Antenatal exercise (ANE) is one of physical activity recommended for antenatal women as the benefits outweigh the risks. Despite knowing the benefits of exercises, most antenatal women are less active during pregnancy. Addressing women's attitude and barriers to exercise are crucial in designing intervention, it should be facilitated by a theory. Hence, this study aims to investigate the effectiveness of antenatal exercise intervention using behavioral theory on exercise adherence among antenatal women. This study will be a cluster randomized controlled trial (CRCT) in six primary health clinics with total sample estimation is 322. A two arm CRCT will be applied, a control group will be received standard antenatal care only while intervention group will be received 12 weeks ANE intervention and standard antenatal care. The ANE intervention module based on behavioral theory is the main study instrument, questionnaires on exercise adherence, perceived benefits, perceived barriers, exercise self-efficacy and HRQOL will be used in this study. Repeated measures logistic regression analysis using generalized estimating equations (GEE) will be used to evaluate the odds ratios of dependent variables between the groups at three-time points. All statistical tests are based on two-tailed test and the level of significance, alpha (α) is set at 0.05. The expected findings of this study will be a significant difference on dependent variables in intervention than control group at two time point post intervention. It could also improve antenatal women's adherence towards ANE, hence promote active lifestyles and reduce maternal implications.

Purpose: Age, sex, and BMI can help to predict knee osteoarthritis (OA) patients from healthy individuals (HV). The metabolome, the comprehensive output of metabolic processes occurring within an individual, and the levels of individual metabolites can also be used to help with disease diagnosis. However, metabolite selection methods and modeling algorithms that best identify metabolites capable of predicting OA have not been well established. We sought to determine a method that was capable of effectively identifying metabolite signatures that were predictive of OA in demographically-stratified populations. Methods: Phosphatidylcholine (lysoPC) and lyso(PC) analogues from plasma of 152 OA patients undergoing total knee replacement and 194 HV (346 total individuals) were measured by metabolomics. Cohorts were stratified by age, sex and BMI. Analogue signatures were determined by generating univariate area under the receiver operator curve (UAUC) values from 1000 bootstrapped training and test sets. Metabolites with UAUC > 0.5 at the 2.5% quantile of the empirical distribution were selected as capable of predicting OA from HV within strata. Three multivariate classification algorithms were tested using each signature. The most consistent algorithm was determined by the minimum difference between training and test set AUC values, derived from 1000 resamplings. The effect of diabetes mellitus on signature elements was also determined by identifying metabolites that were significantly changed in diabetic patients and removing those signature elements from multivariate analyses.

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