ABSTRACT
BACKGROUND: Work-related stress negatively impacts employee wellbeing. Stress-management interventions that reduce workplace stress can be challenging. Immersive technologies, such as virtual reality (VR), may provide an alternative. AIMS: This systematic review aimed to evaluate feasibility, acceptability, and effectiveness of immersive technologies to promote workplace wellbeing (PROSPERO 268460). METHODS: Databases MEDLINE, Web of Science, PsycINFO and Embase were searched until 22nd July 2021. Studies were included if they tested a workforce or were designed for a workplace. Effective Public Health Practice Project quality assessment tool (EPHPP) was used for quality ratings. RESULTS: There were 17 studies (N = 1270), published 2011-2021. Over half were conducted in Europe. Eight studies were controlled trials. Most studies involved brief, single sessions of immersive VR and provided evidence of feasibility, acceptability, and effectiveness when measuring wellbeing-related variables such as stress, relaxation, and restoration. VR environments included relaxation tasks such as meditation or breathing exercises, and nature-based stimuli, such as forests, beaches, and water. Studies tested office workers, healthcare professionals, social workers, teachers, and military personnel. EPHPP ratings were "strong" (N = 1), "moderate" (N = 13), and "weak" (N = 3). CONCLUSIONS: VR relaxation appears helpful for workplaces. However, limited longer-term data, controlled trials, and naturalistic studies mean conclusions must be drawn cautiously.
ABSTRACT
PURPOSE: Vulnerability to stress is linked to poor mental health. Stress management interventions for people with mental health conditions are numerous but they are difficult to implement and have limited effectiveness in this population. Virtual reality (VR) relaxation is an innovative intervention that aims to reduce stress. This review aimed to synthesize evidence of VR relaxation for people with mental health conditions (PROSPERO 269405). METHODS: Embase, Medline, PsycInfo, and Web of Science were searched until 17th September 2021. The review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The Effective Public Health Practice Project (EPHPP) tool assessed methodological quality of studies. RESULTS: Searching identified 4550 studies. Eighteen studies (N = 848) were included in the review. Studies were published between 2008 and 2021. Eleven were conducted in Europe. Thirteen studies were controlled trials. Participants were mostly working-age adult outpatients experiencing anxiety or stress-related conditions. Other conditions included eating disorders, depression, bipolar disorder, and psychosis. Five studies tested inpatients. All studies used a range of nature-based virtual environments, such as forests, islands, mountains, lakes, waterfalls, and most commonly, beaches to promote relaxation. Studies provided evidence of the feasibility, acceptability, and short-term effectiveness of VR relaxation to increase relaxation and reduce stress. EPHPP ratings were 'strong' (N = 11), 'moderate' (N = 4), and 'weak' (N = 3). CONCLUSIONS: VR relaxation has potential as a low-intensity intervention to promote relaxation and reduce stress for adults with mental health conditions, especially anxiety and stress-related problems. Further research is warranted on this promising intervention.
Subject(s)
Mental Disorders , Virtual Reality , Adult , Humans , Mental Health , Anxiety , PsychotherapyABSTRACT
PURPOSE: Therapeutic engagement is a key component of psychological interventions. Robot-assisted psychological interventions appear to have therapeutic benefits for service users that are challenging to engage. However, engagement with robots in robot-assisted psychological interventions is not well understood. The aim of this systematic review is to evaluate the quality of therapeutic engagement in robot-assisted psychological interventions (PROSPERO: 122437). METHODS: Scopus, Web of Science, PsycInfo and Medline were searched until 15 January 2021 for studies which quantitatively evaluated therapeutic engagement in robot-assisted psychological interventions. The Effective Public Health Practice Project (EPHPP) quality assessment tool was used to assess methodological dimensions of studies. RESULTS: 3647 studies were identified through database searching. Thirty studies (N = 1462), published between 2004 and 2020, and from 14 countries, were included. Robots were typically toy animals or humanoids and were used to provide support and improve wellbeing through social interaction. Studies primarily tested robots on older adults with dementia and children with autism and indicated positive therapeutic engagement. Twelve studies included a control group. EPHPP ratings were 'strong' (N = 1), 'moderate' (N = 10) and 'weak' (N = 19). CONCLUSIONS: Therapeutic engagement between service users and robots is generally positive. Methodological limitations of studies, such as small sample sizes, and lack of control groups and longitudinal data, mean that the field is in early stages of its development and conclusions should be drawn with caution. There are important practical and ethical implications for policymakers to consider, such as responsible clinical practice and how service users may understand the therapeutic relationship with robots.
Subject(s)
Robotics , Aged , Humans , Robotics/methodsABSTRACT
Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitting. To further assess the performance, the permutation-based framework was applied to high-dimensional mixed-type data from two independent breast cancer cohorts. Reproducibility and robustness of our approach was demonstrated by the concordance in relative feature importance between the cohorts, along with consistencies in clustering profiles. One of the identified clusters was shown to be prognostic for clinical outcome after standard-of-care adjuvant chemotherapy and outperformed current intrinsic molecular breast cancer classifications.
