2024
Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter
Malamud J, Guloksuz S, van Winkel R, Delespaul P, De Hert M, Derom C, Thiery E, Jacobs N, Rutten B, Huys Q. Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter. PLOS Computational Biology 2024, 20: e1012457. PMID: 39312537, PMCID: PMC11449358, DOI: 10.1371/journal.pcbi.1012457.Peer-Reviewed Original ResearchMeSH KeywordsAffectAlgorithmsComputational BiologyDepressionEcological Momentary AssessmentFemaleHumansMalePsychometricsConceptsEcological momentary assessmentEcological momentary assessment dataMood statesMood disordersDepressive stateMood dynamicsFeatures of mood disordersCombination of ecological momentary assessmentsControl of moodSymptoms of depressionInternal emotional statesAlterations to interactionsPsychopathological featuresMomentary assessmentMoodEmotional statesDepressionEnvironmental stimuli
2023
Coping and sleep quality in youth: An Experience Sampling study
Hagemann N, Kirtley O, Lafit G, Vancampfort D, Wampers M, Decoster J, Derom C, Gülöksüz S, De Hert M, Jacobs N, Menne‐Lothmann C, Rutten B, Thiery E, van Os J, van Winkel R, Wichers M, Myin‐Germeys I. Coping and sleep quality in youth: An Experience Sampling study. Journal Of Adolescence 2023, 95: 566-583. PMID: 36647754, DOI: 10.1002/jad.12137.Peer-Reviewed Original ResearchMeSH KeywordsAdaptation, PsychologicalAdolescentEcological Momentary AssessmentHumansSleepSleep QualitySurveys and QuestionnairesConceptsEmotion-focused copingExperience Sampling StudySleep qualityDaily sleep qualitySample of youthLocus of controlSampling studyUtrecht Coping ListPassive reactionNegative eventsDaily lifeFuture insomniaCopingSleep problemsQuality of sleepMental wellMental healthDisengagementYouthState locusIndividual presentStyleDecreased sleep qualitySleepPsychopathology
2022
General psychopathology and its social correlates in the daily lives of youth
Achterhof R, Kirtley O, Schneider M, Hagemann N, Hermans K, Hiekkaranta A, Lecei A, Decoster J, Derom C, De Hert M, Gülöksüz S, Jacobs N, Menne-Lothmann C, Rutten B, Thiery E, van Os J, van Winkel R, Wichers M, Myin-Germeys I. General psychopathology and its social correlates in the daily lives of youth. Journal Of Affective Disorders 2022, 309: 428-436. PMID: 35500686, DOI: 10.1016/j.jad.2022.04.147.Peer-Reviewed Original Research
2020
Polygenic liability for schizophrenia and childhood adversity influences daily‐life emotion dysregulation and psychosis proneness
Pries L, Klingenberg B, Menne‐Lothmann C, Decoster J, van Winkel R, Collip D, Delespaul P, De Hert M, Derom C, Thiery E, Jacobs N, Wichers M, Cinar O, Lin B, Luykx J, Rutten B, van Os J, Guloksuz S. Polygenic liability for schizophrenia and childhood adversity influences daily‐life emotion dysregulation and psychosis proneness. Acta Psychiatrica Scandinavica 2020, 141: 465-475. PMID: 32027017, PMCID: PMC7318228, DOI: 10.1111/acps.13158.Peer-Reviewed Original ResearchConceptsDaily life stressorsChildhood adversityEmotion dysregulationPositive affectPsychosis pronenessMomentary mental statesEcological momentary assessmentChildhood Trauma QuestionnaireGene-environment correlationNegative affectMental statesMomentary assessmentPsychosis expressionTrauma QuestionnaireAdversityAffectYoung adultsStressorsNovel evidencePolygenic liabilityInteraction effectsPronenessSchizophreniaHigh PRSState domain
2019
Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches
Stamate D, Katrinecz A, Stahl D, Verhagen S, Delespaul P, van Os J, Guloksuz S. Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches. Schizophrenia Research 2019, 209: 156-163. PMID: 31104913, DOI: 10.1016/j.schres.2019.04.028.Peer-Reviewed Original Research
2018
Recurrent Neural Networks in Mobile Sampling and Intervention
Koppe G, Guloksuz S, Reininghaus U, Durstewitz D. Recurrent Neural Networks in Mobile Sampling and Intervention. Schizophrenia Bulletin 2018, 45: 272-276. PMID: 30496527, PMCID: PMC6403085, DOI: 10.1093/schbul/sby171.Peer-Reviewed Original ResearchMeSH KeywordsEcological Momentary AssessmentHumansMachine LearningNeural Networks, ComputerPsychotic DisordersTelemedicineConceptsRecurrent neural networkEcological momentary interventionsEveryday life contextPsychological processesMomentary interventionsData modalitiesDifferent data modalitiesSocial outcomesSocioenvironmental factorsFuture researchMultiple data modalitiesNeural networkTreatment of psychosisOnline feedbackIndividual trajectoriesPsychosisDaily lifeContext-specific interventionsInterventionEmotionsCognitionExperienceStatistical machineContextFitness trackers
2017
The experience sampling method as an mHealth tool to support self‐monitoring, self‐insight, and personalized health care in clinical practice
van Os J, Verhagen S, Marsman A, Peeters F, Bak M, Marcelis M, Drukker M, Reininghaus U, Jacobs N, Lataster T, Simons C, Investigators E, Lousberg R, Gülöksüz S, Leue C, Groot P, Viechtbauer W, Delespaul P. The experience sampling method as an mHealth tool to support self‐monitoring, self‐insight, and personalized health care in clinical practice. Depression And Anxiety 2017, 34: 481-493. PMID: 28544391, DOI: 10.1002/da.22647.Peer-Reviewed Original ResearchMeSH KeywordsEcological Momentary AssessmentHumansMental DisordersMobile ApplicationsPrecision MedicineTelemedicineConceptsExperience sampling methodClinical practiceTrial of MindfulnessESM data collectionPersonalized trajectoriesEmotion dynamicsCognitive abilitiesRoutine clinical practiceDifferent patient groupsNatural rewardsRoutine outcome measurementDaily lifeBlended careMix of faceImplicit patternsMental healthSmartphone usePatient groupOffice treatmentTwin studiesOutcome measurementsProcess of diagnosisMHealth toolsTreatment evaluationDecision making