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Record W2903056119 · doi:10.1007/s40120-018-0121-0

A Videogame-Based Digital Therapeutic to Improve Processing Speed in People with Multiple Sclerosis: A Feasibility Study

2018· article· en· W2903056119 on OpenAlex
Riley Bove, Gillian Rush, Chao Zhao, William Rowles, Priya Garcha, John Morrissey, Adrian Schembri, Titi Alailima, Dawn Langdon, Katherine L. Possin, Adam Gazzaley, Anthony Feinstein, Joaquin A. Anguera

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurology and Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Neurological Disorders and StrokeDoris Duke Charitable Foundation
KeywordsMedicineMultiple sclerosisPhysical therapyExpanded Disability Status ScaleCognitionPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

Self-administered in-home digital therapeutics could expand access to cognitive rehabilitation for individuals with multiple sclerosis (MS), over half of whom experience cognitive impairment (CI). However, feasibility in an MS population must be clarified. This study was conducted to assess the feasibility of deploying a videogame-like digital treatment for CI in MS, including initial efficacy and barriers to adherence. In this pilot study, 21 participants with MS completed an in-clinic baseline neurological evaluation. Cognitive tests included paper-and-pencil Brief International Cognitive Assessment for Multiple Sclerosis [BICAMS—which included the Symbol Digit Modalities Test (SDMT)] and other unsupervised tablet-based tests (including Match: an unsupervised test of executive functions and processing speed, developed at UCSF; and the Cogstate MS Battery). Participants then completed an in-home, tablet-based, videogame-like investigational digital treatment (Project: EVO™) for 25 min daily, 5 days weekly, for 4 weeks. This was followed by a repeat in-clinic evaluation. Of the 21 participants (mean [standard deviation, SD] age 53.8 [11.6] years, median Expanded Disability Status Scale (EDSS) 2.5 [SD 2.0, IQR [2–3.5]]) enrolled to use the digital therapeutic at home (mean [SD] SDMT z score: − 0.21 [1.16]), 18 completed the study, during which they completed an average of 19.7 days (median [SD]: 20.5 [8.4]). Overall, 78% of these 18 participants completed 75% of prescribed days (i.e., at least 15), and 50% completed all 20 days or more. Over the 4-week period, scores of processing speed improved significantly (based on one-sided t test), including SDMT (p = 0.003) and Match (p = 0.006). The Cogstate DET test (psychomotor function) also increased (p = 0.006). Mean increase in SDMT was 3.6 points. Male sex, not being employed, and higher baseline anxiety all were significantly associated with greater improvement in SDMT over the 4-week period. Interestingly, lower baseline cognitive scores were associated with greater number of sessions completed (e.g., SDMT: p = 0.003, R2 = 0.44). Adjusting for employment, a proxy for time available, did not significantly improve the model fit. Deploying an in-home digital tool to improve processing speed in MS is feasible, and shows preliminary efficacy. A larger, randomized controlled clinical trial is ongoing.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.059
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.070
GPT teacher head0.325
Teacher spread0.255 · 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