Working out, at/under calories and still gaining weight

You started exercising. That alone can easily explain the increased weight on the scale: extra water retention for muscle repair.
You need to give it more time. And track other measurements. The fact that your clothes are fitting better is a clear indicator that things are moving in the right direction.

Please don't look at your individual weigh-ins as a fully reliable reflection of what happened the day before. Your weight fluctuates from day to day because of water weight fluctations and fluctuations in food waste in your system. Those fluctuations can sometimes be linked to the day before, but only for things like high sodium meals, a hard workout, etc., they're not a reflection of the calorie deficit you achieved. Fat loss is gradual and is ascertained by looking at the long term trend of your weight, not short term changes. You need at least one month (or menstrual cycle for menstruating women) to evaluate your weight trend.

Also: don't undereat. Your goal is a goal to reach, not to stay significantly under. Undereating stresses the body and can cause water retention, not to mention the health risks involved.

WholeFoods4Lyfe wrote: »

What is your calorie goal? How did you determine that amount? Why are you eating under? And how far under? Are you tracking your calories burned during exercise? How? Do you use something like a Garmin or a SmartWatch or are you using an online calculator? There are so many things to factor in here. And as for your actual weigh-ins, the overall trend is what is most important. Weigh yourself less often, maybe only once per week, and then follow the trend instead of the actual numbers. As long as over time your weight is going down, that is all that matters.

Not specific to weight particularly, but applies to weight: From purely a technical data analysis standpoint, more data points will establish a trend more reliably and possibly more quickly ** than fewer data points, generally speaking. In that sense, there's a data analytics argument for daily weighing.

(As illustration, think about an extreme case where someone weighs only once a month, happens to catch an outlier low day at the start, and outlier high day at the end, when weight has fluctuated through a much wider range - maybe fat loss was happening at a reasonable rate, but day 1 was dehydrated after a low-residue intake day, day N was with some water retention from sodium/exercise/head cold/hormones plus fiber-rich eating the day before. Those two data points describe a trend, in the most simplistic way. That trend says "gaining" when - with the outliers I describe - the reality may be "losing". Weighing on even one or two days in between would be likely to make that trend line less misleading, and the more consistent-conditions weigh-ins in between, the higher odds - though no guarantee - that the trend line is realistic. )

There's not much point IMO in weighing (for recording purpose) much more often than daily. The "first thing in the morning, same state of dress, after bathroom, before eating/drinking" routine (or something similar) will provide the most consistent possible conditions for gathering a data point for trending purposes. Other data points within the day have more confounding factors, like timing of eating/drinking/sweating, etc.

** I get that this is particularly noisy data (in terms of data variability caused by factors we don't really care about, when what we care about most is fat mass changes). In that sense, frequent data observations might seem to have potential to mislead. In the case of weight data particularly, that characteristic of the data will limit the "more quickly" part of my statement above, but not IMO wipe it out entirely, for many people. For some women, who see a new low weight only once per menstrual cycle, it could take more than a month to get a realistic trend. In that scenario, the daily weights at least have the potential to clue that woman in to the fact that she's one of those women who only see a new low once a month, useful info in itself.

Just my opinion, though . . . admittedly, with *a little* data analysis experience behind it.