Wellbeing or Just Not Depressed? A Deep Dive into Mental Health Scales

Hey there! Let’s chat about something pretty important: how we measure how we’re *really* doing inside. You know, our mental health. We’ve got scales for feeling down, anxious, all that stuff we call psychological distress or mental illness symptoms. And then, we’ve got scales for feeling good, thriving, what we call positive mental wellbeing. Seems straightforward, right? Like two different sides of a coin, or maybe even two separate coins?

The Big Question: Are They Really Different?

Here’s the thing. For a long time, we’ve kind of assumed that measuring symptoms of depression or anxiety is totally distinct from measuring how much positive wellbeing someone has. It makes intuitive sense, doesn’t it? Not being sick isn’t the same as being super healthy and happy. But what if the tools we use to measure these things aren’t quite as distinct as we think?

I recently came across a fascinating study that dug into this very question. They looked at two popular scales: the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), designed to measure positive mental wellbeing, and the Patient Health Questionnaire (PHQ-9), a widely used tool for screening depression symptoms. The big goal? To see if the WEMWBS truly measures something *different* – something called discriminant validity in research speak – compared to the PHQ-9, especially in folks who are already seeking help for mild to moderate anxiety or depression.

Why Does This Matter Anyway?

You might be thinking, “So what if they overlap a bit?” Well, it matters quite a lot! In psychology and mental health research, we’re constantly developing new ways to measure things. If a new scale largely measures the same stuff as an old one, it can lead to a bunch of problems:

• Redundancy: We end up with super long questionnaires asking the same things in slightly different ways. Annoying for everyone!
• Misleading Conclusions: If two scales overlap heavily, saying one “impacts” the other might just be saying A impacts A, which isn’t very insightful.
• Confusing Theory: If we think we’re measuring separate concepts (wellbeing and illness) but our tools can’t tell them apart, it makes it hard to build solid theories about how mental health actually works.

So, checking if a new scale measures something truly unique is a pretty big deal.

Meet the Scales (and the Study Participants)

The WEMWBS is designed to capture positive aspects of mental health – feeling optimistic, useful, interested, etc. It’s been used all over the place to see how people are doing and if interventions help boost wellbeing. The idea is that it measures more than just *not* being ill; it’s supposed to be on the positive end of the spectrum.

The PHQ-9, on the other hand, is all about symptoms of depression – feeling down, losing interest, trouble sleeping, etc. It’s a quick way to screen for how severe depressive symptoms might be.

This particular study looked at data from 1690 participants in a Norwegian mental health service called ‘Prompt Mental Health Care’ (PMHC). These were adults seeking help for mild to moderate anxiety and/or depression. Most of the sample were women (about three out of four), and most were between 21 and 50 years old.

To figure out if these scales were measuring distinct things, the researchers used some fancy statistical techniques, including structural equation modeling and latent variable modeling, like bifactor analysis. Think of bifactor analysis as a way to mathematically separate what’s *shared* between two sets of items (like a general mental health factor) from what’s *unique* to each set (like a specific wellbeing factor or a specific depression factor). Pretty neat, right?

What Did They Find? (Spoiler Alert: It’s Complicated!)

Okay, let’s get to the juicy part – the results. The findings were quite striking and, honestly, a bit eye-opening:

High Correlation is the First Clue

First off, they looked at how the scores on the WEMWBS and PHQ-9 related to each other. As you’d expect, they were negatively correlated – meaning if you scored high on wellbeing (WEMWBS), you tended to score low on depression (PHQ-9), and vice versa. But the correlations were *strong*, really strong. When they looked at the underlying concepts (latent variables) rather than just the raw scores, the correlation between the full WEMWBS (14 items) and the PHQ-9 was close to -0.80. Now, a correlation of -0.80 is pretty darn high, suggesting a lot of shared ground.

Essentially Unidimensional? Say What?!

This is where the bifactor modeling comes in. When they threw all the items from the PHQ-9 and either version of the WEMWBS (the 7-item or 14-item version) into the model, the results suggested that the combined set of items was “essentially unidimensional.” What does that mean in plain English? It means that even though there are items about feeling good (WEMWBS) and items about feeling bad (PHQ-9), they largely seem to be tapping into *one* main underlying concept when used together in this population. The statistical indices (like Omega Hierarchical and Explained Common Variance) strongly supported the idea that a single, general factor (which looked a lot like the PHQ-9 factor) explained most of the reliable variance. The specific WEMWBS factor, representing what’s *unique* to wellbeing beyond this general factor, explained much less.

Demographics Tell a Similar Story

They also looked at how scores on these scales related to demographic stuff like age, education, relationship status, etc. Guess what? The patterns were mirror images. Groups that scored higher on depression (PHQ-9) scored lower on wellbeing (WEMWBS) and vice versa. But here’s the kicker: when they used the bifactor model to look at the specific WEMWBS factor (the part supposedly unique to wellbeing), most of its associations with demographics faded away. It seems the shared factor (the one looking like depression severity) was driving most of those relationships.

So, What’s the Takeaway?

Based on these findings from a sample of patients seeking mental health care, the study suggests that the WEMWBS might lack discriminant validity compared to the PHQ-9. In simpler terms, in this context, the WEMWBS doesn’t seem to be measuring something truly distinct from the severity of depressive symptoms as captured by the PHQ-9.

This challenges a few ideas:

• The idea that WEMWBS is purely on the “positive” end of a continuum, unable to measure illness.
• The idea of a “dual continuum model” where wellbeing and illness are related but distinct dimensions.

Instead, the results lean towards a “bipolar conceptualization,” suggesting mental health might be better viewed as a single continuum ranging from poor mental health (high PHQ-9, low WEMWBS) to good mental health (low PHQ-9, high WEMWBS). Essentially, in this sample, scoring low on the WEMWBS seems to mean you have poor mental health, just like scoring high on the PHQ-9 does.

The study authors even bring up the concept of the jangle fallacy – the mistake of assuming two things are different just because they have different names. It seems the WEMWBS, in this population, might be measuring an existing concept (like mental illness severity) but just calling it “wellbeing” and scoring it in the opposite direction.

Limitations and Future Thoughts

Of course, like any good study, this one has its limits. It was based on a sample of patients with mild to moderate issues, not the general population. While another study in England found similar overlap in a general population sample, more research across different groups and cultures is definitely needed to see if these findings hold true everywhere.

Also, they only looked at a few demographic variables. Having more data on people’s backgrounds might reveal unique associations for wellbeing that weren’t captured here. And importantly, these findings are specific to the WEMWBS and PHQ-9. We can’t assume the same overlap exists between PHQ-9 and *other* wellbeing scales out there.

Looking ahead, it would be super interesting to apply these same advanced statistical methods to other wellbeing scales (like PANAS, Satisfaction with Life Scale, etc.) alongside depression measures. And maybe, just maybe, we can identify specific aspects of wellbeing that *are* truly distinct from the absence of illness. The study hinted that things related to social relationships might be one area worth exploring further.

Wrapping It Up

So, there you have it. This study gives us a lot to think about. While the WEMWBS is a popular scale for positive mental wellbeing, in a sample of patients with mild-to-moderate anxiety and depression, it seems to overlap significantly with a measure of depression symptoms (PHQ-9). This raises questions about whether it’s truly measuring a distinct concept or largely reflecting the opposite end of the mental illness spectrum. It’s a great reminder that we need to keep scrutinizing our tools to make sure they’re doing exactly what we think they are!

Source: Springer