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Paolillo EW, Casaletto KB, Clark AL, Taylor JC, Heuer HW, Wise AB, Dhanam S, Sanderson-Cimino M, Saloner R, Kramer JH, Kornak J, Kremers W, Forsberg L, Appleby B, Bayram E, Bozoki A, Brushaber D, Darby RR, Day GS, Dickerson BC, Domoto-Reilly K, Elahi F, Fields JA, Ghoshal N, Graff-Radford N, G H Hall M, Honig LS, Huey ED, Lapid MI, Litvan I, Mackenzie IR, Masdeu JC, Mendez MF, Mester C, Miyagawa T, Naasan G, Pascual B, Pressman P, Ramos EM, Rankin KP, Rexach J, Rojas JC, VandeVrede L, Wong B, Wszolek ZK, Boeve BF, Rosen HJ, Boxer AL, Staffaroni AM. Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study. JMIR Aging 2024; 7:e52831. [PMID: 38922667 DOI: 10.2196/52831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 02/09/2024] [Accepted: 03/07/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown. OBJECTIVE This study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD. METHODS Participants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age. RESULTS The CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases). CONCLUSIONS These findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change.
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Affiliation(s)
- Emily W Paolillo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kaitlin B Casaletto
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Annie L Clark
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jack C Taylor
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Hilary W Heuer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Amy B Wise
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Sreya Dhanam
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Mark Sanderson-Cimino
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Rowan Saloner
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Walter Kremers
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Leah Forsberg
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
| | - Ece Bayram
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina, Chapel Hill, NC, United States
| | - Danielle Brushaber
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - R Ryan Darby
- Department of Neurology, Vanderbilt University, Nashville, TN, United States
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Fanny Elahi
- Department of Neurology, The Deane Center for Wellness and Cognitive Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- James J. Peters Veterans Affairs Medical Center, New York, NY, United States
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Nupur Ghoshal
- Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University, St. Louis, MO, United States
| | | | - Matthew G H Hall
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lawrence S Honig
- Department of Neurology, Columbia University, New York, NY, United States
| | - Edward D Huey
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, United States
| | - Maria I Lapid
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Irene Litvan
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Ian R Mackenzie
- Department of Pathology, University of British Columbia, Vancouver, BC, Canada
| | - Joseph C Masdeu
- Stanley H. Appel Department of Neurology, Nantz National Alzheimer Center, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX, United States
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Carly Mester
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Georges Naasan
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Belen Pascual
- Stanley H. Appel Department of Neurology, Nantz National Alzheimer Center, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX, United States
| | - Peter Pressman
- Department of Neurology, University of Colorado, Aurora, CO, United States
| | - Eliana Marisa Ramos
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine P Rankin
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jessica Rexach
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Julio C Rojas
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lawren VandeVrede
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Bonnie Wong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Adam L Boxer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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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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3
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Lawson L, Mc Ardle R, Wilson S, Beswick E, Karimi R, Slight SP. Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review. J Med Internet Res 2023; 25:e45658. [PMID: 37490331 PMCID: PMC10410386 DOI: 10.2196/45658] [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] [Received: 01/18/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Subtle impairments in instrumental activities of daily living (IADLs) can be a key predictor of disease progression and are considered central to functional independence. Mild cognitive impairment (MCI) is a syndrome associated with significant changes in cognitive function and mild impairment in complex functional abilities. The early detection of functional decline through the identification of IADL impairments can aid early intervention strategies. Digital health technology is an objective method of capturing IADL-related behaviors. However, it is unclear how these IADL-related behaviors have been digitally assessed in the literature and what differences can be observed between MCI and normal aging. OBJECTIVE This review aimed to identify the digital methods and metrics used to assess IADL-related behaviors in people with MCI and report any statistically significant differences in digital endpoints between MCI and normal aging and how these digital endpoints change over time. METHODS A total of 16,099 articles were identified from 8 databases (CINAHL, Embase, MEDLINE, ProQuest, PsycINFO, PubMed, Web of Science, and Scopus), out of which 15 were included in this review. The included studies must have used continuous remote digital measures to assess IADL-related behaviors in adults characterized as having MCI by clinical diagnosis or assessment. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS Ambient technology was the most commonly used digital method to assess IADL-related behaviors in the included studies (14/15, 93%), with passive infrared motion sensors (5/15, 33%) and contact sensors (5/15, 33%) being the most prevalent types of methods. Digital technologies were used to assess IADL-related behaviors across 5 domains: activities outside of the home, everyday technology use, household and personal management, medication management, and orientation. Other recognized domains-culturally specific tasks and socialization and communication-were not assessed. Of the 79 metrics recorded among 11 types of technologies, 65 (82%) were used only once. There were inconsistent findings around differences in digital IADL endpoints across the cognitive spectrum, with limited longitudinal assessment of how they changed over time. CONCLUSIONS Despite the broad range of metrics and methods used to digitally assess IADL-related behaviors in people with MCI, several IADLs relevant to functional decline were not studied. Measuring multiple IADL-related digital endpoints could offer more value than the measurement of discrete IADL outcomes alone to observe functional decline. Key recommendations include the development of suitable core metrics relevant to IADL-related behaviors that are based on clinically meaningful outcomes to aid the standardization and further validation of digital technologies against existing IADL measures. Increased longitudinal monitoring is necessary to capture changes in digital IADL endpoints over time in people with MCI. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42022326861; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=326861.
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Affiliation(s)
- Lauren Lawson
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ríona Mc Ardle
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Sarah Wilson
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Emily Beswick
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Radin Karimi
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Sarah P Slight
- School of Pharmacy, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
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4
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Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J. Older Adults and New Technology: Mapping Review of the Factors Associated With Older Adults' Intention to Adopt Digital Technologies. JMIR Aging 2023; 6:e44564. [PMID: 37191976 DOI: 10.2196/44564] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/19/2023] [Accepted: 03/18/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Ongoing advancements in digital solutions support older adults' healthy aging and well-being. However, a unified synthesis of sociodemographic, cognitive, attitudinal, emotional, and environmental factors that influence older adults' intention to use these new digital technologies is still lacking. Understanding the salient factors that influence older adults' intention to use digital technologies will help to ensure that technology is developed appropriately and contextually. This understanding is also likely to contribute to developing technology acceptance models specifically for the aging generation, by reorganizing principles and constructing objectivity criteria for future research studies. OBJECTIVE This review aims to identify the key factors associated with older adults' intention to use digital technologies and to provide a comprehensive conceptual framework to describe the relationships between these key factors and older adults' intention to use digital technologies. METHODS A mapping review was conducted using 9 databases from inception to November 2022. Articles were selected for review if they had an evaluative component of older adults' intention to use digital technologies. Three researchers independently reviewed the articles and extracted the data. Data synthesis was performed via narrative review and quality appraisal was measured using 3 different tools based on each article's study design. RESULTS We identified a total of 59 articles investigating older adults' intention to use digital technologies. The majority (40/59, 68%) of articles did not use an existing framework or model for technology acceptance. Studies mostly adopted a quantitative research design (27/59, 46%). We found 119 unique factors reported to influence older adults' intention to use digital technologies. These were categorized into 6 distinct themes: Demographics and Health Status, Emotional Awareness and Needs, Knowledge and Perception, Motivation, Social Influencers, and Technology Functional Features. CONCLUSIONS Given the importance of global demographic change toward an aging society, there is surprisingly limited research on the factors that influence older adults' intention to use digital technologies. Our identification of the key factors across different types of digital technology and models supports the future integration of a comprehensive perspective encompassing environmental, psychological, and social determinants for older adults' intention to use digital technologies.
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Affiliation(s)
- Tanja Schroeder
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, Australia
- Centre for Research on Service Sciences (CROSS), Neu-Ulm University, Neu-Ulm, Germany
| | - Laura Dodds
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Westmead, Australia
| | - Andrew Georgiou
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, Australia
| | - Heiko Gewald
- Centre for Research on Service Sciences (CROSS), Neu-Ulm University, Neu-Ulm, Germany
| | - Joyce Siette
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, Australia
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Westmead, Australia
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5
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DuBord AY, Paolillo EW, Staffaroni AM. Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases. J Diabetes Sci Technol 2023:19322968231171399. [PMID: 37102472 DOI: 10.1177/19322968231171399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.
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Affiliation(s)
- Ashley Y DuBord
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Technology Society, Burlingame, CA, USA
| | - Emily W Paolillo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
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French MA, Keatley E, Li J, Balasubramanian A, Hansel NN, Wise R, Searson P, Singh A, Raghavan P, Wegener S, Roemmich RT, Celnik P. The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease. Digit Health 2023; 9:20552076231176160. [PMID: 37214659 PMCID: PMC10192672 DOI: 10.1177/20552076231176160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/28/2023] [Indexed: 05/24/2023] Open
Abstract
Objective Clinical implementation of remote monitoring of human function requires an understanding of its feasibility. We evaluated adherence and the resources required to monitor physical, cognitive, and psychosocial function in individuals with either chronic obstructive pulmonary disease or stroke during a three-month period. Methods Seventy-three individuals agreed to wear a Fitbit to monitor physical function and to complete monthly online assessments of cognitive and psychosocial function. During a three-month period, we measured adherence to monitoring (1) physical function using average daily wear time, and (2) cognition and psychosocial function using the percentage of assessments completed. We measured the resources needed to promote adherence as (1) the number of participants requiring at least one reminder to synchronize their Fitbit, and (2) the number of reminders needed for each completed cognitive and psychosocial assessment. Results After accounting for withdrawals, the average daily wear time was 77.5 ± 19.9% of the day and did not differ significantly between months 1, 2, and 3 (p = 0.30). To achieve this level of adherence, 64.9% of participants required at least one reminder to synchronize their device. Participants completed 61.0% of the cognitive and psychosocial assessments; the portion of assessments completed each month didnot significantly differ (p = 0.44). Participants required 1.13 ± 0.57 reminders for each completed assessment. Results did not differ by disease diagnosis. Conclusions Remote monitoring of human function in individuals with either chronic obstructive pulmonary disease or stroke is feasible as demonstrated by high adherence. However, the number of reminders required indicates that careful consideration must be given to the resources available to obtain high adherence.
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Affiliation(s)
- Margaret A French
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Eva Keatley
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Junyao Li
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Aparna Balasubramanian
- Division of Pulmonary and Critical Care
Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nadia N Hansel
- Division of Pulmonary and Critical Care
Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Robert Wise
- Division of Pulmonary and Critical Care
Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Peter Searson
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
- Department of Materials Science and
Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Anil Singh
- Department of Pulmonary and Critical
Care Medicine, Allegheny Health Network, Pittsburg, PA, USA
| | - Preeti Raghavan
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Stephen Wegener
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Ryan T Roemmich
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
- Kennedy Krieger Institute, Center for Movement Studies, Baltimore, MD, USA
| | - Pablo Celnik
- Department of Physical Medicine and
Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
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7
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Chen SD, Zhang W, Li YZ, Yang L, Huang YY, Deng YT, Wu BS, Suckling J, Rolls ET, Feng JF, Cheng W, Dong Q, Yu JT. A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank. Biol Psychiatry 2022; 93:790-801. [PMID: 36788058 DOI: 10.1016/j.biopsych.2022.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/15/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Considerable uncertainty remains regarding associations of multiple risk factors with Alzheimer's disease (AD). We aimed to systematically screen and validate a wide range of potential risk factors for AD. METHODS Among 502,493 participants from the UK Biobank, baseline data were extracted for 4171 factors spanning 10 different categories. Phenome-wide association analyses and time-to-event analyses were conducted to identify factors associated with both polygenic risk scores for AD and AD diagnosis at follow-up. We performed two-sample Mendelian randomization analysis to further assess their potential causal relationships with AD and imaging association analysis to discover underlying mechanisms. RESULTS We identified 39 factors significantly associated with both AD polygenic risk scores and risk of incident AD, where higher levels of education, body size, basal metabolic rate, fat-free mass, computer use, and cognitive functions were associated with a decreased risk of developing AD, and selective food intake and more outdoor exposures were associated with an increased risk of developing AD. The identified factors were also associated with AD-related brain structures, including the hippocampus, entorhinal cortex, and inferior/middle temporal cortex, and 21 of these factors were further supported by Mendelian randomization evidence. CONCLUSIONS To our knowledge, this is the first study to comprehensively and rigorously assess the effects of wide-ranging risk factors on AD. Strong evidence was found for fat-free body mass, basal metabolic rate, computer use, selective food intake, and outdoor exposures as new risk factors for AD. Integration of genetic, clinical, and neuroimaging information may help prioritize risk factors and prevention targets for AD.
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Affiliation(s)
- Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom; Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Wild K, Marcoe J, Mattek N, Sharma N, Loewy E, Tischler H, Kaye J, Karlawish J. Online monitoring of financial capacity in older adults: Feasibility and initial findings. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12282. [PMID: 35311179 PMCID: PMC8911546 DOI: 10.1002/dad2.12282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/23/2021] [Accepted: 12/07/2021] [Indexed: 11/10/2022]
Abstract
Introduction Impairment in financial capacity places older adults at risk of fraud or abuse and can be a harbinger of loss of independence. Online automated monitoring of financial transactions offers an objective, unobtrusive, and continuous data collection strategy to minimize risk and to detect early changes in an important complex activity of daily living. Methods Ninety‐three participants used an online financial activity monitoring platform that extracted metrics related to use and potential departures from established patterns of financial behavior. Standard neuropsychological assessments and a performance‐based measure of financial capacity at baseline were compared using continuous monitoring metrics. Results Participants demonstrated a willingness to engage with an online financial activity monitoring system. Online metrics were not associated with performance in specific cognitive domains. Performance on an established test of financial capacity was negatively correlated with a ratio of alerts to transactions, that is, a higher likelihood of errors or deviations from previous activity. Discussion To our knowledge, this is the first reported study using secure online technology to link ongoing unobtrusively collected financial activity monitoring data with other objective measures of function and cognition in a cohort of independent living older adults.
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Affiliation(s)
- Katherine Wild
- Department of Neurology Oregon Health and Science University Portland Oregon USA
- NIA‐OHSU Layton Aging and Alzheimer's Disease Center Oregon Health and Science University Portland Oregon USA
- Department of Biomedical Engineering Oregon Health and Science University Portland Oregon USA
- Oregon Center for Aging and Technology Oregon Health and Science University Portland Oregon USA
| | - Jennifer Marcoe
- Department of Neurology Oregon Health and Science University Portland Oregon USA
- NIA‐OHSU Layton Aging and Alzheimer's Disease Center Oregon Health and Science University Portland Oregon USA
- Department of Biomedical Engineering Oregon Health and Science University Portland Oregon USA
- Oregon Center for Aging and Technology Oregon Health and Science University Portland Oregon USA
| | - Nora Mattek
- Department of Neurology Oregon Health and Science University Portland Oregon USA
- NIA‐OHSU Layton Aging and Alzheimer's Disease Center Oregon Health and Science University Portland Oregon USA
- Department of Biomedical Engineering Oregon Health and Science University Portland Oregon USA
- Oregon Center for Aging and Technology Oregon Health and Science University Portland Oregon USA
| | - Nicole Sharma
- Department of Neurology Oregon Health and Science University Portland Oregon USA
- NIA‐OHSU Layton Aging and Alzheimer's Disease Center Oregon Health and Science University Portland Oregon USA
- Department of Biomedical Engineering Oregon Health and Science University Portland Oregon USA
- Oregon Center for Aging and Technology Oregon Health and Science University Portland Oregon USA
| | | | | | - Jeffrey Kaye
- Department of Neurology Oregon Health and Science University Portland Oregon USA
- NIA‐OHSU Layton Aging and Alzheimer's Disease Center Oregon Health and Science University Portland Oregon USA
- Department of Biomedical Engineering Oregon Health and Science University Portland Oregon USA
- Oregon Center for Aging and Technology Oregon Health and Science University Portland Oregon USA
| | - Jason Karlawish
- Departments of Medicine Medical Ethics and Health Policy and Neurology University of Pennsylvania Philadelphia Pennsylvania USA
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