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Introduction: Electronic versions of patient-reported outcome measures (PROMs) seem to have a clear administrative logging advantage to traditional paper versions. However, most of them have not been formally evaluated for their suitability to replace paper outcome measures for assessment of individuals with lower-limb amputations. The aim of this study is to examine the usability and equivalence of electronic to paper versions of PROMs suitable for use in prosthetic clinical care and research for persons with lower-limb loss. Methods: In this cross-sectional study, 10 participants remotely completed the following PROMs online and then on paper: Orthotic and Prosthetic User Survey (OPUS), Modified Falls Efficacy Scale (MFES), Prosthetic Evaluation Questionnaire (PEQ), Patient Health Questionnaire–9 (PHQ-9), and Community Participation Indicators (CPI). Participants also answered open-ended and standardized questions regarding the usability of the electronic surveys. Wilcoxon signed rank tests, comparisons to minimum detectable change, intraclass correlation coefficients, and Bland-Altman plots were used to evaluate differences between the two survey versions, meaningful changes in scores, reliability, and systematic biases, respectively. Results: Electronic surveys had fewer missing or ambiguous responses than paper surveys; however, the PEQ Social Burdens subscale could not be evaluated due to error in the creation of the electronic survey. No significant differences were found between scores of the two versions for any of the measures, but multiple participants had meaningful changes in the Appearance and Sounds PEQ subscales. All measures demonstrated acceptable reliability between versions, except the Appearance, Perceived Response, and Sounds subscales of the PEQ. No systematic biases in scores or usability concerns were found for any measures. Conclusions: This study analysis showed that most of the electronic PROMs studied are easily used and demonstrate equivalence to the paper versions. However, the PEQ Appearance, Perceived Response, Sounds, and Social Burden subscales require further evaluation. [ABSTRACT FROM AUTHOR]

Introduction: Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning., Methods: Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls)., Results: These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips ( p < 0.037)., Discussion: Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum., Competing Interests: S.X. and J.A.R. hold equity in the company Sonica Health, which makes wearable sensors for medical applications. A patent on the wireless sensor and methods for medical use has been filed by Northwestern University with J.A.R., S.X., B.M-H., L.R.C., A.J., and M.K.O. as inventors. The remaining authors have no conflicts of interest to declare., (Copyright © 2021 by S. Karger AG, Basel.)