Science

How nudg works things out.

Every chart in the app has an “i” with a short explanation. This page is the long version: what each measure is, how nudg calculates it, and the research that makes it worth tracking.

nudg score

One number out of 100 for how today compares with your own normal. It is built from four parts, each scored against the median of your previous 28 days, so a good day means good for you rather than for an average person.

If a part has no data that day (you didn't wear your watch to bed, say) it is left out and the rest are scaled up to 100, so missing data doesn't drag the score down. Spending alone never makes a score; at least one health part is needed. Scores of 85 and above read as Great, 70 to 84 Good, 55 to 69 Fair and below 55 Low.

Personal baselines are used because people differ a lot in their normal sleep, HRV and resting heart rate, and the change from your own normal is usually more informative than a population average (Shaffer & Ginsberg 2017; Buchheit 2014).

Sleep

Time actually asleep during your main sleep, not time in bed, as recorded by your watch or tracker in Apple Health. Sleep is counted towards the day you woke up, so Tuesday's sleep is the night before Tuesday.

Adults are advised to get seven or more hours a night (Watson et al. 2015; Hirshkowitz et al. 2015). Short sleep affects attention, mood and decision-making the next day (Killgore 2010; Walker 2009) and is linked with weaker self-control (Pilcher et al. 2015), which is why nudg looks at sleep next to spending and screen time.

Sleep stages

Your tracker estimates how much of the night you spent awake, in REM, light and deep sleep, using movement and heart rate. Deep sleep supports physical recovery and REM supports memory and emotional processing (Walker 2009).

Consumer trackers are good at telling sleep from wake but less precise about individual stages than a sleep lab (de Zambotti et al. 2019), so nudg treats stages as a rough guide and leans on total sleep for the score.

HRV

Heart rate variability is the small variation in time between one heartbeat and the next, measured overnight while you are still. A higher HRV than your usual generally means your body is recovered; a drop below your normal often points to stress, poor sleep, alcohol or illness (Shaffer & Ginsberg 2017; Kim et al. 2018).

HRV varies hugely between people, so nudg only ever compares it with your own baseline. Day-to-day readings are noisy, so trends over several days say more than one night (Plews et al. 2013).

Resting heart rate

Your heart rate when fully at rest, mostly measured while you sleep. A resting heart rate a few beats above your normal can be an early sign of fatigue, strain or illness (Buchheit 2014; Quer et al. 2021).

Steps

Steps counted by your phone or watch across the day. Daily step count is one of the simplest and best-studied measures of activity: in a large meta-analysis, more daily steps were linked with lower risk of death, with benefits levelling off at around 6,000 to 8,000 steps a day for older adults and 8,000 to 10,000 for younger adults (Paluch et al. 2022; Saint-Maurice et al. 2020). nudg gives full activity points from 8,000 steps.

Exercise

Minutes spent at moderate or vigorous effort; light movement is left out. UK and WHO guidelines suggest at least 150 minutes of moderate activity, or 75 of vigorous activity, a week (UK Chief Medical Officers 2019; WHO 2020). nudg gives full exercise points at 30 minutes in a day.

Workouts

Sessions your tracker detected or you logged, with the time spent in each heart-rate zone. Harder zones build fitness; easier time builds your aerobic base. nudg shows workouts alongside recovery so you can see how training affects your HRV and sleep.

Spending

Your share of card and bank payments, read through a regulated open banking provider. Transfers between your own accounts, investments and money you are paid back by friends are taken out, so the figure reflects what you actually spent. Rent and bills are tracked but kept out of the score.

Research on self-control has long linked tiredness and depleted willpower with impulse buying (Baumeister 2002; Vohs & Faber 2007). Putting spending next to sleep and recovery lets you see whether that is true for you.

Correlations

nudg looks for links between pairs of measures over roughly the last six months, for example last night's sleep and today's spending. It uses two methods:

• Group comparisons. Days are grouped (such as nights under six hours against seven hours or more, or your lowest-HRV quarter of days against the rest) and the typical day in each group is compared using the median, which isn't thrown off by one big purchase.
• Rank correlation. A Spearman correlation across all days, which picks up steady relationships without assuming they are straight lines. nudg needs at least 20 days before testing a pair.

A link is called strong when it is unlikely to be chance (p below 0.01) and at least moderate in size (a correlation of 0.3 or more), and directional when p is below 0.05. Weaker links aren't shown.

A correlation is not proof of cause. Both things might be driven by something else, like a busy week at work. But a pattern that keeps showing up in your own data is a good reason to try a change and see if it holds, which is what nudg suggests.

Getting better or worse

Compares your last 30 days with the 30 before, matching like with like (weekdays against weekdays, weekends against weekends), so a change in routine shows up rather than just the mix of days. This is how you can tell whether a change you have made is working.

Air quality

The US Air Quality Index for where you were, averaged over the day, from Open-Meteo. Under 50 is good, 101 to 150 is unhealthy for sensitive groups and above 150 is unhealthy for everyone (US EPA AirNow). Polluted air is linked with worse sleep (Liu et al. 2020) and tends to keep people indoors and less active.

Weather

Temperature, rain and wind for the city you are in (worked out from where you spend), from Open-Meteo. Hot nights shorten sleep (Minor et al. 2022), and heat and rain change how much people move and what they spend on, such as taxis and delivery.

Sources

1. Baumeister RF (2002). Yielding to temptation: self-control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research 28(4), 670–676.
2. Buchheit M (2014). Monitoring training status with HR measures: do all roads lead to Rome? Frontiers in Physiology 5, 73.
3. de Zambotti M, Cellini N, Goldstone A, Colrain IM, Baker FC (2019). Wearable sleep technology in clinical and research settings. Medicine & Science in Sports & Exercise 51(7), 1538–1557.
4. Hirshkowitz M et al. (2015). National Sleep Foundation's sleep time duration recommendations. Sleep Health 1(1), 40–43.
5. Killgore WDS (2010). Effects of sleep deprivation on cognition. Progress in Brain Research 185, 105–129.
6. Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH (2018). Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry Investigation 15(3), 235–245.
7. Liu J, Wu T, Liu Q, Wu S, Chen JC (2020). Air pollution exposure and adverse sleep health across the life course: a systematic review. Environmental Pollution 262, 114263.
8. Minor K, Bjerre-Nielsen A, Jonasdottir SS, Lehmann S, Obradovich N (2022). Rising temperatures erode human sleep globally. One Earth 5(5), 534–549.
9. Paluch AE et al. (2022). Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. The Lancet Public Health 7(3), e219–e228.
10. Pilcher JJ, Morris DM, Donnelly J, Feigl HB (2015). Interactions between sleep habits and self-control. Frontiers in Human Neuroscience 9, 284.
11. Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M (2013). Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring. Sports Medicine 43(9), 773–781.
12. Quer G et al. (2021). Wearable sensor data and self-reported symptoms for COVID-19 detection. Nature Medicine 27, 73–77.
13. Saint-Maurice PF et al. (2020). Association of daily step count and step intensity with mortality among US adults. JAMA 323(12), 1151–1160.
14. Shaffer F, Ginsberg JP (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health 5, 258.
15. UK Chief Medical Officers (2019). UK Chief Medical Officers' Physical Activity Guidelines.
16. US EPA. AQI basics. AirNow.
17. Vohs KD, Faber RJ (2007). Spent resources: self-regulatory resource availability affects impulse buying. Journal of Consumer Research 33(4), 537–547.
18. Walker MP (2009). The role of sleep in cognition and emotion. Annals of the New York Academy of Sciences 1156, 168–197.
19. Watson NF et al. (2015). Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Sleep 38(6), 843–844.
20. World Health Organization (2020). WHO guidelines on physical activity and sedentary behaviour.
21. Weather and air quality data: Open-Meteo.