Advancing Digital Medicine with Wearables in the Wild

McGinnis, Ryan S.; McGinnis, Ellen

doi:10.3390/s22124576

Cited by 11 publications

(13 citation statements)

References 45 publications

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“…By offering an app and platform for active data collection, we aim to aid researchers in charging ahead in the field of digital psychiatry 32 . Future improvements to ChAMP will increase the customizability of our platform to enhance research and ultimately clinical utility 3,32 . Our next steps toward a customizable tool would allow investigation into additional conditions impacting children across a wider range of ages.…”

Section: Discussionmentioning

confidence: 99%

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

Loftness

Halvorson-Phelan

O'Leary

et al. 2023

Preprint

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Objective: Internalizing disorders, such as anxiety and depression, in pre-school aged children are common and often go undiagnosed into adulthood. To augment traditional parental-reports, we have previously presented an objective assessment for early childhood anxiety and depression which leverages movement and vocal biomarkers measured via wearable sensors during brief mood induction tasks that achieves good accuracy (75%-81%). However, these methods required specialized equipment and expertise in data and sensor engineering to administer and analyze. Method: To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes an Android mobile application for collecting movement and audio data during mood induction tasks and an open-source data analysis platform for extracting digital biomarkers and discovering digital phenotypes. As proof of principle, we present data collected using the ChAMP System from 50 children ages 5-8, with and without anxiety or depressive disorders. Results: Movement and vocal features derived from the ChAMP System support the consideration of theory-driven temporal phases within mood induction tasks, and the use of an assessment battery for characterizing childhood internalizing disorders. Results also demonstrate that features significantly differ between diagnostic groups and correlate with symptom severity implying their potential use as digital biomarkers. Conclusions: The ChAMP System provides clinically relevant digital biomarkers of childhood anxiety and depression. This new open-source tool lowers the barrier to entry for those interested in exploring digital phenotyping of childhood mental health.

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Section: Discussionmentioning

confidence: 99%

Section: Champ As An Open-access Digital Toolmentioning

confidence: 99%

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

Loftness

Halvorson-Phelan

O'Leary

et al. 2023

Preprint

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“…This will allow us to identify PASC digital phenotypes that aid in its clinical assessment and treatment. 111,112 Furthermore, we can triangulate evidence of PASC from physiological parameters and self-reported patient outcomes. 113 The study's wider public health implications pertain to the role of mobile technologies in digital medicine.…”

Section: Hypothesismentioning

confidence: 99%

“…113 The study's wider public health implications pertain to the role of mobile technologies in digital medicine. 111,112,114,115 Indeed, mobile technologies have been central to public health response to the COVID-19 pandemic 116,117 -from crowdsourcing platforms for contact tracing 118 to symptom reporting [119][120][121] and sensor data collection. 104,122 Wearables physiological data improve the identi cation of COVID-19 positive cases above and beyond self-reported symptoms, 50 which have been the basis of pilot COVID-19 detection tools, [46][47][48] and show preliminary differentiation between COVID-19 and other respiratory illnesses.…”

Section: Hypothesismentioning

confidence: 99%

A prospective natural history study of post acute sequalae of COVID-19 using digital wearables: Study protocol

El-Toukhy,

Hegeman,

Zuckerman

et al. 2023

Preprint

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Background Post-acute sequelae of COVID-19 (PASC) is characterized by having 1 + persistent, recurrent, or emergent symptoms post the infection’s acute phase. The duration and symptom manifestation of PASC remain understudied in nonhospitalized patients. Literature on PASC is primarily based on data from hospitalized patients where clinical indicators such as respiratory rate, heart rate, and oxygen saturation have been predictive of disease trajectories. Digital wearables allow for a continuous collection of such physiological parameters. This protocol outlines the design, aim, and procedures of a natural history study of PASC using digital wearables. Methods This is a single-arm, prospective, natural history study of a cohort of 550 patients, ages 18 to 65 years old, males or females who own a smartphone and/or a tablet that meets pre-determined Bluetooth version and operating system requirements, speak English, and provide documentation of a positive COVID-19 test issued by a healthcare professional or organization within 5 days before enrollment. The study aims to identify wearables collected physiological parameters that are associated with PASC in patients with a positive diagnosis. The primary endpoint is long COVID-19, defined as ≥ 1 symptom at 3 weeks beyond first symptom onset or positive diagnosis, whichever comes first. The secondary endpoint is chronic COVID-19, defined as ≥ 1 symptom at 12 weeks beyond first symptom onset or positive diagnosis. We hypothesize that physiological parameters collected via wearables are associated with self-reported PASC. Participants must be willing and able to consent to participate in the study and adhere to study procedures for six months. Discussion This is a fully decentralized study investigating PASC using wearable devices to collect physiological parameters and patient-reported outcomes. Given evidence on key demographics and risk profiles associated with PASC, the study will shed light on the duration and symptom manifestation of PASC in nonhospitalized patient subgroups and is an exemplar of use of wearables as population-level monitoring health tools for communicable diseases. Trial registration: ClinicalTrials.gov NCT04927442, Submitted: 6/15/2021, First posted: 6/16/2021.

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“…These transitions have been used extensively to assess patient stability, dating back to the introduction of the "get-up and go" test in 1986 [26]. More modernly, wearable monitoring of such tasks has been applied to augmentation of PBMs and home-monitoring of M symptoms [22], [23], but our understanding of these metrics remains limited [24], [27].…”

Section: Introductionmentioning

confidence: 99%

Digital Phenotypes of Instability and Fatigue Derived from Daily Standing Transitions in Persons with Multiple Sclerosis

VanDyk¹,

Meyer²,

DePetrillo³

et al. 2022

Preprint

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<p>Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of ~6 weeks each. These data were processed using a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS. </p>

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Advancing Digital Medicine with Wearables in the Wild

Abstract: This editorial provides a concise overview of the use and importance of wearables in the emerging field of digital medicine [...]

Cited by 11 publications

References 45 publications

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

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

A prospective natural history study of post acute sequalae of COVID-19 using digital wearables: Study protocol

Digital Phenotypes of Instability and Fatigue Derived from Daily Standing Transitions in Persons with Multiple Sclerosis

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