517 research outputs found
Happier People Live More Active Lives: Using Smartphones to Link Happiness and Physical Activity.
Physical activity, both exercise and non-exercise, has far-reaching benefits to physical health. Although exercise has also been linked to psychological health (e.g., happiness), little research has examined physical activity more broadly, taking into account non-exercise activity as well as exercise. We examined the relationship between physical activity (measured broadly) and happiness using a smartphone application. This app has collected self-reports of happiness and physical activity from over ten thousand participants, while passively gathering information about physical activity from the accelerometers on users' phones. The findings reveal that individuals who are more physically active are happier. Further, individuals are happier in the moments when they are more physically active. These results emerged when assessing activity subjectively, via self-report, or objectively, via participants' smartphone accelerometers. Overall, this research suggests that not only exercise but also non-exercise physical activity is related to happiness. This research further demonstrates how smartphones can be used to collect large-scale data to examine psychological, behavioral, and health-related phenomena as they naturally occur in everyday life.Engineering and Physical Sciences Research Council (UBhave project (Ubiquitous and Social Computing for Positive Behaviour Change, Grant ID: EP/I032673/1))This is the final version of the article. It first appeared from Public Library of Science via https://doi.org/http://dx.doi.org/10.1371/journal.pone.016058
Small-world behavior in time-varying graphs
Connections in complex networks are inherently fluctuating over time and
exhibit more dimensionality than analysis based on standard static graph
measures can capture. Here, we introduce the concepts of temporal paths and
distance in time-varying graphs. We define as temporal small world a
time-varying graph in which the links are highly clustered in time, yet the
nodes are at small average temporal distances. We explore the small-world
behavior in synthetic time-varying networks of mobile agents, and in real
social and biological time-varying systems.Comment: 5 pages, 2 figure
Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data
Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers.
Our experiments using a real-world dataset of 33, 000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood –valence and arousal– with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.This work was supported by the Embiricos Trust Scholarship of Jesus College Cambridge, EPSRC through Grants DTP (EP/N509620/1)
and UBHAVE (EP/I032673/1), and Nokia Bell Labs through the Centre of Mobile, Wearable Systems and Augmented Intelligence
Deep Learning for Mobile Mental Health: Challenges and recent advances
Mental health plays a key role in everyone’s day-to-day lives, impacting our thoughts, behaviours, and emotions. Also, over the past years, given its ubiquitous and affordable characteristics, the use of smartphones and wearable devices has grown rapidly and provided support within all aspects of mental health research and care, spanning from screening and diagnosis to treatment and monitoring, and attained significant progress to improve remote mental health interventions. While there are still many challenges to be tackled in this emerging cross-discipline research field, such as data scarcity, lack of personalisation, and privacy concerns, it is of primary importance that innovative signal processing and deep learning techniques are exploited. Particularly, recent advances in deep learning can help provide the key enabling technology for the development of the next-generation user-centric mobile mental health applications. In this article, we first brief basic principles associated with mobile device-based mental health analysis, review the main system components, and highlight conventional technologies involved. Next, we describe several major challenges and various deep learning technologies that have potentials for a strong contribution in dealing with these challenges, respectively. Finally, we discuss other remaining problems which need to be addressed via research collaboration across multiple disciplines.This paper has been partially funded by the Bavarian
Ministry of Science and Arts as part of the Bavarian Research
Association ForDigitHealth, the National Natural Science
Foundation of China (Grant No. 62071330, 61702370), and
the Key Program of the National Natural Science Foundation
of China (Grant No: 61831022)
The functional link between microsomal prostaglandin E synthase-1 (mPGES-1) and peroxisome proliferator-activated receptor γ (PPARγ) in the onset of inflammation
Many years have elapsed since the discovery of anti-inflammatories as effective therapeutics for the treatment of inflammatory-related diseases, but we are still uncovering their various mechanisms of action. Recent biochemical and pharmacological studies have shown that in different tissues and cell types lipid mediators from thearachidonic acid cascade, play a crucial role in the initiation and resolution of inflammation by shifting from pro-inflammatory prostaglandin (PG)E2 to anti-inflammatory PGD2 and PGJ2. Considering that until now very little is known about the biological effects evoked by microsomal prostaglandin E synthase-1 (mPGES-1) and contextually by peroxisome proliferator-activated receptor γ (PPARγ) modulation (key enzymes involved in PGE2 and PGD2/PGJ2metabolism), in this opinion paper we sought to define the coordinate functional regulation between these two enzymes at the "crossroads of phlogistic pathway" involved in the induction and resolution of inflammation
The architecture of innovation: Tracking face-to-face interactions with UbiComp technologies
The layouts of the buildings we live in shape our everyday lives. In office
environments, building spaces affect employees' communication, which is crucial
for productivity and innovation. However, accurate measurement of how spatial
layouts affect interactions is a major challenge and traditional techniques may
not give an objective view.We measure the impact of building spaces on social
interactions using wearable sensing devices. We study a single organization
that moved between two different buildings, affording a unique opportunity to
examine how space alone can affect interactions. The analysis is based on two
large scale deployments of wireless sensing technologies: short-range,
lightweight RFID tags capable of detecting face-to-face interactions. We
analyze the traces to study the impact of the building change on social
behavior, which represents a first example of using ubiquitous sensing
technology to study how the physical design of two workplaces combines with
organizational structure to shape contact patterns.This is the author accepted manuscript. The final version is available at http://dl.acm.org/citation.cfm?id=2632056&CFID=528294814&CFTOKEN=36484024
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease pro- gression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from dig- ital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to under- stand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.ER
Components in time-varying graphs
Real complex systems are inherently time-varying. Thanks to new communication
systems and novel technologies, it is today possible to produce and analyze
social and biological networks with detailed information on the time of
occurrence and duration of each link. However, standard graph metrics
introduced so far in complex network theory are mainly suited for static
graphs, i.e., graphs in which the links do not change over time, or graphs
built from time-varying systems by aggregating all the links as if they were
concurrent in time. In this paper, we extend the notion of connectedness, and
the definitions of node and graph components, to the case of time-varying
graphs, which are represented as time-ordered sequences of graphs defined over
a fixed set of nodes. We show that the problem of finding strongly connected
components in a time-varying graph can be mapped into the problem of
discovering the maximal-cliques in an opportunely constructed static graph,
which we name the affine graph. It is therefore an NP-complete problem. As a
practical example, we have performed a temporal component analysis of
time-varying graphs constructed from three data sets of human interactions. The
results show that taking time into account in the definition of graph
components allows to capture important features of real systems. In particular,
we observe a large variability in the size of node temporal in- and
out-components. This is due to intrinsic fluctuations in the activity patterns
of individuals, which cannot be detected by static graph analysis.Comment: 12 pages, 4 figures, 3 table
Squared PLGA microPlates as injectable depot for the management of Post-Traumatic Osteoarthritis
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