Toward a new age of patient centricity? The application of eye-tracking to the development of connected self-injection systems

Disease-Agnostic Electronic Adherence Aid for Subcutaneous at-Home and Self-Administration Devices-The Lowest Common Denominator Based on a Cross-Indication Survey

The value of connected devices and health apps with features such as adherence trackers, dosing reminders, and remote communication tools for users and healthcare providers has been assessed to support home-based subcutaneous administration. A comprehensive survey was conducted with 605 participants, including users and caregivers, from eight countries. Medical conditions encompassed ankylosing spondylitis, asthma, cerebral palsy, cluster headaches, Crohn's disease, hemophilia, lupus, migraine, multiple sclerosis, Parkinson's disease, plaque psoriasis, psoriatic arthritis, rheumatoid arthritis, spasticity, spondyloarthritis, and ulcerative colitis. Utilizing a maximum difference scaling methodology, the survey gauged participant preferences regarding specific attributes and features of connected drug delivery devices. Irrespective of demographic factors like age, gender, nationality, or the specific medical condition, the device's ability to verify a successful injection stood out as universally valued. The second and third most valued attributes pertained to temperature-related indicators or warnings. These features do not necessitate the use of a connected device and can be integrated into existing autoinjector platforms. The survey findings support the development of a universal adherence tool for at-home subcutaneous dosing, independent of a specific medical condition. This tool may be gradually improved with disease-specific features. Once established as a platform, manufacturers can launch any subcutaneous medication and later integrate real-world evidence for enhanced educational, treatment, and diagnostic capabilities. This approach is crucial for advancing connected adherence tools in decentralized healthcare, aligning with user and healthcare system needs while translating scientific innovation into practical solutions.

Human Factors Evaluation of HeartMate 3 Left Ventricular Assist Device Peripherals: An Eye Tracking Supported Simulation Study

BACKGROUND

Despite recent design improvements, human factors issues continue to challenge left ventricular assist device (LVAD) therapy. The aim of this study was to evaluate user experience of former non-HeartMate 3 (HM3) LVAD patients post heart transplantation (HTX) and laypersons (LP) with HM3 LVAD peripherals in simulated everyday and emergency scenarios.

METHODS

This single center cohort study included untrained HTX and LP. Seven scenarios, including battery exchanges (without alarm, advisory alarm, dim light, consolidated bag), change of power supply, driveline dis-/reconnection and controller exchange were simulated. Subjects' gaze behavior was recorded using eye tracking technology. Success rate, pump-off-time, duration to success (DTS), percental fixation duration per areas of interest and post-scenario-survey results were defined as outcome measures.

RESULTS

Thirty subjects completed 210 scenarios, initially solving 82.4% (HTX vs. LP, p = 1.00). Changing power supply revealed highest complexity (DTS = 251 ± 93s, p = 0.76): 26.7% succeeded at first attempt (p = 0.68), 56.7% at second attempt, with significantly more LP failing (p = 0.04), resulting in 10 hazards from driveline disconnections (pump-off-time 2-118s, p = 0.25). Comparison on initial success showed differences in fixation durations for seven areas of interest (p < 0.037). Decreasing DTS during battery exchanges (p < 0.001) indicate high learnability. Exchanging batteries within the bag took longer (median DTS = 75.0 (IQR = 45.0)s, p = 0.09), especially in elderly subjects (r = 0.61, p < 0.001). Subjects with less initial success were more afraid of making mistakes (p = 0.048).

CONCLUSION

This eye tracking based human factors study provided insights into user experiences in handling HM3 peripherals. It highlights unintuitive and hazardous characteristics, providing guidance for future user-centered design of LVAD wearables.

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

Eye Tracking Supported Human Factors Testing Improving Patient Training

The handling of left ventricular assist devices (LVADs) can be challenging for patients and requires appropriate training. The devices' usability impacts patients' safety and quality of life. In this study, an eye tracking supported human factors testing was performed to reveal problems during use and test the trainings' effectiveness. In total 32 HeartWare HVAD patients (including 6 pre-VAD patients) and 3 technical experts as control group performed a battery change (BC) and a controller change (CC) as an everyday and emergency scenario on a training device. By tracking the patients' gaze point, task duration and pump-off time were evaluated. Patients with LVAD support ≥1 year showed significantly shorter BC task duration than patients with LVAD support <1 year (p = 0.008). In contrast their CC task duration (p = 0.002) and pump-off times (median = 12.35 s) were higher than for LVAD support patients <1 year (median = 5.3 s) with p = 0.001. The shorter BC task duration for patients with LVAD support ≥1 year indicate that with time patients establish routines and gain confidence using their device. The opposite effect was found for CC task duration and pump-off times. This implies the need for intermittent re-training of less frequent tasks to increase patients' safety.

Value of Eye-Tracking Data for Classification of Information Processing-Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design

Background

In order to give a wide range of people the opportunity to ensure and support home care, one approach is to develop medical devices that are as user-friendly as possible. This allows nonexperts to use medical devices that were originally too complicated to use. For a user-centric development of such medical devices, it is essential to understand which user interface design best supports patients, caregivers, and health care professionals.

Objective

Using the benefits of mobile eye tracking, this work aims to gain a deeper understanding of the challenges of user cognition. As a consequence, its goal is to identify the obstacles to the usability of the features of two different designs of a single medical device user interface. The medical device is a patient assistance device for home use in peritoneal dialysis therapy.

Methods

A total of 16 participants, with a subset of seniors (8/16, mean age 73.7 years) and young adults (8/16, mean age 25.0 years), were recruited and participated in this study. The handling cycle consisted of seven main tasks. Data analysis started with the analysis of task effectiveness for searching for error-related tasks. Subsequently, the in-depth gaze data analysis focused on these identified critical tasks. In order to understand the challenges of user cognition in critical tasks, gaze data were analyzed with respect to individual user interface features of the medical device system. Therefore, it focused on the two dimensions of dwell time and fixation duration of the gaze.

Results

In total, 97% of the handling steps for design 1 and 96% for design 2 were performed correctly, with the main challenges being task 1 insert, task 2 connect, and task 6 disconnect for both designs. In order to understand the two analyzed dimensions of the physiological measurements simultaneously, the authors propose a new graphical representation. It distinguishes four different patterns to compare the eye movements associated with the two designs. The patterns identified for the critical tasks are consistent with the results of the task performance.

Conclusions

This study showed that mobile eye tracking provides insights into information processing in intensive handling tasks related to individual user interface features. The evaluation of each feature of the user interface promises an optimal design by combining the best found features. In this way, manufacturers are able to develop products that can be used by untrained people without prior knowledge. This would allow home care to be provided not only by highly qualified nurses and caregivers, but also by patients themselves, partners, children, or neighbors.