Belinda Smith

a preoccupation with engaging with this content is that it truly taps into something deeply human, right?

That desire to improve ourselves and that its core, it speaks to motivation, aspiration, and that drive to be a better version of who we are.

And that's a very powerful emotional hook, right?

That's why this content resonates so widely.

But what is really interesting and specifically how this navigates on social media is that Fitspiration doesn't just encourage that self-improvement, right?

It's tied to that pursuit of the very specific, often very narrow ideal of what health and success should look like, especially as it taps into that aspirational lifestyle of fitness ingrained within your life.

So often it does encourage that extreme diet, extreme exercise measures to achieve such ideals, right, which can feel motivating.

And that's, you know, rooted in those appearance standards, but it doesn't truly reflect the picture of well-being.

Yeah, absolutely.

So these studies were actually experimental studies.

So it was participants being brought into a lab and they were exposed to these images, whether it was over a period of time or a specific number of images that they were engaging with and seeing.

And what makes this so interesting is that it paints a picture of the immediacy of

of exposure and engagement with this content.

And I think you mentioned that at the start, you know, we usually think that, you know, algorithms are feeding us this kind of content and we're generally just being exposed to this, which is very true.

Right.

But there is a really interesting

Yeah, that's a really important question.

And something that was really interesting about this paper is that it draws on data from multiple countries and the samples themselves, you know, incorporated both men, women and other key demographic factors.

But it's interesting that such factors are reported inconsistently.

And because of that, we didn't see clear differences across demographic groups in terms of responses, largely due to those gaps in the data.