Virtual reality-based relaxation for enhancement of perioperative well-being and quality of life: protocol for a randomised pilot trial

A randomised pilot trial of virtual reality-based relaxation for enhancement of perioperative well-being, mood and quality of life

A cancer diagnosis and subsequent treatment can trigger distress, negatively impact coping resources, and affect well-being as well as quality of life. The aim of this pilot study was to investigate feasibility and clinical effects of a VR intervention on quality of life, well-being and mood in cancer patients undergoing surgery compared to a non-VR intervention and a control group. 54 patients with colorectal cancer or liver metastases from colorectal cancer undergoing elective curatively intended surgery were recruited and randomised to one of two intervention groups or a control group receiving standard treatment. Participants assigned to one of the intervention groups either received a VR-based intervention twice daily or listened to music twice daily. Adherence to the intervention was 64.6% in the music group and 81.6% in the VR group. The VR intervention significantly reduced heart rate (- 1.2 bpm; 95% CI - 2.24 to - 0.22; p = 0.02) and respiratory rate (- 0.7 brpm; 95% CI - 1.08 to - 0.25; p = 0.01). Self-reported overall mood improved in both groups (VR: + 0.79 pts; 95% CI 0.37-1.21; p = 0.001; music: + 0.59 pts; 95% CI 0.22-0.97; p = 0.004). There was no difference in quality of life between the three groups. Both interventions groups reported changes in feelings. Adherence rates favoured the VR intervention over the music group. Observed clinical outcomes showed stronger intragroup effects on mood, feelings, and vital signs in the VR group. The study demonstrated feasibility of a VR intervention in cancer patients undergoing surgery and should encourage further research investigating the potential of VR interventions to positively influence well-being and mood in cancer patients.

Impact of Mental Health on General Surgery Patients and Strategies to Improve Outcomes

Mental Health Disorders (MHD) are a growing concern nationwide. The significant impact MHD have on surgical outcomes has only recently started to be understood. This literature review investigated how mental health impacts the outcomes of general surgery patients and what can be done to make improvements. Patients with schizophrenia had the poorest surgical outcomes. Mental health disorders increased post-surgical pain, hospital length of stay, complications, readmissions, and mortality. Mental health disorders decreased wound healing and quality of care. Optimizing outcomes will be best accomplished through integrating more effective perioperative screening tools and interventions. Screenings tools can incorporate artificial intelligence, MHD data, resilience and its biomarkers, and patient mental health questionnaires. Interventions include cognitive behavioral therapy, virtual reality, spirituality, pharmacology, and resilience training.

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient's behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aimed to predict changes in one's behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a temporal convolutional network, and the behavioral state was predicted through bidirectional long short-term memory auto-encoder, operating jointly. From the comparison with the state of the art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.