National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors

The role of quality of life data as an endpoint for collecting real-world evidence within geroscience clinical trials

With geroscience research evolving at a fast pace, the need arises for human randomized controlled trials to assess the efficacy of geroprotective interventions to prevent age-related adverse outcomes, disease, and mortality in normative aging cohorts. However, to confirm efficacy requires a long-term and costly approach as time to the event of morbidity and mortality can be decades. While this could be circumvented using sensitive biomarkers of aging, current molecular, physiological, and digital endpoints require further validation. In this review, we discuss how collecting real-world evidence (RWE) by obtaining health data that is amenable for collection from large heterogeneous populations in a real-world setting can help speed up validation of geroprotective interventions. Further, we propose inclusion of quality of life (QoL) data as a biomarker of aging and candidate endpoint for geroscience clinical trials to aid in distinguishing healthy from unhealthy aging. We highlight how QoL assays can aid in accelerating data collection in studies gathering RWE on the geroprotective effects of repurposed drugs to support utilization within healthy longevity medicine. Finally, we summarize key metrics to consider when implementing QoL assays in studies, and present the short-form 36 (SF-36) as the most well-suited candidate endpoint.

Why and how we can use data linkage in oral health research: a narrative review

OBJECTIVES

Poor oral health, impacting health and wellbeing across the life-course, is a costly and wicked problem. Data (or record) linkage is the linking of different sets of data (often administrative data gathered for non-research purposes) that are matched to an individual and may include records such as medical data, housing information and sociodemographic information. It often uses population-level data or 'big data'. Data linkage provides the opportunity to analyse complex associations from different sources for total populations. The aim of the paper is to explore data linkage, how it is important for oral health research and what promise it holds for the future.

METHODS

This is a narrative review of an approach (data linkage) in oral health research.

RESULTS

Data linkage may be a powerful method for bringing together various population datasets. It has been used to explore a wide variety of topics with many varied datasets. It has substantial current and potential application in oral health research.

CONCLUSIONS

Use of population data linkage is increasing in oral health research where the approach has been very useful in exploring the complexity of oral health. It offers promise for exploring many new areas in the field.

Transforming healthcare with big data analytics: technologies, techniques and prospects

In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review

Introduction:

Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?”

Methods:

A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review.

Results:

Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%).

Discussion:

The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics.

Conclusions:

Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

Mobile Health Applications for Prenatal Assessment and Monitoring

BACKGROUND

A mobile health application is an exciting, fast-paced domain that is likely to improve prenatal care.

METHODS

In this narrative review, we summarise the use of mobile health applications in this setting with a special emphasis on both the benefits of remote monitoring devices and the potential pitfalls of their use, highlighting the need for robust regulations and guidelines before their widespread introduction into prenatal care.

RESULTS

Remote monitoring devices for four areas of prenatal care are reported: (1) cardio-tocography; (2) blood glucose levels; (3) blood pressure; and (4) prenatal ultrasound. The majority of publications are pilot projects on remote consultation, education, coaching, screening, monitoring and selective booking, mostly reporting potential medical and/or economic benefits by mobile health applications over conventional care for very specific situations, indications and locations, but not always generalizable.

CONCLUSIONS

Despite the potential advantages of these devices, some caution must be taken when implementing this technology into routine daily practice. To date, the majority of published research on mobile health in the prenatal setting consists of observational studies and there is a need for high-quality randomized controlled trials to confirm the reported clinical and economic benefits as well as the safety of this technology. There is also a need for guidance and governance on the development and validation of new apps and devices and for the implementation of mobile health technology into healthcare systems in both high and low-income settings. Finally, digital communication technologies offer perspectives towards exploration and development of the very new domain of tele-pharmacology.

The Role of Data Type and Recipient in Individuals' Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study

Background

The growing field of personal sensing harnesses sensor data collected from individuals’ smartphones to understand their behaviors and experiences. Such data could be a powerful tool within mental health care. However, it is important to note that the nature of these data differs from the information usually available to, or discussed with, health care professionals. To design digital mental health tools that are acceptable to users, understanding how personal sensing data can be used and shared is critical.

Objective

This study aimed to investigate individuals’ perspectives about sharing different types of sensor data beyond the research context, specifically with doctors, electronic health record (EHR) systems, and family members.

Methods

A questionnaire assessed participants’ comfort with sharing six types of sensed data: physical activity, mood, sleep, communication logs, location, and social activity. Participants were asked about their comfort with sharing these data with three different recipients: doctors, EHR systems, and family members. A series of principal component analyses (one for each data recipient) was performed to identify clusters of sensor data types according to participants’ comfort with sharing them. Relationships between recipients and sensor clusters were then explored using generalized estimating equation logistic regression models.

Results

A total of 211 participants completed the questionnaire. The majority were female (171/211, 81.0%), and the mean age was 38 years (SD 10.32). Principal component analyses consistently identified two clusters of sensed data across the three data recipients: “health information,” including sleep, mood, and physical activity, and “personal data,” including communication logs, location, and social activity. Overall, participants were significantly more comfortable sharing any type of sensed data with their doctor than with the EHR system or family members (P<.001) and more comfortable sharing “health information” than “personal data” (P<.001). Participant characteristics such as age or presence of depression or anxiety did not influence participants’ comfort with sharing sensed data.

Conclusions

The comfort level in sharing sensed data was dependent on both data type and recipient, but not individual characteristics. Given the identified differences in comfort with sensed data sharing, contextual factors of data type and recipient appear to be critically important as we design systems that harness sensor data for mental health treatment and support.

Towards an efficient and Energy-Aware mobile big health data architecture

BACKGROUND AND OBJECTIVES

Mobile and ubiquitous devices are everywhere, generating an exorbitant amount of data. New generations of healthcare systems are using mobile devices to continuously collect large amounts of different types of data from patients with chronic diseases. The challenge with such Mobile Big Data in general, is how to meet the growing performance demands of the mobile resources handling these tasks, while simultaneously minimizing their consumption.

METHODS

This research proposes a scalable architecture for processing Mobile Big Data. The architecture is developed around three new algorithms for the effective use of resources in performing mobile data processing and analytics: mobile resources optimization, mobile analytics customization, and mobile offloading. The mobile resources optimization algorithm monitors resources and automatically switches off unused network connections and application services whenever resources are limited. The mobile analytics customization algorithm attempts to save energy by customizing the analytics processes through the implementation of some data-aware schemes. Finally, the mobile offloading algorithm uses some heuristics to intelligently decide whether to process data locally, or delegate it to a cloud back-end server.

RESULTS

The three algorithms mentioned above are tested using Android-based mobile devices on real Electroencephalography (EEG) data streams retrieved from sensors and an online data bank. Results show that the three combined algorithms proved their effectiveness in optimizing the resources of mobile devices in handling, processing, and analyzing EEG data.

CONCLUSION

We developed an energy-efficient model for Mobile Big Data which addressed key limitations in mobile device processing and analytics and reduced execution time and limited battery resources. This was supported with the development of three new algorithms for the effective use of resources, energy saving, parallel processing and analytics customization.

Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study

Background

Using smartphones to enroll, obtain consent, and gather self-reported data from patients has the potential to enhance our understanding of disease burden and quantify physiological impact in the real world. It may also be possible to harness integral smartphone sensors to facilitate remote collection of clinically relevant data.

Objective

We conducted the Patient Rheumatoid Arthritis Data From the Real World (PARADE) observational study using a customized ResearchKit app with a bring-your-own-device approach. Our objective was to assess the feasibility of using an entirely digital approach (social media and smartphone app) to conduct a real-world observational study of patients with rheumatoid arthritis.

Methods

We conducted this observational study using a customized ResearchKit app with a bring-your-own-device approach. To recruit patients, the PARADE app, designed to guide patients through a series of tasks, was publicized via social media platforms and made available for patients in the United States to download from the Apple App Store. We collected patient-reported data, such as medical history, rheumatoid arthritis-related medications (past and present), and a range of patient-reported outcome measures. We included in the assessment a joint-pain map and a novel objective assessment of wrist range of movement, measured by the smartphone-embedded gyroscope and accelerometer.

Results

Within 1 month of recruitment via social media campaigns, 399 participants self-enrolled, self-consented, and provided complete demographic data. Joint pain was the most frequently reported rheumatoid arthritis symptom to bother study participants (344/393, 87.5%). Severe patient-reported wrist pain appeared to be inversely linked with the range of wrist movement measured objectively by the app. At study entry, 292 of 399 participants (73.2%) indicated a preference for participating in a mobile app–based study. The number of participants in the study declined to 45 of 399 (11.3%) at week 12.

Conclusions

Despite the declining number of participants over time, the combination of social media and smartphone app with sensor integration was a feasible and cost-effective approach for the collection of patient-reported data in rheumatoid arthritis. Integral sensors within smartphones can be harnessed to provide novel end points, and the novel wrist range of movement test warrants further clinical validation.

Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

Concurrence of big data analytics and healthcare: A systematic review

BACKGROUND

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care.

PURPOSE

This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges.

DATA SOURCES

A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered.

STUDY SELECTION

Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected.

DATA EXTRACTION

Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare.

RESULTS

A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare.

CONCLUSION

This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

Harnessing interactive technologies to improve health outcomes in juvenile idiopathic arthritis

BACKGROUND

Children and adolescents with Juvenile Idiopathic Arthritis (JIA) typically have reduced physical activity level and impaired aerobic and anaerobic exercise capacity when compared to their non-JIA counterparts. Low intensity exercise regimens appear to be safe in children with JIA and may results in improvements in overall physical function. Poor adherence to paediatric rheumatology treatment may lead to negative clinical outcomes and possibly increased disease activity. This includes symptoms such as pain, fatigue, quality of life, longer term outcomes including joint damage, as well as increase of healthcare associated costs. Low adherence to medications such as methotrexate and biological-drugs remains a significant issue for paediatric rheumatologists, with alarming reports that less than half of the children with JIA are compliant to drug-therapy.

MAIN BODY

The recent advances in interactive technology resulting in a variety of wearable user-friendly smart devices may become a key solution to address important questions in JIA clinical management. Fully understanding the impact that arthritis and treatment complications have upon individual children and their families has long been a challenge for clinicians. Modern interactive technologies can be customised and accessed directly in the hands or wrists of children with JIA. These secured networks could be accessible 'live' at anytime and anywhere by the child, parents and clinicians. Multidisciplinary teams in paediatric rheumatology may benefit from adopting these technologies to better understand domains such as patient biological parameters, symptoms progression, adherence to drug-therapy, quality of life, and participation in physical activities. Most importantly the use of smart devices technologies may also facilitate more timely clinical decisions, improve self-management and parents awareness in the progression of their child's disease. Paediatric rheumatology research could also benefit from the use of these smart devices, as they would allow real-time access to meaningful data to thoroughly understand the disease-patterns of JIA, such as pain and physical activity outcomes. Data collection that typically occurs once every 1 or 3 months in the clinical setting could instead be gathered every week, day, minute or virtually live online. Arguably, few limitations in wearing such interactive technologies still exist and require further developments.

CONCLUSION

Finally, by embracing and adapting these new and now highly accessible interactive technologies, clinical management and research in paediatric rheumatology may be greatly advanced.