25 research outputs found
Genetic interaction with temperature is an important determinant of nematode longevity
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140048/1/acel12658_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140048/2/acel12658.pd
The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study
Research in mental health has implicated sleep pathologies with depression.
However, the gold standard for sleep assessment, polysomnography, is not
suitable for long-term, continuous, monitoring of daily sleep, and methods such
as sleep diaries rely on subjective recall, which is qualitative and
inaccurate. Wearable devices, on the other hand, provide a low-cost and
convenient means to monitor sleep in home settings. The main aim of this study
was to devise and extract sleep features, from data collected using a wearable
device, and analyse their correlation with depressive symptom severity and
sleep quality, as measured by the self-assessed Patient Health Questionnaire
8-item. Daily sleep data were collected passively by Fitbit wristband devices,
and depressive symptom severity was self-reported every two weeks by the PHQ-8.
The data used in this paper included 2,812 PHQ-8 records from 368 participants
recruited from three study sites in the Netherlands, Spain, and the UK.We
extracted 21 sleep features from Fitbit data which describe sleep in the
following five aspects: sleep architecture, sleep stability, sleep quality,
insomnia, and hypersomnia. Linear mixed regression models were used to explore
associations between sleep features and depressive symptom severity. The z-test
was used to evaluate the significance of the coefficient of each feature. We
tested our models on the entire dataset and individually on the data of three
different study sites. We identified 16 sleep features that were significantly
correlated with the PHQ-8 score on the entire dataset. Associations between
sleep features and the PHQ-8 score varied across different sites, possibly due
to the difference in the populations
Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device:Multicenter Longitudinal Observational Study
BACKGROUND: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. RESULTS: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant
Using smartphones and wearable devices to monitor behavioural changes during COVID-19
We aimed to explore the utility of the recently developed open-source mobile
health platform RADAR-base as a toolbox to rapidly test the effect and response
to NPIs aimed at limiting the spread of COVID-19. We analysed data extracted
from smartphone and wearable devices and managed by the RADAR-base from 1062
participants recruited in Italy, Spain, Denmark, the UK, and the Netherlands.
We derived nine features on a daily basis including time spent at home, maximum
distance travelled from home, maximum number of Bluetooth-enabled nearby
devices (as a proxy for physical distancing), step count, average heart rate,
sleep duration, bedtime, phone unlock duration, and social app use duration. We
performed Kruskal-Wallis tests followed by post-hoc Dunns tests to assess
differences in these features among baseline, pre-, and during-lockdown
periods. We also studied behavioural differences by age, gender, body mass
index (BMI), and educational background. We were able to quantify expected
changes in time spent at home, distance travelled, and the number of nearby
Bluetooth-enabled devices between pre- and during-lockdown periods. We saw
reduced sociality as measured through mobility features, and increased virtual
sociality through phone usage. People were more active on their phones,
spending more time using social media apps, particularly around major news
events. Furthermore, participants had lower heart rate, went to bed later, and
slept more. We also found that young people had longer homestay than older
people during lockdown and fewer daily steps. Although there was no significant
difference between the high and low BMI groups in time spent at home, the low
BMI group walked more. RADAR-base can be used to rapidly quantify and provide a
holistic view of behavioural changes in response to public health interventions
as a result of infectious outbreaks such as COVID-19
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
Background and objectives Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients’ activity profiles has the potential to assess the level of MS-induced disability in free-living conditions.
Methods In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months’ duration. We combined these features with participants’ demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS).
Results The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT.
Conclusions This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
A number of challenges exist for the analysis of mHealth data: maintaining
participant engagement over extended time periods and therefore understanding
what constitutes an acceptable threshold of missing data; distinguishing
between the cross-sectional and longitudinal relationships for different
features to determine their utility in tracking within-individual longitudinal
variation or screening individuals at high risk; and understanding the
heterogeneity with which depression manifests itself in behavioral patterns
quantified by the passive features. From 479 participants with MDD, we
extracted 21 features capturing mobility, sleep, and smartphone use. We
investigated the impact of the number of days of available data on feature
quality using the intraclass correlation coefficient and Bland-Altman analysis.
We then examined the nature of the correlation between the 8-item Patient
Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the
features using the individual-mean correlation, repeated measures correlation,
and linear mixed effects model. Furthermore, we stratified the participants
based on their behavioral difference, quantified by the features, between
periods of high (depression) and low (no depression) PHQ-8 scores using the
Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were
needed for reliable calculation of most of the features in the 14-day time
window. We observed that features such as sleep onset time correlated better
with PHQ-8 scores cross-sectionally than longitudinally, whereas features such
as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but
worse cross-sectionally. Finally, we found that participants could be separated
into 3 distinct clusters according to their behavioral difference between
periods of depression and periods of no depression
Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study
Methods
Results
Discussion
References
Abbreviations
Copyright
Abstract
Background:
The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored.
Objective:
We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.
Methods:
Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility.
Results:
This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=−0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, P<.001) the subsequent periodicity of mobility.
Conclusions:
Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
BACKGROUND: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE: We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS: We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS: This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses
Preclinical optimization of an enterotoxigenic Escherichia coli adjuvanted subunit vaccine using response surface design of experiments
International audienceEnterotoxigenic E. coli (ETEC) is a leading cause of moderate-to-severe diarrhoea. ETEC colonizes the intestine through fimbrial tip adhesin colonization factors and produces heat-stable and/or heat-labile (LT) toxins, stimulating fluid and electrolyte release leading to watery diarrhoea. We reported that a vaccine containing recombinant colonization factor antigen (CfaEB) targeting fimbrial tip adhesin of the colonization factor antigen I (CFA/I) and an attenuated LT toxoid (dmLT) elicited mucosal and systemic immune responses against both targets. Additionally, the toll-like receptor 4 ligand second-generation lipid adjuvant (TLR4-SLA) induced a potent mucosal response, dependent on adjuvant formulation. However, a combination of vaccine components at their respective individual optimal doses may not achieve the optimal immune profile. We studied a subunit ETEC vaccine prototype in mice using a response surface design of experiments (DoE), consisting of 64 vaccine dose-combinations of CfaEB, dmLT and SLA in four formulations (aqueous, aluminium oxyhydroxide, squalene-in-water stable nanoemulsion [SE] or liposomes containing the saponin Quillaja saponaria-21 [LSQ]). Nine readouts focusing on antibody functionality and plasma cell response were selected to profile the immune response of parenterally administered ETEC vaccine prototype. The data were integrated in a model to identify the optimal dosage of each vaccine component and best formulation. Compared to maximal doses used in mouse models (10 µg CfaEB, 1 µg dmLT and 5 µg SLA), a reduction in the vaccine components up to 37%, 60% and 88% for CfaEB, dmLT and SLA, respectively, maintained or even maximized immune responses, with SE and LSQ the best formulations. The DoE approach can help determine the best vaccine composition with a limited number of experiments and may accelerate development of multi-antigen/ component ETEC vaccines