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.

Mobile Health in Solid Organ Transplant: The Time Is Now

“…The combination of high willingness and satisfaction rates provides a good prognosis for further studies. These results, however, may be limited by the low response rates in some studies and the apparent discordance in reported willingness and sustained utilization over time (Table ) .…”

“…Another well‐designed mHealth intervention, the Pocket PATH app, was able to demonstrate improved self‐monitoring (OR 5.11, 95% CI 2.95–8.87; p < 0.001) and adherence (OR 1.64, 95% CI 1.01–2.66; p = 0.046) in a large prospective randomized trial . Another study was able to demonstrate a 90% response rate to critical messages with the Pocket PATH, although there was no control group . Demonstrating the effect of multiple layers of mHealth interventions, Reese et al demonstrated significantly improved adherence with both customized reminders (78%) and customized reminders with notification to providers for adherence <90% (88%) compared with usual care (55%, p < 0.001 for each intervention group compared with controls) .…”

eHealth in transplantation

“…Chronically ill patients, however, are expected to use the app for years on a regular basis. It has been shown that gender, income, education, and regular use of technology, as well as the length of stay in a hospital predict the adherence to an eHealth tool [32]. Other studies confirm these findings with varying details [33–36].…”

Perspectives on implementing mobile health technology for living kidney donor follow‐up: In‐depth interviews with transplant providers

“…Only 43% of centers met reporting thresholds for LKDs who donated in 2013, indicating that novel methods for collecting LKD follow-up data are needed. [10][11][12][13][14][15][16][17][18][19][20] However, mHealth technology has not yet been explored as a method for improving LKD follow-up, despite the well-documented challenges of collecting follow-up data. 9 Within the transplant community, mHealth technologies have been used to address numerous challenges in the field, including educating transplant recipients and candidates, improving adherence and self-management behaviors of recipients, helping patients on the waitlist find a living donor, and aiding in the screening and evaluation process of LKD candidates.…”

“…9 Within the transplant community, mHealth technologies have been used to address numerous challenges in the field, including educating transplant recipients and candidates, improving adherence and self-management behaviors of recipients, helping patients on the waitlist find a living donor, and aiding in the screening and evaluation process of LKD candidates. [10][11][12][13][14][15][16][17][18][19][20] However, mHealth technology has not yet been explored as a method for improving LKD follow-up, despite the well-documented challenges of collecting follow-up data. Improved follow-up data collection is needed to improve understanding of long-term risks associated with live donor nephrectomy, as existing analyses are limited by incomplete LKD follow-up data.…”