ArticleInternational journal of medical informatics2016
Using mobile health technology to deliver decision support for self-monitoring after lung transplantation.
Article in International journal of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00818025 (Phase III Trial of Pocket PATH), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Phase III Trial of Pocket PATH: A Computerized Intervention to Promote Self-Care
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Pooled it
- Adult provider role in transition of care for young adult pediatric recipients of liver transplant: An expert position statement.Hepatology communications · 2024Review
- MyKidneyCoach, Patient Activation, and Clinical Outcomes in Diverse Kidney Transplant Recipients: A Randomized Control Pilot Trial.Transplantation direct · 2023Article
- Review and Evaluation of mHealth Apps in Solid Organ Transplantation: Past, Present, and Future.Transplantation direct · 2022Review
- Effectiveness of mobile health-based self-management application for posttransplant cares: A systematic review.Health science reports · 2021Article
- A Mobile App to Support Clinical Diagnosis of Upper Respiratory Problems (eHealthResp): Co-Design Approach.Journal of medical Internet research · 2021Article
- Perspectives on implementing mobile health technology for living kidney donor follow-up: In-depth interviews with transplant providers.Clinical transplantation · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundLung 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.
objectivesTo 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.
methodsA 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.
resultsOf 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.