Dataset Information

Mobile application for monitoring patients under home oxygen therapy: a protocol for a randomized controlled trial.

ABSTRACT:

Background

Mobile technologies have become capable of changing the paradigm of healthcare services. A clear example is that, nowadays, these technologies are an important instrument for data collection processes, epidemiologic surveillance, health promotion and disease prevention. Therefore, technological tools should be exploited to optimize the monitoring of patients with chronic diseases, including patients who require home oxygen therapy.

Purpose

The purpose of this study is to determine the efficacy of a mobile application in the clinical monitoring of patients under home oxygen therapy.

Methods

This is a randomized controlled trial includes subjects of 18 years or older diagnosed with Chronic Obstructive Pulmonary Disease (COPD) who are under home oxygen therapy. Subjects will be divided into two arms: the intervention group will include patients who will be monitored with a mobile application, and the control group will include patients monitored by conventional follow-up methods (periodic visits of a respiratory therapist). The following outcome variables will be considered to measure the effect of the intervention: identification of dyspnea self-management, number of acute exacerbations associated with oxygen therapy, and the occurrence of oxygen supply underuse.

Discussion

This study is expected to assess the efficacy of a mobile application in the follow up of patients under home oxygen therapy. It will also determine whether the monitoring of a six-month intervention by a team comprising a physician, a nurse and respiratory therapists can decrease acute exacerbations, determine the most appropriate oxygen dose, and identify the underuse of oxygen systems and supplies.

Trial registration

The study has been registered at ClinicalTrials.gov ( NCT04820790 ; date of registration: March 29, 2021).

SUBMITTER: Naranjo-Rojas A

PROVIDER: S-EPMC8157712 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Women's College Hospital, Toronto, Canada, offers specialized ambulatory surgical procedures. Patients often travel great distances to undergo surgery. Most patients receiving ambulatory surgery have a low rate of postoperative events necessitating clinic visits. However, regular follow-up is still considered important in the early postoperative phase. Increasingly, telemedicine is used to overcome the distance patients must travel to receive specialized care. Telemedicine data suggest that mobile monitoring and follow-up care is valued by patients and can reduce costs to society. Women's College Hospital has used a mobile app (QoC Health Inc) to complement in-person postoperative follow-up care for breast reconstruction patients. Preliminary studies suggest that mobile app follow-up care is feasible, can avert in-person follow-up care, and is cost-effective from a societal and health care system perspective. OBJECTIVE:We hope to expand the use of mobile app follow-up care through its formal assessment in a randomized controlled trial. In postoperative ambulatory surgery patients at Women's College Hospital (WCH), can we avert in-person follow-up care through the use of mobile app follow-up care compared to conventional, in-person follow-up care in the first 30 days after surgery. METHODS:This will be a pragmatic, single-center, open, controlled, 2-arm parallel-group superiority randomized trial comparing mobile app and in-person follow-up care over the first month following surgery. The patient population will comprise all postoperative ambulatory surgery patients at WCH undergoing breast reconstruction. The intervention consists of a postoperative mobile app follow-up care using the quality of recovery-9 (QoR9) and a pain visual analog scale (VAS), surgery-specific questions, and surgical site photos submitted daily for the first 2 weeks and weekly for the following 2 weeks. The primary outcome is the total number of physician visits related to the surgery over the first 30-days postoperative. The secondary outcomes include (1) the total number of phone calls and emails to a health care professional related to surgery, (2) complication rate, (3) societal and health care system costs, and (4) patient satisfaction over the first 30 days postoperative. Permutated-block randomization will be conducted by blocks of 4-6 using the program ralloc in Stata. This is an open study due to the nature of the intervention. RESULTS:A sample of 72 (36 patients per group) will provide an E-test for count data with a power of 95% (P=.05) to detect a difference of 1 visit between groups, assuming a 10% drop out rate. Count variables will be analyzed using Poisson regression. Categorical variables will be tested using a chi-square test. Cost-effectiveness will be analyzed using net benefit regression. Outcomes will be assessed over the first 30 days following surgery. CONCLUSIONS:We hope to show that the use of a mobile app in follow-up care minimizes the need for in-person visits for postoperative patients. TRIAL REGISTRATION:Clinicaltrials.gov NCT02318953; https://clinicaltrials.gov/ct2/show/NCT02318953 (Archived by WebCite at http://www.webcitation.org/6Yifzdjph).

Project description:BACKGROUND:Nonadherence to immunosuppressive therapy after renal transplantation is associated with poor graft outcomes. We aimed to evaluate whether the use of the Adhere4U mobile medication manager application could improve adherence among renal transplant recipients ?1 year posttransplantation. Adhere4U can provide medication reminders, monitor medication use, and provide information on immunosuppressants. METHODS:We conducted a prospective randomized controlled study to compare the rate of nonadherence to index immunosuppressant (tacrolimus or cyclosporine) in a group using the Adhere4U app (mobile group) and in another group receiving conventional care (control group). The primary outcome was the nonadherence rate, which was evaluated using an electronic medication event monitoring system during the 6-month intervention period. Our secondary outcome included self-reported adherence using the Basel Assessment of Adherence to Immunosuppressive Medication Scale (BAASIS) and the visual analog scale (VAS) based on a 4-week recall on days 28, 90, and 180. Longitudinal data of repeated measures of self-rated adherence were analyzed using generalized estimating equations (GEE) to compare the between-group difference in adherence change over time. RESULTS:Between November 2013 and May 2015, 138 renal transplant recipients were randomly allocated to the control (n = 67) or the mobile group (n = 71). The overall nonadherence rate over the 6-month study period by electronic monitoring was 63.6%, with no between-group difference [mobile group, 65.0% (n = 39/60); control group, 62.1% (n = 36/58); odds ratio 1.14; 95% confidence interval 0.53-2.40; p = 0.89]. Self-rated nonadherence assessed using the BAASIS and VAS at baseline was 53.7% and 51.5%, respectively. Although the self-rated nonadherence by BAASIS of the mobile group was lower than the control group throughout the study period, there was no between-group difference in the change of nonadherence over time (?2 = 2.82, df = 3, p = 0.42 by logistic GEE). There also was no significant between-group difference in the nonadherence by VAS (?2 = 1.71, df = 3, p = 0.63 by logistic GEE) over time. The main limitation of this study was the low rate of patient engagement with the app among the mobile group. The rate of app use was 47.6% (31/65) at 28 days, 33.9% (19/56) at 90 days, and 11.5% (6/52) at 180 days. CONCLUSIONS:The Adhere4U application did not improve adherence to immunosuppressive therapy. Our evidence is limited by the high rate of attrition. Further studies on strategies to facilitate patient engagement with mobile interventions are warranted.

Project description:BACKGROUND:Monitoring signs and symptoms in bipolar disorder (BP) is typically based on regular assessments from patient-clinician interactions. Mobile and wearable technology promises to make monitoring symptoms in BP easier, but little is known about how best to engage individuals with BP in monitoring symptoms. OBJECTIVE:The objective of this study was to provide the rationale and protocol for a randomized controlled trial that investigates engagement strategies for monitoring symptoms of BP, including the strategies of using activity trackers compared with self-reports and reviewing recorded symptoms weekly with an interviewer. METHODS:A total of 50 individuals with BP will be recruited from the Prechter Longitudinal Study of Bipolar Disorder at the University of Michigan to participate in a 6-week study. Participants will monitor their symptoms through an activity tracker (Fitbit Alta HR) and a mobile phone app designed for this study. In addition to monitoring symptoms, participants have a 50-50 chance of being assigned to an arm that reviews self-reports and activity information weekly. Statistical tests will be performed to test hypotheses that participants adhere to activity tracking significantly more than self-reporting, prefer activity tracking significantly more than self-reporting, and better adhere to both activity tracking and self-reporting when reviewing collected information weekly. RESULTS:Recruitment commenced in November 2017. The first group of participants began the study in January 2018. CONCLUSIONS:This study aims to establish strategies to engage individuals with BP in monitoring their symptoms with mobile and wearable technology. Better engagement strategies are expected to aid current efforts in bipolar research and clinical care, from the development of new mobile phone apps to providing the right intervention to the right individual at the right moment. TRIAL REGISTRATION:ClinicalTrials.gov NCT03358238; https://clinicaltrials.gov/ct2/show/NCT03358238 (Archived by WebCite at http://www.webcitation.org/6yebuNfz5). REGISTERED REPORT IDENTIFIER:RR1-10.2196/9899.

Project description:BackgroundThe number of solid organ transplants (SOT) in Canada has increased 33% over the past decade. Hospital readmissions are common within the first year after transplant and are linked to increased morbidity and mortality. Nearly half of these admissions to hospital appear to be preventable. Mobile health (mHealth) technologies hold promise to reduce admission to hospital and improve patient outcomes as they allow real-time monitoring and timely clinical intervention.ObjectiveTo determine whether an innovative mHealth intervention can reduce hospital readmission and unscheduled visits to the emergency department (ED) or transplant clinic. Our second objective is to assess the use of clinical and continuous ambulatory physiologic data to develop machine learning algorithms to predict risk of infection, organ rejection, and early mortality in adult heart, kidney, and liver transplant recipients.MethodsREmote moBile Outpatient mOnitoring in Transplant (Reboot) 2.0 is a two-phased single-center study to be conducted at the University Health Network (UHN) in Toronto, Canada. Phase 1 will consist of a 1-year concealed randomized control trial of 400 adult heart, kidney, and liver transplant recipients. Participants will be randomized to receive either personalized communication using a mHealth application in addition to standard of care phone communication (intervention group), or standard of care communication only (control group). In phase two, the prior collected dataset will be utilized to develop machine learning (ML) algorithms to identify early markers of rejection, infection, and graft dysfunction post-transplantation. The primary outcome will be a composite of any unscheduled hospital admission, visits to the ED or transplant clinic following discharge from the index admission. Secondary outcomes will include: 1) patient-reported outcomes using validated self-administered questionnaires; 2) 1-year graft survival rate; 3) 1-year patient survival rate; and 4) number of standard of care phone voice messages.ResultsAt the time of this manuscript's completion, no results are available.ConclusionsBuilding from previous work, this project will aim to leverage an innovative mHealth application to improve outcomes and reduce hospital readmission in adult SOT recipients. Additionally, the development of ML algorithms to better predict adverse health outcomes will allow for personalized medicine to tailor clinician-patient interactions, and mitigate the healthcare burden of a growing patient population.ClinicaltrialClinicalTrials.gov NCT04721288; https://www.clinicaltrials.gov/ct2/show/NCT04721288.