How data can encourage wellbeing at work
I was recently asked if we should be performing employee wellbeing initiatives because it is the right thing to do or because it saves an organisation money. My answer was to support the latter. It's not because I believe ‘doing the right thing’ is not worthwhile, or that organisations only care if there is a cost saving. Rather, it's because the two options are inextricably linked and are only perceived as separate if poorly managed.
Typical wellbeing initiatives address an issue and consider direct or indirect solutions to resolve them. Let’s take the example of back pain. Common solutions for higher rates of employee back pain are display screen equipment (DSE) assessments, ergonomic equipment or physiotherapy services. These interventions have been shown to help those with back pain return to work faster and have fewer days off, but the number of annual back pain incidences remains roughly the same, so the underlying problem isn’t changing.
Now let’s take that same example and consider that back pain is caused by sitting at a desk too long and slouching to complete a repetitive typing task. This would mean that the intermediate solution would be standing desks and treadmill desks, which can be of benefit, but also run a risk of increasing back pain if used without correct guidance - try standing in one spot for eight hours and see how your back feels.
Therefore, there would be value in taking this solution even further back in the process and looking to understand why employees spend so long sitting at a computer with a common reason being ‘to answer emails’. This signifies that there may be a poor email culture and a poor line of internal communication, which may not be supported by line management. So the solution may be that an internal communications strategy is created, line managers are trained to be advocates, technical solutions are incorporated (such as an auto-redirect of CC emails to a secondary, deprioritised inbox) and emails are turned off completely over lunch.
If a Wellbeing Manager were to propose: ‘We should turn off emails completely at lunch periods’ this would undoubtedly be swiftly declined, but if you link in data, it becomes a more compelling suggestion. Linking data from the start point to the end makes the cost benefit identifiable and curatable. It allows the decision maker to see the abstract interventions in a causative relationship and identify the best model to elicit the best response.
The linear relationship example below highlights how the actions could interconnect and result in the reduction of absenteeism, which is financially measurable.
Turn off emails at lunch > Training/justification/support from line management > Lunch break/away from desk/social contact > Reduced sitting time/spinal loading > Reduced absenteeism caused by back pain*
In this example the Wellbeing Manager would have collated data on the part of the business this affects. This might include: training completion and feedback from line managers; wellbeing survey feedback from employees (including colleague contact time); sitting behaviour and productivity, in order to show the reduction in absenteeism rates.
Their proposal would now be reframed as: ‘We should turn off emails completely for 45 minutes between 1300 - 1345 as this will save the business £30,000 per annum.’ This is a far more compelling wellbeing proposal now it has the evidence of financial reason to support it.
At a human level the end result is that the linking of data has encouraged employees to get up, have a lunch period that benefits their physical and emotional wellbeing and encourages social contact. The data has done the ‘right thing’ at the same time as helping the business perform more efficiently.
*Note this is a theoretical example and not a tested intervention
Tuesday 16 August 2016