16 research outputs found

    An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

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    Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

    Plasma GFAP associates with secondary Alzheimer's pathology in Lewy body disease

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    Abstract Objective Within Lewy body spectrum disorders (LBSD) with α‐synuclein pathology (αSyn), concomitant Alzheimer's disease (AD) pathology is common and is predictive of clinical outcomes, including cognitive impairment and decline. Plasma phosphorylated tau 181 (p‐tau181) is sensitive to AD neuropathologic change (ADNC) in clinical AD, and plasma glial fibrillary acidic protein (GFAP) is associated with the presence of β‐amyloid plaques. While these plasma biomarkers are well tested in clinical and pathological AD, their diagnostic and prognostic performance for concomitant AD in LBSD is unknown. Methods In autopsy‐confirmed αSyn‐positive LBSD, we tested how plasma p‐tau181 and GFAP differed across αSyn with concomitant ADNC (αSyn+AD; n = 19) and αSyn without AD (αSyn; n = 30). Severity of burden was scored on a semiquantitative scale for several pathologies (e.g., β‐amyloid and tau), and scores were averaged across sampled brainstem, limbic, and neocortical regions. Results Linear models showed that plasma GFAP was significantly higher in αSyn+AD compared to αSyn (β = 0.31, 95% CI = 0.065–0.56, and P = 0.015), after covarying for age at plasma, plasma‐to‐death interval, and sex; plasma p‐tau181 was not (P = 0.37). Next, linear models tested associations of AD pathological features with both plasma analytes, covarying for plasma‐to‐death, age at plasma, and sex. GFAP was significantly associated with brain β‐amyloid (β = 15, 95% CI = 6.1–25, and P = 0.0018) and tau burden (β = 12, 95% CI = 2.5–22, and P = 0.015); plasma p‐tau181 was not associated with either (both P > 0.34). Interpretation Findings indicate that plasma GFAP may be sensitive to concomitant AD pathology in LBSD, especially accumulation of β‐amyloid plaques
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