Understanding Well-being Data

chapter 5 Getting a sense of Big Data and well-being

Fit for Purpose? Health and well-being tracking and apps

Recent technological developments have seen a rise in people using wearable technologies and their mobile phones to track their movements and behaviour. These include: periods of activity, menstruation, what they have eaten, how they have slept, how far they have walked and their heart rate, in order to gain an overall picture of their health and general well-being. These practices are frequently called the Quantified Self movement [1], which refers both to the cultural phenomenon of self-tracking using one’s own data, as well as the community of people who use and share data in this way.

The technologies are increasingly popular and are being discussed as cost-savers for the NHS, but there are barriers to their use [2]. Around five years ago, 85% of the general population did not own wearable devices [3]. Therefore, measures which use datasets from these technologies will only account for a proportion of the population, who are most likely to be younger and more affluent [4] and already demonstrating an investment in their current and future well-being by owning such a device in the first place. We also do not yet fully understand the impact of COVID-19 on wearable devices and app use, as at the beginning of the crisis there were stories about governments using these data to monitor compliance with lockdown measures [5]. YouGov polling data[6] indicate that even in July 2020, 65% of the UK had still never owned a wearable device, with 22% currently using one (with everyone else having tried one, or owned one but not currently using one). However, the same YouGov data indicate that usage has increased from 22% to 27% in January 2021, and those who have never owned a device has decreased at a similar rate. Therefore COVID-19 has seen an increase in wearable technology, as people take an interest in their well-being data in new ways.

Self-tracking, or the practice of generating or capturing data about everyday activities like eating, exercise for purposes of self-improvement, puts data and control in the hands of people, as well as the corporations which produce self-tracking devices and the third parties with which these data are shared [7]. The research is ambivalent as to whether the experience of self-tracking has positive benefits, such as perception of control, agency or, in the case of professional or amateur sporting, opportunities for new communities [8]. It is also thought that these practices in and of themselves, and in their relationship to control, may decrease well-being more generally [9].

Data collected via mobile phone apps present similar possibilities for community and compromise. Smartphone access and usage only account for certain sections of a national demographic, much like wearable devices. Similarly, people who download an app to better understand their well-being are already self-selecting as wanting to improve their well-being, and therefore may not be considered a representative sample. A number of apps in the early 2010s wanted to further develop the insights gained from better understanding subjective well-being measurement.

In 2012, experts in geography and the lived environment based at the London School of Economics created a mobile phone app to understand happiness [10]. What they branded a ‘hedonimeter’ (after the nineteenth-century invention we discovered in Chap. 2), the ‘Mappiness’ app asked people to allow the app to collect objective data about where they were (automatically, using GPS data), what activity they were doing, and who they were with (as manual entries). It also asked them to provide hedonic responses (subjective well-being data) as to how awake, happy and relaxed they were. These data were collected using sliders instead of the more traditional scales we have previously encountered. The data collected by the app were used in a number of different ways to appreciate subjective well-being and we will touch on a couple here.

In 2015, a report which drew on this data was published. ‘Cultural Activities, Artforms and Wellbeing’ reported on research commissioned by Arts Council England (ACE). The authors evaluated the hedonic readings of various activities found in the data collected by the app [11]. Table 5.4 shows what the authors describe as ‘happiness activities rankings’, with theatre, dance and concert appearing to have the highest effect, and reading the lowest, unless you incorporate other ‘everyday participation’ activities, such as TV watching. As you can see housework, chores and DIY is negatively associated with happiness.

Table 5.4 ‘Happiness activitiesa rankings’

Theatre, dance, concert8.735***
Singing, performing7.731***
Exhibition, museum, library7.457***
Hobbies, arts, crafts5.737***
Talking, chatting, socialising3.789***
Drinking alcohol3.646***
Listening to music3.518***
Childcare, playing with children2.888***
Watching TV, film2.084***
Housework, chores, DIY−0.651***

Source: Fujiwara and MacKerron (2015)
aThe table shows coefficients, rather than rankings. Compared with the baselines, these coefficients report how much happier participants reported being when participating in these activities on a scale, when relevant variables have been controlled for. The coefficient shows the size of the impact on happiness from doing the activity (where happiness is measured on a scale of 0-100). All variables were statistically significant.

Other studies cited in this report indicate that theatre has less of an effect on life satisfaction, whereas reading fares much better [12]. As we encountered in Chap. 4, there are conceptual differences between life satisfaction and happiness, and common sense might tell us that reading and attending a theatre performance present different kinds of well-being experiences. Yet, seeing that reading looks quite bad for well-being is surprising at first glance. Elsewhere in the report are regression tables[13] for other activities, including birdwatching, gardening and hunting and fishing which are significantly better than watching a film—or indeed—poor old reading that doesn’t win on these happiness scales. Interestingly, when you go back to the Twitter data answering the question: ‘what is happiness?’ (Box 5.1) there were many responses that answered reading, curling up on the sofa and watching a film, and so on. While the limited sample of the Twitter data makes it impossible to generalise, it certainly still poses questions as to what is going on with confounding results in various happiness data. One thing that struck me returning to these cases in 2020, a world changed by COVID-19, is the difference between activities in the home and outside the home.

Interestingly, the app’s inventors co-authored an academic article for the journal Global Environmental Change. Using the same data, they found that outdoor activities were better for well-being. They state:

[T]he predicted happiness of a person who is outdoors (+2.32), birdwatching (+4.32) with friends (+4.38), in heathland (+2.71), on a hot (+5.13) and sunny (+0.46) Sunday early afternoon (+4.30) is approximately 26 scale points (or 1.2 standard deviations) higher than that of someone who is commuting (−2.03), on his or her own, in a city, in a vehicle, on a cold, grey, early weekday morning. Equivalently, this is a difference of about the same size as between being ill in bed (−19.65) vs doing physical exercise (+6.51), keeping all other factors the same.

(MacKerron and Mourato 2013, 997)

The numbers in the brackets refer to ‘the scale points’, showing the increase in probable happiness by where people are, what day of the week it is, what time of day it is. Interestingly, the greener the space you are in and the hotter the day (if sunniness seems less important than you might expect), the better. While this may appear to be common sense in one way, when you think back to how policy relies on evidence to improve well-being, what are the policy messages here from an investment point of view?

I had this app for a while and my results always told me that I was happiest in a pub beer garden with my best friends. Did I know that the data I was ploughing in when the app beeped me to do so was going to potentially be used to inform policy-making? Well, yes, of course, I guessed that, because I was researching well-being data and policy, which was why I downloaded the app in the first place. But did most people who were interested in how they felt doing certain things imagine the contexts of their data’s potential future use?

What policy decisions should be made about beer gardens off the back of my interactions with some sliders on a mobile phone app after a few ciders on a summer’s day? While these data were collected at a scale that means my personal data and my interactions are no longer visible on an individual level, it does pose questions for some of the correlations we make with these data. Are people happier on a weekend because they are not working or because they can go to the pub?


  1. Ruckenstein and Pantzar 2017[]
  2. Jee 2016[]
  3. Lee et al. 2016[]
  4. Strain et al. 2019[]
  5. Digital Initiatives 2020[]
  6. See YouGov (n.d.) ‘Brits use of wearable device’.[]
  7. Kennedy et al. 2020[]
  8. Ajana 2017; Lupton 2019; Pink and Fors 2017[]
  9. Kennedy et al. 2020[]
  10. MacKerron and Mourato 2013[]
  11. Fujiwara and MacKerron 2015[]
  12. Leadbetter et al. 2013[]
  13. A regression table like the one reproduced in Table 5.4 will mainly be concerned with communicating the degree of association between variables. Chapters 7 and 8 go into this in far greater detail. The values will always lie between 0 and 1, and the way this table has been presented shows simplified detail. Ordinarily there is additional information to show not only the degree of association, but how sure we can be that this is a correct estimate. There will always be a degree of error that has to be accounted for. Typically in a regression table, you will find asterisks, as in Table 5.4. Asterisks in a regression table indicate the level of the statistical significance of a regression coefficient.[]