Feasibility and Short‐Term Effects of a Multi‐Component Emergency Department Blood Pressure Intervention: A Pilot Randomized Trial

Spieker, Andrew J.; Nelson, Lyndsay A.; Rothman, Russell L.; Roumie, Christianne L.; Kripalani, Sunil; Coco, Joseph; Fabbri, Daniel; Levy, Phillip D.; Collins, Sean P.; Wang, Thomas J.; Liu, Dandan; McNaughton, Candace D.

doi:10.1161/jaha.121.024339

Cited by 3 publications

(3 citation statements)

References 58 publications

(55 reference statements)

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“…Our results help elucidate how truly complex the nature of engagement is. Although our past work and that of others have demonstrated the importance of behaviorally interacting with mHealth interventions [ 9 , 31 , 40 , 41 ], this measure represents one piece of a larger puzzle. Engagement may be best conceptualized as including both a behavioral dimension and a cognitive dimension.…”

Section: Discussionmentioning

confidence: 99%

The Goldilocks Dilemma on Balancing User Response and Reflection in mHealth Interventions: Observational Study

Nelson,

Spieker,

LeStourgeon

et al. 2024

JMIR Mhealth Uhealth

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Background Mobile health (mHealth) has the potential to radically improve health behaviors and quality of life; however, there are still key gaps in understanding how to optimize mHealth engagement. Most engagement research reports only on system use without consideration of whether the user is reflecting on the content cognitively. Although interactions with mHealth are critical, cognitive investment may also be important for meaningful behavior change. Notably, content that is designed to request too much reflection could result in users’ disengagement. Understanding how to strike the balance between response burden and reflection burden has critical implications for achieving effective engagement to impact intended outcomes. Objective In this observational study, we sought to understand the interplay between response burden and reflection burden and how they impact mHealth engagement. Specifically, we explored how varying the response and reflection burdens of mHealth content would impact users’ text message response rates in an mHealth intervention. Methods We recruited support persons of people with diabetes for a randomized controlled trial that evaluated an mHealth intervention for diabetes management. Support person participants assigned to the intervention (n=148) completed a survey and received text messages for 9 months. During the 2-year randomized controlled trial, we sent 4 versions of a weekly, two-way text message that varied in both reflection burden (level of cognitive reflection requested relative to that of other messages) and response burden (level of information requested for the response relative to that of other messages). We quantified engagement by using participant-level response rates. We compared the odds of responding to each text and used Poisson regression to estimate associations between participant characteristics and response rates. Results The texts requesting the most reflection had the lowest response rates regardless of response burden (high reflection and low response burdens: median 10%, IQR 0%-40%; high reflection and high response burdens: median 23%, IQR 0%-51%). The response rate was highest for the text requesting the least reflection (low reflection and low response burdens: median 90%, IQR 61%-100%) yet still relatively high for the text requesting medium reflection (medium reflection and low response burdens: median 75%, IQR 38%-96%). Lower odds of responding were associated with higher reflection burden (P<.001). Younger participants and participants who had a lower socioeconomic status had lower response rates to texts with more reflection burden, relative to those of their counterparts (all P values were <.05). Conclusions As reflection burden increased, engagement decreased, and we found more disparities in engagement across participants’ characteristics. Content encouraging moderate levels of reflection may be ideal for achieving both cognitive investment and system use. Our findings provide insights into mHealth design and the optimization of both engagement and effectiveness.

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Section: Discussionmentioning

confidence: 99%

The Goldilocks Dilemma on Balancing User Response and Reflection in mHealth Interventions: Observational Study

Nelson,

Spieker,

LeStourgeon

et al. 2024

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

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“…The higher intensity mHealth components were not associated with a greater decline in BP. Hypotheses for the lack of BP difference among the mHealth components include (1) variable engagement with prompted SMBP monitoring, 15% of participants did not text any BPs; (2) access to care barriers outside of scheduling and transportation were not well addressed 28 as evidence by a greater reduction in BP among those taking antihypertensives, (3) no true difference between components. Furthermore, BP reductions were less among Black participants than non-Black participants, requiring further study.…”

Section: Discussionmentioning

confidence: 99%

“…The successful completion of this trial and other ED-based hypertension trials suggests that further efforts to engage the ED in chronic disease management of patients at high risk for cardiovascular disease are warranted. 28,31 Second, Reach Out SMBP monitoring relied on readily scalable text messaging rather than smartphone application-based SMBP monitoring. Notably, a recent study has been shown to have no additional benefit of application-based SMBP monitoring over standard monitoring.…”

Section: Discussionmentioning

confidence: 99%

Reach Out Emergency Department: A Randomized Factorial Trial to Determine the Optimal Mobile Health Components to Reduce Blood Pressure

Skolarus

Dinh

Kidwell

et al. 2023

Circ: Cardiovascular Quality and Outcomes

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Background: Mobile health (mHealth) strategies initiated in safety-net Emergency Departments may be one approach to address the US hypertension epidemic, but the optimal mHealth components or dose are unknown. Methods: Reach Out is an mHealth, health theory-based, 2×2×2 factorial trial among hypertensive patients evaluated in a safety-net Emergency Department in Flint, Michigan. Reach Out consisted of 3 mHealth components, each with 2 doses: (1) healthy behavior text messaging (yes versus no), (2) prompted self-measured blood pressure (BP) monitoring and feedback (weekly versus daily), and (3) facilitated primary care provider appointment scheduling and transportation (yes versus no). The primary outcome was a change in systolic BP from baseline to 12 months. In a complete case analysis, we fit a linear regression model and accounted for age, sex, race, and prior BP medications to explore the association between systolic BP and each mHealth component. Results: Among 488 randomized participants, 211 (43%) completed follow-up. Mean age was 45.5 years, 61% were women, 54% were Black people, 22% did not have a primary care doctor, 21% lacked transportation, and 51% were not taking antihypertensive medications. Overall, systolic BP declined after 6 months (−9.2 mm Hg [95% CI, −12.2 to −6.3]) and 12 months (−6.6 mm Hg, −9.3 to −3.8), without a difference across the 8 treatment arms. The higher dose of mHealth components were not associated with a greater change in systolic BP; healthy behavior text messages (point estimate, mmHG=−0.5 [95% CI, −6.0 to 5]; P =0.86), daily self-measured BP monitoring (point estimate, mmHG=1.9 [95% CI, −3.7 to 7.5]; P =0.50), and facilitated primary care provider scheduling and transportation (point estimate, mmHG=0 [95% CI, −5.5 to 5.6]; P =0.99). Conclusions: Among participants with elevated BP recruited from an urban safety-net Emergency Department, BP declined over the 12-month intervention period. There was no difference in change in systolic BP among the 3 mHealth components. Reach Out demonstrated the feasibility of reaching medically underserved people with high BP cared for at a safety-net Emergency Departments, yet the efficacy of the Reach Out mHealth intervention components requires further study. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03422718.

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The Goldilocks Dilemma in mHealth Interventions: Balancing User Response and Reflection (Preprint)

Nelson¹,

Spieker²,

LeStourgeon³

et al. 2023

Preprint

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BACKGROUND mHealth has the potential to radically improve health behaviors and quality of life; however, there are still key gaps in understanding how to optimize mHealth engagement. Most engagement research reports only on system use without consideration of whether the user is reflecting on the content cognitively. Although interactions with mHealth are critical, cognitive investment is also important for meaningful behavior change. Importantly, content designed to request too much reflection, may lead users to disengage. Understanding how to strike the balance between response and reflection burden has critical implications for achieving effective engagement to impact intended outcomes. OBJECTIVE We sought to understand the interplay between response burden and reflection burden for impacting mHealth engagement. Specifically, we examined how varying the response and reflection burden of mHealth content would impact users’ text message response rate as part of an mHealth intervention. METHODS We recruited support persons of people with diabetes for a randomized controlled trial (RCT) evaluating an mHealth intervention for diabetes management. Support person participants assigned to the intervention (N=148) completed a survey and received text messages for 9 months. During the two-year RCT, we sent four versions of a weekly interactive text message that varied in reflection and response burden. We quantified engagement using response rate. We compared the odds of responding to each text and estimated associations between participant characteristics and response rate. RESULTS Results: The texts requesting the most reflection had the lowest response rates (median 10-23%), regardless of response burden. Response rate was highest for the text requesting the least reflection (90%), yet still relatively high for the text with medium reflection (75%). Lower odds of responding were associated with higher reflection burden (P<.001). Participants who were younger or had lower socioeconomic status had lower response rates to texts with more reflection burden relative to their counterparts (P<.01 for each). CONCLUSIONS Conclusions: As reflection burden increased, engagement decreased, and we found more disparities in engagement by participants’ characteristics. Content encouraging moderate levels of reflection may be ideal for achieving both cognitive investment and system use. Our findings provide insights into mHealth design and optimizing both engagement and effectiveness. CLINICALTRIAL NCT04347291

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Feasibility and Short‐Term Effects of a Multi‐Component Emergency Department Blood Pressure Intervention: A Pilot Randomized Trial

Cited by 3 publications

References 58 publications

The Goldilocks Dilemma on Balancing User Response and Reflection in mHealth Interventions: Observational Study

The Goldilocks Dilemma on Balancing User Response and Reflection in mHealth Interventions: Observational Study

Reach Out Emergency Department: A Randomized Factorial Trial to Determine the Optimal Mobile Health Components to Reduce Blood Pressure

The Goldilocks Dilemma in mHealth Interventions: Balancing User Response and Reflection (Preprint)

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