Month: April 2020

Meet Joanne: Caring for youth with e-health technologies

Interviewed by Sara Loo

Joanne Beames is currently a post-doctoral researcher and clinical psychology registrar at the Black Dog Institute. She is passionate about seeing results come out of the work she does with school students. As one of our ECR cohort, we chatted to her about how new technologies can help youth mental health, and what she loves about psychology.

Dr Joanne Beames

You work at the Black Dog Institute. What is the focus there?

The Institute is the only medical research institute in Australia that is all about improving the mental health of people of all ages. 

We’re working in the early detection, prevention and treatment space for common mental health disorders, like depression and anxiety. There’s a big focus on bridging the gap between research and practice to increase the delivery of effective mental health strategies to the people that need them, when they need them. This is one of the main reasons why I like working at the Institute. I think that equitable access to evidence-based treatments that work is a really important cause to contribute to. 

Using e-health technology is one of the ways that we are trying to increase the availability of mental health care options for people in Australia, and worldwide. There are so many benefits and opportunities for using technology in mental health, like increased access and reduced cost, but we don’t really know much about how to do this in a way that maximises outcomes for the people that use them.

To address this gap, a lot of our work aims to improve the implementation of e-mental health services and programs in real-world contexts. In my work specifically, I look at how to best use e-health technology to prevent mental illness in young people.

What is “e-mental health technology”?

E-mental health technology just means any mental health service or program that is delivered using technology. The program could be delivered via a website, phone app, or a sensor-based monitoring device such as a smart watch.

I look at the school-based delivery of prevention programs to young people using smartphone apps. Because most young people have a phone, they are a great way to reach lots of young people at once, regardless of their location. They help to overcome other barriers of face-to-face therapy, like high cost, embarrassment, and stigma. They also help to increase engagement by building the sense of autonomy that young people have in their own mental health care – autonomy is really important for this age group.

Can you tell us a bit about your research?

I am evaluating whether, and how, a depression prevention app can be implemented at scale in Australian schools. We hope to reach at least 10,000 Year 8 students from a range of schools – public, private – and areas across Australia – rural, regional, and metropolitan. Schools are an ideal environment to implement prevention programs because they provide access to large numbers of young people, at the developmental phase when mental illness first emerges. School staff who have a mental health role, such as counsellors, well-being officers, and teachers, are responsible for supporting the delivery of the app to students.

Information from my research will help to maximise how much young people engage with and use the app and, in turn, preventative effects on depression. It will also inform how mental health prevention technologies can be disseminated at scale in schools, in a way that is sustainable over time.

How do you explore the implementation of the prevention app in schools?

I’m interested in the factors that help school staff to support the delivery of the app in their school. I’m equally interested in the factors that make it difficult for them to do so. I use surveys and interviews to explore how staff perceive the app, as well as their experience with supporting its delivery. Because schools are complex environments and staff are often time-poor, we expect that the delivery is going to vary across schools. This means that staff might provide different levels and types of support across different schools, which will affect student adherence and, in turn, their mental health outcomes.

You interact with a lot of young people, who your research focuses on. What do you enjoy about that?

I think that working with young people is really important in the mental health space. Given that many mental health problems first emerge during early adolescence, prevention and early intervention efforts at this time are critical for improving their trajectory.

I also think that young people can be extremely resilient, and their ability to cope in the face of multiple stressors and major life transitions is inspiring. For me, seeing how young people can respond to mental health strategies is a source of hope.

What drew you to study psychology? And why research in particular?

Psychology was a good fit for me because I was interested in why people feel and do the things that they do. I initially wanted to work as a clinical psychologist rather than a researcher, but during my postgraduate studies I quickly saw how important it was to be a scientist-practitioner. So now I do both! This means that I use gold-standard evidence to guide what I do with clients during psychological therapy, and also use insights from working with clients to guide the types of questions I explore in my research.

I was also drawn to research because there is still so much that we do not know about mental illness – the causes, the individual risk factors that contribute to onset and relapse, and the optimal treatments. I was drawn to the challenge of contributing to something that can have a meaningful impact on the quality of life of many people around the world.

What does a typical day look like for you?

I spend most of my day thinking critically and creatively. I read and write articles, analyse

data, and design new studies. This allows me to do deep dives into topics of interest, helping me to create new ideas and outputs to share with other people. Teamwork also forms a big part of what I do. Collaborating with others is crucial for producing innovative ideas that have real impact on people’s daily lives – the trope of a lonely scientist in a lab or stuck behind a computer screen is not always true!

What do you like to do in your spare time?

I’m all about crime, fiction and non-fiction, so I spend a lot of time reading, watching, and listening to it (but not doing it, obviously). My other hobbies are playing field hockey, baking, and camping. Having different ways to wind down and enjoy life outside of research is really important for increasing motivation and a positive outlook.

Follow Joanne on Twitter.

Reading between the lines: some simple maths in the time of COVID-19

By Sara Loo

You don’t need me to tell you that things are not as they used to be. Unless you have recently emerged from a 3-month long hike, you should be well aware of the current global atmosphere. Working from home, social distancing, lockdown. Words that have entered our everyday vernacular. There is a never-ending stream of news and updated case numbers, late night press conferences and a continually changing list of do’s and don’ts.

Staying informed is important. But in these anxious times, keeping up to date can be exhausting. I have seen more scientific terms in the news than ever, and have been sent articles from family and friends with words I thought were confined to the depths of my research, never to see the light of day.

To non-mathematicians, there is a long list of scientific and mathematical terms to be understood, and just So. Much. News. COVID-19 has taken over the news cycle. And, with it come phrases like enveloped viruses, antivirals, doubling time, mortality and morbidity, exponential growth.

Though I do not by any means claim to be an expert in viral epidemiology, I currently work in understanding the evolution of infectious diseases and can claim to knowing something or other about mathematics. And there may be no better time than now to spread the good news of mathematics and its usefulness to our everyday lives.

Exponential growth

An exponential graph can be scary to look at. When we’re tracking cases of a disease and seeing it get larger, and then get larger even quicker, it’s easy to see how the changing numbers may fan our fear.

However, it’s important to note that exponential growth has its limits, and can be slowed by employing certain preventative measures, as we’ve seen in countries such as Singapore, Japan and South Korea – countries that seem to have successfully ‘flattened the curve’.

Even in the most dire case, the curve will flatten when everyone has become infected; when there’s no one else left to infect.

As an example of exponential growth slowing down, let’s take for example your productivity during a day of working from home. You might start the day with the goal of writing a scientific manuscript, or a blog piece. It might be slow to get going. Other than the inevitable writer’s block, there’s that second coffee you need to go to the kitchen to make, and a child’s IT issues to fix so they can join their online class.

Once you have that hot cuppa and you’ve told your child to “try turning it off and on again”, you get to sit down and focus. Then, the writing just starts. Words flow quickly.

But then you have lunch, and you accidentally eat too much of last night’s leftover pasta. Your kids get more and more irritable as they get bored of being cooped up in front of their computer screen, starting to throw said leftover pasta at each other. Things get crazy at home.

Your manuscript sits forgotten for a time. Nothing new is written. After returning to your desk, you start typing, but slowly. The excess carbs slow your thinking. The length of your article still grows, but slowly.

Like the speed of your writing in a day of working from home, we want to slow down the spread of infection. We want to impose a post-lunch carbo crash onto the outbreak. We want to reduce new cases quickly.

SIR modelling

You may have seen this simulation of little dots getting infected with COVID-19, and the effects of social distancing on the overall spread of the outbreak (if you haven’t, please do. It’s a great visualisation). This is based on a simple mathematical model commonly used in epidemiology known as an SIR model.

The letters stand for Susceptible, Infectious, and Recovered.  Individuals can be susceptible to a disease, be infected by a disease, or have recovered from that disease.

Mathematical modelling is based around tracking changes to different categories of things (in this case, categories of people and their state based on whether they have a certain disease or not), assuming these changes follow a set of average behaviours. An SIR model follows this basic structure.

Based on what we know about these average behaviours, how will the outbreak progress?

An example SIR model, with mathematical detail for those so inclined

If 1 infected individual, say, hops off a plane into Sydney Airport, will an outbreak in Sydney? If there are 100 people in the arrival hall of the airport at the time they arrive, and we know the disease is transmissible at a certain rate, will there be an outbreak in Sydney?  

Will all the susceptible individuals become infectious, provided they move from susceptible to infectious at a certain rate (β)?

R0

Understanding how quickly the outbreak progresses in infectious disease modelling revolves around a value called R0 (pron. arr-naught). The basic description of R0 is that it is a measure of the average number of new infections every infected individual is likely to cause.

Without going into the mathematical details, this is often dependent on the population size, the rate of transmission (how easy it is to transmit – i.e. can I get it by just touching the same elevator button as someone who is sick, or do they need to sneeze directly into my mouth), and how many other people are around that are likely to get sick too.

Numerically, an R0 greater than 1 indicates that a disease will spread throughout a population. Public health initiatives aim to reduce this value to less than 1, in order to minimise the effect of a disease outbreak and eliminate it from a population.

#stayhome aims to reduce this R0 by effectively removing people from the population. If they’re infected they can’t infect any more people. If they’re susceptible, they can’t get infected and subsequently infect more people.

You may have seen this represented in the form of match sticks, dominoes, or a family-tree-like transmission chain. Whatever the medium you’ve seen the information conveyed, the message is the same. If possible, stay home. It may not be possible for everyone, but for those who can: stay home.

While we are living in uncertain times, and though mathematics has the reputation of being stressful and equally anxiety-inducing, the numbers and predictions you see in the news are based on well-studied models. I, for one, take comfort in that.

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