Virtual Reality Tools to Enhance Evidence Based Treatment of Substance Use Disorders (R41/R42 - Clinical Trial Optional)
Virtual Reality Tools to Enhance Evidence Based Treatment of Substance Use Disorders (R41/R42 - Clinical Trial Optional)
批准号:
9898050
负责人:
Erik Muendel
金额:
$21.81万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-15 至 2022-08-14
关键词:
AddressAdultAreaArtificial IntelligenceBackBehavior ControlBehavioralBrainBrain regionChronicClinical TrialsCocaineCognitiveCommunitiesConsciousConsumptionCuesDSM-VDataDatabasesDevelopmentDiseaseDrug AddictionEmotionalEuphoriaEvidence based treatmentExposure toFeedbackFeedsFunctional Magnetic Resonance ImagingGoalsImageImageryImpaired cognitionImpairmentIndividualInformal Social ControlInstitutionIntelligenceInterventionInterviewLeadLong-Term CareModelingMonitorMotivationNear-Infrared SpectroscopyNeurobiologyPatientsPersonsPharmaceutical PreparationsPhasePhysiologicalPilot ProjectsPlayPrevention strategyProtocols documentationRecoveryRegulationResearchRewardsRiskRoleSensoryStimulusStressStructureSubstance Use DisorderSupport SystemSystemTechnologyTestingTimeaddictionbasebehavioral impairmentcare coordinationclinically significantcognitive controlconditioningcravingcue reactivitydisorder later incidence preventionemotion regulationexecutive functionexperiencehead mounted displayimplicit memorymeetingsneurophysiologyphase 1 studyportabilityprofiles in patientsprototyperelapse riskresponsesensorsimulationsmart watchsobrietytoolvirtualvirtual realityvirtual reality simulation
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R&R Other Project Information
7. Project Summary/Abstract
Neurobiological changes caused by addiction impair behavioral control and increase relapse
risk (Volkow, Koob, & McLellan, 2016), substantiating the need for long-term care coordination
and recovery engagement for individuals with Substance Use Disorders (SUD) (Humphreys,
Malenka, Knutson, & MacCoun, 2017). Because addiction creates stress and reward system
dysregulation impairing emotional regulation and executive functioning capacities when
experiencing intense craving (Volkow, Koob & McLellan, 2016), traditional didactic relapse
prevention strategies may have limited efficacy for those in early recovery when they transition
back to their natural communities’ post-discharge from treatment, where they will be exposed to
SUD-related stimuli. New recovery support models that detect implicit cue-induced
neurophysiological dysregulation and restore real-time regulatory capacity to decrease relapse
risk are of clinical significance. This feasibility Phase I study will use Virtual Reality (VR)
technology to 1) simulate a patient-specific drug cue-triggering experience (VR_drug) to
calibrate a personalized neurophysiological relapse risk set-point, captured in-session using
fNIRS (Functional Near-Infrared Spectroscopy) sensors, a portable alternative to fMRI which will
be integrated into the VR HMD (Head Mounted Display), and physiological sensors (smartwatch
Empatica E4) worn during the VR_drug scenario (Aim 1); and 2) simulate a recovery-regulation
experience (VR_recovery) using patient-specific virtually-generated sober-supportive
relationships, recovery-enhanced environmental conditions, and recovery-associated sensory
cues, to calibrate a recovery-regulated neurophysiological set-point, captured by the same
in-session neurophysiological sensors systems (Aim 2). If successful, in Phase II, a mobile
recovery support system product will be developed to help individuals in SUD recovery
modulate real-time craving by monitoring their personalized neurophysiological relapse risk and
activating their recovery-regulation intervention (VR_recovery) to alter urge reactivity in
real-time. This type of mobile intervention that is immediately available to individuals in recovery
as they leave treatment institutions and transition back into their natural communities may help
manage in-the-moment drug urges in ways that allow engagement in other recovery-related
activities (e.g., calling a sponsor, getting to a meeting), decreasing real-time relapse risk
(Matto,& Seshaiyer, 2018; Matto, 2015; Matto, et al, 2014).
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