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Article ; Online: Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

Loftness, Bryn C / Bernstein, Ira / McBride, Carole A / Cheney, Nick / McGinnis, Ellen W / McGinnis, Ryan S

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–4

Abstract: Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid- ... ...

Abstract	Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance- This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.
MeSH term(s)	Pregnancy ; Infant, Newborn ; Humans ; Female ; Pre-Eclampsia/diagnosis ; Premature Birth ; Gestational Age ; Biomarkers ; Hemodynamics
Chemical Substances	Biomarkers
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340404
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

Loftness, Bryn C / Bernstein, Ira / McBride, Carole A / Cheney, Nick / McGinnis, Ellen W / McGinnis, Ryan S

medRxiv : the preprint server for health sciences

2023

Abstract	Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.
Language	English
Publishing date	2023-03-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.28.23286590
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety.

Loftness, Bryn C / Halvorson-Phelan, Julia / O'Leary, Aisling / Cheney, Nick / McGinnis, Ellen W / McGinnis, Ryan S

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 1141–1144

Abstract: Anxiety and depression, collectively known as internalizing disorders, begin as early as the preschool years and impact nearly 1 out of every 5 children. Left undiagnosed and untreated, childhood internalizing disorders predict later health problems ... ...

Abstract	Anxiety and depression, collectively known as internalizing disorders, begin as early as the preschool years and impact nearly 1 out of every 5 children. Left undiagnosed and untreated, childhood internalizing disorders predict later health problems including substance abuse, development of comorbid psychopathology, increased risk for suicide, and substantial functional impairment. Current diagnostic procedures require access to clinical experts, take considerable time to complete, and inherently assume that child symptoms are observable by caregivers. Multi-modal wearable sensors may enable development of rapid point-of-care diagnostics that address these challenges. Building on our prior work, here we present an assessment battery for the development of a digital phenotype for internalizing disorders in young children and an early feasibility case study of multi-modal wearable sensor data from two participants, one of whom has been clinically diagnosed with an internalizing disorder. Results lend support that sacral movement responses and R-R interval during a short stress-induction task may facilitate child diagnosis. Multi-modal sensors measuring movement and surface biopotentials of the chest and trapezius are also shown to have significant redundancy, introducing the potential for sensor optimization moving forward. Future work aims to further optimize sensor placement, signals, features, and assessments to enable deployment in clinical practice. Clinical Relevance- This work considers the development and optimization of technologies for improving the identification of children with internalizing disorders.
MeSH term(s)	Anxiety/diagnosis ; Anxiety Disorders ; Family ; Humans ; Suicide ; Wearable Electronic Devices
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871090
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning.

Loftness, Bryn C / Rizzo, Donna M / Halvorson-Phelan, Julia / O'Leary, Aisling / Prytherch, Shania / Bradshaw, Carter / Brown, Anna Jane / Cheney, Nick / McGinnis, Ellen W / McGinnis, Ryan S

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–4

Abstract: Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report ... ...

Abstract	Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.
MeSH term(s)	Child ; Adolescent ; Humans ; Child, Preschool ; Adult ; Mental Health ; Quality of Life ; Anxiety/diagnosis ; Anxiety/epidemiology ; Supervised Machine Learning ; Phenotype
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340806
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Loftness, Bryn C / Halvorson-Phelan, Julia / O'Leary, Aisling / Bradshaw, Carter / Prytherch, Shania / Berman, Isabel / Torous, John / Copeland, William L / Cheney, Nick / McGinnis, Ryan S / McGinnis, Ellen W

medRxiv : the preprint server for health sciences

2023

Abstract: Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, ... ...

Abstract	Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
Language	English
Publishing date	2023-11-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.19.23284753
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Discovering Digital Biomarkers of Panic Attack Risk in Consumer Wearables Data.

McGinnis, Ellen W / Lunna, Shania / Berman, Isabel / Loftness, Bryn C / Bagdon, Skylar / Danforth, Christopher M / Price, Matthew / Copeland, William E / McGinnis, Ryan S

medRxiv : the preprint server for health sciences

2023

Abstract: Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality ... ...

Abstract	Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Results indicate that objective measures of ambient noise (louder) and resting heart rate (higher) are related to the likelihood of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from data passively collected by consumer wearable devices, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions. Clinical relevance: Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.
Language	English
Publishing date	2023-03-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.01.23286647
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Discovering Digital Biomarkers of Panic Attack Risk in Consumer Wearables Data.

McGinnis, Ellen W / Lunna, Shania / Berman, Isabel / Loftness, Bryn C / Bagdon, Skylar / Danforth, Christopher M / Price, Matthew / Copeland, William E / McGinnis, Ryan S

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–4

Abstract	Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures collected via consumer wearable sensors (referred to as digital biomarkers) to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Mixed Regressions, with an autoregressive covariance structure were used to estimate the prevalence of a next-day panic attack Results indicate that digital biomarkers of ambient noise (louder) and resting heart rate (higher) are indicative of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from digital biomarkers, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions.Clinical Relevance- Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.
MeSH term(s)	Adult ; Humans ; United States ; Panic Disorder/diagnosis ; Panic Disorder/epidemiology ; Panic Disorder/psychology ; Quality of Life ; Self Report ; Affect ; Wearable Electronic Devices
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10339982
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Loftness, Bryn C / Halvorson-Phelan, Julia / OLeary, Aisling / Bradshaw, Carter / Prytherch, Shania / Berman, Isabel / Torous, John / Copeland, William L / Cheney, Nick / McGinnis, Ryan S / McGinnis, Ellen W

IEEE journal of biomedical and health informatics

2023 Volume PP

Abstract	Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
Language	English
Publishing date	2023-11-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3337649
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway.

Meyer, Brett M / Depetrillo, Paolo / Franco, Jaime / Donahue, Nicole / Fox, Samantha R / O'Leary, Aisling / Loftness, Bryn C / Gurchiek, Reed D / Buckley, Maura / Solomon, Andrew J / Ng, Sau Kuen / Cheney, Nick / Ceruolo, Melissa / McGinnis, Ryan S

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 18

Abstract: Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to ... ...

Abstract	Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.
MeSH term(s)	Gait ; Humans ; Multiple Sclerosis ; Postural Balance ; Walking Speed ; Wearable Electronic Devices
Language	English
Publishing date	2022-09-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22186982
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study.

Specht, Ivan / Sani, Kian / Loftness, Bryn C / Hoffman, Curtis / Gionet, Gabrielle / Bronson, Amy / Marshall, John / Decker, Craig / Bailey, Landen / Siyanbade, Tomi / Kemball, Molly / Pickett, Brett E / Hanage, William P / Brown, Todd / Sabeti, Pardis C / Colubri, Andrés

Patterns (New York, N.Y.)

2022 Volume 3, Issue 8, Page(s) 100572

Abstract: An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience- ... ...

Abstract	An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.
Language	English
Publishing date	2022-08-12
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2022.100572
Database	MEDical Literature Analysis and Retrieval System OnLINE

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