Developing effective time management skills can be incredibly beneficial for a healthy work–life balance and boosting productivity. In this piece, BSI member Dr Carolyn Nielsen gives an overview of the ‘deep work’ time management system and explains how it has helped her juggle priorities in her research career.
When you’re someone who grows up devising elaborate schedules for your younger siblings, perhaps it’s inevitable that you’ll become enamoured with various time management strategies in adult life. These days, I am fascinated by what we can learn from the pros in psychology and business to support the development of academic researchers – especially those of us working in immunology. Specifically, I want to know how we can apply established organisational psychology principles to impact the quality of research output, leadership, and work–life balance of the average academic scientist.
Teaching soft skills
In my observation, early career researchers may be well-supervised with respect to developing their technical competencies, but there is often negligible practical guidance on critical ‘soft’ skills required for a successful research career; for example, evidence-based recommendations on how to hire and lead a research team, how to navigate interpersonal dynamics across power structures, and – you guessed it – even just how to manage your time. There is no scarcity of books in these fields (see any airport bookshop) for an interested early career researcher, but what can be difficult is deciphering how Effective time management with ‘deep work’ to apply the broad advice to the specific context of a research job. Yes, we likely have a lot of autonomy over our time, but we are also likely tied to a physical environment (hello flow cytometer!) in a way most other knowledge workers are not.
‘Deep work’ philosophy
For me, the framework that has resonated best thus far comes from Deep Work by Cal Newport. Cal defines ‘deep work’ as ‘professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit… create new value, improve your skill, and are hard to replicate’. As an academic himself (computer science), many of his examples of high priority deep work activities – for example, data analysis and writing papers – will be very familiar to my fellow university-based immunologists. At a high level, the main thrust of the book (and the podcast if you’re uber-keen) is that context switching is cognitively demanding. This goes further than any healthy suspicions you may already have about multi-tasking. A context switch occurs any time you and your brain move from one task, like analysing data, to another task, like just-very-quickly-checking-email-to-see-if-someone-has-sent-me-slides. Importantly, there is a cost to our focus and attention with each context switch. This cost is not only in the form of cumulating delays for the main task at hand, but also to your own energy level and attention span for the day. Professor Sophie Leroy at the University of Minnesota coined the term ‘attention residue’ to describe this detrimental cognitive impact of moving sequentially between tasks.
Focus, not frazzle
A working style where you flit between all your ongoing projects and priorities throughout the day is therefore not efficient, but rather an unnecessarily frazzling approach. You will never hit a flow state with analysis or grant writing, or really be able to focus on a paper if you bounce between these activities or intersperse with tiny chunks of time spent looking at email or ordering random reagents as they pop into mind. The solution isn’t necessarily to start dropping projects (a subject for another blog) or to resign yourself to ending each day in a whirlwind approaching burnout. What ‘deep work’ proposes is that we make a conscious effort to block out time for the activities that require a lot of what Cal Newport calls ‘attention capital’, and then guard this time ferociously.
You will never hit a flow state with analysis or grant writing, or really be able to focus on a paper if you bounce between these activities or intersperse with tiny chunks of time spent looking at email or ordering random reagents as they pop into mind.
Satisfying realism
I have always planned my days and weeks to an extent, but the game changer for me with this philosophy has been to give myself much longer periods of time to complete a given task. This doesn’t mean I do less in a week but, instead of chipping away at everything every day, the most critical projects get prioritised for two- or three-hour blocks of dedicated undistracted time. This lets me more realistically account for the time it takes to get started on something (finding the data files, getting a coffee) and the time to wrap-up at the end in a less pressured way. It’s these transition phases that we often forget about when we make really ambitious plans for what we can get through in a morning or afternoon. Building schedules based on a best-case scenario, rather than reality, is apparently a common problem! But there is a satisfying calm that comes with reaching a stopping point within the amount of time you allocated, rather than saving halfway through a sentence you thought you could finish before realising you were five minutes late for a lab meeting.
It's not a perfect system of course, and it’s certainly not executed perfectly in my own case. I am also aware that depending on your seniority or specific role in a team, it may be more or less straightforward to block out these periods of time for deep work. But it is worth exercising this control where you can. Can you express a preference for a meeting time that keeps your afternoon free for longer? Can you delay replying to a non-urgent email until you have finished your own high-priority work? Can you fit in just one solid two-hour block this week to really make progress with that data set? Specify it ahead of time, don’t risk relying on your willpower in the moment to get started. As one of my favourite quotes in the book goes ‘[Great creative minds] think like artists but work like accountants’.
Can you fit in just one solid two-hour block this week to really make progress with that data set? Specify it ahead of time, don’t risk relying on your willpower in the moment to get started.
Dr Carolyn Nielsen
Senior Immunologist, University of Oxford