PDF(Pubmed)
Abstract：
UNASSIGNED: Hypertension, a key modifiable risk factor for cardiovascular disease, is more prevalent among Black and low-income individuals. To address this health disparity, leveraging safety-net emergency departments for scalable mobile health (mHealth) interventions, specifically using text messaging for self-measured blood pressure (SMBP) monitoring, presents a promising strategy. This study investigates patterns of engagement, associated factors, and the impact of engagement on lowering blood pressure (BP) in an underserved population.
UNASSIGNED: We aimed to identify patterns of engagement with prompted SMBP monitoring with feedback, factors associated with engagement, and the association of engagement with lowered BP.
UNASSIGNED: This is a secondary analysis of data from Reach Out, an mHealth, factorial trial among 488 hypertensive patients recruited from a safety-net emergency department in Flint, Michigan. Reach Out participants were randomized to weekly or daily text message prompts to measure their BP and text in their responses. Engagement was defined as a BP response to the prompt. The k-means clustering algorithm and visualization were used to determine the pattern of SMBP engagement by SMBP prompt frequency-weekly or daily. BP was remotely measured at 12 months. For each prompt frequency group, logistic regression models were used to assess the univariate association of demographics, access to care, and comorbidities with high engagement. We then used linear mixed-effects models to explore the association between engagement and systolic BP at 12 months, estimated using average marginal effects.
UNASSIGNED: For both SMBP prompt groups, the optimal number of engagement clusters was 2, which we defined as high and low engagement. Of the 241 weekly participants, 189 (78.4%) were low (response rate: mean 20%, SD 23.4) engagers, and 52 (21.6%) were high (response rate: mean 86%, SD 14.7) engagers. Of the 247 daily participants, 221 (89.5%) were low engagers (response rate: mean 9%, SD 12.2), and 26 (10.5%) were high (response rate: mean 67%, SD 8.7) engagers. Among weekly participants, those who were older (>65 years of age), attended some college (vs no college), married or lived with someone, had Medicare (vs Medicaid), were under the care of a primary care doctor, and took antihypertensive medication in the last 6 months had higher odds of high engagement. Participants who lacked transportation to appointments had lower odds of high engagement. In both prompt frequency groups, participants who were high engagers had a greater decline in BP compared to low engagers.
UNASSIGNED: Participants randomized to weekly SMBP monitoring prompts responded more frequently overall and were more likely to be classed as high engagers compared to participants who received daily prompts. High engagement was associated with a larger decrease in BP. New strategies to encourage engagement are needed for participants with lower access to care.
UNASSIGNED: We aimed to identify patterns of engagement with prompted SMBP monitoring with feedback, factors associated with engagement, and the association of engagement with lowered BP.
UNASSIGNED: This is a secondary analysis of data from Reach Out, an mHealth, factorial trial among 488 hypertensive patients recruited from a safety-net emergency department in Flint, Michigan. Reach Out participants were randomized to weekly or daily text message prompts to measure their BP and text in their responses. Engagement was defined as a BP response to the prompt. The k-means clustering algorithm and visualization were used to determine the pattern of SMBP engagement by SMBP prompt frequency-weekly or daily. BP was remotely measured at 12 months. For each prompt frequency group, logistic regression models were used to assess the univariate association of demographics, access to care, and comorbidities with high engagement. We then used linear mixed-effects models to explore the association between engagement and systolic BP at 12 months, estimated using average marginal effects.
UNASSIGNED: For both SMBP prompt groups, the optimal number of engagement clusters was 2, which we defined as high and low engagement. Of the 241 weekly participants, 189 (78.4%) were low (response rate: mean 20%, SD 23.4) engagers, and 52 (21.6%) were high (response rate: mean 86%, SD 14.7) engagers. Of the 247 daily participants, 221 (89.5%) were low engagers (response rate: mean 9%, SD 12.2), and 26 (10.5%) were high (response rate: mean 67%, SD 8.7) engagers. Among weekly participants, those who were older (>65 years of age), attended some college (vs no college), married or lived with someone, had Medicare (vs Medicaid), were under the care of a primary care doctor, and took antihypertensive medication in the last 6 months had higher odds of high engagement. Participants who lacked transportation to appointments had lower odds of high engagement. In both prompt frequency groups, participants who were high engagers had a greater decline in BP compared to low engagers.
UNASSIGNED: Participants randomized to weekly SMBP monitoring prompts responded more frequently overall and were more likely to be classed as high engagers compared to participants who received daily prompts. High engagement was associated with a larger decrease in BP. New strategies to encourage engagement are needed for participants with lower access to care.
摘要：
■高血压，心血管疾病的一个关键的可改变的危险因素，在黑人和低收入人群中更为普遍。为了解决这种健康差距，利用安全网应急部门进行可扩展的移动医疗(mHealth)干预，特别是使用短信进行自测血压(SMBP)监测，提出了一个有前途的战略。这项研究调查了参与模式，相关因素，以及参与对服务不足人群降低血压（BP）的影响。
■我们的目标是通过反馈来识别与提示SMBP监控的互动模式，与参与相关的因素，以及参与与降低BP的关联。
■这是对ReachOut数据的二次分析，mHealth,从弗林特安全网急诊科招募的488名高血压患者的析因试验，密歇根。ReachOut参与者被随机分配到每周或每天的短信提示中，以测量他们的BP和回复中的文本。参与定义为BP对提示的反应。使用k均值聚类算法和可视化方法通过每周或每天的SMBP提示频率来确定SMBP参与的模式。在12个月时远程测量BP。对于每个提示频率组，逻辑回归模型用于评估人口统计学的单变量关联，获得护理，和高参与度的合并症。然后，我们使用线性混合效应模型来探索12个月时参与度与收缩压之间的关系，使用平均边际效应估计。
■对于两个SMBP提示组，最佳参与集群数量为2，我们将其定义为高参与和低参与。在241名每周参与者中，189（78.4%）低（应答率：平均20%，SD23.4)接合器，52例（21.6%）高（应答率：平均86%，SD14.7）接合器。在247名每日参与者中，221人(89.5%)是低接班人(应答率：平均9%，SD12.2)，和26（10.5％）高（响应率：平均67％，SD8.7)接合器。在每周参与者中，那些年龄较大（>65岁）的人，上过一些大学(vs没有大学)，已婚或与某人同居，有医疗保险(vs医疗补助)，在初级保健医生的照顾下，并且在过去6个月内服用抗高血压药物的参与几率较高.缺乏预约交通的参与者参与的可能性较低。在两个提示频率组中，与低参与者相比,高参与者的血压下降幅度更大.
■与接受每日提示的参与者相比，随机接受每周SMBP监测提示的参与者总体反应频率更高，并且更有可能被归类为较高的参与者。高参与度与血压下降幅度较大相关。对于获得护理机会较低的参与者，需要采取新的策略来鼓励参与。
■我们的目标是通过反馈来识别与提示SMBP监控的互动模式，与参与相关的因素，以及参与与降低BP的关联。
■这是对ReachOut数据的二次分析，mHealth,从弗林特安全网急诊科招募的488名高血压患者的析因试验，密歇根。ReachOut参与者被随机分配到每周或每天的短信提示中，以测量他们的BP和回复中的文本。参与定义为BP对提示的反应。使用k均值聚类算法和可视化方法通过每周或每天的SMBP提示频率来确定SMBP参与的模式。在12个月时远程测量BP。对于每个提示频率组，逻辑回归模型用于评估人口统计学的单变量关联，获得护理，和高参与度的合并症。然后，我们使用线性混合效应模型来探索12个月时参与度与收缩压之间的关系，使用平均边际效应估计。
■对于两个SMBP提示组，最佳参与集群数量为2，我们将其定义为高参与和低参与。在241名每周参与者中，189（78.4%）低（应答率：平均20%，SD23.4)接合器，52例（21.6%）高（应答率：平均86%，SD14.7）接合器。在247名每日参与者中，221人(89.5%)是低接班人(应答率：平均9%，SD12.2)，和26（10.5％）高（响应率：平均67％，SD8.7)接合器。在每周参与者中，那些年龄较大（>65岁）的人，上过一些大学(vs没有大学)，已婚或与某人同居，有医疗保险(vs医疗补助)，在初级保健医生的照顾下，并且在过去6个月内服用抗高血压药物的参与几率较高.缺乏预约交通的参与者参与的可能性较低。在两个提示频率组中，与低参与者相比,高参与者的血压下降幅度更大.
■与接受每日提示的参与者相比，随机接受每周SMBP监测提示的参与者总体反应频率更高，并且更有可能被归类为较高的参与者。高参与度与血压下降幅度较大相关。对于获得护理机会较低的参与者，需要采取新的策略来鼓励参与。
