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BackgroundIn chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non–health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. ObjectiveThe aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. MethodsIn this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. ResultsIn total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. ConclusionsThe vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non–health-care professionals are involved.

BackgroundWearable devices are valuable assessment tools for patient outcomes in contexts such as clinical trials. To be successfully deployed, however, participants must be willing to wear them. Another concern is that usability studies are rarely published, often fail to test devices beyond 24 hours, and need to be repeated frequently to ensure that contemporary devices are assessed. ObjectiveThis study aimed to compare multiple wearable sensors in a real-world context to establish their usability within an older adult (>50 years) population. MethodsEight older adults wore seven devices for a minimum of 1 week each: Actigraph GT9x, Actibelt, Actiwatch, Biovotion, Hexoskin, Mc10 Biostamp_RC, and Wavelet. Usability was established through mixed methods using semistructured interviews and three questionnaires, namely, the Intrinsic Motivation Inventory (IMI), the System Usability Scale (SUS), and an acceptability questionnaire. Quantitative data were reported descriptively and qualitative data were analyzed using deductive content analysis. Data were then integrated using triangulation. ResultsResults demonstrated that no device was considered optimal as all scored below average in the SUS (median, IQR; min-max=57.5, 12.5; 47.5-63.8). Hexoskin was the lowest scored device based on the IMI (3.6; 3.4-4.5), while Biovotion, Actibelt, and Mc10 Biostamp_RC achieved the highest median results on the acceptability questionnaire (3.6 on a 6-point Likert scale). Qualitatively, participants were willing to accept less comfort, less device discretion, and high charging burdens if the devices were perceived as useful, namely through the provision of feedback for the user. Participants agreed that the purpose of use is a key enabler for long-term compliance. These views were particularly noted by those not currently wearing an activity-tracking device. Participants believed that wrist-worn sensors were the most versatile and easy to use, and therefore, the most suitable for long-term use. In particular, Actiwatch and Wavelet stood out for their comfort. The convergence of quantitative and qualitative data was demonstrated in the study. ConclusionsBased on the results, the following context-specific recommendations can be made: (1) researchers should consider their device selection in relation to both individual and environmental factors, and not simply the primary outcome of the research study; (2) if researchers do not wish their participants to have access to feedback from the devices, then a simple, wrist-worn device that acts as a watch is preferable; (3) if feedback is allowed, then it should be made available to help participants remain engaged; this is likely to apply only to people without cognitive impairments; (4) battery life of 1 week should be considered as a necessary feature to enhance data capture; (5) researchers should consider providing additional information about the purpose of devices to participants to support their continued use.

BackgroundSensor-based recordings of human movements are becoming increasingly important for the assessment of motor symptoms in neurological disorders beyond rehabilitative purposes. ASSESS MS is a movement recording and analysis system being developed to automate the classification of motor dysfunction in patients with multiple sclerosis (MS) using depth-sensing computer vision. It aims to provide a more consistent and finer-grained measurement of motor dysfunction than currently possible. ObjectiveTo test the usability and acceptability of ASSESS MS with health professionals and patients with MS. MethodsA prospective, mixed-methods study was carried out at 3 centers. After a 1-hour training session, a convenience sample of 12 health professionals (6 neurologists and 6 nurses) used ASSESS MS to capture recordings of standardized movements performed by 51 volunteer patients. Metrics for effectiveness, efficiency, and acceptability were defined and used to analyze data captured by ASSESS MS, video recordings of each examination, feedback questionnaires, and follow-up interviews. ResultsAll health professionals were able to complete recordings using ASSESS MS, achieving high levels of standardization on 3 of 4 metrics (movement performance, lateral positioning, and clear camera view but not distance positioning). Results were unaffected by patients’ level of physical or cognitive disability. ASSESS MS was perceived as easy to use by both patients and health professionals with high scores on the Likert-scale questions and positive interview commentary. ASSESS MS was highly acceptable to patients on all dimensions considered, including attitudes to future use, interaction (with health professionals), and overall perceptions of ASSESS MS. Health professionals also accepted ASSESS MS, but with greater ambivalence arising from the need to alter patient interaction styles. There was little variation in results across participating centers, and no differences between neurologists and nurses. ConclusionsIn typical clinical settings, ASSESS MS is usable and acceptable to both patients and health professionals, generating data of a quality suitable for clinical analysis. An iterative design process appears to have been successful in accounting for factors that permit ASSESS MS to be used by a range of health professionals in new settings with minimal training. The study shows the potential of shifting ubiquitous sensing technologies from research into the clinic through a design approach that gives appropriate attention to the clinic environment.

BACKGROUND: Assessing motor functioning is important to monitor the disease course of multiple sclerosis (MS). Video-assisted rating of classic neurologic tests and activities of daily living may improve the detection of changes in motor functioning. We investigated the value of using video-assisted composite measures for the detection of changes in mobility and upper extremity function (UEF). METHODS: Forty-three patients with MS were recorded performing motor function tests before and during treatment with fampridine. Patients were classified as improved or not improved on mobility composite (MOB-COM) and UEF composite (UEF-COM) measures based on neurologists' ratings of the tests. The proportional agreements between the composite measures and the conventional measures--the Timed 25-Foot Walk test (T25FW) and the Nine-Hole Peg Test (NHPT)--were determined and compared with patient-perceived improvement, which was determined using patient-reported ratings of changes in mobility and UEF. RESULTS: Agreement between MOB-COM and T25FW was 79.5%, and agreement between UEF-COM and NHPT was 82.1%. Twenty-six of 39 patients (66.7%) reported mobility improvement; 6 of these reports were confirmed by both T25FW and MOB-COM, 4 were confirmed by T25FW only, and 2 were confirmed by MOB-COM only. For UEF, 13 of 39 patients (33.3%) reported improvement; 3 of these were confirmed by the NHPT and 3 were confirmed by the UEF-COM. CONCLUSIONS: Compared with the conventional NHPT measure, the video-assisted composite measure of UEF detected additional patient-perceived improvement. This was less evident for mobility measures. Video-assisted composite measures may enhance the detection of treatment effects in MS clinical practice and trials. [ABSTRACT FROM AUTHOR]

Purpose: Clinical ordinal rating scales of movements, e.g., the Expanded Disability Status Scale, have poor intra- and interrater reliability, are insensitive to subtle differences and result in coarse-grained ratings compared to relative comparative rating methods. We therefore established video-based setwise comparison as a fine-grained, reliable and efficient rating method of motor dysfunction using algorithmic support. Materials and methods: Eight neurologists rated a set of 40 multiple sclerosis patient videos of the Finger-to-Nose-Test using both the newly developed setwise comparison and the established pairwise comparison techniques, which result in a continuous rating scale. Reliability was assessed by the intra-class correlation coefficient. Construct validity was estimated as Pearson's correlation between the continuous scale and severity ratings according to the Neurostatus scale for upper-extremity tremor/dysmetria and the Nine-hole-peg-test. Comparing the time needed for ratings assessed efficiency. Results: Intra-class correlation coefficient was 0.83 for setwise and 0.7 for pairwise comparison. Correlation to the tremor/dysmetria score of the Neurostatus was 0.86 for both rating procedures and correlation to the Nine-hole-peg-test was 0.64 (setwise) and 0.66 (pairwise). The time needed to rate 40 videos was 22.9 ± 6.9 minutes (setwise) and 77.8 ± 14.5 minutes (pairwise). Conclusions: Setwise comparison is an efficient, valid and reliable method for fine-grained rating of motor dysfunction that can be applied to larger datasets. It is substantially more efficient than pairwise comparison. Disability rating is crucial in clinical neurorehabilitation and in clinical trials. Humans are naturally inconsistent in rating items on ordinal scales leading to poor intra- and interrater reliability, insensitivity to subtle differences and coarse-grained ratings. Video-based setwise comparison is a new rating method enabling fine-grained, reliable and efficient ratings of motor dysfunction using algorithmic support. [ABSTRACT FROM AUTHOR]

Background: Accurate clinical assessment in multiple sclerosis (MS) is challenging. The Assess MS system is being developed to automatically quantify motor dysfunction in MS, including upper extremity function (UEF) and mobility. Objective: To determine to what extent combinations of standardized movements included in the Assess MS system explain accepted measures of UEF and mobility. Methods: MS patients were recruited at four European MS centres. Eight movements were selected, including tasks of activities of daily living (ADL) and classical neurological tests. Movements were recorded on video and rated by experienced neurologists (n = 5). Subsequently, multivariate linear regression models were performed to explain the variance of the Nine-Hole Peg Test (9HPT), Arm Function in Multiple Sclerosis Questionnaire (AMSQ) and Timed-25 Foot Walk test (T25WT). Results: In total, 257 patients were included. The movements explained 62.9% to 80.1% of the variance of the 9HPT models, 43.3% and 44.3% of the AMSQ models and 70.8% of the T25WT. In all models, tasks of ADL contributed most to the variance. Conclusion: Combinations of movements are valuable to assess UEF and mobility. Incorporating ADL tasks into daily clinical practice and clinical trials may be more valuable than the classical neurological examination of UEF and mobility. [ABSTRACT FROM AUTHOR]