Can Pedometric Data (Daily Walks) predict Weight Loss)?

jenkins_macedo

Just  using JMP Pro 12.01 in my Own Life

I want to predict whether these predictor variables (X), # steps, #kCal, Days In Action, Walk Frequency, Distance (mile), Time In Action (minutes), Speed (mph), Week Days, and Hours of Sleep can predict weight lost (kg) (Y Response).

I used SAS JMP Pro 12 and conducted a simple Standard Least Squares Regression. There are multiple of models that can be build in JMP Model Fits Platform including advance Partition using decision trees, boostrap Forest, Boosted Forest and Neural Networks. But just to show how you can use the correlation matrices of your data quickly to develop a model in SAS JMP Pro 12.

From the model output attached from JMP Pro Standard Least Square Regression Model you can see that the Summary of Fit shows that the predictors (X) variables are predicting weight loss (kg) (Y) at R-Square 0.83 with an RMSE  of 0.88, which is pretty good. This shows that we are 83% sure that weight loss can be reduced if all of these factors are considered.

However, the ANOVA also shows that the variation explained by the R Square that is 0.83 is statistically significant at P-Value of <0.0001* with a F-Value of 18.84.

Lastly, because your model needs to be validated and tested, the training (which is the set if data on which the model was developed) are different from the sets of data the the model was validated and tested against and all show an R-Square value of (Training = 0.83, Validation = 0.80 and Test = 0.80), which further demonstrates that you can be more 80% sure that the data is 80% predicting the response effect.

This analysis is part of an on-going personal research using daily pedometric data to predict weight loss and BMI.

Data used in the personal study is currently on-going and would continue to be tracked for the next 5 months.

Standard Least Square Regression Model.jpg

David_Burnham

Thanks for sharing your model.  I suspect that you have fallen into a common trap: JMP makes it easy for us to build models and generate various statistics.  The trap is that we focus on the statistics and disengage our scientific thinking.  You point out the high R-square statistic, but does it make sense?  How is the model achieving this?  Your title question was whether "pedometric data predicts weight loss".  From the science we know that exercise translates to calorie burn and net daily calories should correlate to weight loss - but your model seems to ignore the pedometry data and relies only on 'days in action' to explain the variation in your response data. I would interpret the model as saying "the longer that you exercise the more weight you loose". My guess is that you are measuring cumulative weight loss (i.e. your weight each day), but if you want to understand the effect of your per-day pedometry data then you need to be looking at daily weight change.  I hope you will treat these as positive comments.  I think you've collected a lot of interesting data, and there should be a lot more useful information (including some interesting correlations between your x-variables!).

Dave