Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design

Symptom monitoring is a cornerstone of asthma self-management. Conventional methods of symptom monitoring have fallen short in producing objective data and eliciting patients' consistent adherence, particularly in teen patients. We have recently developed an Automated Device for Asthma Monitoring (ADAM) using a consumer mobile device as a platform to facilitate continuous and objective symptom monitoring in adolescents in vivo.

The objectives of the study were to evaluate the validity of the device using spirometer data, fractional exhaled nitric oxide (FeNO), existing measures of asthma symptoms/control and health care utilization data, and to examine the sensitivity and specificity of the device in discriminating asthma cases from nonasthma cases.

A total of 84 teens (42 teens with a current asthma diagnosis; 42 without asthma) aged between 13 and 17 years participated in the study. All participants used ADAM for 7 consecutive days during which participants with asthma completed an asthma diary two times a day. ADAM recorded the frequency of coughing for 24 hours throughout the 7-day trial. Pearson correlation and multiple regression were used to examine the relationships between ADAM data and asthma control, quality of life, and health care utilization at the time of the 7-day trial and 3 months later. A receiver operating characteristic (ROC) curve analysis was conducted to examine sensitivity and specificity based on the area under the curve (AUC) as an indicator of the device's capacity to discriminate between asthma versus nonasthma cases.

ADAM data (cough counts) were negatively associated with forced expiratory volume in first second of expiration (FEV1) (r=-.26, P=.05), forced vital capacity (FVC) (r=-.31, P=.02), and overall asthma control (r=-.41, P=.009) and positively associated with daily activity limitation (r=.46, P=.01), nighttime (r=.40, P=.02) and daytime symptoms (r=.38, P=.02), and health care utilization (r=.61, P<.001). Device data were also a significant predictor of asthma control (β=-.48, P=.003), quality of life (β=-.55, P=.001), and health care utilization (β=.74, P=.004) after 3 months. The ROC curve analysis for the presence of asthma diagnosis had an AUC of 0.71 (95% CI 0.58-0.84), which was significantly different from chance (χ(2) 1=9.7, P=.002), indicating the device's discriminating capacity. The optimal cutoff value of the device was 0.56 with a sensitivity of 51.3% and a specificity of 72.7%.

This study demonstrates validity of ADAM as a symptom-monitoring device in teens with asthma. ADAM data reflect the current status of asthma control and predict asthma morbidity and quality of life for the near future. A monitoring device such as ADAM can increase patients' awareness of the patterns of cough for early detection of worsening asthma and has the potential for preventing serious and costly future consequences of asthma.