Novel advances in engineering and data analytics are revolutionizing both our ability to monitor Parkinson’s disease (PD) patient symptoms and our understanding of neuropathology. Despite promise, key challenges exist before patient monitoring technologies become standard in clinical settings, including 1) industry standardization of sensor-based analytical approaches; 2) validation of endpoint sensitivity to degree of impairment and medication state; and 3) consensus regarding appropriate devices, algorithms, data requirements, and statistical analysis requirements for symptom measurement outside of the clinic. In addition to the need for better patient monitoring, no disease-modifying therapeutics currently exist and thorough understanding of the neuropathology of PD remains elusive. To this end, large network brain simulations that leverage efficient computational frameworks are beginning to provide insight into mechanisms that facilitate pathological oscillations and may serve to identify new therapeutic targets. To address current limitations in patient monitoring and our understanding of neuropathology, in this dissertation I 1) develop and evaluate validity and reliability of an open-source, wearable sensor-based algorithm for measuring gait in PD patients, 2) evaluate and compare sensitivity of at-home measurements relative to in-clinic measurements, 3) evaluate sensitivity of wearable-derived features for measuring degree of gait impairment and treatment response in PD patients, and 4) investigate the effect of synaptic parameters on beta synchrony and entrainment in a large-scale spiking model of the subthalamic nucleus-globus pallidus externa (STN-GPe) network of the basal ganglia. Importantly, I find that sensor-derived features derived from the at-home environment differ from and are more sensitive to small changes compared to in-clinic, traditional assessments. Furthermore, I demonstrate the capacity for a single, lower back sensor-based algorithm to estimate gait features with sufficient sensitivity to detect degree of gait impairment and treatment effect in a mild-to-moderate PD population. Lastly, I demonstrate that weak synaptic connections between STN and GPe allows the STN-GPe network to entrain to a wide range of frequencies outside of the beta range, thus elucidating constraints on conditions required for beta production. Together, my work provides new insights into the feasibility and benefits of sensor-based symptom monitoring and PD-related neuropathology.

BACKGROUND: While smartphone digital phenotyping smartphone apps today can collect vast amounts of information on participants, less is known about how this data can be shared back with participants. Effective data visualization is critical to ensuring applications of digital signals are more informed, ethical, and impactful. But little is known about how sharing of this data, especially at different levels from raw data to analyzed data, impacts patients’ perceptions. METHODS: We compared five different visualizations strategies, each a graph, generated from data created by the open source mindLAMP app, that reflected different ways to share data from simple amount of data captured to more complex clinical correlations. All graphs were shown to 28 participants during individual video interviews, and the graphs usability was measured via the System Usability Scale (SUS). Additionally, participants were asked about their comfort sharing different kinds of data, administered the Digital Working Alliance Inventory (D-WAI), and if they would want to use these visualizations with care providers. RESULTS: Of the five graphs shown to participants, the graph visualizing change in survey responses over the course of a week, received the highest usability score, with the graph showing multiple metrics changing over a week receiving the lowest usability score. Participants were significantly more likely to be willing to share geolocation data after viewing the graphs, and 25 of 28 participants agreed that they would like to use these graphs to communicate with their clinician. CONCLUSIONS: Data visualization can help participants and patients understand digitally-sourced data and increase trust in how they are sampled and used to create visualizations. As data sourced from digital technology becomes more complex, simple visualizations may fail to capture their multiple dimensions and new interactive data visualizations may be necessary to help realize their full value.

Yes / Introduction The role of digital health in providing psychological treatment and support for the prevention of suicide is well documented. Particular emphasis has been placed on digital health technologies during the COVID-19 pandemic. Providing psychological support reduces the burden of mental health conditions. The challenge is to provide support in the context of patient isolation, which highlights the role of digital technology (video conferencing, smartphone apps and social media). There is, however, a dearth of literature where experts by experience have been involved in the end-to-end process of developing digital health tools for suicide prevention. Methods and analysis This study aims to codesign a digital health tool for suicide prevention focusing on the enablers and barriers. The scoping review protocol is phase I within a three-phase study. The protocol will inform the second phase of the study which is the scoping review. The results of the review will inform a funding application to National Institute for Health and Care Research to codesign a digital health tool for suicide prevention (the third phase). The search strategy will follow the Joanna Briggs Institute Reviewer’s Manual for Scoping Reviews and incorporates the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist to ensure reporting standards are maintained. The methodology will be supplemented by frameworks by Arksey and O’Malley and Levac et al. The search strategy dates for screening are from November 2022 to March 2023. Five databases will be searched: Medline, Scopus, CINAHL, PsycInfo and Cochrane Database of Systematic Reviews. Grey literature searches include government and non-government health websites, Google and Google Scholar. The data will be extracted and organised into relevant categories. The results will be synthesised into themes and inform phase II of the study. Ethics and dissemination Ethics granted by the University of Bradford on 15 August 2022, reference E995. The project team will design a digital health tool, results will be published in a peer-review journal and disseminated through conferences. Study registration number Safety (Mental Health) Innovation Challenge Fund 2022–2023 Protocol RM0223/42079 Ver 0.1. / This research was funded by the National Institute for Health and Care Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC). Project Reference: SICF 2022-02.

Background – Digitalization and the utilization of ICTs in healthcare have revolutionized the healthcare industry through digital healthcare platforms (DHP). ICTs and DHP’s are highly beneficial for those who have the ability to use them, meanwhile increasing the digital segregation for those who cannot, e.g., elderly generations. Although, elderly generations can particularly benefit from utilizing DHP’s, e.g., enabling independent living. Furthermore, the elderly population is expected to grow globally in the upcoming years, which affects governmental expenditures on healthcare. Purpose – The aim of this study is to investigate what causes elderly not to adopt and use DHP’s, i.e., investigate and develop a deeper understanding of the central barriers for elderly. Furthermore, the second aim is to investigate how DHP providers better can facilitate increased adoption and usage among the elderly generations. Method – This study followed an abductive research approach of a single case company that offers healthcare services through a digital platform. To answer research questions, we primarily used a qualitative research method as well as quantitative data to strengthen our findings. In total, 22 qualitative interviews were conducted with respondents over 65. The interviews were divided between previous users of digital health platforms and non-users. The quantitative data were analyzed through SPSS where uni- and bivariate analysis was performed. Findings – Our finding disclosed that elderly experience mainly two barriers for adoption of DHP’s among elderly: negative attitudes and technology anxiety and one barrier for both adoption and usage: trust. This adds to the previous literature which has not made any distinction between adoption and usage of DHP’s among elderly. Furthermore, this study provides direct development suggestions that can enable increased adoption and usage. Theoretical implications – This study contributes to previous digital health literature by considering digital platforms a part of healthcare. We contribute with deepened knowledge regarding barriers that hinders adoption for elderly and by suggesting development suggestions that are able to bridge each barrier. Managerial implications – The managerial implications of this research are directed towards DHP providers on how they can facilitate increased adoption and usage respectively. The implications include design and marketing options that are able to enable the objective.

Patients in hospital can be prone to sudden, life-threatening changes in their cardiovascular state. Haemodynamic parameters such as blood pressure, pulse transit time (PTT) and perfusion can be monitored in clinical situations to identify these changes as early as possible. Continuous blood pressure is usually monitored using a catheter placed into a major artery, but this is invasive and involves risk to the patient. In the last decade, the field of non-contact vital sign monitoring has emerged, with growing evidence that the remote photoplethysmogram (rPPG) signal can be used to estimate vital signs using video cameras. If the analysis of the rPPG signal can be expanded to include the estimation of haemodynamic parameters, it could result in methods for the continuous, non-contact monitoring of a subject's haemodynamic state. In a physiology study, a series of video recordings were made of 43 healthy volunteers. The subjects sat in a purpose-built chamber, and the composition of the air was carefully adjusted to cause the subjects to experience large, controlled changes in blood oxygen levels. To validate the video camera algorithms, reference data were also collected. Along with the volunteer study, a clinical study was performed to acquire data in a challenging clinical environment. Data were collected from patients on haemodialysis in the Renal Unit, a population likely to experience sudden changes in haemodynamics. The reference data from the Renal Unit study were analysed to determine the extent to which PTT and mean arterial pressure (MAP) are related. The correlation coefficients and linear fits were found on a global and a per-subject basis. In addition, the video recordings from the Physiology study were processed to derive rPPG signals, and these signals were analysed to obtain estimates for PTT. Local rPPG signals were also derived for different regions of interest, and the waveforms were analysed using a novel application of the technique of signal averaging to produce spatial maps of perfusion and blood flow. The correlation between conventionally measured PTT and MAP was found to be weaker in the haemodialysis population than has been shown elsewhere in the literature, except for a sub-set of patients. The results of the video analysis showed that PTT could be estimated robustly and consistently, although direct validation of these estimates was not possible because of the different method used to calculate the reference PTT. For most subjects, the spatial mapping methods produced robust maps that were consistent over time. These results suggest that it is possible to detect changes in haemodynamics using a video camera, and that this could have applications in healthcare, providing that challenges such as subject movement and clinical validation can be overcome.

Background: Atrial fibrillation (AF) confers high risk of stroke, but often goes undiagnosed due to difficulties in its diagnosis. AF detection is important in post-stroke populations for secondary prevention and smartwatches have emerged as a promising modality for detecting AF, but little is known about their use in older adults who have experienced a stroke. Methods: This dissertation uses data from the Pulsewatch study, a two-phased trial assessing accuracy, usability, and adherence of smartwatch-based AF detection among older patients after stroke. Analyses performed include: descriptive statistics, linear and logistic regressions, qualitative and mixed-methods analyses, mixed effects modeling, and group-based trajectory modeling. Results: The Pulsewatch system was 91% accurate in detecting AF compared to a clinical gold-standard. Participants found the system easy to use, but indicated that streamlining the smartwatch’s functionalities to focus on passive cardiac monitoring is crucial. Improving battery life to allow for longer wear time would alleviate anxiety in some participants. Participants with previous experience using cardiac rhythm monitors rated the system lower on usability, but overwhelmingly preferred it to previous monitors due to the watch’s comfort, appearance, and convenience. Watch wear decreased over time, and we observed three distinct patterns of decline. No individual-level characteristics were associated with usability or adherence to watch wear. Conclusions: Smartwatches are promising for AF detection in older adults after stroke, though while they offer high accuracy and usability, adherence to wear is low. Strategies to encourage extended watch wear are necessary to realize the potential of smartwatches as a viable cardiac monitoring modality.

Human vital signs are crucial parameters which reflect essential body functions and are often accessed by medical professionals at the first place during clinical diagnostics to provide immediate assistance in health status measurements. However, due to the recent COVID-19 pandemic, measurements made with direct body contact have become increasingly challenging and costly because of the spreading nature of this virus. Therefore, contactless vital sign measurements are highly desirable, and it motivates us to research and develop a new solution which is capable of performing real time heart rate (HR) detection, respiratory (RR) detection, and body temperature (BT) measurement together from a distant human subject under an ambient light environment. The thesis describes a new system framework, which utilizes the power of computer vision to collect remote video image data, processes them using signal processing and machine learning (ML) technologies simultaneously, and produces rapid updates on display. Furthermore, our validation analysis on the system has showed varied results based on different methodologies used, which enables us to apply the most suitable approach on each component for an optimized final integration. At the time of completing this thesis, we have achieved a complete system integrated with remote HR, RR estimations and BT detection, which are all fully functional in both real-time and offline. To further refine the performance on HR estimation, we selected the extreme gradient boost model through a number of ML models we tested, as it not only gives the lowest root mean square error of 8.2 but also produces stable and robust output.

Yes / Stroke is the leading cause of adult disability in the UK. Many stroke patients report a feeling of ‘abandonment’ when they are discharged from therapy services. This in turn, leads to readmissions and re-referrals to both hospital and community services. This paper discusses the components of E-health and how E-health could be used to supplement conventional interventions to improve patient engagement, empowerment and decreased reliance on therapy services. In doing so it will consider how such interventions could better involve carers. An integrated E-health approach has the potential to improve outcomes for stroke survivors. A person centred approach needs to be employed through partnerships between stroke survivor, carer and healthcare professional. Remote monitoring could provide specific targeted interventions, preventing unnecessary hospital admissions or re-referrals and reducing cost of care. While the issues are well defined more work is required on what these integrated, patient centred E-health solutions may look like in order to be successful in supporting stroke survivors. / The full-text of this article will be released for public view at the end of the publisher embargo on 11 Apr 2018.

Background. Advances in healthcare have resulted in a growing population of Children with Medical Complexities (CMC). Medical management of the complex needs of CMC has traditionally taken place in hospital settings, such as clinics, emergency departments, and hospital wards, by specialized teams of healthcare clinicians. While access to expert-level care is necessary, the hospital-based model of care is expensive and inconvenient and has resulted in harms such as medical errors conferred upon CMC. Models of care that allow for expert-delivered hospital-to-home care for CMC are needed. The aim of this dissertation was to investigate the needs, performance, and perceptions of CMC families and clinicians when using a hospital-to-home digital health system called DigiComp Kids. Methods. This study was guided by A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies, which attends to technological, human, and contextual variables influencing virtual care. A scoping review was conducted to provide an overview of how digital healthcare has been used with medically fragile children, before co-designing the DigiComp Kids system with CMC family members and clinicians. Usability testing of DigiComp Kids was conducted measuring user effectiveness, efficiency, satisfaction, and experiences. Results. Across studies in this dissertation, technological, human, and contextual factors each played a role in the usability of digital health systems, including DigiComp Kids. In the scoping review, these factors influenced the acceptability, accessibility, and implementation success of digital healthcare systems for CMC. During DigiComp Kids co-design and usability testing, participants emphasized how these factors affected their willingness to engage with the system, how it fit into their lives and workflows, and where improvements could be made. Conclusions. This study highlights the importance of engaging with end-users as well as attending to technological, human, and contextual factors when designing and testing digital health systems. / Thesis / Doctor of Philosophy (PhD)

People with knee Osteoarthritis (OA) often develop negative psychosocial beliefs like pain catastrophizing and fear avoidance that can interfere with engagement in physical activity and adherence to exercise. This can lead to further pain and disability since exercise and physical activity are the first line treatment for knee OA. Therefore, there is a need for interventions that address negative psychosocial beliefs related to exercise and low adherence along with addressing physical impairments of knee OA. This dissertation examined the safety, feasibility, and acceptability of a novel telehealth mindful exercise intervention for people with knee OA. The mindful exercise intervention trains individuals to incorporate concepts of mindfulness into strengthening exercises recommended for knee OA. The intervention was delivered via telehealth to facilitate access. Study 1 used a decentralized randomized controlled trial (RCT) of mindful exercise (n=21) vs. exercise alone (n=19) in people with knee OA. Mindful exercise was safe with 0 adverse events (vs. 4 in exercise group) and lower use of oral analgesics. The design was feasible for recruitment and retention, but adherence was suboptimal (53% in mindful exercise group) and the cohort was not racially diverse. Participants in the mindful exercise group reported larger clinically meaningful improvements in pain intensity, interference, catastrophizing, quality of life, and global assessment of knee OA compared to the exercise group. Study 2 was to qualitatively determine the acceptability of the mindful exercise intervention. Participants in the mindful exercise group of the RCT (n = 13 of 21) participated in individual interviews that were informed by the Theoretical Framework of Acceptability. Participants valued the content (exercise and mindfulness) and format (telehealth, group) of the intervention. Areas for further refinement included exercise selection and equipment, additional support and education on mindfulness, and greater flexibility with timing and nature of intervention sessions. Study 3 investigated the association between telehealth satisfaction and ehealth literacy in both groups. Participants in this cohort had high ehealth literacy (mean = 31.3 on a 8–40 scale) at baseline and high satisfaction with telehealth (mean = 5.6 on a 1–7 scale) at the end of the intervention. There was no association between ehealth literacy and telehealth satisfaction (R2=0.01, p=0.61). In conclusion, telehealth mindful exercise could be a safe and feasible intervention for people with knee OA. However, further refinement to improve adherence and acceptability are needed prior to efficacy studies.