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Introduction Following reports of respiratory symptoms among service members returning from deployment to South West Asia (SWA), an expert panel recommended pre-deployment spirometry be used to assess disease burden. Unfortunately, testing with spirometry is high cost and time-consuming. The airflow perturbation device (APD) is a handheld monitor that rapidly measures respiratory resistance (APD-Rr) and has promising but limited clinical data. Its speed and portability make it ideally suited for large volume pre-deployment screening. We conducted a pilot study to assess APD performance characteristics and develop normative values. Materials and methods We prospectively enrolled subjects and derived reference equations for the APD from those without respiratory symptoms, pulmonary disease, or tobacco exposure. APD testing was conducted by medical technicians who received a 10-min in-service on its use. A subset of subjects performed spirometry and impulse oscillometry (iOS), administered by trained respiratory therapists. APD measures were compared with spirometry and iOS. Results The total study population included 199 subjects (55.8% males, body mass index 27.7 ± 6.0 kg/m2, age 49.9 ± 18.7 yr). Across the three APD trials, mean inspiratory (APD-Ri), expiratory (APD-Re), and average (APD-Ravg) resistances were 3.30 ± 1.0, 3.69 ± 1.2, and 3.50 ± 1.1 cm H2O/L/s. Reference equations were derived from 142 clinically normal volunteers. Height, weight, and body mass index were independently associated with APD-Ri, APD-Re, and APD-Ravg and were combined with age and gender in linear regression models. APD-Ri, APD-Re, and APD-Ravg were significantly inversely correlated with FEV1 (r = -0.39 to -0.42), FVC (r = -0.37 to -0.40), and FEF25-75 (r = -0.31 to -0.35) and positively correlated with R5 (r = 0.61-0.62), R20 (r = 0.50-0.52), X5 (r = -0.57 to -0.59), and FRES (r = 0.42-0.43). Bland-Altman plots showed that the APD-Rr closely approximates iOS when resistance is normal. Conclusion Rapid testing was achieved with minimal training required, and reference equations were constructed. APD-Rr correlated moderately with iOS and weakly with spirometry. More testing is required to determine whether the APD has value for pre- and post-deployment respiratory assessment.

SleepMapper is a mobile, web-based system that allows patients to self-monitor their positive airway pressure therapy, and provides feedback and education in real time. In addition to the usual, comprehensive support provided at our clinic, we gave the SleepMapper to 30 patients initiating positive airway pressure. They were compared with patients initiating positive airway pressure at our clinic without SleepMapper (controls) to determine whether SleepMapper affected adherence. A total of 61 patients had polysomnographic and adherence data analysed, 30 were given SleepMapper and 31 received our standard of care. The two groups were well matched at baseline to include no significant differences in age, apnea-hypopnea index, percentage receiving split-night polysomnographs and starting pressures. Patients in the control group received significantly more non-benzodiazepine sedative hypnotics the night of their polysomnography and during positive airway pressure initiation. At 11 weeks, patients in the SleepMapper group had a greater percentage of nights with any use (78.0 ± 22.0 versus 55.5 ± 24.0%; P 0.001) and4 h positive airway pressure use (78.0 ± 22.0 versus 55.5 ± 24.0%; P = 0.02). There was a trend toward more patients in the SleepMapper group achieving4 h of use for at least 70% of nights [9/30 (30%) versus 3/31 (9.7%); P = 0.06]. In multivariate linear regression, the SleepMapper remained significantly associated with percentage of nights4 h positive airway pressure use (β coefficient = 0.18; P = 0.02). Added to our usual, comprehensive programme to maximize positive airway pressure adherence in new users, the SleepMapper was independently associated with an 18% increase in nights4 h of use.