Secure and Privacy-aware Data Collection and Processing in Mobile Health Systems

Healthcare systems have assimilated information and communication technologies in order to improve the quality of healthcare and patient's experience at reduced costs. The increasing digitalization of people's health information raises however new threats regarding information security and privacy. Accidental or deliberate data breaches of health data may lead to societal pressures, embarrassment and discrimination. Information security and privacy are paramount to achieve high quality healthcare services, and further, to not harm individuals when providing care. With that in mind, we give special attention to the category of Mobile Health (mHealth) systems. That is, the use of mobile devices (e.g., mobile phones, sensors, PDAs) to support medical and public health. Such systems, have been particularly successful in developing countries, taking advantage of the flourishing mobile market and the need to expand the coverage of primary healthcare programs. Many mHealth initiatives, however, fail to address security and privacy issues. This, coupled with the lack of specific legislation for privacy and data protection in these countries, increases the risk of harm to individuals. The overall objective of this thesis is to enhance knowledge regarding the design of security and privacy technologies for mHealth systems. In particular, we deal with mHealth Data Collection Systems (MDCSs), which consists of mobile devices for collecting and reporting health-related data, replacing paper-based approaches for health surveys and surveillance. This thesis consists of publications contributing to mHealth security and privacy in various ways: with a comprehensive literature review about mHealth in Brazil; with the design of a security framework for MDCSs (SecourHealth); with the design of a MDCS (GeoHealth); with the design of Privacy Impact Assessment template for MDCSs; and with the study of ontology-based obfuscation and anonymisation functions for health data.

The era of smartphones: Back to our biological makeup?

SIN FINANCIACIÓN

HEART: heart exercise and remote technologies: a randomized controlled trial study protocol.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. Cardiac rehabilitation (CR) is aimed at improving health behaviors to slow or reverse the progression of CVD disease. Exercise is a central element of CR. Technologies such as mobile phones and the Internet (mHealth) offer potential to overcome many of the psychological, physical, and geographical barriers that have been associated with lack of participation in exercise-based CR. We aim to trial the effectiveness of a mobile phone delivered exercise-based CR program to increase exercise capacity and functional outcomes compared with usual CR care in adults with CVD. This paper outlines the rationale and methods of the trial.

METHODS: A single-blinded parallel two-arm randomized controlled trial is being conducted. A total of 170 people will be randomized at 1:1 ratio either to receive a mHealth CR program or usual care. Participants are identified by CR nurses from two metropolitan hospitals in Auckland, New Zealand through outpatient clinics and existing databases. Consenting participants are contacted to attend a baseline assessment. The intervention consists of a theory-based, personalized, automated package of text and video message components via participants' mobile phones and the Internet to increase exercise behavior, delivered over six months. The control group will continue with usual CR. Data collection occurs at baseline and 24 weeks (post-intervention). The primary outcome is change in maximal oxygen uptake from baseline to 24 weeks. Secondary outcomes include post-intervention measures on self-reported physical activity (IPAQ), cardiovascular risk factors (systolic blood pressure, weight, and waist to hip ratio), health related quality of life (SF-36), and cost-effectiveness.

DISCUSSION: This manuscript presents the protocol for a randomized controlled trial of a mHealth exercise-based CR program. Results of this trial will provide much needed information about physical and psychological well-being, and cost-effectiveness of an automated telecommunication intervention. If effective, this intervention has enormous potential to improve the delivery of CR and could easily be scaled up to be delivered nationally (and internationally) in a very short time, enhancing the translational aspect of this research. It also has potential to extend to comprehensive CR (nutrition advice, smoking cessation, medication adherence).

Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.

Text message-based diabetes self-management support (SMS4BG): study protocol for a randomised controlled trial.

BACKGROUND: Addressing the increasing prevalence, and associated disease burden, of diabetes is a priority of health services internationally. Interventions to support patients to effectively self-manage their condition have the potential to reduce the risk of costly and debilitating complications. The utilisation of mobile phones to deliver self-management support allows for patient-centred care at the frequency and intensity that patients desire from outside the clinic environment. Self-Management Support for Blood Glucose (SMS4BG) is a novel text message-based intervention for supporting people with diabetes to improve self-management behaviours and achieve better glycaemic control and is tailored to individual patient preferences, demographics, clinical characteristics, and culture. This study aims to assess whether SMS4BG can improve glycaemic control in adults with poorly controlled diabetes. This paper outlines the rationale and methods of the trial. METHODS/DESIGN: A two-arm, parallel, randomised controlled trial will be conducted across New Zealand health districts. One thousand participants will be randomised at a 1:1 ratio to receive SMS4BG, a theoretically based and individually tailored automated text message-based diabetes self-management support programme (intervention) in addition to usual care, or usual care alone (control). The primary outcome is change in glycaemic control (HbA1c) at 9 months. Secondary outcomes include glycaemic control at 3 and 6 months, self-efficacy, self-care behaviours, diabetes distress, health-related quality of life, perceived social support, and illness perceptions. Cost information and healthcare utilisation will also be collected as well as intervention satisfaction and interaction. DISCUSSION: This study will provide information on the effectiveness of a text message-based self-management support tool for people with diabetes. If found to be effective it has the potential to provide individualised support to people with diabetes across New Zealand (and internationally), thus extending care outside the clinic environment. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12614001232628 .

Enhancement of sensor data transmission by inference and efficient data processing

When wearable and personal health device and sensors capture data such as heart rate and body temperature for fitness tracking and health services, they simply transfer data without filtering or optimising. This can cause over-loading to the sensors as well as rapid battery consumption when they interact with Internet of Things (IoT) networks, which are expected to increase and de-mand more health data from device wearers. To solve the problem, this paper proposes to infer sensed data to reduce the data volume, which will affect the bandwidth and battery power reduction that are essential requirements to sensor devices. This is achieved by applying beacon data points after the inferencing of data processing utilising variance rates, which compare the sensed data with ad-jacent data before and after. This novel approach verifies by experiments that data volume can be saved by up to 99.5% with a 98.62% accuracy. Whilst most existing works focus on sensor network improvements such as routing, operation and reading data algorithms, we efficiently reduce data volume to reduce band-width and battery power consumption while maintaining accuracy by implement-ing intelligence and optimisation in sensor devices.

Choisir dans l'abondance : les applications mobiles en santé

Colloque organisé par le Regroupement stratégique d’éthique en santé et le Regroupement stratégique de recherche sur les TIC et la santé (Réseau de recherche en santé des populations du Québec) et par le Centre de recherche sur la communication et la santé (Université du Québec à Montréal).