Skip to main content

Advertisement

SpringerLink
Log in

Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence

  • Sleep Breathing Physiology and Disorders • Review
  • Published:
Sleep and Breathing Aims and scope Submit manuscript

Abstract

Smartphone applications are considered as the prime candidate for the purposes of large-scale, low-cost and long-term sleep monitoring. How reliable and scientifically grounded is smartphone-based assessment of healthy and disturbed sleep remains a key issue in this direction. Here we offer a review of validation studies of sleep applications to the aim of providing some guidance in terms of their reliability to assess sleep in healthy and clinical populations, and stimulating further examination of their potential for clinical use and improved sleep hygiene. Electronic literature review was conducted on Pubmed. Eleven validation studies published since 2012 were identified, evaluating smartphone applications’ performance compared to standard methods of sleep assessment in healthy and clinical samples. Studies with healthy populations show that most sleep applications meet or exceed accuracy levels of wrist-based actigraphy in sleep-wake cycle discrimination, whereas performance levels drop in individuals with low sleep efficiency (SE) and in clinical populations, mirroring actigraphy results. Poor correlation with polysomnography (PSG) sleep sub-stages is reported by most accelerometer-based apps. However, multiple parameter-based applications (i.e., EarlySense, SleepAp) showed good capability in detection of sleep-wake stages and sleep-related breathing disorders (SRBD) like obstructive sleep apnea (OSA) respectively with values similar to PSG. While the reviewed evidence suggests a potential role of smartphone sleep applications in pre-screening of SRBD, more experimental studies are warranted to assess their reliability in sleep-wake detection particularly. Apps’ utility in post treatment follow-up at home or as an adjunct to the sleep diary in clinical setting is also stressed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Walker MP (2009) The role of sleep in cognition and emotion. Ann N Y Acad Sci 1156:168–197

    Article  Google Scholar 

  2. Krause AJ, Simon EB, Mander BA, Greer SM, Saletin JM, Goldstein-Piekarski AN, Walker MP (2017) The sleep-deprived human brain. Nat Rev Neurosci 18(7):404–418

    Article  CAS  Google Scholar 

  3. Raven F, Van der Zee EA, Meerlo P, Havekes R (2017) The role of sleep in regulating structural plasticity and synaptic strength: implications for memory and cognitive function. Sleep Med Rev 39:3–11. https://doi.org/10.1016/j.smrv.2017.05.002

    Article  PubMed  Google Scholar 

  4. Durmer JS, Dinges DF (2005) Neurocognitive consequences of sleep deprivation. Semin Neurol 25:117–129

    Article  Google Scholar 

  5. Killgore WD (2010) Effects of sleep deprivation on cognition. Prog Brain Res 185:105–129

    Article  Google Scholar 

  6. Tempesta D, Couyoumdjian A, Curcio G, Moroni F, Marzano C, De Gennaro L et al (2010) Lack of sleep affects the evaluation of emotional stimuli. Brain Res Bull 82:104–108

    Article  Google Scholar 

  7. Panossian LA, Avidan AY (2009) Review of sleep disorders. Med Clin North Am 93:407–425

    Article  Google Scholar 

  8. Morgan D, Tsai SC (2015) Sleep and the endocrine system. Crit Care Clin 31(3):403–418

    Article  Google Scholar 

  9. Morgan D, Tsai SC (2016) Sleep and the endocrine system. Sleep Med Clin 11(1):115–126

    Article  Google Scholar 

  10. Cassoff J, Bhatti JA, Gruber R (2014) The effect of sleep restriction on neurobehavioural functioning in normally developing children and adolescents: insights from the attention, behaviour and sleep laboratory. Pathol Biol 62(5):319–331

    Article  CAS  Google Scholar 

  11. Kecklund G, Axelsson J (2016) Health consequences of shift work and insufficient sleep. BMJ 355:i5210

    Article  Google Scholar 

  12. Wolkow A, Ferguson S, Aisbett B, Main L (2015) Effects of work-related sleep restriction on acute physiological and psychological stress responses and their interactions: a review among emergency service personnel. Int J Occup Med Environ Health 28(2):183–208

    PubMed  Google Scholar 

  13. Van de Water ATM, Holmes A, Hurley DA (2011) Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography—a systematic review. J Sleep Res 20:183–200

    Article  Google Scholar 

  14. Ko PR, Kientz JA, Choe EK, Kay M, Landis CA, Watson NF (2015) Consumer sleep technologies: a review of the landscape. J Clin Sleep Med 11(12):1455–1461

    Article  Google Scholar 

  15. Behar J, Roebuck A, Domingo JS, Gederi E, Clifford GD (2013) A review of current sleep screening applications for smartphones. Physiol Meas 34:29–46

    Article  Google Scholar 

  16. Bhat S, Ferraris A, Gupta D, Mozafarian M, DeBari V, Gushway-Henry N, Gowda SP, Polos PG, Rubinstein M, Seidu H, Chokroverty S (2015) Is there a clinical role for smartphone sleep apps? Comparison of sleep cycle detection by a smartphone application to polysomnography. J Clin Sleep Med 11:709–715

    PubMed  PubMed Central  Google Scholar 

  17. Ong A, Boyd GM (2016) Overview of smartphone applications for sleep analysis. WJOHNS 2:45–49

    PubMed  Google Scholar 

  18. Fietze I (2016) Sleep applications to assess sleep quality. Sleep Med Clin 11(4):461–468

    Article  Google Scholar 

  19. Toon E, Davey MJ, Hollis SL, Hons BA, Nixon MG, Home R, Biggs NS (2016) Comparison of commercial wrist-based and smartphone accelerometers, actigraphy, and PSG in a clinical cohort of children and adolescents. J Clin Sleep Med 12(3):343–350

    Article  Google Scholar 

  20. Patel P, Kim JY, Brooks LJ (2017) Accuracy of a smartphone application in estimating sleep in children. Sleep Breath 21:505–511

    Article  Google Scholar 

  21. Marino M, Li Y, Rueschman MN, Winkelman JW, Ellenbogen JM, Solet JM, Dulin H, Berkman LF, Buxton OM (2013) Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep 36(11):1747–1755

    Article  Google Scholar 

  22. Meltzer LJ, Walsh CM, Traylor J, Westin AM (2012) Direct comparison of two new actigraphs and polysomnography in children and adolescents. Sleep 35:159–166

    PubMed  PubMed Central  Google Scholar 

  23. de Souza L, Benedito-Silva AA, Nogueira Pires ML, Poyares D, Tufik S, Calil HM (2003) Further validation of actigraphy for sleep studies. Sleep 26(1):81–85

    Article  CAS  Google Scholar 

  24. Sadeh A (2011) The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev 15(4):259–267

    Article  Google Scholar 

  25. Sadeh A, Acebo C (2002) The role of actigraphy in sleep medicine. Sleep Med Rev 6(2):113–124

    Article  Google Scholar 

  26. Carney CE, Buysse DJ, Ancoli-Israel S, Edinger JD, Krystal AD, Lichstein KL, Morin CM (2012) The consensus sleep diary: standardizing prospective sleep self monitoring. Sleep 35:287–302

    Article  Google Scholar 

  27. Girschik J, Fritschi L, Heyworth, Waters F (2012) Validation of self-reported sleep against actigraphy. J Epidemiol 22(5):462–468

    Article  Google Scholar 

  28. Tang NK, Harvey AG (2006) Altering misperception of sleep in insomnia: behavioral experiment versus verbal feedback. J Consult Clin Psychol 74:767–776

    Article  Google Scholar 

  29. Herbert V, Pratt D, Emsley R, Kyle SD (2017) Predictors of nightly subjective-objective sleep discrepancy in poor sleepers over a seven-day period. Brain Sci 7–29 . https://doi.org/10.3390/brainsci7030029

  30. Kay DB, Buysse DJ, Germain A, Hall M, Monk TH (2015) Subjective-objective sleep discrepancy among older adults: associations with insomnia diagnosis and insomnia treatment. J Sleep Res 24(1):32–39

    Article  Google Scholar 

  31. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ (1989) The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 28:193–213

    Article  CAS  Google Scholar 

  32. Johns MW (1991) A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 14:540–545

    Article  CAS  Google Scholar 

  33. Bonzelaar LB, Salapatas AM, Yang J, Friedman M (2017) Validity of the Epworth Sleepiness Scale as a screening tool for obstructive sleep apnea. Laryngoscope 127(2):525–531

    Article  Google Scholar 

  34. Chung F, Yegneswaran B, Liao P, Chung S, Vairavanathan S, Islam S, Khajehdehi A, Shapiro C (2008) Stop questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology 108:812–821

    Article  Google Scholar 

  35. Berger AM, Wielgus KK, Young-McCaughan S, Fischer P, Farr L, Lee KA (2008) Methodological challenges when using actigraphy in research. J Pain Symptom Manag 36(2):191–199

    Article  Google Scholar 

  36. Sadeh A, Sharkey KM, Carskadon MA (1994) Activity-based sleep-wake identification: a empirical test of methodological issues. Sleep 17:201–207

    Article  CAS  Google Scholar 

  37. Paavonen EJ, Fjallberg M, Steenari MR, Aronen ET (2002) Actigraph placement and sleep estimation in children. Sleep 25:235–237

    Article  Google Scholar 

  38. Roebuck A, Monasterio V, Gederi E, Osipov M, Behar J, Malhotra A, Penzel T, Clifford GD (2014) Signal processing of data recorded during sleep. Physiol Meas 35(1):1–57

    Article  Google Scholar 

  39. Carter MC, Burley VJ, Nykjaer C, Cade JE (2013) Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res 15(4):e32

    Article  Google Scholar 

  40. Min YH, Lee JW, Shin YW et al (2014) Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study. J Med Internet Res 16:135

    Article  Google Scholar 

  41. Shin H, Cho J (2014) Unconstrained snoring detection using a smartphone during ordinary sleep. Biomed Eng Online 13:116

    Article  Google Scholar 

  42. Martin JL, Hakim AD (2011) Wrist actigraphy. Chest 139:1514–1527

    Article  Google Scholar 

  43. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP (2003) The role of actigraphy in the study of sleep and circadian rhythms. Sleep 26:342–392

    Article  Google Scholar 

  44. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC (1992) Automatic sleep/wake identification from wrist activity. Sleep 15(5):461–469

    Article  CAS  Google Scholar 

  45. Tal A, Shinar Z, Shaki D, Codish S, Goldbart A (2017) Validation of contact-free sleep monitoring device with comparison to polysomnography. J Clin Sleep Med 13:517–522

    Article  Google Scholar 

  46. Ben-Ari J, Zimlichman E, Adi N, Sorkine P (2010) Contactless respiratory and heart rate monitoring: validation of an innovative tool. J Med Eng Technol 34(7–8):393–398

    Article  CAS  Google Scholar 

  47. Zimlichman E, Szyper-Kravitz M, Shinar Z, Klap T, Levkovich S, Unterman A, Rozenblum R, Rothschild JM, Amital H, Shoenfeld Y (2012) Early recognition of acutely deteriorating patients in non-intensive care units: assessment of an innovative monitoring technology. J Hosp Med 7(8):628–633

    Article  Google Scholar 

  48. Zimlichman E, Shinar Z, Rozenblum R, Levkovich S, Skiano S, Szyper-Kravitz M, Altman A, Amital H, Shoenfeld Y (2011) Using continuous motion monitoring technology to determine patient’s risk for development of pressure ulcers. J Patient Saf 7(4):181–184

    Article  Google Scholar 

  49. Oakley NR (1997) Validation with Polysomnography of the Sleepwatch Sleep/Wake Scoring Algorithm used by the Actiwatch Activity Monitoring System. Technical report, Bend, Ore., Mini-Mitter

  50. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC (1992) Automatic sleep/wake identification from wrist activity. Sleep 15:461–469

    Article  CAS  Google Scholar 

  51. Kripke DF, Hahn EK, Grizas AP et al (2010) Wrist actigraphic scoring for sleep laboratory patients: algorhythm development. J Sleep Res 19:612–619

    Article  Google Scholar 

  52. Natale V, Drejak M, Erbacci A, Tonetti L, Fabbri M, Martoni M (2012) Monitoring sleep with a smartphone accelerator. Sleep Biol Rhythms 10:287–292

    Article  Google Scholar 

  53. Scott H (2018) Methodology A pilot study of a novel smartphone application for the estimation of sleep onset. J Sleep Res 27:90–97

    Article  Google Scholar 

  54. Stippig A, Hübers U, Emerich M (2015) Apps in sleep medicine. Sleep Breath 19:411–417

    Article  Google Scholar 

  55. Min JK, Doryab A, Wiese J, Amini S, Zimmerman J, Hong IJ (2014) Toss ’n’ turn: smartphone as sleep and sleep quality detector. In: Jones M, Palanque P, Schmidt A et al (eds) Proceedings of the SIGCHI conference on human factors in computing systems. Association for Computing Machinery, Toronto, pp 477–86

  56. Behar J, Roebuck A, Shahid M, Daly J, Hallack A, Palmius N, Member S (2015) SleepAp: an automated obstructive sleep apnoea screening application for smartphones. IEEE J Biomed Health Inform 19(1):325–331

    Article  Google Scholar 

  57. Nakano H, Hirayama K, Sadamitsu Y, Toshimitsu A, Fujita H, Shin S, Tanigawa T (2014) Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept. J Clin Sleep Med 10(1):73–78

    PubMed  PubMed Central  Google Scholar 

  58. Camacho M, Robertson M, Abdullatif J, Certal V, Kram YA (2015) Smartphone apps for snoring. J Laryngol Otol 129:974–979

    Article  CAS  Google Scholar 

  59. Grigsby-Toussaint, Shin, Reeves, Beattie, Auguste, Jean-Louis (2017) Sleep apps and behavioral constructs: a content analysis. Prev Med Rep 6:126–129

    Article  Google Scholar 

  60. Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV (2004) Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27(7):1255–1273

    Article  Google Scholar 

  61. Bianchi MT (2015) Consumer sleep apps: when it comes to the big picture, it’s all about the frame. J Clin Sleep Med 11(7):695–696

    PubMed  PubMed Central  Google Scholar 

Download references

Funding

Fondazione Altroconsumo, Italy, provided partial financial support in the form of research funding (to EF). The sponsor had no role in the design or conduct of this research.

Author information

Authors and Affiliations

  1. Department of Experimental, Diagnostic and Speciality Medicine (DIMES), Alma Mater Studiorum Università di Bologna, Viale Berti Pichat 5, 40126, Bologna, Italy

    Edita Fino & Michela Mazzetti

Authors
  1. Edita Fino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michela Mazzetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edita Fino.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This work was performed in Department of Experimental, Diagnostic and Specialty Medicine, (DIMES) Alma Mater Studiorum Università di Bologna. Both authors have seen and approved the manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fino, E., Mazzetti, M. Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence. Sleep Breath 23, 13–24 (2019). https://doi.org/10.1007/s11325-018-1661-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11325-018-1661-3

Keywords

Search

Navigation