Abstract
Objective
Type 2 diabetes is one of the most common chronic diseases worldwide. It also has a high risk of morbidity and mortality in the covid 19 pandemic. Due to pandemic measures, disruptions have emerged in the care treatments of patients with type 2 diabetes. The present study aimed to determine the effects of telehealth monitoring and patient training on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
Methodology
The current study is in the design of a single-blind randomized controlled trial. Patients were randomized into intervention group (n=41) and control group (n = 44). The patients in the intervention group received diabetes training once a week for the first 4 weeks and every other week for weeks 5–12. No training was given to the control group. The data was collected using the socio-demographic information form, the questionnaire of diabetes treatment, the form of metabolic control variables, and the Diabetes Symptoms Checklist. The data was analyzed with Chi-square, independent samples t-test, and paired sample t-test.
Results
The mean age of the patients in the control group was 56.86 ± 9.40, and the mean age of those in the intervention group was 54.12 ± 8.32. After the training, a statistically significant difference was found between the checklist averages of the groups in the subscale of hyperglycemia. However, a statistically significant difference was found between the subscales of neurology, cardiology, cognition, hyperglycemia, and the total checklist averages in the intervention group before and after the training (p < 0.05). In the control group, there was a statistically significant difference between the subscale of hyperglycemia and the total checklist averages at the beginning and 3 months later (p < 0.05).
Conclusion
It has been determined that the disease training given to the patients with diabetes via telehealth monitoring during the COVID-19 process has a positive effect on the diabetes control of the patients. Health education through telehealth methods can be an effective and cost-effective strategy to support patients with diabetes.
Keywords: Diabetes, Patient training, Telehealth, Symptom control, COVID-19
1. Introduction
Diabetes is a serious chronic condition recognized as a major cause of premature death and disability worldwide. There are approximately 422 million people with diabetes all over the world and 1.6 million people die from this disease every year [1], [2].
The most important step in diabetes treatment is patient training [3], [4]. The content of disease education consists of nutrition management, physical activity and exercise, insulin injection techniques, oral antidiabetics and administration forms, self-monitoring of glucose, foot care, prevention from acute and chronic complications, psychosocial adaptation and the rights of the diabetic, and social support resources [5]. Therefore, planning and maintaining patient training at regular intervals increases patients' compliance with the disease, controls symptoms, prevents complications, improves quality of life, and reduces morbidity and mortality [1], [6], [7]. It is known that the training and telehealth monitoring given to the patients about the disease have a positive effect on controlling their metabolic variables. In a study, one-year telehealth monitoring shows that there is a significant difference in the HbA1C level and blood sugar regulation of the patients [8]. Thanks to the developing and changing technological infrastructure, it is possible to follow up the patients before they come to the hospital. There is growing evidence to support the use of advanced and innovative technologies such as telehealth to monitor and manage people with diabetes remotely and as often as they need to. Telehealth is generally defined as the exchange of medical information from one location to another using electronic communication or digital technologies such as desktop, laptop computers, mobile phones, and other wireless devices [9], [10]. Telehealth application has benefits such as better diagnosis and treatment, healthy individuals who can maintain their own health, increased preventive health practices, more effective follow-up of chronic diseases, a sustainable health system, time savings for health workers, less hospitalization and cost reduction [5].
Strong evidence demonstrates the beneficial effects of patient monitoring and the training focused on the important role of individual self-care with the support of healthcare professionals. Telehealth may be a strategy for closer monitoring and intervention to not only achieve better metabolic control but also assist in the global care of individuals with multiple chronic diseases. In the last decade, several studies have addressed the feasibility and effectiveness of telehealth strategies for the management of diabetes patients [11]. The studies have shown that the continuity of monitoring diabetes patients by telephone increases the patient's ability to manage their own care and positive behavioral changes have been observed in patients to prevent complications of diabetes [8], [12].
Patients with Type 2 Diabetes are at high risk in the COVID-19 pandemic [13], [14], [15]. During the COVID-19 process, patients are exposed to physical, psychological, and social changes due to being at home for a long time. This situation negatively affects the symptom and disease management of patients [16]. It is known that both patients and healthcare professionals have difficulties during the pandemic period. It is thought that monitoring by telephone becomes more important in eliminating these problems. In the pandemic, there is no data on the level of patients' control of their disease and their self-management. Therefore, the current study was conducted as a randomized controlled study to determine the effects of telehealth monitoring and patient training on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
1.1. Hypotheses
H0: Telehealth monitoring and patient training have no effect on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
HA: Telehealth monitoring and patient training have an impact on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
2. Methods
2.1. Study design
The study was carried out as a single-blind randomized controlled study to examine the effect of training on the symptoms and metabolic outcomes of the patients who are at risk of COVID-19, and who received Type 2 DM treatment.
2.2. Participants
For the randomization of the study, the number of patients who applied to the XXX University Hospital Endocrinology Polyclinic between January-June 2020 was determined. The patients diagnosed with type 2 diabetes were randomized to intervention and control groups. The patients included in the intervention group were given training about the disease once a week for the first 4 weeks, and every other week for the next 8 weeks. The scales were re-administered to the control group at baseline and 12 weeks later, and no training was given to them. The sample group of the population consisted of a total of 90 patients, 45 for the control group and 45 for the intervention group. The study was planned in accordance with the Declaration of Helsinki and ethical approval for the study was obtained from XXX University Clinical Research Ethics Committee on 03/13/2020. (HRU/20.13.25). In addition, written consent was obtained from the patients.
2.3. Sample size
The population of the study consisted of patients who applied to XXX Hospital, Endocrinology Outpatient Clinic between the dates of January and June 2020. The sample was determined by utilizing the G-Power version 3.17 program and using the known universe sampling method. Odd numbers were randomly selected for the control group, and even numbers were randomly selected for the intervention group using the Microsoft Excel program in the sample distribution. In the power analysis, the effect size was 0.7, the bias level was 0.05, and the representativeness was 0.92.
2.4. Randomization of the sample
The patients were reached through the registry system of the relevant hospital. Between the dates of January and June 2020, 1032 patients applied to the endocrinology outpatient clinic of the hospital with the diagnosis of type 2 DM. Of these patients, 158 patients did not meet the inclusion criteria, and 12 did not speak Turkish. The sample of 862 patients was determined with the G. Power version 3.17 program as 42 patients for the intervention group and 42 patients for the control group. Patients who met the inclusion criteria and accepted to participate in the study according to the list of names participated in the study until the sample number was reached. With the help of randomization of the patients by utilizing the Microsoft Excel program, 90 patients (45 patients for the intervention group and 45 patients for the control group) were randomly selected, with odd numbers in the control group and even numbers in the intervention group. 1 patient in the control group and 3 patients in the intervention group left the study voluntarily, and 1 patient voluntarily left the study due to COVID-19 infection during the training process. Therefore, the study was completed with 85 patients. Since the trainings were given to the patients by tele-monitoring method, the interaction of the patients with each other was prevented.
2.5. Inclusion criteria and exclusion criteria
The criteria for inclusion in the study were (a) being a type 2 DM patient who was diagnosed for at least 6 months longer, (b) having no communication barriers, and (c) being able to use telehealth applications.
The criteria for exclusion in the study were (a) being a patient with a known psychiatric illness and/or using psychiatric medication, (b) not being a volunteer to the study, and (c) not being able to use the telephone.
2.6. Measures
The data was collected by the researchers by interviewing patients through telehealth applications. In data collection, a form containing socio-demographic variables and questions about the disease, the form of metabolic control variables form prepared by the researchers, and the Diabetes Symptoms Checklist were used.
The Socio-demographic Information Form, the Questionnaire for Diabetes Treatment, the Form of Metabolic Control Variables, 31 questions related to the disease and the socio-demographic variables such as age, gender, marital status, place of residence, educational level, occupation, level of income, smoking status, the status of being in a risk environment for COVID-19, use of protective equipment in the environment; and the disease-related questions such as the duration of diabetes, insulin use, measurement of the preprandial and postprandial blood glucose, blood pressure measurement, OAD use, the presence of other diabetes mellitus in the family, meal planning and use of change lists, difficulty in using oral pills, the frequency of insulin use, self-administration of the insulin injections and having difficulty in administering the insulin injections, exercise status, disability in having adequate and regular exercise, the status of controlling the blood sugar, dose changes in blood sugar when injecting insulin at home by the healthcare worker or the patients themselves, the status of hospitalization due to high blood sugar, and getting training about diabetes were asked by the researchers in line with the literature.
The Diabetes Symptoms Checklist, It was developed by Grootenhuis et al., and its Turkish validity and reliability study was performed by Terkes and Bektas (2012). The checklist assesses physical and psychological symptoms and the perceived burden of both type 2 diabetes and complications. The 33-item checklist includes six subscales: neurology, psychology/fatigue, cardiology, ophthalmology, psychology/cognition, and hyperglycemia. Each item on the scale is numbered from 0 to 5. If the person with diabetes says that he/she experiences the related symptom, that is if he/she answers "yes", he/she chooses the perceived discomfort level of the symptom on a scale from 1 to 5. If the person with diabetes says that there are no symptoms, the item is evaluated as “0”. The total score and all subscales' scores in the checklist range from 0 to 5, with higher scores indicating greater symptom burden. In the study, the Cronbach's alpha value of the checklist was found to be 0.91 [17], [18].
2.7. Data collection
The study data were collected between January 2020 and June 2020. The Socio-demographic Information Form, the Questionnaire for Diabetes Treatment, the Form of Metabolic Control Variables Survey and Diabetes Symptoms Checklist analysis were performed for pretest data upon admission in both intervention and control group patients.
Individualized patient education was given to the intervention group following the guide created. İnterview schedule was created with each patient. The patients were given training over the tele-health (Phone, SMS) once a week for the first 4 weeks and every other week for the next 8 weeks. During the interviews, the questions of the patients were answered and planning was made so that training about a topic could be given in each interview.
No training was provided to the patients in the control group. The posttest was performed 12 weeks after the pretest.
2.8. Training
The content of patient education was planned according to the patient education model in Fig. 1 [19]. The educational guideline for patients with type 2 diabetes was prepared by the researchers and reviewed by five experts. Each training given in line with the created guide lasted at least 15–20 min on average. The content of the education given to patients with type 2 diabetes is compatible with the literature and covers topics such as disease information, symptom management, effective drug use, nutrition, and physical activity. Diabetes Mellitus (DM) education content is given in Fig. 2.
Fig. 1.
Patient education models.
Fig. 2.
Diabetes Mellitus (DM) education content.
2.9. Data analysis
In the statistical evaluation of the data obtained as a result of the study, the conformity to the normal distribution was tested with the Shapiro-Wilk test, and it showed a normal distribution. Descriptive statistics such as percentage, mean, and standard deviation (SD) were used to evaluate the demographic profile of individuals. The distribution of individuals according to their socio-demographic information was evaluated with independent samples t-test and chi-square. Comparisons of the Diabetes Symptoms Checklist's scores of the individuals in the intervention and control groups before and after the training were measured with the independent sample t-test. Paired sample t-test analysis was used to compare the Diabetes Symptoms Checklist's scores of the groups before and after the training. Pre–post changes within groups were estimated via the standardized response mean, with mean differences between post-test means and pre-test means divided by the standard deviation of the difference scores. ANCOVA with post-test values as outcomes and intervention group (intervention group/control group) as predictor was used to estimate treatment effects. Adjusted mean differences (AMDs) with 95% confidence intervals and Cohen’s d were calculated to quantify the between-group effects. We computed two models for each outcome [20]. Cohen’s D, or standardized mean difference, is one of the most common ways to measure effect size. The effect size tells us how large the effect of the intervention is. Therefore, Cohens D was calculated as suggested in the literature [21], [22]. SPSS Windows version 24.0 package program was used for statistical analysis, and p < .05 was considered statistically significant.
3. Results
The mean age of the control group was 56.86 ± 9.40, the mean age of the intervention group was 54.12 ± 8.32, and the total mean age was 55.54 ± 8.95. When the socio-demographic characteristics of the intervention and control groups included in the study were examined, there was no significant difference between the groups except for BMI (p > 0.05) ( Table 1).
Table 1.
Socio-demographic Characteristics of the Participants.
| Characteristics | Intervention (41) | Control (44) | Statistics |
|---|---|---|---|
| Mean±SD | Mean±SD | t/p | |
| Age | 54.12 ± 8.32 | 56.86 ± 9.40 | 1.419/0.160 |
| Weight | 88.19 ± 14.56 | 77.88 ± 15.66 | -3.105/0.003 |
| BMI | 31.95± 5.30 | 28.55± 5.72 | -2.803/0.006 |
| Number of people living in the house | 4.70 ± 2.52 | 4.54 ± 2.45 | -.300/ 0.765 |
| n (%) | n (%) | ||
| Gender | |||
| Female | 23 (56.1) | 28 (63.6) | X = 0.503 |
| Male | 18 (43.9) | 16 (36.4) | p = 0.513 |
| Marital status | |||
| Married | 38 (92.7) | 32 (72.7) | X = 5.816 |
| Single | 3 (7.3) | 12 (27.3) | p = 0.022 |
| Place of residence | |||
| Village/town | 6 (14.6) | 4 (9.1) | X = 1.356 |
| District center | 3 (7.3) | 6 (13.6) | p = 0.508 |
| Provincial center | 32 (78.0) | 34 (77.3) | |
| Educational level | |||
| Illiterate | 15 (36.0) | 16 (36.4) | X = 4.537 |
| Primary School | 15 (36.0) | 23 (52.3) | p = 0.209 |
| High School | 7 (17.1) | 2 (4.5) | |
| University | 4 (9.8) | 3 (6.8) | |
| Occupation | |||
| Government officer | 4 (9.8) | 3 (6.8) | X = 0.507 |
| Housewife | 22 (53.7) | 26 (59.1) | p = 0.917 |
| Freelancer | 9 (22.0) | 8 (18.2) | |
| Retired | 6 (14.6) | 7 (15.9) | |
| Income level | |||
| Income is equal to expenses | 39 (95.1) | 36 (81.8) | X = 3.619 |
| Income is less than expenses | 2 (4.9) | 8 (18.2) | p = 0.091 |
| Smoking status | |||
| Yes | 11 (26.8) | 7 (15.9) | X = 5.635 |
| No | 26 (63.4) | 24 (54.5) | p = 0.060 |
| Quitted | 4 (9.8) | 13 (29.5) | |
| Being in a COVID-19 risk environment before | |||
| Yes | 12 (29.3) | 9 (20.5) | X = 0.886 |
| No | 29 (70.7) | 35 (79.5) | p = 0.452 |
| Do you pay attention to the use of personal protective equipment in your environment? | |||
| Yes | 36 (87.8) | 43 (97.7) | X = 3.185 |
| No | 5 (12.2) | 1 (2.3) | p = 0.102 |
The average duration of diabetes diagnosis of the participants was 8.37 ± 5.73, and the duration of insulin use was 6.22 ± 5.01. When the data of the participants about diabetes was examined, it was determined that there was no statistically significant difference between the groups, but only between the mean blood pressure and diastole ( Table 2).
Table 2.
The data on diabetes.
| Characteristics | Intervention | Control | Statistics |
|---|---|---|---|
| Mean±SD | Mean±SD | t/p | |
| How long have you been diabetic? | 9.31 ± 6.17 | 7.50 ± 5.18 | -1.473/ 0.145 |
| How long have you been using insulin? | 8.36 ± 4.17 | 5.05 ± 5.13 | -1.829/0.078 |
| The first measurement of preprandial blood glucose | 163.25 ± 55.94 | 146.67 ± 47.69 | -1.387/ 0.170 |
| The last measurement of preprandial blood glucose | 168.02 ± 55.51 | 150.06 ± 37.95 | -1.537/0.129 |
| Blood pressure (systole) | 139.37 ± 24.89 | 131.08 ± 17.44 | -1.391/0.170 |
| Blood pressure (diastole) | 89.06 ± 12.93 | 81.89 ± 11.26 | -2.034/0.047 |
| The first measurement of postprandial blood glucose | 245.37 ± 106.94 | 218.47 ± 74.47 | -1.234/0.221 |
| The last measurement of postprandial blood glucose | 237.53 ± 91.12 | 217.93 ± 68.58 | -0.979/0.331 |
| n (%) | n(%) | ||
| Use of OAD | |||
| Yes | 32 (78) | 41 (93.2) | X = 4.009 |
| No | 9 (22) | 3 (6.8) | p = 0.062 |
| Use of Insulin | |||
| Yes | 11 (26.8) | 20 (45.5) | X = 3.178 |
| No | 30 (78) | 24 (54.5) | p = 0.075 |
| Is there any other diabetes patient in the family? | |||
| Yes | 23 (56.1) | 31 (70.5) | X = 2.551 |
| No | 19 (43.9) | 13 (29.5) | p = 0.123 |
| Do you use exchange lists or food group lists to plan your meals? | |||
| Yes | 3 (7.3) | 4 (9.1) | X = 0.088 |
| No | 38 (92.7) | 40 (90.9) | p = 1.000 |
| Do you have any difficulties when taking your diabetes pills? | |||
| Yes | 7 (20) | 4 (9.3) | X = 1.823 |
| No | 28 (80) | 39(90.7) | p = 0.206 |
| Would you change the dose and/or timing of your insulin or pills? | |||
| Yes | 9 (22) | 16(36.4) | X = 2.123 |
| No | 32 (78) | 28 (63.6) | p = 0.161 |
| Do you move/exercise? | |||
| Yes | 20 (48.8) | 18 (40.9) | X = 0.532 |
| No | 21 (51.2) | 26 (59.1) | p = 0.517 |
| What reasons prevent you from getting enough and regular exercise? | |||
| I can't find enough time | |||
| Yes | 9 (22) | 10 (22.7) | X = 0.007 |
| No | 32 (78) | 34 (77.3) | p = 1.000 |
| I can't spend too much effort | |||
| Yes | 2 (4.9) | 8 (18.2) | X = 3.619 |
| No | 39 (95.1) | 36 (81.8) | p = 0.091 |
| I can't do it when I have another health problem | |||
| Yes | 1 (2.4) | 5 (11.4) | X = 2.577 |
| No | 40 (97.6) | 39 (88.6) | p = 0.204 |
| Have you been told that you need to take tests to monitor your sugar? | |||
| Yes | 37(90.2) | 36 (81.8) | X = 1.243 |
| No | 4 (9.8) | 8 (18.2) | p = 0.355 |
| Do you control your blood sugar? | |||
| Yes | 29 (70.7) | 34 (77.3) | X = 0.473 |
| No | 12(29.3) | 10 (22.7) | p = 0.621 |
| Has the blood glucose dose been changed at home by the healthcare professional before? | |||
| No | 22 (53.7) | 30 (68.2) | X = 3.244 |
| Yes | 19 (46.3) | 14 (31.8) | p = 0.198 |
| Has the blood glucose dose been changed at home by yourself? | |||
| No | 34 (82.9) | 29 (65.9) | X = 3.204 |
| Yes | 7 (17.1) | 15 (34.1) | p = 0.087 |
| Have you made any changes in the food content according to the blood glucose test at home? | |||
| No | 32 (78) | 29 (65.9) | X = 1.544 |
| Yes | 9 (22) | 15 (34.1) | p = 0.238 |
| Have you ever been hospitalized due to high blood sugar? | |||
| Yes | 6 (14.6) | 6 (13.6) | X = 0.017 |
| No | 35 (85.4) | 38 (86.4) | p = 1.000 |
| Have you received any diabetes training before? | |||
| Yes | 1 (2.4) | 6 (13.6) | X = 3.521 |
| No | 40 (97.6) | 38 (86.4) | p = 2.195 |
| Retinopathy | |||
| Yes | 5 (12.2) | 8 (18.2) | X = 0.587 |
| No | 36 (87.8) | 36 (81.8) | p = 0.552 |
| Hypertension | |||
| Yes | 14 (34.1) | 17 (38.6) | X = 0.185 |
| No | 27(65.9) | 27 (61.4) | p = 0.822 |
When the mean scores of the participants from the total and subscales of the checklist were examined, it was determined 0.88 ± 1.12 for the subscale of neurology, 1.09 ± 0.93 for the subscale of psychology/fatigue, 0.72 ± 0.77 for the subscale of cardiology, 0.41 ± 0.81 for the subscale of ophthalmology, 0.96 ± 0.88 for the subscale of psychology/cognition, 2.49 ± 1.30 for the subscale of hyperglycemia, and the total checklist for 1.03 ± 0.76. There was no statistically significant difference between the mean scores of the pre-intervention groups, except for the hyperglycemia subscale (p > 0.05) ( Table 3). After the intervention, there was a statistically significant difference between the checklist's averages between the groups only in the hyperglycemia subscale. However, a statistically significant difference was found between the neurology, cardiology, cognitive, hyperglycemia subscales and the total checklist's averages after the intervention in the intervention group (p < 0.05) (Table 3). In the control group, a statistically significant difference was found between the post-intervention hyperglycemia subscale and the total checklist averages (p < 0.05) (Table 3). In the current study, it was determined that training had a positive effect on diabetes control in the intervention group compared to the control group.
Table 3.
The distribution and comparison of the scores of the groups from the Diabetes Symptoms Checklist's pre-test and post-test.
| Subscales | Pre-test | Post-test | t * ; p | SMR | AMD (95%Cl) Post-test | Cohen's d |
| Neurology | ||||||
| Intervention | 0.88±1.19 | 0.48±0.66 | -.015; 0.988 | .17 | ||
| Control | 0.88±1.07 | 0.76±0.90 | 1.646; 0.104 | -.18 | 28 (-.01-.59) | 0.35 |
| t;p | 2.839; 0.007 | 1.900; 0.064 | ||||
| Psychology/fatigue | ||||||
| Intervention | 1.23 ± 1.04 | 1.05 ±0.87 | 1.809;0.078 | -.03 | ||
| Control | 0.96 ± 0.80 | 1.00 ± 0.80 | -0.760;0.452 | .03 | .21 (-.01-.43) | 0.06 |
| t;p | -1.359; 0.718 | -.311;0.757 | ||||
| Cardiology | ||||||
| Intervention | 0.80 ± 0.96 | 0.51 ± 0.70 | 3.367; 0.002 | -.11 | ||
| Control | 0.65 ± 0.55 | 0.65 ± 0.58 | -0.062; 0.951 | .10 | .29 (.09-.49) | 0.22 |
| t;p | -.934; 0.353 | .986; 0.327 | ||||
| Ophthalmology | ||||||
| Intervention | 0.56 ± 0.99 | 0.41 ± 0.61 | 1.685; 0.100 | .08 | ||
| Control | 0.27 ± 0.56 | 0.31 ± 0.53 | -1.071; 0.290 | -.08 | 0.19 (-.00-.38) | 0.18 |
| t;p | -1.655; 0.102 | -.770; 0.443 | ||||
| Psychology/cognition | ||||||
| Intervention | 1.05 ± 0.97 | 0.82 ± 0.68 | 2.544; 0.015 | -.03 | ||
| Control | 0.88 ± 0.79 | 0.88 ± 0.72 | 0.000; 1.000 | .03 | .22 (.02-.43) | 0.08 |
| t;p | -.921; 0.360 | .342; 0.733 | ||||
| Hyperglycemia | ||||||
| Intervention | 1.91 ± 1.28 | 1.56 ± 1.20 | 2.040; 0.048 | -.36 | ||
| Control | 3.03 ± 1.09 | 2.42 ± 1.09 | 4.532; <0.001 | .33 | -.26 (-.69-.16) | 0.75 |
| t;p | 4.334; <0.001 | 3.427; 0.001 | ||||
| Total | ||||||
| Intervention | 1.04 ± 089 | 0.77 ± 0.60 | 3.531; 0.001 | -.14 | ||
| Control | 1.02 ± 0.62 | 0.94 ± 0.60 | 2.308; 0.026 | .13 | .18 (.01-35) | 0.28 |
| t;p | -0.088; 0.930 | 1.333; 0.189 | ||||
t * =independent samples t-test;
t = paired sample t-test; SRM: standardised response mean; AMD: adjusted mean difference between intervention group and control group (The Diabetes Symptoms Checklist subscale and total score)
4. Discussion
In type 2 diabetes patients, it was aimed to improve the self-care levels of the patients with the training given over the phone. In the COVID-19 pandemic, telehealth monitoring is important in terms of both providing diabetes management and protecting themselves from COVID-19 infection [23]. During the pandemic process, infection prevention policies have been developed in Turkey, such as quarantine practices, the importance given to social distancing rules, and extending the medication reports of patients with chronic diseases such as diabetes (thus reducing the admissions of patients to the hospital). It is an indispensable part of nursing both to maintain these practices and to continue the training of diabetic patients by telephone and to control the symptoms.
As a result of the present study, it was found that telehealth monitoring and patient training in the patients with type 2 DM who are at risk of COVID-19 were effective in the neurological, cardiological, psychological, hyperglycemia, and the total symptom control of the patients. Hyperglycemia and total symptom control were significantly decreased when compared with the first measurements in both groups. Diabetes patients are in the high-risk group in terms of both disease complication, morbidity and mortality in the COVID-19 pandemic. Therefore, the protection of diabetic patients from infection is closely related to the prognosis of diabetes [14], [24].
In the current study, when the mean scores of the diabetes symptoms checklist were examined, it was determined 0.88 ± 1.12 for the subscale of neurology, 1.09 ± 0.93 for the subscale of psychology/fatigue, 0.72 ± 0.77 for the subscale of cardiology, 0.41 ± 0.81 for the subscale of ophthalmology, 0.96 ± 0.88 for the subscale of psychology/cognition, 2.49 ± 1.30 for the subscale of hyperglycemia, 1.03 ± 0.76 for the total checklist. In the study of Terkeş (2016), individuals' psychology/fatigue subscale mean score was 1.51, neurology subscale mean score was 1.91, cardiology subscale mean score was 0.84, ophthalmology subscale mean score was 1.65, psychology/cognition subscale mean score was 1.75, hyperglycemia subscale mean score was 1.48, and the mean score of the total checklist was 1.47.22 Kumsar et al., in their study to determine the effect of perceived symptoms on HbA1c level in the individuals with type 2 diabetes, found that neurological, ophthalmological, and hyperglycemia subscales increased significantly, and the highest score was found in the hyperglycemia subscale [25]. This finding is similar to the results of the current study.
Telehealth monitoring in patients with diabetes improves the self-management skills of patients, reduces their symptoms, and increases their quality of life [26], [27]. Especially during the pandemic process, telephone follow-up of patients contributed significantly to their self-monitoring [28]. In the study of Alanyalı and Arslan, it was found that as the self-management of the patients increased, the symptoms of diabetes significantly decreased. In a study, it was shown that hyperglycemia and hypoglycemia subscales of the patients improved after 6 months of rehabilitation applied to patients with type 2 DM [29]. A meta-analysis study showed that telehealth monitoring has a positive effect on the HbA1c level of patients in the short and long term [30]. However, in a systematic review study by Tilsdey et al. (2015), it was stated that a 6-month follow-up by telephone reduced HbA1c level, but had no effect in the long-term (12 months) follow-up [31]. As a result of the current study, it has been thought that controlling the symptoms of the patients is effective in the management of the disease, and the most important indicator of this can be associated with the decrease in the average hyperglycemia scores of the patients.
Negative cognitive mood, self-criticism, self-judgment, and over-identification tendency are observed more frequently in the patients with type 2 diabetes. These symptoms cause an increase in the physical symptoms of the patients. In controlling these symptoms, digital interventions are accepted as a fast, easy, and effective method in the evaluation of anxiety, depression, and stress symptoms of patients [26]. In a study, it was found that telerehabilitation can be an alternative method in the management of DM and contributes to the metabolic outcomes, physical exercise capacity, muscle strength, and depression level of the patients [32]. In their study, Scott et al. stated that 75% of the patients with T1DM during the COVID-19 pandemic would continue to make appointments with telehealth monitoring after the pandemic [33]. In another study, it was found that more than 80% of the patients with diabetes had appointments via telehealth in a clinic during the COVID-19 process, so that the problem of patients missing their appointments was quite low [34]. It was thought that the follow-up of patients by phone during the Covid 19 process has provided great convenience.
The patients have been exposed to some restrictions during the COVID-19 pandemic process. With these limitations, there was a decrease in the daily living activities and social interactions of the patients. During this period, patients experienced nutritional and psychological problems such as loss of appetite, malnutrition, depression, anxiety, and anger. The problems experienced caused the patients to control their symptoms, to manage their treatments effectively, to have insufficiencies in physical activity, and to eat irregularly. Both the presence of chronic disease and the difficulties in managing the disease have made patients be at high risk for COVID-19 infection. Due to the risk of COVID-19, the frequency of patients receiving service from the hospital has decreased. Thus, it has been determined that the patients have difficulty in reaching healthcare professionals when they have problems [35].
5. Conclusion
As a result of the current study, telehealth monitoring enabled patients to manage their symptoms and to continue their treatments effectively and adequately. Result of the post-intervention analysis revealed no statistical differences over the subscales of neurology, cardiology, cognition, hyperglycemia, and the total checklist averages between the study groups, while and both groups showed a statistically significant difference between the subscales of hyperglycemia after intervention.
Also a result of the telephone follow-up of the intervention group during the pandemic process, it was found that positive outcomes were obtained in terms of neurological, cardiological, cognitive, and hyperglycemic controls. In addition, it was aimed to maintain the quality of care of the patients with telehealth monitoring, while at the same time, holistic care principles were met. In the COVID-19 pandemic, telehealth monitoring of the patients with diabetes, who are worried about going to a health institution, has improved the health care of patients. {{{ Chart 1}}}.
Chart 1.
Flow chart of the process.
Funding
This research did not receive any specific grant from any kind of funding agencies.
Authorship statement
We assure you that the authors of this article are Derya TÜLÜCE, İbrahim Caner DİKİCİ, Emine KAPLAN SERİN, respectively. The considered research is original and has not previously been published elsewhere (either partly or totally), and is not in the process of being considered for publication in another journal. All authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors. All authors are in agreement with the latest version of manuscript. The authors declare that they have no competing interests. Literature search: DT, ICD, Data collection: DT, ICD, Study design: DT, ICD, EKS, Analysis of data: DT, EKS, Manuscript preparation: DT, ICD, Review of manuscript: DT, ICD.
Conflicts of interest
No conflict of interest has been declared by the authors.
Footnotes
This study was performed in Harran University Research and Application Hospital, Şanlıurfa, Turkey.
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.pcd.2022.12.001.
Appendix A. Supplementary material
Supplementary material
.
References
- 1.Lee P.A., Greenfield G., Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials. BMC Health Serv. Res. 2018;18(1):1–10. doi: 10.1186/s12913-018-3274-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.World Health Organization., “Diabetes,” 2021, 2021. 〈https://www.who.int/health-topics/diabetes#tab=tab_1〉 (accessed Feb. 22, 2022).
- 3.Centers for Disease Control and Prevention, “Education and Support,” Survival Guide for Traders, 2015. 〈https://www.cdc.gov/diabetes/managing/education.html〉 (accessed Feb. 18, 2022).
- 4.N. N. I. of D and D and K. Diseases, “Health İnformation,” 2021, 2021. 〈https://www.niddk.nih.gov/health-information/diabetes/overview/managing-diabetes/4-steps〉 (accessed Feb. 12, 2022).
- 5.S.M. Manemann et al., “The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: A systematic review and meta-analysis of systematic reviews of randomised controlled trials,” 2021, vol. 18, no. 1, pp. 2049–2056, 2021, doi: 10.1186/s12913-018-3274-8. [DOI] [PMC free article] [PubMed]
- 6.Ghoreishi M.S., Vahedian-shahroodi M., Jafari A., Tehranid H. Self-care behaviors in patients with type 2 diabetes: Education intervention base on social cognitive theory. Diabetes Metab. Syndr. Clin. Res. Rev. 2019;13(3):2049–2056. doi: 10.1016/j.dsx.2019.04.045. [DOI] [PubMed] [Google Scholar]
- 7.Fischer H.H., et al. Care by cell phone: text messaging for chronic disease management. Am. J. Manag. Care. 2012;18(2):e42–e47. [PubMed] [Google Scholar]
- 8.Fischer S.H., Capsule Commentary on Lee, et al. Telemonitoring and team-based management of glycemic control on people with type 2 diabetes: a cluster randomized controlled trial. J. Gen. Intern Med. 2020;35(1):402. doi: 10.1007/s11606-019-05493-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Corbett-Nolan A., Bullivant J.Dr, Green M., Parker M. Good Governance Institute; 2011. Better Care for People with Long-term Conditions: the Quality and Good Governance of Telehealth Services. (no. July) [Google Scholar]
- 10.Spanakis E.G., et al. Diabetes management using modern information and communication technologies and new care models. Inter. J. Med Res. 2012;1(5) doi: 10.2196/ijmr.2193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Adepoju O.E., et al. Effects of diabetes self-management programs on time-to-hospitalization among patients with type 2 diabetes: a survival analysis model. Diabetes Technol. Ther. 2015;17(11):S58–S59. doi: 10.1089/dia.2015.1507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kanadli K.A., Ovayolu N., Ovayolu Ö. Does telephone follow-up and education affect self-care and metabolic control in diabetic patients. Holist. Nurs. Pract. 2016;30(2):70–77. doi: 10.1097/HNP.0000000000000137. [DOI] [PubMed] [Google Scholar]
- 13.Roncon L., Zuin M., Zuliani G., Rigatelli G. Patients with arterial hypertension and COVID-19 are at higher risk of ICU admission. Br. J. Anaesth. 2020;125(2):e254–e255. doi: 10.1016/j.bja.2020.04.056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Chen Y., Yang D., Yang C., Zheng L., Huang K. Clinical characteristics and outcomes of patients with diabetes and covid-19 in association with glucose-lowering medication. Diabetes Care. 2020;43(10):e165–e166. doi: 10.2337/dci20-0035. [DOI] [PubMed] [Google Scholar]
- 15.Javanmardi F., Keshavarzi A., Akbari A., Emami A., Pirbonyeh N. Prevalence of underlying diseases in died cases of COVID-19: A systematic review and meta-analysis. PLoS One. 2020;15(10) doi: 10.1371/journal.pone.0241265. (October) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hartmann-Boyce J., et al. Diabetes and COVID-19: risks, management, and learnings from other national disasters. Diabetes Care. 2020;43(8):1695–1703. doi: 10.2337/dc20-1192. [DOI] [PubMed] [Google Scholar]
- 17.Grootenhuis P.A., Snoek F.J., Heine R.J., Bouter L.M. Development of a type 2 diabetes symptom checklist: a measure of symptom severity. Diabet. Med. 1994;vol. 11(3):253–261. doi: 10.1111/j.1464-5491.1994.tb00268.x. [DOI] [PubMed] [Google Scholar]
- 18.Terkes N., Bektas H. Psychometric evaluation of the diabetes symptom checklist-revised in patients with type 2 diabetes in Turkey. Jpn. J. Nurs. Sci. 2016;13(2):273–283. doi: 10.1111/jjns.12104. [DOI] [PubMed] [Google Scholar]
- 19.Scharf D. Knowledge Work; The Knowledge Institute. Rutgers University; New Brunswick, NJ, USA: 2010. A new view of patient education: How information and knowledge management can contribute to pa-tient-centered health care; pp. 139–158. [Google Scholar]
- 20.Schultz K., Jelusic D., Wittmann M., Krämer B., Huber V., Fuchs S., Schuler M. Inspiratory muscle training does not improve clinical outcomes in 3-week COPD rehabilitation: results from a randomised controlled trial. Eur. Respir. J. 2018;51(1) doi: 10.1183/13993003.02000-2017. [DOI] [PubMed] [Google Scholar]
- 21.Lenhard W., Lenhard A. Calculation of effect sizes. Psychometrica. 2016 doi: 10.13140/RG.2.2.17823.92329. [DOI] [Google Scholar]
- 22.Cohen J. Routledge; 2013. Statistical Power Analysis for the Behavioral Sciences. [DOI] [Google Scholar]
- 23.Dehghan K., Zareipour M.A., Zamaniahari S., Azari M.T. Tele education in diabetic patients during coronavirus outbreak. Open Access Maced. J. Med Sci. 2020;vol. 8(T1):610–612. doi: 10.3889/OAMJMS.2020.5587. [DOI] [Google Scholar]
- 24.Bode B., et al. Glycemic characteristics and clinical outcomes of COVID-19 patients hospitalized in the United States. J. Diabetes Sci. Technol. 2020;14(4):813–821. doi: 10.1177/1932296820924469. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Karakoç Kumsar A., Taşkın Yılmaz F., Gündoğdu S. Tip 2 diyabetli bireylerde algılanan semptom düzeyi ile HbA1c ilişkisi. Çukurova Med. J. 2019;44:61–68. doi: 10.17826/cumj.551234. [DOI] [Google Scholar]
- 26.Kane N.S., Hoogendoorn C.J., Tanenbaum M.L., Gonzalez J.S. Physical symptom complaints, cognitive emotion regulation strategies, self-compassion and diabetes distress among adults with Type 2 diabetes. Diabet. Med. 2018;35(12):1671–1677. doi: 10.1111/dme.13830. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bassi G., Gabrielli S., Donisi V., Carbone S., Forti S., Salcuni S. Assessment of psychological distress in adults with type 2 diabetes mellitus through technologies: literature review. J. Med Internet Res. 2021;23(1) doi: 10.2196/17740. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Scott S.N., Fontana F.Y., Züger T., Laimer M., Stettler C. Use and perception of telemedicine in people with type 1 diabetes during the COVID-19 pandemic—Results of a global survey. Endocrinol. Diabetes Metab. 2021;4(1) doi: 10.1002/edm2.180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Alanyalı Z., Arslan S. Diabetes symptoms and self-management perceptions of individuals with type 2 diabetes. Arch. Health Sci. Res. 2020;7(3):238–243. doi: 10.5152/archealthscires.2020.19031. [DOI] [Google Scholar]
- 30.Vadstrup E.S., Frølich A., Perrild H., Borg E., Røder M. Health-related quality of life and self-related health in patients with type 2 diabetes: Effects of group-based rehabilitation versus individual counselling. Health Qual. Life Outcomes. 2011;9(1):1–8. doi: 10.1186/1477-7525-9-110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Eberle C., Stichling S. Effect of telemetric interventions on glycated hemoglobin A1c and management of type 2 diabetes mellitus: systematic meta-review. J. Med. Internet Res. 2021;23(2) doi: 10.2196/23252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Tildesley H.D., Po M.D., Ross S.A. Internet blood glucose monitoring systems provide lasting glycemic benefit in type 1 and 2 diabetes: a systematic review. Diabetes Technol. Ther. 2016;18(1):S5. doi: 10.1089/dia.2016.2501. [DOI] [PubMed] [Google Scholar]
- 33.Duruturk N., Özköslü M.A. Effect of tele-rehabilitation on glucose control, exercise capacity, physical fitness, muscle strength and psychosocial status in patients with type 2 diabetes: a double blind randomized controlled trial. Prim. Care Diabetes. 2019;13(6):542–548. doi: 10.1016/j.pcd.2019.03.007. [DOI] [PubMed] [Google Scholar]
- 34.March C.A., et al. Paediatric diabetes care during the COVID-19 pandemic: lessons learned in scaling up telemedicine services. Endocrinol. Diabetes Metab. 2021;vol. 4(1) doi: 10.1002/edm2.202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Choudhary P., et al. The challenge of sustainable access to telemonitoring tools for people with diabetes in europe: lessons from COVID-19 and beyond. Diabetes Ther. 2021;12(9):2311–2327. doi: 10.1007/s13300-021-01132-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material