Adhesives that involve adhesion to the skin have been of great technological importance in medical or pharmaceutical fields, including recently emerging wearable sensors and electronics. The objective of this work was to evaluate the performances of silicone-based adhesives with skin, using a peel adhesion test. Specifically, we explored the effect of adhesive cleansing, which is an inevitable daily event for patients' comfort in long-term applications. Firstly, three medical grade silicone gels, Silbione® RT 4717, Silbione® RT 4642, and Silpuran® 2130, were used to fabricate adhesive pads. Their peel strength values were subsequently measured and compared, among which Silbione® RT gel 4717 possessed the highest peel strength. Therefore, it was selected as the raw material to fabricate the pads with a thickness range of 640-740 μm. Secondly, the peel adhesion of Silbione® RT 4717 adhesive pad was further compared with a series of commercial products that employ various medical-grade adhesives. The peel strength results indicated that our custom-made adhesive pad had an adequately strong adhesion for clinical use. Thirdly, in order to observe and predict the long-term performance of the adhesives, an aging test was performed in an ambient environment, revealing that Silbione® RT 4717 adhesive remained highly sticky for 5 days. Lastly, adequate cleansing protocols were established by monitoring the changes in peel strength after washing and wiping events. The reusability analysis showed that Silbione® 4717 adhesive pad was reusable in a one-week period for the washing method and 3 days for the wiping method.

The objective of this study was to conduct the first patient usability testing of a mobile health (mHealth) system for in-home swallowing therapy. Five participants with a history of head and neck cancer evaluated the mHealth system. After completing an in-application (app) tutorial with the clinician, participants were asked to independently complete five tasks: pair the device to the smartphone, place the device correctly, exercise, interpret progress displays, and close the system. Quantitative and qualitative methods were used to evaluate the effectiveness, efficiency, and satisfaction with the system. Critical changes to the app were found in three of the tasks, resulting in recommendations for the next iteration. These issues were related to ease of Bluetooth pairing, placement of device, and interpretation of statistics. Usability testing with patients identified issues that were essential to address prior to implementing the mHealth system in subsequent clinical trials. Of the usability methods used, video observation (synced screen capture with videoed gestures) revealed the most information.

Surface electromyography (sEMG) is used as an adjuvant to dysphagia therapy to demonstrate the activity of submental muscles during swallowing exercises. Mechanomyography (MMG) has been suggested as a potential superior alternative to sEMG; however, this advantage is not confirmed for signal acquired from submental muscles. This study compared the signal-to-noise ratio (SNR) obtained from sEMG and MMG sensors during swallowing tasks, in healthy participants and those with a history of head and neck cancer (HNC), a population with altered anatomy and a high incidence of dysphagia. Twenty-two healthy adults and 10 adults with a history of HNC participated in this study. sEMG and MMG signals were acquired during dry, thin liquid, effortful, and Mendelsohn maneuver swallows. SNR was compared between the two sensors using repeated measures ANOVAs and subsequent planned pairwise comparisons. Test-retest measures were collected on 20 % of participants. In healthy participants, MMG SNR was higher than that of sEMG for dry [t(21) = -3.02, p = 0.007] and thin liquid swallows [t(21) = -4.24, p < 0.001]. Although a significant difference for sensor was found in HNC participants F(1,9) = 5.54, p = 0.043, planned pairwise comparisons by task revealed no statistically significant difference between the two sensors. sEMG also showed much better test-retest reliability than MMG. Biofeedback provided as an adjuvant to dysphagia therapy in patients with HNC should employ sEMG technology, as this sensor type yielded better SNR and overall test-retest reliability. Poor MMG test-retest reliability was noted in both healthy and HNC participants and may have been related to differences in sensor application.

Mobile health (mHealth) technologies may offer an opportunity to address longstanding clinical challenges, such as access and adherence to swallowing therapy. Mobili-T is an mHealth device that uses surface electromyography (sEMG) to provide biofeedback on submental muscles activity during exercise. An automated swallow-detection algorithm was developed for Mobili-T. This study evaluated the performance of the swallow-detection algorithm. Ten healthy participants and 10 head and neck cancer (HNC) patients were fitted with the device. Signal was acquired during regular, effortful, and Mendelsohn maneuver saliva swallows, as well as lip presses, tongue, and head movements. Signals of interest were tagged during data acquisition and used to evaluate algorithm performance. Sensitivity and positive predictive values (PPV) were calculated for each participant. Saliva swallows were compared between HNC and controls in the four sEMG-based parameters used in the algorithm: duration, peak amplitude ratio, median frequency, and 15th percentile of the power spectrum density. In healthy participants, sensitivity and PPV were 92.3 and 83.9%, respectively. In HNC patients, sensitivity was 92.7% and PPV was 72.2%. In saliva swallows, HNC patients had longer event durations (U = 1925.5, p < 0.001), lower median frequency (U = 2674.0, p < 0.001), and lower 15th percentile of the power spectrum density [t(176.9) = 2.07, p < 0.001] than healthy participants. The automated swallow-detection algorithm performed well with healthy participants and retained a high sensitivity, but had lowered PPV with HNC patients. With respect to Mobili-T, the algorithm will next be evaluated using the mHealth system.