RESP-FIT: Technology-Enhanced Self-Management in COPD

RESP-FIT. The RESP-FIT program is a 6-week respiratory muscle strength training intervention adapted from previous respiratory muscle strength training (RMST) training regimens, consisting of 1) five training days/week using combined inspiratory and expiratory muscle training, consisting of breathing in and out against a combine inspiratory and expiratory muscle strength trainer (IMST/EMST), 2) individualized, progress-based text message training reminders and prompts related to timing and intensification calibration, and 3) use of a Fitbit for remote monitoring of physical activity and hours slept at night. Similar to other muscle strength training programs, exercises are done at regular intervals during the week (5 breaths, 5 times a day, 5 days a week; the participant will receive graphical illustration of RESP-FIT training frequency and intensity achieved, and based on their training regimen, will be prompted and/or reinforced via SMS text messaging). As the use of an accelerometer or remote tracking device alone may affect physical activity, a control group will receive only the Fitbit. This study proposes to assess the feasibility and initial efficacy of RMST by obtaining estimates of variability in fatigue secondary to dyspnea, using a technologically-enhanced RESP-FIT intervention. Our overarching hypothesis is that a training regimen consisting of RMST along with technology-enhanced symptom-tracking will decrease dyspnea, dyspnea-related activity avoidance, and fatigue. Additionally, this application proposes to further develop Smartphone Airway Monitoring System (SAMS) which currently integrates EMA probe questions covering COPD symptoms and preventive care use with the new functions, use in an adult population with COPD, video captured training technique and logging of RMST training sessions. Up to thirty adults with COPD (age over 40) will complete the study and use the SAMS app for 6 weeks in the natural environment. We will evaluate the acceptability, feasibility, adherence, and performance of RESP-FIT + SAMS through quantitative methods to further optimize the app and related study procedures for the next phase of testing (i.e., large scale efficacy RCT).