Leaving the Lab: Rising to the Challenge of Remote Research

Written by Angela Attwood and Maddy Dyer

COVID-19 impact on research

The coronavirus (COVID-19) pandemic has forced millions of us to embrace remote working, and researchers are no exception. Universities are closed and face-to-face research with human participants has been temporarily halted. This has created challenges for our research, and laboratory and field studies are particularly affected.

As part of a large research group at the University of Bristol, we had to respond to this new situation and develop contingency plans for our research. Our first step was to review ongoing research and identify which studies could be suspended. We were fortunate on two counts: 1) we were able to put data collection on hold for many of our studies because there was enough flexibility in our planned completion times, and 2) we were able to stay busy with other work (such as analysing or writing up data from completed studies or developing new grant applications).

In all honesty, we would probably have stopped there with our contingency plans if it hadn’t been for one study that did not have this flexibility. This study investigates smokers’ experiences of switching to e-cigarettes, and requires participants to vape and complete various tasks across a two-week period. If we weren’t able to deliver on this study by a fixed deadline of the end of 2020, the funding would be withdrawn and one of our research staff would have been out of work. This forced us to think again…

Challenges and opportunities

There have been more challenges than opportunities in the context of the COVID-19 outbreak. However, one thing this global crisis has encouraged is innovation and creativity. We responded to the pressing need to complete this project by adapting our protocol so that the study could be run remotely (with no face-to-face communication). Some studies can move onto online platforms (and some of our changes include the use of online surveys), but our study involves participation over a two-week period with multiple “visits” and the use of electronic cigarettes. This required substantial adaption of the study methods (see below for some examples), but we were able to produce a comprehensive revision that retained the necessary components to ensure valid testing of our original research question.

We had to overcome several practical challenges, such as how to screen participants for smoking status and pregnancy. In the laboratory, we typically verify smoking status using a carbon monoxide (CO) breath monitor – equipment that cannot be used during lockdown. To overcome this, we replaced the CO monitor with a cotinine urine test, which verifies smoking status by detecting a metabolite of nicotine in urine. These are dipstick tests that participants can take themselves and we verify the outcome via a video call by asking them to show us the used dipstick.

Another challenge was how to safely deliver the e-cigarettes, e-liquids, and screening tests to participants. We are doing this via post (using pre-paid postage), with carefully constructed information packs and cleaning instructions. All test sessions that collect primary outcome data are now taking place online. This includes a cue reactivity procedure that participants are led through via pre-recorded instructions that link to our online study materials. We are also exploiting ecological momentary assessment methods (daily messaging via mobile phones) to collect real-time data across the test weeks, and all face-to-face communication has been replaced with phone and video calls. We worked closely with our faculty research ethics committee and university IT services as we developed this protocol to ensure any new ideas were feasible and ethically sound (or to identify problems early and seek alternative solutions).

Our aim was to complete a project that otherwise would not have been possible. However, the important learning point was that in developing essentially what was an “emergency response” protocol, we have unlocked other important benefits. Before the COVID-19 outbreak, our biggest challenge was recruiting participants (we require smokers who are willing to abstain from smoking for one week!). As with all university-based research, we often rely on opportunistic recruitment that means recruiting from the local area (i.e., people who can easily attend the laboratory sessions during university opening hours), and our samples often comprise a relatively high number of students. This not only means we have difficulty recruiting, but that our samples are not always representative, and our results may not generalise to the wider population. This new model of working means we have no geographical restriction (as long as the post delivers and there is Internet provision – we can collect data!), hugely improving our reach and the diversity in our participant samples.

Another benefit is that data are collected in more naturalistic settings (although this comes with a loss of control that needs to be considered or may not be appropriate for some studies). For studies that require participants to attend multiple sessions, it is also likely there will be lower attrition (i.e., fewer drop-outs) as there is less burden on participants to travel to a testing laboratory.

This has not been an easy transition (although we will certainly be well equipped to do it again if we need to). It has been time consuming, and some aspects of the study were simply not possible in the context of the fixed time constraints and funding in place, and without the laboratory facilities. The utility of this approach needs to be considered on a case by case basis. But, for our study, it was doable. We are only at the start of this process – the study will be running throughout 2020 and we look forward to the ongoing challenge and reflecting on how we can optimise this process in future.

The important take-home message is that remote research is not something we will discard after the COVID-19 restrictions are lifted. We will refine these methods and embrace the benefits they offer. Remote research will not be an emergency response option only, but instead it will be an integral part of our research toolkit.

If you are interested in finding out more, please visit our website:

http://www.bristol.ac.uk/psychology/research/brain/targ/participants/smoker-experience-ecigs/

You can also follow us on Twitter: @BristolTARG @AngelaAttwood @MaddyLDyer

 

From number crunching to brains: my experiences of interdisciplinary research

by Michelle Taylor @chelle_bluebird

From TARG to neuroscience

The final six months of a PhD can be a stressful time. Not only are you trying to write up three years of research, wondering whether you have done enough work, but you also need to consider what to do next. I decided to try my hand at something different…

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My PhD was in the area of epidemiology, where I was using large datasets (such as the Avon Longitudinal Study of Parents and Children, based here at the University of Bristol) to determine causes and consequences of using various drugs of abuse. My time was mainly spent designing and conducting statistical analyses on data that had already been collected and were available for secondary analysis. I completed this work in TARG and the School of Social and Community Medicine and was lucky enough to be funded by the Wellcome Trust on a PhD programme in molecular, genetic and lifecourse epidemiology. The Wellcome Trust also fund two other PhD programmes at the University of Bristol, one in ‘Neural Dynamics’ and another in ‘Dynamic Cell Biology’. Towards the end of my PhD an opportunity arose – the Elizabeth Blackwell Institute were offering three researchers fellowships to conduct nine months of research with one of the other Wellcome Trust programmes. This would involve changing research area and learning something completely new – and I decided to go for it. I applied to move to the Neural Dynamics programme. As my past research had focused on addiction and mental health, gaining knowledge of the field of neuroscience seemed fitting.

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After identifying a potential new supervisor and quickly putting together and submitting an application I was told that I had been successful. I was go
ing to become a neuroscientist for the next nine months. The day after handing in my PhD I headed off to the lab of Matt Jones, a neuroscientist whose research interests include sleep, memory and brain circuitry. I was going to be working on a study that aimed to find out more about how genes influence overnight brain activity and memory in humans. I’ve written a little more about this study at the end of this blog post, just in case you’re interested!

My new lab group were very friendly and welcoming, although at times it seemed like they were talking in a different language. I would attend seminars in my new department and be completely confused within minutes. While I did have some knowledge of neuroscience from reading literature, my knowledge was severely lacking compared to that of my new colleagues. Mind you, I could always get my own back by blinding them with statistics!

 

The study involved getting participants to stay in our sleep clinic overnight and measuring their brain activity while they slept. I had to learn new methods of data collection, which involved measuring a person’s head to find specific points and gluing on electrodes to measure their brain activity (known as PSG, or polysomnography) [1]. Once these data were collected, the night’s recording needed to be scored into various stages of sleep. We can determine this from the length, height and frequency of the waves on the sleep recording. There are two main stages of sleep: REM (which stands for rapid eye movement) and non-REM. Non-REM can be broken down further into stages 1, 2 and 3 [2,3]. Stage 3 is the deepest stage of sleep, while stage 2 contains oscillations called spindles and K-complexes which are thought to play a role in memory consolidation while we sleep [3]. Learning to score a night’s sleep was something very new to me. I was used to having my data in the form of numbers in a spreadsheet not as wavy lines dominating the computer screen!

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At the end of the nine months, I found myself understanding the talks that I went to – I even started to sound like a neuroscientist myself at times. Many things which originally seemed overwhelming (such as collecting PSG data) now feel like second nature, and the wavy lines on a computer screen are now meaningful. While at first the experience seemed daunting, it has no doubt opened my mind and expanded my knowledge. The ability to conduct interdisciplinary research is a well-regarded asset, but this experience has not only enhanced my CV. It has increased my confidence when talking to other researchers, as I have realised that we can all learn something from one another. Most importantly I have learned to look at research from a broader perspective – what does my research mean for other fields? How can it inform other research that is different from my own? It is, of course, combining the answers to all of these questions that will enhance science and in turn have more impact on the wider world.

My neuroscience experience has come to an end and for now, it is back to epidemiology. But I will definitely look back on my time in neuroscience fondly, and, who knows, I might even get the chance to integrate epidemiological research with neuroscience in the future…

 

A little more about the study

Different parts of our brains communicate with one another as we learn new information during the day. Overnight brain activity then helps us to file memories for long-term storage. Evidence suggests that this process varies naturally in everyone. To help us understand what causes this variation, we are interested in finding out more about how genes influence overnight brain activity in healthy individuals. Studying our genes by specifically testing those who carry particular (naturally occurring) form of them can help us understand their role in shaping the natural variation we see in brain activity. Importantly, understanding this in healthy people can then go on to help us develop new targets for treatments to help the sick. We therefore carried out a study to look at how naturally occurring variation at a particular gene variant affects memory consolidation during sleep.

The gene variant was chosen based on previous studies that have shown that it affects both brain activity and sleep. To do this, we invited back participants from the Avon Longitudinal Study of Parents and Children who had provided us with a DNA sample. Information about their genes had been processed and based on this information they were identified as being carriers or non-carriers of the gene we were interested in. This is a study design known as ‘recall-by-genotype’. We then asked these people to spend two nights in a sleep laboratory, perform some memory based tasks and complete some questionnaires so that we can measure how genetic differences relate to memory and brain activity during sleep.

motionwatch_wrist_smlWhilst participants were in the sleep facility we attached a number of sensors to their head in order to record their brain waves, eye movements and muscle activity. We also used sensors on the chest to measure heart rate and take video and audio recordings to confirm whether or not participants become unsettled during the night. Participants were asked to complete some questionnaires about their sleep behaviour and to carry out a memory task before and after sleep.

For the two weeks in between visits to the sleep laboratory, we asked participants to wear an ‘actiwatch’. An actiwatch looks like a normal watch and records movement, telling us when the participant usually goes to sleep and wakes up. We asked participants to wear the actiwatch on their wrist at all times and asked them to fill in a sleep diary for the two weeks.

What do we hope to find?

actigraphyWe hope to find that individuals who carry our genetic variant of interest differ from those who do not carry the variant on a range of sleep characteristics including the non-REM stage 2 spindles and slow wave oscillations found on stage 3 of non-REM sleep. We also expect to find difference between genotype groups on ability to complete the memory task, and the speed at which they complete the memory task. Finally, we expect to observe a correlation between the stage 2 sleep spindles and the results of the memory task. If we observe these results in our data, then this will suggest that this genotype can influence brain activity during sleep which then in turn can effect a person’s memory, as this memory is not being consolidated as well over night.

 

Where can I find out more?

A protocol for this study has already been published [4].
Once completed, this study will be published open access within a scientific journal.

References:

[1] Wikipedia – polysomnography

[2] American association of sleep

[3] Wikipedia – sleep (including information on stages, spindles, K-complexes and slow waves)

[4] Hellmich C, Durant C, Jones MW, Timpson NJ, Bartsch U, Corbin LJ (2015) Genetics, sleep and memory: a recall-by-genotype study of ZNF804A variants and sleep neurophysiology. BMC Med Genet 16:96