75,530 research outputs found
Visions and Challenges in Managing and Preserving Data to Measure Quality of Life
Health-related data analysis plays an important role in self-knowledge,
disease prevention, diagnosis, and quality of life assessment. With the advent
of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices
(wearables, home-medical sensors, etc) facilitates data collection and provide
cloud storage with a central administration. More recently, blockchain and
other distributed ledgers became available as alternative storage options based
on decentralised organisation systems. We bring attention to the human data
bleeding problem and argue that neither centralised nor decentralised system
organisations are a magic bullet for data-driven innovation if individual,
community and societal values are ignored. The motivation for this position
paper is to elaborate on strategies to protect privacy as well as to encourage
data sharing and support open data without requiring a complex access protocol
for researchers. Our main contribution is to outline the design of a
self-regulated Open Health Archive (OHA) system with focus on quality of life
(QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System
Manipulating the Circadian and Sleep Cycles to Protect Against Metabolic Disease
Modernization of human society parallels an epidemic of metabolic disorders including obesity. Apart from excess caloric intake, a 24/7 lifestyle poses another important challenge to our metabolic health. Recent research under both laboratory and epidemiological settings has indicated that abnormal temporal organization of sleep and wakeful activities including food intake is a significant risk factor for metabolic disease. The circadian clock system is our intrinsic biological timer that regulates internal rhythms such as the sleep/wake cycle and also responses to external stimuli including light and food. Initially thought to be mainly involved in the timing of sleep, the clock and/or clock genes may also play a role in sleep architecture and homeostasis. Importantly, an extensive body of evidence has firmly established a master regulatory role of the clock in energy balance. Together, a close relationship between well-timed circadian/sleep cycles and metabolic health is emerging. Exploiting this functional connection, an important holistic strategy toward curbing the epidemic of metabolic disorders (e.g. obesity) involves corrective measures on the circadian clock and sleep. In addition to behavioral and environmental interventions including meal timing and light control, pharmacological agents targeting sleep and circadian clocks promise convenient and effective applications. Recent studies, for example, have reported small molecules targeting specific clock components and displaying robust beneficial effects on sleep and metabolism. Furthermore, a group of clock-amplitude enhancing small molecules (CEMs) identified via high-throughput chemical screens are of particular interest for future in vivo studies of their metabolic and sleep efficacies. Elucidating the functional relationship between clock, sleep and metabolism will also have far-reaching implications for various chronic human diseases and aging
Trading Data for Discounts: An Exploration of Unstructured Data Through Machine Learning in Wearable Technology
The development of computing sensor devices with the capability of tracking an individual’s activity changed the way we live and move. The data collected and generated from wearable technology provides implications to the user for leading a healthy, more active lifestyle; however, the potential data uses extend beyond the user. Significant opportunity exists in the insurance industry as it relates to discounting premiums. The purpose of this research was to provide insight as to whether insurance companies should consider offering discount on premiums for policyholders who use wearable technology to track their personal fitness by identifying and suggesting potential groups of consumers to target these discounts toward. Using the platform R, researchers collected and analyzed tweets about four leading wearable technology companies including Fitbit, Jawbone, Misfit, and Withings. Both unsupervised and supervised learning techniques were pursued during the study in the form of topic modeling and artificial intelligence. Through detailed analysis, researchers determined that companies may want to consider reducing premiums for wearable technology users who use the devices for weight loss, as it would benefit both policyholders and insurance companies
Self-monitoring Practices, Attitudes, and Needs of Individuals with Bipolar Disorder: Implications for the Design of Technologies to Manage Mental Health
Objective To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management.
Materials and Methods Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses.
Results Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data.
Discussion Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor.
Conclusion This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management
360 Quantified Self
Wearable devices with a wide range of sensors have contributed to the rise of
the Quantified Self movement, where individuals log everything ranging from the
number of steps they have taken, to their heart rate, to their sleeping
patterns. Sensors do not, however, typically sense the social and ambient
environment of the users, such as general life style attributes or information
about their social network. This means that the users themselves, and the
medical practitioners, privy to the wearable sensor data, only have a narrow
view of the individual, limited mainly to certain aspects of their physical
condition.
In this paper we describe a number of use cases for how social media can be
used to complement the check-up data and those from sensors to gain a more
holistic view on individuals' health, a perspective we call the 360 Quantified
Self. Health-related information can be obtained from sources as diverse as
food photo sharing, location check-ins, or profile pictures. Additionally,
information from a person's ego network can shed light on the social dimension
of wellbeing which is widely acknowledged to be of utmost importance, even
though they are currently rarely used for medical diagnosis. We articulate a
long-term vision describing the desirable list of technical advances and
variety of data to achieve an integrated system encompassing Electronic Health
Records (EHR), data from wearable devices, alongside information derived from
social media data.Comment: QCRI Technical Repor
Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition
Driven by real-world applications such as fitness, wellbeing and healthcare, accelerometry-based activity recognition has been widely studied to provide context-awareness to future pervasive technologies. Accurate recognition and energy efficiency are key issues in enabling long-term and unobtrusive monitoring. While the majority of accelerometry-based activity recognition systems stream data to a central point for processing, some solutions process data locally on the sensor node to save energy. In this paper, we investigate the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes. An empirical energy model is presented and used to evaluate the energy efficiency of both systems, and a practical case study (monitoring the physical activities of office workers) is developed to evaluate the effect on classification accuracy. The results show a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer’s activity
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