Heart failure (HF) is a chronic disease estimated to affect 10 of every 1,000 adults 65 and older (American Heart Association, 2013). It is a leading cause of hospitalization associated with poor prognosis and high medical costs in older adults (Vidan et al., 2016). Prevalence in the United States is approximately 5.8 million cases (Roger, 2013) and is estimated to increase to 8 million by 2030 (Heidenreich et al., 2011). Total costs of treating HF are projected to be $70 billion in 2030, a 44% increase from 2012 (Heidenreich et al., 2011). Morbidity and mortality associated with HF are significant and the disease can be difficult to manage. Readmission after initial HF-related hospitalization is common (Desai & Stevenson, 2012), typically occurring within days after discharge (Dharmarajan et al., 2013). Adults are required to adhere to complex treatment regimens, self-monitor symptoms, interpret physiological changes, and recognize symptoms requiring interventions. Quality of life (QOL) can be affected by the high symptom burden of HF and the associated need for ongoing, vigilant monitoring (Nieminen et al., 2015).
Following hospitalization, patients are typically discharged home to a traditional program of care that includes nursing visits and/or telephone support; this transition can be difficult. Self-care is an essential component of successful HF management and reflects behavioral strategies chosen to support and maintain physiological stability (Riegel, Moser, et al., 2009). Adherence to a prescribed medication regimen and self-monitoring of weight and sodium intake are examples of self-care behaviors that influence the ability to manage HF in the home environment (Evangelista & Shinnick, 2008). Comorbidities are common in HF, complicating self-care and effective decision making (Riegel, Moser, et al., 2009). Older individuals demonstrate limited abilities in early symptom detection and interpretation (Riegel et al., 2010). Delays in reporting and seeking treatment for symptom exacerbation are factors in hospital readmission (Maric, Kaan, Ignaszewski, & Lear, 2009). The current study was based on the idea that self-care is an important mediator variable between nursing interventions and improved health outcomes, and that improving self-care abilities can reduce the frequency of hospital services use and improve QOL in adults with HF.
The introduction of remote monitoring (i.e., telemonitoring [TM]) has led to growing interest in understanding the effectiveness of technology-based care in the management of chronic diseases such as HF. TM typically includes the use of devices for electronic transmission of physiological data (e.g., weight, blood pressure, heart rate) as well as telephonic case management (Inglis, Cark, McAlister, Stewart, & Cleland, 2011). Another component of TM is videoconferencing (VC), broadly defined as simultaneous audio and visual communication between two or more individuals using a device such as a videophone or video camera (Center for Connected Health Policy, n.d.). One of the advantages of VC is that it allows health care providers to visually assess and evaluate patients in real time. The ability to respond quickly to changes in patients' physiological status has been identified as an important factor in the success of remote monitoring for HF (Nakamura, Koga, & Iseki, 2014).
Literature reviews of TM in the management of HF have focused primarily on clinical, cost, and hospital services use outcomes. However, little is known about the individual effect of specific components of TM, such as physiological data transmission, telephone support (Kotb, Cameron, Hsieh, & Wells, 2015), or VC on HF outcomes. VC technology has been used in health care for a variety of purposes, including clinical consultations and provider training, and is increasingly viewed as a potential means of educating individuals with chronic disease about self-care. Empirical studies of the effectiveness of VC to assess HF self-care capabilities and deliver appropriate educational interventions to improve capabilities are limited. In addition, the research has been criticized for lack of methodological rigor and problematic conceptualizations of self-care and self-management (Peeters, Wiegers, & Friele, 2013).
QOL, an important patient-centric outcome, receives less attention in TM research on individuals with HF. The progressive nature of HF and its associated symptomatology affect QOL, and improved clinical outcomes may not result in improved QOL. The burden of care of HF creates challenges for older individuals that may exceed the desired QOL.
Gerontology nurses are familiar with the challenges of caring for older individuals with chronic diseases and multiple comorbidities. Their clinical expertise and proximity to patients in a variety of service settings make them well-suited for leadership roles in the development of strategies to improve self-care abilities and QOL. The growing use of technology to support patients at multiple points of care provides additional opportunities for gerontology nurses to implement novel approaches to caring for older adults.
The purpose of the current integrative review is to describe the use of VC in the management of patients with HF in the home environment and synthesize the research findings across studies. The specific aim of this review is to describe the efficacy of TM interventions that include VC on the outcomes of hospital services use (inpatient and/or emergency department [ED]), self-care, and QOL.
An integrative review was conducted based on Whittemore and Knafl's (2005) method. The five steps of the method include problem identification, literature search, data evaluation, data analysis, and presentation of results. PRISMA reporting guidelines were followed (Moher, Liberati, Tetzlaff, Altman, & The PRISMA Group, 2009) (Figure).
Preferred Reporting for Systematic Reviews and Meta-Analysis (PRISMA) diagram of study selection process.
Articles included in this review met the following criteria: (a) were a primary source of empirical data on the effect of VC in the management of HF patients in the home environment; (b) evaluated primary outcomes of hospital services use, self-care, and/or QOL; (c) studied adults 18 and older; (d) published manuscripts in English; and (e) were peer-reviewed studies.
A scoping strategy recommended by a research librarian was initially conducted to identify possible articles. Manuscripts that met the inclusion criteria were evaluated for subject headings and keywords, which helped inform a comprehensive search. The following databases were searched for relevant studies: Med-line (OvidSP 1946 to August Week 1 2016); CINAHL plus with full text (EBSCO, from inception to November 2016); and SCOPUS (from inception to November 2016). Initial search terms represented various combinations of subject headings related to telehealth, telemedicine, videoconferencing, home care, and HF. Finalized search terms were videoconferencing, remote monitoring, HF, and home care. In addition, reference lists of full-text articles assessed were manually reviewed for additional studies.
Titles and abstracts were reviewed by two reviewers (D.B.F., K.B.) to ensure studies included VC as one component of the management of patients with HF at home. There were no disagreements between reviewers as to which studies met inclusion criteria. One challenge encountered was inconsistency in the definition of TM, which could mean anything from asynchronous transmission of biometric data, telephone case management, computer-based education, or VC. This inconsistency made it difficult to extract the requisite information from the abstracts in determining relevance to the study, therefore requiring a more in-depth review of each article.
The search from inception to November 2016 yielded 1,645 studies. After excluding records that did not meet the inclusion criteria, 30 full-text articles were assessed for eligibility. Of these articles, 11 studies included one or more of the three outcome variables of interest.
Of the 11 studies that met inclusion criteria, nine were randomized controlled trials (RCTs; two were pilot studies), one was a one-group design, and one was a matched-cohort design.
Sample size ranged from 20 to 284 participants. Three studies had mostly male participants, three had mostly female participants, two had an equal representation of male and female participants, and three did not specify gender. Race/ethnicity was predominantly White in three studies, Black in one study, a diverse sample in two studies, and not specified in five studies. In 10 of the 11 studies, mean age was >65. Age was not specified in one study. Sample attrition ranged from 6% to 37% and was not specified in three studies. Most of the studies did not provide complete demographic data for the sample.
Risk of Bias
The National Heart, Lung, and Blood Institute's (2004) Quality Assessment of Controlled Intervention Studies tool for risk of bias was used. This 14-question assessment tool assists reviewers in analyzing key elements of a research study related to internal validity. The tool is not meant to create a numerical rating of each study, but rather provides guidance for judging potential risk of bias. Most studies included in the current review were small sample studies demonstrating varying degrees of bias, and findings were interpreted within the context of their quality assessment. Despite concerns with methodological rigor, they were all included for their potential ability to add to the limited body of knowledge about the efficacy of VC for the management of individuals with HF. Table A (available in the online version of this article) displays the quality assessment of the intervention studies used in the review.
Quality Assessment of Videoconferencing Intervention Studies
Data Abstraction and Synthesis
A standardized data extraction tool was developed and used. An independent reviewer confirmed the accuracy of data extraction and risk of bias assessment in 20% of included studies. Table B (available in the online version of this article) displays data abstracted from each study on research design, sample characteristics, interventions, and outcomes of interest. The use of a display matrix facilitated the comparison of similarities and differences across studies and the synthesis of study findings.
Description of VC Intervention
VC was accomplished through use of monitoring or videophone systems that allowed two-way synchronous audio and video communication; most systems included built-in capabilities for measuring and transmitting physiological data, such as weight, blood pressure, pulse, and electrocardiographic tracings. Asynchronous transmission of biometric data was the most common component used in conjunction with VC (n = 10). VC interventions focused on assessment, monitoring, and management of HF symptoms, as well as patient education.
Ten of the 11 studies included usual home care as the control group (Dansky, Vasey, & Bowles, 2008; de Lusignan, Meredith, Wells, Leatham, & Johnson, 1999; de Lusignan, Wells, Johnson, Meredith, & Leatham, 2001; Idris et al., 2015; Jerant, Azari, Martinez, & Nesbitt, 2003; Jerant, Azari, & Nesbitt, 2001; Pekmezaris et al., 2012; Wakefield et al., 2009; Wakefield et al., 2008; Woodend et al., 2008) and one used a one-group design (Dimmick et al., 2003). All VC interventions were delivered by nurses located at a central office. Eight studies cited duration of interventions as ranging from 2 to 12 months (de Lusignan et al., 2001; Idris et al., 2015; Jerant et al., 2001; Jerant et al., 2003; Pekmezaris et al., 2012; Wakefield et al., 2009; Wakefield et al., 2008; Woodend et al., 2008) and duration was unclear in the remaining three studies (Dansky et al., 2008; de Lusignan et al., 1999; Dimmick et al., 2003). Of the three studies in which duration was unclear, one was estimated to have lasted for ≥60 days; however, patients may have been discharged earlier based on Medicare home health services regulations (Dansky et al., 2008). In two studies, it was noted that poor video and audio quality negatively affected 76% of the VC encounters, resulting in nurses using telephone interviews as an alternative (Jerant et al., 2001; Jerant et al., 2003).
Measurement Instruments Used
Hospital services use was defined as HF-related hospital readmission and ED visits. Use was measured by medical record review and patient self-report. The majority of studies measured either hospital admissions or ED visits but not both. In addition, differentiation between HF-specific hospital readmission versus all-cause readmission was not always clear.
Theoretical definitions of self-care and QOL were not provided. For example, although not explicitly stated, self-care appeared to be conceptualized as the ability to maintain physiological stability by following a prescribed treatment regimen, including medication adherence and self-monitoring of weight, blood pressure, and HF symptoms. Self-monitoring in HF is a self-care behavior that is an important part of chronic disease management. Measurement of self-care was primarily patients' reports of compliance with self-care indicators, such as medication and diet, as well as weight and blood pressure recordings. Conceptual ambiguity of self-care in the literature is common (Peeters et al., 2013). Previous reviews have similarly noted the divergent measures of HF self-care (Radhakrishnan & Jacelon, 2012).
Similarly, QOL seemed to be broadly conceptualized as improved physical, functional, and psychosocial well-being. The five studies measuring QOL used a variety of self-report instruments that were either generic or specific to HF, such as the Minnesota Living with Heart Failure Questionnaire (MLHFQ; Wakefield et al., 2008), General Health Questionnaire 28 (de Lusignan et al., 1999), Chronic Heart Failure Questionnaire (de Lusignan et al., 2001), Medical Outcomes Study Short Form-36 (MOS SF-36; Woodend et al., 2008), and two unnamed instruments described as standardized scales (Dimmick et al., 2003).
VC Intervention Outcomes
Hospital Services Use. Of the seven studies measuring hospital services use as a primary outcome, five reported significant reductions in hospital admissions and/or ED visits (Dansky et al., 2008; Dimmick et al., 2003; Idris et al., 2015; Jerant et al., 2001; Jerant et al., 2003). Of these five studies, two measured ED visits only (Jerant et al., 2001; Jerant et al., 2003), one measured a combination of hospital and ED visits (Dansky et al., 2008), one measured hospital admissions (Idris et al., 2015), and one measured hospital admissions and length of stay (Dimmick et al., 2003). Outcomes for hospital services use were reported inconsistently and varied from lower probability of hospital admission and/or ED visits (Dansky et al., 2008) to reduction in the national benchmark HF hospitalization rate (Dimmick et al., 2003).
No discernible differences in methodology or VC intervention differentiated studies with significant effects on hospital services use. However, there was a wide range of sample size across the studies, varying from N = 28 (Idris et al., 2015) to N = 284 (Dansky et al., 2008). Most studies used a RCT and participants were predominantly White men or women, with the exception of one study in which the VC participants were predominantly Black women and slightly younger (mean age = 66.6 years; Jerant et al., 2003). VC was supplemented with remote physiological monitoring and data transmission in all studies and home nursing visits in two studies (Dansky et al., 2008; Pekmezaris et al., 2012).
Self-Care. Of the four studies measuring outcomes that could broadly be conceptualized as self-care abilities, only two reported improvements, which were not statistically quantified (de Lusignan et al., 1999; de Lusignan et al., 2001). In both studies, participant adherence with recording weight, pulse, and blood pressure and the medication regimen were inferred as indicators of effective self-care. In another study, changes in HF symptoms related to diet, physical activity, and medication effectiveness were inferred as evidence of self-care (Dansky et al., 2008). Study findings indicated that VC was less effective than asynchronous monitoring for improving patients' self-assessment of symptoms related to diet and medication (Dansky et al., 2008). Intervention descriptions did not specify self-care education as a goal or component of a disease management program.
All four studies used a randomized design, but the absence of complete sample characteristics makes it difficult to determine whether differences in participants (e.g., ethnicity, degree of HF) contributed to the mixed findings. Participants' levels of health literacy and the potential impact on use of TM equipment and self-care abilities were not addressed. Attrition rates were high (17% to 35%) in the three studies with smaller sample sizes (de Lusignan et al., 1999; de Lusignan et al., 2001; Jerant et al., 2003) and not reported in the study with the largest sample size (Dansky et al., 2008).
QOL. Five studies measured the effect of VC on QOL, and three reported improvement at various data points in some aspect of quality, such as sleep (Dimmick et al., 2003); physical functioning, physical role, bodily pain, vitality, and mental health (Woodend et al., 2008); or overall disease-specific QOL (Wakefield et al., 2008). However, the non-RCT one-group design study also reported significant decreases in QOL, including increased fatigue, depression, and loss of appetite (Dimmick et al., 2003). QOL was measured using unknown standardized scales (Dimmick et al., 2003), the MLHFQ (Wakefield et al., 2008), and MOS SF-36 (Woodend et al., 2008). VC was conducted once or more per week in all three studies reporting significant effects on QOL, with the exception of one study in which VC was scheduled three times during the first week after hospital discharge (Wakefield et al., 2008). In one of the three studies, the sample was noted as being predominantly female (Dimmick et al., 2003).
Eleven empirical studies evaluated the effect of VC on hospital services use, self-care, or QOL. Findings from the current integrative review suggest that VC interventions can reduce hospital services use and improve some contributors to QOL; improvement in self-care outcomes was less evident. Current research on the efficacy of TM of patients with HF in their home environment primarily consists of comparisons between one or multiple telehealth modalities to usual care. There has been little evidence about the comparative effectiveness of the different telehealth modalities in and of themselves (Kotb et al., 2015), such as VC.
Hospital Services Use
The significant results in the majority of studies measuring hospital services use suggest that VC with remote physiological monitoring in patients with HF may be a means of early detection and treatment of worsening symptoms to help reduce HF-related hospital readmissions and/or ED visits. The long-term cost savings associated with reduced use of hospital services may be substantial (Kotb et al., 2015) and can extend to decreased use of home, outpatient, and alternative level of care services. It is important to note that most of the studies with significant findings had small sample sizes and samples that were not racially/ethnically diverse. Reduction in hospital services use in two studies was not sustained beyond the initial measurement period (Dansky et al., 2008; Idris et al., 2015), despite one intervention combining usual care, weekly VC, and regularly scheduled physician office visits (Idris et al., 2015). Although there is ample literature suggesting that TM with VC can reduce the frequency of rehospitalization for individuals with HF, the risk remains high. The absence of a reliable predictor model for HF hospitalization as well as the associated costs of service have led to efforts to develop programs to prevent readmissions (Desai & Stevenson, 2012).
Self-care has been identified as a strategy to improve HF outcomes (Riegel, Dickson, Goldberg, & Deatrick, 2007). Successful self-care management suggests that a patient would demonstrate increased functional status and fewer troubling symptoms (Wang, Lin, Lee, & Wu, 2011). The effectiveness of VC related to self-care abilities in adults with HF remains unclear. Although VC significantly improved clinical outcomes related to participants' blood pressure and weight at 60 days (de Lusignan et al., 1999) and 120 days (de Lusignan et al., 2001), it was found to be less effective than asynchronous monitoring for improving patients' self-assessment of symptoms related to diet and medication (Dansky et al., 2008). The number of studies measuring self-care was small and the operational definitions varied considerably. The ability to maintain functional capacity in the home has been associated with reduced risk for hospital readmission (Rumsfeld et al., 2013).
Reducing barriers to effective self-care, such as insufficient patient understanding of HF symptoms necessitating immediate intervention, requires ongoing education. Knowledge is required for self-care but is not the only determinant of developing successful self-care abilities (Riegel, 2008). Confidence in self-care may influence outcomes (Riegel, 2008) and it is unknown how the introduction of remote monitoring technology in the home affects confidence. Cognitive impairment is frequently demonstrated in older adults with HF (Shaukat et al., 2014), further limiting self-care abilities. Empirical evidence of a relationship between emotional support and self-care abilities (Sayers, Riegel, Pawlowski, Coyne, & Samaha, 2008) suggests that family members should be included in VC interventions designed to support effective patient self-care. Further research on self-care behaviors is needed to evaluate the types of VC interventions that promote the self-care skills required for successful long-term management of HF symptoms, medications, and diet.
A consistent theoretical and operational definition of self-care should be adopted to guide the development of education programs that remotely facilitate the development of self-care abilities. Furthermore, understanding the relationship between VC-enabled self-care interventions and hospital services use may improve readmission rates for individuals with HF. One instrument that has been developed and used specifically for individuals with HF is the Self-Care of Heart Failure Index (SCHFI), which measures self-care maintenance, management, and confidence through self-report of performance and confidence in self-care abilities (Riegel, Lee, Dickson, & Carlson, 2009). The SCHFI has been revised since its 2004 publication by the addition of questions to the self-care maintenance and confidence scales and changes in scoring procedures (Riegel, Lee, et al., 2009). An additional reason to recommend this instrument for use in future nursing research is its derivation from the self-care of heart failure model, which proposes that self-care maintenance and self-care management are processes required for self-care, with confidence as a moderating variable (Riegel & Dickson, 2008).
Quality of Life
Health is an important determinant of QOL in adults with HF. The concept of health-related quality of life (HRQOL) has been developed to differentiate the impact of a disease and its treatment on physical, social, and emotional well-being (Wilson & Cleary, 1995). HRQOL is a predictor of hospital readmission and mortality in patients with HF (Rodriguez-Artalejo et al., 2005), suggesting that HRQOL be routinely evaluated to guide clinical decision making (Calvert, Freemantle, & Cleland, 2005). Three of the five studies examining QOL in the current review reported significant improvements in some aspect of quality. Although similar to self-care abilities, QOL was broadly conceptualized and inconsistently measured. HRQOL is a multidimensional concept with objective and subjective indicators. One conceptual model identified six dimensions of HRQOL: biological and physiological factors, symptom status, functional status, general health perceptions, characteristics of the individual, and characteristics of the environment (Wilson & Cleary, 1995).
Although measuring functional status is particularly relevant in individuals with HF, using it as the sole indicator of overall HRQOL limits understanding of the concept and the patient's experience, as well as the effectiveness of VC interventions. For individuals with HF, the symptom burden of disease varies and substantially compromises QOL, which declines with disease progression. In studies reporting significant improvement in QOL outcome measures, it may be important to include the patient's state of HF as a variable that affects patients' decision making regarding treatment options. Including patients' state of HF as a variable may be particularly important given equivocal findings of the benefits of self-care interventions on QOL in individuals with HF (Grady, 2008). An additional consideration concerns the effect of caregiver presence on QOL measures. For older patients, video-based nursing visits, which can contribute to a positive perception of social support, may be an adequate substitute for in-person home visits, and will be reflected in QOL scores. Finally, as with any chronic disease, the desired goal for an individual with HF may be an acceptable QOL weighed against the ongoing burden of care.
Implications for Geriatric Nursing
HF costs approximately $30.7 billion each year related to health service use, medications, and missed days from work (Centers for Disease Control and Prevention, 2016), resulting in wide-ranging implications in terms of disease burden and economics. Follow-up nursing care to those diagnosed with HF is essential to managing this health care challenge, and VC provides a viable means for geriatric nurses to counsel and educate older, home-bound patients by monitoring vital signs and weight as well as improving patient medication adherence and dietary and exercise compliance. These measures may assist patients in gaining insight into self-care practices, reducing unnecessary ED and hospital admissions, and improving overall functional health status. Through quick physiological assessment, geriatric nurses can identify patients with subtle changes in symptomatology or deteriorating conditions that may require immediate intervention. Patient safety issues that arise during nighttime hours can also be addressed quickly (Hebert, Korabek, & Scott, 2006).
VC may be particularly advantageous to patients residing in rural areas who may not have the means nor the physical stamina to travel long distances to receive nursing and medical attention. The impact of VC on travel time is also advantageous for home care nurses, as VC allows nurses to provide close clinical monitoring to larger numbers of patients, possibly proving more cost-effective than in-home visits. From a QOL perspective, VC allows patients more independence as they may be able to remain in their homes for longer periods of time, thus avoiding institutionalization.
Gerontological nurse leaders play a significant role in advancing the care of individuals with HF through the integration of telehealth modalities into traditional models of care delivery. Using a framework such as the Foundation of Knowledge model, which explicates the process by which knowledge is acquired, processed, and generated (Kinsella, Albright, Prial, & Hoss, 2018), nurse leaders can help nurses extend the boundaries of their clinical expertise to deliver evidence-based practice and patient-centered care at a distance. Some nurses may be resistant to using VC for assessment of patients with HF, viewing it as a less effective substitute for a face-to-face, hands-on physical examination. Nurse leaders will be instrumental in encouraging staff to be receptive to new practice models and disseminating VC outcome-based data related to health care costs, reduction of health services use, impact on patient safety, QOL, and potential to enhance self-care abilities.
There were several limitations of the current review. The lack of a consistent theoretical framework for conceptualizing and measuring self-care and QOL confounded the ability to compare and analyze results. Some studies lacked control groups whereas others failed to describe the meaning of “usual care.” Demographic characteristics were not fully reported and sample participants were primarily White men, despite the disproportionate prevalence of HF in African American individuals (Sharma, Colvin-Adams, & Yancy, 2014). In general, sample sizes were too small to reach statistical power and attrition rates were high.
The use of VC combined with remote physiological monitoring yielded promising results. Many studies demonstrated reduced hospital services use and increased QOL. However, these findings should be interpreted cautiously due to the methodological limitations described. Additional research is needed to better specify the goals of VC interventions, the mechanisms by which VC interventions improve health outcomes, and the effect of VC interventions with adults of diverse race and ethnicity.
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Quality Assessment of Videoconferencing Intervention Studies
|Methodological details||Dansky, Vasey, & Bowles (2008)||deLusignan, Meredith, Wells, Leatham, & Johnson (1999)||deLusignan, Wells, Johnson, Meredith, & Leatham (2001)||Dimmick et al. (2003)||Idris et al. (2015)||Jerant, Azari, Martinez, & Nesbitt (2003)||Jerant, Azari, & Nesbitt (2001)||Pekmezaris et al. (2012)||Wakefield et al. (2009)||Wakefield et al. (2008)||Woodend et al. (2008)|
|Described as randomized||yes||yes||yes||no||yes||yes||yes||yes||yes||yes||yes|
|Method of randomization adequate (i.e., use of randomly generated assignment)?||no||yes||yes||NA||CD||yes||yes||yes||CD||no||no|
|Treatment allocation concealed (so that assignments could not be predicted)?||no||no||no||no||no||no||no||no||no||no||no|
|Were study participants and providers blinded to treatment group assignment?||no||no||no||no||no||no||no||no||no||no||no|
|Were the people assessing the outcomes blinded to the participants' group assignments?||no||no||no||no||no||no||no||no||no||no||no|
|Were the groups similar at baseline on important characteristics that could affect outcomes (e.g., demographics, risk factors, co-morbid conditions)?||yes||CD||CD||CD||no||yes||yes||yes||CD||yes||no|
|Was the overall drop-out rate from the study at endpoint 20% or lower of the number allocated to treatment?||NR||yes||no||yes||CD||yes||yes||NR||CD||no||yes|
|Was the differential drop-out rate (between treatment groups) at endpoint 15% or lower?||NR||no||yes||CD||CD||no||no||NR||CD||no||CD|
|Was there high adherence to the intervention protocols for each treatment group?||no||no||no||CD||CD||no||no||CD||no||no||no|
|Were other interventions avoided or similar in the groups (e.g., similar background treatments)?||yes||CD||yes||yes||yes||yes||yes||yes||yes||Yes||Yes|
|Were outcomes assessed using valid and reliable measures, implemented consistently across all study participants?||yes||no||no||no||no||no||no||no||yes||yes||no|
|Did the authors report that the sample size was sufficiently large to be able to detect a difference in the main outcome between groups with at least 80% power?||no||no||no||no||no||no||no||no||no||no||yes|
|Were outcomes reported or subgroups analyzed pre-specified (i.e., identified before analyses were completed)?||yes||no||no||no||yes||yes||yes||yes||yes||yes||yes|
|Were all randomized participants analyzed in the group to which they were originally assigned (i.e., did they use an intention-to-treat analysis?||CD||CD||CD||CD||CD||CD||yes||yes||CD||yes||yes|
Summary of Studies
|Criteria||Dansky, Vasey, & Bowles (2008)||deLusignan, Meredith, Wells, Leatham, & Johnson (1999)||deLusignan, Wells, Johnson, Meredith, & Leatham (2001)||Dimmick et al. (2003)||Idris et al. (2015)|
|Study Design||RCT||RCT pilot study||RCT pilot study||One group||RCT|
|Sample size||n = 284
112 = UC
127 = OWM
45 = VC||n = 20
10 = UC
10 = VC||n = 20
10 = UC
10 = VC||n = 34||n = 28
14 = UC
14 = VC|
|Attrition, %||N/A||20% UC||35%||6%||NA|
|Race/ethnicity||N/A||N/A||N/A||N/A||UC 71% Black
VC 86% White|
|Mean age, year||N/A||75.1||75.2||71||UC 66.5
|Sample characteristics||N/A||N/A||N/A||Female 67%
Average education level 8.9 yeas||UC 43% Male
VC 36% Male|
|Intervention description||UC & OWM or
UC & VC||VC twice daily||VC weekly for 3 months, every 2 weeks for second 3 months and monthly thereafter unless unwell||Weekly VC||UC & weekly VC, & MD office visits at 2 weeks, 3 months, and 6 months.|
|Duration of intervention||Data collected @ 60 and120 days||3 months||3 months||N/A||Weekly VC for 3 months|
|Outcome variables of interest||Hospital readmission ED visits HF clinical status||QOL Compliance with medication & self-measurement regimen||QOL Compliance with medication & self-measurement regimen||Hospital readmission QOL||Hospital readmission|
|Measures used to assess outcomes||Patient self-report verified by medical record||General Health
Weight & BP recordings||General Health Questionnaire (QOL)
Chronic Heart Failure Questionnaire (QOL)
Weight & BP recordings||Unknown standardized scales (QOL)
N/A for hospital readmission||Patient report & medical record|
|Technology||OWM or VC||VC||VC||VC||VC|
|Intervention outcomes||– hospitalization OWM and VC @ 60 days
ED visits VC @ 60 days||NSF||NSF||fatigue, depression, loss of appetite sleep problems
14% hospitalized for HF in the first 6 months||NSF|
|Control outcomes||NSF||NSF||NSF||No control group||NSF|
|Criteria||Jerant, Azari, Martinez, & Nesbitt (2003)||Jerant, Azari, & Nesbitt (2001)||Pekmezaris et al. (2012)||Wakefield et al. (2009)||Wakefield et al. (2008)||Woodend et al. (2008)|
|Study design||RCT||RCT||Randomized and matched-cohort study||RCT||RCT||RCT|
13 VC||12 UC
13 VC||84 randomized RPM
84 matched-cohort RPM
80 randomized UC
80 matched-cohort UC||148
52 VC||49 UC
52 VC||59 UC
|Attrition, %||17% TG||17% TG||NA||15–26%||14% UC
37% VC||7% (for all HF subjects)|
UC 33% Black
TG 42% Black
VC 62% Black
UC 33% Black
TG 42% Black
VG 62% Black
92% Matched-cohort UC 65%||94% White||UC 100%White
TG 94% White
VC 88% White||N/A|
|Mean age, year||72.7 UC
66.6 VC||72.7 UC
66.6 VC||81 randomized RPM
84 matched-cohort RPM
83 randomized UC
85 matched-cohortUC||69||67 UC
69 VC||66 UC
|Sample characteristics||UC 50% Male
TG 42% Male
VC 46% Male||UC 50% Male
TG 42% Male
VC 46% Male||Randomized UC 33%
Matched-cohort UC 34%
Randomized VC 43% Male
Matched-cohort VC 40%||99% Male||UC 98% Male
VC 98%||UC 70% Male
UC 58% NYHAclass 3+VC 66%|
|Intervention description||Home nursing visit at baseline and 60 days plus either scheduled telephone calls from nurse or scheduled VC||Home nursing visit at baseline and 60 days plus either scheduled telephone calls from nurse or scheduled VC||Combination of home and VC||Scheduled telephone or VC contact weekly for 90 days post hospital discharge||Scheduled telephone or videophone contact follow-up 3× first week post hospital discharge then weekly for 11 weeks
Symptom monitoring and management and education; patient self-report of symptoms||Minimum of weekly VC|
|Duration of intervention||180 days from the date of first home nurse visit||1 year||NA||180 days||6 months||3 months|
|Outcome variables of interest||Self-care ED visits||Hospital readmission ED visits||Hospital readmission ED visits||Hospital readmission Medication changes & patient knowledge (self-care)||Hospital readmission QOL||Hospital readmission
ED visits QOL|
|Measures used to assess outcomes||Patient report of compliance with self-care indicators and HF medication regimen
Hospital records||Hospital records||Home care database & medical records||Patient report||MLHFQ (QOL) Hospital records||Medical Outcomes Study Short Form – 36 (QOL)
Patient report (admissions/ED visits)|
|Technology||VC||VC||VC with built-in stethoscope, digital scale, BP and pulse meter, BP cuff, pulse oximeter||VC with change of vendor mid-study||VC with change of vendor mid-study||Minimum of weekly VC|
|Control||UC||UC||Home nursing visits||UC||UC||UC|
|Intervention outcomes||HF-related ED visits in each intervention arm.||HF-related ED visits in each intervention arm.||NSF||NSF||hospital readmission rate for intervention groups at 12 months
disease-specific QOL over time||QOL in three subscales at 1 month, five subscales at 3 months, and one subscale at 1 year after discharge|
|Control outcomes||NSF||NSF||NSF||NSF||NSF||QOL over time.|