Data Safety and Monitoring for Research Involving Remote Health Monitoring

Kovach, Karen; Aubrecht, Jill; Dew, Mary Amanda; Myers, Brad A.; Dabbs, Annette DeVito

doi:10.1089/tmj.2010.0219

Cited by 11 publications

(11 citation statements)

References 9 publications

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“…They were instructed to use Pocket PATH to set medication-taking and appointment reminders, record values for the health indicators they were required to self-monitor and follow Pocket PATH’s decision-support messages. These automatic messages instructed recipients to contact their transplant coordinator if they recorded health indicator values that fell outside the pre-established normal range and therefore required clinical attention (e.g., body temperature >99°F, pulse <60 or >120 beats/min) (22). …”

Section: Methodsmentioning

confidence: 99%

Long-Term Follow-up of a Randomized Controlled Trial Evaluating a Mobile Health Intervention for Self-Management in Lung Transplant Recipients

Rosenberger

Dabbs

DiMartini

et al. 2017

American Journal of Transplantation

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Mobile health (mHealth) interventions may help transplant recipients follow their complex medical regimen. Pocket Personal Assistant for Tracking Health (Pocket PATH) is one such intervention, tailored for lung transplant recipients. A randomized controlled trial showed Pocket PATH’s superiority to usual care for promoting the self-management behaviors of adherence, self-monitoring and communication with clinicians during post-transplant year 1. Its long-term impact was unknown. Here, we examine associations between Pocket PATH exposure during year 1 and longer-term clinical outcomes—mortality and bronchiolitis obliterans syndrome (BOS)—among 182 recipients who survived the original trial. Cox regression assessed whether (a) original group assignment and (b) performance of self-management behaviors during year 1 predicted time to outcomes. Median follow-up was 5.7 years post-transplant (range: 4.2–7.2). Pocket PATH exposure had no direct effect on outcomes (p’s>0.05). Self-monitoring was associated with reduced mortality risk (HR, 0.45; 95%CI, 0.22–0.91; p=0.027), and reporting abnormal health indicators to clinicians was associated with reduced risks of mortality (HR, 0.15; 95%CI, 0.04–0.65; p=0.011) and BOS (HR, 0.27; 95%CI, 0.08–0.86; p=0.026), regardless of intervention group assignment. Although Pocket PATH did not have a direct impact on long-term outcomes, early improvements in self-management facilitated by Pocket PATH may be associated with long-term clinical benefit.

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Section: Methodsmentioning

confidence: 99%

Long-Term Follow-up of a Randomized Controlled Trial Evaluating a Mobile Health Intervention for Self-Management in Lung Transplant Recipients

Rosenberger

Dabbs

DiMartini

et al. 2017

American Journal of Transplantation

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“…They were instructed to enter their spirometry data, vital signs, and symptoms into the daily checklist of Pocket PATH. The application was programmed to generate automatic feedback messages via the Smartphone prompting the LTR to take action, such as double checking the values, and reporting any of the following critical values: temperature >101° Fahrenheit (or 38.3° Celsius), blood pressure of systolic >160 or <88 or diastolic >100 mmHg, or pulse <60 or >120 beats/min [33]. …”

Section: Methodsmentioning

confidence: 99%

“…Self-monitoring data were automatically uploaded to a secure server in the research site. Critical feedback messages were assessed and compared with cumulative data by project staff every 72 h [33]. Participants were informed that the transplant team was responsible for managing their clinical care [32].…”

Section: Methodsmentioning

confidence: 99%

“…According to an a priori data monitoring algorithm [33], confirmation about whether the LTR responded appropriately to a feedback message by reporting the critical value to the clinician was obtained by reviewing the transplant coordinators' progress notes. Appropriate responses were also considered when critical values were found to be entered in error, values returned to acceptable levels, or clinicians were already aware of values from other ways (such as clinical visits) [33]. The degree of following decision support was calculated as a percentage by dividing the number of feedback messages to which the LTR appropriately responded by the total number of feedback messages generated over the 12 months, multiplied by 100.…”

Section: Methodsmentioning

confidence: 99%

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Using mobile health technology to deliver decision support for self-monitoring after lung transplantation

Jiang

Sereika

Dabbs

et al. 2016

International Journal of Medical Informatics

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Background Lung transplant recipients (LTR) experience problems recognizing and reporting critical condition changes during their daily health self-monitoring. Pocket PATH®, a mobile health application, was designed to provide automatic feedback messages to LTR to guide decisions for detecting and reporting critical values of health indicators. Objectives To examine the degree to which LTR followed decision support messages to report recorded critical values, and to explore predictors of appropriately following technology decision support by reporting critical values during the first year after transplantation. Methods A cross-sectional correlational study was conducted to analyze existing data from 96 LTR who used the Pocket PATH for daily health self-monitoring. When a critical value is entered, the device automatically generated a feedback message to guide LTR about when and what to report to their transplant coordinators. Their socio-demographics and clinical characteristics were obtained before discharge. Their use of Pocket PATH for health self-monitoring during 12 months was categorized as low (≤25% of days), moderate (>25% to ≤75% of days), and high (>75% of days) use. Following technology decision support was defined by the total number of critical feedback messages appropriately handled divided by the total number of critical feedback messages generated. This variable was dichotomized by whether or not all (100%) feedback messages were appropriately followed. Binary logistic regression was used to explore predictors of appropriately following decision support. Results Of the 96 participants, 53 had at least 1 critical feedback message generated during 12 months. Of these 53 participants, the average message response rate was 90% and 33 (62%) followed 100% decision support. LTR who moderately used Pocket PATH (n = 23) were less likely to follow technology decision support than the high (odds ratio [OR] = 0.11, p = 0.02) and low (OR = 0.04, p = 0.02) use groups. The odds of following decision support were reduced in LTR whose income met basic needs (OR = 0.01, p = 0.01) or who had longer hospital stays (OR = 0.94, p = 0.004). A significant interaction was found between gender and past technology experience (OR = 0.21, p = 0.03), suggesting that with increased past technology experience, the odds of following decision support to report all critical values decreased in men but increased in women. Conclusions The majority of LTR responded appropriately to mobile technology-based decision support for reporting recorded critical values. Appropriately following technology decision support was associated with gender, income, experience with technology, length of hospital stay, and frequency of use of technology for self-monitoring. Clinicians should monitor LTR, who are at risk for poor reporting of recorded critical values, more vigilantly even when LTR are provided with mobile technology decision support.

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“…[6][7][8] Specifically, safety issues during HIT interventions are largely understudied, or at least not often directly discussed in published reports of large interventions. 9 This issue is particularly important to address, as HIT approaches to support self-care and remote monitoring outside of a clinical setting are projected to grow in coming years. Not only will patients need support to respond to potential safety events in a timely manner, but health systems leadership also need to understand these events as they design and disseminate such programs for diverse patient populations.…”

Section: Introductionmentioning

confidence: 99%

Safety Events during an Automated Telephone Self-Management Support Intervention

Lyles

Schillinger

López

et al. 2013

J Diabetes Sci Technol

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Data Safety and Monitoring for Research Involving Remote Health Monitoring

Cited by 11 publications

References 9 publications

Long-Term Follow-up of a Randomized Controlled Trial Evaluating a Mobile Health Intervention for Self-Management in Lung Transplant Recipients

Long-Term Follow-up of a Randomized Controlled Trial Evaluating a Mobile Health Intervention for Self-Management in Lung Transplant Recipients

Using mobile health technology to deliver decision support for self-monitoring after lung transplantation

Safety Events during an Automated Telephone Self-Management Support Intervention

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