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Leveraging Technology to Develop New Trial Endpoints

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30 APPLIED CLINICAL TRIALS December 2018 mHE ALTH celerometer sensor has already shown promise in the understanding of tremor symptoms in PD. 7 This may significantly improve understanding of treatment ef- fects, especially for symptoms that present intermit- tently or may suffer from poor recall properties. Use Case 3: Use of motion-based gaming plat- forms to measure movement/mobility outcomes Motion-based gaming platforms use depth-cameras to detect body movements and enable users to interact with gaming applications in more immersive ways. The same depth-camera technolog y, and its associ- ated software development kits (SDKs), can be used to develop custom software with application in education and health. The most commonly used solution is the Microsoft Kinect depth-camera associated with the Xbox gaming system, although other more advanced (yet similarly low-cost) technologies exist, such as the Intel RealSense camera range. 8 There are numerous applications utilizing this motion capture technology to study or encourage movement in healthcare, particularly in reha- bilitation. Being able to track the 3D position and movement of body joints enables the assessment of movement, and the detection of correct exercising during rehabilitation. Jintronix, for example, have developed games using Microsoft Kinect to encourage adherence and engagement with rehabilitation regimens, which have shown good outcomes in terms of reduced readmission rates in orthopedic and stroke patients. 9 Similarly, being able to track facial landmarks enables the deployment of other health applications, such as rehabilitation systems for patients recovering from facial paralysis—for example, with Bell's palsy and stroke. 10 Depth-camera solutions offer the potential to make objective in- clinic measurements that may previously only have been possible in more specialized motion laboratory settings or by using subjective clinician-reported outcomes (ClinROs). Simple range of motion, gait, and balance performance tests have been developed that leverage simple depth camera technology, both within and outside the con- text of a video game, some of which have shown reasonable perfor- mance in early validation studies. 11 For example, converting the 3D coordinates of body joints into vec- tors representing the spatial orientation of parts of the body enables simple vector algebra to calculate the angles made between joints and thus provides an estimate of the range of joint motion (see Figure 1). Early validation work compared to goniometer measurements has shown promise for upper extremity range of motion measures for example. 12,13 The use of motion-based gaming technolog y to develop move- ment-based outcome measures may enable the low- cost mea- surement of outcomes not possible outside specialist movement laboratories and may provide advantages over subjective ClinROs in providing measures that may be more sensitive, less prone to inter- rater variability, and capable of measuring more subtle aspects of movement and motion. Developing endpoints derived from novel use of technology applications The abilit y to leverage endpoints derived from these novel ap - proaches, and other approaches leveraging existing technologies in novel ways, relies upon the provision of evidence to support the use of the technology and to support the endpoint derived. Specifi- cally, we must be assured that the device faithfully measures what is intended to an acceptable level of reliability, accuracy, and precision; and that endpoints derived are truly measuring a concept of interest of the study, are sensitive to detect changes in health status as a result of an intervention, and that meaningful change is understood. This is, of course, no different to the approach required to validate any measurement approach associated with any clinical endpoint used to measure intervention effects. A comprehensive summary of requirements was published by the Critical Path Institute's ePRO consortium in the context of the use of wearables to develop endpoints to support regulatory decision- making and labelling claims. 14 These are summarized in Figure 2 on facing page, and also below. A. Technology assessment Usability and feasibility: Demonstration that the technology is us- able within the target population and feasible within the context of the specific clinical trial. Reliability: Data generated show satisfactor y intra- and inter- device reliability. Concurrent validity: Demonstration that the technolog y is truly measuring what is intended. Responsiveness: Data generated are able to suitably distinguish changes when they occur. B. Endpoint evaluation Measures a concept of interest, as defined by the study protocol. Content and construct validit y: The endpoint provides a suf- ficiently comprehensive measure of a concept of interest that is Upper arm vector Spine vector (a) Abduction dot product (b) Internal and external rotation in abduction Spine vector Upper arm vector er a or cross product arm Forearm vector dot product Range of Joint Motion Source: Byrom Figure 1. Estimation of range of motion using 3D joint coordinates. For personal, non-commercial use

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