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Butler S, Sculley D, Santos D, Girones X, Singh-Grewal D, Coda A. Using Digital Health Technologies to Monitor Pain, Medication Adherence and Physical Activity in Young People with Juvenile Idiopathic Arthritis: A Feasibility Study. Healthcare (Basel) 2024; 12:392. [PMID: 38338277 PMCID: PMC10855480 DOI: 10.3390/healthcare12030392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 01/26/2024] [Accepted: 01/31/2024] [Indexed: 02/12/2024] Open
Abstract
Juvenile idiopathic arthritis can be influenced by pain, medication adherence, and physical activity. A new digital health intervention, InteractiveClinics, aims to monitor these modifiable risk factors. Twelve children, aged 10 to 18 years, received daily notifications on a smartwatch to record their pain levels and take their medications, using a customised mobile app synchronised to a secure web-based platform. Daily physical activity levels were automatically recorded by wearing a smartwatch. Using a quantitative descriptive research design, feasibility and user adoption were evaluated. The web-based data revealed the following: Pain: mean app usage: 68% (SD 30, range: 28.6% to 100%); pain score: 2.9 out of 10 (SD 1.8, range: 0.3 to 6.2 out of 10). Medication adherence: mean app usage: 20.7% (SD, range: 0% to 71.4%), recording 39% (71/182) of the expected daily and 37.5% (3/8) of the weekly medications. Pro-re-nata (PRN) medication monitoring: 33.3% (4/12), one to six additional medications (mean 3.5, SD 2.4) for 2-6 days. Physical activity: watch wearing behaviour: 69.7% (439/630), recording low levels of moderate-to-vigorous physical activity (mean: 11.8, SD: 13.5 min, range: 0-47 min). To conclude, remote monitoring of real-time data is feasible. However, further research is needed to increase adoption rates among children.
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Affiliation(s)
- Sonia Butler
- School of Bioscience and Pharmacy, University of Newcastle, Ourimbah, NSW 2258, Australia;
| | - Dean Sculley
- School of Bioscience and Pharmacy, University of Newcastle, Ourimbah, NSW 2258, Australia;
| | - Derek Santos
- School of Health Sciences, Queen Margaret University, Edinburgh EH21 6UU, UK;
| | - Xavier Girones
- Department of Research, Universities de Catalunya, Generalitat de Catalunya, 08003 Barcelona, Spain;
| | - Davinder Singh-Grewal
- Department of Rheumatology, Sydney Children’s Hospitals Network (Randwick), Randwick, NSW 2031, Australia;
- Department of Rheumatology, Sydney Children’s Hospitals Network (Westmead), Westmead, NSW 2145, Australia
- John Hunter Children’s Hospital, New Lambton Heights, NSW 2305, Australia
- Discipline of Child and Adolescent Health, University of Sydney, Camperdown, NSW 2050, Australia
- School of Women’s and Children’s Health, University of NSW, Sydney, NSW 2052, Australia
| | - Andrea Coda
- School of Health Sciences, University of Newcastle, Callaghan, NSW 2308, Australia;
- Equity in Health and Wellbeing Research Program, The Hunter Medical Research Institute (HMRI), Newcastle, NSW 2305, Australia
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2
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Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial. Nat Med 2022; 28:1823-1830. [PMID: 36031651 DOI: 10.1038/s41591-022-01979-w] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 07/28/2022] [Indexed: 12/16/2022]
Abstract
Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial ( NCT04250220 ), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19-3.76; P = 0.010) and 2.75 (95% CI, 1.42-5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.
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Harris PA, Swafford J, Serdoz ES, Eidenmuller J, Delacqua G, Jagtap V, Taylor RJ, Gelbard A, Cheng AC, Duda SN. MyCap: a flexible and configurable platform for mobilizing the participant voice. JAMIA Open 2022; 5:ooac047. [PMID: 35673353 PMCID: PMC9165428 DOI: 10.1093/jamiaopen/ooac047] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022] Open
Abstract
This paper provides a description of the MyCap data collection platform, utilization metrics, and vignettes associated with use from diverse research institutions. MyCap is a participant-facing mobile application for survey data collection and the automated administration of active tasks (activities performed by participants using mobile device sensors under semi-controlled conditions). Launched in 2018, MyCap is a no-code solution for research teams conducting longitudinal studies, integrates tightly with REDCap and is available at no cost to research teams at academic, nonprofit, or government organizations. MyCap has been deployed at multiple research institutions with application usage logged across 135 countries in 2021. Vignettes demonstrate that MyCap empowered research teams to explore and implement novel methods of information collection and use. MyCap’s integration with REDCap provides a comprehensive data collection ecosystem and is best suited for longitudinal studies with frequent requests for information from participants.
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Affiliation(s)
- Paul A Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan Swafford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Emily S Serdoz
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jessica Eidenmuller
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Giovanni Delacqua
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Vaishali Jagtap
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert J Taylor
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alexander Gelbard
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alex C Cheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Stephany N Duda
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Goh CY, Parlow S, Di Santo P, Simard T, Jung R, Ahmed Z, Verreault-Julien L, Kuhar P, Chan V, Al-Atassi T, Toeg H, Bernick J, Wells GA, Ruel M, Hibbert B. Utility of a smartphone application in assessing palmar circulation prior to radial artery harvesting for coronary artery bypass grafting: rationale and design of the randomised CAPITAL iRADIAL-CABG trial. BMJ Open 2022; 12:e055580. [PMID: 35396289 PMCID: PMC8995949 DOI: 10.1136/bmjopen-2021-055580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION There is emerging evidence supporting the use of the radial artery (RA) as a preferred secondary conduit for coronary artery bypass grafting (CABG) as it is associated with higher rates of graft patency at 5 years when compared with saphenous vein grafts (SVG). The modified Allen's test (MAT) is traditionally regarded as the standard of care in the assessment of ulnar artery (UA) patency prior to RA harvesting. Unfortunately, due to high false-positive rates, a substantial number of pre-CABG patients are found to have an abnormal MAT despite normal UA patency, resulting in inappropriate exclusion from RA harvesting. The SVG is generally used in its place when this occurs, resulting in potentially lower rates of long-term graft patency. METHODS AND ANALYSIS The CAPITAL iRADIAL-CABG trial is currently enrolling participants 18 years of age or older undergoing CABG for whom the treating physician is considering the use of an RA conduit. Eligible patients will be randomised in a 1:1 fashion to MAT or smartphone-based photoplethysmography application assessment to assess collateral palmar circulation prior to RA harvesting. The primary outcome of the trial is the use of the RA as a conduit during CABG. The primary safety outcome is postoperative palmar ischaemia as determined by clinical assessment or requirement of vascular intervention. Secondary outcomes include vascular complications, early graft failure, need for rescue percutaneous coronary intervention during the index hospitalisation and a composite cardiovascular outcome of myocardial infarction, stroke and cardiovascular death prior to discharge from hospital. A total of 236 participants are planned to be recruited. ETHICS AND DISSEMINATION The study was approved by the Ottawa Heart Science Network Research Ethics Board (approval number 20180865-01H). The study results will be disseminated via conference presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER NCT03810729.
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Affiliation(s)
- Cheng Yee Goh
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Simon Parlow
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Pietro Di Santo
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Trevor Simard
- Department of Cardiovascular Diseases, Mayo Clinic School of Medicine, Rochester, Minnesota, USA
| | - Richard Jung
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Zeeshan Ahmed
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Louis Verreault-Julien
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Peter Kuhar
- Azumio Inc, Redwood City, San Francisco, USA
| | - Vincent Chan
- Division of Cardiac Surgery, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Talal Al-Atassi
- Division of Cardiac Surgery, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Hadi Toeg
- Division of Cardiac Surgery, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Jordan Bernick
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - George A Wells
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Marc Ruel
- Division of Cardiac Surgery, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Benjamin Hibbert
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
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5
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Inomata T, Nakamura M, Sung J, Midorikawa-Inomata A, Iwagami M, Fujio K, Akasaki Y, Okumura Y, Fujimoto K, Eguchi A, Miura M, Nagino K, Shokirova H, Zhu J, Kuwahara M, Hirosawa K, Dana R, Murakami A. Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study. NPJ Digit Med 2021; 4:171. [PMID: 34931013 PMCID: PMC8688467 DOI: 10.1038/s41746-021-00540-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/28/2021] [Indexed: 01/01/2023] Open
Abstract
Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.
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Affiliation(s)
- Takenori Inomata
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Strategic Operating Room Management and Improvement, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan.
| | - Masahiro Nakamura
- Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan.,Precision Health, Department of Engineering, Graduate School of Bioengineering, The University of Tokyo, Tokyo, Japan
| | - Jaemyoung Sung
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,University of South Florida, Morsani College of Medicine, Tampa, FL, USA
| | - Akie Midorikawa-Inomata
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Kenta Fujio
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Strategic Operating Room Management and Improvement, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Atsuko Eguchi
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Maria Miura
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Ken Nagino
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Hurramhon Shokirova
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Jun Zhu
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Mizu Kuwahara
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Reza Dana
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Akira Murakami
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
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6
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Bahmani A, Alavi A, Buergel T, Upadhyayula S, Wang Q, Ananthakrishnan SK, Alavi A, Celis D, Gillespie D, Young G, Xing Z, Nguyen MHH, Haque A, Mathur A, Payne J, Mazaheri G, Li JK, Kotipalli P, Liao L, Bhasin R, Cha K, Rolnik B, Celli A, Dagan-Rosenfeld O, Higgs E, Zhou W, Berry CL, Van Winkle KG, Contrepois K, Ray U, Bettinger K, Datta S, Li X, Snyder MP. A scalable, secure, and interoperable platform for deep data-driven health management. Nat Commun 2021; 12:5757. [PMID: 34599181 PMCID: PMC8486823 DOI: 10.1038/s41467-021-26040-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 08/23/2021] [Indexed: 11/08/2022] Open
Abstract
The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.
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Affiliation(s)
- Amir Bahmani
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Arash Alavi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Thore Buergel
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Sushil Upadhyayula
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Qiwen Wang
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Amir Alavi
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Diego Celis
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Dan Gillespie
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Gregory Young
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Ziye Xing
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
| | - Minh Hoang Huynh Nguyen
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
| | - Audrey Haque
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
| | - Ankit Mathur
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Josh Payne
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ghazal Mazaheri
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Jason Kenichi Li
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Pramod Kotipalli
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Lisa Liao
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Rajat Bhasin
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Kexin Cha
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Benjamin Rolnik
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | | | | | - Emily Higgs
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Wenyu Zhou
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
| | - Camille Lauren Berry
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Katherine Grace Van Winkle
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | | | - Utsab Ray
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA
| | - Keith Bettinger
- Department of Genetics, Stanford University, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA
| | - Somalee Datta
- Technology and Digital Solutions, Stanford Medicine, Stanford, CA, USA
| | - Xiao Li
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biochemistry, The Center for RNA Science and Therapeutics, Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA.
- Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
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7
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Abstract
Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.
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8
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Inomata T, Sung J, Nakamura M, Fujisawa K, Muto K, Ebihara N, Iwagami M, Nakamura M, Fujio K, Okumura Y, Okano M, Murakami A. New medical big data for P4 medicine on allergic conjunctivitis. Allergol Int 2020; 69:510-518. [PMID: 32651122 DOI: 10.1016/j.alit.2020.06.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 05/20/2020] [Indexed: 12/16/2022] Open
Abstract
Allergic conjunctivitis affects approximately 15-20% of the global population and can permanently deteriorate one's quality of life (QoL) and work productivity, leading to societal work force costs. Although not fully understood, allergic conjunctivitis is a multifactorial disease with a complex network of environmental, lifestyle, and host contributory risk factors. To effectively enhance the quality of treatment for patients with allergic conjunctivitis, as well as other allergic diseases, the field must first comprehend the pathology underlying various individualized subjective symptoms and stratify the disease according to risk factors and presentations. Such competent stratification and societal reconstruction that targets the alleviation of the damage due to allergic diseases would greatly help ramify personalized treatments and prevent the projected increase in societal costs imposed by allergic diseases. Owing to the rapid advancements in the information and technology sector, medical big data are greatly accessible and useful to decipher the pathophysiology of many diseases. Such data collected through multi-omics and mobile health have been effective for research on chronic diseases including allergic and immune-mediated diseases. Novel big data containing vast and continuous information on individuals with allergic conjunctivitis and other allergic symptoms are being used to search for causative genes of diseases, gain insights into new biomarkers, prevent disease progression, and, ultimately, improve QoL. The individualized and holistic data accrued from new angles using technological innovations are helping the field realize the principles of P4 medicine: predictive, preventive, personalized, and participatory medicine.
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Affiliation(s)
- Takenori Inomata
- Department of Ophthalmology, Juntendo University Faculty of Medicine, Tokyo, Japan; Department of Strategic Operating Room Management and Improvement, Juntendo University Faculty of Medicine, Tokyo, Japan; Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Faculty of Medicine, Tokyo, Japan; Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Masahiro Nakamura
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan
| | - Kumiko Fujisawa
- Department of Public Policy, Human Genome Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kaori Muto
- Department of Public Policy, Human Genome Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Urayasu Hospital, Juntendo University, Chiba, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Masahiro Nakamura
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Kenta Fujio
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mitsuhiro Okano
- Department of Otorhinolaryngology, International University of Health and Welfare, Narita, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Faculty of Medicine, Tokyo, Japan; Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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9
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Abstract
Cardiovascular diseases (CVDs) are responsible for more deaths than any other cause, with coronary heart disease and stroke accounting for two-thirds of those deaths. Morbidity and mortality due to CVD are largely preventable, through either primary prevention of disease or secondary prevention of cardiac events. Monitoring cardiac status in healthy and diseased cardiovascular systems has the potential to dramatically reduce cardiac illness and injury. Smart technology in concert with mobile health platforms is creating an environment where timely prevention of and response to cardiac events are becoming a reality.
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Affiliation(s)
- Jeffrey W. Christle
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California 94305, USA
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, California 94305, USA
| | - Steven G. Hershman
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California 94305, USA
| | - Jessica Torres Soto
- Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
| | - Euan A. Ashley
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California 94305, USA
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, California 94305, USA
- Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
- Stanford Center for Digital Health, Stanford University, Stanford, California 94305, USA
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10
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Eliasen A, Abildtoft MK, Krogh NS, Rechnitzer C, Brok JS, Mathiasen R, Schmiegelow K, Dalhoff KP. Smartphone App to Self-Monitor Nausea During Pediatric Chemotherapy Treatment: User-Centered Design Process. JMIR Mhealth Uhealth 2020; 8:e18564. [PMID: 32706744 PMCID: PMC7400028 DOI: 10.2196/18564] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/23/2020] [Accepted: 05/13/2020] [Indexed: 12/23/2022] Open
Abstract
Background Nausea and vomiting are common and distressing side effects for children receiving chemotherapy. Limited evidence is available to guide antiemetic recommendations; therefore, prospective and reliable evaluation of antiemetic efficacy is needed. Smartphone apps can be used to effortlessly and precisely collect patient-reported outcomes in real time. Objective Our objective was to develop a smartphone app to monitor nausea and vomiting episodes in pediatric cancer patients aged 0 to 18 years and to test its usability and adherence to its use. Methods We used a user-centered design process and the evolutionary prototype model to develop and evaluate the app. Multidisciplinary group discussions and several rounds of patient feedback and modification were conducted. We translated the validated Pediatric Nausea Assessment Tool to assess nausea severity in children aged 4 to 18 years. The child’s own term for nausea was interactively incorporated in the nausea severity question, with response options expressed as 4 illustrative faces. Parent-reported outcomes were used for children aged 0 to 3 years. Reminders were sent using push notifications in order to ensure high response rates. Children aged 0 to 18 years who were undergoing chemotherapy were recruited from the Department of Pediatric Oncology at Copenhagen University Hospital Rigshospitalet to evaluate the app. Results The app’s most important function was to record nausea severity in children. After assistance from a researcher, children aged 4 to 18 years were able to report their symptoms in the app, and parents were able to report symptoms for their children aged 0 to 3 years. Children (n=20, aged 2.0-17.5 years) and their parents evaluated the app prospectively during a collective total of 60 chemotherapy cycles. They expressed that the app was user-friendly, intuitive, and that the time spent on data entry was fair. The response rates were on average 92%, 93%, and 80% for the day before, the first day of, and the next 3 days after chemotherapy, respectively. Researchers and clinicians were able to obtain an overview of the patient’s chemotherapy dates and responses through a secure and encrypted web-based administrative portal. Data could be downloaded for further analysis. Conclusions The user-friendly app could be used to facilitate future pediatric antiemetic trials and to refine antiemetic treatment during chemotherapy.
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Affiliation(s)
- Astrid Eliasen
- Department of Clinical Pharmacology, Bispebjerg and Frederiksberg University Hospital, Copenhagen, Denmark.,Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark.,Institute of Clinical Medicine, Faculty of Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Catherine Rechnitzer
- Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Jesper Sune Brok
- Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - René Mathiasen
- Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Kjeld Schmiegelow
- Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark.,Institute of Clinical Medicine, Faculty of Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Kim Peder Dalhoff
- Department of Clinical Pharmacology, Bispebjerg and Frederiksberg University Hospital, Copenhagen, Denmark.,Institute of Clinical Medicine, Faculty of Medicine, University of Copenhagen, Copenhagen, Denmark
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11
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Vandenberk T, Storms V, Lanssens D, De Cannière H, Smeets CJ, Thijs IM, Batool T, Vanrompay Y, Vandervoort PM, Grieten L. A Vendor-Independent Mobile Health Monitoring Platform for Digital Health Studies: Development and Usability Study. JMIR Mhealth Uhealth 2019; 7:e12586. [PMID: 31663862 PMCID: PMC7017647 DOI: 10.2196/12586] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 12/22/2018] [Accepted: 06/15/2019] [Indexed: 01/24/2023] Open
Abstract
Background Medical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use. Objective The aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies. Methods DHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting. Results A prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients’ blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization. Conclusions The increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies.
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Affiliation(s)
- Thijs Vandenberk
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium
| | - Valerie Storms
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium
| | - Dorien Lanssens
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium.,Department of Obstetrics & Gynecology, Ziekenhuis Oost-Limburg, Genk, Belgium
| | - Hélène De Cannière
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium.,Department of Cardiology, Hospital Oost-Limburg, Genk, Belgium
| | - Christophe Jp Smeets
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium.,Department of Cardiology, Hospital Oost-Limburg, Genk, Belgium
| | - Inge M Thijs
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium.,Department of Obstetrics & Gynecology, Ziekenhuis Oost-Limburg, Genk, Belgium
| | - Tooba Batool
- Transportation Research Institute (IMOB), Hasselt University, Hasselt, Belgium
| | - Yves Vanrompay
- Transportation Research Institute (IMOB), Hasselt University, Hasselt, Belgium
| | - Pieter M Vandervoort
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium.,Department of Cardiology, Hospital Oost-Limburg, Genk, Belgium
| | - Lars Grieten
- Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.,Future Health Department, Hospital Oost-Limburg, Genk, Belgium
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12
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Ranjan Y, Rashid Z, Stewart C, Conde P, Begale M, Verbeeck D, Boettcher S, Dobson R, Folarin A. RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices. JMIR Mhealth Uhealth 2019; 7:e11734. [PMID: 31373275 PMCID: PMC6694732 DOI: 10.2196/11734] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 11/28/2018] [Accepted: 12/09/2018] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. OBJECTIVE Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. METHODS RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. RESULTS General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. CONCLUSIONS RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.
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Affiliation(s)
- Yatharth Ranjan
- The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom
| | - Callum Stewart
- The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom
| | - Pauline Conde
- The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom
| | | | - Denny Verbeeck
- Janssen Pharmaceutica NV, Turnhoutseweg, Beerse, Belgium
| | - Sebastian Boettcher
- Epilepsy Center, Department of Neurosurgery, University of Hospital Freiburg, Freiburg, Germany
| | - Richard Dobson
- The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Amos Folarin
- The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
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