Abstract

Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed.

Highlights

  • Cloud computing, i.e., the storage and processing of data on the internet, is currently experiencing an upsurge: infrastructure, platform, and software-as-a-service products (IaaS, Paas, Saas) make the acquisition and maintenance of on-premises Information Technology (IT) infrastructure obsolete and promise cost-effective, foolproof, and compliant data processing worldwide

  • Cloud computing paves the way for new opportunities in healthcare as well: electronic patient records, telemedicine, and mHealth are among the first applications already implemented based on cloud computing [1,2,3]

  • The current paper aims to provide a process of requirement collection based on a concrete application, drafting of a reference architecture, as well as an implementation concept, which can serve as a blueprint for cloud based therapy and diagnosis platform applications

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Summary

Introduction

I.e., the storage and processing of data on the internet, is currently experiencing an upsurge: infrastructure-, platform-, and software-as-a-service products (IaaS, Paas, Saas) make the acquisition and maintenance of on-premises Information Technology (IT) infrastructure obsolete and promise cost-effective, foolproof, and compliant data processing worldwide. By combining data from different data sources (including environmental and other data) the context-aware health paradigm of Smart Cities could lead to an improved diagnosis of sleep disorders [4]. Context-aware health services include standard data from structured domains (diagnoses, procedures, laboratory results, vital signs, medication), patient-reported outcomes (PROs), called patient-generated health data (PGHD), as well as health related environmental factors and social determinants. The aim of s-Health is to provide citizens and patients with healthcare applications and services that automatically adapt to discovered context by changing these systems’ behaviour. Insomnia is diagnosed by determining up to eight diagnostic criteria defined by the Diagnostic and Statistical Manual of Mental Disorders in its fifth edition (DSM-5): first, anamnesis and status praesens of the patient are obtained. The patient’s individual factors causing insomnia are analysed during sleep anamnesis: using sleep diaries and surveys, sleep behaviour, subjective sleep quality, and

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