Science

Measurement matters: Part 1 – Why we need to measure mental health and wellbeing scientifically

Rhian Male, MSc

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TABLE OF CONTENTS

The Unmind Index is a unique feature on our platform. It’s a self-report measure of mental health and wellbeing (MHWB) developed by the Science team at Unmind. This series digs into the power of measurement, the thinking behind the Unmind Index, and why it works.

In part one of this series, we’ll explain why it’s important to measure mental health and wellbeing properly, and the difference between a problem-based and positive approach.

In parts two and three, we’ll share the thinking behind the Index, how it’s designed to help you, and how we know it works.

Because if knowledge really is power, then measurement really matters. 

But how do we measure one of the most complex, elusive parts of being human? This series explores the mission behind the Unmind Index and the science behind measuring – and managing – our mental health.

The matter of our minds

Human minds are brilliantly vast and complex. Their processes and experiences include everything from our personality traits and abilities through to our emotions, such as anxiety and happiness.

For centuries, scientists, clinicians and philosophers have tried to find a way to measure these processes. But how do we measure something so important but invisible?

This is exactly what the field of psychometrics does – it builds and evaluates psychological measures in a scientific way.

Measuring mental health and wellbeing (MHWB) is an area of psychometrics and it tends to get the most attention. It’s a broad topic that covers everything from anxiety and depression on one side of the spectrum, to satisfaction and personal growth on the other.

There are many times that it’s valuable to measure MHWB – whether that’s in a clinical setting or a research study or on digital health apps. (And we’ll talk more about that later.)

How can science measure something we can't see?

Scientifically speaking, the underlying thing we’re interested in measuring is called a ‘construct’. Put simply, that is someone’s state of MHWB – like being calm or happy.

But because we can’t see these, we’ve got to ask that person about their experiences, and use this to infer their states of MHWB. 

We assume they’re related. For example, it seems fair to assume that people with high levels of calmness are less likely to report feeling on edge, or spend time worrying. 

Doing this scientifically involves using a measure (also known as a survey, scale or questionnaire).

A measure is a series of questions or statements, which ask people to reflect on various aspects (like their behaviours, thoughts and feelings) that are related to one or more underlying constructs that you’re trying to measure. 

For instance: “In the past two weeks I have felt bright and cheerful in my mood.” 

Asking questions in this way is helpful as:

  • It gives a meaningful and accurate picture of someone’s MHWB: Simply asking, ‘Are you depressed?’ isn’t a good approach, because it relies on that person fully understanding the term. 
  • It’s time-specific: The questions refer to a particular period of time, like ‘over the past week’ or ‘in general’ and include a scale of options for you to choose from (e.g. ‘no days’ through to ‘nearly every day’). 
  • You can calculate a score: Measures often ask people to respond using numbers on a scale, which means a total score for the measure or its constructs can be calculated. 

There are lots of measures of MHWB, how do you find the right one?


More than 100 measures of MHWB now exist. Part of the puzzle for choosing a measure to use is thinking about what you’re looking for, and which approach will give you the best answers.

Some measures use a problem-based approach.

Some of the most well-known measures take a problem-based approach, meaning they focus on symptoms and how these negatively interfere with a person’s life.

Two very common ones are the GAD-7 and PHQ-9, which measure generalised anxiety and low mood, respectively.

Clinicians use these in some therapeutic services, to help diagnose specific mental health conditions or describe problems. 

They’re suited to this because:

  • They’re standardised measures, so we know what a population’s average scores (aka the norms’) look like. When someone completes a measure, you can compare their score to the average, to see if they’re experiencing symptoms in a way that suggests a particular mental health problem (and whether they have mild, moderate or severe symptoms).
  • This helps identify when someone may benefit from support or therapy.
  • Completing the same measures over time (for example, during therapy), is a useful way to monitor mental health and see patterns. In other words, you can see whether therapy is working.

We also use them to help evaluate the Unmind platform.

Here at Unmind, we're passionate about improving the lives of our users, and that means rigorously evaluating our platform, making sure it’s backed by evidence. 

One way we do this is through efficacy studies (see part one and two of our last blog series Research at Unmind: a 3-part introduction).

In short, we compare the change in scores from problem-based measures over time – for participants asked to use specific Unmind content, versus participants in a control group (who don’t have access to our platform).

This means we can see the true effect of Unmind on areas of mental health, such as levels of anxiety. 

We can also use measures to identify people who would find particular studies helpful, like when testing content designed for people who are experiencing symptoms of low mood.

To help everyone, we need approaches that focus on the positive aspects of mental wellbeing

Everybody has mental health. 

This means we also need to consider people who are not experiencing symptoms of a mental health problem, rather than having a ‘best’ score that only indicates the absence of a problem.

That’s why there are also measures that focus on these positive aspects of mental wellbeing, rather than only on difficult feelings, thoughts, behaviours or other symptoms. They’re intended for use by the general population, and tend to focus on aspects of life satisfaction, experience of positive emotions, and fulfilment. 

A good example is the first three questions of the ONS4, which focus on a person’s satisfaction with life, meaning and purpose, as well as their happiness. (e.g. “Overall, how satisfied are you with your life nowadays?”)

This is used by the Office for National Statistics to understand local and national trends, and how quality of life changes in relation to changes in circumstances, policies, and wider events in society. 

Why we created our own measure of MHWB (and how we know it works)

Since there were no existing measures of MHWB that fully met the needs of all our users, we decided to build our own: the Unmind Index. 

At Unmind, we believe if something’s worth measuring, it’s worth measuring properly. This is why we, the science team, went through a rigorous process of developing and validating the Unmind Index. 

We worked with experienced clinical psychologists and academic experts to ensure it was a robust, valid, and reliable measure, and published our work in a peer reviewed journal.

If you want to know more about the Unmind Index and how it works, it’s time you check out Part Two.

Find out more about Unmind

To learn more about nurturing the mental health of your organisation, click here to book a chat with one of our specialists.