eHealth-based patient reported outcome real-time measurements

Modern wearable technology, especially from the health and fitness sector, is increasingly being used for medical purposes. Wearables allow the assessment of daily life in a new dimension of granularity, duration and objectivity. It is generally accepted that parameters collected by such a technique can improve the quality of treatment, e.g. by improving patient "compliance" through SMS messages. On the other hand, smartphone apps facilitate the collection of patient information, such as quality of life, by regularly filling out questionnaires. It is known that CED patients often get a "fatigue", overwhelming feeling of fatigue associated with reduced energy levels and feeling of exhaustion, and the degree of fatigue associated with the severity of the disease. Primary and secondary depressive disorders are also common comorbidities of CED, which may affect activity levels, compliance and outcome. Since fatigue and depression are reflected in disturbed physical activity, we want to use real-time tracking wearables to measure movement, relaxation and sleep quality/pattern with validated algorithms. Applicants postulate that physical activity levels and objective assessment of sleep quality (e.g. by assessing sleep movements) may provide evidence of fatigue/fatigue and depression in patients with CED. The data entered can also be used to assess the disease status during the MMB.
Together with the other collected data from this project, predictive models for the course of the disease will be developed. In order to implement the project, the following working objectives were defined for SP3:
1. Continuous collection of objective parameters on sleep quality, tri-axial trunk accelerometry, physical activity quantification and energy consumption from the home environment with validated portable medical devices and an eHealth platform.

2. The parameters collected in (1) shall be related to molecular and clinical parameters to molecular and clinical parameters of disease control and treatment success, allowing a direct judgement of the accuracy of these parameters to reflect the individual’s wellbeing.

Simplified overview of the system architecture

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