Search Articles

View query in Help articles search

Search Results (1 to 8 of 8 Results)

Download search results: CSV END BibTex RIS


Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study

Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study

Mental health conditions are common, with approximately 25% of the population in the United States experiencing a mental health disorder in a given year [1]. Since the COVID-19 pandemic, there have been increasing rates of anxiety, depression, and other psychological conditions [2]. This has disproportionately impacted health care workers (HCWs) who are at a higher risk of depression, anxiety, insomnia, and distress compared to the general population [3-6].

Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Renata Pyzik, Sparshdeep Kaur, Helena Chang, Drew Helmus, Bruce E Sands, Dennis Charney, Girish Nadkarni, Emilia Bagiella, Laurie Keefer, Zahi A Fayad

JMIR Ment Health 2024;11:e55552

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform

Remote digital study platforms represent a subset of digital platforms comprising a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place [5,6].

Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Georgy Orlov, Jovita Rodrigues, Eugenia Alleva, Ipek Ensari, Erwin Bottinger, Girish N Nadkarni, Thomas J Fuchs, Zahi A Fayad

JMIR Res Protoc 2023;12:e49204

StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials

StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials

Typically, series of N-of-1 trials designed with a specific research aim require ethics approval, while single N-of-1 trials with a clinical aim for a single patient do not require it. Often, however, this distinction is not clear. For example, let us consider a potential N-of-1 trial where physicians have the goal of finding out whether a particular drug is effective in off-label use for patients with chronic conditions such as chronic liver disease.

Stefan Konigorski, Sarah Wernicke, Tamara Slosarek, Alexander M Zenner, Nils Strelow, Darius F Ruether, Florian Henschel, Manisha Manaswini, Fabian Pottbäcker, Jonathan A Edelman, Babajide Owoyele, Matteo Danieletto, Eddye Golden, Micol Zweig, Girish N Nadkarni, Erwin Böttinger

J Med Internet Res 2022;24(7):e35884

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

HRV, a physiological marker of stress, was collected via the Apple Watch, while subjective outcome measures were assessed through standardized surveys. The Apple Watch Series 4 or 5 was worn by subjects on the wrist to capture HRV and was connected via Bluetooth to the participants’ i Phone. A photoplethysmogram sensor on the Apple Watch pairs a green LED (light-emitting diode) light with a light-sensitive photodiode to generate time-series peaks [16].

Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Claudia Calcagno, Robert Freeman, Bruce E Sands, Dennis Charney, Erwin P Bottinger, James W Murrough, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad

J Med Internet Res 2021;23(9):e31295

A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation

A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation

COVID-19 has resulted in over 41 million infections with a worldwide case fatality ratio of approximately 2.6% as of early November 2020 [1,2]. Disease spread has been facilitated by a prolonged incubation period and variable symptomatology and severity. As a result, there is a tremendous burden on the health care system, with health care workers (HCWs) experiencing increased stress and demand and increased risk of COVID-19 infection, particularly for patient-facing staff [3].

Eddye A Golden, Micol Zweig, Matteo Danieletto, Kyle Landell, Girish Nadkarni, Erwin Bottinger, Lindsay Katz, Ricardo Somarriba, Vansh Sharma, Craig L Katz, Deborah B Marin, Jonathan DePierro, Dennis S Charney

JMIR Form Res 2021;5(5):e26590

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

A cosinor model was used to model the daily circadian rhythm over a 24-hour period with the following nonlinear function: where τ is the period (τ=24 h); M is the midline statistic of rhythm (MESOR), a rhythm-adjusted mean; A is the amplitude, a measure of half of the extent of variation within a day; and Φ is the acrophase, a measure of the time at which overall high values recur on each day (Figure S1 in Multimedia Appendix 1).

Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Alexander Charney, Riccardo Miotto, Benjamin S Glicksberg, Matthew Levin, Ismail Nabeel, Judith Aberg, David Reich, Dennis Charney, Erwin P Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad

J Med Internet Res 2021;23(2):e26107

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

A confirmed case of COVID-19 was defined by a positive reverse transcriptase–polymerase chain reaction (RT-PCR) assay of a nasopharyngeal swab. To restrict our data to only primary COVID-19–related encounters, we excluded patients who had a first positive COVID-19 RT-PCR result more than two days after admission. We included all patients who had been discharged, had died, or were still admitted and had stayed in the hospital for at least the amount of time corresponding to the outcome in question.

Akhil Vaid, Sulaiman Somani, Adam J Russak, Jessica K De Freitas, Fayzan F Chaudhry, Ishan Paranjpe, Kipp W Johnson, Samuel J Lee, Riccardo Miotto, Felix Richter, Shan Zhao, Noam D Beckmann, Nidhi Naik, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F O'Reilly, Laura Huckins, Patricia Kovatch, Joseph Finkelstein, Robert M. Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A Aberg, Emilia Bagiella, Carol R Horowitz, Barbara Murphy, Eric J Nestler, Eric E Schadt, Judy H Cho, Carlos Cordon-Cardo, Valentin Fuster, Dennis S Charney, David L Reich, Erwin P Bottinger, Matthew A Levin, Jagat Narula, Zahi A Fayad, Allan C Just, Alexander W Charney, Girish N Nadkarni, Benjamin S Glicksberg

J Med Internet Res 2020;22(11):e24018

Factors Associated With Trial Completion and Adherence in App-Based N-of-1 Trials: Protocol for a Randomized Trial Evaluating Study Duration, Notification Level, and Meaningful Engagement in the Brain Boost Study

Factors Associated With Trial Completion and Adherence in App-Based N-of-1 Trials: Protocol for a Randomized Trial Evaluating Study Duration, Notification Level, and Meaningful Engagement in the Brain Boost Study

Participants enrolled in the Brain Boost Study follow a treatment schedule, guided by a mobile app (N1 app), where they alternate between the two treatments: caffeine (treatment A) or caffeine combined with L-theanine (treatment B) during a prespecified window of time. Participants are also asked to complete an app-based cognitive assessment during a prespecified window of time each day.

Jason R R Bobe, Jacqueline Buros, Eddye Golden, Matthew Johnson, Michael Jones, Bethany Percha, Ryan Viglizzo, Noah Zimmerman

JMIR Res Protoc 2020;9(1):e16362