Interview with Malcolm Emery, originator of the Workwell project and scientist studying the effects of lifestyle on health and well-being. Malcolm was, among others founder of City Healthcare, Senior PWC Consultant and Scientific Advisor to the British Sports Council. He is currently the CEO of Praxis Workwell Ltd., where he focuses on using artificial intelligence and machine learning to improve the quality of life and productivity of employees.
Why did you decide to pursue happiness professionally?
We used in the development phase the Hospital Anxiety Depression Scale (HADS) for mental health and Framingham for disease risk. These methods though internationally accepted and validated are laborious and have a focus on illness. In working life it was felt that a more positive approach to wellbeing assessment was required based on the physiological drivers of the body’s energy levels, resilience to both physical and mental pressures and happiness levels. From previous research as scientific advisor to the UK sports council and whilst working on ageing research at the Royal Brompton Hospital it became clear that these components were central to wellbeing was the body’s repair system. This system is driven by hormones generated by the brain in response to both physical and emotional activity. Catabolic i.e. adrenaline hormones, release energy, from muscles for example. This energy release results in negative changes to the body tissue involved. Anabolic hormones e.g., growth hormone, oestradiol, repair the resulting body changes during sleep. Similarly in the brain, the balance of anabolic dopamines and catabolic catecholamines hormones is central to mental wellbeing and feelings of happiness. Workwell has developed (and is patenting) based on these physiological drivers, a Quality of Life (QOL) wellbeing assessment based on an individual’s responses to their feelings of happiness, resilience and energy.
The general perception is that people are happy mainly because of money, family and health. But there are many more of these factors, aren't there?
The development of the Workwell programme had two initial objectives.
The first was to use AI to identify and quantify the causal factors of wellbeing as defined by the quality of life (QOL) process. This involved by trial and error finding 180 possible causes including:
- lifestyle factors - ranging through activity, diet, sleep and social interaction.
- Personal demographics, - including age, marital status, job description, salary, dependents including children and relatives and access to personal support.
- Current personal health status and history, and family health status and history.
- Local environment - including accommodation, access to parks, medical services, schools, transport, and pollution.
- The workplace environment including, - physical, tools and equipment, training, job difficulty and daily workload, hours at work and work/life balance.
- Corporate culture including - leadership, involvement, autonomy, shared aims.
- Financial status.
Finally - this took some time to discover -
- When life events including, deaths, illnesses accidents, divorce/separation and legal disputes were added.
The AI could predict with over 95% accuracy the quality-of-life status for each individual and identify and quantify the causal factors of their quality of life.
How can a good life be helped by artificial intelligence?
- The AI can determine and rank for each individual the causal factors of their current wellbeing status. This enables a systems approach to improving an individuals wellbeing status to be adopted. Whereby an individual can be directed to making a series of small but achievable changes in the key factors influencing their quality of life. i.e., the through systems approach the total effect is much greater than the sum of the parts. The AI can then also predict if such changes were made the improvements that would result.
- The AI can also be used to predict the likelihood of the respondent having a stress related illness that would require time off work (sickness/absence), or working below capacity whilst still at work (presenteeism)
- A sponsoring organisation can use the aggregated and anonymised data to determine their financial costs attributable to sickness absence, presenteeism and staff turnover.
- The sponsoring organisation can then use AI to interrogate the data and determine the causal factors under their control which are impacting employee wellbeing resulting in their employees’ sickness/absence, presenteeism and staff turnover costs. See example dashboard.
How did you collect data to create Workwell? Did you conduct your own research or gather the results of someone else's research?
The work on quality of life and AI implementation is entirely internal with no external input other than support from our academic partners, Exeter University and Glasgow university. The initial data collected from a nationally representative sample commissioned using Workwell questionnaires from Opiniography a national data collection organisation.
Data has subsequently been further built from commercial use by one legal organisation and eight separate government departments currently using the system.
Do you already know how it has translated into their businesses? Is it known how their productivity has increased, what other benefits have they enjoyed?
Feedback and reaction from individual users have been very positive.
For commercial use the programme provides data and direction it does not mange change! The govt departments have commissioned change management organisations to work with the data provided to implement such changes.
Why should employers care about employee happiness?
One of the biggest challenges facing successful organisations is retaining key staff. Happy employees as well as being more effective – low presenteeism rates, having fewer sickness/absence days - are far more likely to remain loyal to the organisation keeping
