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

Abstract

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.

Keywords: Critical value reporting; Decision support; Health self-monitoring; Lung transplantation; Mobile health technology; Patient compliance.

Conflict of interest statement

Conflicts of interest: All authors declare that they have no conflicts of interest in the research.

Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Figures

Fig. 1

Exploratory research model 1: following technology decision support for reporting recorded critical values during health self-monitoring in lung transplant recipients (use of technology as a mediator).

Fig. 2

Exploratory research model 2: following technology decision support for reporting recorded critical values during health self-monitoring in lung transplant recipients (use of technology as a moderator).