Publications

Huma’s scientific credibility is supported by 90 peer-reviewed publications spanning multiple disease areas, patient populations and care settings, including clinical validation and real-world evidence.

Titles

1. 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
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2. Moore A, Morelli D.
   2024
   conDENSE: Conditional Density Estimation for Time Series Anomaly Detection
   Journal of Artificial Intelligence Research, Vol 79.
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3. 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
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4. Lim, A. et al.
   2022
   An Outpatient Management Strategy Using a Corona taxi Digital Early Warning System Reduces Coronavirus Disease 2019 Mortality
   Open Forum Infectious Diseases, 9, no.4: ofac063.
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5. Gatzoulis, M. et al.
   2022
   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.
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6. Bacciu, D. et al.
   2022
   Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes
   IEEE Transactions on Neural Networks and Learning Systems.
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7. Sarraju A, et al.
   2022
   Pandemic-Proof Recruitment and Engagement in a Fully Decentralized Trial in Atrial Fibrillation Patients (DeTAP)
   Npj Digital Medicine, 5, no. 1: 1-7.
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8. Valentine, S. et al.
   2022
   Smartphone Movement Sensors for the Remote Monitoring of Respiratory Rates: Technical Validation
   DIGITAL HEALTH 8: 20552076221089090.
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9. Rennie, K. L. et al.
   2022
   Engagement with mHealth COVID-19 digital biomarker measurements in a longitudinal cohort study: a mixed methods evaluation
   JMIR Preprints. 29/06/2022: 40602.
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10. Elnakib, S. et al.
    2022
    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.
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11. Yassaee, A. et al.
    2020
    Evaluation of an RPM solution for heart failure in Wales.
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12. In Preparation
    2023
    Managing Asthma Patients With AMAZE: A Novel Disease Management Platform, A Clinical Pilot Study
    ClinicalTrials.gov
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13. 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.
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14. Velardo C, et al.
    2021
    Toward a Multivariate Prediction Model of Pharmacological Treatment for Women With Gestational Diabetes Mellitus: Algorithm Development and Validation
    JMIR Medical Internet Research, 23(3): e21435.
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15. Plans, D. et al.
    2021
    Measuring interoception: The phase adjustment task
    Biological Psychology, 165, p. 108171.
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16. Ponzo, S. et al.
    2021
    Measuring Interoception: The CARdiac Elevation Detection Task
    Frontiers in Psychology, 12, p. 3661.
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17. Dolezalova, N. et al.
    2021
    Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study
    Digital Health. arXiv preprint.
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18. Dolezalova, N. et al.
    2021
    Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study
    European Heart Journal-Digital Health.
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19. Morelli, D. et al.
    2021
    Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population
    Frontiers in Psychiatry, 12, p. 1342.
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20. Clift, A. K. et al.
    2021
    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.
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21. Nikbakhtian, S. et al.
    2021
    Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study
    European Heart Journal-Digital Health.
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22. Morelli, D. et al.
    2021
    SDNN 24 Estimation from Semi-Continuous HR Measures
    Sensors, 21(4), p. 1463.
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23. Obika, B. D. et al.
    2021
    Implementation of a mHealth solution to remotely monitor patients on a cardiac surgical waiting list: service evaluation
    JAMIA Open, 4(3).
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24. Ashraf, H. et al.
    2021
    Feasibility of a perioperative smartphone application in colorectal surgery
    British Journal of Surgery.
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25. Shah, S. et al.
    2021
    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.
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26. Valentine, S. et al.
    2021
    A smartphone-based self-administered test of verbal episodic memory: Development and initial validation
    Alzheimer's Association International Conference 2021, Denver, Colorado, USA.
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27. Shah, S. S. et al.
    2021
    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.
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28. Hemmings, N. R. et al.
    2021
    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.
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29. Booth, A. et al.
    2021
    Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis
    PLOS ONE, 16(3), p. e0247461.
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30. Bacciu, D., Bertoncini, G. and Morelli, D.
    2021
    Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data
    Neural Computing and Applications.
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31. Thornton, J.
    2020
    The virtual wards supporting patients with COVID-19 in the community
    BMJ, 369, p. m2119.
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32. Rossi, A. et al.
    2020
    Multilevel Monitoring of Activity and Sleep in Healthy People
    PhysioNet.
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33. Rossi, A. et al.
    2020
    Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts
    Sensors, 20(24), p. 7122.
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34. Rossi, A. et al.
    2020
    A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults
    Data, 5(4), p. 91.
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35. Ponzo, S. et al.
    2020
    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.
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36. Kawadler, J. M. et al.
    2020
    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.
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37. Dall'Olio, L. et al.
    2020
    Prediction of vascular aging based on smartphone acquired PPG signals
    Scientific Reports, 10(1), p. 19756.
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38. Chelidoni, O. et al.
    2020
    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.
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39. Werhahn, S. M. et al.
    2019
    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.
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40. Plans, D. et al.
    2019
    Use of a Biofeedback Breathing App to Augment Post-stress Physiological Recovery: Randomized Pilot Study
    JMIR Formative Research, 3(1), p. e12227.
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41. Murphy, J. et al.
    2019
    I feel it in my finger: Measurement device affects cardiac interoceptive accuracy
    Biological Psychology, 148, p. 107765.
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42. Morelli, D. et al.
    2019
    Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations
    Sensors, 19(14), p. 3163.
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43. Morelli, D. et al.
    2019
    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.
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44. Lobo, M. et al.
    2019
    A novel non-invasive cuff-less optoelectronic sensor to measure blood pressure: comparison against intra-arterial measurement
    Journal of Hypertension, 37, p. e158.
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45. 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 and uHealth, 6(3): e71.
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46. Morrison, R. L. et al.
    2018
    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.
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47. Morelli, D. et al.
    2018
    Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device
    Healthcare Technology Letters, 5(2), pp. 59-64.
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48. Bacciu, D. et al.
    2018
    Randomized neural networks for preference learning with physiological data
    Neurocomputing, 298, pp. 9-20.
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49. 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.
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50. Cropley, M. et al.
    2017
    The Association between Work-Related Rumination and Heart Rate Variability: A Field Study
    Frontiers in Human Neuroscience, 11, p. 27.
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51. Bacciu, D., Crecchi, F. and Morelli, D.
    2017
    Drop In: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
    arXiv:1705.02643.
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52. 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.
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53. Imrisek, S. D. et al.
    2022
    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.
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54. Lavaysse, L. M. et al.
    2022
    One Drop Improves Productivity for Workers with Type 2 Diabetes
    Journal of Occupational and Environmental Medicine.
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55. Nara, Goldner, Lee, Dachis.
    2022
    Reducing Treatment Burden Among People with Chronic Conditions Using Machine Learning
    JMIR Biomedical Engineering.
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56. Osborn, C. Y. et al.
    2020
    One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial
    JMIR mHealth and uHealth, 2020; 8(9): e16745.
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57. Kumar, S., Moseson, H., Uppal, J., Juusola, J. L.
    2018
    A diabetes mobile app within-app coaching from a Certified Diabetes Educator reduces A1c for individuals with type 2 diabetes
    Diabetes Educ. 2018 Jun; 44(3): 226-236.
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58. Osborn, C. Y. et al.
    2017
    One Drop Mobile: An evaluation of hemoglobin A1c improvement linked to app engagement
    JMIR Diabetes. 2017; 2(2): e21.
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59. Osborn, C. Y. et al.
    2017
    One Drop Mobile on iPhone and Apple Watch: An evaluation of A1c improvement associated with tracking self-care
    JMIR mHealth and uHealth, 2017.
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60. Imrisek, S. D. et al.
    2022
    Leveling Health Disparities Through Digital Health: Associations Between Risk for Health Inequity and Diabetes App Satisfaction, Use, and Outcomes
    2022 American Diabetes Association Conference.
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61. Sears, L. E. et al.
    2022
    CGM Attitudes and Adoption among People with Type 2 Diabetes using One Drop
    2022 American Diabetes Association Conference.
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62. Lavaysse, L. M. et al.
    2022
    The effects of the COVID-19 Pandemic on People with Type 2 Diabetes Using One Drop
    2022 Advanced Technologies and Treatments for Diabetes Conference.
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63. Lavaysse, L. M. et al.
    2022
    Glucose Reduction in Employees with Diabetes After Long-Term One Drop Use
    2022 Advanced Technologies and Treatments for Diabetes Conference.
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64. Sears, L. E. et al.
    2021
    The Effects of One Drop Digital Program on Glucose Control in Employees with Type 1 and 2 Diabetes.
    2021 Diabetes Technology Meeting.
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65. Hirsch, A. et al.
    2019
    A pragmatic randomized control trial evaluates One Drop inhalable vs. injectable insulin
    Diabetes Technology and Therapeutics, 2019; 21: A146.
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66. Hirsch, A., Osborn, C. Y., Heyman, M., Huddleston, B., Dachis, J.
    2019
    Long-Term A1C Benefit from Using One Drop
    Diabetes. 2019; 68(Suppl 1): 48-LB.
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67. Osborn, C. Y. et al.
    2018
    Blood glucose improves among people at risk using One Drop Premium or Plus on iPhone and Apple Watch
    Diabetes Technology and Therapeutics. 2018; 20(Suppl 1): A-118-A-119.
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68. Sears, L. et al.
    2018
    Insulin Adherence Is a Mechanism Underlying Disparities in A1C for Younger Adults with Type 2 Diabetes
    Diabetes Jul 2018, 67(Supplement 1) 890-P.
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69. Osborn, C. Y. et al.
    2018
    Reasons for Insulin Omission: What Matters Most?
    Diabetes Jul 2018, 67(Supplement 1) 889-P.
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70. Kumar, S. et al.
    2017
    Impact of a diabetes mobile app within-app coaching on glycemic control
    Diabetes. 2017; 63-LB.
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71. Imrisek, S. D. et al.
    2022
    Awareness of Cardiovascular Disease Risk in People with Type 2 Diabetes
    2022 American Diabetes Association Conference.
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72. Nagra, H. et al.
    2022
    Health Inequity in Diabetes Technology Use: Are mHealth Apps the Solution We've Been Waiting For?
    Society for Behavioral Medicine Meeting.
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73. Sears, L. E. et al.
    2021
    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.
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74. Sears, L. E. et al.
    2021
    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.
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75. Sears, L. E. et al.
    2021
    The Effects of One Drop Digital Program on Weight Reduction in Overweight and Obese Employees with Prediabetes
    2021 Diabetes Technology Meeting.
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76. Nagra, H., Sears, L., Hoy-Rosas, J.
    2022
    Techquity: Strategies to inform and enhance health equity in digital health design
    Annals of Behavioral Medicine.
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77. Nagra, H., Goel, A., Goldner, D.
    2022
    Reducing Treatment Burden Among People with Chronic Conditions Using Machine Learning
    JMIR Biomed Eng (forthcoming).
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78. Wexler, Y. et al.
    2021
    Measuring Continuous Changes in Individual Cardiovascular Risk for People With Diabetes and PreDiabetes
    Circulation. 2021; 144: A12390.
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79. Wexler, Y. et al.
    2021
    Measuring Continuous Changes in Individual Cardiovascular Risk for People With Diabetes and PreDiabetes
    Circulation. 2021; 144: A12359.
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80. Wexler, Y., Goldner, D.
    2021
    Large-Scale Association of Basal Metabolic Rate and Blood Glucose Outcomes in People with Type 2 Diabetes
    Diabetes, 1 June 2021, 496-P.
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81. Wexler, Y., Goldner, D.
    2021
    Estimating Basal Metabolic Rate in People with Diabetes
    Diabetes, 1 June 2021, 39-LB.
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82. Goldner, D. et al.
    2021
    Poster presentation at: Advanced Technologies and Treatments for Diabetes, June 2021
    Advanced Technologies and Treatments for Diabetes, June 2021 (Virtual).
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83. Wexler, Y. et al.
    2020
    One-to-Six-Month Outcomes Forecasts for Diabetes and Related Conditions
    American Diabetes Association 80th Scientific Sessions, June 12-16, 2020.
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84. Goldner, D.
    2020
    Yes, glucose monitoring can predict the future
    Oral presentation at: Diabetes Technology Meeting; November 2020.
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85. Wexler, Y. et al.
    2020
    Poster presentation at: Diabetes Technology Meeting, November 2020
    Diabetes Technology Meeting; November 2020.
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86. Wexler, Y. et al.
    2020
    Overnight Hypoglycemia Prediction for CGM Users
    American Diabetes Association 80th Scientific Sessions, June 12-16, 2020.
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87. Wexler, Y. et al.
    2020
    Hypo-And Hyperglycemia Prediction From Pooled Continuous Glucose Monitor Data
    Diabetes Technology and Therapeutics, Feb 2020.
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88. Goldner, D. et al.
    2019
    Reported Utility of Automated Blood Glucose Forecasts
    Diabetes Jun 2019, 68(Supplement 1) 49-LB.
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89. Wexler, Y. et al.
    2020
    Blood glucose prediction from pooled continuous glucose monitor data
    Journal of Diabetes Science and Technology, Volume 14 issue 2, pages 361-492.
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90. Goldner, D. et al.
    2018
    A Machine-Learning Model Accurately Predicts Projected Blood Glucose
    Diabetes Jul 2018, 67(Supplement 1) 46-LB.
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