Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

[1]  J. Murray,et al.  The "All of Us" Research Program. , 2019, The New England journal of medicine.

[2]  J. Denny,et al.  The "All of Us" Research Program. , 2019, The New England journal of medicine.

[3]  P. Areán,et al.  Contemporary Views of Research Participant Willingness to Participate and Share Digital Data in Biomedical Research , 2019, JAMA network open.

[4]  Michael Snyder,et al.  Big data and health. , 2019, The Lancet. Digital health.

[5]  Ida Sim,et al.  Mobile Devices and Health. , 2019, The New England journal of medicine.

[6]  Larsson Omberg,et al.  A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health , 2019, KDD.

[7]  M. Elzarrad,et al.  The US Food and Drug Administration's Real‐World Evidence Framework: A Commitment for Engagement and Transparency on Real‐World Evidence , 2019, Clinical pharmacology and therapeutics.

[8]  W. Dixon,et al.  Maximizing Engagement in Mobile Health Studies , 2019, Rheumatic diseases clinics of North America.

[9]  S. Steinhubl,et al.  Real world usage characteristics of a novel mobile health self-monitoring device: Results from the Scanadu Consumer Health Outcomes (SCOUT) Study , 2019, PloS one.

[10]  L. Tarassenko,et al.  Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study , 2019, Scientific Data.

[11]  Everett M. Rogers,et al.  Diffusion of Innovations 1 , 2019, An Integrated Approach to Communication Theory and Research.

[12]  Don W. Stacks,et al.  An Integrated Approach to Communication Theory and Research , 2019 .

[13]  Javier Bajo,et al.  A review of mobile sensing systems, applications, and opportunities , 2019, Knowledge and Information Systems.

[14]  M. May.  Clinical trial costs go under the microscope , 2019, Nature Medicine.

[15]  A. Hrõbjartsson,et al.  Randomized clinical trials with run-in periods: frequency, characteristics and reporting , 2019, Clinical epidemiology.

[16]  A. Philippakis,et al.  The "All of Us" Research Program. , 2019, The New England journal of medicine.

[17]  Kyle Taylor,et al.  Smartphone ownership is growing rapidly around the world, but not always equally , 2019 .

[18]  K. Abdullah,et al.  A systematic review of qualitative studies exploring peer learning experiences of undergraduate nursing students. , 2018, Nurse education today.

[19]  Pamela Berry,et al.  Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study , 2018, JMIR mHealth and uHealth.

[20]  David B. Fogel,et al.  Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review , 2018, Contemporary clinical trials communications.

[21]  S. Mooney,et al.  Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial , 2018, Journal of medical Internet research.

[22]  Eric Smalley,et al.  Clinical trials go virtual, big pharma dives in , 2018, Nature Biotechnology.

[23]  Ming-Chieh Li,et al.  Factors influencing the willingness to participate in medical research: a nationwide survey in Taiwan , 2018, PeerJ.

[24]  Pei Wang,et al.  The asthma mobile health study, smartphone data collected using ResearchKit , 2018, Scientific Data.

[25]  Lena Mamykina,et al.  Designing in the Dark: Eliciting Self-tracking Dimensions for Understanding Enigmatic Disease , 2018, CHI.

[26]  I. Cohen,et al.  A Framework for Ethical Payment to Research Participants , 2018, The New England journal of medicine.

[27]  Jennifer C. Goldsack,et al.  Use of Mobile Devices to Measure Outcomes in Clinical Research, 2010–2016: A Systematic Literature Review , 2018, Digital Biomarkers.

[28]  Eric J. Topol,et al.  Digital medicine, on its way to being just plain medicine , 2018, npj Digital Medicine.

[29]  Ambuj Tewari,et al.  Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support , 2017, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[30]  Michelle Mancher,et al.  Returning Individual Research Results to Participants: Guidance for a New Research Paradigm , 2018 .

[31]  W. Dixon,et al.  Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study , 2017, JMIR mHealth and uHealth.

[32]  M. Doerr,et al.  Consent Processes for Mobile App Mediated Research: Systematic Review , 2017, JMIR mHealth and uHealth.

[33]  Camille Nebeker,et al.  Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists , 2017, JMIR mHealth and uHealth.

[34]  Rebecca W. Stubbs,et al.  Trends and Patterns of Geographic Variation in Cardiovascular Mortality Among US Counties, 1980-2014 , 2017, JAMA.

[35]  Marcus A. Badgeley,et al.  The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit , 2017, Nature Biotechnology.

[36]  Alain Abran,et al.  A systematic literature review: Opinion mining studies from mobile app store user reviews , 2017, J. Syst. Softw..

[37]  C. Suver,et al.  The Mole Mapper Study, mobile phone skin imaging and melanoma risk data collected using ResearchKit , 2017, Scientific Data.

[38]  M. McConnell,et al.  The Use of Smartphones for Health Research. , 2017, Academic medicine : journal of the Association of American Medical Colleges.

[39]  Rachel L. Goldfeder,et al.  Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study , 2017, JAMA cardiology.

[40]  A. Agarwal,et al.  A systematic review of discontinued trials suggested that most reasons for recruitment failure were preventable. , 2016, Journal of clinical epidemiology.

[41]  David C. Atkins,et al.  The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial , 2016, Journal of medical Internet research.

[42]  Alan D. Lopez,et al.  Dissonant health transition in the states of Mexico, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2016, The Lancet.

[43]  Peter Hanlon,et al.  Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies , 2016, BMC Medical Informatics and Decision Making.

[44]  G. Barton,et al.  Randomised controlled trial , 2016 .

[45]  Vikram Patel,et al.  Global burden of diseases, injuries, and risk factors for young people's health during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2016, The Lancet.

[46]  Alaa M Althubaiti,et al.  Information bias in health research: definition, pitfalls, and adjustment methods , 2016, Journal of multidisciplinary healthcare.

[47]  John Wilbanks,et al.  First, design for data sharing , 2016, Nature Biotechnology.

[48]  E. Dorsey,et al.  Smartphones as new tools in the management and understanding of Parkinson’s disease , 2016, npj Parkinson's Disease.

[49]  S. Friend,et al.  The mPower study, Parkinson disease mobile data collected using ResearchKit , 2016, Scientific Data.

[50]  Adam Gazzaley,et al.  Conducting a fully mobile and randomised clinical trial for depression: access, engagement and expense , 2016, BMJ Innovations.

[51]  N. Powe,et al.  Diversity in Clinical and Biomedical Research: A Promise Yet to Be Fulfilled , 2015, bioRxiv.

[52]  Lisa M. Nicholson,et al.  Recruitment and retention strategies in clinical studies with low-income and minority populations: Progress from 2004-2014. , 2015, Contemporary clinical trials.

[53]  Eric J. Topol,et al.  The emerging field of mobile health , 2015, Science Translational Medicine.

[54]  L. Yardley,et al.  The Person-Based Approach to Intervention Development: Application to Digital Health-Related Behavior Change Interventions , 2015, Journal of medical Internet research.

[55]  M. Okano,et al.  Cohort Study , 2020, Definitions.

[56]  M. Goldbaum,et al.  Reasons behind the participation in biomedical research: a brief review. , 2014, Revista brasileira de epidemiologia = Brazilian journal of epidemiology.

[57]  David A Chambers,et al.  Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias , 2014, Clinical and Translational Science.

[58]  S. Cummings,et al.  Web-based trial to evaluate the efficacy and safety of tolterodine ER 4 mg in participants with overactive bladder: REMOTE trial. , 2014, Contemporary clinical trials.

[59]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[60]  R. Ward.  The application of technology acceptance and diffusion of innovation models in healthcare informatics , 2013 .

[61]  Gary G Bennett,et al.  How behavioral science can advance digital health , 2013, Translational behavioral medicine.

[62]  D. Bates,et al.  Patient Perceptions of a Personal Health Record: A Test of the Diffusion of Innovation Model , 2012, Journal of medical Internet research.

[63]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[64]  E. Klein,et al.  Recruitment and retention strategies in longitudinal clinical studies with low-income populations. , 2011, Contemporary clinical trials.

[65]  Michael C. Gibbons,et al.  Use of Health Information Technology among Racial and Ethnic Underserved Communities , 2011 .

[66]  Michael Christopher Gibbons,et al.  Use of health information technology among racial and ethnic underserved communities. , 2011, Perspectives in health information management.

[67]  Income inequality, mortality, and self rated health: meta-analysis of multilevel studies , 2010, BMJ : British Medical Journal.

[68]  J. Neely,et al.  A practical guide to understanding Kaplan-Meier curves , 2010, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[69]  J. Geddes,et al.  What is a randomised controlled trial? , 2009, Epidemiologia e Psichiatria Sociale.

[70]  T. Greenhalgh,et al.  Introduction of shared electronic records: multi-site case study using diffusion of innovation theory , 2008, BMJ : British Medical Journal.

[71]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.

[72]  G. Eysenbach.  The Law of Attrition Revisited – Author’s Reply , 2006, Journal of Medical Internet Research.

[73]  Helen Christensen,et al.  The Law of Attrition Revisited , 2006, Journal of medical Internet research.

[74]  Simon Parsons.  Introduction to Machine Learning by Ethem Alpaydin, MIT Press, 0-262-01211-1, 400 pp , 2005, Knowl. Eng. Rev..

[75]  G. Eysenbach.  The Law of Attrition , 2005, Journal of medical Internet research.

[76]  Kathleen M Griffiths,et al.  A Comparison of Changes in Anxiety and Depression Symptoms of Spontaneous Users and Trial Participants of a Cognitive Behavior Therapy Website , 2004, Journal of medical Internet research.

[77]  J. Ball,et al.  Statistics review 12: Survival analysis , 2004, Critical care.

[78]  J. Bentley,et al.  The influence of risk and monetary payment on the research participation decision making process , 2004, Journal of Medical Ethics.

[79]  Douglas G Altman,et al.  The logrank test , 2004, BMJ : British Medical Journal.

[80]  J Bradshaw,et al.  Fluctuating cognition in dementia with Lewy bodies and Alzheimer’s disease is qualitatively distinct , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

[81]  H. Christensen,et al.  Delivering interventions for depression by using the internet: randomised controlled trial , 2004, BMJ : British Medical Journal.

[82]  Roger Clarke,et al.  Viewpoint Paper: e-Consent: The Design And Implementation of Consumer Consent Mechanisms in an Electronic Environment , 2004, J. Am. Medical Informatics Assoc..

[83]  Teresa Foo,et al.  : SYSTEMATIC LITERATURE REVIEW , 2004 .

[84]  S V Subramanian,et al.  Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures--the public health disparities geocoding project. , 2003, American journal of public health.

[85]  V. Shavers,et al.  Factors that influence African‐Americans' willingness to participate in medical research studies , 2001, Cancer.

[86]  J. Trauth,et al.  Public Attitudes Regarding Willingness to Participate in Medical Research Studies , 2000, Journal of health & social policy.

[87]  C. Grady,et al.  What's the price of a research subject? Approaches to payment for research participation. , 1999, The New England journal of medicine.

[88]  M Blettner,et al.  Traditional reviews, meta-analyses and pooled analyses in epidemiology. , 1999, International journal of epidemiology.

[89]  A. Pablos-Mendez,et al.  Run-in periods in randomized trials: implications for the application of results in clinical practice. , 1998, JAMA.

[90]  M. Pagano,et al.  Survival analysis. , 1996, Nutrition.

[91]  Bengt Klefsjö,et al.  Proportional hazards model: a review , 1994 .

[92]  J. Hörandel,et al.  COSMIC RAYS FROM THE KNEE TO THE SECOND , 2007 .

[93]  R. S. Eliot.  Lessons Learned and Future Directions , 2020, Responsibility of Higher Education Systems.

[94]  Edi Karni,et al.  “Preference Reversals” and the Theory of Decision Making under Risk , 1988 .

[95]  Bertrand Munier,et al.  Risk, decision and rationality , 1988 .

[96]  Rupert G. Miller,et al.  Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.

[97]  J. Affeldt,et al.  The feasibility study , 2019, The Information System Consultant’s Handbook.

[98]  R. C. Macridis.  A review , 1963 .

[99]  L. Gulick.  The Way to Health , 1910 .