Telehealth has been used for more than 2 decades and during the Covid-19 pandemic, its use increased. The use of technology and telecommunications has a positive impact on the provision of remote health services. Pharmacists have used telehealth strategies to facilitate easier and more frequent follow-up or to provide remote patient monitoring (RPM) services¹². This pharmaceutical service is commonly known as telepharmacy¹³^,¹⁴. A meta-analysis by Cao et.al (2022) revealed that telepharmacy in patient with diabetes mellitus showed a positive association in reducing HbA1c and risk of hypoglycemia¹⁵. The use of the telephone in telepharmacy has been shown to improve patients awareness of diabetes, medication adherence, self-care routine and glycemic control¹⁶^,¹⁷.

Telepharmacy in hospitals have been provided in some countries but they are still limited in Indonesia. Most research related to pharmacist intervention through telepharmacy services in DM patients utilized telephone and showed an increase in better diabetes control and quality of life. This present study aims to identify the effectiveness of pharmacist intervention through telepharmacy to improve clinical outcome and quality of life of patients with type 2 diabetes mellitus at Universitas Indonesia Hospital

METHODS:

Study Design and Population:

A quasi-experimental design (non-randomized controlled trial) study design was used. The determination of the sample used the consecutive sampling method in which all consecutive subjects who met the predetermined criteria were included in the study until the required research subjects were fulfilled ¹⁸. After receiving approval from the Universitas Indonesia Hospital Ethics Committee, the study was conducted under the ethical approval number S-032/KETLIT/RSUI/VIII/2022. The research was conducted in September 2022 - February 2023. Sampling and baseline data were obtained in September - October 2022 and the last data were collected after the intervention, which were 3 months from baseline data collection. The study was carried out at Universitas Indonesia Hospital in Depok City.

The size of study sample was determined based on the previous study to obtain a clinically significant difference (µ_1-µ₂) of 0.688% with a standard deviation (ό) of 0.84¹⁶. A 0.05 level of significance and a 90% study power were assumed. A determined sample size of 32 was used for each group. However, it was believed that a sample size of 35 patients for each group would be adequate to account for 10% of patient dropouts that might occur during the follow-up.

Eligibility Criteria:

Outpatients with a diagnosis of Type 2 Diabetes, who were at least 18 years old, and willing to participate in this study, met the inclusion criteria. Patients undergoing hemodialysis, unable to communicate well, and pregnant or lactating women were excluded from the study.

Intervention:

The intervention group received telepharmacy services for 3 months. Interventions were in the form of monitoring the patient's drug use, information related to the drug used by the patient, the patient's lifestyle (diet and exercises), and possible side effects of the drug. The intervention was carried out by the pharmacist using telephone every third week of each month, during 3 months.

Outcomes:

The outcome of this research was to assess patient clinical outcome and the quality of life patient. The HRQOL EQ-5D5L questionnaire was used to assess quality of life. The EQ-5D descriptive system covers five health domains, namely mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. A five-level scale was used to measure each dimension, with higher levels indicating more health problems. The dichotomization of domain scores (level 1 vs. level 2–5) allowed for the identification of both absent and present problem. The Indonesian Value Set was used to calculate the utility value^19,20

Statistical Analysis:

Utilizing IBM SPSS Statistics v22, data were examined. Univariate analysis was performed to describe the distribution of patient characteristics. Chi-Square analysis and logistic regression were used to analyze the relationship between categorical variables. Statistical tests of paired sample t-test and Wilcoxon non-parametric test were used to analyzed a significant difference between the values pre and post the intervention. The t-independent statistical test and the Mann-Whitney non-parametric test were used to analyze the mean difference after intervention between the intervention and the control groups with a significance level of α < 0.05.

Table. 1 Sociodemographic Characteristics of Patients

Characteristics	Group n (%)		Total	p-value
Characteristics	Intervention (35)	Control (35)	Total	p-value
Age, years
Mean + SD	57.06 + 11.88	58.4 + 10.17		0.613^a
< 60	19 (54.3)	18 (51.4)	37 (52.9)	1^b
> 60	16 (45.7)	17 (48.6)	33 (47.1)
Sex, n(%)
Male	15 (42.9)	23 (65.7)	38 (54.3)	0.093^b
Female	20 (57.1)	12 (34.3)	32 (45.7)
Education, n(%)
ES– SHS	12 (34.3)	13 (37.1)	25 (35.7)	1^b
TU	23 (65.7)	22 (62.9)	45 (64.3)
Employment, n(%)
Employed	15 (42.9)	16 (45.7)	31 (44.3)
Unemployed	20 (57.1)	19 (54.3)	39 (55.7)	1^b
Insurance
BPJS	22 (62.9)	25 (71.4)	47 (67.1)	0.284^a
Non-BPJS	9 (25.7)	4 (11.4)	13 (18.6)
Private	4 (11.4)	6 (17.1)	10 (14.3)
BMI, kg/m²
Mean + SD	28.04 + 5.6	27.22 + 4.8		0.512^a

Notes : ^aT-test, ^bChi-square, significance p<0.05

BMI = Body mass index, ES = Elementary school, SHS = Senior high school, TU = Tertiary Education, BPJS = Health Care and Social Security Agency

Table. 2 Clinical Characteristics of Patients

Characteristics	Group		Total	p-value
Characteristics	Intervention (n=35)	Control (n=35)	Total	p-value
HbA1c
Mean + SD	7.65 + 1.94	7.43 + 1.84		0.615^a
DM duration, year
< 5	24 (68.6)	20 (57.1)	44 (62.9)	0.458^b
>5	11 (31.4)	15 (42.9)	26 (37.1)
Family history of DM
No	14 (40)	16 (45.7)	30 (42.9)	0.809^b
Yes	21 (60)	19 (54.3)	40 (57.1)
Clinical Conditions
< 3	9 (25.7)	12 (34.3)	21 (30)	0.602^b
> 3	26 (74.3)	23 (65.7)	49 (70)
Cardiovascular	27 (77.1)	25 (71.4)	52 (74.3)	0.784
Dyslipidemia	14 (40)	14 (40)	28 (40)	1
Kidney Disease	9 (25.7)	7 (20)	16 (22.9)	0.776
Fatty Liver	4 (11.4)	2 (5.7)	6 (8.6)	0.673
Gout	5 (14.3)	2 (5.7)	7 (10)	0.428
Anemia	0 (0)	2 (5.7)	2 (2.9)	0.493
Cholelithiasis	5 (14.3)	0 (0)	5 (7.1)	0.054
Neuropathy/Polyneuropathy	8 (25.7)	10 (28.6)	19 (27.1)	1
Number of Medication
< 3	7 (20)	6 (17.1)	13 (18.6)	0.218^b
4 – 6	12 (34.3)	19 (54.3)	31 (44.3)
> 7	16 (45.7)	10 (28.6)	26 (37.1)
Number of DM drugs
1	9 (25.7)	14 (40)	23 (32.9)	0.093^b
2	11 (31.4)	14 (40)	27 (35.7)
3	13 (37.1)	4 (11.4)	17 (24.3)
4	2 (5.7)	3 (8.6)	5 (7.1)
Types of DM drugs
Oral	28 (80)	27 (77.1)	55 (78.6)	0.601^b
Insulin	0 (0)	1 (2.9)	1 (1.4)
Oral and Insulin	7 (20)	7 (20)	14 (20)
Therapeutic Changes
Yes	11 (31.4)	4 (11.4)	15 (21.4)	0.081^b
No	24 (68.6)	31 (88.6)	55 (78.6)
Drug allergy
Yes	10 (28.6)	4 (11.4)	14 (20)	0.135^b
No	25 (71.4)	31 (88.6)	56 (80)
Use of herbal medicine
Yes	7 (20)	7 (20)	14 (20)	1^b
No	28 (80)	28 (80)	56 (80)
Smoking status
Smoker	3 (8.6)	4 (11.4)	7 (10)	0.248^a
Ex-Smoker	4 (11.4)	9 (25.7)	13 (18.6)
Non-smoker	28 (80)	22 (62.9)	50 (71.4)

Notes : Data in n (%); Statistical test: ^aT-test, ^bChi-square, significance p<0.05

Clinical Characteristics of the Patient:

Based on Table 2, there was no statistically significant difference in clinical characteristics between the intervention and control groups (p-value > 0.05). The mean of HbA1c in the intervention and control group was 7.65 ± 1.94 vs 7.43 ± 1.84 with a prevalence of DM duration <5 years (62.9%), comorbidities >3 (70%), had families with a history of Type 2 DM (57.1%), used OHA drugs (78.6%), and having therapeutically changed during the study (21.4%)

Profile of Quality of Life Patients:

Patients reported having problems with greater problems in the pain dimension than in other dimensions for quality of life, followed by anxiety in both the intervention and control groups (Figure 2). Patient Quality of Life showed a significant improvement with p-value <0.05 in index QoL (Table 3).

Figure 2. Percentage of Patients Reporting Problems in Each QoL Domain

Table. 3 Mean Differences of Clinical Outcomes and Quality of Life of Patients

Variable	Intervention group			p^a- value	Control group			p^b-value	p^c-value (Intervention vs control)
Variable	Pre-test	Post-test	mean difference	p^a- value	Pre-test	Post-test	mean difference	p^b-value	p^c-value (Intervention vs control)
HbA1c	7.65 ± 1.94	7.88 ± 1.36	0.23 (1.52)	0.379	7.43 ±1.84	7.46 ± 1.29	0.04 (1.55)	0.88	0.604
Systolic	134.89 ± 13.28	128 ± 13.28	-6.89 (17.24)	0.024*	133.82 ± 16.74	135.63 ±15.52	1.8 (21.91)	0.63	0.07*
Diastolic	74.69 ± 11.29	69.54 ± 10.12	-5.14 (13.51)	0.031*	75.14 ± 10.79	75.66 ±9.28	0.51 (9.46)	0.75	0.047*
BMI	28.04 ± 5.59	28.24 ± 5.79	0.2 (0.96)	0.217	27.23 ± 4.81	27.20 ±4.63	-0.02 (0.99)	0.925	0.348
Index Quality of life²	0.84 (0.71 – 0.91)	0.91 (0.80 – 1)	0.08 (0 – 0.13)	0.009^e*	0.91 (0.72 – 1)	0.91 (0.77 – 1)	0 (-0.09 – 0.09)	0.702^e	0.026^g*
VAS²	80 (70 – 90)	80 (80 - 90)	5 (-5 – 10)	0.022^e*	77.0 (75 – 80)	80 (75 – 85)	0 (-5 – 10)	0.262^e	0.511^g

Notes: Data in ¹Mean + SD, ²Median (IQT, 25-75 percentiles) *Significant -p value <0.05. a. The p-value between the mean of the Intervention group's pretest and posttest scores. b The p-value between the mean pretest and posttest scores of the control group. c P-values of mean differences between the Intervention and Control groups. c. Dependent T-test, e. Wilcoxon Non-Parametric Analysis, f. Independent T-test, g. Mann-Whitney Non-Parametric Analysis.

Figure 3. The proportion of HbA1c improvement between the intervention and control groups with OR (95%CI) 1.28 (0.48 - 3.37).

Table 4. Factors Affecting HbA1c Improvement

Variable	HbA1c Improvement		Total	p-value	Crude OR (95% CI)
Variable	Yes	No		p-value	Crude OR (95% CI)
Group, n(%)
Telepharmacy	14 (53.8)	21 (47.7)	35 (50)	0.805^a	1.28 (0.48 - 3.37)
Control	12 (46.2)	23 (52.3)	35 (50)
Age, years, n(%)
< 60	15 (57.7)	22 (50)	37 (52.9)	0.708^a	1.36 (0.51 3.62)
> 60	11 (42.3)	22 (50)	33 (47.1)
Sex, n(%)
Male	12 (46.2)	26 (59.1)	38 (54.3)	0.423^a	0.59 (0.22-1.58)
Female	14 (53.8)	18 (40.9)	32 (45.7)
Physical exercise, n(%)
Yes	19 (73.3)	18 (40.9)	37 (52.9)	0.018^a	3.92 (1.37-11.26)
No	7 (26.9)	26 (59.1)	33 (47.1)
Regular diet, n(%)
Ye	25 (96.1)	26 (59.1)	51 (72.9)	0.002^a	17.31 (2.15-139.52)
No	1 (3.8)	18 (40.9)	19 (27.1)
Decreased BMI, kg/m2, n(%)
Yes	8 (30.8)	10 (22.7)	18 (25.7)	0.645^a	1.51 (0.51 - 4.5)
No	18 (69.2)	34 (77.3)	52 (74.3)
Comorbidities
< 3	10 (38.5)	11 (25)	21 (30)	0.359^a	1.88 (0.66 - 5.33)
> 3	16 (61.5)	33 (75)	49 (70)
Number of DM Medication
1	6 (23.1)	17 (38.6)	23 (32.9)	0.219^b	Reff
2	8 (30.8)	17 (38.6)	25 (35.7)		1.33 (0.38-4.67)
3	9 (34.6)	8 (18.2)	17 (24.3)		3.19 (0.84-12.07)
4	3 (11.5)	2 (4.5)	5 (7.1)		4.25 (0.57-31.94)
Type of DM drugs
Oral	21 (80.8)	34 (77.3)	55 (78.6)	0.73^b	NA
Insulin	0 (0)	1 (2.3)	1 (1.4)
Oral and Insulin	5 (19.2)	9 (20.5)	14 (20)

Notes: Statistic analysis chi square, p-value a. Continuity Correction, b. Pearson Chi-square, *significance p<0.05

Table 5. Multivariate Analysis of Factors Affecting HbA1c Improvement

Model	Variable	OR	95% CI		Nilai – P
Model	Variable	OR	Min	Max	Nilai – P
Crude	Telepharmacy
	Intervention	1.28	0.48	3.37	0.805
	Non-Intervention	Reff
Adjusted	Telepharmacy
	Intervensi	1.49	0.4	5.59	0.553
	Non-Intervention	Reff
	Sex, n(%)
	Male	0.87	0.26	2.99	0.830
	Female	Reff
	Regular diet, n(%)
	Yes	30.43	2.88	320.82	0.004*
	No	Reff
	Clinical Condition
	< 3	1.96	0.56	6.91	0.295
	> 3	Reff
	Number of DM Medication
	1	Reff
	2	2.05	0.53	7.96	0.3
	3	4.50	1.03	19.72	0.046
	4	14.95	0.88	255.01	0.062

Notes : Statistic Binary Logistic, *Significant p<0.05

DISCUSSION:

This study aimed to assess the effectiveness of pharmacist interventions through telepharmacy on clinical outcomes and quality of life of the patient. The finding of the follow-up after 3 months showed an increase in HbA1c both in the intervention and control groups but this increase was not statistically significant (p-value >0.05). However, the proportion of patients with an improvement in HbA1c was greater in the intervention group than in the control group with an OR 1.28 (0.48 - 3.37) times but not statistically significant (p-value >0.05). The results of the analysis in this pragmatic study showed no decrease in the patient's HbA1c. Pragmatic trials were analyzed using the intention-to-treat, which required that patients assigned to the intervention group be in that group during the analysis even if the patient does not follow the established protocol. Intention-to-treat analysis was influenced by the patient compliance to the patterns of the protocol in the current study and therefore might not be directly relevant for making decisions in clinical settings with various compliant patterns²¹.

This does not align with the previous studies concerning telepharmacy studies which showed a decrease in HbA1c with a follow-up after 6 months from baseline¹⁷. This difference could be due to the duration of the intervention with only 3 months and the frequency given was once a month. A meta-analysis by Groot et.al (2021) showed that interventions with a duration of around 6 months had a greater decrease in HbA1c -0.626% compared to that less than 6 months. More than 6 months with the frequency of weekly intervention and less than weekly were reported to be more effective at lowering HbA1c (P<0.001) than patients receiving more than weekly interventions²².

No decrease in HbA1c can be caused by the patient's baseline HbA1c average of <8%, which in this study the average of HbA1c was 7.65+1.94% in the intervention group and 7.43+1.84% in the control group (Table 3). Meta-analysis related to telehealth showed that the average HbA1c changed by 1.22% in the telehealth group when the HbA1c baseline was 9.0% or more. Then, the average HbA1c change was only 0.35% when the HbA1c baseline was lower than 9.0%²³. Another Meta-Analysis data by Cao et.al (2022) showed that an HbA1c baseline >10% was associated with the largest average HbA1c reduction (2.37%), followed by an HbA1c mean baseline 8% to 10% with a decrease of 1.13%. And it does not show a significant decrease in HbA1c in subjects with an HbA1c baseline of <8%.¹⁵. In this study, patients had access to the HbA1c results at the baseline of the study. This can lead to the perception that they had achieved the therapeutic goal which causes the patient to no longer maintain their lifestyle and medication adherence, where this study found that the patient’s BMI was increased but statistically not significant. But a previous study by Geetha et.al (2017) showed that BMI and waist circumference had a significant association with the risk of uncontrolled glycemic²⁴. Furthermore studies by Lauffenbuerger et.al (2019) stated that the most common reason for poor glycemic control was due to poor diet or exercise²⁵. In the present study, the analysis of factors affecting the improvement in HbA1c showed a statistically significant between regular diet and HbA1c improvement with OR (95CI) 17.31 (2.15-139.52) (Table 4). This finding is consistent with the study by Leni Nopitasari et.al (2023) that one of the self-care in the management of DM is diet restriction²⁶. The Standard American Diet states that the high proportion of refined starch, high glycemic intake, and added sugar can exacerbate the body's physiological processes that regulate sugar metabolism ²⁷.

A systematic review by Gummeson et.al (2017) revealed that weight loss in obese and overweight patients with type 2 DM was consistently associated by a decrease in HbA1c. The model developed in that study estimates that every 1kg of average weight loss, indicates an average decrease in HbA1c of 0.1%. Additionally, groups with poor glycemic control experience greater reduction in HbA1c than the control group does while losing weight at the same rate²⁸. Diabetes care should be person-centered and geared toward enabling patients to take control of their own diabetes problems. For better control of this chronic condition, diabetics must use both pharmacological and non-pharmacological treatments. When providing diabetes care, medical professionals should encourage patients to take control of their diabetes and lead healthy lifestyles. A care plan that has been discussed and agreed upon with each person and is being executed as part of the care planning process²⁹^,³⁰.

In terms of blood pressure, the intervention given could significantly reduce blood pressure in the intervention group (p-value <0.05). Similar findings had been reported by Sayin Kasar et.al (2022) that the group who received telephone counseling showed a significant reduction in blood pressure. Blood pressure monitoring in patients with type 2 DM is important because hypertension and coronary heart disease is a common complication with prevalence depending on the duration of diabetes, age, and gender³¹^,³².

Patients in the intervention group's quality of life significantly improved with telepharmacy when compared to the control group (p-value < 0.05). This is in line with Groot et.al (2021) that there is a significant relationship in increasing the value of quality of life, especially quality of life related to mental/psychological and physical²². Moreover, other studies concerning pharmacist interventions in improving quality of life showed that pharmacist intervention could improve the quality of life of patients³³^,³⁴. Diabetes mellitus is a chronic disease in which patients usually have comorbidities or complications that will cause a decrease in their quality of life. The presences of comorbidities and/or complications are associated with anxiety, depression and deterioration in numerous dimensions of the patient's quality of life³⁵^,³⁶. There is a need for ongoing pharmaceutical monitoring to improve the better quality of life. The right use of medications and patient education are essential for the management of chronic diseases to prevent the progression of the disease and ultimately minimize the hospitalization³⁷. Intervention from pharmacists will help patients understand and obtain information on how to prevent the risk of complications to minimize the patient's anxiety related to the disease and drug use.

This study does have some limitations. First, it had a small number of samples so it was not sufficiently representative of some categories of variables that may affect the dependent variable. Second, patients got access to information related to HbA1c levels which could have an impact on changing the patient's lifestyle so that it may also affect the patient's glycemic control. Third, the duration of follow-up was only 3 months and the frequency of intervention was once a month.

CONCLUSION:

Telepharmacy has wide coverage and good potential to provide access to patients to obtain health-related information. According to this study, telepharmacy improves patient quality of life. However, it does not show any improvement in HbA1c. Interventions may not adequately address major barriers to glycemic control. Besides the use of medication, poor glycemic control is commonly associated with poor diet or exercise. Future research is expected to be carried out in collaboration between pharmacists and other health workers.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

The author would like to express our sincere gratitude to pharmacists who provided Telepharmacy services, the Internal Medicine Polyclinic, Laboratory staff and Universitas Indonesia Hospital all helped make this research possible, as well as the patients who were willing to take part in this study. This research was funded by PUTI Pascsarjana Grant No.NKB-073/UN2.RST/HKP.05.00/2022

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Received on 08.09.2023 Modified on 02.03.2024

Research J. Pharm. and Tech 2024; 17(9):4282-4290.

DOI: 10.52711/0974-360X.2024.00662