Background:People with sickle cell disease frequently use complementary and integrative therapies to cope with their pain, yet few studies have evaluated their effectiveness. The 3-arm, 3-site pragmatic Hybrid Effectiveness-implementation Trial of Guided Relaxation and Acupuncture for Chronic Sickle Cell Disease Pain (GRACE) has 3 priorities: (1) evaluate guided relaxation and acupuncture to improve pain control; (2) determine the most appropriate and effective treatment sequence for any given patient based on their unique characteristics; and (3) describe the processes and structures required to implement guided relaxation and acupuncture within health care systems.Methods:Participants (N = 366) are being recruited and randomized 1:1:1 to one of 2 intervention groups or usual care. The acupuncture intervention group receives 10 sessions over approximately 5 weeks. The guided relaxation intervention group receives access to video sessions ranging from 2 to 20 min each viewed daily over 5 weeks. The usual care group receives the standard of clinical care for sickle cell disease. Participants are re-randomized at 6 weeks depending on their pain impact score. Assessments occur at 6 weeks, 12 weeks, and 24 weeks. The primary outcome is the change in pain impact score and secondary measures include opioid use, anxiety, depression, sleep, pain catastrophizing, substance use, global impression of change, constipation, and hospitalizations. The GRACE study uses the Consolidated Framework for Implementation Research to plan, execute, and evaluate the associated implementation processes.Conclusion:The results from GRACE will represent a critical step toward improving management of pain affecting patients with sickle cell disease.ClinicalTrials.gov Identifier: NCT04906447.

Sickle cell disease (SCD) is a hemoglobin disorder and the most common genetic disorder that affects 100,000 Americans and millions worldwide. Adults living with SCD have pain so severe that it often requires opioids to keep it in control. Depression is a major global public health concern associated with an increased risk in chronic medical disorders, including in adults living with sickle cell disease (SCD). A strong relationship exists between suicidal ideation, suicide attempts, and depression. Researchers enrolling adults living with SCD in pragmatic clinical trials are obligated to design their methods to deliberately monitor and respond to symptoms related to depression and suicidal ideation. This will offer increased protection for their participants and help clinical investigators meet their fiduciary duties. This article presents a review of this sociotechnical milieu that highlights, analyzes, and offers recommendations to address ethical considerations in the development of protocols, procedures, and monitoring activities related to suicidality in depressed patients in a pragmatic clinical trial.

Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity.

Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges-incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology-that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Introduction: Commercially available wearable devices for physical activity (PA) monitoring may provide objective data to facilitate assessment of NYHA class in patients with heart failure (HF). However, there is limited research exploring their ability to differentiate patients between NYHA classes. Objective: This study aims to assess the relationship between NYHA class and PA using a Fitbit wrist-based activity tracker (Charge 3) in patients with HF. Methods: We conducted a secondary analysis of baseline NYHA and PA data (daily step count and minutes of moderate-to-vigorous PA [MVPA]) collected by the iCardia4HF trial, which examined the feasibility and preliminary efficacy of a mHealth intervention for HF self-care. NYHA class was determined by a trained research assistant at the baseline visit, using standardized criteria. Daily steps and MVPA minutes were measured using Fitbit data from the first 7 days after the baseline visit. To be included in the analysis, patients had to have ≥4 days with ≥600 minutes of Fitbit wear-time/day. Results: We analyzed data from 78 participants (mean age=55.2 years [SD 10.6]; female=43.6%; Black/African American= 79.5%; mean BMI=35.6 kg/m2 [SD 8.7]; HFrEF=65.4%). The Kruskal-Wallis test demonstrated significant differences in the distribution of daily steps (p=.021) and MVPA (p=.023) across NYHA classes. Pairwise comparisons showed that patients in NYHA Class III had significantly lower daily steps and MVPA minutes than patients in Class I. There were also important differences in daily steps and MVPA between patients in NYHA Class III and II, and Class II and I, but these did not reach statistical significance. Conclusions: There was a general decrease in steps and MVPA duration with increasing NYHA class. Wrist-worn activity trackers represent a promising method for estimating NYHA class and assessing HF severity and progression through continuous PA monitoring, but larger studies are needed for validation.

Background Heart failure (HF) is a highly prevalent chronic condition that places a substantial burden on patients, families, and health care systems worldwide. Recent advances in mobile health (mHealth) technologies offer great opportunities for supporting many aspects of HF self-care. There is a need to better understand patients’ adoption of and interest in using mHealth for self-monitoring and management of HF symptoms. Objective The purpose of this study is to assess smartphone ownership and patient attitudes toward using mHealth technologies for HF self-care in a predominantly minority population in an urban clinical setting. Methods We conducted a cross-sectional survey of adult outpatients (aged ≥18 years) at an academic outpatient HF clinic in the Midwest. The survey comprised 34 questions assessing patient demographics, ownership of smartphones and other mHealth devices, frequently used smartphone features, use of mHealth apps, and interest in using mHealth technologies for vital sign and HF symptom self-monitoring and management. Results A total of 144 patients were approached, of which 100 (69.4%) participated in the study (63/100, 63% women). The participants had a mean age of 61.3 (SD 12.25) years and were predominantly Black or African American (61/100, 61%) and Hispanic or Latino (18/100, 18%). Almost all participants (93/100, 93%) owned a cell phone. The share of patients who owned a smartphone was 68% (68/100). Racial and ethnic minorities that identified as Black or African American or Hispanic or Latino reported higher smartphone ownership rates compared with White patients with HF (45/61, 74% Black or African American and 11/18, 61% Hispanic or Latino vs 9/17, 53% White). There was a moderate and statistically significant association between smartphone ownership and age (Cramér V [ΦC]=0.35; P<.001), education (ΦC=0.29; P=.001), and employment status (ΦC=0.3; P=.01). The most common smartphone features used by the participants were SMS text messaging (51/68, 75%), internet browsing (43/68, 63%), and mobile apps (41/68, 60%). The use of mHealth apps and wearable activity trackers (eg, Fitbits) for self-monitoring of HF-related parameters was low (15/68, 22% and 15/100, 15%, respectively). The most popular HF-related self-care measures participants would like to monitor using mHealth technologies were physical activity (46/68, 68%), blood pressure (44/68, 65%), and medication use (40/68, 59%). Conclusions Most patients with HF have smartphones and are interested in using commercial mHealth apps and connected health devices to self-monitor their condition. Thus, there is a great opportunity to capitalize on the high smartphone ownership among racial and ethnic minority patients to increase reach and enhance HF self-management through mHealth interventions.

Background: Heart failure (HF) is a complex and serious condition associated with substantial morbidity, mortality, and health care costs. We conducted a systematic review and meta-analysis to evaluate the effects of mobile health (mHealth) interventions compared with usual care in patients with HF. Methods: We searched MEDLINE, CENTRAL, CINAHL, and EMBASE databases to identify eligible randomized controlled trials (RCTs) of mHealth interventions. Primary outcomes included: all-cause mortality, cardiovascular mortality, HF-related hospitalizations, and all-cause hospitalizations. Meta-analyses using a random effects model were performed for all outcomes. Risk of bias and quality of evidence were evaluated using the Cochrane Tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Results: Sixteen RCTs involving 4389 patients were included. Compared with usual care, mHealth interventions reduced the risk of all-cause mortality (risk ratio [RR], 0.80; 95% confidence interval [CI], 0.65-0.97; absolute risk reduction [ARR], 2.1%; high-quality evidence), cardiovascular mortality (RR, 0.70; 95% CI, 0.53-0.91; ARR, 2.9%; high-quality evidence), and HF hospitalizations (RR, 0.77; 95% CI, 0.67-0.88; ARR, 5%; high-quality evidence), but had no effect on all cause hospitalizations. Results were driven by mHealth interventions with remote monitoring and clinical feedback, which were associated with larger reductions than stand-alone mHealth interventions. However, subgroup differences were not statistically significant. Conclusions: mHealth interventions with remote monitoring and clinical feedback reduce mortality and HF-related hospitalizations, but might not reduce all-cause hospitalizations in patients with HF. Additional studies are needed to determine the efficacy of stand-alone mHealth interventions as well as active features of mHealth that contribute to efficacy.

This randomized controlled trial aims to determine the feasibility and preliminary efficacy of a patient-centered, mobile health technology intervention (iCardia4HF) in patients with chronic Heart Failure (HF). Participants (n = 92) are recruited and randomized 1:1 to the intervention or control group. The intervention group receives a commercial HF self-care app (Heart Failure Storylines), three connected health devices that interface with the app (Withings weight scale and blood pressure monitor, and Fitbit activity tracker), and a program of individually tailored text-messages targeting health beliefs, self-care self-efficacy, HF-knowledge, and physical activity. The control group receives the same connected health devices, but without the HF self-care app and text messages. Follow-up assessments occur at 30 days and 12 weeks. The main outcome of interest is adherence to HF self-care assessed objectively through time-stamped data from the electronic devices and also via patient selfreports. Primary measures of HF self-care include medication adherence and adherence to daily weight monitoring. Secondary measures of HF self-care include adherence to daily self-monitoring of HF symptoms and blood pressure, adherence to low-sodium diet, and engagement in physical activity. Self-reported HF self-care and health-related quality of life are assessed with the Self-care Heart Failure Index and the Kansas City Cardiomyopathy Questionnaire, respectively. Hospitalizations and emergency room visits are tracked in both groups over 12 weeks as part of our safety protocol. This study represents an important step in testing a scalable mHealth solution that has the potential to bring about a new paradigm in self-management of HF.

Introduction Self-efficacy and health beliefs are important modifiable factors influencing adherence to self-care behaviors in patients with heart failure (HF). Text messaging (TM) is a low-cost and scalable solution for delivering behavior change intervention and improving patient outcomes, yet it is largely unexplored in patients with HF. Hypotheses We developed a tailored TM intervention and conducted a pilot randomized control trial (RCT) to assess feasibility and preliminary efficacy in urban-dwelling patients with HF over 60 days. We hypothesized that patients receiving TM would report higher HF self-care efficacy, as well as fewer barriers and more benefits to medication, low-sodium, and self-monitoring compliance compared with patients receiving usual care. Methods We translated a validated, theory-based program of tailored Heart Messages into a Short Message Service TM intervention, and further enhanced its content using stakeholder input. We conducted a pilot RCT with two arms (TM intervention vs usual care). Eligibility criteria were medical diagnosis of HF (Stage C), access to a cell phone, and ability to read and speak English. Self-efficacy was assessed with the Self-Care Heart Failure Index (self-confidence subscale). Health Beliefs (perceived benefits and barriers) were assessed with the Beliefs about Medication, Diet and Self-monitoring Compliance Scales. Participants in the TM group received 3 individually tailored TM per week for 60 days based on their responses to the Health Belief scales at baseline and 30 days. Results Twenty-five patients (mean age 56±8.3, female=11, African American=23) were enrolled and completed the study (TM=11, control=14). Baseline characteristics were similar between groups. Although not reaching statistical significance, participants in the TM group reported better self-efficacy (mean difference (MD) 10.0, 95% Confidence Interval (CI): -2.21 to 22.21; Cohen's d=0.66, p=0.166), and more perceived benefits with medication (MD 1.88, 95% CI: -0.53 to 4.29; Cohen's d=0.64, p=0.15) and low-sodium diet compliance (MD 2.35, 95% CI: -1.20 to 5.90; Cohen's d=0.54, p=0.105). TM significantly improved perceived benefits of HF self-monitoring (MD 3.48, 95% CI: 0.40 to 6.56; Cohen's d=0.86, p=0.047) and trended to reduce perceived barriers (MD -1.32, 95% CI: -6.03 to 3.39; Cohen's d=-0.24, p=0.473). Compared with usual care, TM did not reduce barriers of medication (MD 0.64 95% CI: -2.46 to 3.75; Cohen's d=0.18, p=0.977) or low-sodium diet compliance (MD 1.37 95% CI: -0.56 to 3.30; Cohen's d=0.58, p=0.166). Satisfaction with the TM intervention was high (2.94±0.07; Low=1, High=3). Conclusions The TM program was feasible to deliver and was associated with beneficial trends. It is now ready for testing in intervention studies with larger sample sizes.