The effectiveness of the use of consumer health information technology in patients with heart failure: A meta-analysis and narrative review of randomized controlled trials

Abstract

Purpose

The purpose of this study was to examine whether the use of consumer health information technologies (CHITs) has an impact on outcomes of patients in the self-management of heart failure (HF).

Methods

A literature search of six electronic databases was conducted to identify relevant reports of randomized controlled trials (RCTs) for the analysis. Mortality, hospitalization and length of hospital stay were meta-analyzed and other patient outcomes were synthesized using a narrative approach.

Results

The literature search identified 50 studies, representing 43 RCTs, comparing the use of CHITs with usual care for HF patients. The meta-analysis showed that the use of CHITs reduced the risk of HF-caused mortality (relative risk (RR) = 0.70, 95% confidence interval (CI): 0.54–0.91), p = 0.007), lowered the risk of HF-caused hospitalization (RR = 0.80, 95% CI: 0.66–0.96), p = 0.020), and shortened HF-caused length of hospital stay (mean difference = –0.52, 95% CI: –0.77 to –0.27, p < 0.00), but not all-cause mortality, all-cause hospitalization or all-cause length of hospital stay, compared with usual care. The narrative synthesis indicated that only a small proportion of the trials reported positive effects of CHITs over usual care.

Conclusions

Evidence from RCTs presents mixed results on the impacts of CHITs for HF management. Further studies are required to assess whether and how CHITs would play a role in enhancing health care and patient outcomes and what specific CHIT features and functions are relevant to different HF treatment goals and self-care objectives.

Keywords

Meta-analysis technology-enhanced disease management heart failure

Introduction

The high prevalence of heart failure (HF) worldwide is becoming a significant societal and economic burden due to the high healthcare costs, mortality, and hospitalization rates associated with the disease. HF affects more than 23 million people worldwide¹ and there are about 3.6 million HF-related hospitalizations annually in the western world.² Approximately 50% of HF patients are rehospitalized within six months of discharge and 50% of HF patients die within five years of diagnosis.^3,4 Traditional HF management programs and home care, such as education, counselling, and monitoring through in-person follow-up visits or phone contact, can help to reduce mortality and hospitalization and improve patient outcomes. However, they can be delivered to only a limited number of patients as they are largely constrained by geographical barriers, insufficiently trained caregivers, and logistical inconvenience.^5,6

To overcome these difficulties, efforts have been made to link the home care/management of the disease to two main types of information technology—telemonitoring systems and telephone-based education and counseling systems. These technologies, mainly developed for health care provider users, enable remote and frequent monitoring of health data and symptoms of HF patients and permit early detection of disease deterioration and prompt treatment interventions.^7–9 However, results regarding the effectiveness of this form of monitoring and counseling are mixed.^10–19 During the past decades, there has been a growing interest in the development of health information technologies for consumers/patients for HF self-care, for example, Johnson et al.²⁰ and Or and Tao.²¹ For this type of consumer health information technology (CHIT), we define it as “patient-focused interactive web- or technology-mediated applications that are designed to improve information access and exchange, enhance decision making, provide social and emotional support, and facilitate behavior changes that promote health and well being” (p. 51).²² This technology enables health information and care resources to be accessed remotely by a wider range of chronically ill patients, empowering them to participate more actively in self-care.²³ Active self-care of patients can result in better health and less utilization of hospital services.^24,25 Despite the potential benefits of CHITs, their effectiveness in improving HF patients’ outcomes relative to usual care is yet to be fully determined. While previous review studies focused on evaluating telemonitoring and structured telephone support programs mainly developed for care provider users,^10–19 this present article reports a systematic review and meta-analysis of randomized controlled trials (RCTs) that assessed the current state of evidence regarding the effectiveness of patient-focused interactive web- and technology-mediated applications in HF management (e.g. patient-accessible online medical records and two-way video-conference telecare). In addition, this review provides a summary estimate of the magnitude of these technologies’ effects across trials.

Methods

Literature search and study selection

The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines²⁶ and covered studies published up to 4 September 2015. MEDLINE, Cochrane Central Register of Controlled Trials, Academic Search Premier, CINAHL Plus, PsycINFO, and PsycARTICLES databases were searched using the following keywords: (Web* or Internet or computer* or technolog* or telephone or telemonitor* or telemedic* or ehealth or e-health or informatics*) AND (self-monitor* or monitor* or self-manag* or manag* or self-care or care) AND (heart failure or cardiac failure or heart disease or cardiac disease).

Studies that met the following criteria were included: (a) RCT studies; (b) compared the use of a CHIT with the non-use of the technology; (c) examined the effects of CHITs on patient outcomes; (d) published in peer-reviewed journals; and (e) written in English. We excluded studies that made no quantitative comparison between study groups, or examined non-CHITs, such as health care professional-led telephone support systems^27,28 and non-interactive monitoring technologies that were used “passively” by patients and did not actually provide patients with social support or enhance their decision making.^29,30 For instance, Alsabbagh et al.²⁷ that examined a pharmacist-led telephone support system and Antonicelli et al.²⁸ that studied a nurse-led telephone-based technology were excluded because the interventions were not primarily designed for patients to use.

Titles and abstracts of the articles identified in the initial search were screened to determine their relevance. The potentially relevant articles were then reviewed in full text for final inclusion. We also examined the reference lists of the included articles to catch missed studies. Two authors (CKLO and DT) independently assessed the articles in all of the review steps.

Data extraction

With a similar data extraction method in our previously published reviews,^31,32 two authors (DT and HW) systematically extracted and recorded the following data from each trial using a pre-designed form: first author, year of publication, study country, study objective, sample characteristics, setting, intervention duration, CHIT characteristics, and primary outcomes and their statistical significance. The other author (CKLO) then cross-checked the extracted data. All disagreements that occurred during the screening, study selection, and data extraction were resolved by discussion among the three authors of this paper until consensus was reached.

Data analysis

Data on mortality, hospitalization, and length of hospital stay were pooled using meta-analysis, and data on the other outcomes were analyzed using a narrative synthesis method. In the meta-analysis, mortality and hospitalization were defined as the proportion of patients who underwent the respective events, and the relative risk (RR) and 95% confidence interval (CI) for the outcomes were calculated. For length of hospital stay, the mean difference (MD) and 95% CI were calculated to represent the measure of the effect size. The overall RR or MD for the outcomes were then aggregated using a fixed-effects model unless a moderate or high level of heterogeneity between trials was observed, in which case a random-effects model was applied.³³ Heterogeneity between trials was determined using the I² statistic, with I² values of 25%, 50%, and 75% representing low, moderate, and high levels of heterogeneity, respectively.³⁴ The possibility of publication bias was examined by using Egger’s regression test, with p value < 0.1 representing the existence of publication bias.³⁵ We also performed sensitivity analyses to examine whether the effects of CHITs remained unaltered when trials with a sample size larger than 150, high study quality, small sample attrition rate (less than 20%), long-term intervention duration (more than six months), or older adults (aged 65 years or above) were included in the meta-analysis. The category boundaries for sample size and study duration were determined according to the median values in the reviewed trials. The 65 years and above age group was chosen because this population has a higher prevalence of HF and is more susceptible to death and hospitalization.³⁶ The methodological quality of each of the included trials was assessed using the Cochrane back review group’s 12-item criteria list.³⁷ According to the guidelines,³⁷ a study was considered high quality if it met six or more of the 12 criteria; otherwise, it was rated as low quality. The meta-analysis was performed using Comprehensive Meta-Analysis v2.³³

Results

Figure 1 presents the literature search and study selection process. Fifty studies^38–87 representing 43 RCTs, met the inclusion criteria and were included in the review. Table 1 summarizes the characteristics of the 43 trials. The list of articles excluded from the review is shown in Supplementary Material, Appendix I. The results of the quality assessment of the 43 trials showed that 37 trials were considered high quality. The most frequently identified potential sources of bias concerned blindedness to the intervention, intention-to-treat analysis, and treatment allocation concealment. The technologies examined in three of the 43 trials were a Web- or CD-ROM-based platform that provided medical records, educational materials, self-test resources, and messaging systems for care and communication.^66,73,85 The technological interventions involved in the other 40 trials were mainly telemonitoring systems that were designed to collect, monitor, and transfer patients’ vital signs (e.g. heart rate, body weight, and blood oxygen) and HF-related symptoms. The features that were available in the technologies reviewed for HF self-management included monitoring of signs and symptoms, self-monitoring of HF-related symptoms; communication and patient monitoring by care providers; education and personalized feedback; management of medication, diet, and physical activity; reminders; and education on adherence to treatment.

Figure 1.

Literature search and study selection process. CHITs: consumer health information technologies: HF: heart failure; RCT: randomized controlled trial.

Table 1.

Characteristics of the 43 trials reviewed.

Study characteristics
Study setting, n (%)
Patient home	41 (95%)^{38–42,44–53,55–65,67–69,71,72,74–79,81,82,84,85}
Clinic	2 (5%)^66,73
Sample size^a
Mean (SD)	211 (266)
Median (range)	150 (18–1653)
Total	9071
Age of study sample (year)^b
Median (range)	68.4 (53.5–78.1)
Female in study sample (%)^c
Median (range)	31.4 (6.0–69.9)
Intervention duration (month)
Mean (SD)	7.3 (4.9)
Median (range)	6 (2–26)
Year of publication, n (%)
2001–2004	9 (21%)^{38,40,44,49,52,53,59,66,84}
2005–2008	11 (26%)^{39,46,47,55,60,68,71–73,81,85}
2009–2012	19 (44%)^{41,42,45,48,50,51,56–58,61,63–65,67,69,74–76,78}
2013–2014	4 (9%)^62,77,79,82
Region where the trials were conducted, n (%)^d
North America	24 (57%)^{38,40,41,45,47,52,53,55,57,59,60,62,66,68,69,71,72,74–76,78,82,84,85}
Europe	17 (41%)^{39,42,44,46,48–51,56,58,61,63–65,67,73,77}
Asia	1 (2%)⁷⁹
Study quality, n (%)
Low quality trials	6 (14%)^{46,60,64,75,79,81}
High quality trials	37 (86%)^{38–42,44,45,47–53,55–59,61–63,65–69,71–74,76–78,82,84,85}
Type of CHITs involved, n (%)
Web- or CD-ROM-based platform	3 (7%)^66,73,85
Telemonitoring system	40 (93%)^{38–42,44–52,54–65,67–69,71,72,74–79,81,82,84}
Features that were available in the technologies for HF self-management, n (%)
Monitoring of signs and symptoms	32 (74%)^{38,39,42,45–52,55–58,60–64,67–69,71,72,74–77,79,82,84}
Self-monitoring of HF-related symptoms	25 (58%)^{38,40–42,44,45,47,48,52,55,57–59,63–65,68,69,71,72,75–78,81}
Communication and patient monitoring by care providers	31 (72%)^{41,44–47,49–53,55–57,61,63,65–69,71,72,74–79,81,82,84}
Education and personalized feedback	12 (28%)^{39,40,42,57,59,60,65–67,73,75,81}
Medication management	8 (19%)^{38,51,53,60,67,68,72,76}
Alerts/reminders	9 (21%)^{48,67,69,71,72,74,75,77,84}
Education on adherence to treatment	5 (12%)^{39,40,42,65,85}
Diet management	3 (7%)^38,53,68
Physical activity management	2 (5%)^38,75

CHIT: consumer health information technology; HF: heart failure; SD: standard deviation.

Sample size was based on the number of participants at study entry; ^bfour trials did not report age; ^csix trials did not report the proportion of female participants; ^done trial did not report the region where the trial was conducted.

Meta-analysis of the effects of CHITs

Table 2 summarizes and presents the results of the meta-analyses, sensitivity analyses, and heterogeneity tests. High levels of heterogeneity across trials were not detected for all outcomes, except in the sensitivity analysis for all-cause length of hospital stay when a long-term intervention duration was included (I²= 75%). The Egger’s regression tests indicated the presence of publication bias for HF-caused mortality (t = 4.37, p = 0.022), and HF-caused hospitalization (t = 2.37, p = 0.035), thus caution should be warranted when interpreting the result for the two events.

Table 2.

Results of meta-analyses, sensitivity analyses, and heterogeneity tests.

Outcomes	No. of trials included in the meta-analysis	No. of participants	RR/MD (95% CI)^a	p value	I² (%)
All-cause mortality
All trials	26^{39,41,42,44–46,48–52,55–58,61,64,66–69,71,75–77,85}	7001	0.88 (0.77–1.02)	0.080	27
Sample size > 150	16^{39,41,42,45,46,48,50–52,56,61,64,71,75–77}	6027	0.86 (0.74–1.00)	0.047	44
Mean age ≥ 65 years	17^{39,41,42,46,48–50,56–58,61,67,68,71,75–77}	3956	0.88 (0.73–1.06)	0.181	26
Study duration > 6 months	12^{39,42,44,46,49,51,55,56,58,61,66,77}	2953	0.84 (0.69–1.03)	0.088	0
High quality	23^{39,41,42,44,45,48–52,55–58,61,66–69,71,76,77,85}	6280	0.92 (0.79–1.07)	0.270	31
Attrition rate < 20%	19^{39,44,48–52,55,56,58,61,64,66,68,69,71,75–77}	4113	0.80 (0.66–0.97)	0.025	25
HF-caused mortality
All trials	6^{44,46,51,52,56,71}	2151	0.70 (0.54–0.91)	0.007	0
Sample size > 150	5^{46,51,52,56,71}	2018	0.72 (0.55–0.93)	0.013	0
Mean age ≥ 65 years	3^46,56,71	1278	0.77 (0.57–1.04)	0.091	0
Study duration > 6 months	4^44,46,51,56	1556	0.73 (0.56–0.96)	0.026	0
High quality	5^{44,51,52,56,71}	1898	0.71 (0.53–0.95)	0.021	0
Attrition rate < 20%	5^{44,51,52,56,71}	1898	0.71 (0.53–0.95)	0.021	0
All-cause hospitalization
All trials	19^{41,42,45–48,51,52,56,61,63,66–68,71,75,76,78,82}	6416	0.98 (0.93–1.04)	0.543	42
Sample size > 150	14^{41,42,45–48,51,52,56,61,63,71,75,76}	5914	0.99 (0.94–1.05)	0.812	32
Mean age ≥ 65 years	14^{41,42,46–48,56,61,67,68,71,75,76,78,82}	3561	0.98 (0.91–1.06)	0.629	36
Study duration > 6 months	7^{42,46,51,56,61,63,66}	2586	0.94 (0.83–1.07)	0.379	51
High quality	17^{41,42,45,47,48,51,52,56,61,63,66–68,71,76,78,82}	5847	0.99 (0.93–1.04)	0.621	47
Attrition rate < 20%	14^{47,48,51,52,56,61,63,66,68,71,75,76,78,82}	3803	0.95 (0.84–1.07)	0.400	51
HF-caused hospitalization
All trials	14^{42,45,46,48,51,55,56,58,63,71,76,79,82,85}	4836	0.80 (0.66–0.96)	0.020	50
Sample size > 150	9^{42,45,46,48,51,56,63,71,76}	4498	0.90 (0.80–1.00)	0.048	49
Mean age ≥ 65 years	8^{42,46,48,56,58,71,76,82}	2205	0.80 (0.67–0.96)	0.017	26
Study duration > 6 months	7^{42,46,51,55,56,58,63}	2290	0.75 (0.63–0.88)	0.001	43
High quality	12^{42,45,48,51,55,56,58,63,71,76,82,85}	4544	0.80 (0.65–0.99)	0.036	56
Attrition rate < 20%	10^{48,51,55,56,58,63,71,76,79,82}	2472	0.75 (0.64–0.89)	0.001	49
All-cause length of hospital stay
All trials	11^{39,41,45,46,50,54–57,69,75}	3847	0.06 (–0.81–0.94)	0.885	37
Sample size > 150	7^{39,41,45,46,50,56,75}	3524	0.24 (–0.84–1.32)	0.666	0
Mean age ≥ 65 years	8^{39,41,46,50,54,56,57,75}	2046	0.18 (–1.29–1.66)	0.809	0
Study duration > 6 months	4^39,46,55,56	1225	–0.45 (–5.15–4.26)	0.853	75
High quality	9^{39,41,45,50,54–57,69}	3278	0.24 (–0.70–1.19)	0.612	46
Attrition rate < 20%	7^{39,50,54–56,69,75}	1573	–0.44 (–2.58–1.71)	0.689	60
HF–caused length of hospital stay
All trials	10^{41,46,50,54–57,67,71,84}	2215	–0.52 (–0.77– –0.27)	0.000	37
Sample size > 150	6^{41,46,50,56,71,84}	1872	–0.47 (–0.72– –0.21)	0.000	0
Mean age ≥ 65years	8^{41,46,50,54,56,57,67,71}	1951	–0.42 (–0.90–0.06)	0.086	8
Study duration > 6 months	3^46,55,56	1011	–1.30 (–4.39–1.78)	0.408	67
High quality	9^{41,50,54–57,67,71,84}	1962	–0.52 (–0.78– –0.27)	0.000	44
Attrition rate < 20%	6^{50,54–56,71,84}	1474	–0.96 (–2.13–0.21)	0.107	52

HF: heart failure; MD: mean difference; RR: relative risk.

RR for mortality and hospitalization, and MD for length of hospital stay.

The meta-analysis results showed that compared to the usual care, the use of CHITs significantly reduced the risk of death due to HF (i.e. HF-caused mortality), with a reduction of 30% (RR = 0.70, 95% CI: 0.54–0.91, p = 0.007) (see Figure 2); lowered the risk of HF-caused hospitalization by 20% (RR = 0.80, 95% CI: 0.66–0.96, p = 0.020) (see Figure 3); and decreased the HF-caused length of hospital stay by 0.52 days (MD = –0.52, 95% CI: –0.77 to –0.27, p < 0.001) (see Figure 4). CHITs did not have significant impacts on all-cause mortality, all-cause hospitalization, and all-cause length of hospital stay.

Figure 2.

A meta-analysis of the effect of consumer health information technologies (CHITs) on heart failure (HF)-caused mortality. CI: confidence interval.

Figure 3.

A meta-analysis of the effect of consumer health information technologies (CHITs) on heart failure (HF)-caused hospitalization. CI: confidence interval.

Figure 4.

A meta-analysis of the effect of consumer health information technologies (CHITs) on heart failure (HF)-caused length of hospital stay. CI: confidence interval.

Moreover, the sensitivity analyses revealed that the positive effect of CHITs on HF-caused mortality became non-significant when we excluded trials involving younger adults only. Similarly, for HF-caused length of hospital stay, the reduction became non-significant when we excluded trials with younger adults, trials with short intervention duration, or those with attrition rate equal to or above 20%. Significant findings for HF-caused hospitalization and non-significant findings for the other outcomes did not change in any of the sensitivity analyses.

Narrative synthesis of the effects of CHITs

In the narrative synthesis, we grouped patient outcomes into six categories: clinical, psychosocial, behavioral, knowledge, health care utilization, and cost outcomes. The results are presented in Table 3.

Table 3.

Narrative synthesis results: the number of supporting trials for the effects.

Outcomes	Total number of trials that examined the outcome	Positive effect^a	No effect^b	Negative effect^c	Mixed effect^d
Clinical outcomes
Functional status	9	1⁶⁷	5^{38,56,59,69,85}		3^40,64,78
Presence of HF symptoms	4	2^78,85			2^47,81
Blood pressure	2		2^49,55
Body weight	2		2^49,55
Peak oxygen consumption	1		1⁶⁴
Psychosocial outcomes
Quality of life	15	2^69,82	13^{38,39,43,48,49,52,56,59,64,68,73,84,87}
Self-reported health status	5	1⁶²	4^49,53,66,75
Self-efficacy toward disease management	6	1⁴⁰	4^59,66,83,84		1⁸⁶
Depression	5	1⁸⁴	4^56,,59,65,68
Patient satisfaction with care	3		3^41,53,66
Behavioral outcomes
Adherence to health care activities/self-care behaviors	14	5^{64,69,81,83,85}	4^38,39,53,73		5^{40,65,66,78,83}
Knowledge outcomes
Knowledge about HF and its management	5	4^39,73,82,83	1⁶⁵
Health care utilization outcomes
Amount of patient-provider contact	2	2^41,85
Combined outcome of (re)hospitalization, emergency department visits, and death	13	4^58,67,78,82	9^{42,45,56,61–63,71,72,75}
Number of HF hospitalizations	18	4^51,55,82,84	14^{45,47,50,52,54,57,60,66,69,71,74,76,77,85}
Length of time to HF hospitalization, death, and emergency department visits	9	2^58,84	7^{41,42,45,48,52,68,76}
Number of days lost due to death or hospitalization during the study period	3	2^46,50	1⁵⁶
Number of all-cause or cardiovascular-related emergency department visits	18	3^44,55,78	12^{41,46–48,52,57,60,68,69,71,74,85}	1^66,	2^54,75
Number of primary/special care visits	7	3^60,69,77	2^55,66		2^74,75
Number of days alive and out of hospital	2		2^39,48
Cost outcomes
Overall cost of HF care/management	6		5^{43,48,54,68,77}	1⁷⁰
Annual cost per patient	1	1⁷⁷
Costs of hospital readmission/hospitalization	3	2^51,84	1⁵⁰

HF: heart failure.

Positive effect: significant improvement in the intervention group compared with the control group; ^bno effect: no significant difference between the intervention and control groups; ^cnegative effect: significant deterioration in the intervention group compared with the control group; ^dmixed effect: positive, neutral, and/or negative effects were found.

Clinical outcomes

Five of the nine trials that assessed patient functional status using the New York Heart Association Functional Classification, Six-Minute Walk Test, and/or Short Form Health Survey (SF-36) reported the use of CHITs had no effect on the outcome, three showed mixed effects, and one reported a positive effect. Four trials examined the effects of CHITs on the presence of HF symptoms, revealing significant improvement in two trials and mixed effects in the other two. All five trials that compared blood pressure, body weight, and peak oxygen consumption (as an index of exercise capacity) between CHIT and usual care groups failed to find any significant differences.

Psychosocial outcomes

Two of the 15 trials that examined health-related quality of life detected a positive effect, with the change in that outcome from baseline to follow-up significantly higher in the CHIT group than the control group. Among those trials evaluating the effect of CHIT use on patients’ self-reported health status, patients’ self-efficacy, or depression, only three trials showed positive effect, respectively. No evidence was discovered that CHITs have an effect on satisfaction with care in HF patients.

Behavioral outcomes

Fourteen trials assessed patients’ adherence to health care activities/self-care behavior, of which five observed a significant improvement in adherence to exercise and daily weight and blood pressure measurements in the CHIT groups compared with the control groups. Five of these trials found mixed effects, with the use of CHITs significantly improving adherence to at least one of the following: medical advice, fluid and alcohol restrictions, weighing recommendations, exercise, and stress control. However, they had no effect on adherence to smoking cessation, medication use, appointment keeping, and sodium restrictions. Finally, four trials found no effects.

Knowledge outcomes

Four of the five trials that examined patients’ disease-specific knowledge found a significantly higher increase in knowledge about HF and its management in the CHIT groups than in the control groups.

Health care utilization outcomes

We found that a large proportion of the trials surveyed detected no CHIT effects on the combined outcomes of (re)hospitalization, emergency department visits, and death; number of hospitalizations; length of time to hospitalization, death, and emergency department visits; and number of all-cause or cardiovascular-related emergency department visits. Inconsistent findings were observed for the other health care utilization outcomes.

Cost outcomes

Ten trials examined the effects of CHIT-based HF management on the overall cost of HF care, the annual cost per patient, and the costs of hospital readmission/hospitalization, with only three reporting significantly lower such costs in the technology groups relative to the usual care groups.

Discussion

This review examines published evidence from RCTs assessing the effectiveness of CHITs in improving the outcomes of HF patients. Our meta-analysis shows that the use of CHITs was associated with significant reductions in HF-caused mortality, HF-caused hospitalization, and HF-caused length of hospital stay among HF patients compared with usual care, although narrative synthesis indicates that only a small proportion of the trials examined reported positive effects for CHITs relative to usual care. Some of these results are consistent with some previous reviews of other types of health information technology (e.g. telemonitoring technology and structured telephone support);^13–19 however, some of the results are not consistent (e.g. we found a non-significant reduction in all-cause mortality). Possible reasons for different results across the review studies could be that some of the technology interventions the other reviews examined were not included in ours, the way of how the technologies were analyzed was different, or different inclusion criteria were used. For instance, the reviews conducted by Kitsiou et al.,¹⁸ Kotb et al.,¹⁹ and Pandor et al.¹⁷ examined health care professional-led structured telephone support interventions, but our review excluded most telephone support technologies because they were largely designed for and used by care providers. Also, Pandor et al.¹⁷ showed that the analyses of the technological interventions (e.g. the use of home telemonitoring during office hours or 24/7 versus usual care) were different from the approach that our review used (i.e. we analyzed a technology intervention as a whole). Moreover, Clarke et al.¹⁴ only included trials that had at least 50 patient participants, for example; but our review did not have this inclusion criterion.

Although we can only speculate, it is important and meaningful to try to understand the underlying reasons for the effects on the outcomes that we found. One possible reason may be related to the availability of patient health data. More specifically, CHITs that allow the frequent collection and monitoring of patients’ vital signs and symptoms enable variations in their health conditions to be identified early, permitting patients and caregivers to intervene before further deterioration, thereby possibly reducing the likelihood of mortality and hospitalization.⁸⁸ Another possible reason could be that CHITs empower monitored patients to engage in self-care by providing them with greater access to relevant tools and resources, helping them to quickly obtain effective attention and care, thereby minimizing the likelihood of deterioration and complications.

The technologies examined in this review had no impact on all-cause events. This finding seems to indicate that the technologies suffer a weakness in reducing the incidence of events (i.e. mortality, hospitalization, and length of hospital stay) caused by factors other than HF, such as physical falls and comorbidities. Their designers may thus wish to consider whether CHITs should be extended to cover a more comprehensive monitoring range in an effort to reduce all-cause events. In addition, the issue of whether all-cause events should be considered suitable outcomes in the evaluation of these technologies also merits further discussion. If they should not, then the findings on outcomes reported to date could give the public a biased impression of CHIT effectiveness, which could prevent technology funders and users from making objective decisions on technology adoption.

The results of our sensitivity analyses indicate that older adults obtain no additional benefits from using CHITs rather than usual care. Although many of the trials reviewed claimed that they had older adults in mind in the technology design process, our results suggest a lack of knowledge on how to deliver health care services to the elderly effectively through CHITs. The sensitivity analyses also show that the beneficial effects of CHITs on HF-caused mortality and HF-caused hospitalization persist over the long term, but not on HF-caused length of hospital stay. Meanwhile, a previous study of telemonitoring technologies indicated that the effectiveness is diluted over time.⁸⁹ Therefore, although both studies confirm the short-term effects of the technological interventions, more evidence is necessarily needed to confirm the long-term effects.

The findings of our narrative synthesis are particularly worthy of attention. First, we found that clinical, behavioral, and knowledge outcomes were less frequently examined in the RCTs reviewed than other outcomes, although they are important indicators of patients’ health and functional status, self-management, and quality of care. In addition, only a small portion of the trials demonstrated positive effects on such outcomes as functional status, presence of HF symptoms, quality of life, self-reported health status, self-efficacy toward disease management, depression, and adherence to health care activities/self-care behavior, and none reported positive effects on blood pressure, body weight, peak oxygen consumption, and patient satisfaction with care. Second, we found little information on patients’ compliance with CHIT use. It thus remains unclear whether the technologies’ apparent neutral effects in the trials are attributable to patient non-adherence to them or their ineffectiveness. Finally, the economic impact of CHITs is unclear. We identified only ten trials that assessed the economic effects of CHITs, and they showed mixed results. Accordingly, it is premature to make any definitive statement on the cost-effectiveness of CHITs in HF management.

Our findings have important implications for future evaluation, design, and adoption of CHIT initiatives. First, our review found that many of the outcomes were examined in too few trials to draw conclusions, or their results were mixed. In addition, although our meta-analysis found positive effects on HF-caused mortality and HF-caused hospitalization, it is noted that the two large RCT studies showed no effects of CHITs on the outcomes.^45,56 This suggests a need for more research to further assess whether and how CHITs would play a role in enhancing health care and patient outcomes. Also, it would be more meaningful to scrutinize why the technologies had impacts on some outcomes but not others. Second, it is unclear if certain outcomes are more likely to associate with some CHIT interventions or care situations. In order to develop effective CHITs, future studies are recommended to examine what and how CHIT features and functions are relevant to different HF treatment goals and self-care objectives. Third, since HF patients are mostly elderly, it is necessary to understand the limitations, capabilities, and motivations of older adults and HF patients in order to ensure that the designs support their physical, cognitive, and behavioral performance. Finally, it is noteworthy that while future CHIT initiatives are expected to produce some favorable effects, investment in the technologies may not lead to immediate economic returns.

Compared with previous reviews of telemonitoring technologies for HF patients,^10–19 our study is distinct in several respects. First, our literature search was designed in a way to minimize the likelihood of missing eligible studies. Second, we performed sensitivity analyses to test the robustness of our meta-analysis results with factors likely to bias the results. Third, our review examined a broader range of meaningful outcome dimensions using appropriate data synthesis methods. Finally, different from the previous reviews,^10–19 which largely examined telemonitoring technologies that were built for both patient and care provider users, our review focused on interactive web- or technology-mediated applications that were developed for patient users. Nonetheless, our review also has a few limitations. First, publication bias cannot be ruled out for HF-caused mortality, and HF-caused hospitalization, meaning that our explanation of this outcome is suggestive rather than conclusive. Second, as in previous meta-analyses,^13–19 we calculated hospitalization by the proportion of patients experiencing this event, rather than by its frequency, as hospitalization frequency was reported in few of the RCT studies examined. Accordingly, we may have underestimated the effects of CHITs on this outcome.

Conclusions

The use of CHITs appears to be effective in reducing HF-caused mortality, HF-caused hospitalization, and HF-caused length of hospital stay for HF patients compared with usual care. However, their effectiveness in improving some other patient outcomes still awaits confirmation by future studies. Further efforts are also required to evaluate the technologies in trials particularly with long-term intervention duration and older patients.

Summary

What was already known before this study:

CHITs are feasible technological interventions to facilitate disease self-management for HF patients.

However, the effectiveness of the technologies in improving patients’ health outcomes relative to usual care is yet to be fully determined.

What this study has added to our knowledge:

The present review shows that CHIT-based self-management has the potential to provide additional benefits beyond usual care for HF patients on HF-caused mortality, HF-caused hospitalization, and HF-caused length of hospital stay.

It is premature to make any definitive statement about the effectiveness of CHITs in improving clinical, psychosocial, behavioral, knowledge, and cost outcomes.

Footnotes

Authors’ contributions

Calvin Or and Da Tao developed the review protocol, selected articles for full text assessment, and reviewed the included articles. Da Tao and Hailiang Wang conducted data extraction and data analysis and prepared the initial draft. Calvin Or cross-checked the extracted and analyzed data and prepared the final version of the article.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: the review was conducted with the support of the Health and Medical Research Fund (HMRF) of Food and Health Bureau, The Government of HKSAR (project 12133231; PI: Calvin Or) and the Theme-based Research Scheme of the Hong Kong Research Grants Council (grant number T32-102/14-N).

References

Bui

Horwich

Fonarow

. Epidemiology and risk profile of heart failure. Nat Rev Cardiol 2011; 8: 30–41.

Giamouzis

Triposkiadis

Butler

. Heart failure. Cardiol Res Pract 2011; 2011: 159608.–159608..

Giamouzis

Kalogeropoulos

Georgiopoulou

. Hospitalization epidemic in patients with heart failure: Risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail 2011; 17: 54–75.

Mozaffarian

Roger

. Heart disease and stroke statistics–2013 update: A report from the American Heart Association. Circulation 2013; 127: e6–e245.

Murphy

Chakraborty

Fuat

. Diagnosis and management of patients with heart failure in England. Clin Med 2008; 8: 264–266.

CKL

Valdez

Casper

. Human factors and ergonomics in home care: Current concerns and future considerations for health information technology. Work 2009; 33: 201–209.

Anker

Koehler

Abraham

. Telemedicine and remote management of patients with heart failure. Lancet 2011; 378: 731–739.

Riley

Cowie

. Telemonitoring in heart failure. Heart 2009; 95: 1964–1968.

Hasan

Paul

. Telemonitoring in chronic heart failure. Eur Heart J 2011; 32: 1457–1464.

10.

Chaudhry

Phillips

Stewart

. Telemonitoring for patients with chronic heart failure: A systematic review. J Card Fail 2007; 13: 56–62.

11.

Maric

Kaan

Ignaszewski

. A systematic review of telemonitoring technologies in heart failure. Eur J Heart Fail 2009; 11: 506–517.

12.

Giamouzis

Mastrogiannis

Koutrakis

. Telemonitoring in chronic heart failure: A systematic review. Cardiol Res Pract 2012; 2012: 410820–410820.

13.

Clark

Inglis

McAlister

. Telemonitoring or structured telephone support programmes for patients with chronic heart failure: Systematic review and meta-analysis. Br Med J 2007; 334: 942–942.

14.

Clarke

Shah

Sharma

. Systematic review of studies on telemonitoring of patients with congestive heart failure: A meta-analysis. J Telemed Telecare 2011; 17: 7–14.

15.

Inglis

Clark

McAlister

. Which components of heart failure programmes are effective? A systematic review and meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary component of chronic heart failure management in 8323 patients: Abridged Cochrane review. Eur J Heart Fail 2011; 13: 1028–1040.

16.

Klersy

De Silvestri

Gabutti

. A meta-analysis of remote monitoring of heart failure patients. J Am Coll Cardiol 2009; 54: 1683–1694.

17.

Pandor

Gomersall

Stevens

. Remote monitoring after recent hospital discharge in patients with heart failure: A systematic review and network meta-analysis. Heart 2013; 99: 1717–1726.

18.

Kitsiou

Paré

Jaana

. Effects of home telemonitoring interventions on patients with chronic heart failure: An overview of systematic reviews. J Med Internet Res 2015; 17: e63–e63.

19.

Kotb

Cameron

Hsieh

. Comparative effectiveness of different forms of telemedicine for individuals with heart failure (HF): A systematic review and network meta-analysis. PLOS One 2015; 10: e0118681–e0118681.

20.

Johnson

Valdez

Casper

. Experiences of technology integration in home care nursing. AMIA Annu Symp Proc 2008; 6: 389–393.

21.

CKL

Tao

. Usability study of a computer-based self-management system for older adults with chronic diseases. JMIR Rese Protoc 2012; 1: e13–e13.

22.

Karsh

Severtson

. Factors affecting home care patients' acceptance of a web-based interactive self-management technology. J Am Med Inform Assoc 2011; 18: 51–59.

23.

Riley

Gabe

Cowie

. Does telemonitoring in heart failure empower patients for self-care? A qualitative study. J Clin Nurs 2013; 22: 2444–2455.

24.

Jimison

Gorman

Woods

. Barriers and drivers of health information technology use for the elderly, chronically ill, and underserved. Evid Rep Technol Assess (Full Rep) 2008; 175: 1–1422.

25.

Karsh

. A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc 2009; 16: 550–560.

26.

Moher

Liberati

Tetzlaff

. The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Ann Intern Med 2009; 151: 2008–2012.

27.

Alsabbagh

Lemstra

Eurich

. Pharmacist intervention in cardiac rehabilitation: A randomized controlled trial. J Cardiopulm Rehabil Prev 2012; 32: 394–399.

28.

Antonicelli

Mazzanti

Abbatecola

. Impact of home patient telemonitoring on use of beta-blockers in congestive heart failure. Drugs Aging 2010; 27: 801–805.

29.

Hindricks

Taborsky

Glikson

. Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): A randomised controlled trial. Lancet 2014; 384: 583–590.

30.

Boriani

Da Costa

Ricci

. The MOnitoring Resynchronization dEvices and CARdiac patiEnts (MORE-CARE) randomized controlled trial: Phase 1 results on dynamics of early intervention with remote monitoring. J Med Internet Res 2013; 15: e167–e167.

31.

CKL

Tao

. Does the use of consumer health information technology improve outcomes in the patient self-management of diabetes? A meta-analysis and narrative review of randomized controlled trials. Int J Med Inform 2014; 83: 320–329.

32.

Tao

CKL

. Effects of self-management health information technology on glycaemic control for patients with diabetes: A meta-analysis of randomized controlled trials. J Telemed Telecare 2013; 19: 133–143.

33.

Borenstein

Hedges

Higgins

. Introduction to meta-analysis, Chichester: John Wiley and Sons, 2009.

34.

Higgins

Thompson

Deeks

. Measuring inconsistency in meta-analyses. Br Med J 2003; 327: 557–560.

35.

Egger

Smith

Schneider

. Bias in meta-analysis detected by a simple, graphical test. Br Med J 1997; 315: 629–634.

36.

Chen

Normand

Wang

. National and regional trends in heart failure hospitalization and mortality rates for Medicare beneficiaries, 1998–2008. JAMA 2011; 306: 1669–1678.

37.

Furlan

Pennick

Bombardier

. 2009 updated method guidelines for systematic reviews in the Cochrane Back Review Group. Spine 2009; 34: 1929–1941.

38.

Artinian

Harden

Kronenberg

. Pilot study of a Web-based compliance monitoring device for patients with congestive heart failure. Heart Lung 2003; 32: 226–233.

39.

Balk

Davidse

Dommelen

. Tele-guidance of chronic heart failure patients enhances knowledge about the disease. A multi-centre, randomised controlled study. Eur J Heart Fail 2008; 10: 1136–1142.

40.

Barnason

Zimmerman

Nieveen

. Impact of a home communication intervention for coronary artery bypass graft patients with ischemic heart failure on self-efficacy, coronary disease risk factor modification, and functioning. Heart Lung 2003; 32: 147–158.

41.

Bowles

Hanlon

Glick

. Clinical effectiveness, access to, and satisfaction with care using a telehomecare substitution intervention: A randomized controlled trial. Int J Telemed Appl 2011; 2011: 540138–540138.

42.

Boyne

Vrijhoef

Crijns

. Tailored telemonitoring in patients with heart failure: Results of a multicentre randomized controlled trial. Eur J Heart Fail 2012; 140: 791–801.

43.

Boyne

Van Asselt

Gorgels

. Cost-effectiveness analysis of telemonitoring versus usual care in patients with heart failure: The TEHAF-study. J Telemed Telecare 2013; 19: 242–248.

44.

Capomolla

Pinna

Maestri

. Heart failure case disease management program: A pilot study of home telemonitoring versus usual care. Eur Heart J Suppl 2004; 6: F91–F98.

45.

Chaudhry

Mattera

Curtis

. Telemonitoring in patients with heart failure. N Engl J Med 2010; 363: 2301–2309.

46.

Cleland

JGF

Louis

Rigby

. Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: The Trans-European Network-Home-Care Management System (TEN-HMS) study. J Am Coll Cardiol 2005; 45: 1654–1664.

47.

Dansky

Vasey

Bowles

. Impact of telehealth on clinical outcomes in patients with heart failure. Clin Nurs Res 2008; 17: 182–199.

48.

Dar

Riley

Chapman

. A randomized trial of home telemonitoring in a typical elderly heart failure population in North West London: Results of the Home-HF study. Eur J Heart Fail 2009; 11: 319–325.

49.

de Lusignan

Wells

Johnson

. Compliance and effectiveness of 1 year's home telemonitoring. The report of a pilot study of patients with chronic heart failure. Eur J Heart Fail 2001; 3: 723–730.

50.

Dendale

De Keulenaer

Troisfontaines

. Effect of a telemonitoring-facilitated collaboration between general practitioner and heart failure clinic on mortality and rehospitalization rates in severe heart failure: The TEMA-HF 1 (TElemonitoring in the MAnagement of Heart Failure) study. Eur J Heart Fail 2012; 14: 333–340.

51.

Giordano

Scalvini

Zanelli

. Multicenter randomised trial on home-based telemanagement to prevent hospital readmission of patients with chronic heart failure. Int J Cardiol 2009; 131: 192–199.

52.

Goldberg

Piette

Walsh

. Randomized trial of a daily electronic home monitoring system in patients with advanced heart failure: The Weight Monitoring in Heart Failure (WHARF) trial. Am Heart J 2003; 146: 705–712.

53.

Jerant

Azari

Martinez

. A randomized trial of telenursing to reduce hospitalization for heart failure: Patient-centered outcomes and nursing indicators. Home Health Care Serv Q 2003; 22: 1–20.

54.

Jerant

Azari

Nesbitt

. Reducing the cost of frequent hospital admissions for congestive heart failure: A randomized trial of a home telecare intervention. Med Care 2001; 39: 1234–1245.

55.

Kashem

Droogan

Santamore

. Managing heart failure care using an Internet-based telemedicine system. J Card Fail 2008; 14: 121–126.

56.

Koehler

Winkler

Schieber

. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: The telemedical interventional monitoring in heart failure study. Circulation 2011; 123: 1873–1880.

57.

Kulshreshtha

Kvedar

Goyal

. Use of remote monitoring to improve outcomes in patients with heart failure: A pilot trial. Int J Telemed Appl 2010; 2010: 870959–870959.

58.

Kurtz

Lemercier

Pouchin

. Automated home telephone self-monitoring reduces hospitalization in patients with advanced heart failure. J Telemed Telecare 2011; 17: 298–302.

59.

LaFramboise

Todero

Zimmerman

. Comparison of Health Buddy with traditional approaches to heart failure management. Fam Community Health 2003; 26: 275–288.

60.

Lehmann

Mintz

Giacini

. Impact of telehealth on healthcare utilization by congestive heart failure patients. Dis Manag Health Out 2006; 14: 163–169.

61.

Lynga

Persson

Hagg-Martinell

. Weight monitoring in patients with severe heart failure (WISH). A randomized controlled trial. Eur J Heart Fail 2012; 14: 438–444.

62.

Madigan

Schmotzer

Struk

. Home health care with telemonitoring improves health status for older adults with heart failure. Home Health Care Serv Q 2013; 32: 57–74.

63.

Mortara

Pinna

Johnson

. Home telemonitoring in heart failure patients: The HHH study (Home or Hospital in Heart Failure). Eur J Heart Fail 2009; 11: 312–318.

64.

Piotrowicz

Baranowski

Bilinska

. A new model of home-based telemonitored cardiac rehabilitation in patients with heart failure: Effectiveness, quality of life, and adherence. Eur J Heart Fail 2010; 12: 164–171.

65.

Ramaekers

Janssen-Boyne

Gorgels

. Adherence among telemonitored patients with heart failure to pharmacological and nonpharmacological recommendations. Telemed J E Health 2009; 15: 517–524.

66.

Ross

Moore

Earnest

. Providing a web-based online medical record with electronic communication capabilities to patients with congestive heart failure: Randomized trial. J Med Internet Res 2004; 6: e12–e12.

67.

Scherr

Kastner

Kollmann

. Effect of home-based telemonitoring using mobile phone technology on the outcome of heart failure patients after an episode of acute decompensation: Randomized controlled trial. J Med Internet Res 2009; 11: e34–e34.

68.

Schwarz

Mion

Hudock

. Telemonitoring of heart failure patients and their caregivers: A pilot randomized controlled trial. Prog Cardiovasc Nurs 2008; 23: 18–26.

69.

Seto

Leonard

Cafazzo

. Mobile phone-based telemonitoring for heart failure management: A randomized controlled trial. J Med Internet Res 2012; 14: e31–e31.

70.

Soran

Feldman

Piña

. Cost of medical services in older patients with heart failure: Those receiving enhanced monitoring using a computer-based telephonic monitoring system compared with those in usual care: The Heart Failure Home Care trial. J Card Fail 2010; 16: 859–866.

71.

Soran

Piña

Lamas

. A randomized clinical trial of the clinical effects of enhanced heart failure monitoring using a computer-based telephonic monitoring system in older minorities and women. J Card Fail 2008; 14: 711–717.

72.

Spaeder

Najjar

Gerstenblith

. Rapid titration of carvedilol in patients with congestive heart failure: A randomized trial of automated telemedicine versus frequent outpatient clinic visits. Am Heart J 2006; 151: e1–e10.

73.

Strömberg

Dahlström

Fridlund

. Computer-based education for patients with chronic heart failure. A randomised, controlled, multicentre trial of the effects on knowledge, compliance and quality of life. Patient Educ Couns 2006; 64: 128–135.

74.

Tompkins

Orwat

. A randomized trial of telemonitoring heart failure patients. J Healthc Manag 2010; 55: 312–322.

75.

Wade

Desai

Spettell

. Telemonitoring with case management for seniors with heart failure. Am J Manag Care 2011; 17: e71–e79.

76.

Weintraub

Gregory

Patel

. A multicenter randomized controlled evaluation of automated home monitoring and telephonic disease management in patients recently hospitalized for congestive heart failure: The SPAN-CHF II trial. J Card Fail 2010; 16: 285–292.

77.

Zanaboni

Landolina

Marzegalli

. Cost-utility analysis of the EVOLVO study on remote monitoring for heart failure patients with implantable defibrillators: Randomized controlled trial. J Med Internet Res 2013; 15: e106–e106.

78.

Dansky

Vasey

. Managing heart failure patients after formal homecare. Telemed J E Health 2009; 15: 983–991.

79.

Mizukawa

Moriyama

Naka

. Telemonitoring in patients with chronic heart failure: Multicenter randomized trial in Japan (pilot study). J Card Fail 2014; 20: S144–S144.

80.

Robinson

Stroetmann

. Tele-homecare for chronically-ill patients: Improved outcomes and new development. Journal on Information Technology in Healthcare 2004; 2: 251–262.

81.

Tomita

Tsai

B-M

Fisher

. Improving adherence to exercise in patients with heart failure through internet-based self-management. J Am Geriatr Soc 2008; 56: 1981–1983.

82.

Delaney

Apostolidis

Bartos

. A randomized trial of telemonitoring and self-care education in heart failure patients following home care discharge. Home Health Care Manag Pract 2013; 25: 187–195.

83.

Boyne

Vrijhoef

Spreeuwenberg

. Effects of tailored telemonitoring on heart failure patients' knowledge, self-care, self-efficacy and adherence: A randomized controlled trial. Eur J Cardiovasc Nurs 2014; 13: 243–252.

84.

Benatar

Bondmass

Ghitelman

. Outcomes of chronic heart failure. Arch Intern Med 2003; 163: 347–352.

85.

Albert

Buchsbaum

. Randomized study of the effect of video education on heart failure healthcare utilization, symptoms, and self-care behaviors. Patient Educ Couns 2007; 69: 129–139.

86.

Dansky

Vasey

Bowles

. Use of telehealth by older adults to manage heart failure. Res Gerontol Nurs 2008; 1: 25–32.

87.

Konstam

Gregory

Chen

. Health-related quality of life in a multicenter randomized controlled comparison of telephonic disease management and automated home monitoring in patients recently hospitalized with heart failure: SPAN-CHF II trial. J Card Fail 2011; 17: 151–157.

88.

Bui

Fonarow

. Home monitoring for heart failure management. J Am Coll Cardiol 2012; 59: 97–104.

89.

Xiang

Liu

. Meta-analysis and meta-regression of telehealth programmes for patients with chronic heart failure. J Telemed Telecare 2013; 19: 249–259.