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Background: Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient’s physical activity patterns. Objective: To explore clinicians’ opinions on different visualisations of actigraphy data. Methods: Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis. Results: Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret. Conclusion: The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.

Introduction: Using multi-sensor devices in large scale clinical trials may facilitate development of new digital health solutions for remote monitoring and care of patients. Choice of appropriate parameters and devices for use in such clinical trials poses significant technological and operational challenges. Aim: Conduct a technological, operational and clinical feasibility study to determine the most appropriate multi-sensor devices for use in large scale Asthma and COPD clinical trials Methods: Parameters of clinical relevance to monitor Asthma and COPD patients were shortlisted. Sensor devices were chosen based on required parameter coverage, regulatory status, scientific evidence and technological and operational feasibility. Sensor data was collected and analyzed from 29 patients (moderate to severe COPD or uncontrolled Asthma) in a 4-week observational clinical feasibility study. Results: Parameters with potential clinical relevance (inclusive of heart rate, oxygen saturation, respiratory rate, sleep, physical activity, lung function, FeNO, rescue medication use and environmental data) were assessed using the following devices: Philips Biosensor®, Biovotion®, ActiGraph®, GoSpiro®, NIOX VERO®, MDI Sensor (Propeller Health®) and Emfit® . Patient compliance, burden to patient, usability and data quality, varied between the sensor devices and between asthma and COPD patients. Conclusions: The key considerations for selection of digital sensors for large scale remote monitoring clinical trials are: type and severity of disease, clinically relevant parameters, burden to patient, usability and data-quality of devices.

Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = –4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. Conclusion: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.

Background Wearable devices are valuable assessment tools for patient outcomes in contexts such as clinical trials. To be successfully deployed, however, participants must be willing to wear them. Another concern is that usability studies are rarely published, often fail to test devices beyond 24 hours, and need to be repeated frequently to ensure that contemporary devices are assessed. Objective This study aimed to compare multiple wearable sensors in a real-world context to establish their usability within an older adult (>50 years) population. Methods Eight older adults wore seven devices for a minimum of 1 week each: Actigraph GT9x, Actibelt, Actiwatch, Biovotion, Hexoskin, Mc10 Biostamp_RC, and Wavelet. Usability was established through mixed methods using semistructured interviews and three questionnaires, namely, the Intrinsic Motivation Inventory (IMI), the System Usability Scale (SUS), and an acceptability questionnaire. Quantitative data were reported descriptively and qualitative data were analyzed using deductive content analysis. Data were then integrated using triangulation. Results Results demonstrated that no device was considered optimal as all scored below average in the SUS (median, IQR; min-max=57.5, 12.5; 47.5-63.8). Hexoskin was the lowest scored device based on the IMI (3.6; 3.4-4.5), while Biovotion, Actibelt, and Mc10 Biostamp_RC achieved the highest median results on the acceptability questionnaire (3.6 on a 6-point Likert scale). Qualitatively, participants were willing to accept less comfort, less device discretion, and high charging burdens if the devices were perceived as useful, namely through the provision of feedback for the user. Participants agreed that the purpose of use is a key enabler for long-term compliance. These views were particularly noted by those not currently wearing an activity-tracking device. Participants believed that wrist-worn sensors were the most versatile and easy to use, and therefore, the most suitable for long-term use. In particular, Actiwatch and Wavelet stood out for their comfort. The convergence of quantitative and qualitative data was demonstrated in the study. Conclusions Based on the results, the following context-specific recommendations can be made: (1) researchers should consider their device selection in relation to both individual and environmental factors, and not simply the primary outcome of the research study; (2) if researchers do not wish their participants to have access to feedback from the devices, then a simple, wrist-worn device that acts as a watch is preferable; (3) if feedback is allowed, then it should be made available to help participants remain engaged; this is likely to apply only to people without cognitive impairments; (4) battery life of 1 week should be considered as a necessary feature to enhance data capture; (5) researchers should consider providing additional information about the purpose of devices to participants to support their continued use.

Background: Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity. Objectives: We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness. Methods: We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone. Results: During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning. Conclusions: This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.

Physical activity (PA) is a key component in the treatment of a range of chronic health conditions. It is therefore important for researchers and clinicians to accurately assess and monitor PA. Although advances in wearable technology have improved this, there is a need to investigate PA in greater depth than the sum of its total parts. Specifically, linking deep PA data to patient outcomes offers a valuable, and unexplored use for wearable devices. As a result, this paper extracts useful features from accelerometer data (Actigraph GT3X Link), and applies machine learning algorithms to predict daily pain and stiffness. This was applied to a population of 30 arthritis patients and 15 healthy volunteers. Participants were provided with an Actigraph and asked to wear it continuously for 28 days. Results demonstrate that it is possible to predict both pain and stiffness of patients using the extracted accelerometer features.