Awareness of the importance of maintaining physical health for patients with severe mental illnesses has recently been on the increase. Although there are several elements contributing to poor physical health among these patients as compared with the general population, risk factors for cardiovascular disease such as smoking, diabetes mellitus, hypertension, dyslipidemia, metabolic syndrome, and obesity are of particular significance due to their relationship with mortality and morbidity. These patients present higher vulnerability to cardiovascular risk factors based on several issues, such as genetic predisposition to certain pathologies, poor eating habits and sedentary lifestyles, high proportions of smokers and drug abusers, less access to regular health care services, and potential adverse events during pharmacological treatment. Nevertheless, there is ample scientific evidence supporting the benefits of lifestyle interventions based on diet and exercise designed to minimize and reduce the negative impact of these risk factors on the physical health of patients with severe mental illnesses.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189180PMC
http://dx.doi.org/10.1186/1744-859X-10-22DOI Listing

Publication Analysis

Top Keywords

physical health
16
patients severe
12
severe mental
12
health patients
12
risk factors
12
lifestyle interventions
8
mental illnesses
8
health
5
patients
5
efficacy lifestyle
4

Similar Publications

Aim: To investigate the detection and initial management of first psychotic episodes, as well as established schizophrenia, within the primary care of the Andalusian Health System.

Background: Delay in detecting and treating psychosis is associated with slower recovery, higher relapse risk, and poorer long-term outcomes. Often, psychotic episodes go unnoticed for years before a diagnosis is established.

View Article and Find Full Text PDF

This study examined internal, external training loads, internal:external ratios, and aerobic adaptations for acute and short-term chronic repeated-sprint training (RST) with blood flow restriction (BFR). Using randomised crossover (Experiment A) and between-subject (Experiment B) designs, 15 and 24 semi-professional Australian footballers completed two and nine RST sessions, respectively. Sessions comprised three sets of 5-7 × 5-second sprints and 25 seconds recovery, with continuous BFR (45% arterial occlusion pressure) or without (Non-BFR).

View Article and Find Full Text PDF

This meta-review provides the first meta-analytic evidence from published meta-analyses examining the effectiveness of acute exercise interventions on cognitive function. A multilevel meta-analysis with a random-effects model and tests of moderators were performed in R. Thirty systematic reviews with meta-analyses (383 unique studies with 18,347 participants) were identified.

View Article and Find Full Text PDF

Growth trajectory of Yiling sheep and its related genetic parameters.

Trop Anim Health Prod

January 2025

Laboratory of Small Ruminant Genetics, Breeding and Reproduction, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.

Growth traits are one of the focuses of sheep breeding, and growth curve is an effective method to describe growth traits. The body weight of Yiling sheep at 0, 120, 180, 360 and 540 days of age were fitted with five common nonlinear growth models: Logistic, Gompertz, Von Bertalanffy, Brody and Negative exponential, and the growth models were evaluated by goodness of fit standard. The results showed that the Von Bertalanffy model was suitable for characterizing the growth of Yiling sheep.

View Article and Find Full Text PDF

Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.

Trop Anim Health Prod

January 2025

Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.

Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!