Mental Metrics Research Articles

Efficacy of a multimodal lifestyle intervention (The Lift Project) for improving the mental health of individuals with an affective mood disorder living in South Africa.

Affective disorders are becoming more pervasive worldwide, including in Southern Africa, where treating patients with these conditions is challenging due to social and financial constraints. A variety of non-pharmacological approaches including lifestyle medicine (e.g., exercise, nutrition, sleep) and positive psychology practices (e.g., gratitude, service), are effective for treating mental health (MH) conditions. Twenty-six individuals from South Africa with a diagnosed MH condition participated in a 10-week multimodal intervention incorporating a diverse range of non-pharmacological strategies for improving MH. MH metrics were assessed pre-and post-intervention, including general MH, vitality/energy (VIT), depression, anxiety, stress, and satisfaction with life. MH and VIT were also measured weekly. Improvements were observed in all mental metrics from pre-to post-intervention: MH (59%, p < 0.001, Cohen's D = 1.36), VIT (110%, p < 0.001, Cohen's D = 1.71), depression (-46%, p < 0.001, Cohen's D = -1.06), anxiety (-48%, p < 0.001, Cohen's D = -1.21), stress (-36%, p < 0.001, Cohen's D = -1.08) and life satisfaction (23%, p < 0.001, Cohen's D = 0.66). Significant improvements in MH and VIT were observed after only 1 week of the intervention and progressively increased until the seventh week, after which further improvements were not statistically significant. The findings of this cohort study indicate that a multimodal intervention that incorporates lifestyle and positive psychology practices may benefit individuals living with an affective disorder. Non-pharmacological, multimodal interventions might offer a stigma-free way of providing MH promotion and treatment at a population level.

Patient-Reported Outcomes of Quality of Life, Functioning, and GI/Psychiatric Symptom Severity in Patients with Inflammatory Bowel Disease (IBD).

Patients with inflammatory bowel disease (IBD) are at risk for psychiatric disorders that impact symptom experience and health-related quality of life (HRQOL). Therefore, comprehensive biopsychosocial assessments should be considered in ambulatory care settings. Patient-Reported Outcomes Measurement Information System (PROMIS) measures created by the National Institutes of Health have shown construct validity in a large IBD internet-based cohort, but their validity in ambulatory settings has not been examined. We sought to validate PROMIS patient-reported measures of HRQOL, functioning, and psychiatric symptom severity at a tertiary IBD clinic. Adult patients (n = 110) completed the PROMIS Global Health scale, PROMIS-29, SF-12, and WHODAS 2.0. Pearson's correlation coefficients (r) determined the relationships between scores to validate the PROMIS Global Health Physical and Mental metrics, compared with the SF-12 and WHODAS 2.0. We compared these measures by disease subtype of Crohn's disease or ulcerative colitis. PROMIS measures were highly correlated (r range = 0.64-0.82) with standard measures of HRQOL and functioning. On the PROMIS Global Health measures, 20.9% had impaired physical health, and 13.7% had impaired mental health. Impairments were reported in pain interference (20% of patients), anxiety (18.2%), satisfaction with social role (15.5%), physical functioning (10.9%), fatigue (10%), depression (7.3%), and sleep disturbance (5.5%). Patients with Crohn's disease had worse scores than those with ulcerative colitis on measures of the global physical health (P = 0.027), physical functioning (P = 0.047), and pain interference (P = 0.0009). PROMIS instruments provide valid assessment of HRQOL and functioning in ambulatory adults with IBD. Of note, patients with Crohn's disease demonstrated significantly worse impairments than those with ulcerative colitis.

Show Horse Welfare: Horse Show Competitors' Understanding, Awareness, and Perceptions of Equine Welfare

The purpose of this study was to gain a better understanding of stock-type horse show competitors' understanding of welfare and level of concern for stock-type show horses' welfare. Data were collected through an online questionnaire that included questions relating to (a) interest and general understanding of horse welfare, (b) welfare concerns of the horse show industry and specifically the stock-type horse show industry, (c) decision-making influences, and (d) level of empathic characteristics. The majority of respondents indicated they agree or strongly agree that physical metrics should be a factor when assessing horse welfare, while fewer agreed that behavioral and mental metrics should be a factor. Respondent empathy levels were moderate to high and were positively correlated with the belief that mental and behavioral metrics should be a factor in assessing horse welfare. Respondents indicated the inhumane practices that most often occur at stock-type shows include excessive jerking on reins, excessive spurring, and induced excessive unnatural movement. Additionally, respondents indicated association rules, hired trainers, and hired riding instructors are the most influential regarding the decisions they make related to their horses' care and treatment.

Classification and Regression of Learner’s Scores in Logic Environment

This paper presents the possibility of classifying and regressing learner’s scores according to different cognitive tasks which are grouped with difficulty level, type and category. This environment is namely, Logic environment. It is mainly divided into three main categories: memory, concentration and reasoning. To classify and regress learner’s scores according to the category and the type of cognitive task acquired, we trained and tested different machine learning algorithms such as linear regression, support vector machines, random forests and gradient boosting. Primary results shows that a random forest algorithm is the most suitable model for classifying and regressing the learners’ scores in cognitive tasks, where the features most important for the model are, in descending order: the task difficulty and the task category in the case of regression, the task difficulty, the time taken by the participant before completing it, and his electroencephalogram mental metrics in the case of classification.