6,434 research outputs found
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
Technology has recently been recruited in the war against the ongoing obesity
crisis; however, the adoption of Health & Fitness applications for regular
exercise is a struggle. In this study, we present a unique demographically
representative dataset of 15k US residents that combines technology use logs
with surveys on moral views, human values, and emotional contagion. Combining
these data, we provide a holistic view of individuals to model their physical
exercise behavior. First, we show which values determine the adoption of Health
& Fitness mobile applications, finding that users who prioritize the value of
purity and de-emphasize values of conformity, hedonism, and security are more
likely to use such apps. Further, we achieve a weighted AUROC of .673 in
predicting whether individual exercises, and we also show that the application
usage data allows for substantially better classification performance (.608)
compared to using basic demographics (.513) or internet browsing data (.546).
We also find a strong link of exercise to respondent socioeconomic status, as
well as the value of happiness. Using these insights, we propose actionable
design guidelines for persuasive technologies targeting health behavior
modification
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
PARIS: Personalized Activity Recommendation for Improving Sleep Quality
The quality of sleep has a deep impact on people's physical and mental
health. People with insufficient sleep are more likely to report physical and
mental distress, activity limitation, anxiety, and pain. Moreover, in the past
few years, there has been an explosion of applications and devices for activity
monitoring and health tracking. Signals collected from these wearable devices
can be used to study and improve sleep quality. In this paper, we utilize the
relationship between physical activity and sleep quality to find ways of
assisting people improve their sleep using machine learning techniques. People
usually have several behavior modes that their bio-functions can be divided
into. Performing time series clustering on activity data, we find cluster
centers that would correlate to the most evident behavior modes for a specific
subject. Activity recipes are then generated for good sleep quality for each
behavior mode within each cluster. These activity recipes are supplied to an
activity recommendation engine for suggesting a mix of relaxed to intense
activities to subjects during their daily routines. The recommendations are
further personalized based on the subjects' lifestyle constraints, i.e. their
age, gender, body mass index (BMI), resting heart rate, etc, with the objective
of the recommendation being the improvement of that night's quality of sleep.
This would in turn serve a longer-term health objective, like lowering heart
rate, improving the overall quality of sleep, etc.Comment: 18 pages, 7 figures, Submitted to UMUAI: Special Issue on Recommender
Systems for Health and Wellbeing, 202
Integrating Wearable Devices and Recommendation System: Towards a Next Generation Healthcare Service Delivery
Researchers have identified lifestyle diseases as a major threat to human civilization. These diseases gradually progress without giving any warning and result in a sudden health aggravation that leads to a medical emergency. As such, individuals can only avoid the life-threatening condition if they regularly monitor their health status. Health recommendation systems allow users to continuously monitor their health and deliver proper health advice to them. Also, continuous health monitoring depends on the real-time data exchange between health solution providers and users. In this regard, healthcare providers have begun to use wearable devices and recommendation systems to collect data in real time and to manage health conditions based on the generated data. However, we lack literature that has examined how individuals use wearable devices, what type of data the devices collect, and how providers use the data for delivering solutions to users. Thus, we decided to explore the available literature in this domain to understand how wearable devices can provide solutions to consumers. We also extended our focus to cover current health service delivery frameworks with the help of recommender systems. Thus, this study reviews health-monitoring services by conglomerating both wearable device and recommendation system to come up with personalized health and fitness solutions. Additionally, the paper elucidates key components of an advanced-level real-time monitoring service framework to guide future research and practice in this domain
Heart rate monitoring, activity recognition, and recommendation for e-coaching
Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter
Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach
Mobile health (mHealth) information service makes healthcare management
easier for users, who want to increase physical activity and improve health.
However, the differences in activity preference among the individual, adherence
problems, and uncertainty of future health outcomes may reduce the effect of
the mHealth information service. The current health service system usually
provides recommendations based on fixed exercise plans that do not satisfy the
user specific needs. This paper seeks an efficient way to make physical
activity recommendation decisions on physical activity promotion in
personalised mHealth information service by establishing data-driven model. In
this study, we propose a real-time interaction model to select the optimal
exercise plan for the individual considering the time-varying characteristics
in maximising the long-term health utility of the user. We construct a
framework for mHealth information service system comprising a personalised AI
module, which is based on the scientific knowledge about physical activity to
evaluate the individual exercise performance, which may increase the awareness
of the mHealth artificial intelligence system. The proposed deep reinforcement
learning (DRL) methodology combining two classes of approaches to improve the
learning capability for the mHealth information service system. A deep learning
method is introduced to construct the hybrid neural network combing long-short
term memory (LSTM) network and deep neural network (DNN) techniques to infer
the individual exercise behavior from the time series data. A reinforcement
learning method is applied based on the asynchronous advantage actor-critic
algorithm to find the optimal policy through exploration and exploitation
Health State Estimation
Life's most valuable asset is health. Continuously understanding the state of
our health and modeling how it evolves is essential if we wish to improve it.
Given the opportunity that people live with more data about their life today
than any other time in history, the challenge rests in interweaving this data
with the growing body of knowledge to compute and model the health state of an
individual continually. This dissertation presents an approach to build a
personal model and dynamically estimate the health state of an individual by
fusing multi-modal data and domain knowledge. The system is stitched together
from four essential abstraction elements: 1. the events in our life, 2. the
layers of our biological systems (from molecular to an organism), 3. the
functional utilities that arise from biological underpinnings, and 4. how we
interact with these utilities in the reality of daily life. Connecting these
four elements via graph network blocks forms the backbone by which we
instantiate a digital twin of an individual. Edges and nodes in this graph
structure are then regularly updated with learning techniques as data is
continuously digested. Experiments demonstrate the use of dense and
heterogeneous real-world data from a variety of personal and environmental
sensors to monitor individual cardiovascular health state. State estimation and
individual modeling is the fundamental basis to depart from disease-oriented
approaches to a total health continuum paradigm. Precision in predicting health
requires understanding state trajectory. By encasing this estimation within a
navigational approach, a systematic guidance framework can plan actions to
transition a current state towards a desired one. This work concludes by
presenting this framework of combining the health state and personal graph
model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin
Effect of a moderate-intensity demonstration walk on accuracy of physical activity self-report.
Background/Objective:Providing a demonstration of a 10-minute bout of moderate-to-vigorous intensity physical activity (MVPA) immediately prior to subjective reporting of MVPA could influence self-reported activity by calibrating both duration and intensity. We assessed the effect of a demonstration of MVPA on subsequent MVPA recall, and explored whether this improved agreement with objective measures of MVPA. Methods:A total of 846 individuals participated in four different physical activity interventions; two of which included a 10-minute moderate-intensity demonstration walk on a treadmill at baseline and 6-month visits immediately prior to reporting MVPA. Participants from three studies also wore accelerometers during the week overlapping with self-reported MVPA. Results:Overall, those completing the demonstration walk reported significantly fewer minutes of MVPA per week at baseline (b = -11.69, standard error = 2.53, p < 0.01). The effect of the demonstration walk at 6 months was not significant (p = 0.06). Correlations with accelerometers at baseline were higher in the two studies with the demonstration walk (ρ = 0.28, 0.26) than the study without (ρ = 0.04). Correlations with accelerometers increased overall from baseline to follow-up. Conclusion:A 10-minute demonstration of MVPA was associated with reporting fewer minutes of MVPA and improved agreement with objective PA measures at baseline. These findings support combining self-report PA assessments with hands-on MVPA demonstrations
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