Efficient and Cost-Effective Multimodal System for Pain Management in Low Back Pain
Efficient and Cost-Effective Multimodal System for Pain Management in Low Back Pain
批准号:
9886461
负责人:
Zakia Hammal
金额:
$48.68万
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:
2020-03-16 至 2024-12-31
关键词:
AcuteAcute PainAftercareAmericanArticular Range of MotionBehavioralBiological AssayBrief Pain InventoryCellular PhoneCharacteristicsChronicChronic low back painClinicClinic VisitsClinicalClinical DataClinical assessmentsComputer AssistedComputer Vision SystemsConsentDataData CollectionDepressed moodDetectionDevelopmentDistressEcological momentary assessmentEthnic OriginFaceFamily memberFrequenciesFriendsGenderHeadHead MovementsHumanImpairmentIncidenceIndividualInterventionInterviewLow Back PainMeasurementMeasuresMetadataMethodsModalityModelingMonitorMovementMusculoskeletal PainPainPain DisorderPain MeasurementPain intensityPain managementParticipantPathway interactionsPatient Self-ReportPatientsPersonsPharmaceutical PreparationsPhysiciansProceduresProviderPsychological FactorsPsychosocial FactorPsychosocial StressReportingResearch PersonnelRiskRisk FactorsRotationSacroiliac joint structureSamplingSlipped DiskSpinal StenosisSubgroupSuggestionSyndromeSystemTechnologyTestingThinnessTimeTrainingUnited States National Institutes of HealthVariantVideo RecordingVisitVisualanaloganxiousbasechronic painclinical practicecomorbiditycost effectivedevelopmental diseasedigitalfollow-upimpressionimprovedmovement analysismultimodalityneurocognitive disorderpain scorepreventpsychosocialsocial factorsstandard of caretreatment response
中文摘要
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英文摘要
Project Summary/Abstract
Pain is among the most pervasive and universal forms of human distress. Pain typically is measured by patient
self-report or clinician impressions, either through clinical interview or the visual analog scale. However, self-
reported pain is difficult to interpret and in some circumstances not possible to obtain [Hadjistavropoulos et
al., 2002]. To improve the standard of care and advance pain assessment, monitoring, and intervention, we
propose (1) a savvy technology based on automatic facial, head, and body movement analysis for a reliable and
valid assessment of the occurrence and intensity associated with five causes of acute and chronic low back pain
(LBP); (2) inform our understanding of psychosocial and behavioral indicators of chronic LBP to develop new
means to prevent chronic LBP.
Participants' face, head, and body movement will be recorded during clinical assessment using two
synchronized high-definition digital video cameras during extension, flexion, and rotation movements. The
obtained video-recordings, taken during a first visit to the clinic and 3 follow-up visits after treatment, will be
used for the development of automatic measures of the occurrence and intensity of pain. To investigate the
generalizability of the proposed automatic measures, we will explicitly train and test the proposed classifiers on
five different types of acute and chronic LBP. To do so, face, head, and body movement will be automatically
tracked using our fully- automatic methods. The tracking results will be used to train end-to-end deep-leaning
based classifiers to automatically measure the occurrence and intensity of LBP. To investigate the validity of
the proposed classifiers, we will compare automated measurement to the patient- and clinician- rated visual
analog scale, brief pain inventory, and continuous observer ratings of pain intensity from the video recordings.
MANOVA will be used to quantify the relationship between the individual modalities and their combination for
the measurement of the occurrence and intensity of the five LBP conditions and for chronic and acute
conditions.
To inform our understanding of how LBP evolves into a chronic form, we will use Ecological Momentary
Assessment (EMA) to collect behavioral and contextual information beyond the video-recordings and pain
scores' assessments. Participants will be monitored for 6 months after treatment, at a frequency of 7
consecutive days per month (1 week per month), and 4 prompts per day, to identify those who evolved to
chronic LBP. EMA measures will be used to investigate whether pain intensity differs by psychosocial and
behavioral factors both between and within LBP groups as well as whether psychosocial and behavioral factors
are associated with the development of chronic LBP.
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Efficient and Cost-Effective Multimodal System for Pain Management in Low Back Pain
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批准号:10319006
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项目类别:
-
资助金额:$59.82万
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财政年份:2020
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负责人:Zakia Hammal
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依托单位:
Automatic Multimodal Assessment of Pain in Dementia
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批准号:10288413
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项目类别:
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资助金额:$34.47万
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财政年份:2020
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负责人:Zakia Hammal
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依托单位:
海外基金