Impact of work-related stress
Measuring the impact of work-related stress is a post-hoc self-research project that uses retrospective data to evaluate whether typically stress-associated physiological variables show any deviation from a baseline after the experience of intense job-related stress. Using a retrospective approach it uses resting heart rate, heart rate variability, body weight and step counts to attempt to differentiate between different kinds of stress that were experienced over a period between September 2021 and April/May 2022.
|Related tools||Fitbit, Oura Ring|
|Related topics||Stress, HRV, Sleep, Weight, Activity tracking|
Builds on project(s)
Since around September 2021, I experienced a lot of work/job-related stress: On the one hand, I entered the last year of a three year postdoctoral contract, which meant that I slowly but surely needed to look for my next job. On the other hand, there was also a large external stress-factor at play with my current employer/institute: With funding for the institution at large running out, a (misguided) rebranding scheme took place, leading to a lot of internal uncertainty which ended up with a clear signal that my own kind of work was not seen as important and would no longer be part of the institutes future going forward. Overall, this created a situation that had the hallmarks of overcommitment and effort reward imbalance, both of which are associated with vital exhaustion and burnout.
Within this context, I started looking for my next job between September 2021 and April/May 2022. One of the jobs I applied for would have meant staying permanently with the institution that was undergoing its own stressful transformation, while a second one would give me the opportunity to move away from this environment, which also happens to be the job I will move to later in 2022.
Questions & MethodsEdit
With the stress of the job application period over and having a clear exit strategy to leave my current environment, I was wondering whether any of physiological data from wearables etc. would show signs of this prior stress through deviations from a baseline. This approach would be similar to other projects I have done in the past, e.g. regarding the impact my PhD writing period had on me or the impact of COVID-19 lockdowns.
My two main questions were:
- Do typically stress-related physiological variables show any changes compared to the baseline during this stress-period?
- Can those variables help me distinguish whether the stress was due to my current work environment or due to the job applications?
Variables of interestEdit
To answer those questions I decided to look at four variables that I thought might be indicative of stress over this period and that I already regularly collect through (semi-)passive tracking through my Oura Ring and my Fitbit Aria smart scale.
The first parameter I wanted to look at was my Heart Rate Variability (HRV). The scientific literature describes that a decrease in HRV can be found under stress more broadly, during stressful tasks but also in particular during occupational stress, in particular in settings where there is an effort reward imbalance or overcommitment. Similarly, a higher resting heart rate has been observed in response to acute occupational stress and even during night time has been found to be associated with low job control.
Beyond heart-related metrics, I decided to look at my body weight and physical activity, given that a study found that high workloads are linked to lacking the energy to eat healthy and that burnout is additionally linked to a reduction in physical activity.
For all four metrics, I used my existing data exports from Oura and Fitbit to Open Humans, filtering for data that fell within the time period of interest. I marked down the start of the overall stress period as starting on September 13, 2021 as this corresponds to the end of the general summer vacation period in France and my own end-of-summer vacation (red dashed line in plots). For the "end of stress" date, I marked down April 8, 2022 as the date where I got my job offer for leaving (red solid line in plots). To create a pre- and post-stress baseline view, I additionally exported the 10 weeks of data prior to the stress-onset as well as all remaining data until May 31, 2022 to evaluate the evolution after the stress period. Additionally, I marked two more points of interest within this time period: November 20, 2021 as the start point of my job application period (blue dashed line in plots) and May 20, 2022 as the last day of the job application period as the last application results came in on this date. Rather than fitting potential dates to the observed data, these four dates were picked before plotting any data, as such the corresponding findings aren't a result of tweaking cut-off points.
The raw data consists of a single data point per day for each of the variables and I calculated weekly averages to remove some of the noise due to differences in weekdays/weekends etc. Each of the weekly averages was then plotted on a timeline, alongside an annotation for the time periods to evaluate any changes/trends compared to the baselines. The code to reproduce these visualizations using your own data is available as a Jupyter notebook.
Overall, the visualization of all four metrics shown on the right demonstrates that they develop in line with the expectations from the scientific literature outlined above.
Is there any impact of stress on my different vital signs?Edit
For the heart metrics I notice that my heart rate variability – for which higher values are broadly speaking better values – drops by around 10 points, from pre/post-values of ~40 ms down to ~30 ms. Similarly, my resting heart rate – for which lower values are better – there is a pronounced increase from the low 50s up to the high 50s/low 60s.
I see a similar effect for the weight and step count metrics: The overall average step-count within the stress period decreased by ~2000 steps/day. And my weight crept up from ~70 kg prior to the stress to a peak of 76 kg at the end of the stress period. For the weight there was already an increasing trend prior to the stress period though, which might hint at some prior issue before the denoted stress period and that the stress just exacerbated this problem. At the same time, it is somewhat encouraging that the weight goes down since the end of the stress period.
Overall, despite only including around 2 weeks worth of post-exposure data at this time, it is encouraging that the values seem to all move back towards their baseline values from prior the stress exposure.
Occupational stress or job-search-stress?Edit
The graphs include two potential stress periods, marked down in red and blue: The red dashed/solid lines give the start of the occupational stress, induced by workplace's "transformation" and the end when being offered an exit strategy by a job outside this institution respectively. The blue dashed/solid lines denote the start of my job application period at the end of November and when the application period ends once all job application results are in.
Across all four metrics, the sudden shift in trend aligns quite well with the occupational stress onset and the end of this stress by having a perspective to leave the current burnout behind. In contrast, the beginning/end of the job application period does not seem to line up with pronounced changes across any of the metrics. Only the heart rate variability and resting heart rate values seem to show a slight decrease/increase following the added job search-stress which might hint at a compounding stress-factor.
At the same time, all four metrics seem to return back to their pre-stress baseline as soon as I got a job offer that would allow me to leave my current workplace, supporting the view that the main stress factor in my case was the existing work place rather than the job search.
What did I learn?Edit
On the one hand, I find it quite validating to see that all those metrics pick up on the intense stress and associated misery I experienced over this time period. On the other hand, it is also quite scary to see the physical impact that this stress has beyond my mental health and well-being. Additionally, I find it quite useful to see the data like this and being able to disentangle these two different kind of stressors and were quite useful in my personal sense-making in relation to this experience, For most of this period I had thought that the stress comes mainly from the application processes rather than it being occupational stress. As a result, I thought I would want to stay at my current workplace if given the option.
Only towards the end of this period I had some anecdotal experience that made me suspect that things might be different: Towards the end of the stress-period I developed a persistent back/shoulder pain, which – while not fully disappearing – strongly subsided when getting the news that I would not be offered a job at my current workplace, thus making my exit plan to a different institution would happen (blue solid line). Which made me suspect that there might be a strong sense of relief in this outcome, even if I had thought I would have preferred another outcome. As such it is quite interesting for me to see that the retrospective analysis of my data actually shows that many of those values already went back to the baseline once I had the option to move, despite still nominally being within the job application stress!
Addendum after self-research chat presentationEdit
I presented these findings during the self-research chat of June 16, 2022. During this call a number of questions came up, some of which I investigated afterwards.
I chose the four variables presented based on their known association to stress generally and occupational stress in particular. But of course it could be the case that they basically all show the same effect, as they could be highly correlated: A rising body weight might be directly related to a reduction in physical activity (as measured by step counts as a proxy). Similarly, an overall lower fitness might explain a higher resting heart rate and lower HRV.
For another recent self-research project – What does clustering tell us – I had already looked into how different variables correlate by doing a Principal Component Analysis (PCA). Looking at the variable representation in this PCA (see right), it seems that the only two things which are strongly negatively correlated with each other are my heart rate variability (
hrv) and my resting heart rate (
resting_hr), as indicated by the two arrows more or less pointing in opposite directions. This is not too unexpected to me, as those two quite typically move in opposite directions in response to any factors.
In contrast, the weight measurements (
weight_fitbit) are less well aligned with my resting heart rate/heart rate variability, the corresponding arrow being about 45º off. This indicates that there is still a small correlation, but one that is a lot worse than the case of HRV/heart rate. This is even more extreme when looking at the step count metric (
oura_steps), which moves in a 90º away from the resting heart rate and heart rate variability metrics, indicating that there is basically no correlation between the step count and those metrics.
With the exception of resting heart rate and heart rate variability it thus appears like there isn't too strong a correlation between the different metrics looked at here.
Do you find anything in your sleep metrics?Edit
Another question that came up was whether I felt that the stress also impacted my sleep and whether I could see that in any of the sleep metrics. Anecdotally I could say that it definitely impacted my sleep, in particular leading to problems falling asleep due to ruminating. To investigate this further, I also proceeded to plot four different sleep metrics as measured by the Oura Ring using the same approach as outlined above (weekly averages, same four date annotations) in a similar plot.
As the four metrics I chose:
- Sleep onset latency (time until falling asleep in seconds)
- Sleep efficiency (total time asleep divided by total time asleep plus sleep onset latency and time awake during the night)
- Total sleep amount (in seconds)
- Time awake during the night (in seconds)
These graphs overall seem a lot more noisy, despite being based on weekly averages as well and are also less clearly correlated than the other metrics outlined above. I think partially this is due the inherent problem with detecting sleep that all wearable devices have that do not make use of EEG recordings. Like other wrist/finger-worn wearable devices the Oura Ring uses a combination of accelerometer and heart rate data to predict different sleep stages. Unfortunately, it is not trivial to confidently distinguish between "being asleep in bed" and "laying in bed awake" and as a result a lot of time that is spent being actually awake will be classified as "light sleep" instead. As all of the sleep metrics make explicit or implicit use of identifying sleep-time, a lot of signal here might be lost due to misclassifications. Additionally, as the PCA plot (above) shows all the different sleep metrics seem to be quite correlated between each other one way or another. Despite these limitations, there seems to be a particular period of outlier for in the "time awake" data which is between the onset of the work stress and the start of the job application period, where the awake-time goes up (and the 'sleep efficiency' values go down correspondingly)
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