Introduction: Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping patients manage their symptoms effectively at home, and providing guidance and decision support for primary care. We aim to establish a gold standard of care by systematically analysing symptom clusters and current practices, iteratively improving pathways and systems of care, and working to disseminate better practices. Methods and analysis: This mixed-method, multi-site study is informed by the principles of applied health services research, quality improvement, co-design, and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment; and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: [1] specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access; [2] patient self-management at home, with technology-supported monitoring; and [3] generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and prioritisation criteria across the primary-secondary care interface, along with analysis of costs. Study governance includes an active patient advisory group. Ethics and dissemination: LOCOMOTION is sponsored by the University of Leeds and approved by Yorkshire & The Humber - Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers to influence service specifications and targeted funding streams.

Online shopping environments are becoming more interactive as technology advances. As a result, it is necessary to explore marketing theories and neuro scientific explanations to why this is the case. A reviewed approach of consumer engagement to online interactive shopping environments is considered in this chapter. The online interactive elements of traditional fashion websites that are considered includes; social media, browsing and videos. Measurements of consumer engagement are reviewed via marketing consumer engagement theories (CE) and a cognitive neuroscience technique using an Electroencephalogram (EEG) (A non-invasive procedure measuring the brain's electrical activity). ASOS.com, the U.K. top fashion online pure player, is used as a preliminary research study, the results demonstrate that engagement is significantly different in social media, video and browsing tasks, browsing for jackets elicited more engagement. Originality of this research stems from the novel way to look at engagement and the ability to combine traditional and non-traditional marketing methods thus addressing emerging fields of the future such as virtual shopping.

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<p>Light exposure is a vital regulator of physiology and behaviour in humans. However, monitoring of light exposure is not included in current wearable Internet-of-Things (IoT) devices, and only recently have international standards defined <em>alpha-opic equivalent daylight illuminance</em> measures for how the eye responds to light. This paper reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of equivalent daylight illuminance exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of equivalent daylight illuminance measurements when compared to a calibrated spectral source. This includes consideration of the directional response of the sensor, and a comparison of performance when placed on different parts of the body, and a demonstration of practical use over 7 days. Our device operates for 3.5 days between charges, with a sampling period of 30 s. It has 10 channels of measurement, over the range 415-910 nm, balancing accuracy and cost considerations. Measured alpha-opic Equivalent Daylight Illuminance results for 13 devices show a mean absolute error of less than 0.07 log lx, and a minimum between device correlation of 0.99. These findings demonstrate that accurate light sensing is feasible, including at wrist worn locations. We provide an experimental platform for use in future investigations in real-world light exposure monitoring and IoT based lighting control.</p>