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Blaha et al.
2024
Validation of digital cardiovascular risk score (DiCAVA) in the United States All of Us dataset
Abstract submitted to ESC Congress 2024
Moore A, Morelli D.
conDENSE: Conditional Density Estimation for Time Series Anomaly Detection
Journal of Artificial Intelligence Research, Vol 79.
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Dolezalova, N. et al.
2023
Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity
Nature Scientific Reports, 13: 10581
Lim, A. et al.
2022
An Outpatient Management Strategy Using a Coronataxi Digital Early Warning System Reduces Coronavirus Disease 2019 Mortality
Open Forum Infectious Diseases 9, no. 4: ofac063.
Gatzoulis, M. et al.
Patient monitoring and education over a tailored digital application platform for congenital heart disease: A feasibility pilot study
International Journal of Cardiology. Vol 362, Pages 68-72.
Bacciu, D. et al.
Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes
IEEE Transactions on Neural Networks and Learning Systems PP.
Sarraju A, et al.
Pandemic-Proof Recruitment and Engagement in a Fully Decentralized Trial in Atrial Fibrillation Patients (DeTAP)
Npj Digital Medicine 5, no. 1: 1-7.
Valentine, S. et al.
Smartphone Movement Sensors for the Remote Monitoring of Respiratory Rates: Technical Validation
DIGITAL HEALTH 8: 20552076221089090.
Rennie, K. L. et al.
Engagement with mHealth COVID-19 digital biomarker measurements in a longitudinal cohort study: a mixed methods evaluation
JMIR Preprints. 29/06/2022:40602.
Elnakib, S. et al.
A Novel Score for MHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set
Journal of Medical Internet Research 24, no. 6: e36787.
Yassaee, A. et al.
Evaluation of an RPM solution for heart failure in Wales
In Preparation
Managing Asthma Patients With AMAZE: A Novel Disease Management Platform
Dabbah, M. A. et al.
2021
Machine learning approach to dynamic risk modelling of mortality in COVID-19: a UK Biobank study
Scientific Reports, 11(1), p. 16936.
Velardo C, et al.
Toward a Multivariate Prediction Model of Pharmacological Treatment for Women With Gestational Diabetes Mellitus: Algorithm Development and Validation
J Med Internet Res 2021;23(3):e21435.
Plans, D. et al.
Measuring interoception: The phase adjustment task
Biological Psychology, 165, p. 108171.
Ponzo, S. et al.
Measuring Interoception: The CARdiac Elevation Detection Task
Frontiers in Psychology, 12, p. 3661.
Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study
Digital Health. arXiv preprint.
Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study
European Heart Journal - Digital Health.
Morelli, D. et al.
Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population
Frontiers in Psychiatry, 12, p. 1342.
Clift, A. K. et al.
Development and Validation of Risk Scores for All-Cause Mortality for a Smartphone-Based General Health Score App: Prospective Cohort Study Using the UK Biobank
JMIR mHealth and uHealth, 9(2), p. e25655.
Nikbakhtian, S. et al.
Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study
SDNN24 Estimation from Semi-Continuous HR Measures
Sensors, 21(4), p. 1463.
Obika, B. D. et al.
Implementation of a mHealth solution to remotely monitor patients on a cardiac surgical waiting list: service evaluation
JAMIA Open, 4(3).
Ashraf, H. et al.
Feasibility of a perioperative smartphone application in colorectal surgery
British Journal of Surgery.
Shah, S. et al.
A Prospective Observational Real World Feasibility Study Assessing the Role of App-Based Remote Patient Monitoring in Reducing Primary Care Clinician Workload during the COVID Pandemic
BMC Family Practice 22, no. 1: 248.
A smartphone-based self-administered test of verbal episodic memory: Development and initial validation
Alzheimer's Association International Conference 2021, Denver, Colorado, USA.
Shah, S. S. et al.
Mobile App-Based Remote Patient Monitoring in Acute Medical Conditions: Prospective Feasibility Study Exploring Digital Health Solutions on Clinical Workload During the COVID Crisis
JMIR Formative Research, 5(1), p. e23190.
Hemmings, N. R. et al.
Development and Feasibility of a Digital Acceptance and Commitment Therapy-Based Intervention for Generalized Anxiety Disorder: Pilot Acceptability Study
JMIR Formative Research, 5(2), p. e21737.
Booth, A. et al.
Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis
PLOS ONE, 16(3), p. e0247461.
Bacciu, D., Bertoncini, G. and Morelli, D.
Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data
Neural Computing and Applications.
Thornton, J.
2020
The virtual wards supporting patients with covid-19 in the community
BMJ, 369, p. m2119.
Rossi, A. et al.
Multilevel Monitoring of Activity and Sleep in Healthy people
PhysioNet.
Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts
Sensors, 20(24), p. 7122.
A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults
Data, 5(4), p. 91.
Efficacy of the Digital Therapeutic Mobile App BioBase to Reduce Stress and Improve Mental Well-Being Among University Students: Randomized Controlled Trial
JMIR mHealth and uHealth, 8(4), p. e17767.
Kawadler, J. M. et al.
Effectiveness of a Smartphone App (BioBase) for Reducing Anxiety and Increasing Mental Well-Being: Pilot Feasibility and Acceptability Study
JMIR Formative Research, 4(11), p. e18067.
Dall'Olio, L. et al.
Prediction of vascular aging based on smartphone acquired PPG signals
Scientific Reports, 10(1), p. 19756.
Chelidoni, O. et al.
Exploring the Effects of a Brief Biofeedback Breathing Session Delivered Through the BioBase App in Facilitating Employee Stress Recovery: Randomized Experimental Study
JMIR mHealth and uHealth, 8(10), p. e19412.
Falinska, A. et al.
2019
Novel Way to Deliver Care to Women with GDM through the Use of Cloud Technology
Poster presentation at Diabetes in Pregnancy Conference 2019.
Werhahn, S. M. et al.
Designing meaningful outcome parameters using mobile technology: a new mobile application for telemonitoring of patients with heart failure
ESC Heart Failure, 6(3), pp. 516-525.
Use of a Biofeedback Breathing App to Augment Poststress Physiological Recovery: Randomized Pilot Study
JMIR Formative Research, 3(1), p. e12227.
Murphy, J. et al.
I feel it in my finger: Measurement device affects cardiac interoceptive accuracy
Biological Psychology, 148, p. 107765.
Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations
Sensors, 19(14), p. 3163.
A computationally efficient algorithm to obtain an accurate and interpretable model of the effect of circadian rhythm on resting heart rate
Physiological Measurement, 40(9), p. 095001.
Lobo, M. et al.
A novel non-invasive cuff-less optoelectronic sensor to measure blood pressure: comparison against intra-arterial measurement
Journal of Hypertension, 37, p. e158.
Mackillop L, et al.
2018
Comparing the Efficacy of a Mobile Phone-Based Blood Glucose Management System With Standard Clinic Care in Women With Gestational Diabetes: Randomized Controlled Trial
JMIR Mhealth Uhealth 2018;6(3):e71.
Morrison, R. L. et al.
A computerized, self-administered test of verbal episodic memory in elderly patients with mild cognitive impairment and healthy participants: A randomized, crossover, validation study
Alzheimer's and Dementia, 10, pp. 647-656.
Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device
Healthcare Technology Letters, 5(2), pp. 59-64.
Randomized neural networks for preference learning with physiological data
Neurocomputing, 298, pp. 9-20.
Schack, T. et al.
2017
Computationally efficient algorithm for photoplethysmography-based atrial fibrillation detection using smartphones
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2017, pp. 104-108.
Cropley, M. et al.
The Association between Work-Related Rumination and Heart Rate Variability: A Field Study
Frontiers in Human Neuroscience, 11, p. 27.
Bacciu, D., Crecchi, F. and Morelli, D.
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
arXiv:1705.02643.
Farmer, A. et al.
2015
Acceptability and User Satisfaction of a Smartphone-Based, Interactive Blood Glucose Management System in Women With Gestational Diabetes Mellitus
Journal of Diabetes Science and Technology. Vol. 9(1) 111-115.
Imrisek SD, et al.
Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults with Type 2 Diabetes Using One Drop: A Retrospective Cohort Study
JMIR Diabetes.
Lavaysse LM, et al.
One Drop Improves Productivity for Workers with Type 2 Diabetes
Journal of Occupational and Environmental Medicine.
Nara, Goldner, Lee, Dachis
Reducing Treatment Burden Among People with Chronic Conditions Using Machine Learning
JMIR Biomedical Engineering.
Osborn CY, et al.
One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial
JMIR Mhealth Uhealth 2020;8(9):e16745.
Kumar S, Moseson H, Uppal J, Juusola JL
A diabetes mobile app with in-app coaching from a Certified Diabetes Educator reduces A1c for individuals with type 2 diabetes
Diabetes Educ. 2018 Jun; 44(3):226-236.
Osborn CY, van Ginkel JR, Rodbard D, et al.
One Drop Mobile: An evaluation of hemoglobin A1c improvement linked to app engagement
JMIR Diabetes. 2017;2(2):e21.
One Drop Mobile on iPhone and Apple Watch: An evaluation of A1c improvement associated with tracking self-care
JMIR mHealth and uHealth, 2017.
Hirsch MA, Heyman M, Raymond J, Huddleston B, Dachis J, Osborn CY
Osborn CY, Hirsch MA, Heyman M, Raymond J, Huddleston B, Dachis J
Hirsch MA, Dachis J, Heyman M, Raymond J, Huddleston B, Osborn CY
Imrisek S, Hirsch A, Sears L, Chapman M, Portela S, Goldner D, Dachis J
Sears L, Chapman M, Imrisek S, Hirsch A, Portela S, Goldner D, Dachis J
Chapman M, Sears L, Imrisek S, Hirsch A, Portela S, Goldner D, Dachis J
Leveling Health Disparities Through Digital Health: Associations Between Risk for Health Inequity and Diabetes App Satisfaction, Use, and Outcomes
2022 American Diabetes Association Conference.
Sears LE, et al.
CGM Attitudes and Adoption among People with Type 2 Diabetes using One Drop
The effects of the COVID-19 Pandemic on People with Type 2 Diabetes Using One Drop
2022 Advanced Technologies and Treatments for Diabetes Conference.
Glucose Reduction in Employees with Diabetes After Long-Term One Drop Use
Directionality of One Drop's Glucose Predictions is Associated with Improved Glucose Management Among Adults with Type 2 Diabetes
2022 Society for Behavioral Medicine Meeting.
One Drop Improves Productivity for Older Workers with Type 2 Diabetes
The Effects of One Drop Digital Program on Glucose Control in Employees with Type 1 and 2 Diabetes
2021 Diabetes Technology Meeting.
Hirsch A, et al.
A pragmatic randomized control trial evaluates one drop with inhalable vs. injectable insulin
Diabetes Technol Ther. 2019;21:A146.
Hirsch A, Osborn CY, Heyman M, Huddleston B, Dachis J
Long-Term A1C Benefit from Using One Drop
Diabetes. 2019;68(Suppl1):48-LB.
One drop improves A1c among people with type 1 diabetes
Ann Behav Med. 2019;53(Suppl1):S509.
One drop improves A1c among people with type 2 diabetes
Ann Behav Med. 2019;53(Suppl1):S586.
Improvement and maintenance of healthy blood glucose levels in people with type 1 diabetes paying for One Drop expert coaching
Ann Behav Med. 2018;52(Suppl1):S719.
People with Type 2 diabetes paying for a One Drop expert coach improve and maintain at goal blood glucose
Ann Behav Med. 2018;52(Suppl1);S156.
Blood glucose improves among people at risk using One Drop Premium or Plus on iPhone and Apple Watch
Diabetes Technol Ther. 2018;20(Suppl1):A-118-A-119.
Sears L, et al.
Insulin Adherence Is a Mechanism Underlying Disparities in A1C for Younger Adults with Type 2 Diabetes
Diabetes Jul 2018, 67(Supplement 1) 890-P.
Reasons for Insulin Omission: What Matters Most?
Diabetes Jul 2018, 67(Supplement 1) 889-P.
Using the One Drop mobile app to track self-care and remember medications is associated with improved glycemia
International Diabetes Federation Congress. 2017; Abu Dhabi, UAE.
The One Drop mobile app with in-app coaching improves blood glucose and self-care
Diabetes (Abstract Book). June 2017;66 (pp228).
Osborn CY, Heyman M, Dachis J
The One Drop Mobile app and Experts program is evidence-based and improves blood glucose
Ann Behav Med (Annual Meeting). 2017;51(Suppl 1):S1-S2867.
Osborn CY
The One Drop diabetes iOS and WatchOS app with in-app coaching from Certified Diabetes Educators improves blood glucose, carbohydrate intake, and physical activity
Stanford MedX. 2017; Palo Alto, CA.
Quisel T, Foschini L, Kerr D
Self-care tracking and blood glucose stability among One Drop mobile app users
Ann Behav Med. 2017;51(Suppl 1):S1-S2867.
Dachis J, Osborn CY, Rodbard D, Huddleston B
One Drop app users report improved glycemic control
Ann Behav Med. 2017;51(Suppl 1):S1097-S1098.
Osborn CY, Rodbard D, Huddleston B, Heyman M, Dachis J
Kumar S, et al.
Impact of a diabetes mobile app with in-app coaching on glycemic control
Diabetes. 2017;63-LB.
Sears LE
One Drop Multi-condition Program Clinical Outcomes Validation
Validation Institute.
Awareness of Cardiovascular Disease Risk in People with Type 2 Diabetes
Nagra H, et al.
Health Inequity in Diabetes Technology Use: Are mHealth Apps the Solution We've Been Waiting For?
Society for Behavioral Medicine Meeting.
One Drop Digital App and Coaching Improves Lifestyle Risks, Glycemic Control and Psychological Well-being in People with Hypertension and Type 2 Diabetes
Circulation 2021;144:A9597.
One Drop's Multicondition Program is Associated with Blood Pressure Reduction in Employees with High Blood Pressure and Maintenance for Employees with Blood Pressure in Range
Circulation 2021;144:A11410.
The Effects of One Drop Digital Program on Weight Reduction in Overweight and Obese Employees with Prediabetes
Herrod C, et al.
A Socio-Ecological Approach to Understanding Barriers and Facilitators of Cardiovascular Disease Prevention
Annals of Behavioral Medicine. In press.
Nagra H, Sears L, Hoy-Rosas J
Techquity: Strategies to inform and enhance health equity in digital health design
Annals of Behavioral Medicine.
A Systematic Review of Behavior Change Techniques for Medication Compliance in Cardiovascular Disease Prevention mHealth Apps
A Systematic Review of Accessibility Frameworks and Behavior Change Techniques in mHealth Apps
Rickles L, et al.
A Behavioral Analysis to Support a Digital Blood Pressure Solution
Nagra H, Goel A, Goldner D
JMIR Biomed Eng (forthcoming).
Wexler Y, Guerra G, Patel P, Sears L, Goldner D, Dachis J
Measuring Continuous Changes in Individual Cardiovascular Risk for People With Diabetes and PreDiabetes
Circulation. 2021;144:A12390.
Circulation. 2021;144:A12359.
Wexler Y, Goldner D
Large-Scale Association of Basal Metabolic Rate and Blood Glucose Outcomes in People with Type 2 Diabetes
Diabetes 1 June 2021, 496-P.
Estimating Basal Metabolic Rate in People with Diabetes
Diabetes 1 June 2021, 39-LB.
Goldner D, et al.
Poster presentation at: Advanced Technologies and Treatments for Diabetes, June 2021
Advanced Technologies and Treatments for Diabetes, June 2021 (Virtual).
Wexler Y, et al.
One- to Six-Month Outcomes Forecasts for Diabetes and Related Conditions
American Diabetes Association 80th Scientific Sessions. June 12-16, 2020.
Goldner D
Yes, glucose monitoring can predict the future
Oral presentation at: Diabetes Technology Meeting; November 2020.
Poster presentation at: Diabetes Technology Meeting, November 2020
Diabetes Technology Meeting; November 2020.
Overnight Hypoglycemia Prediction for CGM Users
Hypo- And Hyperglycemia Prediction From Pooled Continuous Glucose Monitor Data
Diabetes Technology and Therapeutics, Feb 2020.
Reported Utility of Automated Blood Glucose Forecasts
Diabetes Jun 2019, 68(Supplement 1) 49-LB.
Blood glucose prediction from pooled continuous glucose monitor data
Journal of Diabetes Science and Technology. Volume 14 issue 2, pages 361-492.
A Machine-Learning Model Accurately Predicts Projected Blood Glucose
Diabetes Jul 2018, 67(Supplement 1) 46-LB.