Taking an extra dose of Simvastatin, and then ordering double cheese on the pizza–no, screw it, let’s get double pepperoni, too! This may not be the recommended usage of statin drugs for cholesterol, but it is how they are used by many more people than are willing to admit to the practice. While health care becomes increasingly data-driven, and IoT technology continues to find ways to gather more data about our vital signs and blood chemistry, while diagnostics increase in sensitivity–why do we keep doing things we know will harm us and how is human behaviour and change connected?
Estimated reading time: 5 minutes
Okay, first, they won’t harm us today, though perhaps a vicious hangover would put up a vigorous debate on the subject. We like our hot dogs, our milkshakes, our Crown Royal appletinis. We like to collapse on the couch after supper instead of taking a nice walk. We sit in front of our computers for hours at a time instead of standing and doing a few tai chi moves. We all know, without a doubt, the things we are doing that are impacting our health in a negative way. So what is it going to take to change this behaviour?
That’s why we may need AIs with their big neural networks to help us. How is behaviour change motivated? How can health care help people change behaviour that is impacting health? What is it going to take to get us off the couch and eating greens? How self-deluded are we, and how self-deluded do we need to remain?
Motivation to change human behaviour
Motivating human behaviour change is complex and deeply personal, bound up in a web of intimate and strange beliefs, dreams, and fears. There is no one-size-fits-all approach to motivating behaviour change. And changing health behaviour is the only thing that will save us.
In healthcare, our best efforts at this point involve asking people what is keeping them from changing, an approach that is successful with the mildly self-aware; and educating about how to change, an approach that works until the buzz of wanting to please the doctor wears off, usually eighteen hours or so. Less effective are being shown threatening pictures of shriveled, blackened lungs, which makes us want to make a rude hand gesture and light up, even if we’ve never smoked in our lives.
Asking a person, “What would it take for you to lose weight?” may work to clarify thinking for those who are ready to change, have been thinking about change, and have made the first steps. But many people never get to that place where they are ready to make a change.
Fear is the deeply buried emotion that prevents us from making changes in our behaviour. Fear that we can’t do it, that nothing we could possibly do will give us the pleasure of a warm chocolate chip cookie, that if we change, we’ll find that we still aren’t popular or pretty, fear that it’s not the size of our butt keeping us from an interesting social life, but maybe we’re just not that likable. If we change, who knows what else will change? And what if we don’t like it? What if we make life worse? What if…
Our fears can only be revealed by slowly and carefully peeling back the layers of narrative and excuses and justifications we’ve used to hide them. And the best way to do that is by a non-threatening, non-judgmental therapist with a big neural network of a brain. We need personal therapists, private AIs who can nudge us in the direction of changing our behaviour gently. A therapist who can work with us, and understand the strangely shifting nature of human motivations (human behaviour and change), a therapist with no personal agenda and an erase button we control.
The work in natural language processing and the analysis of big data, including qualitative behavioural data, has been growing by leaps and bounds. So has the IoT world of personal health monitoring. But knowing our blood sugar when we wake in the morning is not enough to keep most of us on the straight and narrow for more than a few hours, and for some, a good reading will be an excuse for a maple-bacon donut to celebrate. We need to talk these things over with someone. We need someone who will listen to us as we run through all of the reasons we deserve a maple-bacon donut. Someone to listen, without judgment.
Can technology help?
Big data and machine learning platforms are in a unique position to analyze one of the most challenging aspects of medical research: behavioral variables that are not reported accurately by common assessment methods. While EMRs (electronic medical records) prompt healthcare providers to collect a great deal of subjective information that impacts healthcare, such as compliance with medication regimes or alcohol intake, the validity of the information collected is questionable.
The subjective nature of the reasons people conceal or alter information given to a health care provider are as complicated at the whole of the human population. People feel social pressures to conform and please a questioner. They don’t want to admit to money problems that impact health care. They do not accurately see their own behaviour. Cultural norms regarding personal information vary widely, as does disclosure by age and gender and social class. But new methods of gathering and quantifying data across populations has the potential to give greater insights into human behaviour that can change the results of medical research.
Relying solely on patient reports of behaviour is a method of gathering data that is extremely limited and may significantly impact the results of healthcare research. But gathering self reports, along with subjective research reports, pharmacy records, laboratory test results, social media, buying behaviour, financial records, employment records, and other sources of data, and then analyzing across populations, can give a more accurate picture of what people are actually doing and how human behaviour and change interacts. By having a more accurate picture of human behavioural variables, healthcare research can more accurately assess the impact of human behaviour on health care outcomes and propose treatment modalities that are fine-tuned to the people we actually are.
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