Research in Gerontological Nursing

Empirical Research 

Telephone Support and Telemonitoring for Low-Income Older Adults

Suyong Jeong, PhD, RN; Hanna Choi, PhD, RN; Seok Hyun Gwon, PhD, RN; Jeongeun Kim, PhD, RN

Abstract

The objective of the current pilot study was to determine whether nurse-led telephone counseling improves health behavior, self-care, and physiological indices for low-income older adults using a telemonitoring system. The control group (n = 15) was provided with weekly health education only, and the intervention group (n = 20) was given additional telephone support by nurses. At baseline and 8 weeks, data on health and self-care behaviors were collected using a self-reported questionnaire, and blood pressure and fasting blood glucose levels were assessed. Nurse-led telephone support had a medium effect on improving health behavior (Cohen's d = 0.58, 95% confidence interval [CI] [−0.10, 1.27]), reducing systolic blood pressure (Cohen's d = −0.61, 95% CI [−1.29, 0.08]), and improving self-care behavior for hypertension (Cohen's d = 1.16, 95% CI [0.05, 2.27]). Findings support that nurse-led telephone support may be effective for improvements in health behavior, systolic blood pressure, and hypertension self-care in disadvantaged older adults under remote monitoring. Further studies are needed to obtain a powered sample size and investigate the long-term effects of personalized elements surrounding telehealth in community-based settings.

[Res Gerontol Nurs. 2018; 11(4):198–206.]

Abstract

The objective of the current pilot study was to determine whether nurse-led telephone counseling improves health behavior, self-care, and physiological indices for low-income older adults using a telemonitoring system. The control group (n = 15) was provided with weekly health education only, and the intervention group (n = 20) was given additional telephone support by nurses. At baseline and 8 weeks, data on health and self-care behaviors were collected using a self-reported questionnaire, and blood pressure and fasting blood glucose levels were assessed. Nurse-led telephone support had a medium effect on improving health behavior (Cohen's d = 0.58, 95% confidence interval [CI] [−0.10, 1.27]), reducing systolic blood pressure (Cohen's d = −0.61, 95% CI [−1.29, 0.08]), and improving self-care behavior for hypertension (Cohen's d = 1.16, 95% CI [0.05, 2.27]). Findings support that nurse-led telephone support may be effective for improvements in health behavior, systolic blood pressure, and hypertension self-care in disadvantaged older adults under remote monitoring. Further studies are needed to obtain a powered sample size and investigate the long-term effects of personalized elements surrounding telehealth in community-based settings.

[Res Gerontol Nurs. 2018; 11(4):198–206.]

South Korea has had the steepest increase in annual growth of health care spending among member countries of the Organisation for Economic Co-operation and Development (2015). The introduction of long-term care insurance, high prevalence of chronic diseases in the older adult population, and rapid aging of the population will increase the costs of health care, which has become an urgent national concern. In addition, the high poverty rate (approximately 50%) and high suicide rate (72 per 100,000 individuals) among older adults (age ≥65) are serious social problems in the nation (Jones & Urasawa. 2014). Cognitive and mood problems occur more frequently in older adult populations with lower socioeconomic status (Won & Kim, 2008). When caring for vulnerable older adults, interventions improving health behavior should be applied in community settings (Choi, 2009). In this regard, national social and health policies may be needed, especially for low-income older adults, and sustainable self-management programs for those with chronic diseases are needed to increase their knowledge and access to resources for better health.

Telemonitoring enables health counseling and education between health professionals and patients without limits of time or space. Special interests and skills for its use are essential to the telemonitoring infrastructure. Because computer and health literacy in older adults may be influenced by their education and income (Chang et al., 2004), support and assistance for effective telemonitoring are important to increase the usability of the service and active participation during the initial phase.

In this context, it is likely that telephone support can be used as an inexpensive and comfortable method for Korean low-income older adults. Researchers found that telephone support was effective for behavioral modification and clinical outcome improvements for participants with hypertension (Chiu & Wong, 2010; Jung & Lee, 2017), diabetes (Aliha et al., 2013; Song & Kim, 2009), metabolic syndrome (Lin et al., 2016), and postoperative symptoms (Malmstrom et al., 2016). Furthermore, telephone interventions were useful in a telemonitoring context by helping participants integrate information themselves and improve self-care behaviors (Bove et al., 2013; Martin-Lesende et al., 2013; Shea & Chamoff, 2012). Nurse-led telephone support and self-monitoring programs targeting frail older adults reported improved clinical outcomes and reduced unnecessary use of health services (Barlow, Singh, Bayer, & Curry, 2007). However, when comparing the telephone support group with the standard care group, it remained questionable as to how the additional telephone support was effective within the telemonitoring group. In addition, few studies suggested strategies for an effective telehealth application in vulnerable older adult populations with digital and health literacy limitations and for establishing evidence for the management of chronic diseases in primary health care settings.

Researchers have reported that telemonitoring improved behavioral, clinical, and health care system outcomes in older adults with chronic diseases (Fursse, Clarke, Jones, Khemka, & Findlay, 2008; Martin-Lesende et al., 2013; McLean et al., 2013; Townley & Yalowich, 2015). However, some disputes about its effectiveness and safety remain, and no evidence is available regarding which aspects of the interventions are successful. These mixed findings may be due to the lack of full description of the intervention modalities and why interventions succeeded or failed (McLean et al., 2013), as well as the absence of human support in offering digital behavior change interventions to promote effective engagement (Yardley et al., 2016).

To achieve better outcomes of telehealth applications, humanistic factors such as individualized or personalized approaches are critical for health care providers and patients to engage in a participatory role (Demiris et al., 2010). As more modern technology is developed, more vulnerable populations may face challenges with accessing technology-related health care services.

In the current study, telephone support was designed as a telehealth strategy for low-income older adults based on the transtheoretical model (TTM) developed by Prochaska and DiClemente (1983). This randomized controlled trial sought to determine whether nurse-led telephone support influences health behavior, self-care, and physiological indices for low-income older adults with hypertension and diabetes in the context of telemonitoring.

Method

Design

A randomized, controlled, single-blinded, parallel pilot trial was performed to examine whether an intervention via telephone support improves health behavior, self-care, and physiological indices in low-income older adults with hypertension and type 2 diabetes mellitus. The Figure presents a flowchart of the research.

CONSORT flowchart of the research.

Figure.

CONSORT flowchart of the research.

Randomization

Randomization was undertaken by a researcher (S.J.) who was blinded to the trial and had no prior knowledge of participants. The researcher randomly assigned participants to either an intervention group or a control group using sequentially numbered, opaque, sealed envelopes method. Stratification was used to balance participants by disease type (i.e., hypertension and diabetes).

Participants

In May 2013, low-income older adults who had been registered in the database of a national home telemonitoring project that commenced on June 23, 2012, were recruited. In total, 40 participants were randomly allocated to an intervention group (n = 20) and control group (n = 20). Inclusion criteria were participants who: (a) were age ≥60, (b) had a history of hypertension or diabetes, (c) were newly diagnosed by the research team with hypertension (systolic blood pressure [BP] ≥140 mmHg or diastolic BP ≥90 mmHg) or diabetes (fasting blood glucose [FBG] ≥126 mg/dL for at least 8 hours after a meal), and (d) were able to communicate verbally with the research team. Exclusion criteria were participants with: (a) a history of hospitalization due to acute and severe diseases, (b) mental disease, (c) dementia, or (d) an inability to communicate verbally. According to Julious (2005), if prior information on studied group and setting is lacking, a minimum of 12 individuals per group is recommended for pilot studies. Written consent was obtained from all participants and the study protocol was approved by the researchers' university Institutional Review Board.

Telemonitoring and Group Health Education

Telemonitoring and group health education were provided to both groups. Telemonitoring was performed using an automated web-based system, enabling participants to manage chronic diseases by monitoring BP and FBG themselves at home. It was assumed that all participants recognized the telemonitoring service and were capable of using it because they had received training for at least 4 weeks prior to pretest measurements. The telemonitoring system collected all biomedical data from participants, who were automatically classified into high-risk, medium-risk, and normal groups.

Weekly group health education was provided to both groups by two nurses over a period of 8 weeks. The primary purpose was to improve knowledge of overall health management given that participants were low-income older adults with potentially low health literacy. Additional purposes were to exchange health information, establish positive social supports, encourage interest in health, and encourage formation of friendships among participants. After printed materials were distributed, group health education was implemented at two different senior centers. This education covered the definition, causes, symptoms, treatment, and daily management of the disease; measurements of BP and FBG; and complications related to hyper-tension and diabetes. The duration of group health education sessions averaged 40 minutes.

Intervention: Telephone Support

Two nurses provided telephone support to the intervention group for 8 weeks. The intervention group participated in 30-minute telephone calls twice per week. Using the TTM (Prochaska & DiClemente, 1983), researchers developed an algorithm for appropriate stage-matched intervention. At baseline, after assessing each participant's intent and maintenance of health behavior, health education and counseling were offered to meet each individual's needs in compliance with each strategy. The purpose of telephone support was to help participants meet their individual exercise, diet, and self-monitoring goals. Details of the telephone support are shown in Table 1.

Telephone Support for Low-Income Older Adults With Hypertension and Diabetes

Table 1:

Telephone Support for Low-Income Older Adults With Hypertension and Diabetes

Instruments

Baseline information were collected, including demographic characteristics such as gender, age, educational level, monthly income, living arrangements, and duration of residence in the current home.

Primary Outcomes: Health Behavior and Physiological Indices. The Health Practice Index (HPI; Belloc & Breslow, 1972) was used to assess health behavior. HPI scores are higher in individuals exhibiting the following behaviors: (a) no tobacco use; (b) no more than four glasses of alcohol per week; (c) exercise at least 3 days per week; (d) average 7 to 8 hours of sleep nightly; (e) breakfast daily; (f) no frequent snacking; and (g) appropriate weight maintenance. Body mass index (BMI) was used to evaluate weight maintenance: BMI <18.5 kg/m2 was categorized as underweight (0), BMI 18.5 to 25 kg/m2 as normal (1), and BMI >25 kg/m2 as overweight (0). Participants engaging in behavior promoting health were assigned a score of 1; those not engaging in this behavior were assigned a score of 0. HPI scores ranged from 0 to 7, with higher scores indicating greater compliance with health behaviors.

Physiological indices included systolic and diastolic BP and FBG level. BP was measured using an automated device on the upper arm with the participant seated, and FBG levels were evaluated in blood samples collected by a monitoring device via reference to the timing of the last meal.

Secondary Outcome: Self-Care Behavior. Two different instruments were used to measure self-care behavior, depending on disease type. A subjective instrument developed by Lee (1994) was used to assess self-care of participants with hypertension. This instrument includes 16 items related to diet, weight control, exercise, smoking cessation, medication use, and stress management. Each item is scored on a 5-point Likert scale, with higher scores indicating greater compliance with self-care activities (range = 16 to 80 points). Cronbach's alpha at the time the instrument was developed was 0.72 and was 0.68 in the current study.

An instrument developed by Choi (1998) was used to assess self-care of participants with diabetes. This instrument includes 15 items related to diet, medication use, exercise, and self-management. Each item is scored on a 4-point Likert scale, with higher scores indicating greater compliance with self-care activities (range = 15 to 60 points). Cronbach's alpha at the time the instrument was developed was 0.83 and was 0.93 in the current study.

Data Collection

Trained research assistants and surveyors visited each participant's household and completed face-to-face interviews for the pretest. The surveyor read each question and the participant answered verbally. The survey took 30 minutes on average, and the survey booklet was collected by the research assistant and surveyor. Post-test examination was performed after 8 weeks using the same procedure as for the pretest.

Data Analysis

Descriptive statistics were used to assess demographic information of the sample. Chi-square test, Fisher's exact test, and independent t test were used, as appropriate, to analyze the homogeneity of the baseline characteristics. Statistical significance was set at 0.05, two-tailed. All outcome variables, including health behaviors, physiological indicators, and self-care behaviors, were treated as continuous variables. Statistical analysis of self-care behavior was completed separately by disease type for hypertension and diabetes. To explain the effect size of the intervention, Cohen's d and its 95% confidence interval (CI) were calculated by examining the mean difference and standard deviation in outcome variables between the intervention and control groups. Cohen's d was interpreted according to the following criteria (Cohen, 1988): 0.2 ≤ |d| < 0.5 (small); 0.5 ≤ |d| < 0.8 (medium); |d| ≥ 0.8 (large).

Results

Baseline Characteristics

Five (12.5%) participants in the control group were excluded due to long-term absence from home (n = 2), refusal to participate in group health education (n = 2), or hospitalization (n = 1). No statistically significant differences were observed in the demographic characteristics of participants between the intervention and control groups at baseline (Table 2). The majority of both groups were female, ages 61 to 80 years, and had an educational level of middle school or less. More than 70% of participants answered that their monthly income was <500,000 South Korean Won ($454.54). The largest portion of participants in the intervention group was living with children, but the largest portion of the control group was living alone. Overall, 60% to 78.9% of participants had lived in their current residence for 10 to 20 years.

Baseline Characteristics of Study Sample

Table 2:

Baseline Characteristics of Study Sample

Comparison of Health Behavior, Blood Pressure, and Fasting Blood Glucose Levels

After telephone support, 14 (70%) participants in the intervention group had improved health behavior (1.7 increase in HPI score), and 11 (55%) participants had lower systolic BP (−19.8 mmHg) compared with pretest values. Nurse-led telephone support had a medium effect on improving health behavior (Cohen's d = 0.58, 95% CI [−0.10, 1.27]) and reducing systolic BP (Cohen's d = −0.61, 95% CI [−1.29, 0.08]) (Table 3). However, mean differences in diastolic BP and FBG were small between groups.

Comparisons of Health Behaviors, Blood Pressure, Fasting Blood Glucose Levels, and the Effects of Telephone Support Between Intervention (n = 20) and Control (n = 15) Groups

Table 3:

Comparisons of Health Behaviors, Blood Pressure, Fasting Blood Glucose Levels, and the Effects of Telephone Support Between Intervention (n = 20) and Control (n = 15) Groups

Comparison of Self-Care Behavior by Disease Type

After 8 weeks of intervention, six of nine participants with hypertension in the intervention group had higher self-care behavior scores (6.7-point increase). The telephone support intervention provided by nurses had a medium effect on improving self-care behavior for hypertension (Cohen's d = 1.16, 95% CI [0.05, 2.27]) (Table 4). However, mean differences in diabetes self-care behavior were small between groups.

Comparisons of Self-Care Behavior by Disease Type and the Effect of Telephone Support

Table 4:

Comparisons of Self-Care Behavior by Disease Type and the Effect of Telephone Support

Discussion

The current pilot study assessed an intervention that assists low-income older adults using remote monitoring to manage chronic disease. The effect size indicated that nurse-led telephone support may have the potential to improve health behavior and reduce systolic BP, compared with telemonitoring and group health education only. In addition, better self-care behavior was evident in participants with hypertension in the intervention group. However, small differences in FBG level were observed between groups, and minimal changes in self-care behavior were apparent among participants with diabetes.

Although several limitations exist because this exploratory pilot study did not obtain a powered sample size, the telephone support provided is considered clinically meaningful for the improvement in health behaviors, systolic BP, and hypertension self-care in low-income older adults who use telemonitoring. The level of change in systolic BP (−8.75 mmHg) in the current study was higher than that (−3.5 mmHg) reported in a meta-analysis on nurse-led telephone monitoring (Clark, Smith, Taylor, & Campbell, 2010). The current researchers' attempts to increase self-efficacy and psychologically support social interaction may be important. Speaking with a nurse on the telephone may improve self-efficacy, provide a feeling of social support, and fulfill personal needs, as well as reduce any sense of isolation among individuals receiving community-based assistance. The current findings are consistent with those of previous empirical studies indicating that telephone support improved self-efficacy and was perceived as a form of social support, improving health behavior, hypertension self-care, and BP control (Chiu & Wong, 2010; Jung & Lee, 2017). One Korean study involving frail older adults found that health behavior improved when self-control increased (Choi, 2009), and hypertension self-care improved when social support was offered (Yang, Jeong, Kim, & Lee, 2014). Improvements in health behavior positively influence perceived health status (Noguchi et al., 2015).

Tailored components need to be included in telemonitoring programs for older adult–friendly service delivery. In particular, telephone procedures accommodating personal needs are required to strengthen empowerment and promote health literacy among older adults. Home-based telemonitoring requires patient involvement and specific knowledge and skills. It is vital that health professionals (e.g., nurses) engage and communicate with individuals in efforts to empower them for self-care (Shea & Chamoff, 2012; Zullig, Melnyk, Goldstein, Shaw, & Bosworth, 2013). According to an expert panel, the health of older adults may be affected by not only health literacy but also computer literacy, due to recent technological advances (Chang et al., 2004). Green et al. (2011) reported that participation rates in web-based BP monitoring may decrease among older populations and tho se from lower educational and socioeconomic backgrounds. Health literacy is associated with healthy behavior and access to health information, specifically via communication between older patients and health care providers (Carollo, 2015; Suka et al., 2015).

When caring for low-income older adults, it is essential to use a service that is convenient and simple, and to prioritize the continuity of the relationship between patients and health care professionals. Telephone support is a cost-effective, useful, and familiar tool that should not encounter technological resistance from frail older adults and should facilitate the efficacy of telemonitoring. In addition, one-to-one telephone support helps group sessions have a high degree of individualization rather than one-sided communication (Wilson, 1997). These combined approaches can be more beneficial to participants than group sessions solely because telephone contacts by the health provider enable participants to be more engaged in their experiences and adapt to their individual problems after or between group sessions.

Telephone support, however, did not significantly affect diabetes control across groups in the current study. The complex protocols may have been associated with these poor results. Other researchers have reported that telemonitoring for patients with diabetes requires more effort and skills for procedures such as self-collection of blood samples. Vassilev et al. (2015) reported that the success of a telehealth program was dependent on three elements of the intervention: relationship, fit, and visibility. Among these criteria, telemonitoring for diabetes control in the current study may not have been a good fit due to low-income older adults' lack of knowledge and skills. This lack of understanding may have caused the low frequency of self-monitoring and poor quality of assessment.

In summary, study findings support the potential effectiveness of implementing telephone support for behavior modification and hypertensive control in disadvantaged older adults under remote monitoring. Human factors, such as the engagement of health professionals, may be critical when trying to use telehealth to improve the outcomes of disadvantaged older adult populations. When using clinical telemonitoring, practitioners should base their actions on a combination of appropriate action, response, or escalation in the care of patients and the associated decision support (Nangalia, Prytherch, & Smith, 2010). In the initial phase of a technology-based intervention, economically vulnerable older adults may require a level of human contact that encourages them to cooperate with health professionals and adapt their daily lives to allow management of chronic conditions. Telephone support appears to be a feasible user interface for low-income older adults using telemonitoring; the telephone is a familiar and traditional form of contact, improving telehealth targeting of an aging population. Older adults may be reassured by direct communication with health professionals, even if it is not face-to-face, allowing them to maintain positive perceptions and attitudes about technology, eventually reducing barriers to technological applications.

Nurse-led supplemental telephone support within a community setting and telemonitoring system were feasible to improve health behavior, self-care, and BP control among low-income older adults with chronic diseases. This finding suggests that the applicability of telehealth programs for vulnerable populations depends on the use of personalized interventions in the primary care setting. These results will be helpful for future nursing practice, research, and policy to improve access to health care and health equity for vulnerable older adults.

Limitations

The current study had limitations. First, this exploratory pilot study had a small sample size. Larger randomized trials are required for generalizability and statistical inference by obtaining adequate powered sample sizes. Second, the measures of health behavior and self-care were self-reported; therefore, a subjective response error was possible. In addition, there may have been errors in measuring the physiological indices. For example, it was not possible to determine whether participants had prepared appropriately for blood glucose testing (e.g., not eating for 8 hours prior to testing). More intervention studies are needed to measure more reliable and valid biomarkers.

Conclusion

Findings support that nurse-led telephone support may be effective for improvements in health behavior, systolic BP, and hypertension self-care in disadvantaged older adults under remote monitoring. Further studies are needed to obtain a powered sample size and investigate the long-term effects of personalized elements surrounding telehealth in community-based settings.

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Telephone Support for Low-Income Older Adults With Hypertension and Diabetes

WeekIntervention
1Introduce telephone support, review previous telemonitoring data
2Establish personalized goals for exercise, nutrition, and blood pressure or fasting blood glucose self-monitoring; help individual understand self-care management, medication, and resources for health access
3–4Communicate barriers and usefulness of behavioral changes and healthy aging; follow up regarding clinical data and symptoms and the level of support needed to achieve initial goals; manage physical and psychological stress
5–6Monitor risk factors for complications and symptoms; identify social support; give complimentary remarks; reward goal attainment
7–8Encourage health maintenance and self-efficacy for health behavior, helping create supportive relationships and encourage sharing health information with others

Baseline Characteristics of Study Sample

Variablen (%)p Value
Intervention Group (n = 20)Control Group (n = 15)
Age (mean, SD) (years)73 (6.5)75.4 (6.8)0.300a
Gender0.430b
  Female17 (85)11 (73.3)
  Male3 (15)4 (26.7)
Educational level0.680b
  Middle school or lower16 (80)13 (86.7)
  High school or above4 (20)2 (13.3)
Monthly income (10,000 KRW)0.700b
  <$5014 (70)12 (80)
  $50 to $2006 (30)3 (20)
Living arrangement0.678b
  With children9 (45)5 (33.3)
  Alone6 (30)7 (46.7)
  With spouse5 (25)3 (20)
Duration of residence in same home (years)0.266b
  <104 (20)6 (40)
  10 to 2016 (80)9 (60)
Type of chronic disease1.000b
  Hypertension9 (45)6 (40)
  Diabetes11 (55)9 (60)

Comparisons of Health Behaviors, Blood Pressure, Fasting Blood Glucose Levels, and the Effects of Telephone Support Between Intervention (n = 20) and Control (n = 15) Groups

Outcome VariableMean (SD)Cohen's d (95% CI)
PretestPosttestDifference
Health behaviors0.58 [−0.10, 1.27]
  Intervention group4.0 (1.3)5.1 (1.1)1.1 (1.2)
  Control group3.8 (1.1)4.2 (1.1)0.4 (1.2)
Systolic blood pressure (mmHg)−0.61 [−1.29, 0.08]
  Intervention group134.3 (21.9)125.6 (16.2)−8.75 (17.85)
  Control group134.4 (24.2)136.9 (25.5)2.53 (19.54)
Diastolic blood pressure (mmHg)−0.18 [−0.85, 0.49]
  Intervention group75.0 (9.3)75.6 (8.8)0.65 (7.74)
  Control group74.9 (10.8)77.1 (13.1)2.13 (8.49)
Fasting blood glucose level (mg/dL)−0.33 [−1.01, 0.34]
  Intervention group175.7 (61.4)175.5 (63.1)−0.20 (63.62)
  Control group132.9 (53.0)152.5 (70.8)19.60 (52.40)

Comparisons of Self-Care Behavior by Disease Type and the Effect of Telephone Support

Outcome VariableMean (SD)Cohen's d (95% CI)
PretestPosttestDifference
Hypertension self-care behavior1.16 [0.05, 2.27]
  Intervention group (n = 9)62.2 (7.0)66.0 (5.5)3.8 (5.0)
  Control group (n = 6)60.0 (9.9)56.3 (6.5)−3.7 (8.3)
Diabetes self-care behavior0.34 [−0.55, 1.23]
  Intervention group (n = 11)43.9 (12.5)45.0 (7.8)1.1 (8.0)
  Control group (n = 9)47.1 (7.6)45.4 (4.5)−1.7 (8.4)
Authors

Dr. Jeong is Researcher, and Dr. Kim is Professor, Research Institute of Nursing Science, College of Nursing, Seoul National University, Seoul, and Dr. Choi is Assistant Professor, Department of Nursing, Nambu University, Gwangju, Republic of Korea; and Dr. Gwon is Assistant Professor, College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01061329), and partially supported by the Development of the Technology for Age-friendly Smart Home based on health care funded by the Ministry of Land, Transport and Maritime Affairs (12CHUD-B056165-03-000000). The authors acknowledge Jiye Sin and Sangmi Ku for assisting with telephone support and group health education.

Address correspondence to Jeongeun Kim, PhD, RN, Professor, Research Institute of Nursing Science, College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea; e-mail: jeongeunkim0424@gmail.com.

Received: July 21, 2017
Accepted: March 06, 2018
Posted Online: May 16, 2018

10.3928/19404921-20180502-01

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