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Reliability and Validity of Smartphone Cognitive Testing for Frontotemporal Lobar Degeneration

2024· article· en· W4393375382 on OpenAlexaffabout
Adam M. Staffaroni, Annie L Clark, Jack C. Taylor, Hilary W. Heuer, Mark Sanderson‐Cimino, Amy B. Wise, Sreya Dhanam, Yann Cobigo, Amy Wolf, Masood Manoochehri, Leah K. Forsberg, Carly Mester, Katherine P. Rankin, Brian S. Appleby, Ece Bayram, Andrea Bozoki, David Clark, R. Ryan Darby, Kimiko Domoto‐Reilly, Julie A. Fields, Douglas Galasko, Daniel H. Geschwind, Nupur Ghoshal, Neill R. Graff‐Radford, Murray Grossman, Ging‐Yuek Robin Hsiung, Edward D. Huey, David T. Jones, Maria I. Lapid, Irene Litvan, Joseph C. Masdeu, Lauren Massimo, Mario F. Mendez, Toji Miyagawa, Belén Pascual, Peter Pressman, Vijay K. Ramanan, Eliana Marisa Ramos, Katya Rascovsky, Erik D. Roberson, Maria Carmela Tartaglia, Bonnie Wong, Bruce L. Miller, John Kornak, Walter K. Kremers, Jason Hassenstab, Joel H. Kramer, Bradley F. Boeve, Howard J. Rosen, Adam L. Boxer, Liana G. Apostolova, Sami J. Barmada, Hugo Botha, Danielle Brushaber, Bradford Dickerson, Dennis W. Dickson, Fanny M. Elahi, Kelley Faber, Anne M. Fagan, Jamie Fong, Tatiana M. Foroud, Ralitza H. Gavrilova, Tania F. Gendron, Jill Goldman, Jonathan Graff‐Radford, Ian Grant, Matthew Hall, Chadwick M. Hales, Lawrence S. Honig, Eric J. Huang, David J. Irwin, Noah R. Johnson, Kejal Kantarci, David S. Knopman, Tyler Kolander, Justin Kwan, Argentina Lario Lago, Shannon B. Lavigne, Suzee Lee, Gabriel C. Léger, Peter A. Ljubenkov, Diane Lucente, Ian R. Mackenzie, Scott McGinnis, Corey T. McMillan, Joie Molden, Georges Naasan, Chiadi U. Onyike, Alexander Pantelyat, Emily W. Paolillo, Henry L. Paulson, Leonard Petrucelli, Rosa Rademakers, Meghana Rao, Kristoffer Rhoads, Jessica E. Rexach, Aaron Ritter, Emily Rogalskı, Julio C. Rojas, Rodolfo Savica, William W. Seeley, Allison Snyder, Anne C. Sullivan, Jeremy M. Syrjanen, Philip W. Tipton, Marijne Vandebergh, Arthur W. Toga, Lawren VandeVrede, Sandra Weıntraub, Dylan Wint, Zbigniew K. Wszołek, Jennifer Yokoyoma

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsOccupational Cancer Research CentreUniversity of TorontoUniversity of British Columbia
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute on AgingNational Institutes of HealthLarry L. Hillblom Foundation
KeywordsFrontotemporal lobar degenerationIntraclass correlationMedicineNeuropsychologyNeuropsychological assessmentCognitionConcurrent validityMemory clinicCohortReliability (semiconductor)Physical medicine and rehabilitationPhysical therapyPsychologyDementiaClinical psychologyFrontotemporal dementiaDiseasePsychiatryPsychometricsCognitive impairmentInternal consistencyInternal medicine

Abstract

fetched live from OpenAlex

Importance: Frontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD. Objective: To evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations. Design, Setting, and Participants: In this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)-based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes. Exposure: Participants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests. Main Outcomes and Measures: Internal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy. Results: In the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized β range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, -2.49 [95% CI, -0.19 to -0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back β = -0.49 [95% CI, -0.72 to -0.25]; P < .001) but not a composite of traditional neuropsychological measures (β = -0.14 [95% CI, -0.42 to 0.14]; P = .32). Conclusions and Relevance: The findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.367
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations20
Published2024
Admission routes2
Has abstractyes

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