The endometriosis daily diary: qualitative research to explore the patient experience of endometriosis and inform the development of a patient-reported outcome (PRO) for endometriosis-related pain

Purpose Endometriosis is a chronic disorder of the female reproductive system characterized by debilitating symptoms, particularly endometriosis-related pain (ERP). Patient-reported outcome (PRO) measures of symptoms and impacts are required to assess disease severity in ERP clinical studies and clinical practice. A content-valid instrument was developed by modifying the Dysmenorrhea Daily Diary (DysDD) to form the Endometriosis Daily Diary (EDD), an electronic PRO administered via handheld device. Methods Qualitative research with US females with ERP was conducted in three stages: (1) Development of an endometriosis conceptual model based on qualitative literature and conduct of concept elicitation (CE) interviews (N = 30). (2) Cognitive debriefing (CD) interviews (N = 30) conducted across two rounds to assess relevance and understanding of the EDD, with modifications between interview rounds. (3) Pilot testing to assess usability/feasibility of administrating the EDD daily on an electronic handheld device (N = 15). Clinical experts provided guidance throughout the study. Results The conceptual model provided a comprehensive summary of endometriosis to inform modifications to the DysDD, forming the EDD. CD results demonstrated that EDD items were relevant for most participants. Instructions, items, response scales, and recall period were well-understood. The resulting daily diary assesses severity of cyclic and non-cyclic pelvic pain, dyspareunia, impact of ERP on functioning and daily life, symptoms associated with ERP, and bowel symptoms. Participants were able to complete the diary daily and found the device easy to use. Conclusion The EDD demonstrated good content validity in females experiencing ERP. The next step is to perform psychometric validation in an ERP sample.

Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

Digitally Distanced Inspection & Maintenance at Drilling Rigs : Applied Augmented Reality

For drilling contractors, the moment of truth is the operations at the site. If the technician at the site encounters a problem he can't solve, then everything stops. The team has to wait for a subject matter expert (SME) to arrive at the site to diagnose rectify the problem. Such process of SME mobilization and till that time Non-Productive Time (NPT) results in loss of hundreds of thousands of dollars. Hence the key challenge is converting the Sparse to Adequate availability of Right Knowledge at Right Time at Right Place, for the support of technicians. This paper is focused on the approach of moving from Hand Held devices to Hands-Free environment at sites and connecting local/global support to site support systems, to reduce cost, improve HSE and enhance operational performance. The augmented reality technology-enabled, smart glass laced headsets are rugged, zone 1 certified, and are voice-operated which are better than smart tablets which were considered during Technology Qualification Process. Evaluation criteria were: 1. Availability and follow up of the digital work instruction while operating. Moreover, not missing a single step of work instruction while inspection or maintenance continues was noted carefully. 2. Reduced travel/accommodation cost : Normally at the time of shutdown, the rig crew contacts subject matter experts (SME) and (at times) in turn the SME contacts the OEM support team to mobilize service engineers globally. 3. Response time improvement-Availability of support by SME right at the time of need results from better response time to diagnose and fix the issue at hand. Call logging till final resolution process improvement is considered an important metric. Travel restrictions imposed by Covid-19, are also being addressed through the distanced inspection. A hands-free environment is compared vis a vis handheld device. Better training and knowledge transfer are achieved through better communication methods and this goes better with learning by doing. Subsequent text (NLP-speech to text) analysis is planned through deep learning models to derive related predictions. Sparse to Adequate availability of support to rig staff with Right Knowledge at Right Place at Right Time is the key outcome of this Proof of Value project.

Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

Smart handheld device with flexible wrist and electrical bioimpedance sensor for tissue inspection

With the evolving demands of surgical intervention, there is a strong need for smaller and functionally augmented instruments to improve surgical outcomes, operational convenience, and diagnostic safety. Owing to the narrow and complicated anatomy, the probe head of the medical instrument is required to possess both good maneuverability and compact size. In addition, the development of medical instrument is moving toward patient-specialized, of which the articulation positions can be customized to reach the target position. To fulfill these requirements, this study presents the design of a smart handheld device which equips with a low cost, easy control, disposable flexible wrist, and an electrical bioimpedance sensor for medical diagnosis. Prototype of the device is made and tested. The experimental results demonstrate that the proposed device can provide accurate manipulation and effective tissue detection, showing a great potential in various medical applications.