Background: Web-based interventions may have the potential to support self-care in patients with chronic disease, yet little is known about the feasibility of Web-based interventions in patients with heart failure (HF).
Objective: The objective of our study was to develop and pilot a Web-based self-care intervention for patients with HF.
Methods: Following development and pretesting, we pilot tested a Web-based self-care intervention using a randomized controlled design. A total of 28 participants completed validated measures of HF knowledge, self-care, and self-efficacy at baseline and 1-month follow-up.
Results: Change scores and effect size estimates showed that the mean differences in HF knowledge (d=0.06), self-care (d=0.32), and self-efficacy (d=0.37) were small. Despite email reminders, 7 of 14 participants (50%) of the sample accessed the site daily and 4 of 14 (28%) had no record of access.
Conclusions: Larger randomized controlled trials are needed that attend to all sources of self-efficacy and include more comprehensive educational tools to improve patient outcomes.
Despite improvements in treatment and prevention, chronic heart failure (HF) remains a serious health burden and carries a poor prognosis . Given the complex and progressive nature of HF, interventions are needed that slow disease progression and prevent hospital admissions [ ]. One strategy to improve health outcomes in patients with HF is to enhance self-care behavior related to physical activity, nutrition, fluid management, and treatment adherence [ ]. Self-care is defined as “a process of maintaining physiological stability by monitoring symptoms, adhering to the treatment regimen (self-care maintenance), and promptly identifying and responding to symptoms (self-care management)” [ ]. Self-care education is a critical strategy in empowering HF patients to be informed and actively engaged in monitoring their condition and adjusting treatment accordingly [ ].
Although findings are mixed, previous research suggests that Web-based interventions may have the potential to improve clinical outcomes and reduce hospitalizations in patients with chronic diseases including HF [, ]. Using a Web-based app as a cost-effective intervention might facilitate access to education and improve self-care skills [ ]. Yet there are very few clearly conceptualized studies that have evaluated Web-based interventions for HF patients. Two descriptive studies reported high levels of patient satisfaction with educational websites providing information [ ] and a daily communication method between HF patients and health care providers [ ]. Findings indicated that older HF patients with limited computer skills were willing to engage with Web-based education if provided adequate resources and instruction [ ].
Only two controlled studies could be identified that examined the effectiveness of a Web-based HF self-care program. Westlake et al  tested a Web-based HF and symptom management education progam specifically designed for older HF patients (≥60 years). In addition to educational modules, patients were encouraged to use the website at home to email a clinical nurse specialist for support, link to video-based Web content, and monitor personal clinical data. A total of 40 patients attending an outpatient HF clinic were recruited to the 12-week Web-based intervention and were compared with 40 age- and sex-matched historical controls who received HF care as usual. Groups were similar at baseline, and results showed a modest benefit for perceived control over health status and mental health quality of life, but not for physical health quality of life.
Another study  also tested a Web-based HF self-care program. In addition to usual care, 16 HF patients randomly assigned to the treatment group received a computer with Internet access, as well as basic computer training. The website provided self-care information and videos, access to record and monitor vital signs, health behavior, and HF symptoms daily, and emails from health professionals for feedback on progress and emotional support as needed. It was found that at both 6- and 12-month follow-ups, only the treatment group showed significantly improved HF knowledge, amount of exercise, and quality of life, as well as reduced HF symptoms, blood pressure, and health care utilization [ ].
The preliminary findings of the HF studies above, combined with systematic reviews [, ], suggest that Web-based educational interventions may have a role in supporting HF patients in the community. In this study, we pilot tested a Web-based self-care intervention that provided education and self-monitoring tools for ambulatory HF patients. Our primary aim was to evaluate the feasibility of a Web-based intervention in improving HF patients’ knowledge, self-care, and self-efficacy.
The website development phase involved seeking expert feedback and pretesting the user friendliness and appropriateness of the intervention and outcome measures with HF patients. We recruited an expert panel comprising two cardiologists, four cardiac nurse researchers, and one HF nurse practitioner to advise on the development and content of the website. The software was developed by an information technology team that also provided feedback on the design. We recruited a group of 10 patients with chronic HF visiting a community outpatient HF service to pretest the intervention. During face-to-face meetings, patients provided their feedback and recommendations on the Web-based app content, ease of navigation, and user friendliness. To examine the feasibility of the Web-based HF self-care intervention, we then piloted the intervention using a randomized controlled design over a 1-month period.
The pilot sample comprised 29 patients consecutively recruited from a tertiary hospital HF service and a university health clinic located in Brisbane, Australia, between December 2013 and May 2014. Patients were eligible if they were English-speaking adults, had cardiologist- diagnosed class I–III HF according to the New York Heart Association (NYHA) classification and had a left ventricle ejection fraction <40%. Participants were required to have home or mobile Internet access. Exclusion criteria included nursing home residents, severe cognitive impairment, and critical illness.
After providing informed consent at an introductory face-to-face session, participants completed a baseline questionnaire and were randomly assigned to the intervention or control group. Participants in the intervention group were assigned a username and password and were provided basic training on how to use each component of the website. They could then access the website educational modules each day, and record and monitor personal data such as HF-specific symptoms. The intervention group also received a weekly personalized email to improve engagement with the intervention. Both intervention and control groups received usual care from the HF service or clinic, which included comprehensive educational information consisting of topics such as medication, nutrition, exercise, and psychosocial issues. Individual patients were referred to the programs for a duration of 12 weeks and were followed up by HF nurses, and physical and occupational therapists. At 1-month follow-up, all participants completed the same questionnaire as at baseline. Due to the scope of the study, the primary researcher collected all data, as blinding was not possible. All study procedures were approved by the relevant hospital and university human research ethics committees. The intervention was also made available to the control group after completion of the research.
The pedagogical principals underpinning the Web-based app were based on two key elements of self-efficacy theory [, ]: role modelling and mastery experience. Helping adults to improve their self-efficacy is a powerful and central factor in increasing chronic disease self-care [ , ]. Patients with greater self-efficacy have been found to be more willing to learn and commit to achieving goals, whereas low self-efficacy has been associated with task avoidance [ ]. We anticipated that the use of informational sources such as role modelling and mastery experience would help patients to increase their self-efficacy. Role modelling can take the form of observing the actions of others [ ]. As such, we created female and male avatars to resemble role models and to help users engage with educational materials on the website. We encouraged mastery experience by asking participants to monitor and record their signs and symptoms related to HF fluid overload. The daily weight measurements and HF symptoms were able to be recorded to show trends over time. We used a further source of self-efficacy, verbal persuasion, through weekly emails to each participant.
We developed the Web-based app based on feedback from three groups: HF experts, an information technology team, and HF patients. The role of the HF expert panel was to ensure integrity of the content according to evidence-based guidelines, applicability of study instruments, appropriate language and images, and appropriate user interfaces. HF patients provided their feedback and recommendations on the content, ease of navigation, and user friendliness. We developed the Web-based educational materials based on the National Heart Foundation of Australia and the Cardiac Society of Australia and New Zealand chronic HF guidelines . The website was password protected, and both HF patients and their health care professionals had access to content including interactive HF teaching tools, self-care tools, a chart for recording daily measures, and self-care questionnaires.
This section consisted of educational topics including an explanantion of HF (with animations), HF signs and symptoms, daily weighings (related to fat and fluid differentiation), suggestions for healthy eating and being active, methods to avoid salty foods and tips for reading food lables, and taking appropriate action when symptoms were exacerbated.
To reinforce self-monitoring, patients could enter daily clinical data including daily weight and any signs of fluid retention such as tight shoes or socks.
This tool allowed patients to visually monitor their daily weight and severity of HF symptoms through simple graphs. In addition, health care professionals were able to monitor their patients’ self-reported data by accessing their charts.
The final section was designed to help patients assess their knowledge and self-care skills over time. It included questionnaires such as the Self-Care of Heart Failure Index (SCHFI) and the Dutch Heart Failure Knowledge Scale (DHFK).
In addition to baseline demographics, questionnaires assessed the primary outcomes including HF knowledge, self-care, and self-efficacy. The DHFK  consists of 15 multiple-choice items concerning HF in general (4 items), HF treatment (6 items on diet, fluid restriction, and activity), and HF symptoms and symptom recognition (5 items). The scale has a minimum score of 0 (no knowledge) and a maximum score of 15 points (optimal knowledge). It is a frequently used measure of HF knowledge and has been validated in previous research evaluating educational intervention [ ].
We measured self-care skills using the SCHFI version 6.2 [, ]. This tool has 21 items, which are scored as three subscales: maintenance (symptom monitoring and adherence behaviors performed to prevent HF exacerbation), management (patients’ abilities to recognize symptoms when they occur, treatment implementation in response to symptoms, and treatment evaluation), and confidence (measures task-specific self-efficacy behaviors to manage the process of self-care) [ ]. The management subscale was answered only if the patient reported having trouble breathing or ankle swelling in the past 4 weeks. Each standardized subscale score ranged from 0–100, with a score of ≥70 or more indicating adequate self-care [ ].
We also used the 6-item Self-Efficacy for Managing Chronic Disease Scale (SEMCD)  as a general measure of chronic disease self-efficacy. It covers several domains relevant to chronic disease self-care, including symptom control, role-function, emotional functioning, and communicating with physicians. Each item is rated on a 10-point scale ranging from “not at all confident” (1) to “totally confident” (10). A mean score is calculated, with higher scores indicating higher perceived self-efficacy.
We analyzed data using IBM SPSS Statistics version 21 (IBM). We compared baseline characteristics using Fisher exact and t tests. Descriptive statistics were examined for all variables, and pre-post change scores were calculated for outcome measures. Given the small sample size and nonnormal distribution of outcome variables and to analyze between-group differences over time, we compared change scores using Mann-Whitney U tests. We considered P<.05 to be significant and report effect sizes where possible.
Study Recruitment and Baseline Characteristics
Of 75 HF patients who were assessed for eligibility, 29 agreed to participate and were enrolled into the study (see). shows the baseline demographic and clinical characteristics of the 28 patients who participated in the pilot study. One participant dropped out. Of the total sample, the majority were men (n=22, 79%) and the mean age was 60.8 (SD 11.9) years. The mean self-reported disease duration was 4.0 (SD 6.9) years. Most participants were asymptomatic (class I, n=14, 50%) or had mild HF (class II, n=12, 43%) according to the NYHA functional classification. Comparisons showed no significant differences between the intervention and control groups at baseline.
|Characteristic||Control (n=14)||Intervention (n=14)|
|Age in years, mean (SD)||60.0 (14.0)||61.7 (9.9)|
|Sex, n (%)|
|Male||11 (79)||11 (79)|
|Female||3 (21)||3 (21)|
|Marital status, n (%)|
|Married||8 (57)||9 (64)|
|Widowed, divorced, or never married||6 (43)||5 (36)|
|Living arrangement, n (%)|
|Live alone||6 (46)||5 (36)|
|Live with others||7 (54)||9 (64)|
|Level of education, n (%)|
|<12 years or high school diploma||7 (50)||8 (57)|
|Some college/associate degree, bachelor’s degree, or postgraduate degree||7 (50)||6 (43)|
|Total household income, n (%)|
|≤A$40,000/year||4 (29)||9 (64)|
|≥A$70,000/year, or do not know, or refused||10 (71)||5 (36)|
|Overall perceived health, n (%)|
|Excellent, very good, or good||7 (50)||7 (50)|
|Fair or poor||7 (50)||7 (50)|
|Angiotensin-converting enzyme inhibitor||10 (71)||10 (71)|
|Angiotensin II receptor blocker||4 (29)||2 (14)|
|Beta-blocker||14 (100)||11 (79)|
|New York Heart Association classification|
|Class I||8 (57)||6 (43)|
|Class II||6 (43)||6 (43)|
|Class III||0 (0)||2 (14)|
|Ejection fraction, mean (SD)||33.7 (8.9)||33.4 (8.51)|
|Duration of heart failure in years, mean (SD)||3.0 (4.8)||4.9 (8.5)|
Heart Failure Knowledge
compares the change in knowledge scores for the intervention and control groups. As the maximum possible knowledge score is 15, the baseline mean scores of 12.5 (SD 1.1) for the intervention group and 12.2 (SD 2.4) for the control group indicated that participants had relatively high levels of HF knowledge at the outset. While change scores at 1 month showed slight improvement in HF knowledge, there was no significant difference between the intervention and control groups (U=91.5, P=.75), with a negligible effect size (d=0.06).
|Scale||Intervention group (n=14)||Pre-post difference, mean (95% CI)||Control group (n=14)||Pre-post difference, mean (95% CI)||Mann-Whitney U test||P value||Effect size (d)|
|Pre, mean (SD)||Post, mean (SD)||Pre, mean (SD)||Post, mean (SD)|
|DHFKa||12.5 (1.1)||12.9 |
|0.35 (–0.67 to 1.3)||12.2 (2.4)||12.8 (1.7)||0.57 (–0.05 to 1.2)||91.5||.75||0.06|
|SCHFIb (maintenance)||61.9 (20.9)||71.9 (15.1)||9.9 (–3.6 to –23.6)||70.2 (17.4)||66.6 (17.7)||–3.5 (–10.3 to 1.3)||59.5||.07||0.32|
|SCHFI (confidence)||67.1 (20.3)||73.0 (18.0)||3.8 (–4.3 to 12.0)||65.5 (21.4)||69.9 (19.2)||3.8 (–4.3 to 12.0)||89.5||.94||0.16|
|SEMCDc||7.3 (1.6)||8.0 (1.7)||0.70 (–0.54 to 1.9)||7.1 (1.4)||7.4 (1.5)||0.35 (–0.21 to 0.92)||87.0||.61||0.37|
aDHFK: Dutch Heart Failure Knowledge Scale.
bSCHFI: Self-Care of Heart Failure Index.
cSEMCD: Self-Efficacy for Managing Chronic Disease Scale.
As presented in, change scores showed an improvement in the SCHFI maintenance subscale in the intervention group to above the minimum level of self-care adequacy (mean difference 9.9, 95% CI –3.6 to –23.6), whereas the control group scores decreased over time (mean difference –3.5, 95% CI –10.3 to 1.3). While the effect size suggests an important trend (d=0.32), given the small sample size it did not reach statistical significance (U=59.9, P=.07). In addition, while we observed small improvements in the SCHFI confidence subscale, we found no significant between-group differences (U=89.5, P=.94). As answers to the management subscale were indicated by only 5 patients, we did not compare this subscale.
shows that both groups also reported high levels of self-efficacy at baseline. Change scores indicated an improvement among the intervention group (mean difference 0.70, 95% CI –0.54 to 1.9, d=0.37), although this was not statistically significant (U=87.0, P=.61).
Use of the Intervention
The intervention group accessed the website on average 16.8 times during the 1-month intervention period. Of the 14 participants, 7 (50%) accessed the intervention site every day, 4 (28%) had no record of access, 11 (78%) accessed the website from home and 3 (22%) through a mobile phone. The most reviewed section of the website was “How do I learn to read food labels?” followed by “How do I reduce salt in my daily meal?,” “What is the difference between fat and fluid weight gain?,” and “How do I become more physically active?” Based on personal data recorded, 7 participants entered their data every day, 1 participant on 25 days, 1 on 12 days, and 1 on 4 days; 4 participants did not enter any data.
To explore a possible dose-response relationship, we ran correlations between participants’ change scores and frequency of usage of the website. Within the intervention group, Spearman correlations between frequency of usage of the intervention and outcome change scores suggested greater use was associated with higher knowledge (ρ=.34), SCHFI subscales (ρ range .40–.43), and self-efficacy scores (ρ=.45).
In this pilot study, we developed and investigated the feasibility of a Web-based self-care intervention for HF outpatients. Overall, results showed that the intervention did not improve patients’ outcomes at 4 weeks’ follow-up. While there was a trend toward improved self-care and self-efficacy in the intervention group for behaviors such as symptom monitoring and strategies to prevent HF exacerbation, given the small sample size, the results were not statistically significant. In contrast to previous studies [, ] we also found that a Web-based educational intervention was challenging for older HF patients, and usage was low. Despite face-to-face training and email reminders, only 50% of the sample accessed the site daily and 28% had no record of access.
Comparison With Prior Work
This study differed in important ways from previous intervention studies of Web-based HF self-care [, ], which may account for the negative findings. Our cohort had shorter disease duration and less severe HF according to the NYHA classification than in previous studies [ , ]. The follow-up period was relatively short to examine changes in patient outcomes, and we acknowledge that a minimum of 6 months’ follow-up is recommended in the literature [ , ]. There were also differences in the scope of the intervention. The educational content of the website was at a basic level and focused on self-care skills. The standard face-to-face patient education received by both groups in the HF clinic, therefore, may not have been comprehensive enough to diminish the effect of the Web-based intervention. Our primary outcome measures of self-care skills and self-efficacy differed from previous studies, which focused on HF knowledge, symptoms, and quality of life [ , ]. Nonetheless, using validated measures was a strength of this study. Yet, because most of the participants in the sample were asymptomatic, we did not compare the management subscale of the SCHFI. In addition, as the maximum score for the DHFK questionnaire was 15 and a score >10 indicated adequate knowledge [ ], the high knowledge scores of participants at baseline (mean 12.5, SD 1.1) likely created a ceiling effect.
Notwithstanding the above limitations, our study findings are consistent with a body of literature that points to mixed evidence of the effectiveness of Web-based self-care interventions for chronic disease [, ]. Despite the development of a plethora of Internet-based interventions over recent decades, systematic reviews have not shown any benefit of Web-based self-care interventions for chronic disease [ , ]. Moreover, meta-analyses have not demonstrated a significant benefit in the use of Web-based interventions when combining samples [ , ]. This is attributed, in the first instance, to issues of methodology and variability in research design [ , - ]. There are significant differences in configuration (eg, educational materials, asynchronous discussion, live conferencing), instructional methods (eg, practice exercise, cognitive interactivity), and presentation [ ]. These also extend to types of assessments, study populations, the etiology of symptoms, and times of intervention [ , ]. A further and related issue is a lack of appropriate theoretical frameworks [ , ]. Many studies are missing the theoretical rationale for multiple assessments and how these informed the development of the Web-based interventions. The authors of a previous study [ ] reported that a limited number of interventions applied evidence-based theory and, more recently, a meta-analysis of research on reducing blood pressure with Internet-based interventions [ ] emphasized that a priority for future research is to design and evaluate interventions according to theoretically grounded hypotheses.
In contrast to other kinds of educational intervention studies in HF patients , the role of theory in developing Internet-based interventions has been largely disregarded. While precise measures of key constructs and outcomes and well-defined associations improve research precision, theory is needed to inform the choice of study design and to enhance an understanding of causal relationships [ ]. Robust theory is also critical in identifying the effectiveness of the specific components of the interventions and optimizing their intensity [ - ]. Key theoretical constructs and associations should therefore be applied in efficacy trials, tests of effectiveness, and adoption and sustainability studies. There is some evidence for the use of Bandura’s self-efficacy theory as the most important element in developing self-care interventions [ ].
Implications for Future Research
This pilot study presented a fundamental phase of the development of a Web-based self-care intervention for patients with HF that can be used to inform future research. To examine the effectiveness of the proposed Web-based intervention, a larger, adequately powered randomized controlled trial is needed. Longer follow-up periods at 3, 6, and 12 months are required to examine clinically meaningful change over time. In addition, we recommend that researchers actively support older HF patients to engage with Web-based interventions through face-to-face education sessions for basic computer and Internet skills. Greater resources may improve adherence and resolve possible barriers or difficulties in using Web-based self-care tools.
Well-designed educational strategies grounded in theory and contemporary evidence are crucial in improving HF self-care and patient outcomes . More comprehensive educational content is needed, including nutrition and diet, physical activity, effects of alcohol and smoking, medications and their side effects [ ], as well as psychosocial and emotional issues [ , ]. Moreover, as Tomita et al [ ] argued, interventions should target improved physical activity and provide practical advice about appropriate types and levels of exercise. This would include walking, breathing, stretching, range of motion, and upper and lower extremity strength training. For better outcomes and in order to encourage HF patients to maintain healthy lifestyles, interventions would also incorporate more information on recommended diet and nutrition [ ].
To strengthen the efficacy of the Web-based intervention, we recommend drawing on the sources and mediators of self-efficacy theory. Feedback, goal setting, videos of peer storytelling, monitoring tools such as for blood pressure, daily weight, and physical activities, and diaries are some examples of self-efficacy information sources . We strongly recommend using a combination of the four primary information sources in Bandura’s work to promote a stronger sense of self-efficacy and a greater willingness to undergo behavioral change and thus produce optimal results [ ]. There are examples [ , ] of previous studies that have used the four primary dimensions of self-efficacy to enhance self-care in adults with chronic diseases. The primary focus of these interventions was the provision of efficacy-based information sources, including the mastery of performance accomplishments, role modelling (vicarious learning), social persuasion (verbal encouragement), and the interpretation of physiological and emotional responses [ ].
Recruitment was slow in part due to HF patients’ lack of interest in the intervention, Internet access, or computer skills. Among the HF patients who were invited to participate in the study, 26 were not eligible based on the study inclusion and exclusion criteria. This exploratory pilot study was confined to an HF management program provided at a single-hospital HF service and university health clinic. To participate, it was essential that HF patients had access to the Internet at home or workplace or via a mobile phone. Although a large proportion of the HF population did have access to the Internet, a significant minority (22 participants) did not. As a result, our study was based on a small sample size, which limited statistical power. Finally, the study follow-up was relatively short for the examination of the Web-based intervention on patient knowledge, self-care, and self-efficacy.
This study examined the feasibility of a Web-based self-care intervention in improving HF knowledge, self-care, and self-efficacy. Although preliminary, our findings are consistent with current literature that demonstrates negative to small effects of Web-based self-care interventions in chronic disease. Larger, theoretically informed, and more comprehensive studies are needed to support or refute this proposition. Nonetheless, the challenges of this study and lessons learned give some support to the argument that Web-based programs, as discrete interventions, will not be the remedy for the cost and social burden of chronic illness.
Thanks to Associate Professor John Atherton, Mrs Linda Prentice, Mr Carlos Fernandez, and HF clinic staff and participating patients at the Royal Brisbane and Women’s Hospital. Special thanks to Professor Robyn Clark from Flinders University and Mrs Chris Horton and Dr Johannes Moolman from Ipswich Community Health and Ipswich Hospital for their generous support during phase one of the study.
Conflicts of Interest
- Boyde M, Song S, Peters R, Turner C, Thompson DR, Stewart S. Pilot testing of a self-care education intervention for patients with heart failure. Eur J Cardiovasc Nurs 2013 Feb;12(1):39-46. [CrossRef] [Medline]
- Shively MJ, Gardetto NJ, Kodiath MF, Kelly A, Smith TL, Stepnowsky C, et al. Effect of patient activation on self-management in patients with heart failure. J Cardiovasc Nurs 2013;28(1):20-34. [CrossRef] [Medline]
- National Heart Foundation of Australia, Cardiac Society of Australia and New Zealand, Chronic Heart Failure Guidelines Expert Writing Panel. Guidelines for the Prevention, Detection and Management of Chronic Heart Failure in Australia. Sydney, Australia: CSANZ; 2011 Jul. URL: http://www.csanz.edu.au/wp-content/uploads/2014/12/2011_HF_CSANZ_Chronic_Heart_Failure.pdf [accessed 2016-05-26] [WebCite Cache]
- Vellone E, Fida R, D'Agostino F, Mottola A, Juarez-Vela R, Alvaro R, et al. Self-care confidence may be the key: A cross-sectional study on the association between cognition and self-care behaviors in adults with heart failure. Int J Nurs Stud 2015 Nov;52(11):1705-1713. [CrossRef] [Medline]
- Barnason S, Zimmerman L, Hertzog M, Schulz P. Pilot testing of a medication self-management transition intervention for heart failure patients. West J Nurs Res 2010 Nov;32(7):849-870. [CrossRef] [Medline]
- Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care 2006 Nov;44(11):964-971. [CrossRef] [Medline]
- Lorig K, Ritter PL, Plant K, Laurent DD, Kelly P, Rowe S. The South Australia health chronic disease self-management Internet trial. Health Educ Behav 2013 Feb;40(1):67-77. [CrossRef] [Medline]
- Griffiths F, Lindenmeyer A, Powell J, Lowe P, Thorogood M. Why are health care interventions delivered over the internet? A systematic review of the published literature. J Med Internet Res 2006;8(2):e10 [FREE Full text] [CrossRef] [Medline]
- Evangelista LS, Strömberg A, Westlake C, Ter-Galstanyan A, Anderson N, Dracup K. Developing a Web-based education and counseling program for heart failure patients. Prog Cardiovasc Nurs 2006;21(4):196-201. [Medline]
- Delgado DH, Costigan J, Wu R, Ross HJ. An interactive Internet site for the management of patients with congestive heart failure. Can J Cardiol 2003 Nov;19(12):1381-1385. [Medline]
- Westlake C, Evangelista LS, Strömberg A, Ter-Galstanyan A, Vazirani S, Dracup K. Evaluation of a Web-based education and counseling pilot program for older heart failure patients. Prog Cardiovasc Nurs 2007;22(1):20-26. [Medline]
- Tomita MR, Tsai B, Fisher NM, Kumar NA, Wilding G, Stanton K, et al. Effects of multidisciplinary Internet-based program on management of heart failure. J Multidiscip Healthc 2008 Dec 1;2009(2):13-21 [FREE Full text] [Medline]
- Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: relevance for cancer survivors. J Med Internet Res 2013;15(2):e37 [FREE Full text] [CrossRef] [Medline]
- Im E, Chang SJ. Web-based interventions in nursing. Comput Inform Nurs 2013 Feb;31(2):94-102. [CrossRef] [Medline]
- Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 1977 Mar;84(2):191-215. [Medline]
- Bandura A. Guide for constructing self-efficacy scales. In: Urdan T, Pajares F, editors. Self-Efficacy Beliefs of Adolescents. Greenwich, CT: Information Age Publishing; 2006:307-337.
- Lorig KR, Holman HR. Self-management education: history, definition, outcomes, and mechanisms. Ann Behav Med 2003 Aug;26(1):1-7. [CrossRef]
- Bandura A. Self-Efficacy: The Exercise of Control. New York, NY: Freeman; 1997.
- van der Wal M, Jaarsma T, Moser DK, van Veldhuisen D. Development and testing of the Dutch Heart Failure Knowledge Scale. Eur J Cardiovasc Nurs 2005 Dec;4(4):273-277. [CrossRef] [Medline]
- Riegel B, Carlson B, Glaser D. Development and testing of a clinical tool measuring self-management of heart failure. Heart Lung 2000;29(1):4-15. [Medline]
- Vellone E, Riegel B, Cocchieri A, Barbaranelli C, D'Agostino F, Antonetti G, et al. Psychometric testing of the Self-Care of Heart Failure Index Version 6.2. Res Nurs Health 2013 Oct;36(5):500-511. [CrossRef] [Medline]
- Lorig KR, Sobel DS, Ritter PL, Laurent D, Hobbs M. Effect of a self-management program on patients with chronic disease. Eff Clin Pract 2001;4(6):256-262 [FREE Full text] [Medline]
- Ditewig JB, Blok H, Havers J, van Veenendaal H. Effectiveness of self-management interventions on mortality, hospital readmissions, chronic heart failure hospitalization rate and quality of life in patients with chronic heart failure: a systematic review. Patient Educ Couns 2010 Mar;78(3):297-315. [CrossRef] [Medline]
- Eland-de KP, van Os-Medendorp H, Vergouwe-Meijer A, Bruijnzeel-Koomen C, Ros W. A systematic review of the effects of e-health on chronically ill patients. J Clin Nurs 2011 Nov;20(21-22):2997-3010. [CrossRef] [Medline]
- Samoocha D, Bruinvels DJ, Elbers NA, Anema JR, van der Beek AJ. Effectiveness of web-based interventions on patient empowerment: a systematic review and meta-analysis. J Med Internet Res 2010;12(2):e23 [FREE Full text] [CrossRef] [Medline]
- Stellefson M, Chaney B, Barry AE, Chavarria E, Tennant B, Walsh-Childers K, et al. Web 2.0 chronic disease self-management for older adults: a systematic review. J Med Internet Res 2013 Feb;15(2):e35 [FREE Full text] [CrossRef] [Medline]
- Macea DD, Gajos K, Daglia Calil YA, Fregni F. The efficacy of Web-based cognitive behavioral interventions for chronic pain: a systematic review and meta-analysis. J Pain 2010 Oct;11(10):917-929. [CrossRef] [Medline]
- Bossen D, Veenhof C, Dekker J, de Bakker D. The effectiveness of self-guided web-based physical activity interventions among patients with a chronic disease: a systematic review. J Phys Act Health 2014 Mar;11(3):665-677. [CrossRef] [Medline]
- Bewick BM, Trusler K, Barkham M, Hill AJ, Cahill J, Mulhern B. The effectiveness of web-based interventions designed to decrease alcohol consumption: a systematic review. Prev Med 2008 Jul;47(1):17-26. [CrossRef] [Medline]
- Bond GE, Burr R, Wolf FM, Price M, McCurry SM, Teri L. The effects of a web-based intervention on the physical outcomes associated with diabetes among adults age 60 and older: a randomized trial. Diabetes Technol Ther 2007 Feb;9(1):52-59 [FREE Full text] [CrossRef] [Medline]
- Kerr AJ, Broad J, Wells S, Riddell T, Jackson R. Should the first priority in cardiovascular risk management be those with prior cardiovascular disease? Heart 2009 Feb;95(2):125-129. [CrossRef] [Medline]
- García-Lizana F, Sarría-Santamera A. New technologies for chronic disease management and control: a systematic review. J Telemed Telecare 2007;13(2):62-68. [CrossRef] [Medline]
- Cotter AP, Durant N, Agne AA, Cherrington AL. Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence. J Diabetes Complications 2014;28(2):243-251 [FREE Full text] [CrossRef] [Medline]
- Liu S, Dunford SD, Leung YW, Brooks D, Thomas SG, Eysenbach G, et al. Reducing blood pressure with Internet-based interventions: a meta-analysis. Can J Cardiol 2013 May;29(5):613-621. [CrossRef] [Medline]
- Campbell-Voytal K. The role of theory in clinical prevention research. Fam Pract 2010 Aug;27(4):357-358 [FREE Full text] [CrossRef] [Medline]
- Neville LM, O'Hara B, Milat AJ. Computer-tailored dietary behaviour change interventions: a systematic review. Health Educ Res 2009 Aug;24(4):699-720 [FREE Full text] [CrossRef] [Medline]
- Phillips R, Kennedy G, McNaught C. The role of theory in learning technology evaluation research. Australas J Educ Technol 2012;28(7):1103-1118.
- Boyde M, Peters R. Education material for heart failure patients: what works and what does not? Curr Heart Fail Rep 2014 Sep;11(3):314-320. [CrossRef] [Medline]
- Tung H, Lin C, Chen K, Chang C, Lin Y, Chou C. Self-management intervention to improve self-care and quality of life in heart failure patients. Congest Heart Fail 2013;19(4):E9-E16. [CrossRef] [Medline]
- Yu CH, Parsons J, Mamdani M, Lebovic G, Shah BR, Bhattacharyya O, et al. Designing and evaluating a web-based self-management site for patients with type 2 diabetes: systematic website development and study protocol. BMC Med Inform Decis Mak 2012;12:57 [FREE Full text] [CrossRef] [Medline]
- Chao Y, Scherer YK, Wu Y, Lucke KT, Montgomery CA. The feasibility of an intervention combining self-efficacy theory and Wii Fit exergames in assisted living residents: a pilot study. Geriatr Nurs 2013;34(5):377-382. [CrossRef] [Medline]
|DHFK: Dutch Heart Failure Knowledge Scale|
|HF: heart failure|
|NYHA: New York Heart Association|
|SCHFI: Self-Care of Heart Failure Index|
|SEMCD: Self-Efficacy for Managing Chronic Disease Scale|
Edited by G Eysenbach; submitted 01.09.15; peer-reviewed by F Fatehi, W Groen, E Vellone; comments to author 12.11.15; revised version received 02.12.15; accepted 04.01.16; published 20.06.16Copyright
©Nazli Bashi, Carol Windsor, Clint Douglas. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 20.06.2016.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.