG, mg/dl Baseline 3 months 6 months Health behavior score† Baseline 3 months 6 months Body weight (kg) Baseline 3 months 6 months Depression score Baseline 3 months 6 months Quality of life score Baseline 3 months 6 months
Mean (SD) DSMS program Usual care
Unadjusted Mean difference‡ (95% CI)
Adjusted mean Difference§ (95% CI)
P-value
8.2 7.9 7.8
(1.5) (1.5) (1.4)
8.5 8.4 8.2
(1.6) (1.7) (1.8)
−0.21 (−0.51, 0.08) −0.48 (−0.80, −0.16) −0.33 (−0.65, −0.01)
– −0.29 (−0.48, −0.09) −0.12 (−0.37, 0.13)
– 0.004 0.334
165.7 157.5 149.5
(48.9) (48.7) (32.4)
163.1 162.7 162.4
(43.2) (49.9) (50.9)
2.53 (−6.52, 11.58) −5.15 (−14.95, 4.65) −12.91 (−21.59, −4.23)
– −6.16 (−14.31, 1.99) −13.08 (−21.07, −5.10)
– 0.138 0.001
124.4 130.7 135.9
(12.5) (9.8) (10.5)
125.1 126.9 130.5
(10.9) (10.9) (10.2)
−0.54 (−1.11, 1.0) 1.88 (0.85, 2.92) 1.78 (0.86, 2.69)
66.6 64.5 64.7
(12.2) (12.6) (12.7)
64.7 64.8 65.2
(12.3) (12.6) (12.9)
1.87 (−0.54, 4.28) −0.15 (−2.67, 2.36) −0.53 (−3.11, 2.05)
4.3 4.4 3.5
(4.1) (3.3) (3.2)
5.1 3.8 3.4
(4.2) (3.5) (3.9)
58.4 62.1 64.1
(6.2) (6.1) (5.8)
60.1 61.8 62.4
(6.2) (14.2) (6.8)
– 4.09 (2.24, 5.93) 5.49 (3.54, 7.44)
– < 0.001 < 0.001
– −1.79 (−3.02, −0.56) −2.28 (−3.61, −0.94)
– 0.005 0.001
−0.83 (−1.64, −0.001) 0.57 (−0.11, 1.25) 0.04 (−0.69, 0.77)
– 0.88 (0.33, 1.43) 0.24 (−0.45, 0.92)
– 0.002 0.495
−1.73 (−2.95, −0.50) 0.29 (−1.88, 2.45) 1.70 (0.41, 2.99)
– 1.25 (−0.87, 3.38) 2.54 (1.37, 3.71)
– 0.247 < 0.001
†Health behavior consists of dietary, foot care, medication, and general self-care. ‡Independent t tests. §ancova was used to analyze mean difference adjusted for age, duration of diabetes, complication, marital status, and baseline values of each outcome.
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Table 3. Adjusted mean differences over six months by GEE analysis
Outcomes HbA1c (%) FPG (mg/dL) Health behavior score Body weight (kg) Depression score Quality of life score
Unadjusted Mean difference (95% CI) −0.34 −5.08 2.77 0.52 −0.10 0.10
(−0.62, −0.06) (−12.42, 2.26) (1.09, 4.45) (−1.87, 2.90) (− 0.69, 0.49) (−1.05, 1.24)
Adjusted† Mean difference (95% CI) −0.14 −6.37 3.31 −1.35 0.32 1.41
(− 0.26, −0.02) (−10.78, −1.95) (2.27, 4.34) (−2.20, −0.51) (0.00, 0.64) (0.69, 2.12)
P-value 0.025 0.005 < 0.001 0.002 0.050 < 0.001
†Adjusted for age, duration of diabetes, marital status, complication, and baseline values of each outcome.
59.0% were willing to retain the program, and 64.1% intended to persuade other patients to attend the program.
Implementation of the DSMS program Of four adopting PHCs, two applied the DSMS program on-site and launched the program at community settings by integration with community care visits. Around 190 (93.6%) patients completed the program sessions. Findings from the focus group revealed that the DSMS program helped the PHCs to achieve their primary mission and all supporters appreciated the program process. The video and guidebook greatly facilitated the supporter team’s DSMS program delivery. Moreover, group participation and lunch meetings enhanced patients’ acquaintance. The main enabling factors contributing to the program’s success were the PHC administrator’s policy and encouragement, sufficient budget and equipment support, consideration for patients’ well-being, good relationship between healthcare provider and patients, and teamwork. The main barriers to implementation were inadequate staff and information technology, and lack of tools and budgets.
Maintenance At each follow-up, the means of health behavior score were significantly improved in both groups (P < 0.05), especially in the intervention group. Improvements in HbA1c, FPG, health behavior, and QOL were significantly sustained in the long term (Table 3). The proportion of patients who achieved optimal HbA1c < 7% (53 mmol/mol) was higher in the intervention group, although not statistically significant (18.5% vs. 15.5%, P = 0.435). Having finished the study, four out of five PHCs continued providing the program within their healthcare services. However, some adaptations were also made to suit their contexts, including shorter duration and fewer numbers of participants in each session, and higher frequency of foot examination practice.
DISCUSSION Based on the RE-AIM framework, this study showed that the DSMS program with CAI was effective and applicable within routine care in PHCs in Bangkok, Thailand. Significant
improvements in glycemic control, health behavior, body weight and QOL were observed in intervention group. Four PHCs adopted the program within their diabetes care services after the research period. The supporters and participants very satisfied with the program. About 78.3% of invited patients were willing to participate in the program. Although the period of recruitment was limited (one month before launching the study), it was long enough to reach sufficient eligible patients. Most patients were willing to participate in a program that was integrated with their primary care visit (Glasgow et al., 2006). The most reported barriers to participation were similar to those in some recent studies. Those barriers more likely depend on patient-level factors, such as patients not concerned about the DSMS (Schäfer et al., 2013), distance to work (Dubuy et al., 2013), literacy, transportation, and reimbursement (Balamurugan et al., 2006). At six months, 194 (95.6%) in intervention group and 184 (92.0%) in the control group completed the follow-up, with no significant difference in participation rate (P = 0.134). Sustained improvements in the FPG, health behavior, and QOL were also demonstrated. The findings seem to comply with the theory that behavior change and improved psychosocial outcomes result in increased glycemic control. Ongoing reinforcement of health behavior, diabetes education, and patient self-efficacy are also needed for successful long-term interventions (Sperl-Hillen et al., 2013). The optimal strategies are that the supporters deliver the DSMS to improve selfefficacy, provide peer and cognitive behavioral support, and launch automatic telephone reminders, while patients are mostly responsible for their own diabetes management (Jones et al., 2013). Some recent studies revealed beneficial effects of DSMS programs on HbA1c, FPG, psychosocial outcomes, knowledge, QOL, and self-management behavior (Schillinger et al., 2009; Pimouguet et al., 2011; Steinsbekk et al., 2012). Diabetes education class and individual home visit methods were able to encourage and empower participants to be more confident in their abilities to deal with their diabetes (Wattana et al., 2007). Our findings also confirm the effects of the DSMS program on the positive change in all outcomes, but only in the short term. Although the adjusted mean change of the HbA1c was not significantly different at six months, the percentage of patients with the optimal HbA1c < 7% © 2015 Wiley Publishing Asia Pty Ltd.
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(53 mmol/mol), as recommended by the American Diabetes Association (2013) was, however, higher in the intervention than in the control groups. A study revealed that 1% reduction of mean HbA1c was associated with a 21% reduction in deaths related to diabetes, and 14% for myocardial infarction (Stratton et al., 2000). Accordingly, 0.3–0.4% reduction of mean HbA1c in this study may be clinically important. Some studies also found no significant effects on patient outcomes for long-term conditions (Kennedy et al., 2013; Sperl-Hillen et al., 2013). This might be due to short training interventions, which are ineffective for enhancing the SMS in routine primary care (Kennedy et al., 2013). In this study, most healthcare staff indicated that the main barriers to implementation and adoption of the DSMS program were the lack of staff, tools, and budgets, and inadequate information technology. These reasons are consistent with previous reports (Bodenheimer & Laing, 2007; Battersby et al., 2010). The challenge for busy healthcare settings is how to conduct and apply a DSMS program in an efficient way that does not require additional staff within the team (Glasgow, 2010). The flexibility and acceptance of the program contributes to its adoption and continuous delivery in usual care practice. Adoption of the DSMS into routine care or maintenance in the long term may require considerable additional incentives to encourage supporters to engage with a self-management agenda (Kennedy et al., 2013). Consequently, we developed the CAI to facilitate the supporters in part of the education sessions and to resolve work-load issues. We hope that the CAI can be routinely used in the PHCs’ services since all diabetes patients should have continuing diabetes knowledge and self-management support. However, long-term impact and cost effectiveness of the DSMS program need further evaluation. Strengths of the study include the large number of participants with a relatively high participation rate (93.8%). Participants’ characteristics at the baseline in both groups were similar. The CRT design of the study also simplified data collection and reduced contamination. However, the results should be interpreted cautiously because of some limitations. First, CRTs are more complex because recruitment and follow-up processes differ from those in individually randomized trials. Such process is susceptible to recruitment bias. Participants were differentially recruited among clusters. (Giraudeau & Ravaud, 2009). Second, we were not able to separate the effect of CAI from that of nurse support. Third, health behavior scores and QOL were based on selfreport and such measures may differ from other studies. Finally, the data concerning patients’ hypoglycemic drug and other medications used, and their knowledge and skills for diabetic management, were incomplete. Some factors could thereby confound the effects of the intervention.
CONCLUSION Our findings indicated that the DSMS program with CAI was clearly effective in terms of RE-AIM evaluation. Participant rate (78.3%) for reaching the program and attendance rate (93.8%) for the training sessions were generally high. Over six months, the program improved HbA1c, FPG, health © 2015 Wiley Publishing Asia Pty Ltd.
J. Jaipakdee et al.
behavior, and QOL, and had higher achievement in optimal HbA1c level in the intervention group. Although the estimates of the effects were quite small, there was trend towards improvement compared with the baseline. Four out of five PHCs adopted the program into their routine healthcare services.The program facilitates nurse supporters in accomplishing their practices, and can be applied to other PHCs.
ACKNOWLEDGMENTS This study was supported by the National Health Security Office (NHSO) of Thailand, Region 13 Bangkok, via grant no. GUC7/2551/23-04-2551; and the 90th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund). We would like to thank the executives and healthcare staff in the PHCs of the Bangkok Metropolitan Administration for their cordial cooperation with the provision of the program performance and data collection.
CONTRIBUTIONS Study Design: WJ, JJ. Data Collection and Analysis: JJ, VL, SL. Manuscript Writing: JJ, WJ, VL, SL.
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