Assessment of non-directed computer-use behaviours in the home can indicate early cognitive impairment: A proof of principle longitudinal study

Stringer, Gemma; Couth, Samuel; Heuvelman, Hein; Bull, Christopher; Gledson, Ann; Keane, John; Rayson, Paul; Sutcliffe﻿, Alistair; Sawyer, Pete; Zeng, Xiao‐Jun; Montaldi, Daniela; Brown, Laura J. E.; Leroi, Iracema

doi:10.1080/13607863.2022.2036946

Cited by 5 publications

(12 citation statements)

References 39 publications

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“…Only 5 (71%) of the 7 aforementioned IADL domains were found to have been digitally assessed across the studies. These included activities outside of the home (8/15, 53%) [32,33,[37][38][39][40][41]44], everyday technology use (7/15, 47%) [30,[34][35][36]38,42,43], medication management (3/15, 20%) [31,36,39], household and personal management (1/15, 7%) [39], and orientation (2/15, 13%) [40,44]. No studies digitally assessed behaviors related to socialization and communication or culturally specific tasks.…”

Section: Digital Methods To Measure Iadlsmentioning

confidence: 99%

Section: Digital Methods To Measure Iadlsmentioning

confidence: 99%

“…Ambient technology in the home was the most popular method of assessment, with 93% (14/15) of the studies measuring IADLs using this method [30][31][32][33][34][35][36][37][38][39][40][42][43][44]. The types of ambient technologies that were used included passive infrared (PIR) motion sensors (5/15, 33%) [32,33,35,39,44], contact sensors (5/15, 33%) [32,33,35,39,44], mouse movement and keystroke logging (4/15, 27%) [30,35,42,43], computer monitoring software (3/15, 20%) [34,36,38], electronic pillboxes (3/15, 20%) [31,36,39], passive driving sensors (2/15, 13%) [37,38], proximity beacon tags (1/15, 7%) [39], and Bluetooth beacons (1/15, 7%) [40]. PIR motion sensors were usually placed in each room to monitor activity throughout the home and routinely coupled with contact sensors placed in doorways to measure the opening and closing of doors; these sensors monitored the amount of time participants spent at home in the activities outside of the home and orientation domains.…”

Section: Digital Methods To Measure Iadlsmentioning

confidence: 99%

“…Cognitive assessments such as the MMSE or the clinical dementia rating (CDR) were used by 4 (27%) of the 15 papers to characterize MCI [30][31][32][33]. Of the 15 papers, 4 (27%) used validated diagnostic criteria such as the National Institute on Aging-Alzheimer's Association workgroup criteria [1] to classify MCI [34][35][36][37], whereas 6 (40%) reported a consensus with ≥1 clinicians, alongside validated measures or cognitive assessment [38][39][40][41][42][43]. In 1 (7%) of the 15 papers, half of the sample with MCI was classified by cognitive assessment (CDR), whereas the remaining half was assessed by a clinician using validated criteria [44].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…In 1 (7%) of the 15 papers, half of the sample with MCI was classified by cognitive assessment (CDR), whereas the remaining half was assessed by a clinician using validated criteria [44]. Comparison groups included cognitively intact older adult controls (13/15, 87%) [30][31][32][33][34][35][36][37][38][39][40][41]43,44], people with subjective cognitive decline (SCD; 1/15, 7%) [42], people with Alzheimer disease (1/15, 7%) [41], and people with unspecified dementia (1/15, 7%) [40].…”

Section: Study Characteristicsmentioning

confidence: 99%

See 4 more Smart Citations

Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review

Lawson¹,

Ardle²,

Wilson³

et al. 2023

J Med Internet Res

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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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Section: Digital Methods To Measure Iadlsmentioning

confidence: 99%

Section: Digital Methods To Measure Iadlsmentioning

confidence: 99%

Section: Digital Methods To Measure Iadlsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

See 3 more Smart Citations

Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review

Lawson¹,

Ardle²,

Wilson³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

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Identification of Imaging Genetics Association for Mild Cognitive Impairment Based on Adaptive Constrained Canonical Correlation Analysis

Du,

Luo

2024

Learning and Analytics in Intelligent Systems

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Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study

Paolillo,

Casaletto,

Clark

et al. 2024

JMIR Aging

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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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Assessment of non-directed computer-use behaviours in the home can indicate early cognitive impairment: A proof of principle longitudinal study

Cited by 5 publications

References 39 publications

Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review

Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review

Identification of Imaging Genetics Association for Mild Cognitive Impairment Based on Adaptive Constrained Canonical Correlation Analysis

Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study

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