Heart rate and skin conductance associations with physical aggression, psychopathy, antisocial personality disorder and conduct disorder: An updated meta-analysis

Looff, Peter de; Cornet, Liza J. M.; Kogel, C.H. de; Fernández-Castilla, Belén; Embregts, P.J.C.M.; Didden, Robert; Nijman, Henk

doi:10.1016/j.neubiorev.2021.11.003

Cited by 43 publications

(52 citation statements)

References 175 publications

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“…Wearables seem to hold great potential for healthcare ( Cheung et al, 2018 ; Johnson and Picard, 2020 ; Pedrelli et al, 2020 ) in disease monitoring ( Regalia et al, 2019 ), including COVID-19 ( Ates et al, 2021 ; Channa et al, 2021 ), predict risk for disease ( Pedrelli et al, 2020 ), predict risk for dangerous behavior ( de Looff P. et al, 2019 ; Goodwin et al, 2019 ; de Looff et al, 2022 ), or provide continuous feedback to stimulate emotional awareness and behavioral change ( Derks et al, 2019 ). However, wearables also hold promise as an additional tool in the clinician’s current toolbox to provide opportunities for in-depth and careful investigation of physiological responses in daily life ( Johnson and Picard, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…They provide innovative ways to bring neuroscience from the lab to real-life settings ( Johnson and Picard, 2020 ). Researchers study associations between physiological signals and a plethora of emotional, cognitive, and physical diseases, such as epilepsy ( Regalia et al, 2019 ), depression ( Pedrelli et al, 2020 ), burnout ( de Looff P. C. et al, 2019 ), suicide, aggressive behavior ( de Looff P. et al, 2019 ; Goodwin et al, 2019 ; de Looff et al, 2022 ), or mood ( Sano et al, 2018 ; Umematsu et al, 2019 ). These wearable devices can also be used by clinicians and researchers to study psychological-physiological associations in day-to-day and moment-to-moment situations, which is known as digital phenotyping ( Onnela and Rauch, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Looff

Duursma²,

Noordzij

et al. 2022

Front. Behav. Neurosci.

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Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Looff

Duursma²,

Noordzij

et al. 2022

Front. Behav. Neurosci.

View full text Add to dashboard Cite

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“…So, although more than two-thirds of these young adults reported committing an aggressive crime and higher levels of reactive aggression were found in the delinquent group compared to aged-matched controls, our sample could not be considered a severely violent offender group. In a recent meta-analysis, it was shown that psychophysiological effects are largest for the most violent offenders and for psychopathy, and smaller for physical aggression and aggression measured in laboratory tasks [ 2 ]. Furthermore, by focusing on indicators of SNS and PNS separately, we might have lost sight of the importance of the cooperation between these two subsystems in understanding emotion dysregulation and aggressive behavior [ 29 , 36 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…Aggressive and violent behavior in young adulthood is a major concern in forensic psychiatry and society, given the negative impact of concomitant social problems and delinquency on victims and perpetrators, as well as the high costs for health care and society [ 1 , 2 ]. Although treatment-associated risk reductions in violent recidivism have been reported in several studies [ 3 ], the current overall efficacy of psychological and psychosocial interventions aimed at reducing aggressive behavior in forensic patients is found to be limited [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, measures can be taken at rest or in response to arousal-inducing events (i.e., reactivity measures), such as exposure to social stressors, provocation or emotion-eliciting pictures or film clips. In aggression research, lower heart rate (HR) at rest has most consistently been found to be positively associated with antisocial behavior in general [ 2 ] and proactive aggression, although the overall effect size is found to be small [ 24 ]. However, the findings regarding reactivity of the ANS, SNS and PNS, which is the focus of our study, are less robust.…”

Section: Introductionmentioning

confidence: 99%

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Neurobiological Responses towards Stimuli Depicting Aggressive Interactions in Delinquent Young Adults and Controls: No Relation to Reactive and Proactive Aggression

et al. 2022

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Neurobiological measures underlying aggressive behavior have gained attention due to their potential to inform risk assessment and treatment interventions. Aberrations in responsivity of the autonomic nervous system and electrophysiological responses to arousal-inducing stimuli have been related to emotional dysregulation and aggressive behavior. However, studies have often been performed in community samples, using tasks that induce arousal but not specifically depict aggression. In this study, we examined differences in psychophysiological (i.e., heart rate, respiratory sinus arrhythmia, skin conductance level) and electrophysiological responses (i.e., P3, late positive potential, mu suppression) to aggressive versus neutral scenes in a sample of 118 delinquent young adults and 25 controls (all male, aged 18–27). With respect to group differences, we only found significant higher SCL reactivity during the task in the delinquent group compared to controls, but this was irrespective of condition (aggressive and neutral interactions). Within the delinquent group, we also examined associations between the neurobiological measures and reactive and proactive aggression. No significant associations were found. Therefore, although we found some indication of emotional dysregulation in these delinquent young adults, future studies should further elucidate the neurobiological mechanisms underlying emotional dysregulation in relation to different types of aggression.

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Wearable Biosensing to Predict Imminent Aggressive Behavior in Psychiatric Inpatient Youths With Autism

Imbiriba,

Demirkaya,

Singh

et al. 2023

JAMA Netw Open

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ImportanceAggressive behavior is a prevalent and challenging issue in individuals with autism.ObjectiveTo investigate whether changes in peripheral physiology recorded by a wearable biosensor and machine learning can be used to predict imminent aggressive behavior before it occurs in inpatient youths with autism.Design, Setting, and ParticipantsThis noninterventional prognostic study used data collected from March 2019 to March 2020 from 4 primary care psychiatric inpatient hospitals. Enrolled participants were 86 psychiatric inpatients with confirmed diagnoses of autism exhibiting operationally defined self-injurious behavior, emotion dysregulation, or aggression toward others; 16 individuals were not included (18.6%) because they would not wear the biosensor (8 individuals) or were discharged before an observation could be made (8 individuals). Data were analyzed from March 2020 through October 2023.Main Outcomes and MeasuresResearch staff performed live behavioral coding of aggressive behavior while inpatient study participants wore a commercially available biosensor that recorded peripheral physiological signals (cardiovascular activity, electrodermal activity, and motion). Logistic regression, support vector machines, neural networks, and domain adaptation were used to analyze time-series features extracted from biosensor data. Area under the receiver operating characteristic curve (AUROC) values were used to evaluate the performance of population- and person-dependent models.ResultsThere were 70 study participants (mean [range; SD] age, 11.9 [5-19; 3.5] years; 62 males [88.6%]; 1 Asian [1.4%], 5 Black [7.1%], 1 Native Hawaiian or Other Pacific Islander [1.4%], and 63 White [90.0%]; 5 Hispanic [7.5%] and 62 non-Hispanic [92.5%] among 67 individuals with ethnicity data). Nearly half of the population (32 individuals [45.7%]) was minimally verbal, and 30 individuals (42.8%) had an intellectual disability. Participant length of inpatient hospital stay ranged from 8 to 201 days, and the mean (SD) length was 37.28 (33.95) days. A total of 429 naturalistic observational coding sessions were recorded, totaling 497 hours, wherein 6665 aggressive behaviors were documented, including self-injury (3983 behaviors [59.8%]), emotion dysregulation (2063 behaviors [31.0%]), and aggression toward others (619 behaviors [9.3%]). Logistic regression was the best-performing overall classifier across all experiments; for example, it predicted aggressive behavior 3 minutes before onset with a mean AUROC of 0.80 (95% CI, 0.79-0.81).Conclusions and RelevanceThis study replicated and extended previous findings suggesting that machine learning analyses of preceding changes in peripheral physiology may be used to predict imminent aggressive behaviors before they occur in inpatient youths with autism. Further research will explore clinical implications and the potential for personalized interventions.

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Heart rate and skin conductance associations with physical aggression, psychopathy, antisocial personality disorder and conduct disorder: An updated meta-analysis

Cited by 43 publications

References 175 publications

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Neurobiological Responses towards Stimuli Depicting Aggressive Interactions in Delinquent Young Adults and Controls: No Relation to Reactive and Proactive Aggression

Wearable Biosensing to Predict Imminent Aggressive Behavior in Psychiatric Inpatient Youths With Autism

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