Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare

Features of a mobile health intervention to manage chronic obstructive pulmonary disease: a qualitative study

Background: The use of mobile health (mHealth) interventions has the potential to enhance chronic obstructive pulmonary disease (COPD) treatment outcomes. Further research is needed to determine which mHealth features are required to potentially enhance COPD self-management. Aim: The aim of this study was to explore the potential features of an mHealth intervention for COPD management with healthcare providers (HCPs) and patients with COPD. It could inform the development and successful implementation of mHealth interventions for COPD management. Methods: This was a qualitative study. We conducted semi-structured individual interviews with HCPs, including nurses, pharmacists and physicians who work directly with patients with COPD. Interviews were also conducted with a diverse sample of patients with COPD. Interview topics included demographics, mHealth usage, the potential use of medical devices and recommendations for features that would enhance an mHealth intervention for COPD management. Results: A total of 40 people, including nurses, physicians and pharmacists, participated. The main recommendations for the proposed mHealth intervention were categorised into two categories: patient interface and HCP interface. The prevalent features suggested for the patient interface include educating patients, collecting baseline data, collecting subjective data, collecting objective data via compatible medical devices, providing a digital action plan, allowing patients to track their progress, enabling family members to access the mHealth intervention, tailoring the features based on the patient’s unique needs, reminding patients about critical management tasks and rewarding patients for their positive behaviours. The most common features of the HCP interface include allowing HCPs to track their patients’ progress, allowing HCPs to communicate with their patients, educating HCPs and rewarding HCPs. Conclusion: This study identifies important potential features so that the most effective, efficient and feasible mHealth intervention can be developed to improve the management of COPD

Oscillatory Positive Expiratory Pressure (OPEP) therapy in COPD

People with chronic obstructive pulmonary disease (COPD) commonly have a productive cough due to mucus hypersecretion. Clearing mucus from the chest can be difficult, as lung hyperinflation, respiratory muscle dysfunction and premature airway collapse impede the ability to generate an effective cough. Airway Clearance Techniques (ACTs) with the use of oscillating positive expiratory pressure (OPEP) devices can be added to the usual care for sputum clearance. However, assessment of the effect of OPEP devices is so far based on short-term studies with low-grade evidence and there is a lack of information regarding their long-term impact and effectiveness. In this thesis, I have four results chapters to discuss this gap. First, using accepted systematic review methodology to rigorously examine the current evidence on the use of OPEP devices for the treatment of cough and sputum clearance in patients with COPD who frequently produce sputum. Second, conduct a randomised clinical trial (acronym: O-COPD) to evaluate the impact of an OPEP device (the Acapella) on the health-related quality of life in patients with COPD over three-months. Third, study cough characteristics and its relationship to overnight sleep disturbances. Fourth, evaluate the impact of an OPEP device (the Acapella) on cough frequency and sleep actigraphy in a subset of the O-COPD group. In summary, results from the O-COPD trial, coupled with the systematic review, can address the concerns raised regarding the long-term effectiveness of OPEP devices in treating sputum aspects in stable COPD patients. COPD patients with sputum production who received OPEP treatment for three months, compared to the usual care, demonstrated better disease management and improvement in general and cough-related quality of life (LCQ). The findings suggest that adding the OPEP device is effective in optimising the usual care and, perhaps, can be the new mode of usual care in managing cough and sputum production in COPD patients. Larger and longer clinical trials are required to guide the long-term use of OPEP and patient selection.Open Acces

The use of mobile health in the management of Chronic Obstructive Pulmonary Disease

The prevalence and mortality rates of chronic obstructive pulmonary disease (COPD) are increasing worldwide. Therefore, COPD remains a major public health problem. Using an mobile health (mHealth) intervention has the potential to enhance COPD treatment outcomes while mitigating healthcare costs. However, the complexity of the process of developing an mHealth intervention for COPD management is poorly understood, and in-depth assessment of the development process of mHealth interventions for COPD management is currently lacking. This thesis advances our understanding of how to apply the human-centered design process when developing an mHealth intervention for COPD management. The thesis is composed of the following five interconnected journal articles: 1. A systematic review and meta-analysis to summarize and quantify the effect of mHealth interventions on patients with COPD; 2. A qualitative study to explore the perceptions of healthcare providers regarding an mHealth intervention for COPD management; 3. A mixed methods study to explore the perceptions of patients with COPD regarding an mHealth intervention for COPD management; 4. A qualitative study to identify the features of an mHealth intervention for COPD management; and, 5. A mixed methods approach, the iterative convergent design, to guide the usability testing process for mHealth interventions. The outcomes of this research contribute to knowledge about the use of mHealth in COPD management. Firstly, this thesis provides an overview of the effectiveness of mHealth in COPD management. Secondly, it provides an understanding of how to actively and efficiently involve users in the design and development of health information technology. Thirdly, it provides recommendations regarding the features of an mHealth intervention to enhance COPD management. Lastly, it proposes a mixed methods framework for mHealth usability testing. The application of the proposed methods is demonstrated using different case studies. This program of research highlights the process of developing an mHealth intervention for COPD management. Application of the findings could help others in the field to further investigate the development of mHealth interventions in this area

Identifying Physical Activity Profiles in COPD Patients Using Topic Models

With the growing amount of physical activity (PA) measures, the need for methods and algorithms that automatically analyse and interpret unannotated data increases. In this paper PA is seen as a combination of multi-modal constructs that can co-occur in different ways and proportions during the day. The design of a methodology able to integrate and analyse them is discussed and its operation is illustrated by applying it to a data set comprising data from COPD patients and healthy subjects acquired in daily life. The method encompasses different stages. The first stage is a completely automated method of labelling low-level multi-modal PA measures. The information contained in the PA labels are further structured using topic modelling techniques, a machine learning method from the text processing community. The topic modelling discovers the main themes that pervade a large set of data. In our case, topic models discover PA routines that are active in the assessed days of the subjects under study. Applying the designed algorithm to our data provides new learnings and insights. As expected, the algorithm discovers that PA routines for COPD patients and healthy subjects are substantially different regarding their composition and moments in time in which transitions occur. Furthermore, it shows consistent trends relating to disease severity as measured by standard clinical practice