A Novel Adherence Sensor System for Valved Holding Chambers Suitable for Children and Adults With Asthma

Poor adherence to inhaled drug therapy is considered to be a major factor in asthma related morbidity and it has been shown, in a significant number of patients, that estimates of adherence by patients or parents of children is not accurate. Inaccurate reporting may lead to higher dose of drug or more potent drugs being prescribed. A number of adherence sensors are available for some commercial inhaler devices. However, existing adherence monitoring mainly focuses on how the hardware of an inhaler has been actuated and does not provide evidence of drug aerosol inhalation. Valved holding chambers are now first choice for aerosol drug delivery for young children, who inhale drug by breathing in and out of the device several times and are increasingly used by older children and adults who inhale drug aerosol by taking a single deep breath. To the best knowledge of the authors, there is no adherence sensor in the market that provides evidence of actuation of the drug into a valved holding chamber and evidence that it is inhaled by different age groups. We therefore developed a novel adherence sensor system embedded into a valved holding chambers to achieve this. Our customised algorithm ensures that correct use is recorded, and incorrect use flagged. It also distinguishes a deep breath from tidal breaths and in combination measurement of actuation into the device triggers different classifiers for registering usage as good technique or not. A good technique or poor technique is immediately fed back to users using a visual light signal, while the data recorded is Bluetoothed to a paired mobile device for historical recording and statistical analysis. The entire system, including battery, micro-controller, sensors and Bluetooth, is integrated on a
$66 \times 22$
mm
²
printed circuit board and weighs only 8 grams. The system has been evaluated on a custom-made artificial lung which mimics the breathing patterns confirming recognition of tidal and deep breaths. The low-power device operates from a 3.3 V, 560 mAh battery and lasts 14 months if used daily.