Major controllable behavioral risks factors such as unhealthy eating, physical inactivity, smoking, alcohol use and other emerging risk factors such as poor sleep, stress, electronic gadget use, increase sedentary time attribute to a total of 87% of deaths from noncommunicable diseases. Screening these risky lifestyle practices and being able to measure them in a quantifiable risk score allows healthcare providers to facilitate confrontation, counselling, and lifestyle education.
The main purpose of this study is to determine an optimum score to quantify healthy lifestyle risk. Healthy lifestyle tools have been available, however, there has not been a standard tool that classifies lifestyle risk that may assist in preventing NCDs. Despite the enormous challenge, this study aspires to fill in that gap by using the Healthy Lifestyle Screening Tool (HLST) as an instrument that is able to screen risky lifestyle practices and classify them into particular risk categories. This study used the Receiver Operating Curve to determine the ability of HLST to discriminate among non-diagnosed and diagnosed groups by looking into the HLST scores.
A quantitative method was adopted and convenience sampling was administered to collect data from 311 subjects from the United States of America who are 18-65 years old and consented to participate in the study. The subjects were divided into two groups, the healthy group which are not diagnosed with any diseases and three groups diagnosed with diabetes, hypertension and hyperlipidemia.
Results revealed that the HLST was able to discriminate among the positive and negative groups with an Area Under the Curve ranging from .878 to .932 among groups and determined a cut-off score of 114 between the low risk and the moderate risk group. The cut-off score of 114 provides a 55% Sn and 90% Sp in determining risk for DM, a 55.4% Sn and 96.9% Sp in determining risk for HTN, and a 55.4% Sn and 96.4% Sp in determining risk for HLP.
The HLST may not be able to replace gold standard tools for identifying addictions, however, it may be confidently used in assessing lifestyle behaviors that may lead to or predict alcohol and smoking addiction as components and item questions of HLST are closely associated to the increase or decline of AUDIT and FTCD scores. The score of 114 was determined as cut-off between the low risk and moderate risk categories and a cut-off score of 92 was determined for the moderate risk and high risk categories for addiction. The nutrition, exercise and temperance components show significant predictability of alcohol and smoking addictions. The cut-off scores determined from this study may potentially be able to discriminate among those who are low risk, moderate risk and high risk for addictions through assessing the HLST scores.
Utilizing this cut-off score to classify risky lifestyles is essential and indispensable in this era of growing prevalence of NCDs and addictions, identifying the risk classification of the individual will allow individuals' to be aware of the risks of their lifestyle. The HLST can be utilized in Screening, Brief Intervention and Referral to Treatment programs which has been proven as an effective way in combatting addictions and may manifest the same effectiveness in combatting NCDs.