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Kohli M, Moore DJ, Moore RC. Using health technology to capture digital phenotyping data in HIV-associated neurocognitive disorders. AIDS 2021; 35:15-22. [PMID: 33048886 PMCID: PMC7718372 DOI: 10.1097/qad.0000000000002726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Maulika Kohli
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology
- HIV Neurobehavioral Research Program, Department of Psychiatry, University of California, San Diego, San Diego, California, USA
| | - David J Moore
- HIV Neurobehavioral Research Program, Department of Psychiatry, University of California, San Diego, San Diego, California, USA
| | - Raeanne C Moore
- HIV Neurobehavioral Research Program, Department of Psychiatry, University of California, San Diego, San Diego, California, USA
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Marsch LA. Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology 2021; 46:191-196. [PMID: 32653896 PMCID: PMC7359920 DOI: 10.1038/s41386-020-0761-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/25/2020] [Accepted: 06/15/2020] [Indexed: 12/20/2022]
Abstract
Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals' behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.
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Affiliation(s)
- Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Lebanon, NH, USA.
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Lee J, Solomonov N, Banerjee S, Alexopoulos GS, Sirey JA. Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges. Front Psychiatry 2021; 12:732773. [PMID: 34777042 PMCID: PMC8580874 DOI: 10.3389/fpsyt.2021.732773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022] Open
Abstract
Late-life depression is heterogenous and patients vary in disease course over time. Most psychotherapy studies measure activity levels and symptoms solely using self-report scales, administered periodically. These scales may not capture granular changes during treatment. We introduce the potential utility of passive sensing data collected with smartphone to assess fluctuations in daily functioning in real time during psychotherapy for late life depression in elder abuse victims. To our knowledge, this is the first investigation of passive sensing among depressed elder abuse victims. We present data from three victims who received a 9-week intervention as part of a pilot randomized controlled trial and showed a significant decrease in depressive symptoms (50% reduction). Using a smartphone, we tracked participants' daily number of smartphone unlocks, time spent at home, time spent in conversation, and step count over treatment. Independent assessment of depressive symptoms and behavioral activation were collected at intake, Weeks 6 and 9. Data revealed patient-level fluctuations in activity level over treatment, corresponding with self-reported behavioral activation. We demonstrate how passive sensing data could expand our understanding of heterogenous presentations of late-life depression among elder abuse. We illustrate how trajectories of change in activity levels as measured with passive sensing and subjective measures can be tracked concurrently over time. We outline challenges and potential solutions for application of passive sensing data collection in future studies with larger samples using novel advanced statistical modeling, such as artificial intelligence algorithms.
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Affiliation(s)
- Jihui Lee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Nili Solomonov
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
| | - Samprit Banerjee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - George S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
| | - Jo Anne Sirey
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
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Self-Management of Rheumatoid Arthritis: Mobile Applications. Curr Rheumatol Rep 2020; 23:2. [DOI: 10.1007/s11926-020-00968-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2020] [Indexed: 02/07/2023]
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Rosa C, Marsch LA, Winstanley EL, Brunner M, Campbell ANC. Using digital technologies in clinical trials: Current and future applications. Contemp Clin Trials 2020; 100:106219. [PMID: 33212293 DOI: 10.1016/j.cct.2020.106219] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 10/05/2020] [Accepted: 11/10/2020] [Indexed: 12/20/2022]
Abstract
In 2015, we provided an overview of the use of digital technologies in clinical trials, both as a methodological tool and as a mechanism to deliver interventions. At that time, there was limited guidance and limited use of digital technologies in clinical research. However, since then smartphones have become ubiquitous and digital health technologies have exploded. This paper provides an update to our earlier publication and an overview of how technology has been used in the past five years in clinical trials, providing examples with varying levels of technological integration and across different health conditions. Digital technology integration ranges from the incorporation of artificial intelligence in diagnostic devices to the use of real-world data (e.g., electronic health records) for study recruitment. Clinical trials can now be conducted entirely virtually, eliminating the need for in-person interaction. Much of the published research demonstrates how digital approaches can improve the design and implementation of clinical trials. While challenges remain, progress over the last five years is encouraging, and barriers can be overcome with careful planning.
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Affiliation(s)
- Carmen Rosa
- National Institutes of Health, National Institute on Drug Abuse, Bethesda, MD, USA.
| | - Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, USA.
| | - Erin L Winstanley
- West Virginia University, School of Medicine and Rockefeller Neuroscience Institute, Department of Behavioral Medicine and Psychiatry, Morgantown, West Virginia, USA; West Virginia University, School of Medicine, Department of Neuroscience Morgantown, West Virginia, USA.
| | - Meg Brunner
- Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, USA.
| | - Aimee N C Campbell
- New York State Psychiatric Institute, Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute, New York, NY, USA.
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Behar JA, Liu C, Kotzen K, Tsutsui K, Corino VDA, Singh J, Pimentel MAF, Warrick P, Zaunseder S, Andreotti F, Sebag D, Kopanitsa G, McSharry PE, Karlen W, Karmakar C, Clifford GD. Remote health diagnosis and monitoring in the time of COVID-19. Physiol Meas 2020; 41:10TR01. [PMID: 32947271 PMCID: PMC9364387 DOI: 10.1088/1361-6579/abba0a] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.
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Affiliation(s)
- Joachim A Behar
- Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
| | - Chengyu Liu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China
- Equal contribution
| | - Kevin Kotzen
- Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
- Equal contribution
| | - Kenta Tsutsui
- Department of Cardiovascular Medicine, Saitama Medical University International Medical Center, Saitama, Japan
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy
| | | | - Marco A F Pimentel
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | | | - Patrick E McSharry
- Carnegie Mellon University Africa, Kigali, Rwanda
- African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
- Oxford Man Institute of Quantitative Finance, Oxford University, Oxford, United Kingdom
| | - Walter Karlen
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Equal senior authorship
| | - Chandan Karmakar
- School of Information Technology, Deakin University, Geelong, Australia
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- Equal senior authorship
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States of America
- Equal senior authorship
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Biebl JT, Huber S, Rykala M, Kraft E, Lorenz A. Attitudes and Expectations of Health Care Professionals Toward App-Based Therapy in Patients with Osteoarthritis of the Hip or Knee: Questionnaire Study. JMIR Mhealth Uhealth 2020; 8:e21704. [PMID: 33112255 PMCID: PMC7657727 DOI: 10.2196/21704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/19/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023] Open
Abstract
Background The use of mobile health (mHealth) apps is becoming increasingly widespread. However, little is known about the attitudes, expectations, and basic acceptance of health care professionals toward such treatment options. As physical activity and behavior modification are crucial in osteoarthritis management, app-based therapy could be particularly useful for the self-management of this condition. Objective The objective of the study was to determine the expectations and attitudes of medical professionals toward app-based therapy for osteoarthritis of the hip or knee. Methods Health care professionals attending a rehabilitation congress and employees of a university hospital were asked to fill out a questionnaire consisting of 16 items. A total of 240 questionnaires were distributed. Results A total of 127 participants completed the questionnaire. At 95.3% (121/127), the approval rate for app-based therapy for patients with osteoarthritis of the hip or knee was very high. Regarding possible concerns, aspects related to data protection and privacy were primarily mentioned (41/127, 32.3%). Regarding potential content, educational units, physiotherapeutic exercise modules, and practices based on motivation psychology were all met with broad approval. Conclusions The study showed a high acceptance of app-based therapy for osteoarthritis, indicating a huge potential of this form of treatment to be applied, prescribed, and recommended by medical professionals. It was widely accepted that the content should reflect a multimodal therapy approach.
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Affiliation(s)
- Johanna Theresia Biebl
- Department of Orthopaedics, Physical Medicine and Rehabilitation, University Hospital, LMU Munich, Munich, Germany
| | | | - Marzena Rykala
- Department of Orthopaedics, Physical Medicine and Rehabilitation, University Hospital, LMU Munich, Munich, Germany
| | - Eduard Kraft
- Department of Orthopaedics, Physical Medicine and Rehabilitation, University Hospital, LMU Munich, Munich, Germany
| | - Andreas Lorenz
- Department of Orthopaedics, Physical Medicine and Rehabilitation, University Hospital, LMU Munich, Munich, Germany
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Sandifer P, Knapp L, Lichtveld M, Manley R, Abramson D, Caffey R, Cochran D, Collier T, Ebi K, Engel L, Farrington J, Finucane M, Hale C, Halpern D, Harville E, Hart L, Hswen Y, Kirkpatrick B, McEwen B, Morris G, Orbach R, Palinkas L, Partyka M, Porter D, Prather AA, Rowles T, Scott G, Seeman T, Solo-Gabriele H, Svendsen E, Tincher T, Trtanj J, Walker AH, Yehuda R, Yip F, Yoskowitz D, Singer B. Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters. Front Public Health 2020; 8:578463. [PMID: 33178663 PMCID: PMC7593336 DOI: 10.3389/fpubh.2020.578463] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/31/2020] [Indexed: 01/08/2023] Open
Abstract
The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.
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Affiliation(s)
- Paul Sandifer
- Center for Coastal Environmental and Human Health, College of Charleston, Charleston, SC, United States
| | - Landon Knapp
- Center for Coastal Environmental and Human Health, College of Charleston, Charleston, SC, United States
| | - Maureen Lichtveld
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Ruth Manley
- Master's Program in Environmental and Sustainability Studies, College of Charleston, Charleston, SC, United States
| | - David Abramson
- School of Global Public Health, New York University, New York, NY, United States
| | - Rex Caffey
- Department of Agricultural Economics and Agribusiness, Louisiana State University, Baton Rouge, LA, United States
| | - David Cochran
- School of Biological, Environmental, and Earth Sciences, University of Southern Mississippi, Hattiesburg, MS, United States
| | - Tracy Collier
- Huxley College of the Environment, Western Washington University, Bellingham, WA, United States
| | - Kristie Ebi
- Department of Global Health, University of Washington, Seattle, WA, United States
| | - Lawrence Engel
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - John Farrington
- Woods Hole Oceanographic Institution, Woods Hole, MA, United States
| | | | - Christine Hale
- Harte Research Institute, Texas A&M University-Corpus Christi, Corpus Christi, TX, United States
| | - David Halpern
- Scripps Institution of Oceanography, La Jolla, CA, United States
| | - Emily Harville
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Leslie Hart
- Department of Health and Human Performance, College of Charleston, Charleston, SC, United States
| | - Yulin Hswen
- Computational Epidemiology Lab, Harvard Medical School, Boston, MA, United States
- Department of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Barbara Kirkpatrick
- Gulf of Mexico Coastal Ocean Observing System, Texas A&M University, College Station TX, United States
| | - Bruce McEwen
- Laboratory of Neuroendocrinology, Rockefeller University, New York, NY, United States
| | - Glenn Morris
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
| | - Raymond Orbach
- Department of Mechanical Engineering, University of Texas, Austin, TX, United States
| | - Lawrence Palinkas
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States
| | - Melissa Partyka
- Mississippi-Alabama Sea Grant Consortium, Mobile, AL, United States
| | - Dwayne Porter
- Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Aric A. Prather
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Teresa Rowles
- National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Silver Spring, MD, United States
| | - Geoffrey Scott
- Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Teresa Seeman
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Helena Solo-Gabriele
- Department of Civil, Architectural, and Environmental Engineering, University of Miami, Coral Gables, FL, United States
| | - Erik Svendsen
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Terry Tincher
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Juli Trtanj
- Office of Oceanic and Atmospheric Research, National Oceanic and Atmospheric Administration, Silver Spring, MD, United States
| | | | - Rachel Yehuda
- Icahn School of Medicine at Mount Sinai, Bronx, NY, United States
| | - Fuyuen Yip
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - David Yoskowitz
- Harte Research Institute, Texas A&M University-Corpus Christi, Corpus Christi, TX, United States
| | - Burton Singer
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
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Ferrari M, Schick A. Teenagers, screens and social media: a commentary on Orben's narrative review. Soc Psychiatry Psychiatr Epidemiol 2020; 55:973-975. [PMID: 32377761 DOI: 10.1007/s00127-020-01858-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 03/04/2020] [Indexed: 12/01/2022]
Affiliation(s)
- Manuela Ferrari
- Department of Psychiatry, McGill University, Montreal, Canada.
| | - Anita Schick
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Abstract
Digital technologies are rapidly changing how we understand and promote health. A robust and growing line of research has examined how digital health may enhance our understanding and treatment of addiction. This manuscript highlights innovations in the application of digital health approaches to addiction medicine, with a particular emphasis on advances in (1) real-time measurement of drug use events, (2) real-time measurement of the confluence of factors that surround drug use events, and (3) research examining how real-time measurement can inform responsive, in-the-moment interventions to prevent and treat substance use disorder. Although this manuscript focuses on addiction medicine as one exemplar of the striking impact of digital health, science-based digital health offers generalizable solutions to scaling-up unprecedented models of precision healthcare delivery across a broad spectrum of diseases across the globe.
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Affiliation(s)
- Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 315, Lebanon, New Hampshire USA
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Huang EJ, Onnela JP. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3706. [PMID: 32630752 PMCID: PMC7374287 DOI: 10.3390/s20133706] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 11/16/2022]
Abstract
Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.
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Affiliation(s)
- Emily J. Huang
- Department of Mathematics and Statistics, Wake Forest University, Winston Salem, NC 27106, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA;
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Towards clinically actionable digital phenotyping targets in schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:13. [PMID: 32372059 PMCID: PMC7200667 DOI: 10.1038/s41537-020-0100-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/12/2020] [Indexed: 12/12/2022]
Abstract
Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, "Circadian Routine" and "Weekend Day Routine," and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ρ ranged from -0.23 to -0.30, p < 0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ρ ranged from 0.20 to 0.44, p < 0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.
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63
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Milne-Ives M, Lam C, De Cock C, Van Velthoven MH, Meinert E. Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review. JMIR Mhealth Uhealth 2020; 8:e17046. [PMID: 32186518 PMCID: PMC7113799 DOI: 10.2196/17046] [Citation(s) in RCA: 141] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/03/2019] [Accepted: 01/26/2020] [Indexed: 01/16/2023] Open
Abstract
Background With a growing focus on patient interaction with health management, mobile apps are increasingly used to deliver behavioral health interventions. The large variation in these mobile health apps—their target patient group, health behavior, and behavioral change strategies—has resulted in a large but incohesive body of literature. Objective This systematic review aimed to assess the effectiveness of mobile apps in improving health behaviors and outcomes and to examine the inclusion and effectiveness of behavior change techniques (BCTs) in mobile health apps. Methods PubMed, EMBASE, CINAHL, and Web of Science were systematically searched for articles published between 2014 and 2019 that evaluated mobile apps for health behavior change. Two authors independently screened and selected studies according to the eligibility criteria. Data were extracted and the risk of bias was assessed by one reviewer and validated by a second reviewer. Results A total of 52 randomized controlled trials met the inclusion criteria and were included in the analysis—37 studies focused on physical activity, diet, or a combination of both, 11 on drug and alcohol use, and 4 on mental health. Participant perceptions were generally positive—only one app was rated as less helpful and satisfactory than the control—and the studies that measured engagement and usability found relatively high study completion rates (mean 83%; n=18, N=39) and ease-of-use ratings (3 significantly better than control, 9/15 rated >70%). However, there was little evidence of changed behavior or health outcomes. Conclusions There was no strong evidence in support of the effectiveness of mobile apps in improving health behaviors or outcomes because few studies found significant differences between the app and control groups. Further research is needed to identify the BCTs that are most effective at promoting behavior change. Improved reporting is necessary to accurately evaluate the mobile health app effectiveness and risk of bias.
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Affiliation(s)
- Madison Milne-Ives
- Digitally Enabled Preventative Health Research Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Ching Lam
- Digitally Enabled Preventative Health Research Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Caroline De Cock
- Digitally Enabled Preventative Health Research Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Michelle Helena Van Velthoven
- Digitally Enabled Preventative Health Research Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Edward Meinert
- Digitally Enabled Preventative Health Research Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,Department of Primary Care and Public Health, Imperial College London, London, United Kingdom
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64
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Milne-Ives M, Lam C, Van Velthoven MH, Meinert E. Mobile Apps for Health Behavior Change: Protocol for a Systematic Review. JMIR Res Protoc 2020; 9:e16931. [PMID: 32012109 PMCID: PMC7055785 DOI: 10.2196/16931] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 01/13/2023] Open
Abstract
Background The popularity and ubiquity of mobile apps have rapidly expanded in the past decade. With a growing focus on patient interaction with health management, mobile apps are increasingly used to monitor health and deliver behavioral interventions. The considerable variation in these mobile health apps, from their target patient group to their health behavior, and their behavioral change strategy, has resulted in a large but incohesive body of literature. Objective The purpose of this protocol is to provide an overview of the current landscape, theories behind, and effectiveness of mobile apps for health behavior change. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols will be used to structure this protocol. The focus of the systematic review is guided by a population, intervention, comparator, and outcome framework. A systematic search of Medline, EMBASE, CINAHL, and Web of Science will be conducted. Two authors will independently screen the titles and abstracts of identified references and select studies according to the eligibility criteria. Any discrepancies will then be discussed and resolved. One reviewer will extract data into a standardized form, which will be validated by a second reviewer. Risk of bias was assessed using the Cochrane Collaboration Risk of Bias tool, and a descriptive analysis will summarize the effectiveness of all the apps. Results As of November 2019, the systematic review has been completed and is in peer review for publication. Conclusions This systematic review will summarize the current mobile app technologies and their effectiveness, usability, and coherence with behavior change theory. It will identify areas of improvement (where there is no evidence of efficacy) and help inform the development of more useful and engaging mobile health apps. Trial Registration PROSPERO CRD42019155604; https://tinyurl.com/sno4lcu International Registered Report Identifier (IRRID) PRR1-10.2196/16931
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Affiliation(s)
- Madison Milne-Ives
- Department of Paediatrics, Digitally Enabled Preventative Health Research Group, University of Oxford, Oxford, United Kingdom
| | - Ching Lam
- Department of Paediatrics, Digitally Enabled Preventative Health Research Group, University of Oxford, Oxford, United Kingdom
| | - Michelle Helena Van Velthoven
- Department of Paediatrics, Digitally Enabled Preventative Health Research Group, University of Oxford, Oxford, United Kingdom
| | - Edward Meinert
- Department of Paediatrics, Digitally Enabled Preventative Health Research Group, University of Oxford, Oxford, United Kingdom.,Department of Primary Care and Public Health, Imperial College London, London, United Kingdom
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Miaskowski C, Blyth F, Nicosia F, Haan M, Keefe F, Smith A, Ritchie C. A Biopsychosocial Model of Chronic Pain for Older Adults. PAIN MEDICINE 2019; 21:1793-1805. [DOI: 10.1093/pm/pnz329] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Abstract
Population
Comprehensive evaluation of chronic pain in older adults is multifaceted.
Objective and Methods
Research on chronic pain in older adults needs to be guided by sound conceptual models. The purpose of this paper is to describe an adaptation of the Biopsychosocial Model (BPS) of Chronic Pain for older adults. The extant literature was reviewed, and selected research findings that provide the empiric foundation for this adaptation of the BPS model of chronic pain are summarized. The paper concludes with a discussion of specific recommendations for how this adapted model can be used to guide future research.
Conclusions
This adaptation of the BPS model of chronic pain for older adults provides a comprehensive framework to guide future research in this vulnerable population.
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Affiliation(s)
| | - Fiona Blyth
- School of Medicine, The University of Sydney, Sydney, Australia
| | - Francesca Nicosia
- School of Medicine, University of California, San Francisco, California
| | - Mary Haan
- School of Medicine, University of California, San Francisco, California
| | - Frances Keefe
- School of Medicine, Duke University, Durham, North Carolina, USA
| | - Alexander Smith
- School of Medicine, University of California, San Francisco, California
| | - Christine Ritchie
- School of Medicine, University of California, San Francisco, California
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