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Aizenstein H, Moore RC, Vahia I, Ciarleglio A. Deep Learning and Geriatric Mental Health. Am J Geriatr Psychiatry 2024; 32:270-279. [PMID: 38142162 PMCID: PMC10922602 DOI: 10.1016/j.jagp.2023.11.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/25/2023]
Abstract
The goal of this overview is to help clinicians develop basic proficiency with the terminology of deep learning and understand its fundamentals and early applications. We describe what machine learning and deep learning represent and explain the underlying data science principles. We also review current promising applications and identify ethical issues that bear consideration. Deep Learning is a new type of machine learning that is remarkably good at finding patterns in data, and in some cases generating realistic new data. We provide insights into how deep learning works and discuss its relevance to geriatric psychiatry.
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Affiliation(s)
- Howard Aizenstein
- Department of Psychiatry (HA), University of Pittsburgh School of Medicine, Pittsburgh, PA.
| | - Raeanne C Moore
- Department of Psychiatry (RCM), University of California San Diego, San Diego, CA
| | - Ipsit Vahia
- Division of Geriatric Psychiatry (IV), Harvard Medical School, Boston, MA
| | - Adam Ciarleglio
- Department of Biostatistics and Bioinformatics (AC), George Washington University, Washington, D.C
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2
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Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc 2024; 13:e031247. [PMID: 38226518 PMCID: PMC10926806 DOI: 10.1161/jaha.123.031247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.
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Affiliation(s)
- Zachary Popp
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Akwaugo Igwe
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Md Salman Rahman
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Minzae Kim
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Raiyan Khan
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Emily Oh
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ankita Kumar
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Huitong Ding
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Preeti Sunderaraman
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Rhoda Au
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
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Lott SA, Streel E, Bachman SL, Bode K, Dyer J, Fitzer-Attas C, Goldsack JC, Hake A, Jannati A, Fuertes RS, Fromy P. Digital Health Technologies for Alzheimer's Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort. J Prev Alzheimers Dis 2024; 11:1480-1489. [PMID: 39350395 PMCID: PMC11436391 DOI: 10.14283/jpad.2024.103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2024]
Abstract
Digital health technologies offer valuable advantages to dementia researchers and clinicians as screening tools, diagnostic aids, and monitoring instruments. To support the use and advancement of these resources, a comprehensive overview of the current technological landscape is essential. A multi-stakeholder working group, convened by the Digital Medicine Society (DiMe), conducted a landscape review to identify digital health technologies for Alzheimer's disease and related dementia populations. We searched studies indexed in PubMed, Embase, and APA PsycInfo to identify manuscripts published between May 2003 to May 2023 reporting analytical validation, clinical validation, or usability/feasibility results for relevant digital health technologies. Additional technologies were identified through community outreach. We collated peer-reviewed manuscripts, poster presentations, or regulatory documents for 106 different technologies for Alzheimer's disease and related dementia assessment covering diverse populations such as Lewy Body, vascular dementias, frontotemporal dementias, and all severities of Alzheimer's disease. Wearable sensors represent 32% of included technologies, non-wearables 61%, and technologies with components of both account for the remaining 7%. Neurocognition is the most prevalent concept of interest, followed by physical activity and sleep. Clinical validation is reported in 69% of evidence, analytical validation in 34%, and usability/feasibility in 20% (not mutually exclusive). These findings provide clinicians and researchers a landscape overview describing the range of technologies for assessing Alzheimer's disease and related dementias. A living library of technologies is presented for the clinical and research communities which will keep findings up-to-date as the field develops.
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Affiliation(s)
- S A Lott
- Sarah Averill Lott, Digital Medicine Society (DiMe), Boston, MA, USA, , 970-408-0780
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Anda-Duran ID, Hwang PH, Popp ZT, Low S, Ding H, Rahman S, Igwe A, Kolachalama VB, Lin H, Au R. Matching science to reality: how to deploy a participant-driven digital brain health platform. FRONTIERS IN DEMENTIA 2023; 2:1135451. [PMID: 38706716 PMCID: PMC11067045 DOI: 10.3389/frdem.2023.1135451] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Introduction Advances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples. Methods A participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time. Results Participants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support. Discussion The importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.
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Affiliation(s)
- Ileana De Anda-Duran
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Phillip H. Hwang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Zachary Thomas Popp
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Spencer Low
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Huitong Ding
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Salman Rahman
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Akwaugo Igwe
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Rhoda Au
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
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5
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Liu Y, Zhang G, Tarolli CG, Hristov R, Jensen-Roberts S, Waddell EM, Myers TL, Pawlik ME, Soto JM, Wilson RM, Yang Y, Nordahl T, Lizarraga KJ, Adams JL, Schneider RB, Kieburtz K, Ellis T, Dorsey ER, Katabi D. Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response. Sci Transl Med 2022; 14:eadc9669. [PMID: 36130014 DOI: 10.1126/scitranslmed.adc9669] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.
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Affiliation(s)
- Yingcheng Liu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Guo Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher G Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | | | - Stella Jensen-Roberts
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Emma M Waddell
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Taylor L Myers
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Meghan E Pawlik
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Julia M Soto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Renee M Wilson
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Yuzhe Yang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Timothy Nordahl
- Department of Physical Therapy & Athletic Training, Center for Neurorehabilitation, Boston University College of Health and Rehabilitation: Sargent College, Boston, MA 02215, USA
| | - Karlo J Lizarraga
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Ruth B Schneider
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Karl Kieburtz
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Terry Ellis
- Department of Physical Therapy & Athletic Training, Center for Neurorehabilitation, Boston University College of Health and Rehabilitation: Sargent College, Boston, MA 02215, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Dina Katabi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Emerald Innovations Inc., Cambridge, MA 02142, USA
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Corman BHP, Rajupet S, Ye F, Schoenfeld ER. The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Postacute Sequelae of SARS CoV-2. J Med Internet Res 2022; 24:e32713. [PMID: 34932496 PMCID: PMC8989385 DOI: 10.2196/32713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 11/15/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Amid the COVID-19 pandemic, it has been reported that greater than 35% of patients with confirmed or suspected COVID-19 develop postacute sequelae of SARS CoV-2 (PASC). PASC is still a disease for which preliminary medical data are being collected-mostly measurements collected during hospital or clinical visits-and pathophysiological understanding is yet in its infancy. The disease is notable for its prevalence and its variable symptom presentation, and as such, management plans could be more holistically made if health care providers had access to unobtrusive home-based wearable and contactless continuous physiologic and physical sensor data. Such between-hospital or between-clinic data can quantitatively elucidate a majority of the temporal evolution of PASC symptoms. Although not universally of comparable accuracy to gold standard medical devices, home-deployed sensors offer great insights into the development and progression of PASC. Suitable sensors include those providing vital signs and activity measurements that correlate directly or by proxy to documented PASC symptoms. Such continuous, home-based data can give care providers contextualized information from which symptom exacerbation or relieving factors may be classified. Such data can also improve the collective academic understanding of PASC by providing temporally and activity-associated symptom cataloging. In this viewpoint, we make a case for the utilization of home-based continuous sensing that can serve as a foundation from which medical professionals and engineers may develop and pursue long-term mitigation strategies for PASC.
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Affiliation(s)
- Benjamin Harris Peterson Corman
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
- Program in Public Health, Stony Brook University, Stony Brook, NY, United States
| | - Sritha Rajupet
- Department of Family, Population & Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Fan Ye
- Department of Electrical and Computer Engineering, College of Engineering and Applied Science, Stony Brook University, Stony Brook, NY, United States
| | - Elinor Randi Schoenfeld
- Department of Family, Population & Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
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Ramsey M, Lee EE. Digital Dementia Care for the Future: Opportunities and Challenges. Am J Geriatr Psychiatry 2022; 30:12-14. [PMID: 34134922 PMCID: PMC8752056 DOI: 10.1016/j.jagp.2021.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 05/07/2021] [Indexed: 01/03/2023]
Affiliation(s)
- Marina Ramsey
- Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California
| | - Ellen E. Lee
- Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California,Department of Psychiatry, University of California San Diego, San Diego, California,VA San Diego Healthcare System, La Jolla, California
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8
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Haslam-Larmer L, Shum L, Chu CH, McGilton K, McArthur C, Flint AJ, Khan S, Iaboni A. Real-time location systems technology in the care of older adults with cognitive impairment living in residential care: A scoping review. Front Psychiatry 2022; 13:1038008. [PMID: 36440422 PMCID: PMC9685159 DOI: 10.3389/fpsyt.2022.1038008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/24/2022] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION There has been growing interest in using real-time location systems (RTLS) in residential care settings. This technology has clinical applications for locating residents within a care unit and as a nurse call system, and can also be used to gather information about movement, location, and activity over time. RTLS thus provides health data to track markers of health and wellbeing and augment healthcare decisions. To date, no reviews have examined the potential use of RTLS data in caring for older adults with cognitive impairment living in a residential care setting. OBJECTIVE This scoping review aims to explore the use of data from real-time locating systems (RTLS) technology to inform clinical measures and augment healthcare decision-making in the care of older adults with cognitive impairment who live in residential care settings. METHODS Embase (Ovid), CINAHL (EBSCO), APA PsycINFO (Ovid) and IEEE Xplore databases were searched for published English-language articles that reported the results of studies that investigated RTLS technologies in persons aged 50 years or older with cognitive impairment who were living in a residential care setting. Included studies were summarized, compared and synthesized according to the study outcomes. RESULTS A total of 27 studies were included. RTLS data were used to assess activity levels, characterization of wandering, cognition, social interaction, and to monitor a resident's health and wellbeing. These RTLS-based measures were not consistently validated against clinical measurements or clinically important outcomes, and no studies have examined their effectiveness or impact on decision-making. CONCLUSION This scoping review describes how data from RTLS technology has been used to support clinical care of older adults with dementia. Research efforts have progressed from using the data to track activity levels to, most recently, using the data to inform clinical decision-making and as a predictor of delirium. Future studies are needed to validate RTLS-based health indices and examine how these indices can be used to inform decision-making.
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Affiliation(s)
- Lynn Haslam-Larmer
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Leia Shum
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Charlene H Chu
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada
| | - Kathy McGilton
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada
| | - Caitlin McArthur
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
| | - Alastair J Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Shehroz Khan
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Andrea Iaboni
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Centre for Mental Health, University Health Network, Toronto, ON, Canada
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9
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Smith E, Storch EA, Vahia I, Wong STC, Lavretsky H, Cummings JL, Eyre HA. Affective Computing for Late-Life Mood and Cognitive Disorders. Front Psychiatry 2021; 12:782183. [PMID: 35002802 PMCID: PMC8732874 DOI: 10.3389/fpsyt.2021.782183] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022] Open
Abstract
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
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Affiliation(s)
- Erin Smith
- The PRODEO Institute, San Francisco, CA, United States
- Organisation for Economic Co-operation and Development (OECD), Paris, France
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, United States
- Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Eric A. Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Ipsit Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Boston, MA, United States
- Division of Geriatric Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Stephen T. C. Wong
- Systems Medicine and Biomedical Engineering Houston Methodist, Houston, TX, United States
| | - Helen Lavretsky
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jeffrey L. Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States
| | - Harris A. Eyre
- The PRODEO Institute, San Francisco, CA, United States
- Organisation for Economic Co-operation and Development (OECD), Paris, France
- Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, VIC, Australia
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10
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Khan NC, Pandey V, Gajos KZ, Gupta AS. Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia. THE CEREBELLUM 2021; 21:368-379. [PMID: 34302287 PMCID: PMC8302464 DOI: 10.1007/s12311-021-01306-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/08/2021] [Indexed: 11/12/2022]
Abstract
With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T. A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task. Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen’s d = 1.48) and fewer high intensity movements (p < 0.001; Cohen’s d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen’s d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71–0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67–0.92; p < 0.01), demonstrated high reliability (r: 0.86–0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group. Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.
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Affiliation(s)
- Nergis C Khan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,School of Medicine, Stanford University, Stanford, CA, USA
| | - Vineet Pandey
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Krzysztof Z Gajos
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Anoopum S Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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