Term papers writing service

Obesity as an unhealthy lifestyle and not a disease

Navigation menu

Published online 2016 Aug 19. Whatnall ,1 Clare E. Collins ,1 Robin Callister ,2 and Melinda J. Find articles by Megan C.

  • Trading in your chips for healthier snacks You can begin by cutting out the junk food and snacking on things such as unsalted nuts, which stabilise your blood sugar levels, and dairy products such as yogurts, which are high in protein;
  • Talk to your health care provider about how much weight gain is right for you during pregnancy;

Find articles by Clare E. Find articles by Melinda J. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution CC-BY license http: Abstract Unhealthy lifestyle behaviours are known modifiable risk factors for cardiovascular disease CVD.

This cross-sectional analysis aimed to describe lifestyle behaviours and CVD risk markers in young overweight and obese Australian women and explore associations between individual and combined lifestyle behaviours with CVD risk markers.

Lifestyle behaviours assessed were diet quality, alcohol intake, physical activity, sitting time and smoking status, and were combined to generate a Healthy Lifestyle Score HLS 0—5.

Analysis included 49 women aged 18—35 years, with BMI 25.

Section Navigation

All participants were non-smokers. The proportion of participants outside normal reference ranges was 83. Insufficient physical activity was the primary lifestyle factor associated with increased CVD risk markers, which suggests interventions targeting physical activity in young women may potentially improve cardiovascular health. Introduction Cardiovascular disease CVD is the leading cause of total and premature death in women globally [ 12 ].

Lifestyle disease

Lifestyle behaviours known to increase CVD risk include high alcohol consumption, poor diet quality, physical inactivity, extended sitting time and smoking [ 3456 ].

Several observational studies have demonstrated that higher CVD risk is conferred by a greater number of risky lifestyle behaviours [ 171819202122 ]. For example, a prospective cohort study of Swedish women aged 49—83 years investigated the association between a low risk lifestyle pattern healthy diet, moderate alcohol consumption, never smoking, being physically active, healthy body mass index BMI and stroke risk.

This relationship is supported by similar studies in middle and older-aged males and females [ 181920 ]. Over a median follow-up of 14. The high prevalence of risk behaviours in young adults currently supports this trend [ 6781011121314151624 ].

One study has specifically explored the direct association between lifestyle behaviours during young adulthood and CVD risk [ 25 ].

2. Change your lifestyle

These findings suggest that unhealthy lifestyle behaviours in young adulthood are having a direct impact on cardiovascular health, and that further exploration of such an association in this age group is needed. The current study investigated these relationships at baseline in young overweight and obese women 18—35 years enrolled in a weight-loss study.

The second aim was to examine associations between lifestyle behaviours and CVD risk markers.

  1. Fat cells may release hormones that affect cell growth, leading to cancer. High Blood Pressure What is high blood pressure?
  3. A smaller airway can make breathing difficult or loud because of snoring , or breathing may stop altogether for short periods of time. It is like alcoholic liver disease, but it isn't caused by alcohol and can occur in people who drink little or no alcohol.

The final aim was to explore associations between a total Healthy Lifestyle Score HLS that summarises overall diet quality, alcohol intake, physical activity, sitting time and smoking status, and CVD risk markers.

It was hypothesised that the study population would have unhealthy lifestyle behaviours and elevated CVD risk markers, and that unhealthy lifestyle behaviours and a lower HLS would be associated with higher CVD risk. Materials and Methods 2. Study Design This study was a cross-sectional analysis of baseline data from the Be Positive Be Healthe randomised controlled trial RCTwhich evaluated efficacy of a weight-loss program targeting young 18—35 years overweight and obese women.

Participants and Recruitment RCT inclusion criteria were: Participants were excluded if: Participants were recruited via media releases by the University of Newcastle and Hunter Medical Research Institute, posters at the university campus, local technical college, local businesses and organisations known to engage with the target group, and social media pages of these settings [ 26 ].

Email invitation was also sent to consenting participants of a previous research study. All participants gave written informed consent prior to participating in the study.

1. Introduction

The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the University of Newcastle Human Research Ethics Committee H-2014-0138. Measures An online questionnaire collected self-reported data on dietary and alcohol intakes, physical activity, sitting time and smoking status using Survey Monkey www. Participants attended the University of Newcastle campus for objective measurement of CVD risk markers.

The online questionnaires were completed within the two days prior to or during the baseline measurement session. Measurement of Lifestyle Behaviours Dietary intake was assessed using the validated Australian Eating Survey food frequency questionnaire AES FFQ [ 27 ], a self-administered 120-item semi-quantitative FFQ which asks respondents to report usual intake over the previous six months.

A higher score is indicative of greater dietary variety, more optimal nutrient intakes [ 28 ] and dietary patterns more aligned with the Australian Dietary Guidelines [ 29 ].

Participants were asked how many standard drinks they usually consumed on an occasion where they drank alcohol. Participants indicated the number of sessions per week and time minutes per session spent performing vigorous, moderate and mild intensity physical activity, based on the previous month. Participants were asked if they currently smoke tobacco products, and if so if they have smoked at least 100 cigarettes or a similar amount of tobacco in their life.

All five lifestyle behaviours diet quality, alcohol intake, physical activity, sitting time and smoking were included in defining the HLS. Similar to a number of previous studies [ 171819202122 ] participants were awarded one point for each healthy lifestyle behaviour, therefore the HLS ranged from zero to five points, with a higher score indicating a healthier lifestyle. Points were attributed based on meeting population-based guidelines if available [ 3334 ]. Therefore, participants were awarded one point for: Waist circumference was measured to 0.

Participants were seated for five minutes prior to the first blood pressure measurement, with two minutes rest between additional measures. Blood samples were obtained by finger prick and analysed using the validated Cardiochek reflectance spectrophotometer lipid measurement tool Point of Care Diagnostics Pty Ltd, Artarmon, NSW, Australia [ 3738 ].

All measurements for height, weight, waist circumference and blood pressure were taken twice for accuracy, with a third measurement also taken in cases where either of the first two values fell outside a predetermined acceptable range. Socio-Demographic Characteristics Socio-demographic data were collected including age, education, qualifications, income, ethnicity and postcode.

Statistical Analysis Data analysis was conducted using Stata statistical software version 14. Descriptive statistics were used to describe lifestyle behaviours, CVD risk markers and socio-demographic variables including standard deviations SD and means for continuous variables, and percentages for categorical variables. Each linear regression model was then repeated adjusted modelto also include the other lifestyle behaviours, due to potential confounding.

All linear regression models were controlled for age, ethnicity and socio-economic status. Socio-Demographic Status A total of 57 women were recruited, with 49 included in the analyses. The majority was currently studying 55. The greatest proportion of participants had obesity as an unhealthy lifestyle and not a disease a university or higher university degree 42. The majority were born in Australia 93.

Table 1 Baseline characteristics of young 18—35 years overweight and obese women BMI 25—34.