This study was part of a randomized intervention study: Person-centered Care after Acute Coronary Syndrome (PACS study, Swedish registry, Researchweb.org, ID NR 65 791) [18]. The PACS study evaluated the effects of a PCC intervention in patients with ACS throughout 3 health care levels (hospital, outpatient clinics, and primary care) compared with usual care alone. A detailed description of the study methods and findings has been reported previously [18]. In summary, patients were eligible for study inclusion if they were younger than 75 years, admitted for suspected ACS, and subsequently diagnosed with either myocardial infarction or unstable angina pectoris. Patients were included at 2 hospital sites within a university hospital setting in the western part of Sweden. Patients were excluded at admission if they met at least one of the following exclusion criteria: aged ≥75 years; not willing to participate; currently listed at a private primary care center or at a primary care center in another region; having no permanent address; being planned for heart surgery, such as coronary artery bypass grafting; having cognitive impairment; having known alcohol or drug abuse; having a survival expectancy of <1 year; or participating in a conflicting study. A total of 199 patients were randomly assigned in the main PACS study, with 105 patients in the control group and 94 patients in the intervention group.

For this substudy, all of the patients in the control group of the original PACS study were included and compared with those in the intervention group of PACS who chose an eHealth tool (eHealth group). The patients who were included in the eHealth group received the same structured PCC approach as described in the main PACS study [18] and were also given the choice to use the optional eHealth tool as a complement. Based on a structured PCC approach, every patient received the PCC intervention regardless of whether they chose the eHealth tool. Briefly, this approach builds upon the patients’ narrative used to identify their personal opportunities and barriers during cardiac rehabilitation after ACS. The condensed narrative, agreed on by the patient, physician, and registered nurse (PCC team), is documented in a PCC health plan. The PCC health plan includes the patient’s goals, expectations, and follow-up actions (date, time, and place). The focus is on each person’s resources and is the joint responsibility of both the health care professionals and the patients [9]. The PCC teams at each health care level (hospital, outpatient, and primary care) had access to the PCC health plan throughout this continuum of care, and discussed and revaluated or altered the PCC health plan with the patient if necessary [18].

The eHealth tool consisted of a mobile app and access to a webpage, and the patient had the option to use the webpage or the mobile app, or both. Patients who were enrolled in the control group were managed according to standard rehabilitation, which followed guideline-directed care that was compliant with Swedish standards. Patients in the control group answered questionnaires and instruments, similar to the eHealth group, at baseline, 4 weeks, 8 weeks, and 6 months.

We evaluated patient-reported scores on the General Self-Efficacy Scale (GSES) using the Swedish version [21] of the original GSES [22]. The GSES, a unidimensional scale and universal construct, is validated in several countries [23]. The Swedish validated version of the GSES has high internal consistency (alpha = .90) [21]. The GSES is a 10-item instrument that measures patients’ beliefs and confidence in accomplishing certain tasks, rather than the actual execution and outcome of these tasks. Each item is rated by the patient on a 4-point Likert scale, in which 1 = not at all true, 2 = hardly true, 3 = moderately true, and 4 = exactly true. Total scores ranging from 10 to 40 are calculated, with higher totals indicating higher levels of general self-efficacy.

Patients in the control and eHealth groups filled out the GSES instrument at baseline at the hospital, and at 4 weeks, 8 weeks, and 6 months.

The primary end point was a composite of changes in general self-efficacy, return to work or prior activity level, and rehospitalization or death. Each patient was classified as improved, deteriorated, or unchanged. An increase of 4.6 units in the GSES has been suggested to show the minimal clinical important difference for patients [24]. A patient was classified at 6 months as improved in the composite score as follows: self-efficacy had increased by ≥5 units and the patient was not readmitted for unscheduled cardiovascular reasons or death; and the patient had returned to work or previous physical activity level (improved from sedentary to moderate activities or better, or maintained or improved from moderate to demanding or strenuous activities) [25].

Those patients who had neither deteriorated nor improved were considered unchanged. Patients were dichotomized into improved versus deteriorated or unchanged status.

Patients in the PCC intervention group who had used the eHealth tool at least once after discharge were included into this substudy and compared with the control group. We used descriptive statistics, such as frequency, mean, median, range, and SD, to describe user patterns. Between-group differences were tested using Fisher exact test for dichotomous variables and the Mann-Whitney U test was used for continuous variables. Logistic regression was used to calculate the odds ratios (ORs) between groups, with a 95% CI. We analyzed the data using SPSS 22 (IBM Corporation) statistical software package.

The Regional Ethics Committee of the University of Gothenburg approved the study (DNr 275-11). The study adhered to the rules of the Declaration of Helsinki of ethical principles.

This study showed feasibility, to a limited extent, for use of an eHealth tool by patients after an ACS event because approximately 40% of the eligible patients used the eHealth intervention after discharge. Patients were offered use of the eHealth tool as a completely optional supplement without any reminders. ACS is an overwhelming event inducing several concerns during hospitalization [26]; therefore, the adherence rate for using the eHealth tool might have been increased if the patients had been asked again if they wanted to use an eHealth tool when their health status had been stabilized, that is, after hospital discharge. The findings from this study are congruent with a systematic review by Munro and coworkers [27] showing use of eHealth programs for targeting patients with different diagnoses of cardiovascular disease, ranging from 36% to 97%. Interestingly, a meta-analysis by Inglis and coworkers [28] in patients with congestive heart failure showed a slightly higher use of eHealth, ranging from 66% to 98%. Nevertheless, because of the design of the study, the use pattern may indicate the present uptake levels for these kinds of self-management tools in the clinical setting. Similar to our findings regarding the use pattern of a mobile app versus a computer-based app, a study with patients with myocardial infarction evaluating uptake of technology-assisted cardiac rehabilitation showed that patients used the mobile app more frequently than a computer-based tool [29]. In this study the eHealth tool had more functions on the Web than on the mobile app, which may have negatively affected the adherence rate.

Clark et al [2] suggested that the duration of the interventions could have a negative effect on eHealth use for patients with ACS because interventions shorter than 2 months involved more users than interventions lasting longer than 2 months. In this study, the average use of the eHealth tool increased during the study period, indicating that patients who used the eHealth app for 2 months or more also used it most often. This finding needs to be further examined in future studies.

Whereas we observed no difference between groups regarding death or rehospitalization, the primary end point was determined by an improvement in the patients’ self-efficacy level. Self-efficacy is a person’s belief in his or her own ability to execute the behavior required to achieve desired outcomes [14,30]. According to this theory, self-efficacy can be influenced and strengthened, which is probably relevant to patients with chronic conditions. Dickson et al [31] suggested that self-efficacy in patients with congestive heart failure is a dynamic, oscillating resource that is enhanced or diminished by the context and situation. Nevertheless, because health care professionals focus on the medical needs of hospitalized patients, they seldom systematically consider patients’ personal resources, independence, and preferences [9]. The essence of PCC is the partnership between the patient and health care professionals. This partnership is based on a shared knowledge and mutual agreement about living with illness (patient) and generic knowledge about the disease (health care professionals). The way in which patients view their illness is as important as the disease itself and an essential factor for improving health outcomes and returning to professional work after an ACS event [32]. The eHealth tool in this study was developed to be used as a self-care device. Increased knowledge about oneself in relation to the illness and disease in question could be an important step in strengthening patients’ role as an expert about their everyday life and an active partner in the interaction with health care professionals.

The eHealth tool also made it possible for patients and health care professionals to develop a partnership through their communication via the chat function on the webpage and by patients presenting their trend graphs during follow-up visits. Since this is a complex intervention it is difficult to differentiate which component of the PCC intervention contributed most to the improvement in general self-efficacy. This study suggested that an eHealth tool in addition to a PCC intervention was associated with even higher improvement levels in the composite score in this selected group of patients than in the control group. The effect was driven by improved general self-efficacy, which suggests that an eHealth tool added to a PCC intervention can improve patients’ beliefs in their ability to successfully respond to challenges across a wide range of situations. This in turn was shown to contribute to improved disease management and clinical outcomes, such as health status and health care utilization [33]. The potential of digital technologies to become disruptive innovations in traditional power structures such as health care lies not only in making processes more transparent and easily accessible for the end user. We believe that the disruptive force also could change health care providers’ perception of the patients’ own view of their capacities to manage different situations. Nevertheless, research shows that most eHealth studies in the field of chronic care management take a professionally driven approach to monitoring signs [16,17], where professionals try to activate patients by pushing content and information that the professionals believe is of importance for the patient. Instead, we propose a more active patient role where the patients themselves seek information and create knowledge that is of importance to them in order to develop a productive interaction that fits the need of both the patients and the health care professionals. Hence, more studies need to investigate the potential in changing the perception of the patient’s role as a passive provider of data to an active cocreator of knowledge.

While 94 patients were included in the PCC intervention arm, only 37 (39%) chose the eHealth tool. Nevertheless, 11 patients (30% of the active users, or 12% of the total intervention group) improved even more in the primary end point when they complemented PCC with the eHealth tool in comparison with PCC alone. A comparison of this study outcome with that in the original paper published by Fors et al [18] indicates that the eHealth tool could have an augmenting effect on PCC. The primary end point was a composite score, combining patient experiences with clinical outcomes, which have been shown to be sensitive in differentiating treatment outcomes [34]. We defined improvement very restrictively as requiring no rehospitalization or death in combination with improvement on the GSES by ≥5 units (equivalent to almost 1 SD; in general, 0.5 SD is considered of clinical importance [35]). We also included return to work or previous activity level as a measure of improvement. Our specific definition of improvement might explain why only a minority benefitted from the intervention. For the effects of the intervention to be reflected as a hard outcome, a larger sample and longer follow-up period are needed. In general, there is a low power to evaluate subgroup analyses in clinical trials. Thus, the effects on clinical outcomes need to be studied in larger studies [36].

eHealth was still not considered as a viable support tool by the majority of patients in this study. Qualitative studies in telecare suggest that patients with congestive heart failure emphasize the value of the relationship with their health care professional [37]. Additionally, the patient’s primary concern with telecare is that it should not disrupt or compensate for ordinary face-to-face services [38]. Patients’ fear that eHealth is a replacement for face-to-face meetings could affect the decision not to choose an eHealth tool. Therefore, future studies should examine how the interaction and communication aimed at improving self-efficacy in people with long-term diseases can be developed through eHealth solutions or other means. In addition, studies need to investigate how patients and their relatives could better understand their own role in PCC. We believe that the interactive communication as manifested by the partnership in PCC can provide substantial support for eHealth and differentiate this technology from telemedicine. However, this needs to be confirmed in future studies.

This study has several limitations. Results should be interpreted based on the limitation that this was a substudy. Only approximately 40% of the patients included in the intervention agreed to participate in this study, which used an eHealth tool at least once after discharge. This group could consist of the most motivated individuals. Additionally, comparing these patients with the entire control group was a limitation of this study. However, there were no significant differences at baseline in demographic variables between the control and intervention groups. Despite this limitation, our study suggests that, for a selected group of people, this type of eHealth tool adds value in combination with PCC. Finally, another limitation is that we did not know whether the patients actively used the eHealth solution as part of the follow-up visits at the outpatient clinic or in primary care. While patients who used the eHealth tool had significantly higher general self-efficacy levels compared with the control group, the effect of using eHealth tools on shared decision making in a PCC setting still needs to be investigated. More studies, also using a qualitative approach, need to evaluate the potential of the intervention in terms of understanding the tool and patients’ own role in PCC and among a broader study population.

An eHealth diary and symptom-tracking tool in combination with a structured PCC intervention is associated with improved combined scores, comprising self-efficacy, return to work or prior activity level, rehospitalization, and death, in a selected group of patients with ACS compared with usual care. Future research should address the effects and efficiency of an eHealth tool throughout PCC interventions compared with traditional care.