PsychologiCALL

On growing up in noisy households, with Dr Sam Wass

August 09, 2020 SalvesenResearch Season 1 Episode 11
PsychologiCALL
On growing up in noisy households, with Dr Sam Wass
Show Notes Transcript

Dr Sam Wass is a developmental psychologist at the University of East London who specialises in investigating how early development is affected by the environments we grow up in, and by people around us. He talks to Sue about why children exposed to noisy and chaotic home environments show such widespread patterns of impairment during later life - affecting both academic performance, and long-term mental health outcomes.

You can see Sam's website here and his lab site here. You can also follow him on Twitter.

The paper we discussed in this podcast is:
Wass, S. V., Smith, C. G., Daubney, K. R., Suata, Z. M., Clackson, K., Begum, A., & Mirza, F. U. (2019). Influences of environmental stressors on autonomic function in 12‐month‐old infants: understanding early common pathways to atypical emotion regulation and cognitive performance. Journal of Child Psychology and Psychiatry, 60(12), 1323-1333.

Sue:

[Podcast jingle] [ringtone] Hello? Oh, it is recording. I see the little figure. Okay, great . I will do my little spiel and then I'll introduce you. Nice. Okay. Here I go. Hi, I'm Sue from the Salvesen Mindroom Research Centre at the University of Edinburgh, and we're recording another PsychologiCALL, and that's our little podcast! We're trying to make a kind of evidence-based contribution to the conversations that people are having, quite a lot nowadays during , um , the COVID-19 pandemic, about child and adolescent development and wellbeing and learning and things like that. And today I'm talking to Sam Wass from the University of East London, and he's going to talk to me about , um, infant development and particularly the influence of stress early on in life, on how infants develop and learn. Hello, Sam, how are you?

Sam:

Hi Sue, great thanks! Thanks for having me!

Sue:

You're welcome! Um , so why don't you start by telling me what you think was the kind of main headline finding from this bit of research that you did?

Sam:

Yeah, so we were kind of building on previous research, looking at early life stress , uh , suggesting that babies who have more stressful and particularly noisy and chaotic home environments do worse, um , at lots of different outcomes later in life. So academic outcomes, but also mental health outcomes. It really is kind of an across the board, like increased risk if you have a noisy, chaotic, unpredictable, early life home environment you're worst at kind of lots of different cognitive performance, but also some emotional regulation, mental health, that kind of thing. So what we did was we measured kind of actual noise in children , uh, you know, there's a household noise that children were having and we measured their stress systems at the time when they're experiencing it. And basically our main take-homes were: babies being raised in noisier and more chaotic, less predictable home environment, they were more up and down in their stress system . So they had bigger, faster increases and decreases in their levels of stress. And we managed to link those to kind of problems that they were having with sustaining attention, to paying attention to one thing and also to emotion regulation. And the last part of this story though was: it's not all bad for these children that are growing up in noisier, you know, more chaotic households, that they're not just kind of performing more poorly at these cognitive assessments compared to other children, they actually perform better at some. So they're better at kind of fast learning. So rapid onset learning where they haven't got long to learn something, you know, they're actually outperforming children raised in quieter households. Well, but while there are definitely other areas, things like the sustained attention , things like emotional regulation where they're not doing so well.

Sue:

Mmmh... So I suppose the first thing that I'm curious about that, then is the... is this idea of kind of noisy household, right? So there's lots of different ways that that mi-, that noisiness, noisiness might come through. So if you have, you know, lots of brothers and sisters that could lead to a noisy household, or maybe you live in a kind of really busy urban environment, so there's a lot of kind of external traffic noise, you live, you know, like close to Heathrow Airport or something. So was there, what, what kinds of noisy households are we talking about? Um, and is that something that you, a particular factor that you measured?

Sam:

Yeah. So this is quite a tricky thing and actually it's one of the things that , um, you know, really motivated us to do the study. So pretty much all the research that's out there at the moment is , um, either using something called a chaos scale, which is basically a parent rating scale [chukle from Sue] where they basically, I know it's one of those ones you could see it, it's an acronym. And they... It's like, "how can we get it to stand for chaos?" [laugh] "Confusion Hubbub Order something and something", you know, they must've spent ages on that! [laugh]

Sue:

Oh "Hubbub" is great! [laugh] I mean, I really want to fill in the Hubbub subscale . That's fantastic! [laugh ]

Sam:

[Inaudible] But anyway, so basically, that's a self rating questionnaire where parents basically just rate a series of statements like "it's a zoo in our household" on a scale from 1 to 10. Um , and you just average the score. The other research into , um, uh , looking specifically at noise in early life, it tends to be done, you know, as you were mentioning about traffic noise, kind of looking at average levels of noise and one of the big , um, uh, kind of things that motivates us to do this is both the measures are looking at noise, kind of as a static thing, like just, you just get one kind of number, which is that child's level of, you know , noise / chaos exposure. Um, and that we kind of argued, kind of we had to write a grant application to get this in . And we argued that in fact, that's actually quite a bad way of measuring noise because you know, noise is obviously fluctuating! It's always going up and down , um , at the time. And what nobody's really measured before was your reactions to noises as it happens. Um, so, you know, this is kind of quite ironic because you know, as stress is by definition a compensatory mechanism, so, you know, Hans Selye who defined stress , defined it as "general adaptation syndrome". So it's how our body responds to change. So we have this irony that, you know , the point of stress is to allow us to respond to change, but the way everyone's measuring it is in a way that, you know , we don't see that change happening, cause we just got one number, cause we just measured it through a questionnaire. So what we did was we did something differently. We actually put little microphones on the babies and little stress monitors on the babies. Um, and uh, we send them home with it. Well in fact we had a researcher go to their home and, you know, put this equipment on at the start of the day. And then we just left it with them . We , we captured a typical day in their life. Um , and then the researcher returned to their homes to pick it up at the end of the day. So, you know, trying to do something different, you know, different to what a been done before.

Sue:

Um , so tell me more about this measurement process. Cause this is something you're kind of known for Sam is this, you know kind of innovative , um, often quite kind of data-rich measurements. So I guess I'm curious about , um, I don't know... What , what that was maybe like for the babies, you know, did you get many babies kind of yanking their microphones off and discarding them after a while? [chuckle].

Sam:

No, so we jus-

Sue:

And then- [laugh]

Sam:

Yeah, we had a very painful process , but it wasn't a very... It was quite a , um, not a very tight budget grant . So we spent ages trying to work out how we were going to actually design the equipment. I'd have. .. And I had like three or four guys sat there staring at the... this wiring diagram of this printed circuit board, [laughs] cause we were trying to save 50 quid by wiring everything off ourselves. So it was an incredibly painful process. So we had the printed circuit board designed by someone in Germany, printed in China, and then send to these guys in Mongolia, who we found on the internet, who were writing the firmware. And we were stuck in the middle of trying to , um, you know, to kind of integrate the whole process . And it was very, very painful! Don't ever, if you're in this situation, [laughs] try and save a few quid by doing it the cheap way! [Laughs] Um , and we also had , uh, you know, a fashion student who was designing these , this kind of baby equipment. So the clothes that they were wearing, so we had a little baby grow , uh , cause one of the things in it was an accelerometer , so it needed to be really tight for them. Um, so that the accelerometer doesn't jiggle cause that messes up the basis. So we've got like a skin tight, you know, just a baby grow tunic. And we , we sewed in various pockets and stuff. So, and then the microphone obviously had to be at the top level. So we had some kind of things coming through. So yeah, it was, it actually worked pretty well in the end. Um, we didn't have many babies, you know, cause they were napping during the day and they were fine, napping with it on and everything like that, but yeah, it wasn't half painful getting there.

Sue:

Ah... So I can imagine that the data you get from that is incredibly kind of detailed and rich. And so this could get really complicated very quickly, but perhaps you could pick out , um, a little detail of , of something about how you sort of process and manage that data to try and turn it into the sorts of variables that you were interested in. Can you tell me anything about the , the techniques that you use for that?

Sam:

Yeah, so that's a good question. So yeah, it's very, very complex. So it , it does. So yes so, I do... I like to work like this where you collect , you know, big data, which are very complex and unconstrained and you know, in the world. So basically, you know , most of the research, you know, most of the research I do still is in the lab where you're trying to look at one particular thing and you basically, you know, you use a subtractive method where you present, you know, you get them to do one thing and then you get them to do exactly the same thing again, but it differs. But for the presence of one thing, basically, and you did the average, you know, the difference between your blocks, you know, and everything else is controlled for. So the environment is identical between the conditions, you don't have everything else, anything else to worry about. And I like to do stuff where you're... which is competely the opposite of that in a way where you don't put any constraints, you know, you don't cut out any environmental things. You just go for, you know , collect a real life sample. It's one of the things that's very controversial among scientists. I've had, you know , I've had a very angry , um , uh , very traumatic for me meeting at the Welcome Trust where one grant that I spent ages on , um , with , I was really getting ripped apart by a panel saying, you know, you can't do it like this , cause it can't be hypothesis driven if you do it like this , you know, you have to have, you know, an idea. And I was like, "yeah, but I know it is hypothesis driven and I've put my hypotheses in bold point ", but you know, a lot of people have this idea that, you know, when we set out to do an experiment, we have to, you know, there's only one way to do it, which is in this very, very tightly controlled way. I personally think you can have very, very clearly defined, very hypothesis driven analyses apply to big, noisy, complex naturalistic datasets, but you have to be very disciplined and you, you really have to write down exactly what you're planning to ask because there are so many rabbit holes that you can disappear down . So for this one, we, you know, it's quite simple in a way, you know, our first thing was how go do fluctuations in environments , noise, associate with, you know , fluctuations and stress. So that's the question that, you know, is important, one that nobody's actually answered before , um, for the reason that nobody has actually recorded the noise in the home that it's happening. So, so that was our first one. And that was just, you know, it was quite simple in a way, you know, we, we chucked out, you know , the bits , we decided to consider only the sections where they're at home and the baby is awake. Cause we thought that was the only thing giving us the clean answer. You know, obviously the microphone was picking up everything. So we got people to, we got , um , um, uh , researchers to go through and code, you know, what was happening in each microphone, kind of chunk of the microphone data, and anyone where the mic- the baby was vocalising to themselves, we cut that out, cause we only wanted to look at ambient noise. Um, but then we basically just had like a continuous time series of the day, you know, one sample per , uh , we averaged the basis of kind of minute long chunks of how much noise there was going on. We have that stress , that physiological stress, which , um, I could tell you more about how we measured that in a second, if you're interested, but basically we have that as a simple, you know , variable that fluctuated during the course of the day. And so we had two kind of variables that just fluctuate during the course of the day and we basically do something called a cross correlation. So you basically, that's a way of looking at, do you get time lag associations between two times series? So, you know, basically at times during the day when the ambient noise is louder, "is their , uh , are their levels of physiological stress, you know, higher" was basically our question. So, you know , I , you know , I would definitely say, you know, we managed to answer that question despite the fact that our data is very, very noisy. You know, some of the time, you know, they're picking their nose, some of the time, you know, they were doing all the different things that a baby naturally does at home , you know , so, you know , so yeah, that's how we did it.

Sue:

That's amazing. Um, so I could ask you more about measurement, but I'm going to move on to ask you what you think we can kind of learn from this . So, so right at the beginning you mentioned, you know, you talked about how the big motivation for the study was trying to sort of chart the way this noisy, chaotic environment could change the baby's kind of stress levels, and that in turn would lead to, or relate to , um, you know, things like their , um, educational attainment at school, and stuff like that. So did you look at that in your babies or, or, or , um, you know, like... Can you say a bit more about what we can sort of take from this in terms of how we should be , um, you know, monitoring kind of risk and resilience in babies or supporting children as they grow up that kind of thing.

Sam:

Yeah. So , so the outcome measures that we looked at were... So we also brought the same babies into the lab. And , um, so , and did you know, you know, well validated measures of , uh , kind of sustained attention. So basically how, if you , just... The way you measure it in babies is basically if you just flashed up something interesting, you know, a static , you know, interesting stimulus that they haven't seen before, you know, how long do they engage with it for. So that has been shown by lots of other people to be predictive of quite a lot of different types of longterm , kind of academic and mental health outcomes. I think mental health, definitely academic . And then we also did emotional regulation, which is basically we did a thing. Um, called a "still face paradigm" where the mum interact with the baby then freezes for two minutes, um , and then unfreezes , um , and we look at how, how much of an increase in the baby's stress levels they get while during the period where the mum is frozen. Um, so those are the main measures that we did in their lab, you know, as I say, they were chosen because they were very, very well used by other people , um, to, you know, to link, to , to predict later development. We didn't have the funding to , to look at the later development. It was just a cross-sectionnal study for this one, although we're doing it longitudinally later on. Um , and basically the basic take-home was , um , if you look , um, between different babies, depending on their average levels of noise exposure during their day, basically the children who've got more household noise, they're more kind of unstable in their patterns of stress, which kind of makes sense because, you know, as I was saying, our stress is by definition, it's a dynamic compensating mechanism. That's what our stress systems do. It help us to respond to change. And if I'm being raised in a very fast changing environment , um, you know, I'm having to continuously raise out more often. So it kind of makes sense that, you know, children who are raised in faster, noisier, more care-free environments, are more up and down, more unstable in their stress patterns . But the reason that they , you know, affects on academic outcomes and cognitive outcomes, as well as mental health outcomes is because particularly during early development also later on, but it's especially true. We think during early development, we really use our stress systems for everything. So we use our stress systems to pay attention. So, you know, if I flash off the picture, you know, a novel picture to a baby, you know, just similar to what we were doing in the lab, you get a measurable response on the babies , you know , um, uh , kind of stress system, so our autonomic nervous system. So what we were finding was the babies in the lab, you know, we flashed something new up, all of the babies showed a reaction in their stress systems , just to see something new and interesting. But maybe to erase the noise in a household, they couldn't sustain that change for as long. They tend to, you know , bounce back to kind of mean much more quickly, which meant that behaviourally they couldn't sustain this kind of , um, you know, sustain their attention to it for as long. Yeah? And when it came to emotional regulation, we found that the baby being raised in noisier household tends to get more upset more quickly , um, which was kind of linking to that kind of work , emotional regulation. So, I mean, I think this is stuff that does provide kind of, you know, even though it's the very early days, you know, nobody's done a study like this before, you know, we definitely need to do it longitudinally, um, you know, I think it does provide some hints around this idea. You know, the , it provides some insight into these mechanisms why, you know, stress, early life stress seems to be impacting so widely later development, you know, and that's just simply because our stress systems are involved in so many different things, but also potentially , you know, what we can do about it.

Sue:

Mm . Uh , such great work Sam! Um, well, I think we should draw to a close because we are trying to , uh, stick to our bite-size podcast tagline, though I always get too interested in it's hard to do, but , um, before we finish, we are trying to , um, kind of think about any early career researchers or, or PhD students who might be listening or undergrad students who might be listening, perhaps don't have the usual kind of peer support networks around them , um, during lockdown. So I wondered if you had any , um, you know, little insights or encouragement for those , uh, those kinds of listeners.

Sam:

Are you saying specifically people coping with , um, coping with kind of being stuck at home or...

Sue:

No. No. It's just to , just to kind of , um, you know, fill in the gap that you might normally have by going to seminar series and meeting people and stuff like that. Right. So it doesn't have to be about how to cope right now, but , um , you know...

Sam:

Yeah. Okay . What I was going to say , um , to that thing, it's just, it's kind of about reading, and just how... So I d id what, you know, hopefully quite a lot of people, you know, at home, as they're now , when I started my PhD, I just , um, I was actually, you know , doing a... I had, I worked for 10 years and I was living out in Berlin, on a different job, actually worked in, in opera ... And I was living out in Berlin, I had three, three or four months rent . Um , I could afford to like , not work cause I had enough money from the work and I just sat and just read. Um , and it's really interesting. So the way research works is you kind of dig yourself into a canal. Um , I think cause a lot of people like to think that they know what they're talking about, you know , cause... and a lot of people are scared about talking about topics where they don't necessarily know everything that there is to know on a topic. So a lot of people say, you know, this is my specialist area and I will only talk about this, you know. But the problem with that is that you get kind of entrenched in a particular line of research. So I guess the most important thing that I try and encourage people to do is to, you know , try and do stuff that is different to what's been done before, you know , trying to really think outside the box and try to say "okay, there's a lot of research in this area. Um, but there's absolutely no research in this area". So spend a lot of time, you know, typing, you know, kind of unusual combinations of different words into your , uh , search box. [Laugh] You know , try and find an area where there hasn't been research very well before. Compare to what most people did, you know, certainly what I did when I was choosing a PhD, which was you find some research and you think, "Oh, this is interesting. They've done that. They've done that, but nobody's done this", you know, making a slight difference from what people have done before. And that means that you know, where there's a lot of research, you get more and more research built onto an area where there's a lot of research. So, you know, the way I characterise the , uh , you know, the field of research at the moment is: there are some questions where there's a massive amount that we know about it. And there's some other questions that I think are really equally important where there's just nobody has really looked before. So now that's what- That would be my main, you know, message to people , you know, "try and , you know, literally think outside the search box".

Sue:

[Laughs] Very nice. Yeah. That's great advice. Um, so I think we will wrap up there. Thank you so much for your time Sam, and I will let anyone listening, know that they will be able to find out more about Sam's work by following the links on the podcast page, which is at ed .ac. uk/salvesen-research. Thank you very much Sam! Bye!

Sam:

Great thanks! Thanks for having me, Sue! [ringtone]

Sue:

Okay we did it! I thought that went quite smoothly! [Podcast jingle].