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Althobiani MA, Ranjan Y, Jacob J, Orini M, Dobson RJB, Porter JC, Hurst JR, Folarin AA. Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study. JMIR Form Res 2023; 7:e51507. [PMID: 37999935 DOI: 10.2196/51507] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/23/2023] [Accepted: 10/20/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Patients with chronic respiratory diseases and those in the postdischarge period following hospitalization because of COVID-19 are particularly vulnerable, and little is known about the changes in their symptoms and physiological parameters. Continuous remote monitoring of physiological parameters and symptom changes offers the potential for timely intervention, improved patient outcomes, and reduced health care costs. OBJECTIVE This study investigated whether a real-time multimodal program using commercially available wearable technology, home-based Bluetooth-enabled spirometers, finger pulse oximeters, and smartphone apps is feasible and acceptable for patients with chronic respiratory diseases, as well as the value of low-burden, long-term passive data collection. METHODS In a 3-arm prospective observational cohort feasibility study, we recruited 60 patients from the Royal Free Hospital and University College Hospital. These patients had been diagnosed with interstitial lung disease, chronic obstructive pulmonary disease, or post-COVID-19 condition (n=20 per group) and were followed for 180 days. This study used a comprehensive remote monitoring system designed to provide real-time and relevant data for both patients and clinicians. Data were collected using REDCap (Research Electronic Data Capture; Vanderbilt University) periodic surveys, Remote Assessment of Disease and Relapses-base active app questionnaires, wearables, finger pulse oximeters, smartphone apps, and Bluetooth home-based spirometry. The feasibility of remote monitoring was measured through adherence to the protocol, engagement during the follow-up period, retention rate, acceptability, and data integrity. RESULTS Lowest-burden passive data collection methods, via wearables, demonstrated superior adherence, engagement, and retention compared with active data collection methods, with an average wearable use of 18.66 (SD 4.69) hours daily (77.8% of the day), 123.91 (SD 33.73) hours weekly (72.6% of the week), and 463.82 (SD 156.70) hours monthly (64.4% of the month). Highest-burden spirometry tasks and high-burden active app tasks had the lowest adherence, engagement, and retention, followed by low-burden questionnaires. Spirometry and active questionnaires had the lowest retention at 0.5 survival probability, indicating that they were the most burdensome. Adherence to and quality of home spirometry were analyzed; of the 7200 sessions requested, 4248 (59%) were performed. Of these, 90.3% (3836/4248) were of acceptable quality according to American Thoracic Society grading. Inclusion of protocol holidays improved retention measures. The technologies used were generally well received. CONCLUSIONS Our findings provide evidence supporting the feasibility and acceptability of remote monitoring for capturing both subjective and objective data from various sources for respiratory diseases. The high engagement level observed with passively collected data suggests the potential of wearables for long-term, user-friendly remote monitoring in respiratory disease management. The unique piloting of certain features such as protocol holidays, alert notifications for missing data, and flexible support from the study team provides a reference for future studies in this field. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/28873.
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Affiliation(s)
- Malik A Althobiani
- Respiratory Medicine, University College London, London, United Kingdom
- Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom
- Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Joseph Jacob
- Respiratory Medicine, University College London, London, United Kingdom
- Satsuma Lab, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Richard James Butler Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom
| | - Joanna C Porter
- Respiratory Medicine, University College London, London, United Kingdom
- Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom
| | - John R Hurst
- Respiratory Medicine, University College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom
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Nguyen TM, Leow AD, Ajilore O. A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning. Brain Sci 2023; 13:959. [PMID: 37371437 DOI: 10.3390/brainsci13060959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.
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Affiliation(s)
- Theresa M Nguyen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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Čermák J, Pietrucha S, Nawka A, Lipone P, Ruggieri A, Bonelli A, Comandini A, Cattaneo A. An Observational Pilot Study using a Digital Phenotyping Approach in Patients with Major Depressive Disorder Treated with Trazodone. Front Psychiatry 2023; 14:1127511. [PMID: 37032913 PMCID: PMC10080076 DOI: 10.3389/fpsyt.2023.1127511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 02/24/2023] [Indexed: 04/11/2023] Open
Abstract
This 8-week study was designed to explore any correlation between a passive data collection approach using a wearable device (i.e., digital phenotyping), active data collection (patient's questionnaires), and a traditional clinical evaluation [Montgomery-Åsberg Depression Rating Scale (MADRS)] in patients with major depressive disorder (MDD) treated with trazodone once a day (OAD). Overall, 11 out of 30 planned patients were enrolled. Passive parameters measured by the wearable device included number of steps, distance walked, calories burned, and sleep quality. A relationship between the sleep score (derived from passively measured data) and MADRS score was observed, as was a relationship between data collected actively (assessing depression, sleep, anxiety, and warning signs) and MADRS score. Despite the limited sample size, the efficacy and safety results were consistent with those previously reported for trazodone. The small population in this study limits the conclusions that can be drawn about the correlation between the digital phenotyping approach and traditional clinical evaluation; however, the positive trends observed suggest the need to increase synergies among clinicians, patients, and researchers to overcome the cultural barriers toward implementation of digital tools in the clinical setting. This study is a step toward the use of digital data in monitoring symptoms of depression, and the preliminary data obtained encourage further investigations of a larger population of patients monitored over a longer period of time.
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Affiliation(s)
- Jan Čermák
- Psychiatrie Říčany s.r.o., Říčany, Czechia
| | | | - Alexander Nawka
- Institut Neuropsychiatrické Péče (INEP) (Psychiatric Outpatient Clinic), Praha, Czechia
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Capell WH, Wynia MK, Hurley EA, Bonaca MP. Should Participants in Clinical Trials Be Able to Withdraw from Passive Follow-Up? Ethics Hum Res 2021; 43:32-36. [PMID: 33463078 DOI: 10.1002/eahr.500077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A research participant's right to withdraw from all research procedures is widely accepted, but there can be justifiable limits to a participant's exercise of autonomy to withdraw from some procedures. Clinical outcomes trials depend on complete subject follow-up for accurate assessment of the safety and efficacy of investigational therapies. Subjects' refusal to complete follow-up, even through passive medical record review, can cause failure to detect safety signals, inaccurate estimation of efficacy, or lack of acceptance of trial results, which alters the study's benefit-risk ratio. Allowing participant refusal of follow-up data collection therefore creates tension between respect for persons and beneficence. With minimal risk study procedures that can help preserve trial benefit, such as passive data collection, we argue that the importance of upholding the principle of beneficence outweighs individual autonomy concerns. Furthermore, a consent process that prospectively informs participants of mandatory passive follow-up is ethically justified and optimizes the balance between autonomy and beneficence.
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Affiliation(s)
- Warren H Capell
- Associate professor of medicine at CPC Clinical Research at the University of Colorado Anschutz Medical Campus in the Department of Medicine at the University of Colorado Center for Bioethics and Humanities
| | - Matthew K Wynia
- Professor of medicine and public health at the University of Colorado Anschutz Medical Campus in the Department of Medicine at the University of Colorado Center for Bioethics and Humanities
| | - Elisa A Hurley
- Executive director at Public Responsibility in Medicine & Research
| | - Marc P Bonaca
- Professor of medicine at CPC Clinical Research at the University of Colorado Anschutz Medical Campus in the Department of Medicine
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van Heerden A, Wassenaar D, Essack Z, Vilakazi K, Kohrt BA. In-Home Passive Sensor Data Collection and Its Implications for Social Media Research: Perspectives of Community Women in Rural South Africa. J Empir Res Hum Res Ethics 2019; 15:97-107. [PMID: 31631742 DOI: 10.1177/1556264619881334] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
There has been a recent increase in debates on the ethics of social media research, passive sensor data collection, and big data analytics. However, little evidence exists to describe how people experience and understand these applications of technology. This study aimed to passively collect data from mobile phone sensors, lapel cameras, and Bluetooth beacons to assess people's understanding and acceptance of these technologies. Seven households were purposefully sampled and data collected for 10 days. The study generated 48 hr of audio data and 30,000 images. After participant review, the data were destroyed and in-depth interviews conducted. Participants found the data collected acceptable and reported willingness to participate in similar studies. Key risks included that the camera could capture nudity and sex acts, but family review of footage before sharing helped reduce concerns. The Emanuel et al. ethics framework was found to accommodate the concerns and perspectives of study participants.
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Affiliation(s)
- Alastair van Heerden
- Human Sciences Research Council, Pietermaritzburg, South Africa.,University of the Witwatersrand, Johannesburg, South Africa
| | - Doug Wassenaar
- University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Zaynab Essack
- Human Sciences Research Council, Pietermaritzburg, South Africa.,University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Khanya Vilakazi
- Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Brandon A Kohrt
- George Washington School of Medicine and Health Sciences, Washington, DC, USA
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Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Areán PA. The accuracy of passive phone sensors in predicting daily mood. Depress Anxiety 2019; 36:72-81. [PMID: 30129691 PMCID: PMC8491547 DOI: 10.1002/da.22822] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 04/25/2018] [Accepted: 07/01/2018] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Smartphones provide a low-cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone-based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood. METHOD Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants. RESULTS Sample-wide estimates showed a marginally significant association between physical mobility and self-reported daily mood (B = -0.04, P < 0.05), but the predictive models performed poorly for the sample as a whole (median R2 ∼ 0). Focusing on individuals, 13.9% of participants showed significant association (FDR < 0.10) between a passive feature and daily mood. Personalized models combining features provided better prediction performance (median area under the curve [AUC] > 0.50) for 80.6% of participants and very strong prediction in a subset (median AUC > 0.80) for 11.8% of participants. CONCLUSIONS Passive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra- and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS-derived mobility being the top most important feature in the present sample.
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Affiliation(s)
- Abhishek Pratap
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington,Sage Bionetworks, Seattle, Washington
| | - David C. Atkins
- Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, Washington
| | - Brenna N. Renn
- Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, Washington
| | | | - Sean D. Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington
| | - Joaquin A. Anguera
- Department of Neurology, University of California, San Francisco (UCSF), San Francisco, California
| | - Patricia A. Areán
- Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, Washington
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Abstract
INTRODUCTION Self-management is widely promoted but less attention is focused on the work required from patients. To date, many individuals struggle to practise self-management. 'Patient work', a concept that examines the 'work' involved in self-management, is an approach to understanding the tasks, effort, time and context from patient perspective. The purpose of our study is to use a novel approach combining non-obstructive observations via digital devices with in-depth qualitative data about health behaviours and motivations, to capture the full range of patient work experienced by people with type 2 diabetes and chronic comorbidities. It aims to yield comprehensive insights about 'what works' in self-management, potentially extending to populations with other chronic health conditions. METHODS AND ANALYSIS This mixed-methods observational study involves a (1) prestudy interview and questionnaires, (2) a 24-hour period during which participants wear a camera and complete a time-use diary, and a (3) poststudy interview and study feedback. Adult participants living with type 2 diabetes with at least one chronic comorbidity will be recruited using purposive sampling to obtain a balanced gender ratio and of participants using insulin and those using only oral medication. Interviews will be analysed using thematic analysis. Data captured by digital devices, diaries and questionnaires will be used to analyse the duration, time, context and patterns of health-related behaviours. ETHICS AND DISSEMINATION The study was approved by the Macquarie University Human Research Ethics Committee for Medical Sciences (reference number 5201700718). Participants will carry a wallet-sized card that explains the purpose of the study to third parties, and can remove the camera at any stage. Before the poststudy interview begins, participants will view the camera images in private and can delete any images. Should any images be used in future publications or presentations, identifying features such as human faces and names will be obscured.
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Affiliation(s)
- Kathleen Yin
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Teresa Harms
- Department of Sociology, Centre for Time Use Research, University of Oxford, Oxford, UK
- Planning and Transport Research Centre, Business School, University of Western Australia, Perth, Western Australia, Australia
| | - Kenneth Ho
- Faculty of Medical and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Frances Rapport
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Sanjyot Vagholkar
- Faculty of Medical and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Liliana Laranjo
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Jonathan Gershuny
- Department of Sociology, Centre for Time Use Research, University of Oxford, Oxford, UK
| | - Annie Y S Lau
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
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Abstract
Advances in technology have ushered in exciting potential for smartphone sensors to inform mental health care. This commentary addresses the practical challenges of collecting smartphone-based physical activity data. Using data (N = 353) from a large scale, fully remote randomized clinical trial for depression, we discuss findings and limitations associated with using passively collected mobility data to make inferences about depressive symptom severity. We highlight a range of issues in associating mobility data with mental health symptoms, including a high degree of variability, data featurization, granularity, and sparsity. Given the considerable efforts toward leveraging technology in mental health care, it is important to consider these challenges to optimize assessment and guide best practices. Clinical Trials.gov identifier: NCT01808976.
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Affiliation(s)
- Brenna N Renn
- Department of Psychiatry & Behavioral Sciences, University of Washington, 1959 NE Pacific St. Box 356560, Seattle, WA 98195, USA
| | - Abhishek Pratap
- Department of Biomedical Informatics and Medical Education, University of Washington, 1959 NE Pacific St. Box 358047, Seattle, WA 98195, USA
- Sage Bionetworks, 1100 Fairview Ave N, M1-C119, Seattle, WA 98115, USA
| | - David C Atkins
- Department of Psychiatry & Behavioral Sciences, University of Washington, 1959 NE Pacific St. Box 356560, Seattle, WA 98195, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, 1959 NE Pacific St. Box 358047, Seattle, WA 98195, USA
| | - Patricia A Areán
- Department of Psychiatry & Behavioral Sciences, University of Washington, 1959 NE Pacific St. Box 356560, Seattle, WA 98195, USA
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