A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.

Mobile technologies, such as smart phone applications, wearables, ingestibles, and implantables, are increasingly used in clinical research to capture study endpoints. On behalf of the Clinical Trials Transformation Initiative, we aimed to conduct a systematic scoping review and compile a database summarizing pilot studies addressing mobile technology sensor performance, algorithm development, software performance, and/or operational feasibility, in order to provide a resource for guiding decisions about which technology is most suitable for a particular trial. Our systematic search identified 275 publications meeting inclusion criteria. From these papers, we extracted data including the medical condition, concept of interest captured by the mobile technology, outcomes captured by the digital measurement, and details regarding the sensors, algorithms, and study sample. Sixty-seven percent of the technologies identified were wearable sensors, with the remainder including tablets, smartphones, implanted sensors, and cameras. We noted substantial variability in terms of reporting completeness and terminology used. The data have been compiled into an online database maintained by the Clinical Trials Transformation Initiative that can be filtered and searched electronically, enabling a user to find information most relevant to their work. Our long-term goal is to maintain and update the online database, in order to promote standardization of methods and reporting, encourage collaboration, and avoid redundant studies, thereby contributing to the design and implementation of efficient, high-quality trials.

Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson's disease.

INTRODUCTION: Clinical assessment of motor symptoms in Parkinson's disease (PD) is subjective and may not reflect patient real-world experience. This two-part pilot study evaluated the accuracy of the NIMBLE wearable biosensor patch (containing an accelerometer and electromyography sensor) to record body movements in clinic and home environments versus clinical measurement of motor symptoms. METHODS: Patients (Hoehn & Yahr 2-3) had motor symptom fluctuations and were on a stable levodopa dose. Part 1 investigated different sensor body locations (six patients). In Part 2, 21 patients wore four sensors (chest, and most affected side of shin, forearm and back-of-hand) during a 2-day clinic- and 1-day home-based evaluation. Patients underwent Unified Parkinson's Disease Rating Scale assessments on days 1-2, and performed pre-defined motor activities at home on day 3. An algorithm estimated motor-symptom severity (predicted scores) using patch data (in-clinic); this was compared with in-clinic motor symptom assessments (observed scores). RESULTS: The overall correlation coefficient between in-clinic observed and sensor algorithm-predicted scores was 0.471 (p = 0.031). Predicted and observed scores were identical 45% of the time, with a predicted score within a ±1 range 91% of the time. Exact accuracy for each activity varied, ranging from 32% (pronation/supination) to 67% (rest-tremor-amplitude). Patients rated the patch easy-to-use and as providing valuable data for managing PD symptoms. Overall patch-adhesion success was 97.2%. The patch was safe and generally well tolerated. CONCLUSIONS: This study showed a correlation between sensor algorithm-predicted and clinician-observed motor-symptom scores. Algorithm refinement using patient populations with greater symptom-severity range may potentially improve the correlation.

Assessment of Postural Sway in Individuals with Multiple Sclerosis Using a Novel Wearable Inertial Sensor.

Balance impairment is common in individuals with multiple sclerosis (MS). However, objective assessment of balance usually requires clinical expertise and/or the use of expensive and obtrusive measuring equipment. These barriers to the objective assessment of balance may be overcome with the development of a lightweight inertial sensor system. In this study, we examined the concurrent validity of a novel wireless, skin-mounted inertial sensor system (BioStamp®, MC10 Inc.) to measure postural sway in individuals with MS by comparing measurement agreement between this novel sensor and gold standard measurement tools (force plate and externally validated inertial sensor). A total of 39 individuals with MS and 15 healthy controls participated in the study. Participants with MS were divided into groups based on the amount of impairment (MSMild: EDSS 2-4, n = 19; MSSevere: EDSS ≥6, n = 20). The balance assessment consisted of two 30-s quiet standing trials in each of three conditions: eyes open/firm surface, eyes closed/firm surface, and eyes open/foam surface. For each trial, postural sway was recorded with a force plate (Bertec) and simultaneously using two accelerometers (BioStamp and Xsens) mounted on the participant's posterior trunk at L5. Sway metrics (sway area, sway path length, root mean square amplitude, mean velocity, JERK, and total power) were derived to compare the measurement agreement among the measurement devices. Excellent agreement (intraclass correlation coefficients >0.9) between sway metrics derived from the BioStamp and the MTx sensors were observed across all conditions and groups. Good to excellent correlations (r >0.7) between devices were observed in all sway metrics and conditions. Additionally, the acceleration sway metrics were nearly as effective as the force plate sway metrics in differentiating individuals with poor balance from healthy controls. Overall, the BioStamp sensor is a valid and objective measurement tool for postural sway assessment. This novel, lightweight and portable sensor may offer unique advantages in tracking patient's postural performance.

Use of Mobile Devices to Measure Outcomes in Clinical Research, 2010-2016: A Systematic Literature Review.

BACKGROUND: The use of mobile devices in clinical research has advanced substantially in recent years due to the rapid pace of technology development. With an overall aim of informing the future use of mobile devices in interventional clinical research to measure primary outcomes, we conducted a systematic review of the use of and clinical outcomes measured by mobile devices (mobile outcomes) in observational and interventional clinical research. METHOD: We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles on clinical research published between January 2010 and May 2016 in which mobile devices were used to measure study outcomes. We screened each publication for specific inclusion and exclusion criteria. We then identified and qualitatively summarized the use of mobile outcome assessments in clinical research, including the type and design of the study, therapeutic focus, type of mobile device(s) used, and specific mobile outcomes reported. RESULTS: The search retrieved 2,530 potential articles of interest. After screening, 88 publications remained. Twenty-five percent of the publications (n = 22) described mobile outcomes used in interventional research, and the rest (n = 66) described observational clinical research. Thirteen therapeutic areas were represented. Five categories of mobile devices were identified: (1) inertial sensors, (2) biosensors, (3) pressure sensors and walkways, (4) medication adherence monitors, and (5) location monitors; inertial sensors/accelerometers were most common (reported in 86% of the publications). Among the variety of mobile outcomes, various assessments of physical activity were most common (reported in 74% of the publications). Other mobile outcomes included assessments of sleep, mobility, and pill adherence, as well as biomarkers assessed using a mobile device, including cardiac measures, glucose, gastric reflux, respiratory measures, and intensity of head-related injury. CONCLUSION: Mobile devices are being widely used in clinical research to assess outcomes, although their use in interventional research to assess therapeutic effectiveness is limited. For mobile devices to be used more frequently in pivotal interventional research - such as trials informing regulatory decision-making - more focus should be placed on: (1) consolidating the evidence supporting the clinical meaningfulness of specific mobile outcomes, and (2) standardizing the use of mobile devices in clinical research to measure specific mobile outcomes (e.g., data capture frequencies, placement of device). To that aim, this manuscript offers a broad overview of the various mobile outcome assessments currently used in observational and interventional research, and categorizes and consolidates this information for researchers interested in using mobile devices to assess outcomes in interventional research.

Highly flexible, wearable, and disposable cardiac biosensors for remote and ambulatory monitoring.

Contemporary cardiac and heart rate monitoring devices capture physiological signals using optical and electrode-based sensors. However, these devices generally lack the form factor and mechanical flexibility necessary for use in ambulatory and home environments. Here, we report an ultrathin (~1 mm average thickness) and highly flexible wearable cardiac sensor (WiSP) designed to be minimal in cost (disposable), light weight (1.2 g), water resistant, and capable of wireless energy harvesting. Theoretical analyses of system-level bending mechanics show the advantages of WiSP's flexible electronics, soft encapsulation layers and bioadhesives, enabling intimate skin coupling. A clinical feasibility study conducted in atrial fibrillation patients demonstrates that the WiSP device effectively measures cardiac signals matching the Holter monitor, and is more comfortable. WiSP's physical attributes and performance results demonstrate its utility for monitoring cardiac signals during daily activity, exertion and sleep, with implications for home-based care.

Monitoring gait in multiple sclerosis with novel wearable motion sensors.

BACKGROUND: Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices. METHODS: A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA. RESULTS: Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6-2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01). CONCLUSION: BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.

Multiple Wearable Sensors in Parkinson and Huntington Disease Individuals: A Pilot Study in Clinic and at Home.

BACKGROUND: Clinician rating scales and patient-reported outcomes are the principal means of assessing motor symptoms in Parkinson disease and Huntington disease. However, these assessments are subjective and generally limited to episodic in-person visits. Wearable sensors can objectively and continuously measure motor features and could be valuable in clinical research and care. METHODS: We recruited participants with Parkinson disease, Huntington disease, and prodromal Huntington disease (individuals who carry the genetic marker but do not yet exhibit symptoms of the disease), and controls to wear 5 accelerometer-based sensors on their chest and limbs for standardized in-clinic assessments and for 2 days at home. The study's aims were to assess the feasibility of use of wearable sensors, to determine the activity (lying, sitting, standing, walking) of participants, and to survey participants on their experience. RESULTS: Fifty-six individuals (16 with Parkinson disease, 15 with Huntington disease, 5 with prodromal Huntington disease, and 20 controls) were enrolled in the study. Data were successfully obtained from 99.3% (278/280) of sensors dispatched. On average, individuals with Huntington disease spent over 50% of the total time lying down, substantially more than individuals with prodromal Huntington disease (33%, p = 0.003), Parkinson disease (38%, p = 0.01), and controls (34%; p < 0.001). Most (86%) participants were "willing" or "very willing" to wear the sensors again. CONCLUSIONS: Among individuals with movement disorders, the use of wearable sensors in clinic and at home was feasible and well-received. These sensors can identify statistically significant differences in activity profiles between individuals with movement disorders and those without. In addition, continuous, objective monitoring can reveal disease characteristics not observed in clinic.

A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.

Audible implantable cardioverter defibrillator alarms detect intermittent conductor discontinuity.

The lead conductor integrity of implantable cardioverter defibrillator devices is inferred from impedance measurements; however, intermittent discontinuity can be difficult to detect or confirm. Newer devices can perform daily lead impedance self-testing, and some even have audible alarms that promptly warn patients of anomalies. In the present case, the audible alarms were solely responsible for the timely identification of an intermittent, otherwise clinically nonreproducible, form of potentially fatal implantable cardioverter defibrillator system failure.

Rate dependent far-field R wave sensing in an atrial tachyarrhythmia therapy device.

A case is reported of far-field R wave (FFRW) oversensing resulting in inappropriate atrial tachycardia (AT) detection by a dual chamber pacemaker incorporating atrial autoadjusting sensitivity (AAS). FFRW oversensing occurred during periods of functional atrial undersensing (FAU) with PR interval prolongation. Limitations of the pacemaker's ability to reject FFRWs and programming considerations for addressing this unique behavior are discussed.