Table 2.

Analysis plan: objectives, outcomes, hypotheses, and methods of analysis

Objective	Hypothesis	Outcome measure (type of outcome: B = binary or C = continuous)	Methods of analysis
1. Primary	An experimental combined Nutrition + Exercise intervention will increase the percentage of pregnant women who achieve GWG within current recommendations when compared with standard care provided in the primary care community setting	Proportion of women who are within the BMI appropriate GWG according to the IOM guideline for GWGs (B)	Logistic regression
2. Secondary	An experimental combined Nutrition + Exercise intervention will lead to better maternal and child bone health outcomes when compared to standard care	• Maternal and cord blood circulating bone markers (C)   ○ Bone biomarkers: PINP, CTX-I, IGF-1, 25(OH)D, 1,25(OH)2D  • Maternal bone status at 6 months postpartum (C)   ○ Whole body bone mineral content, whole body BMD, lumbar spine bone mineral density by DXA scan  • Maternal fat mass (C)  • Maternal blood glucose, lipid profile, leptin, and adiponectin  • Maternal blood pressure (C)   ○ Diastolic BP   ○ Systolic BP  • Maternal pregnancy outcomes (B)   ○ Gestational diabetes   ○ Pre-eclampsia  • Infant bone status at 6 months of age (C)   ○ Whole body minus the head bone mineral content by DXA scan  • Infant outcomes   ○ Birth weight z-score(C)   ○ Body fat mass (B)	Regression analysis *We will use logistic regression for binary outcomes and linear regression for continuous outcomes
3. Subgroup analyses	The percentage of women within each of the normal, overweight, and obese BMI categories will be similar with respect to being with the IOM target GWG for each category	Proportion of women in each BMI category who reach appropriate GWG according to the IOM guideline for GWGs	Regression analysis including the interaction term of BMI group X Intervention group
4. Sensitivity analyses	Combined Nutrition + Exercise Intervention leads to a greater percentage of women who achieve GWG within current recommendations when compared to standard care	Primary outcome only	• Generalized estimating equations  • Random-effects model

IMPORTANT REMARKS:

In all analyses, results will be expressed as difference or OR (95% CI) and associated p values, as appropriate

Bonferroni method will be used to adjust the overall level of significance for multiple secondary outcomes

We will examine residuals to assess model assumptions

The GEE [76] is a technique that allows to specify the correlation structure between patients within a site and this approach produces unbiased estimates under the assumption that missing observations will be missing at random. An amended approach of weighted GEE will be employed if missingness is found not to be at random [77]

*Infant growth outcomes at 6 months will be adjusted for feeding type (duration of breast feeding from birth to 6 months)