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Schrempf MC, Petzold J, Petersen MA, Arndt TT, Schiele S, Vachon H, Vlasenko D, Wolf S, Anthuber M, Müller G, Sommer F. A randomised pilot trial of virtual reality-based relaxation for enhancement of perioperative well-being, mood and quality of life. Sci Rep 2022; 12:12067. [PMID: 35835944 PMCID: PMC9282619 DOI: 10.1038/s41598-022-16270-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/07/2022] [Indexed: 11/12/2022] Open
Abstract
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.
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Affiliation(s)
- Matthias C Schrempf
- Department of General, Visceral and Transplantation Surgery, University Hospital Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany.
| | - Julian Petzold
- Department of General, Visceral and Transplantation Surgery, University Hospital Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Morten Aa Petersen
- Palliative Care Research Unit, Department of Palliative Medicine GP, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Tim Tobias Arndt
- Department of Computational Statistics and Data Analysis, Institute of Mathematics, University of Augsburg, Augsburg, Germany
| | - Stefan Schiele
- Department of Computational Statistics and Data Analysis, Institute of Mathematics, University of Augsburg, Augsburg, Germany
| | - Hugo Vachon
- Quality of Life Department, European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Dmytro Vlasenko
- Department of General, Visceral and Transplantation Surgery, University Hospital Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Sebastian Wolf
- Department of General, Visceral and Transplantation Surgery, University Hospital Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Matthias Anthuber
- Department of General, Visceral and Transplantation Surgery, University Hospital Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Gernot Müller
- Department of Computational Statistics and Data Analysis, Institute of Mathematics, University of Augsburg, Augsburg, Germany
| | - Florian Sommer
- Department of General, Visceral and Transplantation Surgery, University Hospital Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
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Josephs CA, Shaffer VO, Kucera WB. Impact of Mental Health on General Surgery Patients and Strategies to Improve Outcomes. Am Surg 2022:31348221109469. [PMID: 35730505 DOI: 10.1177/00031348221109469] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
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.
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Affiliation(s)
- Cooper A Josephs
- 364432Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - Virginia O Shaffer
- Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA
| | - Walter B Kucera
- Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA
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Diraco G, Siciliano P, Leone A. Behavioral Change Prediction from Physiological Signals Using Deep Learned Features. SENSORS (BASEL, SWITZERLAND) 2022; 22:3468. [PMID: 35591158 PMCID: PMC9105250 DOI: 10.3390/s22093468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 06/15/2023]
Abstract
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.
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