Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study.

BACKGROUND: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. OBJECTIVE: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. METHODS: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. RESULTS: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. CONCLUSIONS: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.

Capturing Rest-Activity Profiles in Schizophrenia Using Wearable and Mobile Technologies: Development, Implementation, Feasibility, and Acceptability of a Remote Monitoring Platform.

BACKGROUND: There is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis. OBJECTIVE: This study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes. METHODS: A total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater. RESULTS: Overall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001). CONCLUSIONS: Extended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse.

A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data.

Most image analysis pipelines rely on multiple channels per image with subcellular reference points for cell segmentation. Single-channel phase-contrast images are often problematic, especially for cells with unfavorable morphology, such as induced pluripotent stem cells (iPSCs). Live imaging poses a further challenge, because of the introduction of the dimension of time. Evaluations cannot be easily integrated with other biological data sets including analysis of endpoint images. Here, we present a workflow that incorporates a novel CellProfiler-based image analysis pipeline enabling segmentation of single-channel images with a robust R-based software solution to reduce the dimension of time to a single data point. These two packages combined allow robust segmentation of iPSCs solely on phase-contrast single-channel images and enable live imaging data to be easily integrated to endpoint data sets while retaining the dynamics of cellular responses. The described workflow facilitates characterization of the response of live-imaged iPSCs to external stimuli and definition of cell line-specific, phenotypic signatures. We present an efficient tool set for automated high-content analysis suitable for cells with challenging morphology. This approach has potentially widespread applications for human pluripotent stem cells and other cell types.

A high-content platform to characterise human induced pluripotent stem cell lines.

Induced pluripotent stem cells (iPSCs) provide invaluable opportunities for future cell therapies as well as for studying human development, modelling diseases and discovering therapeutics. In order to realise the potential of iPSCs, it is crucial to comprehensively characterise cells generated from large cohorts of healthy and diseased individuals. The human iPSC initiative (HipSci) is assessing a large panel of cell lines to define cell phenotypes, dissect inter- and intra-line and donor variability and identify its key determinant components. Here we report the establishment of a high-content platform for phenotypic analysis of human iPSC lines. In the described assay, cells are dissociated and seeded as single cells onto 96-well plates coated with fibronectin at three different concentrations. This method allows assessment of cell number, proliferation, morphology and intercellular adhesion. Altogether, our strategy delivers robust quantification of phenotypic diversity within complex cell populations facilitating future identification of the genetic, biological and technical determinants of variance. Approaches such as the one described can be used to benchmark iPSCs from multiple donors and create novel platforms that can readily be tailored for disease modelling and drug discovery.