Statistical Analysis
HL
Demographics were analyzed using descriptive statistics. The 2-sided unpaired HL scores were calculated, and parents’ HL category was considered “high” when scores equaled 80% or more, “fair” when scores equaled 60% to less than 80%, and “low” when scores were less than 60% for the total scale and for all domains [The physician’s assessment of the child’s health was used as the standard. Parents’ health assessments that matched the physician’s assessment were considered accurate. The chi-square test was used to determine the differences between the proportions of parent-physician congruence of child health assessments between the app group and the MCHH group. The McNemar test was used to determine the differences in proportions of parent-physician congruence of child health assessments at baseline and visit 2 within groups.For convenience of use, ratings were first grouped into “very easy to easy” and “difficult to very difficult,” and then proportions were calculated. The Pearson chi-square or Fisher exact test was used to determine the differences between the proportions of convenience of use between both groups.We considered possible differences between subgroups and conducted a post hoc subgroup analysis in parents who were male, single, and had more than 1 child. The rationale is that males may be more willing to adopt mHealth interventions, single parents who may have less support could have more incentive to use apps, and parents could have been previously exposed to the app and MCHH if the child included was not the first child. For the subgroup analysis, we used the Bonferroni correction to adjust the α for significance, given 4 subgroups regarding parental status (parents of the 1st child, parents have more than 1 child, single parents, and parents who are men), with 2 groups (intervention vs control). An adjusted α is considered statistically significant when the 