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Comparison of a newly established emotional stimulus approach to a classical assessment-driven approach in BLS training: a randomised controlled trial.

ABSTRACT: OBJECTIVE:The study objective was to implement two strategies (short emotional stimulus vs announced practical assessment) in the teaching of resuscitation skills in order to evaluate whether one led to superior outcomes. SETTING:This study is an educational intervention provided in one German academic university hospital. PARTICIPANTS:First-yearmedical students (n=271) during the first3 weeks of their studies. INTERVENTIONS:Participants were randomly assigned to one of two groups following a sequence of random numbers: the emotional stimulus group (EG) and the assessment group (AG). In the EG, the intervention included watching an emotionally stimulating video prior to the Basic Life Support (BLS) course. In the AG, a practical assessment of the BLS algorithm was announced and tested within a 2 min simulated cardiac arrest scenario. After the baseline testing, a standardised BLS course was provided. Evaluation points were defined 1 week and 6 months after. PRIMARY OUTCOME MEASURES:Compression depth (CD) and compression rate (CR) were recorded as the primary endpoints for BLS quality. RESULTS:Within the study, 137 participants were allocated to the EG and 134 to the AG. 104 participants from EG and 120 from AG were analysed1 week after the intervention, where they reached comparable chest-compression performance without significant differences (CR P=0.49; CD P=0.28). The chest-compression performance improved significantly for the EG (P<0.01) and the AG (P<0.01) while adhering to the current resuscitation guidelines criteria for CD and CR. CONCLUSIONS:There was no statistical difference between both groups' practical chest-compression-performance. Nevertheless, the 2 min video sequence used in the EG with its low production effort and costs, compared with the expensive assessment approach, provides broad opportunities for applicability in BLS training.

SUBMITTER: Kuckuck K

PROVIDER: S-EPMC5855479 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Comparison of a newly established emotional stimulus approach to a classical assessment-driven approach in BLS training: a randomised controlled trial.

Kuckuck Karl K Schröder Hanna H Rossaint Rolf R Stieger Lina L Beckers Stefan K SK Sopka Sasa S

BMJ open 20180222 2

<h4>Objective</h4>The study objective was to implement two strategies (short emotional stimulus vs announced practical assessment) in the teaching of resuscitation skills in order to evaluate whether one led to superior outcomes.<h4>Setting</h4>This study is an educational intervention provided in one German academic university hospital.<h4>Participants</h4>First-yearmedical students (n=271) during the first3 weeks of their studies.<h4>Interventions</h4>Participants were randomly assigned to one ...[more]

PMID: 29472255

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Project description:BackgroundMental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues.ObjectiveThis study aims to present a machine learning-based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations.MethodsPassively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient.ResultsProbabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data.ConclusionsThese findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.

Dataset Information

Comparison of a newly established emotional stimulus approach to a classical assessment-driven approach in BLS training: a randomised controlled trial.

Publications

Comparison of a newly established emotional stimulus approach to a classical assessment-driven approach in BLS training: a randomised controlled trial.

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