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Acceptability of a Text Message‐Based Mobile Health Intervention to Promote Physical Activity in Cardiac Rehabilitation Enrollees: A Qualitative Substudy of Participant Perspectives

Originally publishedhttps://doi.org/10.1161/JAHA.123.030807Journal of the American Heart Association. 2024;13:e030807

    Abstract

    Background

    Mobile health (mHealth) interventions have the potential to deliver longitudinal support to users outside of episodic clinical encounters. We performed a qualitative substudy to assess the acceptability of a text message‐based mHealth intervention designed to increase and sustain physical activity in cardiac rehabilitation enrollees.

    Methods and Results

    Semistructured interviews were conducted with intervention arm participants of a randomized controlled trial delivered to low‐ and moderate‐risk cardiac rehabilitation enrollees. Interviews explored participants' interaction with the mobile application, reflections on tailored text messages, integration with cardiac rehabilitation, and opportunities for improvement. Transcripts were thematically analyzed using an iteratively developed codebook. Sample size consisted of 17 participants with mean age of 65.7 (SD 8.2) years; 29% were women, 29% had low functional capacity, and 12% were non‐White. Four themes emerged from interviews: engagement, health impact, personalization, and future directions. Participants engaged meaningfully with the mHealth intervention, finding it beneficial in promoting increased physical activity. However, participants desired greater personalization to their individual health goals, fitness levels, and real‐time environment. Generally, those with lower functional capacity and less experience with exercise were more likely to view the intervention positively. Finally, participants identified future directions for the intervention including better incorporation of exercise physiologists and social support systems.

    Conclusions

    Cardiac rehabilitation enrollees viewed a text message‐based mHealth intervention favorably, suggesting the potentially high usefulness of mHealth technologies in this population. Addressing participant‐identified needs on increased user customization and inclusion of clinical and social support is crucial to enhancing the effectiveness of future mHealth interventions.

    Registration

    URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882.

    Nonstandard Abbreviations and Acronyms

    mHealth

    mobile health

    SUS

    System Usability Scale

    VALENTINE

    Virtual Application Supported Environment to Increase Exercise

    Clinical Perspective

    What Is New?

    • In this qualitative study, cardiac rehabilitation enrollees viewed a mobile application‐based just‐in‐time adaptive intervention to be highly usable and beneficial in promoting increased physical activity but still identified the need for more personalization and inclusion of clinical and social support to increase the efficacy of mHealth interventions.

    What Are the Clinical Implications?

    • The acceptability of wearable device use and granular activity tracking, even among older cardiac rehabilitation enrollees, suggests the potential for mHealth technologies to support longitudinal disease self‐management for patients enrolled in cardiac rehabilitation as well as those with other chronic cardiovascular conditions.

    • Further research on greater contextual alignment, use of reinforcement learning algorithms, cost‐effective provision of clinical and social support, and specific needs of underrepresented groups is necessary to enhance the effectiveness of and equitable access to mHealth interventions.

    Approximately 20 million American adults aged ≥20 years have coronary artery disease, and up to 45% of patients who have experienced a myocardial infarction may suffer a recurrent adverse cardiac event or die due to heart disease within 5 years.1, 2 There is thus a need for effective secondary prevention strategies. Cardiac rehabilitation is an evidence‐based risk reduction program that includes supervised exercise and education on healthy lifestyle habits.3 Cardiac rehabilitation has been shown to reduce hospital readmissions and all‐cause and cardiovascular mortality, and it is a current Class 1A recommendation to refer patients to cardiac rehabilitation after experiencing a cardiac event.3, 4, 5 Although cardiac rehabilitation improves patients' quality of life and functional capacity, access to and participation in cardiac rehabilitation remains low.6, 7, 8 Additionally, studies have shown that even for those patients who graduate cardiac rehabilitation, benefits are not always long lasting due to lack of reinforcement and gradual return to previous sedentary lifestyles.9, 10

    Mobile health (mHealth) technologies have recently gained attention for their potential to longitudinally address chronic health conditions outside of episodic clinical encounters and to improve access for diverse patient populations.11, 12, 13 Given increased smartphone ownership broadly, mHealth technologies can be harnessed to extend the long‐term benefits of cardiac rehabilitation.14 However, additional work is needed to ensure that health outcome improvements from mHealth technologies are sustainable given evidence of habituation and diminished relevance over time.15, 16, 17 Just‐in‐time adaptive interventions, an emerging type of mHealth intervention, have the potential to overcome that limitation by leveraging contextual environmental data from mobile devices to deliver individualized support to users at times most likely to modify behavior.18, 19 There is limited research, however, on the acceptability of and engagement with mHealth technologies in older cardiovascular disease populations, especially among those who participate in and have completed cardiac rehabilitation.

    The VALENTINE (Virtual Application Supported Environment to Increase Exercise) study was a prospective, randomized controlled, remotely administered trial to evaluate a text message‐based just‐in‐time adaptive intervention designed to augment and extend the benefits of cardiac rehabilitation in low‐ and moderate‐risk patients.20 The primary objective of this qualitative substudy was to assess the acceptability of different components of the VALENTINE mHealth intervention, specifically in relation to mobile feature preferences, lifestyle impact, and opportunities for improvement. Through qualitative methods, we explored whether participants would find the mHealth intervention to be acceptable and beneficial for promoting sustained engagement with physical activity during and after completion of cardiac rehabilitation. Furthermore, we anticipated that the qualitative interviews would reveal valuable information that could guide the design of future mHealth interventions intended to support longitudinal disease self‐management for patients enrolled in cardiac rehabilitation as well as those with other cardiovascular conditions.

    Methods

    Overview

    We conducted a secondary descriptive qualitative analysis using data originally obtained from the VALENTINE study. The VALENTINE study was a 6‐month prospective, remotely administered, randomized controlled trial of 220 participants enrolled in center‐based cardiac rehabilitation at 2 health care centers within the state of Michigan. The study design has previously been described in full.20 In brief, low‐ and moderate‐risk patients aged 18 to 75 years with a compatible smartphone and enrolled in center‐based cardiac rehabilitation based on a qualifying diagnosis were considered eligible. Patients with potentially high‐risk cardiovascular disease who were deemed unsafe for home physical activity were excluded (full inclusion and exclusion criteria listed in Data S1). All participants were enrolled within 8 weeks of initiating center‐based cardiac rehabilitation and were randomized to the control or intervention arms of the study. Participants in both groups received usual cardiac rehabilitation care and a compatible smartwatch (iPhone owners were given an Apple Watch Series 4, whereas Android owners were given a Fitbit Versa 2). Those in the intervention arm additionally were (1) given access to the VALENTINE mobile application that allowed for activity tracking and goal setting, (2) received microrandomized, contextually tailored text messages promoting physical activity and exercise, and (3) received weekly email summaries of their smartwatch physical activity. Participants in the intervention arm generally experienced the mHealth intervention for at least 2 to 3 months after graduation from center‐based cardiac rehabilitation. At the end of the study, all participants in the intervention arm completed a modified version of the System Usability Scale (SUS) using a 6‐point Likert scale to provide quantitative feedback on their experiences interacting with the mobile intervention.21 For this qualitative substudy, semistructured interviews were conducted with intervention arm participants after study completion. The substudy was approved by the University of Michigan Health Institutional Review Board (HUM00202685). Data and study materials will not be made available given the sensitive nature of interviews and the potential for reidentification.

    Mobile Intervention Description

    As described in the full protocol, the mobile intervention included access to a mobile application called MyDataHelps, an application by CareEvolution for conducting health‐science studies.20 This application allowed participants to track activity data (step count, exercise minutes) and set and complete activity goals, which could be adjusted based on performance (Figure). Another key component of the intervention was the delivery of microrandomized, contextually tailored text messages to participants' phones and smartwatches to promote physical activity. Messages were designed by the study team and cardiac rehabilitation exercise physiologists using conceptual behavioral health theories and adapted from prior studies.22, 23, 24 Messages were of 2 types: activity messages and exercise planning messages. Activity messages were designed to encourage low‐level physical activity and were tailored based on weather, day of week, time of day, and phase of cardiac rehabilitation (Table S1). Exercise planning messages were designed to remind participants to plan exercise within their target heart rate zones and suggested activities to increase their exercise repertoires. These were tailored based on season and phase of cardiac rehabilitation. All text messages were delivered in an automated manner based on passively sensed data from participants' mobile devices. The intervention was designed for participants to receive 1 activity message per day and 3 to 4 exercise planning messages per week on average. Finally, all intervention arm participants were sent weekly email summaries that contained encouraging messages and an activity summary with comparisons with earlier phases of the study.

    Figure 1. Sample screenshot of VALENTINE mobile application.

    Participants can use the mobile application to set and complete physical activity goals and review prior text messages. VALENTINE indicates Virtual Application Supported Environment to Increase Exercise.

    Study Design and Sampling

    Participants randomized to the intervention arm of the primary study (N=111) were recruited for this qualitative substudy through a combination of emails and telephone calls (up to 3 contact attempts). Purposive sampling was used to identify participants with focused efforts to contact older adults, women, and racial minorities. In total, the study team made at least 1 contact attempt to 74 (67%) intervention group participants. Interviews were conducted until a representative sample of the primary study population was recruited and thematic saturation reached, yielding a total sample size of 17 participants. All participants underwent remote informed consent before participating in the interviews, with consent forms signed in the mobile study application or through a secure web dashboard.

    Data Collection

    We used grounded theory to create a semistructured interview guide consisting of questions about interaction with the mobile application, thoughts on the content and frequency of tailored text messages, integration with cardiac rehabilitation, and general experiences with the intervention. Grounded theory is a qualitative approach for collecting and analyzing data without imposing previously constructed theoretical frameworks.25 Interview guides were created by a group of investigators with experience in qualitative research and clinical medicine (Data S1). Researchers' biases were limited by creating a semistructured interview guide containing a series of broad, open‐ended questions to encourage participants to share their thoughts or opinions.

    Interviews were conducted by 3 clinician investigators using the same interview guide across all interviews. Given the potential diversity of participants' experiences, individual interviews were conducted to allow participants space to freely share their thoughts and unbiased views toward the mHealth intervention. All investigators received training on qualitative interviewing techniques by senior members of the team experienced in qualitative research. Initially, interviews included 2 investigators (N=8), with 1 leading and 1 observing. Over time, interviews became limited to 1 interviewer (N=9). All interviews were conducted over the telephone, with interviews audio‐recorded and subsequently transcribed. Interviews lasted ≈30 minutes and were conducted between January 2022 and September 2022. No additional incentives beyond those delivered as part of the clinical trial was offered for participation.

    Statistical Analysis

    Clinical characteristics are described as means and SDs for continuous symmetric variables, and categorical variables are presented as counts and percentages. The SUS was scored using a modified scoring system with each item's score contribution ranging from 0 to 5.22 For odd questions, the score contribution was the scale position, and for even questions, the contribution was 5 minus the scale position, as published. Scores were multiplied by 2 to obtain a measure of overall system usability, with scores ranging from 0 to 100. As interviews were conducted, transcripts were concurrently reviewed independently by 4 researchers (N.A., S.R.M., T.A., and J.R.G.), who each separately developed sets of keyword phrases. Across meetings spanning several weeks, an iterative process was used to compare keyword phrases and develop a progressive collective list of codes. Once this list of codes stabilized, a codebook was formally developed consisting of a list of codes with definitions and inclusion and exclusion guidelines. Transcripts were then coded in full by 1 of the researchers (N.A.). With the involvement of senior research team members (B.K.N., J.R.G.), the coded transcripts were thematically analyzed using the Attride‐Sterling Framework for qualitative analysis, through which a system of global themes and subthemes was developed.26 Coding and thematic analysis was conducted using NVivo 12.0 (QSR International).

    Results

    Interviews were conducted with 17 participants from the intervention arm (N=111) of the VALENTINE study. Baseline characteristics of participants are described in Table 1 and Table S2. Participants' mean age was 65.6 (SD 8.2) years. There were 5 (29%) female participants, and 2 (12%) participants self‐identified as non‐White. Five (29%) participants were determined to have low baseline functional capacity as determined by their baseline 6‐minute walk distance (≤450 meters versus >450 meters), and 6 (35%) participants reported prior experience with cardiac rehabilitation.

    Table 1. Participant Clinical and Demographic Characteristics

    CharacteristicNMean (SD) or %
    Demographics
    Age, y1765.6 (8.2)
    Sex
    Men1271%
    Women529%
    Race
    Asian00%
    Black16%
    White1588%
    Other16%
    Phone type
    iPhone (Apple Watch Series 4)953%
    Android phone (Fitbit Versa 2)847%
    Functional capacity
    High1271%
    Low529%
    Indication for cardiac rehabilitation
    PCI847%
    CABG318%
    Valve repair or replacement529%
    PCI or CABG and valve repair or replacement00%
    CAD or ACS, not revascularized16%

    ACS indicates acute coronary syndrome; CABG, coronary artery bypass grafting; CAD, coronary artery disease; and PCI, percutaneous coronary intervention.

    The mean SUS score for the 17 interview participants was 79.2 (SD 12.5; range, 48–98), a high score indicating that participants generally found the mHealth intervention to be efficient and easy to use.27 The mean SUS score for intervention arm participants who completed the SUS but were not interviewed (N=76/94) was similar at 77.8 (SD 17.0; range, 34–100). Additional characteristics for all intervention arm participants are described in Table S3. Overall, participants held positive attitudes toward the mHealth intervention. Fourteen (82%) participants reported that they found the intervention helpful, and all participants said they would recommend the intervention to others. Generally, participants with lower baseline functional capacity and less experience with exercise were more likely to view the intervention positively and report it to be beneficial. Four main themes emerged from the perspectives participants shared in the semistructured interviews (Table 2).

    Table 2. Main Themes From Interviews

    ThemeDescription
    EngagementHow participants used and liked the different features of the mobile health intervention.
    Health impactBenefits of the mobile health intervention and the positive behavioral changes it motivated in participants' lives.
    PersonalizationHow the mobile health intervention fit into participants' lives and did or did not meet their individual needs.
    Future directionsImprovements and new features that participants desired in the mobile health intervention.

    Theme 1: Engagement

    Participants engaged with 4 main features of the mHealth intervention: activity tracking, tailored text messages, goal setting, and weekly email summaries. All participants, even those with no prior experience using wearable devices, reported using the study smartwatch to track their physical activity. They enjoyed reviewing their physical activity throughout the day and learning the impact of their exercise routines on their daily step counts, heart rates, and calories burned. Furthermore, some participants also used the VALENTINE mobile application to track their physical activity, because it included trends of their daily step counts and exercise minutes as well as centralized data collection and review beyond the features in their smartwatch.

    “I did both. I'd look at how many rings I had [on the watch], and I would open the study app and look at the actual numbers and compare that to the previous day.” ID 758, Woman, Low Functional Capacity

    Participants reported reviewing the study's text messages, most often in the text message application of their smartphones and less often in the VALENTINE mobile application directly. Participants had mixed responses about message content. Some individuals preferred messages that encouraged quick bouts of physical activity throughout the day. They felt the activities recommended were usually simple and could be done in any environment, and so they were more likely to engage with these types of messages than with the exercise planning messages.

    “I like the stretching one because I may have just been sitting down, and I get the text message saying ‘why don't you stand up and stretch’. And I go, okay, I can do that right now.” ID 128, Woman, Low Functional Capacity

    “The ones telling me to, if you have a few minutes, to get up and do whatever set exercise from that message. Those were the ones that I liked the most. Because they were really quick and it was something that I could do, pretty much at any time.” ID 844, Man, High Functional Capacity

    Other participants, however, found these messages to be too frequent and simplistic, especially those with high baseline functional capacity.

    “Hey [NAME], why don't you stand up and touch your toes 5 times? I thought those were, you know adorable, but I didn't really ever take the prompts.” ID 167, Man, High Functional Capacity

    A smaller subset of participants preferred messages related to exercise planning, because these messages were delivered less frequently (at most once daily) and encouraged them to set goals for the subsequent day. They also liked how the messages were open‐ended, allowing them to customize their goals as appropriate, rather than the activity messages that they viewed as prescriptive.

    “At the end of the day, I would always set my goals up for the next day. It's making you think constantly, you know before bed, what am I going to do for tomorrow. You get up in the morning, and you're getting a notification.” ID 236, Man, High Functional Capacity

    Despite individuals seeing value in exercise planning, use of the goal‐setting feature within the mobile application was more limited. Those who felt that the feature allowed them to plan their day and kept them accountable reported using it regularly. Even select participants with established exercise routines reported using it for the confidence boost engendered from checking off a goal.

    “I think setting the goals was a good thing to do. Checking that box is a way of giving you a sense of accomplishment and feeling good about your activity and exercising.” ID 433, Man, High Functional Capacity

    Some participants, however, reported that they found it difficult to remember to set goals or anticipate what might be possible the next day. Other participants, especially those with higher physical activity levels at baseline, reported never using the goal‐setting feature, because they felt intrinsically motivated.

    “As far as goal setting not really. I already had a method of what my goals were every day. So I didn't utilize anything else.” ID 501, Man, High Functional Capacity

    Participants also used the weekly email summaries to get a bigger picture view of their physical activity and identify areas for improvement.

    “It's just nice to see what you've done sometimes.” ID 577, Man, High Functional Capacity

    Theme 2: Health Impact

    Interviews identified 3 main ways that participants felt the intervention aided in their recovery: accountability, reflection, and attitude shift. Participants commented on how the intervention, especially the text messages, served as constant reminders to engage in physical activity as a means of progressing toward their health goals.

    “When I got a notification, it was a reminder that I need to get up and do something. So, I would comply with what it asked for. If not right then, then a little bit later in the day.” ID 807, Woman, Low Functional Capacity

    Participants noted that it especially helped them stay on track during times when they were too busy or did not feel like exercising.

    “I have a very physical job, and I would be kind of tired from doing work. I would see the messages, and I would actually get up and go do something according to the message. I tried to do it even though I still didn't want to.” ID 844, Man, High Functional Capacity

    Participants also noted that the intervention, through activity tracking and email summaries, made them reflect on their physical activity levels and motivated them to make changes in their routines.

    “I might look at that and say, ‘oh, well, it says I haven't really been very active Tuesday and Wednesday.’ So, I probably should be more active on Thursday and Friday.” ID 771, Man, High Functional Capacity

    In terms of attitude shift, participants reported that the notifications about engaging in low level activity throughout the day positively affected their mindset toward being physically active.

    “I think getting the notifications throughout the day, it really changed my mindset about having to do physical activity. It's something that you can really do throughout the day. Taking 5, 10 minutes walking [or] climbing stairs, rather than trying to group an hour to go to the gym.” ID 236, Man, High Functional Capacity

    By the end of the intervention, participants reported that the consistent reminders, awareness of their daily activity levels, and the subsequent self‐motivated changes resulted in them performing more physical activity daily than they normally would otherwise.

    “The messages motivate you to do something, that you can do a little more. Looking at [a message] ‘can you do a little more?’ Yeah, I guess I can.” ID 758, Woman, Low Functional Capacity

    Theme 3: Personalization

    Interviews highlighted how the mHealth intervention fit within participants' lives with respect to usability, applicability to participants' goals, and contextual appropriateness. Even those who had little experience with mHealth technology generally felt that the mobile application was easy to navigate and integrate into their lives.

    Participants were also motivated to improve their health and physical fitness generally, and they viewed the mobile application as a means by which to achieve that goal. However, some participants had more specific goals, such as improving diet or reducing their waist circumference, that were not directly targeted by the application. Participants often commented that the messages were not always appropriate for their functional status and activity levels, and such messages usually were ignored. Participants desired more personalization and felt individually tailored messages would be more likely to motivate action.

    “So if it was giving me smart feedback, [where] it knew what I was doing and said, ‘Hey you just walked 2 miles, can you do another half mile kind of a thing,’ then it would make me aware that ‘oh, you know what I am doing, and you think it would be better for me to do that.” ID 771, Man, High Functional Capacity

    “I set my goal with 10,000 steps and I did 5,500 steps. I would have preferred a message saying, ‘5,500 steps, 4,500 more to meet your goal.’ I would have liked a more quantifiable message to meet my goal.” ID 807, Woman, Low Functional Capacity

    Additionally, participants reflected on limited contextual alignment at times, especially in regard to recent physical activity. For example, in some instances, they received a message encouraging exercise shortly after completion of a workout session or were encouraged to walk outside during poor weather. Although some participants were bothered by these inappropriately delivered messages, others viewed them simply as a reminder to be active and focused less on the content of the messages themselves.

    “Actually, I never looked at them that way. I took it as you know, hey it's a good reminder not necessarily to go outside and walk around but just to get up and move.” ID 236, Man, High Functional Capacity

    Despite these limitations, participants still generally viewed the text messages positively. Those who found the messages beneficial were most likely to report that the frequency of messages was appropriate, whereas those who found less usefulness in the messages wished that they were less frequent.

    Theme 4: Future Directions

    Interviews identified additional ways in which the mHealth experience could be enhanced for future iterations. Participants desired greater tailoring of messages to their unique environments. Although participants acknowledged the trade‐off with respect to potential invasion of their privacy due to increased tracking, they felt it was an acceptable sacrifice to improve their health.

    “I joined the study because it was a way for me to be more accurate as far as measuring the outcome of what I was actually doing. And if I'm doing that and you're watching that part of it, I really don't see how I could have an objection to seeing what I'm doing in real time.” ID 444, Man, High Functional Capacity

    Participants also noted that they highly valued the guidance from an exercise physiologist when they initially enrolled in cardiac rehabilitation. They felt the individualized support and customized exercise routines were essential to their recovery. They did note, however, that even virtual interactions with an exercise physiologist might achieve the same effect.

    “An exercise physiologist [is] labor‐intensive and expensive, but I think that's what's needed long term. An expert who's looking at what you're doing and saying ‘bravo’.” ID 807, Woman, Low Functional Capacity

    Additionally, some participants highlighted the importance of social support. Some participants found a sense of community by engaging with other patients at center‐based cardiac rehabilitation and reported the friendly competition made them more motivated to increase their physical activity. They desired a similar feature within the mHealth intervention that could help them connect with a larger social network.

    “Like in my case, [I had] a few people that I actually associated with at rehab. If you could connect to a bigger group of people to see how other people were doing, I think that would have probably made us a lot better than we were.” ID 844, Man, High Functional Capacity

    Similarly, participants noted increased accountability and commitment to exercise when their spouse or other loved one engaged with the mHealth intervention, either by reviewing the mobile study application and email summaries or by using a wearable device themselves. Other suggestions for intervention improvement and future directions are summarized in Table 3.

    Table 3. Participant‐Identified Areas for Improvement of the mHealth Intervention

    Future directionsRepresentative quotes
    Customization of data capture and display within the mobile application and email summaries“That first week is not a good baseline. So, I felt like I kept getting sort of comparisons to a bad baseline. So, I do not think I really looked at it. [I] did not feel like the summary information was very helpful to me.” ID 771
    Targeting more diverse health goals

    “If you are having a big craving for chocolate, try this. Try to do this or take little bites.” ID 739, Man, High Functional Capacity

    “My goal wasn't to lose weight, it was really more about reducing the midsection fat. So having that ability to log and track that kind of information.” ID 433, Man, High Functional Capacity

    Greater personalization of text message content

    “You know if you if you set a goal, and you achieve it, [it would] be nice to get an ‘Attaboy’ or something.” ID 460, Man, High Functional Capacity

    “Like here were your stats from yesterday, you know. See if you can meet or beat them.” ID 577, Man, High Functional Capacity

    Discussion

    In this qualitative substudy, we highlight the diverse perspectives of patients with cardiovascular disease on the acceptability of the VALENTINE mHealth intervention for promoting physical activity during and after cardiac rehabilitation. Despite being an older population with limited prior experience with mobile applications and wearable devices, participants viewed the mHealth intervention favorably in general. They found the mobile application simple to incorporate into their daily lives and liked monitoring their physical activity levels through the smartwatch, mobile application, and weekly email summaries. Although participants had varying preferences on the content and frequency of text messages, text messages generally were well received and thought to motivate positive changes in health behavior. By promoting self‐reflection and serving as a constant reminder to set and achieve exercise goals, the mHealth intervention was noted to increase accountability and habit formation and to promote physical activity after completion of cardiac rehabilitation. Despite these benefits, participants still desired greater tailoring of the intervention, especially the text messages, to their diverse health goals, baseline physical fitness levels, and real‐time environment. Participants also highlighted the significant roles their exercise physiologists and social support systems (ie, family and community of peers) played in their recovery and wished future mHealth interventions could be enhanced to reflect their value.

    Our study adds to a growing body of literature that supports the acceptability and usability of mHealth interventions in older patient populations.28, 29, 30, 31 One of the major limitations of prior mHealth interventions, however, is their loss of relevance over time.15, 16, 17 Just‐in‐time adaptive interventions were conceptualized to counteract this habituation by providing in the moment support to motivate behavior change.18, 24, 32 The VALENTINE study's use of a just‐in‐time adaptive intervention, in the form of microrandomized text messages promoting low‐level physical activity or exercise planning, was thought to be beneficial by some participants who felt that the frequent reminders made them realize that small bouts of activity can easily be incorporated into their days. This finding is consistent with other studies that have also found promising results from text message‐based interventions.30, 33

    Several participants, however, reported that the lack of contextual alignment at times, in terms of both their baseline functional capacity and their real‐time activity trends, limited the usefulness of these messages. Other studies examining tailored text messages also have shown that participants often ignored or reacted negatively to messages that they perceived to be automated or not applicable.33, 34, 35, 36, 37 These findings highlight the need for messages that account for participants' baseline and current activity levels (ie, a message to walk an extra 5 minutes to meet a specific step count goal). This level of customization requires systems to process and respond to activity‐tracking data in real time. Such granular monitoring could be perceived as a privacy invasion by some users and may be unacceptable to older adults.38, 39 However, some studies have suggested that more invasive tracking is acceptable to users if appropriate security mechanisms are in place.35 Contrary to some of the earlier evidence, participants in our study reported a willingness to sacrifice their privacy in exchange for greater message tailoring due to the perceived higher value of these messages and potential to accrue greater health benefits. This suggests that with adequate counseling on the benefits and appropriate reassurance about security, many patients may be agreeable to increased data monitoring.

    One of the key findings of our study is that participants desired even greater tailoring of the mobile intervention in a variety of novel ways. In addition to tailoring based on contextual environment and functional capacity, participants often had strong preferences on message type, with some participants preferring messages promoting low‐level physical activity and others preferring messages related to exercise planning. Incorporating reinforcement learning algorithms into future iterations of the mHealth intervention may be more effective by allowing the system to learn which types of messages users prefer and at what frequencies so that message delivery can be adapted in real time.40, 41, 42 Given that participants desired greater personalization, reinforcement learning algorithms can be leveraged to recognize the message content and contextual factors that motivated action and then increase the delivery of similarly designed messages to continually modify behavior. Additionally, participants identified health goals not targeted by the current mHealth intervention (ie, improving diet, reducing visceral abdominal fat). Future iterations of the mHealth intervention could focus on incorporating more comprehensive features that address other key components of cardiac rehabilitation beyond just physical activity.

    Finally, our study underscores how participants valued their interactions with an exercise physiologist at cardiac rehabilitation and their social support networks, emphasizing that they viewed the mHealth intervention as an adjunct to but not a replacement for traditional center‐based cardiac rehabilitation. Participants reported needing the guidance of an exercise physiologist to feel comfortable with their exercise routines and believed this ongoing supervision and advice was essential to their recovery. Participants also spoke highly of the camaraderie they developed with their peers at cardiac rehabilitation as well as the importance of their spouse or loved one in promoting accountability. Thus, future mHealth interventions should consider innovative strategies to build on these human connections, such as longitudinal, structured review of patients' wearable device data by an exercise physiologist, creation of a web‐based community to promote social support, or sharing of mobile application and wearable device data with patients' support systems. Additional research is needed, however, to determine how best to incorporate these additional strategies into both remote and center‐based cardiac rehabilitation delivery platforms to promote accountability while also minimizing use of scarce resources (eg, clinician time).

    Our study has several strengths. First, the VALENTINE study trial used an inclusive study design where participants were provided with a smartwatch compatible with their existing phones (Fitbit for Android users or Apple watch for iPhone users), rather than simply enrolling patients who already owned a smartwatch or targeting a specific device ecosystem. As a result, we were able to gather diverse perspectives from both Android and Apple phone users as well as from participants with and without prior experience with smartwatches. Second, by using purposive sampling with a focus on increasing representation, our study included many older adults (47% of sample aged >65 years). Given that mHealth technologies are understudied in older populations, our study offers important and necessary insights from older adults who may not be as comfortable with technology but represent a large portion of cardiac rehabilitation enrollees. Finally, we approached participants for exit interviews irrespective of their level of engagement with or completion of the clinical trial. We additionally waited until 6 months after participants enrolled in the trial to perform interviews, allowing us to capture their perspectives on the usefulness of the mHealth intervention both during and after cardiac rehabilitation.

    Important limitations of our study should be noted. First, we only interviewed 17 of the 111 intervention arm participants, so we may have missed important insights from participants not interviewed. However, the similarity in mean SUS scores between interview participants and total intervention arm participants as well as the data saturation achieved over interviews suggest that the thoughts shared by our sample is representative of the larger cohort of participants. Second, the study only enrolled low‐ and moderate‐risk patients who participated in center‐based cardiac rehabilitation and already owned smartphones. As a result, our sample may not be representative of all cardiac rehabilitation enrollees and may reflect the perspectives of patients with at least some comfort with technology. Third, our participants were predominantly White (88%) and men (71%). Given that women and racial and ethnic minorities participate in center‐based cardiac rehabilitation at lower rates than other groups, and enrollment in center‐based cardiac rehabilitation was a requirement for study participation, we were limited in our ability to recruit these individuals into the interview substudy despite focused efforts. Although representative of center‐based cardiac rehabilitation overall, this lack of diversity may have prevented us from gaining insights about the unique barriers these individuals face in engaging with mHealth technologies. Fourth, given our older interview population, interviews were conducted by telephone rather than video conferencing. Finally, in an attempt to keep the interview guide broad and maintain consistency across interviews, certain ideas may not have been thoroughly explored if not independently brought up by participants and not included in the semistructured interview guide.

    In conclusion, our study shows high acceptability of a novel text message‐based mHealth intervention among patients enrolled in cardiac rehabilitation. Participants engaged meaningfully with the intervention and reported several benefits that they felt supported them in increasing and then sustaining higher physical activity levels. To further enhance the efficacy of mHealth interventions in augmenting and extending the long‐term benefits of cardiac rehabilitation, additional research is necessary on the incorporation of greater contextual alignment and user social support systems. Future studies should also investigate the specific needs of underrepresented groups for effectively engaging with mHealth interventions and how to prevent and address potential digital disparities that may emerge.

    Sources of Funding

    This work was supported by institutional grants at the University of Michigan including Precision Health, MCubed, and Aikens Innovation awards.

    Disclosures

    Dr Mishra receives salary support by an American Heart Association grant (grant number 20SFRN35370008). Dr Nallamothu is a principal investigator or coinvestigator on research grants from the National Institutes of Health, Veterans Affairs Health Services Research and Development, and the American Heart Association. He also receives compensation as Editor‐in‐Chief of Circulation: Cardiovascular Quality & Outcomes, a journal of the American Heart Association. Finally, he is a coinventor on US Utility Patent Number US15/356012 (US20170148158A1) titled Automated Analysis of Vasculature in Coronary Angiograms that uses software technology with signal processing and machine learning to automate the reading of coronary angiograms, held by the University of Michigan. The patent is licensed to AngioInsight, Inc., in which Dr Nallamothu holds ownership shares and receives consultancy fees. Dr Golbus receives funding from the National Institutes of Health (L30HL143700) and receives salary support by an American Heart Association grant (grant number 20SFRN35370008). The remaining authors have no disclosure to report.

    Footnotes

    * Correspondence to: Namratha Atluri, MD, Department of Internal Medicine, University of Michigan, MI, 3116 TC, SPC 5368, 1500 E. Medical Center Dr, Ann Arbor, MI 48109‐5368. Email:

    This article was sent to Francoise A. Marvel, MD, Guest Editor, for review by expert referees, editorial decision, and final disposition.

    Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.030807

    For Sources of Funding and Disclosures, see page 10.

    References

    • 1 Heidenreich PA, Fonarow GC, Breathett K, Jurgens CY, Pisani BA, Pozehl BJ, Spertus JA, Taylor KG, Thibodeau JT, Yancy CW, et al. 2020 ACC/AHA clinical performance and quality measures for adults with heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Performance measures. Circ Cardiovasc Qual Outcomes. 2020; 13:e000099. doi: 10.1161/HCQ.0000000000000099LinkGoogle Scholar
    • 2 Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore‐Mensah Y, et al. Heart disease and stroke statistics‐2022 update: a report from the American Heart Association. Circulation. 2022; 145:e153–e639. doi: 10.1161/CIR.0000000000001052LinkGoogle Scholar
    • 3 Taylor RS, Brown A, Ebrahim S, Jolliffe J, Noorani H, Rees K, Skidmore B, Stone JA, Thompson DR, Oldridge N. Exercise‐based rehabilitation for patients with coronary heart disease: systematic review and meta‐analysis of randomized controlled trials. Am J Med. 2004; 116:682–692. doi: 10.1016/j.amjmed.2004.01.009CrossrefMedlineGoogle Scholar
    • 4 Dunlay SM, Pack QR, Thomas RJ, Killian JM, Roger VL. Participation in cardiac rehabilitation, readmissions, and death after acute myocardial infarction. Am J Med. 2014; 127:538–546. doi: 10.1016/j.amjmed.2014.02.008CrossrefMedlineGoogle Scholar
    • 5 Sandesara PB, Lambert CT, Gordon NF, Fletcher GF, Franklin BA, Wenger NK, Sperling L. Cardiac rehabilitation and risk reduction. J Am Coll Cardiol. 2015; 65:389–395. doi: 10.1016/j.jacc.2014.10.059CrossrefMedlineGoogle Scholar
    • 6 Beatty AL, Brown TM, Corbett M, Diersing D, Keteyian SJ, Mola A, Stolp H, Wall HK, Sperling LS. Million Hearts Cardiac Rehabilitation Think Tank: Accelerating New Care Models. Circ Cardiovasc Qual Outcomes. 2021; 14:e008215. doi: 10.1161/CIRCOUTCOMES.121.008215LinkGoogle Scholar
    • 7 Ritchey MD, Maresh S, McNeely J, Shaffer T, Jackson SL, Keteyian SJ, Brawner CA, Whooley MA, Chang T, Stolp H, et al. Tracking cardiac rehabilitation participation and completion among Medicare beneficiaries to inform the efforts of a national initiative. Circ Cardiovasc Qual Outcomes. 2020; 13:e005902. doi: 10.1161/CIRCOUTCOMES.119.005902LinkGoogle Scholar
    • 8 Wall HK, Stolp H, Wright JS, Ritchey MD, Thomas RJ, Ades PA, Sperling LS. The Million Hearts initiative: catalyzing utilization of cardiac rehabilitation and accelerating implementation of new care models. J Cardiopulm Rehabil Prev. 2020; 40:290–293. doi: 10.1097/HCR.0000000000000547CrossrefMedlineGoogle Scholar
    • 9 Hansen D, Dendale P, Raskin A, Schoonis A, Berger J, Vlassak I, Meeusen R. Long‐term effect of rehabilitation in coronary artery disease patients: randomized clinical trial of the impact of exercise volume. Clin Rehabil. 2010; 24:319–327. doi: 10.1177/0269215509353262CrossrefMedlineGoogle Scholar
    • 10 Ramadi A, Haennel RG. Sedentary behavior and physical activity in cardiac rehabilitation participants. Heart Lung. 2019; 48:8–12. doi: 10.1016/j.hrtlng.2018.09.008CrossrefMedlineGoogle Scholar
    • 11 DeVore AD, Wosik J, Hernandez AF. The future of wearables in heart failure patients. JACC Heart Failure. 2019; 7:922–932. doi: 10.1016/j.jchf.2019.08.008CrossrefMedlineGoogle Scholar
    • 12 Ernsting C, Dombrowski SU, Oedekoven M, O Sullivan JL, Kanzler M, Kuhlmey A, Gellert P. Using smartphones and health apps to change and manage health behaviors: a population‐based survey. J Med Internet Res. 2017; 19:e101. doi: 10.2196/jmir.6838CrossrefMedlineGoogle Scholar
    • 13 Whitehead L, Seaton P. The effectiveness of self‐management mobile phone and tablet apps in long‐term condition management: a systematic review. J Med Internet Res. 2016; 18:e97. doi: 10.2196/jmir.4883CrossrefMedlineGoogle Scholar
    • 14 Vogels EA. Millennials stand out for their technology use, but older generations also embrace digital life. Pew Research Center. 2019. Accessed April 4, 2023. https://www.pewresearch.org/short‐reads/2019/09/09/us‐generations‐technology‐use/Google Scholar
    • 15 Burke LE, Ma J, Azar KMJ, Bennett GG, Peterson ED, Zheng Y, Riley W, Stephens J, Shah SH, Suffoletto B, et al. Current science on consumer use of mobile health for cardiovascular disease prevention. Circulation. 2015; 132:1157–1213. doi: 10.1161/CIR.0000000000000232LinkGoogle Scholar
    • 16 Golbus JR, Klasna P, Nallamothu BK. The CHAT trial:time for a heart to heart talk. Circ Cardiovasc Qual Outcomes. 2019; 12:e005643. doi: 10.1161/CIRCOUTCOMES.119.005643LinkGoogle Scholar
    • 17 Steinhubl SR, Muse ED, Topol EJ. Can mobile health technologies transform health care?JAMA. 2013; 310:2395–2396. doi: 10.1001/jama.2013.281078CrossrefMedlineGoogle Scholar
    • 18 Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall A, Tewari A, Murphy SA. Microrandomized trials: an experimental design for developing just‐in‐time adaptive interventions. Health Psychol. 2015; 34:1220–1228. doi: 10.1037/hea0000305CrossrefGoogle Scholar
    • 19 Klasnja P, Hekler EB. Rethinking evaluations of mHealth systems for behavior change. GetMobile. 2018; 22:11–14. doi: 10.1145/3276145.3276149CrossrefMedlineGoogle Scholar
    • 20 Jeganathan VS, Golbus JR, Gupta K, Luff E, Dempsey W, Boyden T, Rubenfire M, Mukherjee B, Klasnja P, Kheterpal S, et al. Virtual AppLication‐supported Environment To INcrease Exercise (VALENTINE) during cardiac rehabilitation study: rationale and design. Am Heart J. 2022; 248:53–62. doi: 10.1016/j.ahj.2022.02.012CrossrefMedlineGoogle Scholar
    • 21 Brooke J. SUS: a quick and dirty usability scale. Usability Eval Ind. 1995; 189:189–194.Google Scholar
    • 22 Klasnja P, Smith S, Seewald NJ, Lee A, Hall A, Luers B, Hekler EB, Murphy SA. Efficacy of contextually tailored suggestions for physical activity: a micro‐randomized optimization trial of HeartSteps. Ann Behav Med. 2018; 53:573–582. doi: 10.1093/abm/kay067CrossrefGoogle Scholar
    • 23 Nahum‐Shani I, Hekler EB, Spruijt‐Metz D. Building health behavior models to guide the development of just‐in‐time adaptive interventions: a pragmatic framework. Health Psychol. 2015; 34:1209–1219. doi: 10.1037/hea0000306CrossrefGoogle Scholar
    • 24 Nahum‐Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just‐in‐time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018; 52:446–462. doi: 10.1007/s12160-016-9830-8CrossrefMedlineGoogle Scholar
    • 25 Chapman AL, Hadfield M, Chapman CJ. Qualitative research in healthcare: an introduction to grounded theory using thematic analysis. J R Coll Physicians Edinb. 2015; 45:201–205. doi: 10.4997/jrcpe.2015.305CrossrefMedlineGoogle Scholar
    • 26 Attride‐Stirling J. Thematic networks: an analytic tool for qualitative research. Qual Res. 2001; 1:385–405. doi: 10.1177/146879410100100307CrossrefGoogle Scholar
    • 27 Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009; 4:114–123.Google Scholar
    • 28 Guo Y, Chen Y, Lane DA, Liu L, Wang Y, Lip GYH. Mobile health technology for atrial fibrillation management integrating decision support, education, and patient involvement: MAF App Trial. Am J Med. 2017; 130:1388–1396.e6. doi: 10.1016/j.amjmed.2017.07.003CrossrefMedlineGoogle Scholar
    • 29 Mehra A, Visser B, Cila N, Helder JVD, Engelbert RHH, Weijs PJ, Kröse BJ. Supporting older adults in exercising with a tablet: a usability study. JMIR Hum Factors. 2019; 6:e11598. doi: 10.2196/11598CrossrefMedlineGoogle Scholar
    • 30 Müller AM, Khoo A, Morris T. Text messaging for exercise promotion in older adults from an upper‐middle‐income country: randomized controlled trial. J Med Internet Res. 2016; 18:e5235. doi: 10.2196/jmir.5235CrossrefGoogle Scholar
    • 31 Portz JD, Vehovec A, Dolansky MA, Levin JB, Bull A, Boxer R. The development and acceptability of a mobile application for tracking symptoms of heart failure among older adults. Telemed J E Health. 2018; 24:161–165. doi: 10.1089/tmj.2017.0036CrossrefMedlineGoogle Scholar
    • 32 Golbus JR, Dempsey W, Jackson EA, Nallamothu BK, Klasnja P. Micro‐randomized trial design for evaluating just‐in‐time‐adaptive‐interventions through mobile health technologies for cardiovascular disease. Circ Cardiovasc Qual Outcomes. 2021; 14:e006760. doi: 10.1161/CIRCOUTCOMES.120.006760LinkGoogle Scholar
    • 33 Smith DM, Duque L, Huffman JC, Healy BC, Celano CM. Text message interventions for physical activity: a systematic review and meta‐analysis. Am J Prev Med. 2020; 58:142–151. doi: 10.1016/j.amepre.2019.08.014CrossrefMedlineGoogle Scholar
    • 34 Beatty AL, Magnusson SL, Fortney JC, Sayre GG, Whooley MA. VA FitHeart, a mobile app for cardiac rehabilitation: usability study. JMIR Hum Factors. 2018; 5:e8017. doi: 10.2196/humanfactors.8017CrossrefGoogle Scholar
    • 35 Elnaggar A, von Oppenfeld J, Whooley MA, Merek A, Park LG. Applying mobile technology to sustain physical activity after completion of cardiac rehabilitation: acceptability study. JMIR Hum Factors. 2021; 8:e25356. doi: 10.2196/25356CrossrefMedlineGoogle Scholar
    • 36 Lunde P, Bye A, Bergland A, Grimsmo J, Jarstad A, Nilsson BB. Long‐term follow‐up with a smartphone application improves exercise capacity post cardiac rehabilitation: a randomized controlled trial. Eur J Prev Cardiol. 2020; 27:1782–1792. doi: 10.1177/2047487320905717CrossrefMedlineGoogle Scholar
    • 37 Saner H. eHealth and telemedicine: current situation and future challenges. Eur J Prev Cardiol. 2013; 20:1–2. doi: 10.1177/2047487313487483CrossrefMedlineGoogle Scholar
    • 38 Wang J, Du Y, Coleman D, Peck M, Myneni S, Kang H, Gong Y. Mobile and connected health technology needs for older adults aging in place: cross‐sectional survey study. JMIR Aging. 2019; 2:e13864. doi: 10.2196/13864CrossrefMedlineGoogle Scholar
    • 39 Zhou L, Bao J, Watzlaf V, Parmanto B. Barriers to and facilitators of the use of mobile health apps from a security perspective: mixed‐methods study. JMIR Mhealth Uhealth. 2019; 7:e11223. doi: 10.2196/11223CrossrefMedlineGoogle Scholar
    • 40 Albers N, Neerincx MA, Brinkman WP. Addressing people's current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active. PLoS One. 2022; 17:e0277295. doi: 10.1371/journal.pone.0277295CrossrefMedlineGoogle Scholar
    • 41 Liao P, Greenewald K, Klasnja P, Murphy S. Personalized HeartSteps: a reinforcement learning algorithm for optimizing physical activity. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020; 4:18–22. doi: 10.1145/3381007CrossrefMedlineGoogle Scholar
    • 42 Lauffenburger JC, Yom‐Tov E, Keller PA, McDonnell ME, Bessette LG, Fontanet CP, Sears ES, Kim E, Hanken K, Buckley JJ, et al. REinforcement learning to improve non‐adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial. BMJ Open. 2021; 11:e052091. doi: 10.1136/bmjopen-2021-052091CrossrefMedlineGoogle Scholar

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