Feasibility, Acceptability, and Intervention Description of a Mobile Health Intervention in Patients With Heart Failure : Journal of Cardiovascular Nursing

Secondary Logo

Journal Logo

Articles

Feasibility, Acceptability, and Intervention Description of a Mobile Health Intervention in Patients With Heart Failure

Schmaderer, Myra S. PhD, MSN, RN; Struwe, Leeza PhD, MSN, RN; Loecker, Courtney APRN-NP, AGACNP-BC, MSN; Lier, Lauren DNP, AGACNP-BC, RN; Lundgren, Scott W. DO; Pozehl, Bunny PhD, APRN-NP, FHFSA, FAHA, FAAN; Zimmerman, Lani PhD, RN, FAHA, FAAN

Author Information
The Journal of Cardiovascular Nursing: October 24, 2022 - Volume - Issue - 10.1097/JCN.0000000000000955
doi: 10.1097/JCN.0000000000000955
  • Free
  • PAP

Abstract

Heart failure (HF) is a chronic progressive disease with a high likelihood of exacerbations and adverse health outcomes. Self-management in patients with HF is important because it contributes to symptom improvement, increased quality of life, fewer HF-related hospitalizations, and decreased healthcare costs.1,2 An important element of self-management is the regular monitoring of symptoms to recognize changes and subsequently take action according to evidence-based guidelines.3 Diet and activity modifications; medication management; and monitoring for changes in blood pressure, weight, edema, or shortness of breath are examples of routine self-management activities of patients with HF.4

Mobile health (mHealth) is a subset of a broader term, telehealth, that uses mobile technology to improve the heath goals of individuals. Mobile technologies, such as mHealth (eg, apps) and telemedicine (eg, virtual visits by healthcare professionals), have increasingly been utilized to support self-management efforts of patients with chronic disease, including those with HF. These novel modalities have been used to encourage adherence to prescribed medication regimens and improve healthy behaviors (eg, physical activity and low-sodium diet).5,6 There has been an uptake of mHealth use during the current SARS-CoV2 pandemic that necessitated social distancing.7 The explosion of mainstream Internet accessibility and ubiquity of mobile phone applications (apps) has created an environment conducive to alternative healthcare delivery methods using mHealth technologies.8

Many telemonitoring strategies are used in the care of patients with HF. Telemonitoring interventions inclusive of mHealth to support patients with HF are associated with reduced all-cause mortality and hospitalizations.9,10 A recent meta-analysis identified that mobile phone text messaging nearly doubled the odds of self-reported medication adherence.11 mHealth has the advantage of real-time communication and portability and is consistent with societal trends of increasing smartphone utilization. Using telemonitoring with mHealth to support patients with HF is a worthwhile strategy, but the app needs to be engaging and easy to use to reap the benefits.12

Similar to mHealth apps used for self-monitoring in HF management, many adults download commercially available health apps on mobile phones to keep track of physical activity, nutrition, and weight. Apps to track daily habits are common, but individuals discontinued use because they lost interest, apps were too confusing, or entering data was too time-consuming.12 Experts recognize mHealth as a promising technology to support self-management in HF13; however, there remains a gap in understanding what mHealth strategies work best for individual patients with HF.14,15 Examining the acceptability and usability of technology intended to support and enhance self-management strategies in HF management is critical to ensure opportunity for increased adherence to prescribed regimens.

We developed self-management interventions using an mHealth platform for patients with HF. The study arms included enhanced usual care, mHealth, and mHealth plus that incorporated a nurse practitioner/community health worker team. The purpose of this article is to describe the intervention and feasibility testing of the study. Specifically, the aims were to (1) describe the study interventions and evaluate the feasibility of recruitment and enrollment of patients to the study and (2) describe the fidelity, usability, and acceptability of the intervention and adherence to medication and weight monitoring by group.

Methods

Study Design

This pilot study used a randomized 3-group repeated-measure design to test the overall feasibility of delivering an mHealth intervention to patients with HF. Institutional review board approval was received. Before this study, we conducted a small feasibility study with 10 patients with HF enrolled over 2 months to evaluate the enrollment process and ease of app use and determine technology capabilities. Data from this preliminary work were used to refine our methods and address technological challenges with the app and the virtual visit platform before implementing this pilot study.

App Description

The commercially available app Play-It Health was customized for our study. The app was compatible with iOS and Android platforms and available on phones as well as tablets. The app allowed patients to record prescribed medications at scheduled times and weights were integrated using a Bluetooth-enabled scale. The app also contained customized HF-related educational tips and personalized appointment scheduling for general provider and mHealth research team appointments. The app was used to deliver intervention components (knowledge, self-monitoring skills, and self-efficacy/confidence) to patients in the mHealth and mHealth plus groups. Specific examples of how the intervention and app were used to deliver these mechanistic components are described in Table 1. Hibbard's conceptualization of patient activation provided a theoretical foundation for the study intervention. Patient activation is the patient's “willingness and ability to take independent actions to manage their health” with an understanding of one's role in this process, equipped with the knowledge, skill, and confidence to do so.16

TABLE 1 - Intervention Components Described by Group
Intervention Components EUC mHealth mHealth Plus
Knowledge
 Heart failure related educational tip sent daily via app. X X
 Best practice guidelines used to educate using simple terms. X X
 Evaluated heart failure knowledge during virtual visit. X
 Evaluated the linkage of red flag symptoms (eg, shortness of breath and lower extremity edema) to weights during virtual visits. X
Skills
 App downloaded on mobile device and demonstrated how to record weights and medication. Bluetooth-enabled scale synched to mobile device. X X X
 Patients asked to record scheduled medication and weight daily with reminders sent if not completed. X X
 Patients asked to answer daily heart failure related educational tips with reminders sent if not answered. X X
 Discussed ability to weigh and record medications during virtual visits. X
Confidence/self-efficacy
 Positive reinforcement (eg, “nice job”) after correctly answering educational tip sent through the app. X X
 Graphs for weekly weight trends in app. X X
 Calendar available to input scheduled appointments. X X
 Virtual visits with community health worker at week 1, 3, 4, and 6. X
 Virtual visits with nurse practitioner at week 2, 5, and 8. X
 Reviewed graphs of weekly weight and medications during virtual visits. X
 Identified realistic self-management strategies that positively modify behavior. X
 Affirmed positive behaviors taken to improve health. Encouraged a “can-do” attitude. X
 Worked with patient to focus on patient-centered strategies with simple behavior changes. X
 Worked with patient to tailor attainable patient goals and action plans. X
 Worked with patient on utilizing motivational strategies for self-management. X
 Worked with patient to identify community resources available to promote wellness. X
Abbreviations: EUC, enhanced usual care; mHealth, mobile health.

We specifically used strategies to increase patient activation, such as teach-back, using clear and simple language when educating, and shared decision making. Patient activation has been shown to improve adherence and promote engagement in self-management behaviors through education, skill building, self-efficacy, and confidence.16

Groups and Intervention Description

All 3 groups (enhanced usual care, mHealth, and mHealth plus) received usual care provided by the enrolling institution. Usual care included pharmacist and nutritionist inpatient consultation and HF education, including an HF resource booklet and HF videos (available for 90 days postdischarge). Usual care also included outpatient reinforcement phone calls with the HF educator over the first month postdischarge; patients received a range of 1 to 4 calls depending on patient need as determined by the HF educator. All 3 groups received training on the app regarding how to record medications and weights using the provided Bluetooth-enabled scale. All patients were encouraged to access the app daily. App settings were customized according to the intervention group (Table 1).

Enhanced Usual Care

The customized app for the enhanced usual care group allowed for medication recording and weight documentation only. No reminders, alerts, or educational tips were available.

Mobile Health

The mHealth group was given added features on the app that included educational tips and reminders to weigh and take medications at scheduled times. The daily HF-related educational tip was followed by a question to answer. Tips were derived from the American Heart Association guidelines and evidence-based resources used at the enrolling institute. A reminder was sent each morning to weigh on the provided Bluetooth-enabled scale. Reminders were sent 15 minutes before each scheduled medication time, and up to 2 additional reminders were sent if the medication was not documented. A calendar feature was available for patients to input scheduled appointments with the option for a reminder. A weekly graph was available for the patient to see trends in weights and medication recording.

Mobile Health Plus

In addition to the features mentioned above, the mHealth plus group received virtual visits with a nurse practitioner/community health worker team via a cloud-based videoconferencing platform (Zoom) during the first 2 months of the 3-month intervention. The nurse practitioner was a specialist in cardiology and had more than 25 years of experience in an HF clinic. The community health worker was an unlicensed layperson with training in community health worker concepts (eg, teach-back, coaching, communication, and community resources). Virtual visits with the nurse practitioner occurred at weeks 2, 5, and 8 postdischarge, and virtual visits with the community health worker occurred at weeks 1, 3, 4, and 6 postdischarge. At week 1, the community health worker met with the patient to validate that the participant had medications, food, and shelter. The community health worker identified community resources available for the HF patient and identified barriers to adherence. The focus of the interventions was to enhance patient activation (knowledge, skill-building, self-efficacy, and confidence) and subsequently improve self-management. The nurse practitioner was the primary interventionist and developed self-management strategies with the HF patient. Attainable goals were identified, and an action plan was created with the patient. Before the virtual visits, the nurse practitioner and community health worker reviewed weekly graphs of trends for weighing and recording medications taken for discussion during the virtual visit. During the virtual visits, the nurse practitioner and the community health worker revisited goals and, if needed, assisted the patient in making a new goal and action plan. The nurse practitioner focused on assisting the patient to link symptoms they were experiencing to appropriate actions to take. There was ongoing communication between the nurse practitioner and the community health worker. To promote engagement, the virtual visits were scheduled at convenient times chosen by the patient. Reminders were sent via the app 1 day and 1 hour before the virtual visit. Details of the intervention components and strategies used are further described in Table 1.

Sample and Setting

The sample included 74 patients with HF who were recruited from inpatient and observational units at a large academic hospital in the Midwest. Inclusion criteria for the study were (a) adult patients (19 years and older) with a primary HF diagnosis or an episode of acute decompensated HF resulting in this current hospitalization, (b) discharged to home, and (c) able to hear, speak, and read English. Exclusion criteria were (a) moderate or severe cognitive impairment (Montreal Cognitive Assessment score ≤17), (b) life expectancy <6 months (healthcare provider documentation), (c) organ transplant, or (d) had a left ventricular assist device.

Procedures

The principal investigator trained and monitored all research personnel. The HF educators employed by the hospital identified and recruited potential patients during their regular visit with patients in the hospital. The HF educators asked potential patients if they were interested in the research study and willing to discuss the study with the researchers. Research personnel thoroughly explained the study and obtained written informed consent. To confirm that participants had the cognitive ability to self-manage, the Montreal Cognitive Assessment was administered by research personnel and participants were excluded if the Montreal Cognitive Assessment score was ≤17 (moderate or severe cognitive impairment). Two patients were ineligible for the study because of moderate or severe cognitive impairment; they were thanked for their time and willingness to participate. Life expectancy of less than 6 months was validated by healthcare provider documentation in the electronic health record. Patients were randomized to groups using a randomization schedule prepared in advance by the biostatistician. Demographic and baseline characteristics were then obtained from self-report or the electronic health record by research personnel.

Research personnel guided the patients in installing the app and connecting the Bluetooth-enabled scale on their smart device. Teach-back was used to validate understanding of how to use the app. All groups received as many teach-back sessions as needed until participants could demonstrate the use of the customized features for the group they were randomized to (eg, documentation of medications taken, synchronizing the Bluetooth scale for all groups, and virtual visits for the mHealth plus group).

Eight patients did not have a smart device (iPad or iPhone) and were loaned an iPhone and taught how to use it for participation in the study. An ongoing spreadsheet of all patients with HF admitted to the hospital, anticipated discharge plan (home or facility), access to mobile devices, and reasons for declining to be in the study was collected and summarized.

The intervention was initiated when the patient was discharged from the hospital. Research personnel monitored app engagement (weighing and documenting medications) throughout the intervention and placed phone calls to patients in the intervention groups if they did not engage in the app for 3 days in a row. Outcome data were collected at months 1, 2, and 3 via phone calls by research registered nurses who did not participate in the enrollment process or the intervention. A maximum of 5 follow-up phone calls were made to patients on different days of the week and at different times of the day to improve response rate. Outcome calls lasted approximately 30 minutes. All patients were paid $10 for each outcome call they completed, for a possible total of $30. Those in the mHealth and mHealth plus groups were paid an additional $10 for each month they participated in the intervention.

Measures

Demographic and Inclusion Measures

Demographic variables (eg, age, race, ethnicity, gender, and insurance status) and clinical variables (eg, New York Heart Association classification and ejection fraction) were collected from the electronic medical record or self-report. Cognition was evaluated with the Montreal Cognitive Assessment and administered by trained research personnel. The Montreal Cognitive Assessment is a 10-minute 30-item assessment instrument recommended for use in individuals with HF17 and assesses 6 cognitive domains, including executive function, attention, concentration, working memory, language, and visuospatial abilities. The Montreal Cognitive Assessment has demonstrated sensitivity of 90% and specificity of 87%.18

Feasibility Measures

Feasibility was measured by calculating recruitment, retention, and attrition rates.

Fidelity Measures

For delivery and receipt, daily reminders to weigh and take medications were sent via the app to the mHealth and mHealth plus groups. Medication specific for HF and weight engagement were assessed by measuring the frequency and percentage of the times the patient documented taking their HF medications and weights in the app at 2 and 3 months. We monitored the use of specific guideline-based HF medication (diuretics, angiotensin-converting enzyme inhibitor, angiotensin-receptor blocker, β-blockers, or aldosterone antagonists) prescribed daily to calculate engagement.19

Usability and Acceptability Measures for the Intervention Groups

A 21-item investigator-developed survey was administered. Of the 21 items, 12 Likert-type items from 1 (strongly disagree) to 4 (strongly agree) were asked of both mHealth intervention groups. Sample questions included topics regarding the ease and confidence of using the app, satisfaction with the intervention, benefits of the app, and ideas to improve the app (Table 2). Cronbach's α for the 12 items was 0.91. Patients in the mHealth plus intervention group received an additional 5 items on the survey related to virtual visits, specifically regarding ease of use, satisfaction, and benefit.

TABLE 2 - Usability and Acceptability Questions (Mobile Health and Mobile Health Plus) at the End of the Intervention
Items on the Tool mHealth mHealth Plus
n Mean SD % Agree or Strongly Agree n Mean SD % Agree or Strongly Agree
1. I learned a lot about how to self-manage my heart failure symptoms. 16 3.13 0.5 93 17 3.35 0.49 100
2. The technology platform was easy to use. 16 2.94 0.44 87.6 18 3.06 0.64 83.3
3. I received enough information about heart failure through the platform. 16 2.94 0.57 81.3 18 3.06 0.80 83.4
4. The platform made me feel more confident. 16 2.94 0.57 81.3 18 3.11 0.83 83.3
5. The platform gave the right number of prompts or reminders. 16 2.94 0.44 87.6 18 3.06 0.72 88.7
6. I believe this program would be useful for all people with heart failure. 16 3.13 0.62 87.6 17 3.18 0.81 87.9
7. I am very satisfied with the intervention I received. 16 3.13 0.50 93.8 18 3.28 0.57 94.1
8. The length of the intervention via the app was about right. 14 3.0 0.55 85.7 18 3.12 0.78 87.9
9. I found the app was not too complex. 15 2.0 0.38 87.9 18 2.17 0.71 71.1
10. I thought the app was easy to use. 15 3.0 0.53 86.6 17 3.06 0.75 88.2
11. I believe most people would learn to uses this app very quickly. 15 2.93 0.46 86.6 18 3.00 0.69 88.9
12. I felt very confident using the app. 15 3.07 0.46 93.3 18 3.11 0.76 88.9
mHealth plus only
 13. I received enough information about my heart failure through the virtual visits. 18 3.12 0.76 83.3
 14. Setting goals for reducing my heart failure symptoms was helpful. 18 3.06 0.83 83.3
 15. The virtual visits with the Community health worker made me feel more confident. 18 3.11 0.76 88.9
 16. The virtual visits with the Nurse Practitioner made me feel more confident. 18 3.12 0.78 88.9
 17. The length of the virtual visits was about right. 18 3.11 0.76 88.9
Open-ended survey questions (mHealth and mHealth plus groups)
 18. Do you have suggestions to make the intervention better in the future?
 19. What were the benefits of receiving this heart failure self-management program?
 20. What things have helped you follow the program?
 21. Were there any barriers or obstacles to following this heart failure self-management program?
1 = Strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree.
Abbreviation: mHealth, mobile health.

Four open-ended questions were also asked of all mHealth and mHealth plus intervention patients at the end of the intervention to seek participants' perceptions of the intervention. These questions sought suggestions to improve the intervention, benefits of receiving the intervention, strategies that assisted patients to follow the program, and the barriers to following the intervention (Table 2).

Adherence to Medication and Weight Monitoring

Patients were asked to weigh daily and document when they took their medication in the app. Adherence was not measured in this study as an outcome. Given this was a feasibility study, we wanted to explore adherence from the prospective of the feasibility of daily recording of self-management behaviors (weighing and recording medications taken) for future studies. For this study, adherence to daily weights was operationally defined as the percentage of days weighed out of the possible 90 days and medication adherence was defined as percentage of HF-specific medications documented as taken out of the possible opportunities to document HF-specific medications over 90 days. Data were bimodal in nature, and therefore we categorized weighing and recording medication 50% of the time as adherent.

Data Analysis

Descriptive statistics were reported as percentage of frequency counts and means and standard deviations. All data were cleaned and checked for errors. All variables were examined for meeting the assumptions of the statistical tests used. For enrollment (recruitment efficiency and attrition), intervention delivery, usability, and acceptability counts (%) were calculated. Adherence to medication and weight monitoring were summarized descriptively.

Results

Of the 74 patients with HF in the sample, 26 were in the enhanced usual care group, 23 were in the mHealth group, and 25 were in the mHealth plus group. The demographic and clinical characteristics of the sample are reported by group and total (Table 3). There were no significant differences between groups. Interestingly, the mean (SD) age of participants was rather young, 56.3 (14.2) years, with a range of 26 to 91 years. Most were female (40, 54%) and half had an educational level of high school or lower (37, 50%). In relation to socioeconomic status, 37.8% (n = 28) of the patients reported an annual income less than $20 000 and half of those had an income <$10 000. In relation to diversity, just over a third (25, 34%) were Black, 4 (5%) were American Indian/Pacific Islander, and 3 (4%) were Hispanic, compared with 41 (55%) Whites. Most had private insurance or Medicare (45, 61%); however, 19 (26%) were uninsured. Heart failure was a new diagnosis during this admission for 15 (20%) of the study sample and most, 32 (43%), were New York Heart Association class III (32, 43%), whereas 16 (21.5%) were class IV.

TABLE 3 - Demographic and Clinical Characteristics of the Sample (N = 74)
EUC Group mHealth Group mHealth Plus Group Groups Combined
Gender
 Male 10 (13.5) 12 (16.2) 12 (16.2) 34 (45.9)
 Female 16 (21.6) 11 (14.8) 13 (17.6) 40 (54.1)
Race
 White 15 (20.3) 14 (18.9) 12 (16.2) 41 (55.4)
 Black 6 (8.1) 8 (10.8) 11 (14.8) 25 (33.8)
 American Indian or Pacific Islander 2 (2.7) 0 (0) 2 (2.7) 4 (5.4)
 Hispanic 3 (4.0) 0 (0) 0 (0) 3 (4.0)
 More than 1 race 0 (0) 1 (1.3) 0 (0) 1 (1.3)
Employment
 Yes 13 (17.6) 12 (16.2) 10 (13.5) 35 (47.3)
 No 12 (16.2) 6 (8.1) 12 (16.2) 30 (40.5)
 Retired 1 (1.3) 5 (6.7) 3 (4.0) 9 (12.2)
Educational level
 High school or less 12 (16.2) 13 (17.6) 12 (16.2) 37 (50.0)
 Some college 10 (13.5) 7 (9.4) 5 (6.7) 23 (31.1)
 Bachelor's degree 2 (2.7) 1 (1.3) 7 (9.4) 10 (13.5)
 Post college 1 (1.3) 2 (2.7) 1 (1.3) 4 (5.4)
Income
 <$9999 5 (6.7) 4 (5.4) 5 (6.7) 14 (18.9)
 $10 000–19 999 6 (8.1) 3 (4.0) 5 (6.7) 14 (18.9)
 $20 000–39 999 4 (5.4) 4 (5.4) 4 (5.4) 12 (16.2)
 $40 000–99 999 8 (10.8) 7 (9.4) 7 (9.4) 22 (29.8)
 ≥$100 000 1 (1.3) 2 (2.7) 1 (1.3) 4 (5.4)
 Refused to report 2 (2.7) 3 (4.0) 3 (4.0) 8 (10.8)
Insurance
 Uninsured 6 (8.1) 6 (8.1) 7 (9.4) 19 (25.7)
 Medicaid 2 (2.7) 3 (4.0) 0 (0) 5 (6.7)
 Medicare 6 (8.1) 7 (9.4) 7 (9.4) 20 (27.0)
 Private insurance 10 (13.5) 8 (10.8) 8 (10.8) 26 (35.1)
 Unreported 2 (2.7) 0 (0) 3 (4.0) 5 (6.7)
NYHA classification
 Class II 7 (9.4) 4 (5.4) 7 (9.4) 18 (24.3)
 Class III 11 (14.9) 13 (17.6) 8 (10.8) 32 (43.2)
 Class IV 5 (6.7) 4 (5.4) 7 (9.4) 16 (21.5)
 Class not reported 3 (4.0) 2 (2.7) 3 (4.0) 8 (10.8)
Ejection fraction
 <50% 15 (20.3) 20 (27.0) 19 (25.7) 54 (73.0)
 ≥50% 9 (12.2) 3 (4.0) 5 (6.7) 17 (23.0)
 Not reported 2 (2.7) 0 (0) 1 (1.3) 2 (2.7)
Newly diagnosed HF 8 (10.8) 6 (8.1) 7 (9.4) 21 (28.4)
Data are presented as n (%).
Abbreviations: EUC, enhanced usual care; NYHA, New York Heart Association, HF, heart failure.

Feasibility

Recruitment

Of all the patients seen by the HF educators, 602 were discharged to home, and more than half (367) met criteria. Of those, 144 agreed to discuss the study with researchers and 80 (56%) signed consent; 64 declined to participate. Some reasons patients declined to participate included uncomfortable with technology, already had a routine they did not want to change, too overwhelmed with their HF, the phone was too slow, or they did not want additional apps on their phone. The CONSORT diagram (Figure 1) was used with permission and is published in a manuscript assessing differences and potential efficacy of the interventions compared with enhanced usual care.20

F1
FIGURE 1:
CONSORT diagram.

Enrollment

Of the 80 patients who consented during hospitalization, 6 became ineligible and were dropped from the study (ie, discharged to a facility or received home healthcare). Thus, 74 patients were eligible and enrolled over a 14-month period between November 2018 and January 2020.

Attrition

Of the 74 patients, we obtained at least partial to complete data on 63 (85%) patients during follow-up outcome phone calls at 1, 2, and 3 months. Fifty-one (69%) completed all 3 follow-up calls, and 11 (15%) did not respond to any outcome calls. The nonresponders were nearly equally divided among the 3 groups (enhanced usual care = 3, mHealth = 4, mHealth plus = 4).

Fidelity

Daily reminders to weigh and take medications were sent via the app. To evaluate engagement, we calculated the frequency of the patient performing the self-management skill (daily weights and taking their medications). For weights, 38 of 48 (79%) patients in the intervention groups weighed at least 1 time. We did not receive any weight data on 5 patients in each intervention group. In relation to recording medications, most patients in the mHealth group, 19 (83%), and 21 (84%) patients in the mHealth plus group recorded medications. Four patients in each group did not record medications. There was an overall decrease over time in recording weights and medications.

For the 25 patients in the mHealth plus group, 18 (72%) received at least 1 virtual visit, and 8 (32%) of the patients participated in all scheduled virtual visits with both the nurse practitioner and the community health worker. Six (24%) of the patients completed all 3 virtual visits with the nurse practitioner, compared with only 2 (8%) patients engaged in all the 4 virtual visits with the community health worker. Examples of reasons that the virtual visits did not occur were related to work conflicts, hospitalizations, or the patient's inability to be reached by phone, text, or email for scheduling.

Usability and Acceptability

From the Usability and Acceptability Questionnaire, 93% of patients in the mHealth group and 100% of the mHealth plus group agreed or strongly agreed that they learned how they could self-manage their HF. Most were satisfied with the intervention (mHealth, 93.8%; mHealth plus, 94.1%) and felt confident using the app (mHealth, 93.3%; mHealth plus, 88.9%). Nearly 30% of the mHealth plus group felt that the app was too complex, whereas only 12.1% of the mHealth group thought the app was too complex. However, more than 83% of patients in both groups agreed or strongly agreed that the technology platform was easy to use. Most patients in both groups agreed or strongly agreed that the intervention would be useful for all people with HF (mHealth, 87.6%; mHealth plus. 87.9%). Table 2 presents each item on the usability and acceptability instrument.

Concerning virtual visits, most patients agreed or strongly agreed that they received enough information about HF during virtual visits (83.3%) and agreed or strongly agreed that setting goals for reducing HF symptoms was helpful (83.3%), whereas 88.9% of the patients agreed or strongly agreed that the virtual visits with both the nurse practitioner and community health worker made them feel more confident.

Open-Ended Comments From the Usability and Acceptability Instrument

Of the 34 patients who completed the usability and acceptability instrument, 27 (56%) also reported open-ended comments. One theme identified was technology issues. Eight (23%) patients reported poor cell phone service, slow bandwidth, or connection problems. For example, 1 patient became frustrated with the virtual visit “cutting out” and requested to continue with the visit on the phone. Beyond technical comments, 2 patients suggested that the intervention should be extended for a longer time, whereas 1 patient wanted it shorter. Medication reminders were burdensome for some (4, 12%), whereas others appreciated the reminders (10, 29%). For example, some patients wanted a notification when medications were scheduled; other patients wanted 1 reminder for the entire day. When asked about the benefits of the intervention, there were positive comments about the intervention; there were 7 (20%) comments on how the participants felt more confident in caring for themselves and were able to take charge of their health. One participant stated that she felt the app was like a “partner” checking on her and she was not alone.

Adherence

Medication Adherence by Group

Within the first month, the proportions of patients whose average app use for recording medications was 50% or greater differed across the groups, with 95.0% of patients in the mHealth plus group using the app 50% or greater, compared with the mHealth group (36.8%) and the enhanced usual care group (72.2%). At month 2, the proportions of patients whose average app use was 50% or greater differed across groups, with 67% of the patients in the enhanced usual care group compared with 55% of the mHealth plus group and 37% of the mHealth group. At 3 months, both the mHealth plus and the enhanced usual care groups further decreased medication adherence, whereas the mHealth group improved by a small amount.

Weight Monitoring Adherence by Group

No differences in adherence to weight monitoring were noted at months 1 and 2. At month 3, there was a trend toward the mHealth group having improved weight adherence, with 61% of the patients recording weight at least half of the time compared with 25% in the mHealth plus group and 37% in the enhanced usual care group. Over time, the monthly percentage of patients in the mHealth group who weighed more than 50% of the time nearly stayed the same (56% to 61%), whereas both the enhanced usual care (63% to 37%) and mHealth plus (75% to 25%) groups experienced a decrease over the 3 months. The mHealth plus group had the greatest fluctuation from month 1 to month 3 and the mHealth group was the most consistent (Figure 2).

F2
FIGURE 2:
Adherence to weight and medication recording in the app. This figure depicts the percentage of patients that recorded medications and weights in the app 50% of the time or more. EUC indicates enhanced usual care; mHealth, mobile health; mHealth +, mobile health plus.

Discussion

This pilot randomized controlled trial with the 2 interventions (mHealth and mHealth plus) was feasible to administer over the 3-month study period. We successfully recruited 80 (22%) hospitalized patients with acute HF over 14 months. In future studies, we could anticipate potentially enrolling more patients who meet inclusion criteria owing to the impact of the global pandemic increasing the accessibility of telehealth. People are becoming more familiar and reliant on technology for healthcare delivery.

The intervention was delivered and received as intended; however, we anticipated more engagement with the mHealth app. Our goal was to have patients weigh themselves daily and record taking medication(s) in all groups. We stressed the importance of weighing oneself, taking necessary HF medication, and contacting research personnel via email, text, or phone calls with concerns. Despite these strategies to enhance engagement, nearly 20% of all patients in each group did not weigh even 1 time. Given the nature of this intervention, several technological limitations were identified. Most notably, all patients were given a Bluetooth-enabled scale and trained on using the scale with the app; however, fewer data points were collected than anticipated. Technological issues may have played a role and true adherence to daily weights may be unknown. Comprehension of technological steps or simply the capacity to learn additional information surrounding discharge could be factors. Similar to other studies using technology-based interventions,21 open-ended comments from the Usability and Acceptability Questionnaire indicate that patients in this study reported issues with poor cell phone service, slow bandwidth, and connection problems. Internet, phone service providers, and bandwidth/speed were not assessed before the initiation of the study; this may be a priority for those living in the rural regions of the Midwest and/or those with lower socioeconomic status. For future studies, we suggest assessing Internet capabilities and additional personalized technological support as deemed necessary for patients.

Reminder fatigue could have played a part in the lack of documenting medication and weighing in the mHealth plus group. Frequently, patients with HF take medications several times a day; thus, some patients received many reminders. For future studies, we suggest customizing the intervention with strategies such as seeking further input from participants to tailor how often and when reminders are sent and teaching patients how to decrease notification fatigue by adjusting settings on their devices based on their desires. Existing literature describing mHealth for self-management in HF reports the clinical team plays a key role in engaging patients to use the app and clarify symptomology.22 Additional clinical support may increase regular app use and overall adherence and has potential to benefit clinical outcomes.

Our study sample included a large percentage of patients with lower socioeconomic status, nearly one-fourth were uninsured, and nearly half were non-White. Income of less than $20 000 annually was reported by 28 (37.8%) patients; thus, those participants may have had a delay in seeking care or got a late diagnosis of their HF. A total of 8 phones were lent to patients, and of those, 7 patients reported lack of finances as the reason for not having a smartphone. We recommend future studies to focus on these vulnerable and underserved populations. Furthermore, studies should consider ways to include vulnerable adults representing a large proportion of patients with HF who may benefit from interventions that enhance self-management.23

Despite study participants being relatively young (mean age, 56.3 years), more than half (64.7%) had documented New York Heart Association class III or IV HF, suggesting poor health status. Adjusting to changes posthospitalization requires time and effort. Many working-age participants may have been especially overwhelmed with employment or family stressors, which could have negatively affected app engagement. De novo HF affected 28.4% of participants and nearly half were of a minority ethnicity or race. Language barriers or health literacy surrounding a new diagnosis may have created additional challenges in self-management. User-friendly apps that incorporate culturally sensitive material for newly diagnosed patients with HF may be worthwhile to consider.

Consistent with previous research,24 we recommend clinical personnel to encourage and empower patients to self-monitor for changes in signs or symptoms as means to improve adherence. Offering a booster session within the first few weeks to reinforce the importance of weighing and taking HF specific medications could possibly improve engagement.

There were some differences of opinions among participants regarding the mHealth interventions. For example, some patients wanted the intervention longer, whereas others wanted it shorter; some patients wanted fewer reminders, whereas others were satisfied with the reminders provided. It may be beneficial to individualize patient requests to increase adherence to skill monitoring with a more tailored, personalized app. These findings are consistent with previous research suggesting that patients desired app customization to overcome app redundancy.22 Furthermore, this pilot study suggests that additional time before or after discharge would be beneficial to demonstrate and practice virtual visits as patients had some difficulties with this technology postdischarge despite the direction and practice provided. As patients are learning a great deal of information surrounding discharge, we suggest supportive individuals (ie, a family member or caregiver) to be present for learning technical skills. Furthermore, we would recommend the same individuals be present during the virtual visits to support and encourage self-management.

Hospitalization for acute HF is common in adults older than 65 years.25 Our study sample's relatively low mean age (56 years) and excluding patients who transferred to another facility at discharge (ie, skilled nursing facility) may indicate that some older patients with HF were omitted from our study, limiting generalization. A limitation of this study was that the survey measures were asked over the phone during an interview. Despite having research personnel who were not involved in the intervention make the phone calls, there was a potential for bias. An alternative could be to use REDCap for electronic entry by the patients. The usability and acceptability instrument were investigator developed and had good reliability; however, an established instrument with documented psychometrics was not used. In the future, we intend to compare this instrument with a validated instrument such as the Health Information Technology Usability Evaluation Scale.26

Overall, patients in the intervention groups found the intervention to be usable and acceptable. Learning self-management, receiving information about HF, and gaining confidence were reported as beneficial to most patients. Several patients agreed or strongly agreed to 1 usability item related to the complexity of the app. Further consultation with the app developer using patient feedback to decrease the complexity of the app features is planned.

Patients with HF continue to experience increasing rates of HF-related hospitalizations and mortality despite advances in medical care.27 Advances in mHealth technologies and the need for alternative healthcare delivery options during a global pandemic underscore the importance of examining the feasibility of delivering interventions to improve self-management.

In summary, interventions that patients find useful and acceptable may have potential impact on adverse health outcomes. The intervention implemented in this study demonstrated feasibility in terms of enrollment and intervention delivery, usability, and acceptability. Regarding fidelity, the intervention was delivered and received as intended; however, patients were not as engaged as we expected and thus impacted their adherence to daily medication recording and weighing themselves. Slight modifications to this intervention have been made and will be tested in a fully powered study with a focus on vulnerable populations (eg, those who face financial and educational hardships) and underserved populations (eg, racial and ethnic minorities, low income, and cultural barriers).

What’s New and Important

  • This mHealth intervention was feasible to implement and most patients with HF enrolled found the intervention usable and acceptable.
  • Most patients in the intervention groups learned how to self-manage their HF, were satisfied with the intervention, and felt confident using the app.
  • This mHealth intervention provided insight for future intervention improvements.

Acknowledgment

The authors would like to acknowledge Kimberly Gandy MD, PhD, and Play-It Health for collaboration and the use of the app.

REFERENCES

1. Howie-Esquivel J, Carroll M, Brinker E, et al. A strategy to reduce heart failure readmissions and inpatient costs. Cardiol Res. 2015;6(1):201–208. doi:10.14740/cr384w.
2. Toback M, Clark N. Strategies to improve self-management in heart failure patients. Contemp Nurse. 2017;53(1):105–120. doi:10.1080/10376178.2017.1290537.
3. Hanlon P, Daines L, Campbell C, McKinstry B, Weller D, Pinnock H. Telehealth interventions to support self-management of long-term conditions: a systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. J Med Internet Res. 2017;19(5):e172. doi:10.2196/jmir.6688.
4. Toukhsati SR, Driscoll A, Hare DL. Patient self-management in chronic heart failure—establishing concordance between guidelines and practice. Card Fail Rev. 2015;1(2):128–131. doi:10.15420/cfr.2015.1.2.128.
5. Athilingam P, Jenkins B. Mobile phone apps to support heart failure self-care management: integrative review. JMIR Cardio. 2018;2(1):e10057. doi:10.2196/10057.
6. Paula TR, Menezes AP, de Guedes NG, Silva VM, Cardoso MVLML, Ramos E de S. Efetividade de aplicativos móveis para mudanças comportamentais em saúde: revisão sistemática. Rev Rene. 2020;21:e43845–e43845. doi:10.15253/2175-6783.20202143845.
7. Neubeck L, Hansen T, Jaarsma T, Klompstra L, Gallagher R. Delivering healthcare remotely to cardiovascular patients during COVID-19: a rapid review of the evidence. Eur J Cardiovasc Nurs. 2020;19(6):486–494. doi:10.1177/1474515120924530.
8. Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The impact of mHealth interventions: systematic review of systematic reviews. JMIR Mhealth Uhealth. 2018;6(1):e23. doi:10.2196/mhealth.8873.
9. Indraratna P, Biswas U, McVeigh J, et al. A smartphone-based model of care to support patients with cardiac disease transitioning from hospital to the community (TeleClinical care): pilot randomized controlled trial. JMIR Mhealth Uhealth. 2022;10(2):e32554. doi:10.2196/32554.
10. Ding H, Chen SH, Edwards I, et al. Effects of different telemonitoring strategies on chronic heart failure care: systematic review and subgroup meta-analysis. J Med Internet Res. 2020;22(11):e20032. doi:10.2196/20032.
11. Thakkar J, Kurup R, Laba TL, et al. Mobile telephone text messaging for medication adherence in chronic disease: a Meta-analysis. JAMA Intern Med. 2016;176(3):340. doi:10.1001/jamainternmed.2015.7667.
12. Krebs P, Duncan DT. Health app use among US Mobile phone owners: a national survey. JMIR Mhealth Uhealth. 2015;3(4):e101. doi:10.2196/mhealth.4924.
13. Bezerra Giordan L, Tong HL, Atherton JJ, et al. The use of mobile apps for heart failure self-management: systematic review of experimental and qualitative studies. JMIR Cardio. 2022;6(1):e33839. doi:10.2196/33839.
14. Kitsiou S, Vatani H, Paré G, et al. Effectiveness of mobile health technology interventions for patients with heart failure: systematic review and Meta-analysis. Can J Cardiol. 2021;37(8):1248–1259. doi:10.1016/j.cjca.2021.02.015.
15. Kitsiou S, Paré G, Jaana M. Effects of home telemonitoring interventions on patients with chronic heart failure: an overview of systematic reviews. J Med Internet Res. 2015;17(3):e63. doi:10.2196/jmir.4174.
16. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff (Millwood). 2013;32(2):207–214. doi:10.1377/hlthaff.2012.1061.
17. Davis KK, Allen JK. Identifying cognitive impairment in heart failure: a review of screening measures. Heart Lung. 2013;42(2):92–97. doi:10.1016/j.hrtlng.2012.11.003.
18. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–699. doi:10.1111/j.1532-5415.2005.53221.x.
19. Bozkurt B. What is new in heart failure management in 2017? Update on ACC/AHA heart failure guidelines. Curr Cardiol Rep. 2018;20(6):39. doi:10.1007/s11886-018-0978-7.
20. Schmaderer MS, Struwe L, Loecker C, et al. Mobile health self-management interventions for patients with heart failure: a pilot study. J Cardiovasc Nurs. 2022;37(5):E149–E159. doi:10.1097/JCN.0000000000000846.
21. Cajita MI, Hodgson NA, Budhathoki C, Han HR. Intention to use mHealth in older adults with heart failure. J Cardiovasc Nurs. 2017;32(6):E1–E7. doi:10.1097/JCN.0000000000000401.
22. Alnosayan N, Chatterjee S, Alluhaidan A, Lee E, Houston Feenstra L. Design and usability of a heart failure mHealth system: a pilot study. JMIR Hum Factors. 2017;4(1):e9. doi:10.2196/humanfactors.6481.
23. Zavertnik JE. Self-care in older adults with heart failure: an integrative review. Clin Nurse Spec. 2014;28(1):19–32. doi:10.1097/NUR.0000000000000021.
24. Evangelista LS, Ghasemzadeh H, Lee JA, Fallahzadeh R, Sarrafzadeh M, Moser DK. Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure. Technol Health Care. 2017;25(3):425–433. doi:10.3233/THC-161279.
25. Mozaffarian D, Benjamin EJ, Go AS, et al. Executive summary: heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation. 2016;133(4):447–454. doi:10.1161/CIR.0000000000000366.
26. Yen PY, Wantland D, Bakken S. Development of a customizable health IT usability evaluation scale. AMIA Annu Symp Proc. 2010;2010:917–921.
27. Ziaeian B, Fonarow GC. The prevention of hospital readmissions in heart failure. Prog Cardiovasc Dis. 2016;58(4):379–385. doi:10.1016/j.pcad.2015.09.004.
Keywords:

feasibility study; heart failure; mHealth; mobile health; self-management

Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.