Dose of Telehealth to Improve Community-Based Care for Adults Living with Multiple Chronic Conditions: A Systematic Review


The purpose of this systematic review is to identify evidence of the appropriate dose of telehealth intervention services provided to community dwelling adults experiencing chronic illness or disability related to effectiveness, quality, safety, and cost. Academic Search Complete, CINAHL, MEDLINE, Cochrane, and JBI were searched using combinations of “telehealth or telemedicine or telemonitoring or telepractice or telenursing or telecare AND chronic illness or chronic disease”. Of the identified 449 articles, 47 articles met the inclusion criteria. Most study designs were quasi-experimental one group pre-test post-test (N = 16) with few Randomized Controlled Trials (N = 12). Twenty-three published articles studied the effect of telehealth for one chronic condition (49.9%) while 24 (51.1%) examined the effectiveness of telehealth for multiple chronic conditions. Measurement of telehealth outcomes varied and included efficacy, healthcare utilization, quality, adherence, cost, and safety. No standard measure of dose could be extrapolated. Length of intervention was measured and reported differently in each study. The dose of telehealth services that improve care effectiveness, quality, safety, and cost is still unknown for community dwelling adults experiencing chronic illness. The findings from this systematic review do indicate that longer duration of telehealth services (51 weeks), regardless of modality, produced positive outcomes as opposed to those with shorter durations (37 - 38 weeks) that produced neutral or mixed results. Collecting and reporting data related to clinical workflow such as dose of intervention specific to disease and type of modality is recommended. Rigorous study design including standard measurement at the RCT and Comparative Effectiveness level is still needed.

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Mallow, J. , Davis, S. , Herczyk, J. , Pauly, N. , Klos, B. , Jones, A. , Jaynes, M. and Theeke, L. (2021) Dose of Telehealth to Improve Community-Based Care for Adults Living with Multiple Chronic Conditions: A Systematic Review. E-Health Telecommunication Systems and Networks, 10, 20-39. doi: 10.4236/etsn.2021.101002.

1. Introduction

Living with Multiple Chronic Conditions (MCC) increases the likelihood of premature death, disability, and cost of care [1]. Currently, six in ten adults in the United States have a chronic illness and four in ten adults have two or more chronic conditions [2]. People with MCC account for most of healthcare spending, placing them at the center of initiatives to improve health system performance [3]. However, patients who experience MCC often delay or decline care due to transportation costs to multiple visits to clinicians at different care sites, which increases the risks of errors and poor care coordination [4]. Hence, well-coordinated, cost-effective, and safe care is critical for patients with chronic illnesses, especially those with MCC. Initiatives to improve outcomes and decrease cost for care of people with MCC often seek to alter care from intervening only after complications arise to maintaining health with an emphasis on productive healthcare teams, use of technology, access to community resources and patient engagement; known as the Chronic Care Model [5].

Users of the Chronic Care Model have long recognized the benefit of effective use of home and community-based services to improve the health of individuals wanting to live in their own homes instead of long-term care facilities [6]. Evidence shows that keeping individuals in their homes, instead of long-term care facilities, requires multiple essential components including: a knowledgeable, proactive interprofessional team; dynamic communication between caregivers, patients, healthcare system members and community resource providers; establishing mutual goals of care; tailored coordinated consistent education for both the patient and caregivers; and, use of evidence-based practice [7]. In rural areas, this level of care coordination is challenging due to social determinants of health including poor access to care due to cost, inadequate numbers of primary care providers and specialists, transportation difficulties worsened by decreased functional ability, and lack of knowledge of available community resources [8]. Thus, persons living with MCC in rural states require additional interventions to stay in their communities. Telehealth has been used as a solution to deliver care to patients with MCC with limited access to healthcare, while eliminating barriers of access, distance, and transportation [9].

Telehealth, for the purposes of this article, is the provision of remote health care, directly to the patient, using a variety of technology, including telephones, smartphones, and mobile wireless devices, with or without a video connection [10]. The most consistently published benefits of the use of telehealth interventions include increased communication with healthcare providers who provide education, counseling, or remote monitoring of chronic conditions to improve health outcomes [11]. In addition, telehealth has been shown to improve health, reduce costs, and holds promise for scalability to a larger population [12]. While the current evidence supports moving from research that investigates the effectiveness of telehealth to implementation and practice-based research, little evidence can be found related to the dose of telehealth intervention needed to improve outcomes or reduce costs [11]. Hence, there is a gap in knowledge when designing rigorous implementation projects related to the dose of telehealth intervention needed.

The purpose of this review is to identify evidence of the appropriate dose of telehealth intervention services provided to community dwelling adults experiencing chronic illness or disability related to effectiveness, quality, safety, and cost. The question being addressed is, “In community dwelling adults experiencing chronic illness or disability, what dose of telehealth services improve care effectiveness, quality, safety, and cost?”.

2. Methods

2.1. PRISMA Methods

This review was performed and reported according to the PRISMA-P 2015 checklist [13]. The eligibility criteria for the review included the following databases: Academic Search Complete, CINAHL, MEDLINE, Cochrane, and JBI. The MeSH Terms/Key Words used were “telehealth or telemedicine or telemonitoring or telepractice or telenursing or telecare AND chronic illness or chronic disease”. The search mode chosen was “Find all search terms” and the limitations set were: Scholarly Journals, English, Human, Outpatients, All Adult. This search resulted in 449 abstracts and this initial search was replicated by 2 authors (JM, AJ). All information obtained was only from what was presented in the published article. All records were maintained using separate Endnote Libraries for included and excluded articles and an Excel database of findings that were imported into SPSS version 26.

Each of the 449 abstracts was reviewed for the following inclusion criteria: quantitative analysis of data that involved adults greater than the age of 18, at least 1 chronic illness, direct to patient telehealth services, and community dwelling participants. Exclusion for review included studies with qualitative design, only participants under age 18, or no chronic illness addressed. In addition, articles that were preliminary communications, meeting updates, conference abstracts, study protocols, case studies, obtained no outcome measures, telehealth was not the intervention, telehealth provided is not in the home (i.e., provider to provider), participants were non-community dwelling (i.e., nursing home), review articles, and technology methodology papers with no patient participants were excluded.

After the initial review of abstracts, 373 were excluded and 76 full text articles were identified as meeting the above criteria for inclusion and were retrieved for further review. Reasons for further exclusion included: no mention of telehealth, the only use of telephones was to collect study data, review article, pediatric population, editorial, for education purposes or commentary, only provider to provider communication, study protocol, surgically implanted telecommunications, technology development process evaluation, and qualitative design. Each of the 76 full-text articles was separately reviewed by 2 authors for inclusion/exclusion criteria. If the article met inclusion criteria, the article was critiqued and placed into a literature matrix developed a priori for congruence of interpretation. If a differing opinion occurred, the article was reviewed by a third author (SD or JH) for consistency and resolution. See Figure 1 for the full literature review flow chart according to PRISMA guidelines [14].

The review matrix included the authors’ location, year published, study aims, design, total sample size, telehealth intervention sample size, sample description, number of chronic illnesses studied, type of chronic illness, measures, type of telehealth (Remote Patient Monitoring (RPM), asynchronous, synchronous, mixed), length/dose of intervention, results, and study limitations. The Cochrane Collaboration’s tool for assessing risk of bias across studies and presented in the limitations section [15]. Since meta-analysis of the findings will not be performed, grading the body of evidence is not appropriate.

Figure 1. PRISMA literature review flow chart.

2.2. Variables, Analysis, and Interpretation

2.2.1. Study Description

Year published was extracted from each article and is analyzed and presented in the findings as a range and mode.

Location was normalized to the Country of the location of the interventions as well as the number of interventions occurring in each country.

Only quantitative study design studies were included. Type of trials was collected as Randomized Controlled Trial (RCT), quasi-experimental 1 group, quasi-experimental 2 group, quasi-experimental pre/post, retrospective, and descriptive is presented via mode of use. Types of trials are presented via mode of use.

The total number of intervention participants in each study and the total number of participants receiving telehealth in each intervention, the number of participants receiving telehealth across all studies, and the mean and standard deviation of telehealth participants are presented.

2.2.2. Participant Description

The sample description of age was collected from each article as mean age for each study. Income, where reported, was abstracted from each article in US dollars and is reported as frequencies. Education, where reported, was collected as the mode from each study and is reported as frequency. Urban or Rural, was recorded from the article when reported and is reported as frequency. Race of participants was abstracted from each article when available as percentage of non-white participants. Gender of participants was recorded when available in each article and is reported as percent male and female.

Chronic illness number and type were collected from each article. If an article included multiple chronic conditions, each individual condition was noted and included as both an individual chronic condition and as MCC. Number of chronic illness described in each article was recorded as a continuous variable and is reported using mean and standard deviation. Types of chronic illness are reported as frequencies.

2.2.3. Study Measures

Study measures were normalized into types of measures and included adherence, cost, efficacy, healthcare utilization, quality, and safety are reported as frequencies.

The type of telehealth services evaluated in each study is reported as frequencies. The type of telehealth offered was normalized into the three major types of telehealth, synchronous, asynchronous, and remote patient monitoring (RPM).

Due to differing ways dose was collected in each study, the dose of intervention collected from each article was transformed into weeks, and is reported in range, mean, and standard deviation.

2.2.4. Study Results & Limitations

The results of each study were reviewed. If the aims of the study were reached the results were recorded as positive. If the aims of the study were not achieved, the results were recorded as negative. If no change was found between telehealth and usual care, the results were recorded as neutral. If the aims of the study were partially met, or not fully achieved, the findings were recorded as mixed. All results are reported in frequencies. The limitations for each study will be presented in a table.

The relationship between dose, type of telehealth intervention, and results (positive, negative, mixed, and neutral) was examined. Differences between types of chronic illness and telehealth and dose (in weeks) were assessed using analysis of variance techniques with an alpha threshold of 0.05. All analyses were performed using SPSS version 26. The authors held no preformed assumptions of the findings. Hence, power was not calculated prior to analysis.

3. Results

After reviewing the 76 full-text articles, 47 articles met the inclusion criteria for this systematic review. See Table 1 for full literature matrix. The year of publication ranged from 2006 to 2020 with most articles being published in or before 2016 (57.4%) and the most observed year of publication was 2018 (n = 12). The studies were conducted in 19 different countries, with one study conducted in more than one country. The United States of America had the most published articles (N = 20), followed by the United Kingdom (N = 5). The number of participants in the telehealth intervention ranged from 8 - 17,319 and a sum of 37,713 participants across all studies. The most used study designs were quasi-experimental one group pre-test post-test (N = 16) and Randomized Controlled Trials (N = 12). Two of the RCT studies recruited participants from two different countries (with the US being one of the two countries) and three were located solely in the US. In half of RCT designed studies, MCC was the focus of the intervention (N = 6). Efficacy was the most common type of measure to examine (N = 8). The weeks of intervention for the RCTs ranged from 4 - 36 weeks and all but one RCT study had positive findings.

Table 1. Literature matrix by year of publication.

Async = Asynchronous; MCC = Multiple Chronic Conditions; RPM = Remote Patient Monitoring; Sync = Synchronous.

3.1. Participant Description

Mean age of telehealth participants was found in 34 of the articles and ranged from 40 - 83 with mean age at 66.1 (SD, 11.48). Income was reported in 7 articles. Two studies reported income as “comfortable”, two studies reported income as less than $14,000 and two studies reported income between $20,000 - $40,000. The remaining study reported that 25% of the participants had an income that was “inadequate to cover bills.” Eleven reported the education level of participants with eight of the eleven reporting a high school education or less. Of the 15 studies that published rural or urban status of participants, 9 were in urban areas, 4 were in rural areas and 2 included participants from both rural and urban areas. Eight articles presented the race of participants. Across all studies, the mean of non-white participants was 33.0% (SD, 21.5%) with a range of 0% - 65%. Thirty-two of the studies contained descriptive data about the gender of participants with the mean percent of female participants at 51.6% (SD, 16.2%) with a range of 9.3% - 75.0%.

The studies differed in the number and type of chronic illness included. Twenty-three studied the effect of telehealth for one chronic condition (49.9%) while 24 (51.1%) examined the effectiveness of telehealth for multiple chronic conditions. Number of chronic illnesses described in each article ranged from 1 - 7 with a mean of 2.5 (D = 1.92). Type of chronic illness included diabetes (n = 25), cardiovascular disease, hypertension or congestive heart failure (n = 21), chronic obstructive pulmonary disease (COPD) or lung disease (n = 17), cancer (n = 4), stroke (n = 3), smoking (n = 2), amyotrophic lateral sclerosis (n = 2), epilepsy (n = 2), pain (n = 2), aphasia (n = 1), arthritis (n = 1), burn (n = 1), deep vein thrombosis (n = 1), multiple sclerosis (n = 1), osteoarthritis (n = 1), pancreatitis (n = 1), and psoriasis (n = 1). When examining type of chronic illness and dose, there were no significant differences between heart disease, lung disease, diabetes, multiple chronic conditions, and other conditions (F(4, 41) = 0.20, p = 0.94). Nineteen studies (40.4%) measured more than one outcome.

3.2. Study Measures & Outcomes

Efficacy was measured by the majority of studies (61.8%, n = 29) followed by Healthcare Utilization (34.0%, n = 16), Quality (31.9%, n = 15), Adherence (10.6%, n = 5), Cost (8.5%, n = 4), and Safety (2.1%, n = 1). See Figure 2 for a visual of dose of telehealth in weeks with the type of telehealth and chronic illness type.

Figure 3 displays the type of telehealth services evaluated in each study.

The types of measures varied for each category in all articles. Asynchronous telehealth was used by 7 studies (14.9%) for a mean of 18.85 weeks (SD, 16.77), synchronous by 12 (25.5%) for a mean of 57.93 weeks (SD, 114.62), remote monitoring by 13 (27.7%) for a mean of 44.92 weeks (SD, 33.79), and mixed telehealth interventions were used by 15 studies (31.9%) for a mean of 64.15 weeks (SD, 50.20).

Figure 2. Dose of telehealth in weeks for chronic illness and type of telehealth.

Figure 3. Percent of type of telehealth.

3.3. Dose

Length of intervention was measured and reported differently in each study. Most articles listed the amount of time that the intervention was funded, measured, or observed. The amount of time each participant received the intervention, or the amount of time spent engaging with telehealth was not reported. Hence, dose of intervention was normalized to weeks participants received telehealth. The weeks of telehealth evaluated in each study ranged from 0 - 520 (mean of 50.2 weeks, SD, 94.7). There were no significant differences between the type of telehealth offered in a study and the mean length of time telehealth was provided (F(3, 42) = 0.38, p = 0.77). Nearly all of the studies (89.4%, n = 42) reported positive outcomes from telehealth interventions. Studies with positive outcomes were offered for a mean of 51 weeks (SD, 100.16). Those with neutral outcomes were provided for a mean of 37.33 weeks (SD, 25.40) and studies with mixed outcomes for a mean of 38.00 weeks (SD, 19.80). The frequency, percent, dosage in weeks, and number of positive findings for each type of measure can be seen in Table 2.

3.4. Limitations

Of the 47 articles reviewed, a small number were RCT designs (N = 12). Of the RCT designs, limitations were vast. Lack of standard quality measurement across studies suggests that intervention effect estimates are inconsistent and unpredictable. Small sample sizes, short duration of the intervention, lack of fidelity to intervention, self-report survey response, convenience samples, lack of generalizability, and participants lost to follow-up lead to a high risk of bias. The quality of reporting in articles and the details of the intervention were often not described, hence replication would be difficult. A matrix table of the RCT studies can be seen in Table 3.

4. Discussion

In community dwelling adults experiencing chronic illness, the dose of telehealth services that improve care effectiveness, quality, safety, and cost is still unknown. Dose of intervention based on these domains of measurement cannot be determined from this review. Dose is recorded and reported inconsistently across studies. In the literature the most information is based on weeks of delivery

Table 2. Frequency of measures, dose, & findings.

Table 3. RCT design.

Async = Asynchronous; MCC = Multiple Chronic Conditions; RPM = Remote Patient Monitoring; Sync = Synchronous.

but not the time spent with the provider, in education, or interacting with the intervention. The findings from this systematic review do indicate that longer duration of telehealth services (51 weeks), regardless of modality, produced positive outcomes as opposed to those with shorter durations (37 - 38 weeks) that produced neutral or mixed results. As the use of telehealth due to COVID-19 increases, the main problem that remains is how to best implement telehealth in existing work structures and treatment protocols. Concentrated effort on workflow issues such as dose of intervention and clinical practice standards are necessary to ensure that telehealth is being used in the most appropriate ways to improve the patient and clinician experience. Without assessing for the appropriateness of offering telehealth, patient-centered, timely, efficient, and equitable telehealth provision is not possible.

The widespread international study locations found in this systematic review are a true indicator that telehealth is viewed as a global initiative to improve the care of people with MCC. Telehealth has been associated with having a positive impact on outcomes for all chronic conditions. Most frequently, telehealth was used for multiple chronic conditions using a mixed telehealth intervention; however, the studies presented in this review varied in quantitative design, methodology, and quality. The evidence related to telehealth has been building for over 2 decades. At this point in knowledge development, study design should be RCT and Comparative Effectiveness. Additionally, while using telehealth for rural populations has been promoted [16], only 15 of the 47 articles in this review noted the location of their intervention. Of those 15, only four were in rural areas and none were a RCT design. Hence, rigorous evaluation of the use of telehealth for rural populations is still needed, especially in regard to dose of telehealth interventions.

This systematic review highlights that demographic information about participants is collected infrequently and inconsistently. Telehealth is often proposed as a solution to decrease health disparity such as a higher burden of illness, injury, disability, or mortality experienced by those with chronic conditions. However, health care disparity such as lack of insurance, access, and quality are rarely measured in telehealth research. In addition, large amounts of missing data in relation to the determinants of health, such as access to care, were observed. The digital divide makes accessing telehealth an issue [17]. Understanding the results of telehealth trials in the context of the population will continue to be important as we attempt to diminish health disparity in the context of the determinants of health. Age, gender, income, education, rurality, and race all impact efficacy of health interventions. Understanding these demographics in relation to the effectiveness, cost, cost-effectiveness, and the patient experience are domains of measurement suggested by the National Quality Forum guidelines for telehealth [18]. These domains must be present in all telehealth work moving forward and should be evaluated in the context of the determinants of health.

5. Conclusion

After completion of this review, drawing generalizations about dose and impact on outcomes of care is not yet possible. Collecting and reporting data related to clinical workflow such as dose of intervention specific to disease and type of modality is recommended. In addition, focus must be placed on patient-centered, timely, efficient, and equitable care. Rigorous study design at the RCT and Comparative Effectiveness level is still needed. Future rigorous studies must use a standardized tool to measure demographic information that includes individual person factors of the determinants of health so that future interventions can impact health disparity. Developing clinical implementation knowledge for community dwelling individuals who experience MCC in relation to the effectiveness, cost, cost-effectiveness, and the patient experience will be crucial to realizing the promise and potential of telehealth.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.


[1] Bashshur, R.L., Shannon, G.W., Smith, B.R, Alverson, D.C, Antoniotti, N., Barsan, W.G., et al. (2014) The Empirical Foundations of Telemedicine Interventions for Chronic Disease Management. Telemedicine & e-Health, 20, 769-800.
[2] U.S. Department of Health & Human Services (2020) National Center for Chronic Disease Prevention and Health Promotion.
[3] Zulman, D.M., Lewis, E.T., Houston, T.K. and Asch, S.M. (2015) How Can eHealth Technology Address Challenges Related to Multimorbidity? Perspectives from Patients with Multiple Chronic Conditions. Journal of General Internal Medicine, 30, 1063-1070.
[4] Schoen, C., Osborn, R., How, S.K.H., Doty, M.M. and Peugh, J. (2008) In Chronic Condition: Experiences of Patients with Complex Health Care Needs, In Eight Countries, 2008. Health Affairs, 27, w1-w16.
[5] Glasgow, R.E., Tracy Orleans, C., Wagner, E.H., Curry, S.J. and Solberg, L.I. (2001) Does the Chronic Care Model Serve Also as a Template for Improving Prevention? The Milbank Quarterly, 79, 579-612.
[6] Arora, S., Kalishman, S., Dion, D., Som, D., Thornton, K., Bankhurst, A., et al. (2011) Partnering Urban Academic Medical Centers and Rural Primary Care Clinicians to Provide Complex Chronic Disease Care. Health Affairs, 30, 1176-1184.
[7] Hirschman, K.B. and Hodgson, N.A. (2018) Evidence-Based Interventions for Transitions in Care for Individuals Living with Dementia. The Gerontologist, 58, S129-S140.
[8] Cockerham, W.C., B.W. Hamby, and Oates G.R. (2017) The Social Determinants of Chronic Disease. American Journal of Preventive Medicine, 52, S5-S12.
[9] Mallow, J.A., Theeke, L.A., Theeke, E. and Mallow, B.K. (2018) The Effectiveness of mI SMART: A Nurse Practitioner Led Technology Intervention for Multiple Chronic Conditions in Primary Care. International Journal of Nursing Sciences, 5, 131-137.
[10] Dorsey, E.R., Achey, M.A., Beck, C.A., Beran, D.B., Biglan, K.M., Boyd, C.M., et al. (2016) National Randomized Controlled Trial of Virtual House Calls for People with Parkinson’s Disease: Interest and Barriers. Telemedicine & e-Health, 22, 590-598.
[11] Totten, A.M., Womack, D.M., Eden, K.B., McDonagh, M.S., Griffin, J.C., Grusing, S., et al. (2016) Telehealth: Mapping the Evidence for Patient Outcomes from Systematic Reviews. Agency for Healthcare Research and Quality, Rockville.
[12] Quinn, W.V., O’Brien, E. and Springan, G. (2018) Using Telehealth to Improve Home-Based Care for Older Adults and Family Caregivers.
[13] Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., et al. (2015) Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 Statement. Systematic Reviews, 4, Article No. 1.
[14] Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G. and the PRISMA Group (2010) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. International Journal of Surgery, 8, 336-341.
[15] Higgins, J.P., Altman, D.G., Gøtzsche, P.C., Jüni, P., Moher, D., Oxman, A.D., et al. (2011) The Cochrane Collaboration’s Tool for Assessing Risk of Bias in Randomised Trials. BMJ, 343, d5928.
[16] Barreiro, M., Coles, A., Conradt, C., Hales, E. and Zellmer, E. (2020) Barriers to the Implementation of Telehealth in Rural Communities and Potential Solutions. Nursing Undergraduate Work, 12.
[17] Cortelyou-Ward, K., Atkins, D.N., Noblin, A., Rotarius, T., White, P., et al. (2020) Navigating the Digital Divide: Barriers to Telehealth in Rural Areas. Journal of Health Care for the Poor and Underserved, 31, 1546-1556.
[18] Hollander, J.E., Davis, T.M., Doarn, C., Goldwater, J.C., Klasko, S., Lowery, C., et al. (2018) Recommendations from the First National Academic Consortium of Telehealth. Population Health Management, 21, 271-277.
[19] Kim, H., Kim, N. and Ahn, S. (2006) Impact of a Nurse Short Message Service Intervention for Patients with Diabetes. Journal of Nursing Care Quality, 21, 266-271.
[20] Sarkar, U., Handley, M.A., Gupta, R., Tang, A., Murphy, E., Seligman, H.K., et al. (2008) Use of an Interactive, Telephone-Based Self-Management Support Program to Identify Adverse Events among Ambulatory Diabetes Patients. Journal of General Internal Medicine, 23, 459-465.
[21] Fursse, J., Clarke, M., Jones, R., Khemka, S. and Findlay, G. (2008) Early Experience in Using Telemonitoring for the Management of Chronic Disease in Primary Care. Journal of Telemedicine & Telecare, 14, 122-124.
[22] Hamar, B., Wells, A., Gandy, W., Haaf, A., Coberley, C., Pope, J.E., et al. (2010) The Impact of a Proactive Chronic Care Management Program on Hospital Admission Rates in a German Health Insurance Society. Population Health Management, 13, 339-345.
[23] Rai, A., Prichard, P., Hodach, R. and Courtemanche, T. (2011) Using Physician-Led Automated Communications to Improve Patient Health. Population Health Management, 14, 175-180.
[24] Vinson, M.H., McCallum, R., Thornlow, D.K. and Champagne, M.T. (2011) Design, Implementation, and Evaluation of Population-Specific Telehealth Nursing Services. Nursing Economics, 29, 265-277.
[25] Gellis, Z.D., Kenaley, B., McGinty, J., Bardelli, E., Davitt, J. andTen Have, T. (2012) Outcomes of a Telehealth Intervention for Homebound Older Adults with Heart or Chronic Respiratory Failure: A Randomized Controlled Trial. Gerontologist, 52, 541-552.
[26] Akematsu, Y. and Tsuji, M. (2012) Measuring the Effect of Telecare on Medical Expenditures Without Bias Using the Propensity Score Matching Method. Telemedicine & e-Health, 18, 743-747.
[27] Steventon, A., Bardsley, M., Billings, J., Dixon, J., Doll, H., Hirani, S., et al. (2012) Effect of Telehealth on Use of Secondary Care and Mortality: Findings from the Whole System Demonstrator Cluster Randomized Trial. British Medical Journal, 345, e3874.
[28] Jaglal, S.B., Haroun, V.A., Salbach, N.M., Hawker, G., Voth, J., Lou, W., et al. (2013) Increasing Access to Chronic Disease Self-Management Programs in Rural and Remote Communities Using Telehealth. Telemedicine & e-Health, 19, 467-473.
[29] Balato, N., Megna, M., Di Costanzo, L., Balato, A. and Ayala, F. (2013) Educational and Motivational Support Service: A Pilot Study for Mobile-Phone-Based Interventions in Patients with Psoriasis. British Journal of Dermatology, 168, 201-205.
[30] Baker, L.C., Macaulay, D.S., Sorg, R.A., Diener, M.D., Johnson, S.J. and Birnbaum, H.G. (2013) Effects of Care Management and Telehealth: A Longitudinal Analysis Using Medicare Data. Journal of the American Geriatrics Society, 61, 1560-1567.
[31] Chung, H., Kim, A., Neighbors, C.J., Johnine Cummings, L.C.S.W.-R., Ricketts, S., O’Grady, M.A., et al. (2013) Early Experience of a Pilot Intervention for Patients with Depression and Chronic Medical Illness in an Urban ACO. General Hospital Psychiatry, 35, 468-471.
[32] Surate Solaligue, D.E., Hederman, L. and Martin, C.M. (2014) What Weekday? How Acute? An Analysis of Reported Planned and Unplanned GP Visits by Older Multi-Morbid Patients in the Patient Journey Record System Database. Journal of Evaluation in Clinical Practice, 20, 522-526.
[33] Cecil, A.H. (2014) An 8-Year Telephone Support Group for Home-Bound People with Multiple Sclerosis: Adapting Therapeutic Methods to Overcome Isolation and Immobility. Social Work with Groups, 37, 129-141.
[34] Gellis, Z.D., Kenaley, B.L. and Have, T.T. (2014) Integrated Telehealth Care for Chronic Illness and Depression in Geriatric Home Care Patients: The Integrated Telehealth Education and Activation of Mood (I-TEAM) Study. Journal of the American Geriatrics Society, 62, 889-895.
[35] Hsieh, H.-L., Tsai, C.-H., Chih, W.-H. and Lin, H.-H. (2015) Factors Affecting Success of an Integrated Community-Based Telehealth System. Technology & Health Care, 23, S189-S196.
[36] Leboeuf-Yde, C., Krüger Jensen, R. and Wedderkopp, N. (2015) Persistence of Pain in Patients with Chronic Low Back Pain Reported via Weekly Automated Text Messages over One Year. BMC Musculoskeletal Disorders, 16, Article ID: 299.
[37] Chatwin, M., Hawkins, G., Panicchia, L., Woods, A., Hanak, A., Lucas, R., et al. (2016) Randomized Crossover Trial of Telemonitoring in Chronic Respiratory Patients (TeleCRAFT Trial). Thorax, 71, 305-311.
[38] Doñate-Martínez, A., Ródenas, F. and Garcés, J. (2016) Impact of a Primary-Based Telemonitoring Programme in HRQOL, Satisfaction and Usefulness in a Sample of Older Adults with Chronic Diseases in Valencia (Spain). Archives of Gerontology & Geriatrics, 62, 169-175.
[39] Gonçalves, N., Aparecida Ciol, M., Dantas, R.A.S., Farina Jr., J.A. and Rossi, L.A. (2016) A Randomized Controlled Trial of An Educational Programme with Telephone Reinforcement to Improve Perceived Health Status of Brazilian Burn Victims at 6-Month Post Discharge. Journal of Advanced Nursing, 72, 2508-2523.
[40] Kekäle, M., Söderlund, T., Koskenvesa, P., Talvensaari, K. and Airaksinen, M. (2016) Impact of Tailored Patient Education on Adherence of Patients with Chronic Myeloid Leukaemia to Tyrosine Kinase Inhibitors: A Randomized Multicentre Intervention Study. Journal of Advanced Nursing, 72, 2196-2206.
[41] Schougaard, L., Larsen, L.P., Jessen, A., Sidenius, P., Dorflinger, L., de Thurah, A., et al. (2016) AmbuFlex: Tele-Patient-Reported Outcomes (TelePRO) as the Basis for Follow-Up in Chronic and Malignant Diseases. Quality of Life Research, 25, 525-534.
[42] Hamad, G.A., Crooks, M. and Morice, A.H. (2016) The Value of Telehealth in the Early Detection of Chronic Obstructive Pulmonary Disease Exacerbations: A Prospective Observational Study. Health Informatics Journal, 22, 406-413.
[43] Dhillon, J.S., Wünsche, B. and Lutteroth, C. (2016) Designing and Evaluating a Patient-Centred Health Management System for Seniors. Journal of Telemedicine & Telecare, 22, 96-104.
[44] Aivaliotis, V., Lee,Y., Zia, J., Wassef, W., Abramson, M. and Park, W. (2017) Telephone-Based Mindfulness Therapy Intervention for Patients with Chronic Pancreatitis. Digestive Diseases & Sciences, 62, 502-509.
[45] Albert, N.M., Dinesen, B., Spindler, H., Southard, J., Bena, J.F, Catz, S., et al. (2017) Factors Associated with Telemonitoring Use among Patients with Chronic Heart Failure. Journal of Telemedicine & Telecare, 23, 283-291.
[46] Cobos-Campos, R., de Larrinoa, A.A.F., de Lafuente Moriñigo, A.S., Diez, N.P. and Barandiaran, F.A. (2017) Effectiveness of Text Messaging as an Adjuvant to Health Advice in Smoking Cessation Programs in Primary Care. A Randomized Clinical Trial. Nicotine & Tobacco Research, 19, 901-907.
[47] Dario, C., Toffanin, R., Calcaterra, F., Saccavini, C., Stafylas, P., Mancin, S., et al. (2017) Telemonitoring of Type 2 Diabetes Mellitus in Italy. Telemedicine & e-Health, 23, 143-152.
[48] Guilkey, R.E., Draucker, C.B., Wu, J.W., Yu, Z.S. and Kroenke, K. (2018) Acceptability of a Telecare Intervention for Persistent Musculoskeletal Pain. Journal of Telemedicine & Telecare, 24, 44-50.
[49] Kamei, T., Yamamoto, Y., Kanamori, T., Nakayama, Y. and Porter, S.E. (2018) Detection of Early-Stage Changes in People with Chronic Diseases: A Telehome Monitoring-Based Telenursing Feasibility Study. Nursing & Health Sciences, 20, 313-322.
[50] Kurland, J., Anna, L. and Stokes, P. (2018) Effects of a Tablet-Based Home Practice Program with Telepractice on Treatment Outcomes in Chronic Aphasia. Journal of Speech, Language & Hearing Research, 61, 1140-1156.
[51] Oddone, E.Z., Gierisch, J.M., Sanders, L.L., Fagerlin, A., Sparks, J., McCant, F., et al. (2018) A Coaching by Telephone Intervention on Engaging Patients to Address Modifiable Cardiovascular Risk Factors: A Randomized Controlled Trial. Journal of General Internal Medicine, 33, 1487-1494.
[52] Taylor, S.S., Oddone, E.Z., Coffman, C.J., Jeffreys, A.S., Bosworth, H.B. and Allen, K.D. (2018) Cognitive Mediators of Change in Physical Functioning in Response to a Multifaceted Intervention for Managing Osteoarthritis. International Journal of Behavioral Medicine, 25, 162-170.
[53] Hamar, G.B., Coberley, C., Pope, J.E., Cottrill, A., Verrall, S. and Larkin, S., et al. (2018) Effect of Post-Hospital Discharge Telephonic Intervention on Hospital Readmissions in a Privately Insured Population in Australia. Australian Health Review, 42, 241-247.
[54] Harnett, P., Jones, M., Almond, M., Ballasubramaniam, G. and Kunnath, V. (2018) A Virtual Clinic to Improve Long-Term Outcomes in Chronic Kidney Disease. Clinical Medicine, 18, 356-363.
[55] Cameron, J.E., Voth, J., Jaglal, S.B., Guilcher, S.J.T., Hawker, G. and Salbach, N.M. (2018) “In This Together”: Social Identification Predicts Health Outcomes (via Self-Efficacy) in a Chronic Disease Self-Management Program. Social Science & Medicine, 208, 172-179.
[56] Litke, J., Spoutz, L., Ahlstrom, D., Perdew, C., Llamas, W., Erickson, K., et al. (2018) Impact of the Clinical Pharmacy Specialist in Telehealth Primary Care. American Journal of Health-System Pharmacy, 75, 982-986.
[57] Bakas, T., Sampsel, D., Israel, J., Chamnikar, A., Ellard, A., Clark, J.G., et al. (2018) Satisfaction and Technology Evaluation of a Telehealth Robotic Program to Optimize Healthy Independent Living for Older Adults. Journal of Nursing Scholarship, 50, 666-675.
[58] Herold, R., van den Berg, N., Dörr, M. and Hoffmann, W. (2018) Telemedical Care and Monitoring for Patients with Chronic Heart Failure Has a Positive Effect on Survival. Health Services Research, 53, 532-555.
[59] Herold, R., Hoffmann, W. and van den Berg, N. (2018) Telemedical Monitoring of Patients with Chronic Heart Failure Has a Positive Effect on Total Health Costs. BMC Health Services Research, 18, Article No. 271.
[60] Walker, P.P., Pompilio, P.P., Zanaboni, P., Bergmo, T.S., Prikk, K., Malinovschi, A., Montserrat, J.M., et al. (2018) Telemonitoring in Chronic Obstructive Pulmonary Disease (CHROMED). A Randomized Clinical Trial. American Journal of Respiratory and Critical Care Medicine, 198, 620-628.
[61] Frederix, I., Vanderlinden, L., Verboven, A.-S., Welten, M., Wouters, D., De Keulenaer, G., et al. (2019) Long-Term Impact of a Six-Month Telemedical Care Programm eon Mortality, Heart Failure Readmissions and Healthcare Costs in Patients with Chronic Heart Failure. Journal of Telemedicine and Telecare, 25, 286-293.
[62] Brain, M.M., Reynolds, C., Nesbeda, H., Walsh, M., Fem, R., Magruder, A., et al. (2019) Intervisit Management of Patients with Diabetes: Synchronized Phone Calls. The Journal for Nurse Practitioners, 15, e157-e160.
[63] Piette, J.D., Striplin, D., Fisher, L., Aikens, J.E., Lee, A., Marinec, N., et al. (2020) Effects of Accessible Health Technology and Caregiver Support Posthospitalization on 30-Day Readmission Risk: A Randomized Trial. The Joint Commission Journal on Quality and Patient Safety, 46,109-117.
[64] Kotsani, K., Antonopoulou, V., Kountouri, A., Grammatiki, M., Rapti, E., Karras, S., Trakatelli, C., et al. (2018) The Role of Telenursing in the Management of Diabetes Type 1: A Randomized Controlled Trial. International Journal of Nursing Studies, 80, 29-35.

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