Patterns of Telehealth Visits After the COVID-19 Pandemic Among Individuals With or at Risk for Cardiovascular Disease in the United States.

BACKGROUND: Prior studies have shown that cardiovascular disease (CVD) can be effectively managed through telehealth. However, there are little national data on the use of telehealth in people with CVD or CVD risk factors. We aimed to determine the prevalence of telehealth visits and visit modality (video versus audio-only) in people with CVD and CVD risk factors. We also assessed their rationale and satisfaction with telehealth visits. METHODS AND RESULTS: A nationally representative sample of 6252 participants from the 2022 Health Information National Trends Survey 6 was used. We defined the CVD risk categories as having no self-reported CVD (coronary heart disease or heart failure) or CVD risk factors (hypertension, diabetes, obesity, or current smoking), CVD risk factors alone, and CVD. Multivariable logistic regression, adjusting for major sociodemographic factors, assessed the relationship between CVD risk and telehealth uptake. The weighted prevalence of using telehealth was 50% (95% CI, 44%-56%) for individuals with CVD and 40% (95% CI, 37%-43%) for those with CVD risk factors alone. Individuals with CVD had the highest odds of using any telehealth (audio-only or video) (adjusted odds ratio [OR], 2.02 [95% CI, 1.39-2.93]) when compared with those without CVD or CVD risk factors. Notably, 21% (95% CI, 16.3%-25.6%) of patients with CVD used audio-only visits (adjusted OR, 2.38 [95% CI, 1.55-3.64]) compared with patients without CVD or CVD risk factors. CONCLUSIONS: In a nationally representative survey, there was high prevalence of any (video or audio-only) telehealth visits in people with CVD, and audio-only visits comprised a significant proportion of telehealth visits in this population.

Associations of Demographic, Socioeconomic, and Cognitive Characteristics With Mobile Health Access: MESA (Multi-Ethnic Study of Atherosclerosis).

Background Mobile health (mHealth) has an emerging role in the prevention of cardiovascular disease. This study evaluated possible inequities in mHealth access in older adults. Methods and Results mHealth access was assessed from 2019 to 2020 in MESA (Multi-Ethnic Study of Atherosclerosis) telephone surveys of 2796 participants aged 62 to 102 years. A multivariable logistic regression model adjusted for general health status assessed associations of mHealth access measures with relevant demographic, socioeconomic, and cognitive characteristics. There were lower odds of all access measures with older age (odds ratios [ORs], 0.37-0.59 per 10 years) and annual income <$50 000 (versus ≥$50 000 ORs, 0.55-0.62), and higher odds with higher Cognitive Abilities Screening Instrument Score (ORs, 1.22-1.29 per 5 points). Men (versus women) had higher odds of internet access (OR, 1.32 [95% CI,1.05-1.66]) and computing device ownership (OR, 1.31 [95% CI, 1.05-1.63]) but lower fitness tracker ownership odds (OR, 0.70 [95% CI, 0.49-0.89]). For internet access and computing device ownership, we saw lower odds for Hispanic participants (versus White participants OR, 0.61 [95% CI, 0.44-0.85]; OR, 0.69 [95% CI, 0.50-0.95]) and less than a high school education (versus bachelor's degree or higher OR, 0.27 [95% CI, 0.18-0.40]; OR, 0.32 [95% CI, 0.28-0.62]). For internet access, lower odds were seen for Black participants (versus White participants OR, 0.64 [95% CI, 0.47-0.86]) and other health insurance (versus health maintenance organization/private OR, 0.59 [95% CI, 0.47-0.74]). Chinese participants (versus White participants) had lower internet access odds (OR, 0.63 [95% CI, 0.44-0.91]) but higher computing device ownership odds (OR, 1.87 [95% CI, 1.28-2.77]). Conclusions Among older-age adults, mHealth access varied by major demographic, socioeconomic, and cognitive characteristics, suggesting a digital divide. Novel mHealth interventions should consider individual access barriers. Registration URL: https://www.clinicaltrials.gov/; Unique identifier: NCT00005487.

Correction: Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

[This corrects the article DOI: 10.2196/14124.].

Continuous heart rate dynamics preceding in-hospital pulseless electrical activity or asystolic cardiac arrest of respiratory etiology.

INTRODUCTION: Respiratory failure is a common cause of pulseless electrical activity (PEA) and asystolic cardiac arrest, but the changes in heart rate (HR) pre-arrest are not well described. We describe HR dynamics prior to in-hospital cardiac arrest (IHCA) among PEA/asystole arrest patients with respiratory etiology. METHODS: In this retrospective study, we evaluated 139 patients with 3-24 hours of continuous electrocardiogram data recorded preceding PEA/asystole IHCA from 2010-2017. We identified respiratory failure cases by chart review and evaluated electrocardiogram data to identify patterns of HR changes, sinus bradycardia or sinus arrest, escape rhythms, and development right ventricular strain prior to IHCA. RESULTS: A higher proportion of respiratory cases (58/73, 79 %) fit a model of HR response characterized by tachycardia followed by rapid HR decrease prior to arrest, compared to non-respiratory cases (30/66, 45 %, p < 0.001). Among the 58 respiratory cases fitting this model, 36 (62 %) had abrupt increase in HR occurring 64 (IQR 23-191) minutes prior to arrest, while 22 (38 %) had stable tachycardia until time of HR decrease. Mean peak HR was 123 ± 21 bpm. HR decrease occurred 3.0 (IQR 2.0-7.0) minutes prior to arrest. Sinus arrest occurred during the bradycardic phase in 42/58 of cases; escape rhythms were present in all but 2/42 (5 %) cases. Right ventricular strain ECG pattern, when present, occurred at a median of 2.2 (IQR -0.05-17) minutes prior to onset of HR decrease. CONCLUSION: IHCAs of respiratory etiology follow a model of HR increase from physiologic compensation to hypoxia, followed by rapid HR decrease prior to arrest.

The Impact of Transitioning From In-Person to Virtual Heart Transplantation Selection Committee Meetings: Observational Study.

BACKGROUND: Heart transplant selection committee meetings have transitioned from in-person to remote video meetings during the COVID-19 pandemic, but how this impacts committee members and patient outcomes is unknown. OBJECTIVE: The aim of this study is to determine the perceived impact of remote video transplant selection meetings on usability and patient care and to measure patient selection outcomes during the transition period from in-person to virtual meetings. METHODS: A 35-item anonymous survey was developed and distributed electronically to the heart transplant selection committee. We reviewed medical records to compare the outcomes of patients presented at in-person meetings (January-March 2020) to those presented during video meetings (March-June 2020). RESULTS: Among 83 committee members queried, 50 were regular attendees. Of the 50 regular attendees, 24 (48%) were physicians and 26 (52%) were nonphysicians, including nurses, social workers, and coordinators; 46 responses were received, 23 (50%) from physicians and 23 (50%) from nonphysicians, with 41 responses fully completed. Overall, respondents were satisfied with the videoconference format and felt that video meetings did not impact patient care and were an acceptable alternative to in-person meetings. However, 54% (22/41) preferred in-person meetings, with 71% (15/21) of nonphysicians preferring in-person meetings compared to only 35% (7/20) of physicians (P=.02). Of the 46 new patient evaluations presented, there was a statistically nonsignificant trend toward fewer patients initially declined at video meetings compared with in-person meetings (6/24, 25% compared to 10/22, 45%; P=.32). CONCLUSIONS: The transition from in-person to video heart transplant selection committee meetings was well-received and did not appear to affect committee members' perceived ability to deliver patient care. Patient selection outcomes were similar between meeting modalities.

Correction: Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study.

[This corrects the article DOI: 10.2196/16391.].

Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery.

BACKGROUND: Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone. METHODS: A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty. RESULTS: The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone. CONCLUSIONS: Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.

Health Information Technology Use Among Persons With Self-reported Atherosclerotic Cardiovascular Disease: Analysis of the 2011-2018 National Health Interview Survey.

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality in the United States. Health information technologies (HITs) have recently emerged as a viable intervention to mitigate the burden of ASCVD. Approximately 60% of US adults report searching the internet for health information; however, previous research has not examined the prevalence of general technology or HIT use among adults with and without ASCVD. In addition, social determinants in HIT use among adults with ASCVD are not well understood. OBJECTIVE: The aim of this study was to evaluate the prevalence and social determinants of HIT use among US adults with versus without self-reported ASCVD. METHODS: We pooled cross-sectional data from the 2011-2018 National Health Interview Survey (NHIS) to examine the general technology and HIT use among adults aged ≥18 years with and without self-reported ASCVD (coronary heart disease, stroke, or both). General technology use was defined as mobile phone ownership, internet use, and computer use. HIT use was defined as looking up health information on the internet, filling a web-based prescription, scheduling a medical appointment on the internet, communicating with a health care provider by email, or using web-based group chats to learn about health topics. We evaluated sociodemographic differences in HIT use among respondents by using Poisson regression. Analyses were weighted according to NHIS standards. RESULTS: A total sample of 256,117 individuals were included, of which 2194 (0.9%) reported prior ASCVD. Among adults with prior ASCVD, the mean age was 70.6 (SD 11.5) years, and 47.4% (1048/2194) of the adults were females. General technology use differed between participants with and without prior ASCVD, with 36.0% (614/1826) and 76.2% (157,642/213,816) indicating internet usage and 24.6% (374/1575) and 60.7% (107,742/184,557) indicating using a computer every day, respectively. Similarly, adults with ASCVD were less likely to use HIT than those without ASCVD (515/2194, 25.1% vs 123,966/253,923, 51.0%; P<.001). Among adults with prior ASCVD, social determinants that were associated with HIT use included younger age, higher education, higher income, being employed, and being married. CONCLUSIONS: HIT use was low among adults with a history of ASCVD, which may represent a barrier to delivering care via emerging HIT. Given the associations with social determinants such as income, education, and employment, targeted strategies and policies are needed to eliminate barriers to impact HIT usage.

Digital Health Intervention in Acute Myocardial Infarction.

BACKGROUND: Thirty-day readmissions among patients with acute myocardial infarction (AMI) contribute to the US health care burden of preventable complications and costs. Digital health interventions (DHIs) may improve patient health care self-management and outcomes. We aimed to determine if patients with AMI using a DHI have lower 30-day unplanned all-cause readmissions than a historical control. METHODS: This nonrandomized controlled trial with a historical control, conducted at 4 US hospitals from 2015 to 2019, included 1064 patients with AMI (DHI n=200, control n=864). The DHI integrated a smartphone application, smartwatch, and blood pressure monitor to support guideline-directed care during hospitalization and through 30-days post-discharge via (1) medication reminders, (2) vital sign and activity tracking, (3) education, and (4) outpatient care coordination. The Patient Activation Measure assessed patient knowledge, skills, and confidence for health care self-management. All-cause 30-day readmissions were measured through administrative databases. Propensity score-adjusted Cox proportional hazard models estimated hazard ratios of readmission for the DHI group relative to the control group. RESULTS: Following propensity score adjustment, baseline characteristics were well-balanced between the DHI versus control patients (standardized differences <0.07), including a mean age of 59.3 versus 60.1 years, 30% versus 29% Women, 70% versus 70% White, 54% versus 54% with private insurance, 61% versus 60% patients with a non ST-elevation myocardial infarction, and 15% versus 15% with high comorbidity burden. DHI patients were predominantly in the highest levels of patient activation for health care self-management (mean score 71.7±16.6 at 30 days). The DHI group had fewer all-cause 30-day readmissions than the control group (6.5% versus 16.8%, respectively). Adjusting for hospital site and a propensity score inclusive of age, sex, race, AMI type, comorbidities, and 6 additional confounding factors, the DHI group had a 52% lower risk for all-cause 30-day readmissions (hazard ratio, 0.48 [95% CI, 0.26-0.88]). Similar results were obtained in a sensitivity analysis employing propensity matching. CONCLUSIONS: Our results suggest that in patients with AMI, the DHI may be associated with high patient activation for health care self-management and lower risk of all-cause unplanned 30-day readmissions. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03760796.

A Mobile Health Intervention to Increase Physical Activity in Pulmonary Arterial Hypertension.

BACKGROUND: Supervised exercise training improves outcomes in patients with pulmonary arterial hypertension (PAH). The effect of an unsupervised activity intervention has not been tested. RESEARCH QUESTION: Can a text-based mobile health intervention increase step counts in patients with PAH? STUDY DESIGN AND METHODS: We performed a randomized, parallel arm, single-blind clinical trial. We randomized patients to usual care or a text message-based intervention for 12 weeks. The intervention arm received three automated text messages per day with real-time step count updates and encouraging messages rooted in behavioral change theory. Individual step targets increased by 20% every 4 weeks. The primary end point was mean week 12 step counts. Secondary end points included the 6-min walk test, quality of life, right ventricular function, and body composition. RESULTS: Among 42 randomized participants, the change in raw steps between baseline and week 12 was higher in the intervention group (1,409 steps [interquartile range, -32 to 2,220] vs -149 steps [interquartile range, -1,010 to 735]; P = .02), which persisted after adjustment for age, sex, baseline step counts, and functional class (model estimated difference, 1,250 steps; P = .03). The intervention arm took a higher average number of steps on all days between days 9 and 84 (P < .05, all days). There was no difference in week 12 six-minute walk distance. Analysis of secondary end points suggested improvements in the emPHasis-10 score (adjusted change, -4.2; P = .046), a reduction in visceral fat volume (adjusted change, -170 mL; P = .023), and nearly significant improvement in tricuspid annular plane systolic excursion (model estimated difference, 1.2 mm; P = .051). INTERPRETATION: This study demonstrated the feasibility of an automated text message-based intervention to increase physical activity in patients with PAH. Additional studies are warranted to examine the effect of the intervention on clinical outcomes. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No. NCT03069716; URL: www.clinicaltrials.gov.

Digital Health Interventions for Cardiac Rehabilitation: Systematic Literature Review.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. Despite strong evidence supporting the benefits of cardiac rehabilitation (CR), over 80% of eligible patients do not participate in CR. Digital health technologies (ie, the delivery of care using the internet, wearable devices, and mobile apps) have the potential to address the challenges associated with traditional facility-based CR programs, but little is known about the comprehensiveness of these interventions to serve as digital approaches to CR. Overall, there is a lack of a systematic evaluation of the current literature on digital interventions for CR. OBJECTIVE: The objective of this systematic literature review is to provide an in-depth analysis of the potential of digital health technologies to address the challenges associated with traditional CR. Through this review, we aim to summarize the current literature on digital interventions for CR, identify the key components of CR that have been successfully addressed through digital interventions, and describe the gaps in research that need to be addressed for sustainable and scalable digital CR interventions. METHODS: Our strategy for identifying the primary literature pertaining to CR with digital solutions (defined as technology employed to deliver remote care beyond the use of the telephone) included a consultation with an expert in the field of digital CR and searches of the PubMed (MEDLINE), Embase, CINAHL, and Cochrane databases for original studies published from January 1990 to October 2018. RESULTS: Our search returned 31 eligible studies, of which 22 were randomized controlled trials. The reviewed CR interventions primarily targeted physical activity counseling (31/31, 100%), baseline assessment (30/31, 97%), and exercise training (27/31, 87%). The most commonly used modalities were smartphones or mobile devices (20/31, 65%), web-based portals (18/31, 58%), and email-SMS (11/31, 35%). Approximately one-third of the studies addressed the CR core components of nutrition counseling, psychological management, and weight management. In contrast, less than a third of the studies addressed other CR core components, including the management of lipids, diabetes, smoking cessation, and blood pressure. CONCLUSIONS: Digital technologies have the potential to increase access and participation in CR by mitigating the challenges associated with traditional, facility-based CR. However, previously evaluated interventions primarily focused on physical activity counseling and exercise training. Thus, further research is required with more comprehensive CR interventions and long-term follow-up to understand the clinical impact of digital interventions.

Heart Rate Measurements in Patients with Obstructive Sleep Apnea and Atrial Fibrillation: Prospective Pilot Study Assessing Apple Watch's Agreement With Telemetry Data.

BACKGROUND: Patients with obstructive sleep apnea (OSA) are at a higher risk for atrial fibrillation (AF). Consumer wearable heart rate (HR) sensors may be a means for passive HR monitoring in patients with AF. OBJECTIVE: The aim of this study was to assess the Apple Watch's agreement with telemetry in measuring HR in patients with OSA in AF. METHODS: Patients with OSA in AF were prospectively recruited prior to cardioversion/ablation procedures. HR was sampled every 10 seconds for 60 seconds using telemetry and an Apple Watch concomitantly. Agreement of Apple Watch with telemetry, which is the current gold-standard device for measuring HR, was assessed using mixed effects limits agreement and Lin's concordance correlation coefficient. RESULTS: A total of 20 patients (mean 66 [SD 6.5] years, 85% [n=17] male) participated in this study, yielding 134 HR observations per device. Modified Bland-Altman plot revealed that the variability of the paired difference of the Apple Watch compared with telemetry increased as the magnitude of HR measurements increased. The Apple Watch produced regression-based 95% limits of agreement of 27.8 - 0.3 × average HR - 15.0 to 27.8 - 0.3 × average HR + 15.0 beats per minute (bpm) with a mean bias of 27.8 - 0.33 × average HR bpm. Lin's concordance correlation coefficient was 0.88 (95% CI 0.85-0.91), suggesting acceptable agreement between the Apple Watch and telemetry. CONCLUSIONS: In patients with OSA in AF, the Apple Watch provided acceptable agreement with HR measurements by telemetry. Further studies with larger sample populations and wider range of HR are needed to confirm these findings.

Efficacy of Mobile Health for Self-management of Cardiometabolic Risk Factors: A Theory-Guided Systematic Review.

BACKGROUND: Although mobile health (mHealth) technologies are burgeoning in the research arena, there is a lack of mHealth interventions focused on improving self-management of individuals with cardiometabolic risk factors (CMRFs). OBJECTIVE: The purpose of this article was to critically and systematically review the efficacy of mHealth interventions for self-management of CMRF while evaluating quality, limitations, and issues with disparities using the technology acceptance model as a guiding framework. METHODS: PubMed, CINAHL, EMBASE, and Lilacs were searched to identify research articles published between January 2008 and November 2018. Articles were included if they were published in English, included adults, were conducted in the United States, and used mHealth to promote self-care or self-management of CMRFs. A total of 28 articles were included in this review. RESULTS: Studies incorporating mHealth have been linked to positive outcomes in self-management of diabetes, physical activity, diet, and weight loss. Most mHealth interventions included modalities such as text messaging, mobile applications, and wearable technologies. There was a lack of studies that are (1) in resource-poor settings, (2) theoretically driven, (3) community-engaged research, (4) measuring digital/health literacy, (5) measuring and evaluating engagement, (6) measuring outcomes related to disease self-management, and (7) focused on vulnerable populations, especially immigrants. CONCLUSION: There is still a lack of mHealth interventions created specifically for immigrant populations, especially within the Latino community-the largest growing minority group in the United States. In an effort to meet this challenge, more culturally tailored mHealth interventions are needed.

Early blood pressure assessment after acute myocardial infarction: Insights using digital health technology.

OBJECTIVE: There is rising interest in digital health in preventive cardiology, particularly for blood pressure (BP) management. In a digital health study of early BP assessment following acute myocardial infarction (AMI), we sought to examine feasibility and the (1) proportion of post-AMI patients with controlled BP and hypotension, and (2) association between prior cardiovascular disease (CVD) and BP post-AMI. METHODS: In this substudy of the parent Myocardial infarction, COmbined-device, Recovery Enhancement (MiCORE) study, type 1 AMI patients were enrolled between October 2017 and April 2019. Participants self-monitored their BP through 30 days after hospital discharge using an FDA-approved wireless BP monitor connected with a smartphone application. Linear mixed-effects models assessed the association between prior CVD and BP trajectory post-discharge, adjusting for antihypertensive medications and a propensity score inclusive of CVD risk factors. RESULTS: Sixty-eight AMI patients (mean age 58 â€‹± â€‹10 years, 75% male, 68% white race, 68% history of hypertension, 24% prior CVD) provided 2638 measurements over 30 days. The percentage of BP control <130/80 â€‹mmHg was 59.6% (95% CI: 54.3-64.9%) and <140/90 â€‹mmHg was 83.7% (95% CI: 80.3-87.2%). The percentage of systolic BP â€‹<90 â€‹mmHg was 1.1% (95% CI: 0.17-2.0%) and the percentage of diastolic BP â€‹<60 â€‹mmHg was 3.9% (95% CI: 2.6-5.2%). Prior CVD was associated with 12.2 â€‹mmHg higher mean daily systolic BP during admission (95% CI: 3.5-20.9 â€‹mmHg), which persisted over follow-up. There was no association between prior CVD and diastolic BP. CONCLUSION: The digital health program was feasible and ~40% of post-AMI patients who engaged in it had uncontrolled BP according to recent guideline cutpoints, while hypotension occurred rarely. The gap in BP control was especially large in patients in whom AMI represented recurrent CVD. These data suggest an opportunity for more aggressive secondary prevention early after MI as care models integrate digital health.

Using Mobile Health Tools to Assess Physical Activity Guideline Adherence and Smoking Urges: Secondary Analysis of mActive-Smoke.

BACKGROUND: Rates of cigarette smoking are decreasing because of public health initiatives, pharmacological aids, and clinician focus on smoking cessation. However, a sedentary lifestyle increases cardiovascular risk, and therefore, inactive smokers have a particularly enhanced risk of cardiovascular disease. OBJECTIVE: In this secondary analysis of mActive-Smoke, a 12-week observational study, we investigated adherence to guideline-recommended moderate-to-vigorous physical activity (MVPA) in smokers and its association with the urge to smoke. METHODS: We enrolled 60 active smokers (≥3 cigarettes per day) and recorded continuous step counts with the Fitbit Charge HR. MVPA was defined as a cadence of greater than or equal to 100 steps per minute. Participants were prompted to report instantaneous smoking urges via text message 3 times a day on a Likert scale from 1 to 9. We used a mixed effects linear model for repeated measures, controlling for demographics and baseline activity level, to investigate the association between MVPA and urge. RESULTS: A total of 53 participants (mean age 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite, and 38% [20/53] obese) recorded 6 to 12 weeks of data. Data from 3633 person-days were analyzed, with a mean of 69 days per participant. Among all participants, median daily MVPA was 6 min (IQR 2-13), which differed by sex (12 min [IQR 3-20] for men vs 3.5 min [IQR 1-9] for women; P=.004) and BMI (2.5 min [IQR 1-8.3] for obese vs 10 min [IQR 3-15] for nonobese; P=.04). The median total MVPA minutes per week was 80 (IQR 31-162). Only 10% (5/51; 95% CI 4% to 22%) of participants met national guidelines of 150 min per week of MVPA on at least 50% of weeks. Adjusted models showed no association between the number of MVPA minutes per day and mean daily smoking urge (P=.72). CONCLUSIONS: The prevalence of MVPA was low in adult smokers who rarely met national guidelines for MVPA. Given the poor physical activity attainment in smokers, more work is required to enhance physical activity in this population.

Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study.

BACKGROUND: As smartphone ownership continues to rise, health care systems and technology companies are driven to develop mobile health (mHealth) interventions as both diagnostic and therapeutic tools. An important consideration during mHealth intervention development is how to achieve health equity despite demographic differences in smartphone ownership. One solution is through the recirculation of loaner smartphones; however, best practices for implementing such programs to optimize security, privacy, scalability, and convenience for participants are not well defined. OBJECTIVE: In this tutorial, we describe how we implemented our novel Corrie iShare program, a 30-day loaner iPhone and smartwatch recirculation program, as part of a multi-center mHealth intervention to improve recovery and access to guideline-directed therapy following acute myocardial infarction. METHODS: We conducted a prospective study utilizing a smartphone app and leveraged iOS enterprise features as well as cellular data service to automate recirculation. RESULTS: Our configuration protocol was shortened from 1 hour to 10 minutes. Of 200 participants, 92 (46.0%) did not own an iPhone and would have been excluded from the study without iShare. Among iShare participants, 72% (66/92) returned their loaned smartphones. CONCLUSIONS: The Corrie iShare program demonstrates the potential for a sustainable and scalable mHealth loaner program, enabling broader population reach while optimizing user experience. Implementation may face institutional constraints and software limitations. Consideration should be given to optimizing loaner returns.

Mobile Health Access and Use Among Individuals With or At Risk for Cardiovascular Disease: 2018 Health Information National Trends Survey (HINTS).

Background Mobile health (mHealth) technologies can deliver interventions to prevent and manage cardiovascular disease (CVD), but mHealth uptake among those with or at risk for CVD remains incompletely explored. Therefore, in this group, we assessed the prevalence of mHealth access and usage, and the association between CVD risk and mHealth uptake. Methods and Results Data were from 3248 adults in the 2018 Health Information National Trends Survey. We defined CVD risk as reporting a heart condition, diabetes mellitus, hypertension, and/or current smoking (n=1903). Multivariable logistic regression, adjusting for demographics, was used to assess the relationship between CVD risk and mHealth uptake. Most individuals with CVD risk owned a smartphone (73%, 95% CI: 69%-76%) and 48% (95% CI: 44%-52%) had a health app. Among men, those with CVD risk were more likely to use a wearable device (odds ratio 2.43, 95% CI: 1.44-4.10) than those without CVD risk, while there was no difference among women. In both sexes, CVD risk was associated with sharing information from a smartphone/wearable with a clinician (odds ratio 1.63, 95% CI 1.12-2.35 in women; odds ratio 3.99, 95% CI 2.30-6.95 in men). However, there was no difference in the odds of using mHealth to track health progress, make health decisions, aid healthcare discussions, or text a clinician. Conclusions In a nationally representative sample, there was high prevalence of smartphone ownership but incomplete mHealth uptake. Having CVD or its risk factors was associated with sharing information from smartphone/wearables, suggesting potential to leverage clinically validated mHealth interventions for CVD prevention.

The role of a clinician amid the rise of mobile health technology.

Mobile health (mHealth) interventions have demonstrated promise in improving outcomes by motivating patients to adopt and maintain healthy lifestyle changes as well as improve adherence to guideline-directed medical therapy. Early results combining behavioral economic strategies with mHealth delivery have demonstrated mixed results. In reviewing these studies, we propose that the success of a mHealth intervention links more strongly with how well it connects patients back to routine clinical care, rather than its behavior modification technique in isolation. This underscores the critical role of clinician-patient partnerships in the design and delivery of such interventions, while also raising important questions regarding long-term sustainability and scalability. Further exploration of our hypothesis may increase opportunities for multidisciplinary clinical teams to connect with and engage patients using mHealth technologies in unprecedented ways.

Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

The explosion of mobile health (mHealth) interventions has prompted significant investment and exploration that has extended past industry into academia. Although research in this space is emerging, it focuses on the clinical and population level impact across different populations. To realize the full potential of mHealth, an intimate understanding of how mHealth is being used by patients and potential differences in usage between various demographic groups must also be prioritized. In this viewpoint, we use our experiences in building an mHealth intervention that incorporates an iOS app, Bluetooth-enabled blood pressure cuff, and Apple Watch to share knowledge on (1) how user interaction data can be tracked in the context of health care privacy laws, (2) what is required for effective, nuanced communication between clinicians and engineers to design mHealth interventions that are patient-centered and have high clinical impact, and (3) how to handle and set up a process to handle user interaction data efficiently.

Digital health technology and mobile devices for the management of diabetes mellitus: state of the art.

Diabetes mellitus is a disease that can be difficult to manage and requires high levels of health literacy and numeracy, self-monitoring and frequent contact with clinicians. If not optimally controlled, diabetes can lead to kidney failure, blindness and cardiovascular complications, which, in turn, contribute to increasing healthcare costs. Although not yet widely used, mobile health (mHealth) tools have enhanced diabetes management and prevention and are likely to play an increasing role with the growth of smartphone ownership and medical device innovations. Recent mHealth interventions targeting type 1 and type 2 diabetes are diverse in their goals and components, and include insulin management applications, wearable blood glucose meters, automated text messages, health diaries and virtual health coaching. In this paper, we review the modalities and components of various impactful interventions for insulin management, diabetes education, self-management and prevention. More work is needed to investigate how individual demographic, socioeconomic, behavioural and clinical characteristics contribute to patient engagement and the efficacy of mHealth tools for diabetes.