Abhishek Pratap | University of Maryland School of Medicine (original) (raw)

Papers by Abhishek Pratap

Scientific Data

Most people with mental health disorders cannot receive timely and evidence-based care despite bi... more Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a tim...

arXiv (Cornell University), Nov 28, 2018

Clinical machine learning applications are often plagued with confounders that can impact the gen... more Clinical machine learning applications are often plagued with confounders that can impact the generalizability and predictive performance of the learners. Confounding is especially problematic in remote digital health studies where the participants self-select to enter the study, thereby making it challenging to balance the demographic characteristics of participants. One effective approach to combat confounding is to match samples with respect to the confounding variables in order to balance the data. This procedure, however, leads to smaller datasets and hence impact the inferences drawn from the learners. Alternatively, confounding adjustment methods that make more efficient use of the data (e.g., inverse probability weighting) usually rely on modeling assumptions, and it is unclear how robust these methods are to violations of these assumptions. Here, rather than proposing a new approach to control for confounding, we develop novel permutation based statistical methods to detect and quantify the influence of observed confounders, and estimate the unconfounded performance of the learner. Our tools can be used to evaluate the effectiveness of existing confounding adjustment methods. We illustrate their application using real-life data from a Parkinson's disease mobile health study collected in an uncontrolled environment.

Recent growth in remote studies has shown the effectiveness of digital health technologies in rec... more Recent growth in remote studies has shown the effectiveness of digital health technologies in recruiting and monitoring the health and behavior of large and diverse populations of interest in real-world settings. However, retaining and engaging participants to monitor their long-term health trajectories has remained a significant challenge. Uneven participant engagement combined with attrition over the course of the study could lead to imbalanced study cohort and data collection, which may severely impact the generalizability of real-world evidence.We report findings from long-term participant retention and engagement patterns in a multinational remote digital depression study with up to two years of real-world behavior monitoring. In total, real-world engagement data from 614 participants with 14,964 surveys and 135,014 days of phone passive and wearable (Fitbit) data were analyzed using survival and unsupervised clustering methods. A considerable proportion of participants (N=415;...

How safe and effective are mental health apps? What’s the impact of social media on youth? Insigh... more How safe and effective are mental health apps? What’s the impact of social media on youth? Insights here from presenters at APA 2019.

arXiv: Applications, 2017

In this work we provide a couple of contributions to the analysis of longitudinal data collected ... more In this work we provide a couple of contributions to the analysis of longitudinal data collected by smartphones in mobile health applications. First, we propose a novel statistical approach to disentangle personalized treatment and "time-of-the-day" effects in observational studies. Under the assumption of no unmeasured confounders, we show how to use conditional independence relations in the data in order to determine if a difference in performance between activity tasks performed before and after the participant has taken medication, are potentially due to an effect of the medication or to a "time-of-the-day" effect (or still to both). Second, we show that smartphone data collected from a given study participant can represent a "digital fingerprint" of the participant, and that classifiers of case/control labels, constructed using longitudinal data, can show artificially improved performance when data from each participant is included in both training...

arXiv: Applications, 2017

Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data... more Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data of each subject randomly assigned to both training and test sets (record-wise data split) can lead to massive underestimation of the cross-validation prediction error, due to the presence of "subject identity confounding" caused by the classifier's ability to identify subjects, instead of recognizing disease. To solve this problem, the authors recommended the random assignment of the data of each subject to either the training or the test set (subject-wise data split). The adoption of subject-wise split has been criticized in Little et al (2017), on the basis that it can violate assumptions required by cross-validation to consistently estimate generalization error. In particular, adopting subject-wise splitting in heterogeneous data-sets might lead to model under-fitting and larger classification errors. Hence, Little et al argue that perhaps the overestimation of predicti...

Code sharing related to DREAM challenges

Frontiers in Psychiatry, 2021

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets wi... more Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clini...

BACKGROUND Background: Several app-based studies share similar characteristics of a ‘light touch’... more BACKGROUND Background: Several app-based studies share similar characteristics of a ‘light touch’ approach that recruit, enroll, and onboard via a smartphone app and attempt to minimize burden through low-friction active study tasks, while emphasizing the collection of passive data with minimal human contact. However, engagement is a common challenge across these studies reporting low retention and adherence. OBJECTIVE To describe an alternative to a ‘light touch’ digital health study that involved a participant centric design including high friction app-based assessments, semi-continuous passive data from wearable sensors and a digital engagement strategy centered on providing knowledge and support to participants. METHODS The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study included US frontline healthcare workers followed between May-November 2020. The study comprised 3 main components: 1) active and passive assessments of stress and symptoms from a smartphone a...

Frontiers in Cardiovascular Medicine, 2020

Clinical and Translational Science, 2020

JAMA Network Open, 2019

IMPORTANCE Using social media to recruit participants is a common and cost-effective practice. Wi... more IMPORTANCE Using social media to recruit participants is a common and cost-effective practice. Willingness to participate (WTP) in biomedical research is a function of trust in the scientific team, which is closely tied to the source of funding and institutional connections. OBJECTIVE To determine whether WTP and willingness to share social media data are associated with the type of research team and online recruitment platform. DESIGN, SETTING, AND PARTICIPANTS This mixed-methods longitudinal survey and qualitative study was conducted over 2 points (T1 and T2) using Amazon's Mechanical Turk (MTurk) platform. Participants were US adults aged 18 years or older who use at least 1 social media platform. Recruitment was stratified to match race/ethnicity proportions of the 2010 US Census. The volunteer sample consisted of 914 participants at T1, and 655 participants completed the follow-up survey 5 months later (T2). MAIN OUTCOMES AND MEASURES Outcomes were (1) past experience with online research and sharing social media data for research; (2) WTP in research advertised online; (3) WTP in a study sponsored by a pharmaceutical company, a university, or a federal agency; and (4) willingness to share social media data. Opinions were solicited regarding the European Union's General Data Protection Regulation statute, which came into effect between T1 and T2. RESULTS Of 914 participants completing the first survey (T1), 604 (66.1%) were aged 18 to 39 years and 494 (54.0%) were female. Of these, 655 participants (71.7%) responded at T2. While 680 participants (74.4%) indicated WTP in biomedical research, only 454 (49.3%) were willing to share their social media data. Participants were significantly less likely to participate in federally sponsored

World Psychiatry, 2019

and it is a condition of accessing publications that users recognise and abide by the legal requi... more and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

PloS one, 2018

Neurofibromatosis 2 (NF2) is a rare tumor suppressor syndrome that manifests with multiple schwan... more Neurofibromatosis 2 (NF2) is a rare tumor suppressor syndrome that manifests with multiple schwannomas and meningiomas. There are no effective drug therapies for these benign tumors and conventional therapies have limited efficacy. Various model systems have been created and several drug targets have been implicated in NF2-driven tumorigenesis based on known effects of the absence of merlin, the product of the NF2 gene. We tested priority compounds based on known biology with traditional dose-concentration studies in meningioma and schwann cell systems. Concurrently, we studied functional kinome and gene expression in these cells pre- and post-treatment to determine merlin deficient molecular phenotypes. Cell viability results showed that three agents (GSK2126458, Panobinostat, CUDC-907) had the greatest activity across schwannoma and meningioma cell systems, but merlin status did not significantly influence response. In vivo, drug effect was tumor specific with meningioma, but not ...

Iproceedings, 2017

We summarize the key learnings from two large-scale fully mobile clinical trials targeting (> 2,0... more We summarize the key learnings from two large-scale fully mobile clinical trials targeting (> 2,000 enrolled people) depressed individuals. BRIGHTEN v1 was open to the general US population and BRIGHTEN v2 was designed to enroll both English-speaking and an underserved Latino/Hispanic population. Noticeable differences in user recruitment, engagement and daily self reported mood observed across two BRIGHTEN studies are highlighted here.

World Psychiatry, 2017

The rapid advances and adoption of smartphone technology presents a novel opportunity for deliver... more The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g50.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g50.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g50.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/ computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p50.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.

Nature communications, Aug 23, 2016

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to... more Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predi...

Scientific Data, 2016

Current measures of health and disease are often insensitive, episodic, and subjective. Further, ... more Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of o...

F1000Research, 2015

DREAM challenges are community competitions designed to advance computational methods and address... more DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scorin...

Nature Plants, 2015

Alga-derived lipids represent an attractive potential source of biofuels. However, lipid accumula... more Alga-derived lipids represent an attractive potential source of biofuels. However, lipid accumulation in algae is a stress response tightly coupled to growth arrest, thereby imposing a major limitation on productivity. To identify transcriptional regulators of lipid accumulation, we performed an integrative chromatin signature and transcriptomic analysis to decipher the regulation of lipid biosynthesis in the alga Chlamydomonas reinhardtii. Genome-wide histone modification profiling revealed remarkable differences in functional chromatin states between the algae and higher eukaryotes and uncovered regulatory components at the core of lipid accumulation pathways. We identified the transcription factor, PSR1, as a pivotal switch that triggers cytosolic lipid accumulation. Dissection of the PSR1-induced lipid profiles corroborates its role in coordinating multiple lipid-inducing stress responses. The comprehensive maps of functional chromatin signatures in a major clade of eukaryotic life and the discovery of a transcriptional regulator of algal lipid metabolism will facilitate targeted engineering strategies to mediate high lipid production in microalgae.

Scientific Data

Most people with mental health disorders cannot receive timely and evidence-based care despite bi... more Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a tim...

arXiv (Cornell University), Nov 28, 2018

Clinical machine learning applications are often plagued with confounders that can impact the gen... more Clinical machine learning applications are often plagued with confounders that can impact the generalizability and predictive performance of the learners. Confounding is especially problematic in remote digital health studies where the participants self-select to enter the study, thereby making it challenging to balance the demographic characteristics of participants. One effective approach to combat confounding is to match samples with respect to the confounding variables in order to balance the data. This procedure, however, leads to smaller datasets and hence impact the inferences drawn from the learners. Alternatively, confounding adjustment methods that make more efficient use of the data (e.g., inverse probability weighting) usually rely on modeling assumptions, and it is unclear how robust these methods are to violations of these assumptions. Here, rather than proposing a new approach to control for confounding, we develop novel permutation based statistical methods to detect and quantify the influence of observed confounders, and estimate the unconfounded performance of the learner. Our tools can be used to evaluate the effectiveness of existing confounding adjustment methods. We illustrate their application using real-life data from a Parkinson's disease mobile health study collected in an uncontrolled environment.

Recent growth in remote studies has shown the effectiveness of digital health technologies in rec... more Recent growth in remote studies has shown the effectiveness of digital health technologies in recruiting and monitoring the health and behavior of large and diverse populations of interest in real-world settings. However, retaining and engaging participants to monitor their long-term health trajectories has remained a significant challenge. Uneven participant engagement combined with attrition over the course of the study could lead to imbalanced study cohort and data collection, which may severely impact the generalizability of real-world evidence.We report findings from long-term participant retention and engagement patterns in a multinational remote digital depression study with up to two years of real-world behavior monitoring. In total, real-world engagement data from 614 participants with 14,964 surveys and 135,014 days of phone passive and wearable (Fitbit) data were analyzed using survival and unsupervised clustering methods. A considerable proportion of participants (N=415;...

How safe and effective are mental health apps? What’s the impact of social media on youth? Insigh... more How safe and effective are mental health apps? What’s the impact of social media on youth? Insights here from presenters at APA 2019.

arXiv: Applications, 2017

In this work we provide a couple of contributions to the analysis of longitudinal data collected ... more In this work we provide a couple of contributions to the analysis of longitudinal data collected by smartphones in mobile health applications. First, we propose a novel statistical approach to disentangle personalized treatment and "time-of-the-day" effects in observational studies. Under the assumption of no unmeasured confounders, we show how to use conditional independence relations in the data in order to determine if a difference in performance between activity tasks performed before and after the participant has taken medication, are potentially due to an effect of the medication or to a "time-of-the-day" effect (or still to both). Second, we show that smartphone data collected from a given study participant can represent a "digital fingerprint" of the participant, and that classifiers of case/control labels, constructed using longitudinal data, can show artificially improved performance when data from each participant is included in both training...

arXiv: Applications, 2017

Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data... more Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data of each subject randomly assigned to both training and test sets (record-wise data split) can lead to massive underestimation of the cross-validation prediction error, due to the presence of "subject identity confounding" caused by the classifier's ability to identify subjects, instead of recognizing disease. To solve this problem, the authors recommended the random assignment of the data of each subject to either the training or the test set (subject-wise data split). The adoption of subject-wise split has been criticized in Little et al (2017), on the basis that it can violate assumptions required by cross-validation to consistently estimate generalization error. In particular, adopting subject-wise splitting in heterogeneous data-sets might lead to model under-fitting and larger classification errors. Hence, Little et al argue that perhaps the overestimation of predicti...

Code sharing related to DREAM challenges

Frontiers in Psychiatry, 2021

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets wi... more Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clini...

BACKGROUND Background: Several app-based studies share similar characteristics of a ‘light touch’... more BACKGROUND Background: Several app-based studies share similar characteristics of a ‘light touch’ approach that recruit, enroll, and onboard via a smartphone app and attempt to minimize burden through low-friction active study tasks, while emphasizing the collection of passive data with minimal human contact. However, engagement is a common challenge across these studies reporting low retention and adherence. OBJECTIVE To describe an alternative to a ‘light touch’ digital health study that involved a participant centric design including high friction app-based assessments, semi-continuous passive data from wearable sensors and a digital engagement strategy centered on providing knowledge and support to participants. METHODS The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study included US frontline healthcare workers followed between May-November 2020. The study comprised 3 main components: 1) active and passive assessments of stress and symptoms from a smartphone a...

Frontiers in Cardiovascular Medicine, 2020

Clinical and Translational Science, 2020

JAMA Network Open, 2019

IMPORTANCE Using social media to recruit participants is a common and cost-effective practice. Wi... more IMPORTANCE Using social media to recruit participants is a common and cost-effective practice. Willingness to participate (WTP) in biomedical research is a function of trust in the scientific team, which is closely tied to the source of funding and institutional connections. OBJECTIVE To determine whether WTP and willingness to share social media data are associated with the type of research team and online recruitment platform. DESIGN, SETTING, AND PARTICIPANTS This mixed-methods longitudinal survey and qualitative study was conducted over 2 points (T1 and T2) using Amazon's Mechanical Turk (MTurk) platform. Participants were US adults aged 18 years or older who use at least 1 social media platform. Recruitment was stratified to match race/ethnicity proportions of the 2010 US Census. The volunteer sample consisted of 914 participants at T1, and 655 participants completed the follow-up survey 5 months later (T2). MAIN OUTCOMES AND MEASURES Outcomes were (1) past experience with online research and sharing social media data for research; (2) WTP in research advertised online; (3) WTP in a study sponsored by a pharmaceutical company, a university, or a federal agency; and (4) willingness to share social media data. Opinions were solicited regarding the European Union's General Data Protection Regulation statute, which came into effect between T1 and T2. RESULTS Of 914 participants completing the first survey (T1), 604 (66.1%) were aged 18 to 39 years and 494 (54.0%) were female. Of these, 655 participants (71.7%) responded at T2. While 680 participants (74.4%) indicated WTP in biomedical research, only 454 (49.3%) were willing to share their social media data. Participants were significantly less likely to participate in federally sponsored

World Psychiatry, 2019

and it is a condition of accessing publications that users recognise and abide by the legal requi... more and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

PloS one, 2018

Neurofibromatosis 2 (NF2) is a rare tumor suppressor syndrome that manifests with multiple schwan... more Neurofibromatosis 2 (NF2) is a rare tumor suppressor syndrome that manifests with multiple schwannomas and meningiomas. There are no effective drug therapies for these benign tumors and conventional therapies have limited efficacy. Various model systems have been created and several drug targets have been implicated in NF2-driven tumorigenesis based on known effects of the absence of merlin, the product of the NF2 gene. We tested priority compounds based on known biology with traditional dose-concentration studies in meningioma and schwann cell systems. Concurrently, we studied functional kinome and gene expression in these cells pre- and post-treatment to determine merlin deficient molecular phenotypes. Cell viability results showed that three agents (GSK2126458, Panobinostat, CUDC-907) had the greatest activity across schwannoma and meningioma cell systems, but merlin status did not significantly influence response. In vivo, drug effect was tumor specific with meningioma, but not ...

Iproceedings, 2017

We summarize the key learnings from two large-scale fully mobile clinical trials targeting (> 2,0... more We summarize the key learnings from two large-scale fully mobile clinical trials targeting (> 2,000 enrolled people) depressed individuals. BRIGHTEN v1 was open to the general US population and BRIGHTEN v2 was designed to enroll both English-speaking and an underserved Latino/Hispanic population. Noticeable differences in user recruitment, engagement and daily self reported mood observed across two BRIGHTEN studies are highlighted here.

World Psychiatry, 2017

The rapid advances and adoption of smartphone technology presents a novel opportunity for deliver... more The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g50.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g50.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g50.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/ computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p50.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.

Nature communications, Aug 23, 2016

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to... more Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predi...

Scientific Data, 2016

Current measures of health and disease are often insensitive, episodic, and subjective. Further, ... more Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of o...

F1000Research, 2015

DREAM challenges are community competitions designed to advance computational methods and address... more DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scorin...

Nature Plants, 2015

Alga-derived lipids represent an attractive potential source of biofuels. However, lipid accumula... more Alga-derived lipids represent an attractive potential source of biofuels. However, lipid accumulation in algae is a stress response tightly coupled to growth arrest, thereby imposing a major limitation on productivity. To identify transcriptional regulators of lipid accumulation, we performed an integrative chromatin signature and transcriptomic analysis to decipher the regulation of lipid biosynthesis in the alga Chlamydomonas reinhardtii. Genome-wide histone modification profiling revealed remarkable differences in functional chromatin states between the algae and higher eukaryotes and uncovered regulatory components at the core of lipid accumulation pathways. We identified the transcription factor, PSR1, as a pivotal switch that triggers cytosolic lipid accumulation. Dissection of the PSR1-induced lipid profiles corroborates its role in coordinating multiple lipid-inducing stress responses. The comprehensive maps of functional chromatin signatures in a major clade of eukaryotic life and the discovery of a transcriptional regulator of algal lipid metabolism will facilitate targeted engineering strategies to mediate high lipid production in microalgae.