Not all sensors are created equal: a framework for evaluating human performance measurement technologies.

Recent years have witnessed an explosion in the number of wearable sensing devices and associated apps that target a wide range of biomedical metrics, from actigraphy to glucose monitoring to lung function. This offers big opportunities for achieving scale in the use of such devices in application contexts such as telehealth, human performance and behaviour research and digitally enabled clinical trials. However, this increased availability and choice of sensors also brings with it a great challenge in optimising the match between the sensor and a specific application context. There is a need for a structured approach to first refining the requirements for a specific application, and then evaluating the available devices against those requirements. In this paper we will outline the main features of such an evaluation framework that has been developed with input from stakeholders in academic, clinical and industry settings.

The Burden of a Remote Trial in a Nursing Home Setting: Qualitative Study.

BACKGROUND: Despite an aging population, older adults are typically underrecruited in clinical trials, often because of the perceived burden associated with participation, particularly travel associated with clinic visits. Conducting a clinical trial remotely presents an opportunity to leverage mobile and wearable technologies to bring the research to the patient. However, the burden associated with shifting clinical research to a remote site requires exploration. While a remote trial may reduce patient burden, the extent to which this shifts burden on the other stakeholders needs to be investigated. OBJECTIVE: The aim of this study was to explore the burden associated with a remote trial in a nursing home setting on both staff and residents. METHODS: Using results from a grounded analysis of qualitative data, this study explored and characterized the burden associated with a remote trial conducted in a nursing home in Dublin, Ireland. A total of 11 residents were recruited to participate in this trial (mean age: 80 years; age range: 67-93 years). To support research activities, we also recruited 10 nursing home staff members, including health care assistants, an activities co-ordinator, and senior nurses. This study captured the lived experience of this remote trial among staff and residents and explored the burden associated with participation. At the end of the trial, a total of 6 residents and 8 members of staff participated in semistructured interviews (n=14). They reviewed clinical data generated by mobile and wearable devices and reflected upon their trial-related experiences. RESULTS: Staff reported extensive burden in fulfilling their roles and responsibilities to support activities of the trial. Among staff, we found eight key characteristics of burden: (1) comprehension, (2) time, (3) communication, (4) emotional load, (5) cognitive load, (6) research engagement, (7) logistical burden, and (8) product accountability. Residents reported comparatively less burden. Among residents, we found only four key characteristics of burden: (1) comprehension, (2) adherence, (3) emotional load, and (4) personal space. CONCLUSIONS: A remote trial in a nursing home setting can minimize the burden on residents and enable inclusive participation. However, it arguably creates additional burden on staff, particularly where they have a role to play in locally supporting and maintaining technology as part of data collection. Future research should examine how to measure and minimize the burden associated with data collection in remote trials.

Context aware falls risk assessment: A case study comparison.

This paper describes three retrospective case studies to illustrate the potential clinical value of a system capable of capturing objective gait metrics and environment data from older adults with a history of falls while they go about their daily lives. Participants in this study wore an inertial sensor above each ankle and a wearable camera around their neck for seven consecutive days. Selected metrics are presented to illustrate scenarios where the data collected by the system could be of clinical value. Evidence suggests that obtaining objective gait metrics and environment data from older adults may not only allow healthcare professionals to assess gait more accurately, but also to design treatment plans and falls prevention strategies that are more specifically tailored to each individual.

Context focused older adult mobility and gait assessment.

This paper presents an initial overview of insights gained into how older adults mobilize in the home and community, based on data from inertial sensors which were worn by study participants over a 7-day period. The addition of a wearable camera provided additional contextual information which can be used to assess mobility and understand the factors that influence it in the free living environment. Seven days of data collected from a group of older adults who had experienced one or more falls in the previous six months was compared to that of a control group with no history of falling. Results showed that both groups spent relatively little time walking in challenging environmental conditions, and that the fallers spent significantly less time walking under regular conditions (no effect on gait) and outdoors. Analysis of gait metrics showed that the fallers were slightly slower in general, and more noticeable differences were observed when the participants were regrouped according to mobility levels determined from baseline assessments using traditional methods.

Does external walking environment affect gait patterns?

The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting.