The effectiveness of telehealth on self-management for older adults with a chronic condition: A comprehensive narrative review of the literature

Abstract

Introduction

Given the rapid global growth of older adults with chronic condition(s), the present study explored the effects of telehealth technologies on self-management among older adults with chronic condition(s).

Methods

Clinical trial studies on telehealth self-management for older adults with chronic condition(s) published in peer reviewed journals were reviewed systematically. In addition, the potential biases were assessed for included studies.

Results

This review identiﬁed 31 articles regarding the effectiveness of telehealth technologies on self-management among older adults.

Discussion

According to the findings of the present study, telehealth technologies have shown effectiveness on improving self-care skills, improving self-monitoring behaviours and improving clinical outcomes among older adults with chronic condition(s) in different settings. Using telehealth technologies is an advisable intervention for promoting older patients’ self-management. However, it is critical to consider racial diversity and culture-related health behaviour differences when using telehealth technologies.

Keywords

Telehealth technology self-management chronic condition older adults

Introduction

Owing to the rapid changes in diets and lifestyles, chronic disease health-care demands are a great challenge worldwide.¹ By 2020, 57% of global diseases will be chronic conditions, which will contribute to over 75% of global death.² By the US Centers for Diseases Control’s definition, if an individual lives with a disease for three months or more, it is a chronic condition, and the diseases are called chronic diseases. With global aging, older adults become the major victims of chronic diseases. In the United States, over 80% of older adults (55+ years old) live with at least one chronic condition, and 68% live with at least two.³ Chronic conditions can place substantial burdens on health, emotion, economic status and quality of life of older adults and their families because chronic diseases are usually incurable.⁴ The seven most common chronic illnesses are arthritis, hypertension, heart disease, diabetes mellitus, respiratory disease, stroke and cancer.⁴ These common chronic diseases are significant causes of disability and death.

The heavy burden of chronic diseases among older adults accelerates the development of self-management, which is irreversibly altering the traditional doctor–patient relationship.⁵ Self-management includes daily tasks that an individual must take to control or reduce the impact of disease on physical health status.⁶ Such self-management tasks should be undertaken with the collaboration and guidance of the individual’s physician and other health-care providers.⁶

The goals of self-management are increasing self-care ability (e.g. medical management, medicine adherence and skills for health-care), using self-monitoring behaviours (e.g. diet and exercise) and improving clinical outcomes.^7,8 In daily life, 95% of health-related decisions of people who are living with chronic condition(s) are made without being monitored by health-care professionals.⁹ Therefore, it is important for chronic disease patients to master self-care skills. Self-care ability means that patients grasp self-care knowledge to provide routine medical copings for chronic condition(s), such as prescription adherence, maintaining an insulin regimen, blood glucose self-monitoring, blood-pressure self-measuring, chronic depression management skills, etc.^10–12

Furthermore, chronic disease patients need to monitor their daily health behaviours, including diet, weight control, tobacco smoking, physical activity, etc.^9,13 Clinical outcomes refers to symptoms of illness, mental health state and the effects of a disease on the patient’s functions.¹⁴

With the development of technologies and the increasing demand of self-management for patients with chronic condition(s), telehealth technologies for self-management appear to be an important topic in the health-care field and a major research interest. Telehealth is defined by Picot as ‘the use of communications technology (e.g., audio, video, and website) to deliver health and health-care services and information over large and small distances’.¹⁵ The positive effect of self-management on chronic conditions has been well documented.^16–18 It is inevitable for patients with chronic condition(s) to self-manage their illnesses, including healthy diet, doing physical activities, having basic health-care skills and consuming prescriptions.⁵

However, many patients with chronic diseases have problems self-managing their chronic condition(s) due to lack of awareness/knowledge, poor physician communication, low social support, physical symptoms, transportation problems and financial problems.¹⁹ Telehealth techniques have been employed actively in self-management programmes, and older adults with chronic condition(s) can get irreplaceable benefits from telehealth technologies. Telehealth is especially promising for older adults with chronic condition(s) to improve access to health services, obtain self-management skills, enhance communication with health-care providers, improve clinical outcomes and have better quality of life.^20,21 Furthermore, telehealth technologies have been identified as cost-effective strategies to significantly reduce the medical expense of hospital and nursing home stays.^22–24 Based on prior studies, the main available telehealth programmes supporting self-management include patient monitoring and professional assessment,^25,26 decision support tools,²⁷ 24-hour access lines,²⁸ personal alarm messages,²⁹ web-based education programmes,³⁰ teleinterventions for coping with depression³¹ and rehabilitation support.²²

In a broader sense, telehealth is an attitude and a way of acting locally and thinking globally to provide health care for patients with chronic condition(s) by using communication technologies. Given the rapid global growth of older adults with chronic condition(s), our study explored the effects of telehealth technologies on self-management among older adults with chronic condition(s). We systematically reviewed clinical trial studies on telehealth self-management for older adults with chronic condition(s) that had been published in peer reviewed journals. According to the goals of self-management discussed above, we sought to answer the following research questions:

Are telehealth technologies effective on improving self-care skills among older adults with chronic condition(s)?

Are telehealth technologies effective on improving self-monitoring behaviours among older adults with chronic condition(s)?

Are telehealth technologies effective on improving clinical outcomes among older adults with chronic condition(s)?

Methods

Search strategies

The present review used the PRISMA Statement guidelines³² to examine the effectiveness of telehealth technologies on self-management for older adults with chronic condition(s) (Figure 1). Potential peer-reviewed articles published by 16 March 2017 were identified through searching six medical databases:³³ PubMed, Psych Info, Web of Science, Cochrane library, CINAHL and Ovid Medline. The searching strategy used Boolean operators combined with terms related to ‘telehealth strategies’, ‘self-management’, ‘chronic condition’ and ‘older adults’. The details of the searching strategy and all searching terms with wildcards are presented in Table 1. The search terms were built up by a scoping search. To decide the search terms, two investigators conducted a scoping search before the main search to identify keywords that are used to index telehealth on self-management for chronic conditions, and then the identified keywords were used to begin the main search.³³

Figure 1.

Flow Diagram of Study Selection Process.

Table 1.

Databases and searching strategy.

Search	Databases	Search terms	Additional restrictions
Telehealth strategy Self-management Chronic condition Older adults	PubMed; Psych Info; Web of Science; Cochrane library databases; CINAHL; and Medline	((ehealth OR (website or web?based program or online program or internet?based program) OR (telemedicine or telehealth* or tele?monitoring or videophone) OR (m?health or mobile?health)) AND ((chronic disease* or chronic condition* or chronic illness) OR (arthritis or hypertension or heart disease or diabetes mellitus or respiratory disease or stroke or and cancer)) AND ((older people or older adult) OR (senior or old? age) OR geriatric*)) AND ((self?management) OR (self?care))	Peer reviewed Language: English Age: 55+ years Clinical trial

Selection process

The present review was restricted to those articles: (a) written in the English language; (b) published in peer-reviewed journals; (c) where participants live with or are diagnosed with chronic condition(s) and their average age should be 55 years or older; (d) that were clinical trials (experimental designs or quasi-experimental designs); and (e) focused on using telehealth technologies. This systematic review only included clinical trial studies because clinical trial studies are the highest quality evidence of effectiveness of a(n) intervention/strategy/programme among a group of participants³⁴ and the majority of medical studies are clinical trial designs. Studies were excluded if the study did not focus on examining outcomes related to self-management. Two investigators independently selected articles by screening titles and abstracts, and then full texts of relevant studies were verified by investigators based on the above inclusion criteria. During this process, any disagreement regarding eligibility was discussed by the investigators until consent was achieved. Moreover, a research team of six professional researchers reviewed and discussed every step of this systematic review. The initial search yielded 366 potential articles (PubMed – 179; Cochrane – 16; Psych Info – 74; Web of Science – 53, CINAHL – 42 and Ovid Medline – 2). After duplicates were removed, 325 records were left. Of those remaining 325 records, 68 articles were included based on screening title and abstract, and then 31 were identified for full-text review.

Coding strategy

The content data were analysed from included articles. To prepare for responses to our three research questions, two investigators identified and coded characteristics of participants, telehealth settings, type of telehealth technologies and results of using telehealth from the content of included articles. Results reported in the included articles were counted to help determine the effectiveness tone of telehealth on self-management and then were labelled to categorize the meaning.^33,35 All disagreements were discussed and solved through research meetings.

Results

The present review identiﬁed 31 articles that met the inclusion criteria. Within the included 31 articles, there were 14 studies of long-distance communication technologies (LDCs), three studies of web-based technologies (WBTs), nine studies of home-health monitoring systems (HHMSs), four studies of tele-education technologies (TETs) and one study regarding a telehealth programme applying multiple telehealth technologies, including LDC, WBT, HHMS and TET. LDC refers to programmes or technologies that support interactions and communications between health-care providers and patients through long distances, such as using cellphone and/or videophone to communicate with patients. WBT refers to network applications accessible over the Internet, including blogs, discussion boards, online multimedia and online surveys. The HHMSs enable patients to monitor their health factors (e.g. blood pressure) from their homes and share the information electronically with their health-care providers. The TETs were used to deliver health-care education electronically from health-care providers to patients using TV channels to provide educational materials to patients with chronic heart failure.²⁴

The included studies cover chronic diseases, including general chronic conditions, chronic heart failure, diabetes, chronic pain, chronic obstructive pulmonary diseases, chronic lung diseases, hypertension, rheumatoid arthritis, overactive bladder, and chronic anti-vitamin K antagonists. The included studies come from all over the world. We have two included studies^36,37 focusing on Hispanic populations and one study focusing on the Canadian population and the British population.³⁸ Detailed information of technologies, participants, programme settings, measurements and results of included studies are exhibited in Table 2.

Table 2.

Summary of included studies.

Studies	Participants and settings	Telehealth technologies	Results
Dang et al. (2017)	Patients with chronic heart failure Sample size n = 61 Mean age 55 years Three-months intervention	LDC	Self-care efficacy improved (p = 0.00) Quality of life improved (p = 0.00)
Rocque et al. (2017)	Cancer patients with chronic pain and fatigue -Sample size n = 42 Mean age 67 years Eight-weeks educational intervention	TET	No clinical results were reported due to the high attrition rate
Ritchie et al. (2016)	Heart failure patients Sample size n = 478 Mean age 63 years 30-days intervention	LDC	Fewer days in the hospital and better self-management (p = 0.03)
Hale et al. (2016)	Chronic heart failure patients Sample size n = 29 Mean age 71.7 years 90-days intervention	HHMS	Reductions in risk of all-cause hospitalization and the number of all-cause length of stay (p = .04) Medication adherence improved Health-related quality of life decreased (SD = 22.3, p = 0.002)
Lilholt et al. (2016)	Patients with chronic obstructive pulmonary disease Sample size n = 60 Mean age 70 years Two-months intervention	HHMS	No significant impact on functional health literacy (p > 0.05)
Piette et al. (2015)	Chronic heart failure patients Sample size n = 331 Mean age 67.8 years 12-months intervention Frequency: weekly	LDC	Increased medication adherence (B = 0.5092; 95% CI 0.0857 to 0.9329) Less weight gain (B = −0.0148; 95% CI −0.0232 to −0.0064). Less shortness of breath (B = −0.0114; 95% CI −0.0206 to −0.0022)
Allam et al. (2015)	Patients with rheumatoid arthritis Sample size n = 156 Mean age 57.95 years Four-months follow-up	LDC	Increased physical activity (B = 3.39) Decreased health care utilization and medication overuse (B = −0.33) Empowerment (B = 2.29)
Gellis et al. (2014)	Patients with chronic conditions Sample size n = 94 Mean age 80.1 ± 7.8 years Three-months intervention	LDC	Problem-solving skills improved Self-efficacy improved Less doctor-visiting and hospital stay Fewer depression symptoms
Barberan-Garcia et al. (2014)	Patients with chronic obstructive pulmonary disease Sample size n = 154 Mean age 64 ± 6 years Eight-weeks intervention Three days per week	LDC	Daily physical activity increased (p = 0.03)
Young et al. (2014)	Patients with diabetes Sample size n = 101 36.6% of participants in the 55–65-year-old age group Nine-months intervention	LDC	Higher self-efficacy scores (p < 0.05) Better physical and mental health outcomes (p < 0.05)
Knight (2014)	Patients with chronic conditions Sample size n = 45 Mean age 63 ± 5 years 12-weeks intervention	WBT	Improvements in physical activity (F(2247) = 1.615, p = 0.201, η = 0.013) Improvement in controlling body weight (F(2247) = 2.147, p = 0.119, η = 0.017) Reducing blood pressure (F(2247) = 1.260, p = 0.286, η = 0.010) Lower blood glucose (F(2247) = 0.520, p = 0.595, ηP2 = 0.004)
Tabak et al. (2014)	Patients with chronic conditions Sample size n = 29 Mean age 64.1 ± 9 years Nine-months intervention	MTP	Clinical health outcome improved
De San Miguel et al. (2013)	Patients with chronic conditions Sample size n = 80 Age range 54–88 years	LDC	Use of telehealth technology has reported good outcomes in terms of reduced hospitalizations and improved quality of life
Piette et al. (2013)	Latino patients with diabetes or hypertension Sample size n = 268 Mean age 55.1 years 6–12-weeks intervention Frequency: weekly		86% reporting that the calls helped them a great deal in managing their health problems.
Barcellona et al. (2013)	Patients with chronic anti-vitamin K antagonists Sample size n = 114 Mean age 61 years Six-months follow-up	HHMS	The therapeutic range with home monitoring increased (p < 0.001)
Pecina et al. (2013)	Patients with chronic conditions Sample size n = 205 Mean age 79.8 years 12-months follow-up	HHMS	No significant improvement of physical component summary score (p = 0.32) No significant improvement of mental component summary (p = 0.12)
Gellis et al. (2012)	Patients with chronic heart or respiratory failure Sample size n = 102 Mean age 79.1 years Three-months follow-up for health condition 12-months follow-up for health care utilization	HHMS	General health improved (F = 3.91, p < 0.016) Social functioning improved (F = 3.64, p < 0.014) Fewer depression symptoms/more exercises (F = 6.47, p < 0.008) Less emergency room visiting (p = 0.03)
Ruiz et al. (2011)	Patients with overactive bladder Sample size n = 25 Mean age 62.9 years Six-months follow-up	LDC	Increased knowledge (SMD = 4.17, large effect size) Clinical symptoms improved (SMD = 1.20–1.30, large effect sizes, p < 0.001) Self-efficacy improved (SMD = 1.84, large effect size, p < 0.001) Self-efficacy in performing pelvic muscle exercises (SMD = 1.41, large effect size, p < 0.001) Self-efficacy in controlling urge symptoms (SMD = 1.32, large effect size, p < 0.001) Health-related quality of life increased (SMD = 1.13, large effect size, p < 0.001)
Glasgow et al. (2011)	Patients with diabetes Sample size n = 270 Mean age 60 years Weekly evaluation	WBT	Improvement in healthy eating (r = 0.20, p = 0.04) Reduction of dietary fat (r = −0.31, p = 0.001) Improvement in self-monitoring and physical exercise (r = 0.20, p = 0.033) No significant improvement in medication adherence
Hawkins (2010)	Patients with diabetes Sample size n = 66 Mean age 64.9 years Six-months intervention Frequency: weekly and monthly	LDC	Diabetes knowledge increased (p = 0.023) Diabetes self-efficacy increased (p = 0.002) Decrease in HbA1c (p = 0.043)
Wu et al. (2010)	Patients with type 2 diabetes Sample size n = 46 Mean age 58 years	LDC	Being reminded to comply with their regimen Prompting and motivating adherence to diabetes self-care behaviours Improved self-esteem Feeling ‘worthy of interest’
Davis et al. (2010)	Patients with diabetes Sample size n = 165 Mean age 59.9 years 12-months intervention	TET	Reduction in glycated haemoglobin (p = 0.004)
Berman et al. (2009)	Patients with chronic pain Sample size n = 78 Mean age 55 years Six-week intervention	TET	Improvement in using non-medical self-care techniques to manage pain (SMD = 0.81, p < 0.0001) Reducing in pain intensity (SMD = −0.86, p < 0.0001)
Seto et al. (2009)	Patients with diabetes type 2 from the UK and Canada Sample size UK: n = 137; Canada: n - = 33 Mean age UK: nine-months follow-up; Canada: four-months intervention Frequency: 2 days/week	HHMS	Among UK patients: -drop in systolic blood pressure B = −6.5, 95% CI −0.8 to −12.2 Among Canadian patients: -no significant decrease in blood pressure or blood glucose
Woodend et al. (2008)	Patients with chronic heart failure or angina Sample size total n = 249 patients (121 with heart failure and 128 with angina) Mean age heart failure: 67 ± 13 years; angina: 66 ± 12 years Three-months intervention	WBT	51% reduction in health care utilization (p = <0.05) No significant difference on symptoms and functional status between treatment group and control group improvements in physical functioning (p = 0.04) Improvements in bodily pain (p = 0.03) Improvements in social functioning (p = 0.04)
Balk et al. (2008)	Patients with chronic heart failure Sample size n = 214 Median age 66 years Average 288-day follow-up	TET	No differences for the clinical outcomes (p > 0.05) No difference for quality of life or self-care behaviors (p > 0.05) Increase in knowledge about heart failure (p < 0.0001)
Cole et al. (2006)	Patients with depression and congestive heart failure Sample size n = 35 Mean age 73.8 years 24-weeks intervention	LDC	Fewer depression symptoms and PHQ score reduced (p = 0.0003)
Riegel et al. (2006)	Latino patients with chronic heart failure Sample size n = 134 Mean age 72 ± 11 years Six-months intervention	LDC	No significant difference in heart failure hospitalizations and clinical outcomes between treatment group and control group (p < 0.05)
Noel et al. (2004)	Patients with heart failure, chronic lung disease, and/or diabetes mellitus Sample size n = 104 Mean age 71 years 6–12 months intervention	LDC	Decrease at six months for bed-days-of-care (p < 0.0001) Decrease at six months for urgent clinic/emergency room visits (p = 0.023) Decrease at six months for A1C levels (p < 0.0001) Improvement at 12 months for cognitive status (p = 0.028) Improvement at three months for patient satisfaction (p < 0.001)
De Lusignan et al. (2001)	Patients with chronic heart failure Sample size n = 20 Mean age 74 years 12-months follow-up	WBT	No significant differences in the quality of life (p > 0.05) No significant difference in clinical outcomes (p > 0.05)
Johnston (2000)	Patients with chronic conditions Sample size n = 212 Mean age 69 years	LDC	Medication compliance Knowledge of disease Ability for self-care

LDC: long-distance communication technology; WBT: web-based technology; HHMS: home-health monitoring system; TET: tele-education technology; MTP: telehealth programme applying multiple telehealth technologies; CI: confidence interval; SMD: standardized mean difference; PHQ: Patient Health Questionnaire

To guarantee that included studies represent trusted evidence, the following potential biases were assessed for low risk, unclear risk and high risk: selection biases, performance biases, detection biases, attrition biases, and reporting biases. The quality of included studies was evaluated by using Cochran’s quality of evidence guideline.³⁹ Studies were marked as low risk for detection bias if their outcomes were assessed from clinical medical records or from standard medical forms. Attrition bias was caused by amount, nature or handling of missing outcome data or failure of follow-up. The majority of included studies reported all studies’ pre-specified outcomes and were marked low risk for reporting bias. A few studies ignored some selective outcomes, and they were evaluated as high risk. Any disagreement on the evaluation was discussed by the authors. More information about the evaluation of quality is available in Table 3 and can be requested from the authors.

Table 3.

Risk of bias of included studies.

	Selection		Performance		Detection	Attrition	Reporting
Study	Sequence generation	Allocation concealment	Blinding of participants	Blinding of care providers	Blinding of outcome assessment	Incomplete outcome data	Selective outcome reporting
Dang et al. (2017)	LR	LR	LR	UR	LR	LR	LR
Rocque et al. (2017)	HR	HR	LR	LR	UR	HR	LR
Ritchie et al. (2016)	LR	UR	HR	UR	LR	LR	HR
Hale et al. (2016)	LR	LR	UR	HR	LR	LR	HR
Lilholt et al. (2016)	LR	LR	UR	HR	LR	LR	UR
Piette et al. (2015)	LR	LR	LR	HR	LR	LR	LR
Allam et al. (2015)	LR	LR	HR	LR	UR	UR	LR
Knight et al. (2014)	LR	LR	LR	LR	LR	UR	LR
Tabak et al. (2014)	UR	UR	HR	HR	HR	HR	LR
Gellis et al. (2014)	LR	LR	LR	HR	LR	UR	LR
Barberan-Garcia et al. (2014)	HR	HR	LR	HR	LR	HR	LR
Young et al. (2014)	UR	UR	HR	HR	LR	UR	HR
De San Miguel et al. (2013)	LR	UR	LR	HR	HR	LR	HR
Barcellona et al. (2013)	HR	HR	LR	LR	HR	LR	LR
Piette et al. (2013)	LR	LR	LR	HR	HR	LR	HR
Pecina et al. (2013)	LR	LR	HR	LR	LR	LR	LR
Gellis et al. (2012)	LR	LR	LR	LR	HR	LR	LR
Glasgow et al. (2011)	LR	LR	HR	LR	LR	UR	LR
Ruiz et al. (2011)	HR	HR	LR	LR	HR	LR	HR
Hawkins (2010)	LR	LR	LR	UR	LR	UR	LR
Wu et al. (2010)	LR	UR	HR	UR	HR	LR	LR
Davis et al. (2010)	LR	LR	HR	UR	LR	LR	LR
Seto et al. (2009)	LR	LR	LR	LR	LR	UR	LR
Berman et al. (2009)	LR	UR	UR	LR	UR	LR	LR
Woodend et al. (2008)	LR	UR	LR	UR	LR	LR	LR
Balk et al. (2008)	UR	UR	HR	LR	LR	HR	UR
Cole et al. (2006)	LR	LR	HR	HR	LR	LR	LR
Riegel et al. (2006)	LR	UR	LR	HR	HR	LR	LR
Noel et al. (2004)	LR	UR	LR	UR	UR	LR	UR
Lusignan et al. (2001)	LR	LR	UR	UR	UR	LR	LR
Johnston (2000)	HR	HR	UR	HR	HR	LR	HR

Low Risk (LR) = Plausible bias unlikely to seriously alter the results.

UR = plausible bias that raises some doubts about the results. HR = plausible bias that seriously weakens confidence in the results.

LR: low risk; UR: unclear risk; HR: high risk

Discussion

Effectiveness for self-care skills

On the whole, the literature suggests success of telehealth for self-management among patients with chronic condition(s). The findings document the feasibility of telehealth technologies that can have benefits for participants’ self-care behaviours. The LDCs show effectiveness on a wide range of chronic conditions for older adults, as follows: improving medication adherence for patients with chronic heart failure;^40–42 improving patients’ quality of life;⁴³ improving health-problem skills and self-care efficacy and medication compliance knowledge for patients with general chronic diseases;^36,44 improving self-care efficacy,^45,46 adherence to self-care behaviours,⁴⁷ health-care knowledge⁴⁶ and managing health-care problems for patients with diabetes;⁴⁶ and improving health-care management skills for patients with hypertension.³⁷ Using LDCs is an efficient strategy to help with routine communication between patients and health-care providers, through which health-care providers can mentor and assist patients’ self-care effectively, as well as saving time and cost for both patients and health-care providers.^36,42,44

Some included studies suggest that WBTs (e.g. discussion boards, online multimedia and online surveys) are effective in improving self-care skills for older adults. Online discussion boards and game-like user experience tools are found to be effective at empowering patients with rheumatoid arthritis⁴⁸ and improving overactive-bladder patients’ self-efficacy in controlling urge symptoms and health-related quality of life.⁸ TET also demonstrates effectiveness at promoting non-medical self-care behaviours, such as mind–body exercises, among patients with chronic pain.⁴⁹ In addition, HHMS was suggested to be effective on promoting chronic heart failure patients’ medication adherence and quality of life.⁵⁰

Effectiveness toward self-monitoring behaviours

Telehealth technologies are also found to improve self-monitoring behaviours. LDCs, HHMS, TETs and WBTs were found to be effective at helping patients with controlling weight among patients with chronic heart failure⁴⁰ and patients with general health conditions,⁵¹ increasing daily physical activities among patients with chronic obstructive pulmonary⁵² and patients with rheumatoid arthritis,⁴⁸ improving physical exercises^46,53 and healthy diets⁵³ among diabetes patients. Based on the findings, we propose that telehealth is a cost-effective strategy to deliver health education to promote self-monitoring behaviours to older adults with chronic conditions.

Clinical outcome effectiveness

Within included studies, many chronic illnesses’ clinical outcomes were improved.⁵⁴ After telehealth interventions, older patients have better clinical outcomes for patients with heart failure,^23,55,56 patients with overactive bladder⁸ and patients with rheumatoid arthritis.⁴⁸ In more detail, older patients with chronic illness have lower blood pressure, lower blood glucose⁵¹ and fewer depression symptoms;⁴⁴ older patients with heart failure have less shortness of breath,⁴⁰ decreased A1C level and better cognitive status⁵⁷ and fewer depression symptoms;^56,58 older adults with chronic pain have reduction in pain intensity;^49,59 older adults with diabetes have decreased HbA1c⁴⁶ and reduction in glycated haemoglobin⁶⁰ by using telehealth technologies; older adults with chronic anti-vitamin K antagonists have improved quality of oral anticoagulation.⁶¹

Ineffectiveness

On the other side, there are several included studies that reported ineffectiveness of telehealth technologies as clinical interventions on self-management among older adults.^{23,24,38,53,55,62–64} One included study even proposed that telemonitoring technology could compromise quality of life, reduce health-care outcomes, decrease physical functions and cause increased mortality for older adults with chronic condition(s).⁶³ Pecina and his colleagues confessed that such results are contrary to their expectations, and they do not have a definite explanation for the negative findings.⁶³ However, the hints of ineffectiveness of telehealth technologies might be obtained when reviewing across the included 31 studies. The poor persistence of telehealth’s effectiveness might contribute to the ineffective results. De Lusignan and his colleagues suggested that telehealth started with enthusiasm but became less useful as time went on.⁶² This finding is supported by the study of Gellis et al., where the intervention group had better clinical outcomes at the three- and six-month measurements but no significant difference with the control group at the 12-month follow-up measurement.⁴⁴ These ineffective results of telehealth technologies also have important implications. Leaving aside the current widespread enthusiasm for telehealth utilization for disease management, the long-term effectiveness of telehealth care needs to be considered.

In our review, several factors associated with telehealth’s effectiveness emerged. The effectiveness of telehealth technologies varies according to different health problems, ethnicity groups, cultures, physical operations and intervention settings. First, consistent communication between patients and their providers is critical for effective telehealth care.³⁸ It is necessary to keep the patients communicating regularly with health-care providers through telehealth technologies. Seto and his colleagues’ study provided evidence of the linkage between consistent communication between patients and their providers and the effectiveness of telehealth technology on older patients’ self-management.³⁸ An HHMS effectively improved clinical outcomes and self-management well-being among older patients in the United Kingdom but not in Canada when the health-care providers did not provide consistent interactions to the patients.³⁸ In addition, racial differences in telehealth care utilization were observed. For example, one study reported that telehealth intervention has no significant effect on older Hispanic patients with chronic heart failure.^23,37 Lack of receptiveness and acceptance of telehealth might play a mediation role in compromising the success of telehealth.^65,66 Due to the different cultures of telehealth recipients, cultural minority groups might lack acceptance of telehealth, a new way of health-care delivery, which could be a key barrier to telehealth’s effectiveness. However, there is big research gap in the current literature to study racial differences on telehealth care utilization. Even though our review identified racial differences, the current literature failed to move beyond barrier identification toward recognizing solutions that can promote racial equality on telehealth care use. Developing culture-friendly telehealth is imperative for future roll-outs of telehealth technologies. We propose researchers further explore racial differences on telehealth utilization and health-care providers design and deliver culture-friendly telehealth services to their target patients for a successful intervention.

Limitations

It is important to note the limitations of this review. Despite applying a comprehensive search process, it is possible that we may have missed some potentially eligible studies. Several strategies have been applied to prevent this risk. We had a very extensive search process and multiple integrators screened to verify eligible studies for the present study. Studies not available in English and grey literatures (e.g. unpublished studies, dissertations and abstracts) were not included in this study. Another limitation exists in the inclusion criteria that guided this review. This review is limited to the participant characteristics of the included studies. The inclusion criteria require the mean age of included sample studies to be 55 years or older. However, the mean age cannot represent that all of participants were older adults in the included studies, which potentially influenced the implication of the findings of this review to general elderly populations. Despite these limitations, our review provides an important contribution to the development of telehealth for treatment of chronic conditions.

Conclusion

In this review, we critically examined and summarized 31 experimental studies addressing the effects of telehealth technologies on self-management among older adults with chronic condition(s). According to current literature, telehealth technologies are generally effective on self-management for older adults with chronic condition(s) with patient-expressed high-level satisfaction. Therefore, we concluded that using telehealth technologies is an advisable intervention for promoting older patients’ self-management.

However, it is critical to consider racial diversity and culture-related acceptance differences when using telehealth technologies. Future studies need to focus on deeply exploring diverse populations’ experiences and needs for using telehealth technologies. In addition, health providers play an important role in the success of telehealth interventions. Active interaction between patients and health-care providers can prevent patients from dropping out of telehealth programmes.³⁸ Consistent interaction/communication between patients and their health-care providers is necessary for effective telehealth interventions. Unfavourably, many existing studies do not examine the role of health-care providers. Experimental design studies are definitely suggested for future studies to examine the performance of health-care providers on the effectiveness of telehealth technologies.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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