Tracking Long-Term Sleep and Activity Differences across Chronotypes: A Case Study in Finland Using Oura Rings

Chandreyee Roy; Kunal Bhattacharya; Kimmo Kaski

doi:10.53941/jsp.2026.100007

Abstract

Non-invasive mobile wearables like fitness trackers, smartwatches, and smart rings allow for an easy and relatively less expensive approach to studying patterns of everyday human sleep and activity patterns compared to traditional longitudinal methods. We used smart rings manufactured by Oura to obtain granular data from 19 healthy participants over the time span of one year (October 2023–September 2024), to investigate longitudinal sleep and activity patterns of three chronotype groups among them: morning type (MT), neither type (NT), and evening type (ET). We find that while ET individuals do not seem to lead as healthy patterns as MT or NT individuals in terms of overall sleep and activity, they seem to have significantly improved their habits over the duration of the study. The activity in all chronotype groups varies throughout the year with the ET individuals showing an increasing trend. Furthermore, we show that the Daylight Saving Time changes in autumn affect the sleep quality of the MT individuals. Finally, using a mixed-effects regression model, we show that the perceived stress of an individual is significantly associated with their time spent in bed during nighttime sleep, monthly survey response time, chronotype and age, while accounting for individual variability.

References

1.
Huhn, S.; Axt, M.; Gunga, H.C.; et al. The impact of wearable technologies in health research: Scoping review. JMIR mHealth uHealth 2022, 10, e34384.
2.
Kazanskiy, N.L.; Khonina, S.N.; Butt, M.A. A review on flexible wearables—Recent developments in non-invasive continuous health monitoring. Sens. Actuators A Phys. 2024, 366, 114993.
3.
Lee, T.; Cho, Y.; Cha, K.S.; et al. Accuracy of 11 wearable, nearable, and airable consumer sleep trackers: Prospective multicenter validation study. JMIR mHealth uHealth 2023, 11, e50983.
4.
Perez, M.V.; Mahaffey, K.W.; Hedlin, H.; et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N. Engl. J. Med. 2019, 381, 1909–1917.
5.
Ferguson, T.; Olds, T.; Curtis, R.; et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: A systematic review of systematic reviews and meta-analyses. Lancet Digit Health 2022, 4, e615–e626. https://doi.org/10.1016/S2589-7500(22)00111-X.
6.
Mishra, T.; Wang, M.; Metwally, A.A.; et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat. Biomed. Eng. 2020, 4, 1208–1220.
7.
Grzesiak, E.; Bent, B.; McClain, M.T.; et al. Assessment of the feasibility of using noninvasive wearable biometric monitoring sensors to detect influenza and the common cold before symptom onset. JAMA Netw. Open 2021, 4, e2128534.
8.
Li, X.; Dunn, J.; Salins, D.; et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 2017, 15, e2001402.
9.
Mayer, C.; Tyler, J.; Fang, Y.; et al. Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression. Cell Rep. Med. 2022, 3, 100601.
10.
Kinnunen, H.; Rantanen, A.; Kentt¨a, T.; et al. Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG. Physiol. Meas. 2020, 41, 04NT01.
11.
Alzueta, E.; de Zambotti, M.; Javitz, H.; et al. Tracking sleep, temperature, heart rate, and daily symptoms across the menstrual cycle with the Oura ring in healthy women. Int. J. Women’s Health 2022, 14, 491–503.
12.
Walker, M. Why We Sleep: The New Science of Sleep and Dreams; Penguin: London, UK, 2017.
13.
Durmer, J.S.; Dinges, D.F. Neurocognitive consequences of sleep deprivation. Semin. Neurol. 2005, 25, 117–129.
14.
Knutson, K.L.; Spiegel, K.; Penev, P.; et al. The metabolic consequences of sleep deprivation. Sleep Med. Rev. 2007, 11, 163–178.
15.
Grandner, M.A. Sleep, health, and society. Sleep Med. Clin. 2022, 17, 117–139.
16.
Jafari, B.; Mohsenin, V. Polysomnography. Clin. Chest Med. 2010, 31, 287–297.
17.
Fekedulegn, D.; Andrew, M.E.; Shi, M.; et al. Actigraphy-based assessment of sleep parameters. Ann. Work. Expo. Health 2020, 64, 350–367.
18.
Binkley, P.F. Predicting the potential of wearable technology. IEEE Eng. Med. Biol. Mag. 2003, 22, 23–27.
19.
Ko, Y.F.; Kuo, P.H.; Wang, C.F.; et al. Quantification analysis of sleep based on smartwatch sensors for Parkinson’s disease. Biosensors 2022, 12, 74.
20.
Chandrasekaran, R.; Sadiq T.M.; Moustakas, E. Usage trends and data sharing practices of healthcare wearable devices among US adults: cross-sectional study. J. Med. Internet Res. 2025, 27, e63879.
21.
Portier, F.; Portmann, A.; Czernichow, P.; et al. Evaluation of home versus laboratory polysomnography in the diagnosis of sleep apnea syndrome. Am. J. Respir. Crit. Care Med. 2000, 162, 814–818.
22.
Bruyneel, M.; Sanida, C.; Art, G.; et al. Sleep efficiency during sleep studies: results of a prospective study comparing home-based and in-hospital polysomnography. J. Sleep Res. 2011, 20, 201–206.
23.
Leeder, J.; Glaister, M.; Pizzoferro, K.; et al. Sleep duration and quality in elite athletes measured using wristwatch actigraphy. J. Sport. Sci. 2012, 30, 541–545.
24.
Ko, P.R.T.; Kientz, J.A.; Choe, E.K.; et al. Consumer sleep technologies: a review of the landscape. J. Clin. Sleep Med. 2015, 11, 1455–1461.
25.
Nauha, L.; Farrahi, V.; Jurvelin, H.; et al. Comparison and agreement between device-estimated and self-reported sleep periods in adults. Ann. Med. 2023, 55, 2191001.
26.
Shelgikar, A.V.; Anderson, P.F.; Stephens, M.R. Sleep tracking, wearable technology, and opportunities for research and clinical care. Chest 2016, 150, 732–743.
27.
Koskimaki, H.; Kinnunen, H.; Kurppa, T.; et al. How do we sleep: A case study of sleep duration and quality using data from oura ring. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore, 8–12 October 2018; pp. 714–717.
28.
Kinnunen, H.; Smarr, B.; Laakkonen, H.; et al. Correlation between MEQ Questionnaire Based Chronotype and Sleep Parameters Automatically Obtained by Commercially Available Wearable Ring. Sleep 2018, 41, A24.
29.
Kristiansson, E.; Fridolfsson, J.; Arvidsson, D.; et al. Validation of Oura ring energy expenditure and steps in laboratory and free-living. BMC Med. Res. Methodol. 2023, 23, 50.
30.
Svensson, T.; Madhawa, K.; Hoang, N.; et al. Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs. Sleep Med. 2024, 115, 251–263.
31.
Willoughby, A.R.; Golkashani, H.A.; Ghorbani, S.; et al. Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use. Sleep Health 2024, 10, 356–368.
32.
Foster, R.G. Sleep, circadian rhythms and health. Interface Focus 2020, 10, 20190098.
33.
Merikanto, I.; Lahti, T.; Kronholm, E.; et al. Evening types are prone to depression. Chronobiol. Int. 2013, 30, 719–725.
34.
Roenneberg, T. Having trouble typing? What on earth is chronotype? J. Biol. Rhythm. 2015, 30, 487–491.
35.
Horne, J.A.; Ostberg, O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int. J. Chronobiol. 1976, 4, 97–110.
36.
Roenneberg, T.; Kuehnle, T.; Juda, M.; et al. Epidemiology of the human circadian clock. Sleep Med. Rev. 2007, 11, 429–438.
37.
Roy, C.; Monsivais, D.; Bhattacharya, K.; et al. Morningness—Eveningness assessment from mobile phone communication analysis. Sci. Rep. 2021, 11, 14606.
38.
Salfi, F.; D’Atri, A.; Amicucci, G.; et al. The fall of vulnerability to sleep disturbances in evening chronotypes when working from home and its implications for depression. Sci. Rep. 2022, 12, 12249.
39.
Chellappa, S.L.; Morris, C.J.; Scheer, F.A. Circadian misalignment increases mood vulnerability in simulated shift work. Sci. Rep. 2020, 10, 18614.
40.
van de Ven, H.A.; Hulsegge, G.; Zoomer, T.; et al. The acute effects of working time patterns on fatigue and sleep quality using daily measurements of 6195 observations among 223 shift workers. Scand. J. Work. Environ. Health 2021, 47, 446–455.
41.
Merikanto, I.; Kortesoja, L.; Benedict, C.; et al. Evening-types show highest increase of sleep and mental health problems during the COVID-19 pandemic—Multinational study on 19,267 adults. Sleep 2022, 45, zsab216.
42.
Makarem, N.; Paul, J.; Giardina, E.G.V.; et al. Evening chronotype is associated with poor cardiovascular health and adverse health behaviors in a diverse population of women. Chronobiol. Int. 2020, 37, 673–685.
43.
Naeck, R.; D’Amore, D.; Mateo, M.F.; et al. Sleep diversity index for sleep fragmentation analysis. J. Nonlinear Syst. Appl. 2009, 151–154.
44.
Gelman, A. Data Analysis Using Regression and Multilevel/Hierarchical Models; Cambridge University Press: Cambridge, UK, 2007.
45.
Bates, D.; Maechler, M.; Bolker, B.; et al. Package ‘lme4’. Version 2.0-1. 2015. Available online: http://dk.archive.ubuntu.com/pub/pub/cran/web/packages/lme4/lme4.pdf (accessed on 13 March 2026).
46.
Allebrandt, K.V.; Teder-Laving, M.; Kantermann, T.; et al. Chronotype and sleep duration: the influence of season of assessment. Chronobiol. Int. 2014, 31, 731–740.
47.
Mattingly, S.M.; Grover, T.; Martinez, G.J.; et al. The effects of seasons and weather on sleep patterns measured through longitudinal multimodal sensing. NPJ Digit. Med. 2021, 4, 76.
48.
Vitale, J.A.; Roveda, E.; Montaruli, A.; et al. Chronotype influences activity circadian rhythm and sleep: Differences in sleep quality between weekdays and weekend. Chronobiol. Int. 2015, 32, 405–415.
49.
Maukonen, M.; Havulinna, A.S.; Mannisto, S.; et al. Genetic associations of chronotype in the Finnish general population. J. Biol. Rhythm. 2020, 35, 501–511.
50.
Bailey, S.L.; Heitkemper, M.M. Circadian rhythmicity of cortisol and body temperature: Morningness-eveningness effects. Chronobiol. Int. 2001, 18, 249–261.
51.
Medina, D.; Ebben, M.; Milrad, S.; et al. Adverse effects of daylight saving time on adolescents’ sleep and vigilance. J. Clin. Sleep Med. 2015, 11, 879–884.
52.
Lahti, T.A.; Leppamaki, S.; Lonnqvist, J.; et al. Transitions into and out of daylight saving time compromise sleep and the rest-activity cycles. BMC Physiol. 2008, 8, 3.
53.
Harrison, Y. The impact of daylight saving time on sleep and related behaviours. Sleep Med. Rev. 2013, 17, 285–292.

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