DESIGN: This is a multisite observational study.
SETTING: The study was conducted in four tertiary care hospitals in Australia.
SUBJECTS: A total of 225 participants, following cardiac surgery, were involved in the study.
INTERVENTION: Participants completed the original 13-item FDQ and other measures of physical function, pain and health-related quality of life.
METHOD: Item reduction was utilized to develop the shortened version. Reliability was evaluated using intraclass correlation coefficients (ICCs), the smallest detectable change and Bland-Altman plots. The validity and responsiveness were evaluated using correlation. Anchor and distribution-based calculation was used to calculate the minimal clinical important difference (MCID).
RESULTS: Item reduction resulted in the creation of a 10-item shortened version of the questionnaire (FDQ-s). Within the cohort of cardiac surgery patient, the mean (SD) for the FDQ-s was 38.7 (19.61) at baseline; 15.5 (14.01) at four weeks and 7.9 (12.01) at three months. Validity: excellent internal consistency (Cronbach's α > 0.90) and fair-to-excellent construct validity (>0.4). Reliability: internal consistency was excellent (Cronbach's α > 0.8). The FDQ-s had excellent test-retest reliability (ICC = 0.89-0.92). Strong responsiveness overtime was demonstrated with large effect sizes (Cohen's d > 1.0). The MCID of the FDQ-s was calculated between 4 and 10 out of 100 (in cm).
CONCLUSION: The FDQ-s demonstrated robust psychometric properties as a measurement tool of physical function of the thoracic region following cardiac surgery.
OBJECTIVE: The main objective of this paper is to develop a robust algorithm to extract respiration rate using the contactless displacement sensor.
METHODS: In this study, chest movements were used as an indicative of inspiration and expiration to measure respiratory rate using the contactless displacement sensor. The contactless optical signals were recorded from 32 healthy subjects in four different controlled breathing conditions: rest, coughing, talking and hand movement to obtain the motion artifacts that the patients may have in the emergency department. The Empirical mode decomposition (EMD) algorithm was used to derive continuous RR signal from the contactless optical signal.
RESULTS: The analysis showed that there is a good correlation (0.9702) with RMSE of 0.33 breaths per minutes between the contact respiration rate and contactless respiration rate using empirical mode decomposition method.
CONCLUSION: It can be concluded that the empirical mode decomposition method can extract the respiration rate of the contactless optical signal from chest movement.