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Record W2794461357 · doi:10.1093/schbul/sby017.750

F219. NOVEL OBJECTIVE ASSESSMENT OF ACTIVITY ENGAGEMENT IN SCHIZOPHRENIA USING WIRELESS MOTION CAPTURE

2018· article· en· W2794461357 on OpenAlexaff
Ishraq Siddiqui, Gary Remington, Gagan Fervaha, Paul Fletcher, Aristotle N. Voineskos, Sarah Saperia, Konstantine K. Zakzanis, George Foussias

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsThe Scarborough HospitalUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsApathyPsychologySchizophrenia (object-oriented programming)Task (project management)Clinical psychologyDevelopmental psychologyCognitionPhysical medicine and rehabilitationMedicinePsychiatry

Abstract

fetched live from OpenAlex

Amotivation and reduced engagement in goal-directed activities are prominent features of schizophrenia. Previous investigations of patients’ engagement in activities have largely relied on accounts of daily living activities rather than objective task-based measures. The current study used wireless motion capture in an open-field setting to evaluate activity preference when individuals are provided an explicit choice between an active engagement option versus a passive engagement option. Twenty stable adult outpatients with schizophrenia and twenty matched healthy controls completed the Activity Preference Task, in which participants play a physical motion-based video game (active engagement) or watch a film (passive engagement) for fifteen minutes. No incentive was associated with either activity, and participants could engage in either activity at any time. Duration of engagement on the active option and number of switches between activity options were computed as the primary task outcome measures using objective motion data. Participants’ behaviour during active engagement was further quantified by computation of physical intensity (average hand speed) and persistence (tendency for sustained continuous engagement). Clinical assessments of positive and negative symptoms, apathy, cognition, depression, medication side-effects, motor ability, and community functioning were also administered. Schizophrenia participants’ duration, intensity, and persistence of active engagement were correlated with apathy (|ρ|=0.72–0.79, p<0.01) and community functioning (ρ=0.50–0.67, p<0.05). Although no significant group differences were detected in the individual comparisons of task measures, exploratory cluster analysis based on the two primary task measures identified three clusters of individuals with distinct profiles of engagement intensity (F(2,36)=9.141, p<0.001) and persistence (F(2,36)=13.954, p<0.001), and clinical apathy (F(2,37)=4.183, p=0.023). Further, there were significant diagnostic group by cluster assignment interaction effects for engagement intensity (F(2,33)=4.551, p=0.018) and apathy (F(2,34)=3.445, p=0.043) that highlighted substantial behavioural heterogeneity specific to schizophrenia; these interaction effects appeared to be driven primarily by a subgroup of patients who exhibited reduced engagement and increased apathy compared to individuals in other clusters as well as within-cluster healthy control counterparts. The Activity Preference Task provides a means of quantifying activity engagement in schizophrenia, which may be particularly valuable given the lack of objective assessments that measure non-incentivized, intrinsically motivated behaviours. Our initial findings suggest that patients with schizophrenia as a group are equally inclined as healthy individuals towards actively engaging activities when presented an explicit choice, but provision of such opportunities may be insufficient for amotivated patients to initiate and maintain engagement in functional behaviours.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.402
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations0
Published2018
Admission routes1
Has abstractyes

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