Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions

Abstract

Rapidly accumulating data from mobile assessments are facilitating our ability to track patterns of emotions, behaviors, biologic rhythms, and their contextual influences in real time. These approaches have been widely applied to study the core features, traits, changes in states, and the impact of treatments in bipolar disorder (BD). This paper reviews recent evidence on the application of both passive and active mobile technologies to gain insight into the role of the circadian system and patterns of sleep and motor activity in people with BD. Findings of more than two dozen studies converge in demonstrating a broad range of sleep disturbances, particularly longer duration and variability of sleep patterns, lower average and greater variability of motor activity, and a shift to later peak activity and sleep midpoint, indicative of greater evening orientation among people with BD. The strong associations across the domains tapped by real-time monitoring suggest that future research should shift focus on sleep, physical/motor activity, or circadian patterns to identify common biologic pathways that influence their interrelations. The development of novel data-driven functional analytic tools has enabled the derivation of individualized multilevel dynamic representations of rhythms of multiple homeostatic regulatory systems. These multimodal tools can inform clinical research through identifying heterogeneity of the manifestations of BD and provide more objective indices of treatment response in real-world settings. Collaborative efforts with common protocols for the application of multimodal sensor technology will facilitate our ability to gain deeper insight into mechanisms and multisystem dynamics, as well as environmental, physiologic, and genetic correlates of BD.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Figure 1 illustrates the domains assessed in the NIMH Family Study through simultaneous objective and subjective ratings tracked in real time.
Fig. 2: Individual variation from application of JIVE to the NIMH Family Study actigraphy data.

References

  1. 1.

    Denicoff KD, Ali SO, Sollinger AB, Smith-Jackson EE, Leverich GS, Post RM. Utility of the daily prospective National Institute of Mental Health Life-Chart Method (NIMH-LCM-p) ratings in clinical trials of bipolar disorder. Depress Anxiety. 2002;15:1–9. https://doi.org/10.1002/da.1078.

    Article  Google Scholar 

  2. 2.

    Post RM, Denicoff KD, Leverich GS, Altshuler LL, Frye MA, Suppes TM, et al. Morbidity in 258 bipolar outpatients followed for 1 year with daily prospective ratings on the NIMH life chart method. J Clin Psychiatry. 2003;64:680–90. https://doi.org/10.4088/jcp.v64n0610. quiz 738-9.

    Article  Google Scholar 

  3. 3.

    Meyer N, Faulkner SM, McCutcheon RA, Pillinger T, Dijk DJ, MacCabe JH. Sleep and circadian rhythm disturbance in remitted schizophrenia and bipolar disorder: a systematic review and meta-analysis. Schizophr Bull. 2020. https://doi.org/10.1093/schbul/sbaa024.

  4. 4.

    Bauer M, Glenn T, Whybrow PC, Grof P, Rasgon N, Alda M, et al. Changes in self-reported sleep duration predict mood changes in bipolar disorder. Psychol Med. 2008;38:1069–71. https://doi.org/10.1017/S0033291708003280.

    Article  Google Scholar 

  5. 5.

    Plante DT, Winkelman JW. Sleep disturbance in bipolar disorder: therapeutic implications. Am J Psychiatry. 2008;165:830–43. https://doi.org/10.1176/appi.ajp.2008.08010077.

    Article  Google Scholar 

  6. 6.

    Lockley SW, Skene DJ, Arendt J. Comparison between subjective and actigraphic measurement of sleep and sleep rhythms. J Sleep Res. 1999;8:175–83. https://doi.org/10.1046/j.1365-2869.1999.00155.x.

    CAS  Article  Google Scholar 

  7. 7.

    Ancoli-Israel S, Martin JL, Blackwell T, Buenaver L, Liu L, Meltzer LJ, et al. The SBSM guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med. 2015;13:S4–S38.

    Article  Google Scholar 

  8. 8.

    Smith CS, Reilly C, Midkiff K. Evaluation of three circadian rhythm questionnaires with suggestions for an improved measure of morningness. J Appl Psychol. 1989;74:728–38. https://doi.org/10.1037/0021-9010.74.5.728.

    CAS  Article  Google Scholar 

  9. 9.

    Scott J, Murray G, Henry C, Morken G, Scott E, Angst J, et al. Activation in bipolar disorders: a systematic review. JAMA Psychiatry. 2017;74:189–96. https://doi.org/10.1001/jamapsychiatry.2016.3459.

    Article  Google Scholar 

  10. 10.

    De Crescenzo F, Economou A, Sharpley AL, Gormez A, Quested DJ. Actigraphic features of bipolar disorder: a systematic review and meta-analysis. Sleep Med Rev. 2017;33:58–69. https://doi.org/10.1016/j.smrv.2016.05.003.

    Article  Google Scholar 

  11. 11.

    Wee ZY, Yong SWL, Chew QH, Guan C, Lee TS, Sim K. Actigraphy studies and clinical and biobehavioural correlates in schizophrenia: a systematic review. J Neural Transm. 2019;126:531–58. https://doi.org/10.1007/s00702-019-01993-2.

    Article  Google Scholar 

  12. 12.

    Zipunnikov V, Caffo B, ousem DM, Davatzikos C, Schwartz BS, Crainiceanu C. Multilevel functional principal component analysis for high-dimensional data. J Comput Graph Stat. 2011;20:852–73.

  13. 13.

    Bertz JW, Epstein DH, Reamer D, Kowalczyk WJ, Phillips KA, Kennedy AP, et al. Sleep reductions associated with illicit opioid use and clinic-hour changes during opioid agonist treatment for opioid dependence: measurement by electronic diary and actigraphy. J Subst Abus Treat. 2019;106:43–57. https://doi.org/10.1016/j.jsat.2019.08.011.

    Article  Google Scholar 

  14. 14.

    Faedda GL, Ohashi K, Hernandez M, McGreenery CE, Grant MC, Baroni A, et al. Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls. J Child Psychol Psychiatry. 2016;57:706–16. https://doi.org/10.1111/jcpp.12520.

    Article  Google Scholar 

  15. 15.

    Doherty A, Jackson D, Hammerla N, Plotz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: The UK Biobank Study. PLoS ONE. 2017;12:e0169649. https://doi.org/10.1371/journal.pone.0169649.

    Article  Google Scholar 

  16. 16.

    Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, et al. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry. 2019;19:72. https://doi.org/10.1111/jcpp.12520.

    CAS  Article  Google Scholar 

  17. 17.

    Leroux A, Di J, Smirnova E, McGuffey EJ, Cao Q, Bayatmokhtari E, et al. Organizing and analyzing the activity data in NHANES. Stat Biosci. 2019;11:262–87. https://doi.org/10.1249/mss.0000000000000778.

    Article  Google Scholar 

  18. 18.

    Bagot KS, Matthews SA, Mason M, Squeglia LM, Fowler J, Gray K, et al. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Dev Cogn Neurosci. 2018;32:121–9. https://doi.org/10.1016/j.dcn.2018.03.008.

    CAS  Article  Google Scholar 

  19. 19.

    Guidi A, Salvi S, Ottaviano M, Gentili C, Bertschy G, de Rossi D, et al. Smartphone application for the analysis of prosodic features in running speech with a focus on bipolar disorders: system performance evaluation and case study. Sensors 2015;15:28070–87. https://doi.org/10.3390/s151128070.

    Article  Google Scholar 

  20. 20.

    Karam ZN, Provost EM, Singh S, Montgomery J, Archer C, Harrington G, et al. Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech. Proc IEEE Int Conf Acoust Speech Signal Process. 2014;2014:4858–62. https://doi.org/10.1109/ICASSP.2014.6854525.

    Article  Google Scholar 

  21. 21.

    Torous J, Summergrad P, Nassir, Ghaemi S. Bipolar disorder in the digital age: new tools for the same illness. Int J Bipolar Disord. 2016;4:25. https://doi.org/10.1186/s40345-016-0065-1.

    Article  Google Scholar 

  22. 22.

    Torous J, Brady R. Advancing care for bipolar disorder today and breakthroughs in access and treatments tomorrow with mobile health and smartphone apps. Bipolar Disord. 2020;22:211–2. https://doi.org/10.1111/bdi.12928.

    Article  Google Scholar 

  23. 23.

    Gliddon E, Barnes SJ, Murray G, Michalak EE. Online and mobile technologies for self-management in bipolar disorder: a systematic review. Psychiatr Rehabil J. 2017;40:309–19. https://doi.org/10.1037/prj0000270.

    Article  Google Scholar 

  24. 24.

    Faurholt-Jepsen M, Vinberg M, Frost M, Christensen EM, Bardram JE, Kessing LV. Smartphone data as an electronic biomarker of illness activity in bipolar disorder. Bipolar Disord. 2015;17:715–28. https://doi.org/10.1111/bdi.12332.

    Article  Google Scholar 

  25. 25.

    Faurholt-Jepsen M, Frost M, Ritz C, Christensen EM, Jacoby AS, Mikkelsen RL, et al. Daily electronic self-monitoring in bipolar disorder using smartphones - the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trial. Psychol Med. 2015;45:2691–704. https://doi.org/10.1017/S0033291715000410.

    CAS  Article  Google Scholar 

  26. 26.

    Seppala J, De Vita I, Jamsa T, Miettunen J, Isohanni M, Rubinstein K, et al. Mobile phone and wearable sensor-based mhealth approaches for psychiatric disorders and symptoms: systematic review. JMIR Ment Health. 2019;6:e9819. https://doi.org/10.2196/mental.9819.

    Article  Google Scholar 

  27. 27.

    Nicholas J, Larsen ME, Proudfoot J, Christensen H. Mobile apps for bipolar disorder: a systematic review of features and content quality. J Med Internet Res. 2015;17:e198 https://doi.org/10.2196/jmir.4581.

    Article  Google Scholar 

  28. 28.

    Merikangas KR, Swendsen J, Hickie IB, Cui L, Shou H, Merikangas AK, et al. Real-time mobile monitoring of the dynamic associations among motor activity, energy, mood, and sleep in adults with bipolar disorder. JAMA Psychiatry. 2019;76:190–8. https://doi.org/10.1001/jamapsychiatry.2018.3546.

    Article  Google Scholar 

  29. 29.

    Johnson E, Grondin O, Barrault M, M. F, Helbig S, M. H, et al. Computerized ambulatory monitoring in psychiatry: a multi-site collaborative study of acceptability, compliance, and reactivity. Int Jourmal Methods Psychiatry Res. 2009;18:48–57.

    Article  Google Scholar 

  30. 30.

    Granholm E, Loh C, Swendsen J. Feasibility and validity of computerized ecological momentary assessment in schizophrenia. Schizophr Bull. 2008;34:507–14.

    Article  Google Scholar 

  31. 31.

    Johnson EI, Barrault M, Nadeau L, Swendsen J, Trull TJ, Solhan MB, et al. Feasibility and validity of computerized ambulatory monitoring in drug-dependent women. Drug Alcohol Depend. 2009;99:322–6. https://doi.org/10.1093/schbul/sbm113.

    Article  Google Scholar 

  32. 32.

    Husky MM, Gindre C, Mazure CM, Brebant C, Nolen-Hoeksema S, Sanacora G, et al. Computerized ambulatory monitoring in mood disorders: feasibility, compliance, and reactivity. Psychiatry Res. 2010;178:440–2. https://doi.org/10.1016/j.psychres.2010.04.045.

    Article  Google Scholar 

  33. 33.

    Lemey C, Larsen ME, Devylder J, Courtet P, Billot R, Lenca P, et al. Clinicians’ concerns about mobile ecological momentary assessment tools designed for emerging psychiatric problems: prospective acceptability assessment of the MEmind app. J Med Internet Res. 2019;21:e10111. https://doi.org/10.1016/j.drugalcdep.2018.03.016.

    Article  Google Scholar 

  34. 34.

    Mackesy-Amiti ME, Boodram B. Feasibility of ecological momentary assessment to study mood and risk behavior among young people who inject drugs. Drug Alcohol Depend. 2018;187:227–35. https://doi.org/10.2196/jmir.7602.

    Article  Google Scholar 

  35. 35.

    Johnson EI, Sibon I, Renou P, Rouanet F, Allard M, Swendsen J, et al. Feasibility and validity of computerized ambulatory monitoring in stroke patients. Neurology. 2009;73:1579–83. https://doi.org/10.1016/j.jagp.2016.11.019.

    CAS  Article  Google Scholar 

  36. 36.

    Moore RC, Kaufmann CN, Rooney AS, Moore DJ, Eyler LT, Granholm E, et al. Feasibility and acceptability of ecological momentary assessment of daily functioning among older adults with HIV. Am J Geriatr Psychiatry. 2017;25:829–40.

    Article  Google Scholar 

  37. 37.

    Solk P, Gavin K, Fanning J, Welch W, Lloyd G, Cottrell A, et al. Feasibility and acceptability of intensive longitudinal data collection of activity and patient-reported outcomes during chemotherapy for breast cancer. Qual Life Res. 2019;28:3333–46. https://doi.org/10.1089/dia.2018.0064.

    Article  Google Scholar 

  38. 38.

    Mulvaney SA, Vaala S, Hood KK, Lybarger C, Carroll R, Williams L, et al. Mobile momentary assessment and biobehavioral feedback for adolescents with type 1 diabetes: feasibility and engagement patterns. Diabetes Technol Ther. 2018;20:465–74. https://doi.org/10.1089/dia.2018.0064.

    CAS  Article  Google Scholar 

  39. 39.

    Knell G, Gabriel KP, Businelle MS, Shuval K, Wetter DW, Kendzor DE. Ecological momentary assessment of physical activity: validation study. J Med Internet Res. 2017;19:e253.

    Article  Google Scholar 

  40. 40.

    Gunn PJ, Middleton B, Davies SK, Revell VL, Skene DJ. Sex differences in the circadian profiles of melatonin and cortisol in plasma and urine matrices under constant routine conditions. Chronobiol Int. 2016;33:39–50. https://doi.org/10.3109/07420528.2015.1112396.

    CAS  Article  Google Scholar 

  41. 41.

    Carrier J, Semba K, Deurveilher S, Drogos L, Cyr-Cronier J, Lord C, et al. Sex differences in age-related changes in the sleep-wake cycle. Front Neuroendocrinol. 2017;47:66–85. https://doi.org/10.1016/j.yfrne.2017.07.004.

    Article  Google Scholar 

  42. 42.

    Nicolaides NC, Charmandari E, Kino T, Chrousos GP. Stress-related and circadian secretion and target tissue actions of glucocorticoids: impact on health. Front Endocrinol (Lausanne). 2017;8:70. https://doi.org/10.3389/fendo.2017.00070.

    Article  Google Scholar 

  43. 43.

    Lewy AJ, Wehr TA, Goodwin FK, Newsome DA, Rosenthal NE. Manic-depressive patients may be supersensitive to light. Lancet. 1981;1:383–4. https://doi.org/10.1016/s0140-6736(81)91697-4.

    CAS  Article  Google Scholar 

  44. 44.

    Lewy AJ, Nurnberger JI Jr, Wehr TA, Pack D, Becker LE, Powell RL, et al. Supersensitivity to light: possible trait marker for manic-depressive illness. Am J Psychiatry. 1985;142:725–7. https://doi.org/10.1176/ajp.142.6.725.

    CAS  Article  Google Scholar 

  45. 45.

    Hallam KT, Olver JS, Norman TR. Effect of sodium valproate on nocturnal melatonin sensitivity to light in healthy volunteers. Neuropsychopharmacology. 2005;30:1400–4. https://doi.org/10.1038/sj.npp.1300739.

    CAS  Article  Google Scholar 

  46. 46.

    Hallam KT, Olver JS, Horgan JE, McGrath C, Norman TR. Low doses of lithium carbonate reduce melatonin light sensitivity in healthy volunteers. Int J Neuropsychopharmacol. 2005;8:255–9. https://doi.org/10.1017/S1461145704004894.

    CAS  Article  Google Scholar 

  47. 47.

    Wirz-Justice A, Reme C, Prunte A, Heinen U, Graw P, Urner U. Lithium decreases retinal sensitivity, but this is not cumulative with years of treatment. Biol Psychiatry. 1997;41:743–6. https://doi.org/10.1016/S0006-3223(97)00001-2.

    CAS  Article  Google Scholar 

  48. 48.

    Abe M, Herzog ED, Block GD. Lithium lengthens the circadian period of individual suprachiasmatic nucleus neurons. Neuroreport. 2000;11:3261–4. https://doi.org/10.1097/00001756-200009280-00042.

    CAS  Article  Google Scholar 

  49. 49.

    Abreu T, Braganca M. The bipolarity of light and dark: a review on bipolar disorder and circadian cycles. J Affect Disord. 2015;185:219–29. https://doi.org/10.1016/j.jad.2015.07.017.

    CAS  Article  Google Scholar 

  50. 50.

    Novakova M, Prasko J, Latalova K, Sladek M, Sumova A. The circadian system of patients with bipolar disorder differs in episodes of mania and depression. Bipolar Disord. 2015;17:303–14. https://doi.org/10.1111/bdi.12270.

    CAS  Article  Google Scholar 

  51. 51.

    Esaki Y, Obayashi K, Saeki K, Fujita K, Iwata N, Kitajima T. Association between light exposure at night and manic symptoms in bipolar disorder: cross-sectional analysis of the APPLE cohort. Chronobiol Int. 2020:1–10. https://doi.org/10.1080/07420528.2020.1746799.

  52. 52.

    Paksarian D, Rudolph KE, Stapp EK, Dunster GP, He J, Mennitt D, et al. Association of outdoor artificial light at night with mental disorders and sleep patterns among US adolescents. JAMA Psychiatry. 2020. https://doi.org/10.1001/jamapsychiatry.2020.1935.

  53. 53.

    Robillard R, Naismith SL, Rogers NL, Scott EM, Ip TK, Hermens DF, et al. Sleep-wake cycle and melatonin rhythms in adolescents and young adults with mood disorders: comparison of unipolar and bipolar phenotypes. Eur Psychiatry. 2013;28:412–6. https://doi.org/10.1016/j.eurpsy.2013.04.001.

    CAS  Article  Google Scholar 

  54. 54.

    Dallaspezia S, Benedetti FMelatonin. circadian rhythms, and the clock genes in bipolar disorder. Curr Psychiatry Rep. 2009;11:488–93. https://doi.org/10.1007/s11920-009-0074-1.

    Article  Google Scholar 

  55. 55.

    Nurnberger JI Jr, Adkins S, Lahiri DK, Mayeda A, Hu K, Lewy A, et al. Melatonin suppression by light in euthymic bipolar and unipolar patients. Arch Gen Psychiatry. 2000;57:572–9. https://doi.org/10.1001/archpsyc.57.6.572.

    CAS  Article  Google Scholar 

  56. 56.

    Lewy AJ. Circadian misalignment in mood disturbances. Curr Psychiatry Rep. 2009;11:459–65. https://doi.org/10.1007/s11920-009-0070-5.

    Article  Google Scholar 

  57. 57.

    Havermans R, Nicolson NA, Berkhof J, deVries MW. Patterns of salivary cortisol secretion and responses to daily events in patients with remitted bipolar disorder. Psychoneuroendocrinology. 2011;36:258–65. https://doi.org/10.1016/j.psyneuen.2010.07.016.

    CAS  Article  Google Scholar 

  58. 58.

    Nikitopoulou G, Crammer JL. Change in diurnal temperature rhythm in manic-depressive illness. Br Med J. 1976;1:1311–4. https://doi.org/10.1136/bmj.1.6021.1311.

    CAS  Article  Google Scholar 

  59. 59.

    Carr O, Saunders KEA, Bilderbeck AC, Tsanas A, Palmius N, Geddes JR, et al. Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder. Transl Psychiatry. 2018;8:79 https://doi.org/10.1038/s41398-018-0125-7.

    Article  Google Scholar 

  60. 60.

    Murray G, Gottlieb J, Hidalgo MP, Etain B, Ritter P, Skene DJ, et al. Measuring circadian function in bipolar disorders: empirical and conceptual review of physiological, actigraphic, and self-report approaches. Bipolar Disord. 2020. https://doi.org/10.1111/bdi.12963.

  61. 61.

    Tazawa Y, Wada M, Mitsukura Y, Takamiya A, Kitazawa M, Yoshimura M, et al. Actigraphy for evaluation of mood disorders: a systematic review and meta-analysis. J Affect Disord. 2019;253:257–69. https://doi.org/10.1016/j.jad.2019.04.087.

    Article  Google Scholar 

  62. 62.

    Carr O, Saunders KEA, Tsanas A, Bilderbeck AC, Palmius N, Geddes JR, et al. Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder. Sci Rep. 2018;8:1649. https://doi.org/10.1038/s41598-018-19888-9.

    CAS  Article  Google Scholar 

  63. 63.

    McGowan NM, Goodwin GM, Bilderbeck AC, Saunders KEA. Circadian rest-activity patterns in bipolar disorder and borderline personality disorder. Transl Psychiatry. 2019;9:195. https://doi.org/10.1038/s41398-019-0526-2.

    Article  Google Scholar 

  64. 64.

    Shou H, Cui L, Hickie I, Lameira D, Lamers F, Zhang J, et al. Dysregulation of objectively assessed 24-hour motor activity patterns as a potential marker for bipolar I disorder: results of a community-based family study. Transl Psychiatry. 2017;7:e1211. https://doi.org/10.1038/tp.2017.136.

    CAS  Article  Google Scholar 

  65. 65.

    Slyepchenko A, Allega OR, Leng X, Minuzzi L, Eltayebani MM, Skelly M, et al. Association of functioning and quality of life with objective and subjective measures of sleep and biological rhythms in major depressive and bipolar disorder. Aust N. Z J Psychiatry. 2019;53:683–96. https://doi.org/10.1177/0004867419829228.

    Article  Google Scholar 

  66. 66.

    Gold AK, Sylvia LG. The role of sleep in bipolar disorder. Nat Sci Sleep. 2016;8:207–14. https://doi.org/10.2147/NSS.S85754.

    Article  Google Scholar 

  67. 67.

    Levenson J, Frank E. Sleep and circadian rhythm abnormalities in the pathophysiology of bipolar disorder. Curr Top Behav Neurosci. 2011;5:247–62. https://doi.org/10.1007/7854_2010_50.

    Article  Google Scholar 

  68. 68.

    Pandi-Perumal SR, Moscovitch A, Srinivasan V, Spence DW, Cardinali DP, Brown GM. Bidirectional communication between sleep and circadian rhythms and its implications for depression: lessons from agomelatine. Prog Neurobiol. 2009;88:264–71. https://doi.org/10.1016/j.pneurobio.2009.04.007.

    CAS  Article  Google Scholar 

  69. 69.

    Wehr TA, Turner EH, Shimada JM, Lowe CH, Barker C, Leibenluft E. Treatment of rapidly cycling bipolar patient by using extended bed rest and darkness to stabilize the timing and duration of sleep. Biol Psychiatry. 1998;43:822–8. https://doi.org/10.1016/s0006-3223(97)00542-8.

    CAS  Article  Google Scholar 

  70. 70.

    Ritter PS, Marx C, Bauer M, Leopold K, Pfennig A. The role of disturbed sleep in the early recognition of bipolar disorder: a systematic review. Bipolar Disord. 2011;13:227–37. https://doi.org/10.1111/j.1399-5618.2011.00917.x.

    Article  Google Scholar 

  71. 71.

    Vancampfort D, Firth J, Schuch F, Rosenbaum S, De Hert M, Mugisha J, et al. Physical activity and sedentary behavior in people with bipolar disorder: a systematic review and meta-analysis. J Affect Disord. 2016;201:145–52. https://doi.org/10.1016/j.jad.2016.05.020.

    Article  Google Scholar 

  72. 72.

    Varma VR, Dey D, Leroux A, Di J, Urbanek J, Xiao L, et al. Re-evaluating the effect of age on physical activity over the lifespan. Prev Med. 2017;101:102–8. https://doi.org/10.1016/j.ypmed.2017.05.030.

    Article  Google Scholar 

  73. 73.

    Horne JA, Ostberg O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int J Chronobiol. 1976;4:97–110.

    CAS  Google Scholar 

  74. 74.

    Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: daily temporal patterns of human chronotypes. J Biol Rhythms. 2003;18:80–90. https://doi.org/10.1177/0748730402239679.

    Article  Google Scholar 

  75. 75.

    Gershon A, Kaufmann CN, Depp CA, Miller S, Do D, Zeitzer JM, et al. Subjective versus objective evening chronotypes in bipolar disorder. J Affect Disord. 2018;225:342–9. https://doi.org/10.1016/j.jad.2017.08.055.

    Article  Google Scholar 

  76. 76.

    Melo MCA, Abreu RLC, Linhares Neto VB, de Bruin PFC, de Bruin VMS. Chronotype and circadian rhythm in bipolar disorder: a systematic review. Sleep Med Rev. 2017;34:46–58. https://doi.org/10.1016/j.smrv.2016.06.007.

    Article  Google Scholar 

  77. 77.

    Fischer D, Lombardi DA, Marucci-Wellman H, Roenneberg T. Chronotypes in the US—influence of age and sex. PLoS ONE. 2017;12:e0178782. https://doi.org/10.1371/journal.pone.0178782.

    Article  Google Scholar 

  78. 78.

    Geoffroy PA, Scott J, Boudebesse C, Lajnef M, Henry C, Leboyer M, et al. Sleep in patients with remitted bipolar disorders: a meta-analysis of actigraphy studies. Acta Psychiatr Scand. 2015;131:89–99. https://doi.org/10.1111/acps.12367.

    CAS  Article  Google Scholar 

  79. 79.

    Gonzalez R, Suppes T, Zeitzer J, McClung C, Tamminga C, Tohen M, et al. The association between mood state and chronobiological characteristics in bipolar I disorder: a naturalistic, variable cluster analysis-based study. Int J Bipolar Disord. 2018;6:5. https://doi.org/10.1186/s40345-017-0113-5.

    Article  Google Scholar 

  80. 80.

    Krane-Gartiser K, Steinan MK, Langsrud K, Vestvik V, Sand T, Fasmer OB, et al. Mood and motor activity in euthymic bipolar disorder with sleep disturbance. J Affect Disord. 2016;202:23–31. https://doi.org/10.1016/j.jad.2016.05.012.

    Article  Google Scholar 

  81. 81.

    Krane-Gartiser K, Henriksen TEG, Morken G, Vaaler AE, Fasmer OB. Motor activity patterns in acute schizophrenia and other psychotic disorders can be differentiated from bipolar mania and unipolar depression. Psychiatry Res. 2018;270:418–25. https://doi.org/10.1016/j.psychres.2018.10.004.

    Article  Google Scholar 

  82. 82.

    Pagani L, St Clair PA, Teshiba TM, Service SK, Fears SC, Araya C, et al. Genetic contributions to circadian activity rhythm and sleep pattern phenotypes in pedigrees segregating for severe bipolar disorder. Proc Natl Acad Sci USA. 2016;113:E754–61. https://doi.org/10.1073/pnas.1513525113.

    CAS  Article  Google Scholar 

  83. 83.

    Sebela A, Novak T, Kemlink D, Goetz M. Sleep characteristics in child and adolescent offspring of parents with bipolar disorder: a case control study. BMC Psychiatry. 2017;17:199. https://doi.org/10.1186/s12888-017-1361-8.

    Article  Google Scholar 

  84. 84.

    Gehrman PR, Ghorai A, Goodman M, McCluskey R, Barilla H, Almasy L, et al. Twin-based heritability of actimetry traits. Genes Brain Behav. 2019;18:e12569 https://doi.org/10.1111/gbb.12569.

    Article  Google Scholar 

  85. 85.

    aan het Rot M, Hogenelst K, Schoevers RA. Mood disorders in everyday life: a systematic review of experience sampling and ecological momentary assessment studies. Clin Psychol Rev. 2012;32:510–23. https://doi.org/10.1016/j.cpr.2012.05.007.

    Article  Google Scholar 

  86. 86.

    Malik A, Goodwin GM, Holmes EA. Contemporary approaches to frequent mood monitoring in bipolar disorder. J Exp Psychopathol. 2012;3:572–81. https://doi.org/10.5127/jep.014311.

    Article  Google Scholar 

  87. 87.

    Ebner-Priemer UW, Trull TJ. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol Assess. 2009;21:463–75. https://doi.org/10.1037/a0017075.

    Article  Google Scholar 

  88. 88.

    Trull TJ, Ebner-Priemer UW. Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: introduction to the special section. Psychol Assess. 2009;21:457–62. https://doi.org/10.1037/a0017653.

    Article  Google Scholar 

  89. 89.

    Husky M, Olie E, Guillaume S, Genty C, Swendsen J, Courtet P. Feasibility and validity of ecological momentary assessment in the investigation of suicide risk. Psychiatry Res. 2014;220:564–70. https://doi.org/10.1016/j.psychres.2014.08.019.

    Article  Google Scholar 

  90. 90.

    Serre F, Fatseas M, Debrabant R, Alexandre JM, Auriacombe M, Swendsen J, et al. Ecological momentary assessment in alcohol, tobacco, cannabis and opiate dependence: a comparison of feasibility and validity. Drug Alcohol Depend. 2012;126:118–23. https://doi.org/10.1002/da.22949.

    Article  Google Scholar 

  91. 91.

    Knowles R, Tai S, Jones SH, Highfield J, Morriss R, Bentall RP. Stability of self-esteem in bipolar disorder: comparisons among remitted bipolar patients, remitted unipolar patients and healthy controls. Bipolar Disord. 2007;9:490–5. https://doi.org/10.1111/j.1399-5618.2007.00457.x.

    Article  Google Scholar 

  92. 92.

    Havermans R, Nicolson NA, Berkhof J, deVries MW. Mood reactivity to daily events in patients with remitted bipolar disorder. Psychiatry Res. 2010;179:47–52. https://doi.org/10.1016/j.psychres.2009.10.020.

    Article  Google Scholar 

  93. 93.

    Havermans R, Nicolson NA, Devries MW. Daily hassles, uplifts, and time use in individuals with bipolar disorder in remission. J Nerv Ment Dis. 2007;195:745–51. https://doi.org/10.1097/NMD.0b013e318142cbf0.

    Article  Google Scholar 

  94. 94.

    Schwartz S, Schultz S, Reider A, Saunders EF. Daily mood monitoring of symptoms using smartphones in bipolar disorder: a pilot study assessing the feasibility of ecological momentary assessment. J Affect Disord. 2016;191:88–93. https://doi.org/10.1016/j.jad.2015.11.013.

    Article  Google Scholar 

  95. 95.

    Axelson DA, Bertocci MA, Lewin DS, Trubnick LS, Birmaher B, Williamson DE, et al. Measuring mood and complex behavior in natural environments: use of ecological momentary assessment in pediatric affective disorders. J Child Adolesc Psychopharmacol. 2003;13:253–66. https://doi.org/10.1089/104454603322572589.

    Article  Google Scholar 

  96. 96.

    Myin-Germeys I, Peeters F, Havermans R, Nicolson NA, DeVries MW, Delespaul P, et al. Emotional reactivity to daily life stress in psychosis and affective disorder: an experience sampling study. Acta Psychiatr Scand. 2003;107:124–31. https://doi.org/10.1034/j.1600-0447.2003.02025.x.

    CAS  Article  Google Scholar 

  97. 97.

    Lamers F, Swendsen J, Cui L, Husky M, Johns J, Zipunnikov V, et al. Mood reactivity and affective dynamics in mood and anxiety disorders. J Abnorm Psychol. 2018;127:659–69. https://doi.org/10.1037/abn0000378.

    Article  Google Scholar 

  98. 98.

    Johns JT, Di J, Merikangas K, Cui L, Swendsen J, Zipunnikov V. Fragmentation as a novel measure of stability in normalized trajectories of mood and attention measured by ecological momentary assessment. Psychol Assess. 2019;31:329–39. https://doi.org/10.1037/pas0000661.

    Article  Google Scholar 

  99. 99.

    Reinertsen E, Clifford GD. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol Meas. 2018;39:05TR1. https://doi.org/10.1088/1361-6579/aabf64.

    Article  Google Scholar 

  100. 100.

    Egan KJ, Knutson KL, Pereira AC, von Schantz M. The role of race and ethnicity in sleep, circadian rhythms and cardiovascular health. Sleep Med Rev. 2017;33:70–8. https://doi.org/10.1016/j.smrv.2016.05.004.

    Article  Google Scholar 

  101. 101.

    Schrack JA, Zipunnikov V, Goldsmith J, Bai J, Simonsick EM, Crainiceanu C, et al. Assessing the “physical cliff”: detailed quantification of age-related differences in daily patterns of physical activity. J Gerontol A Biol Sci Med Sci. 2014;69:973–9. https://doi.org/10.1111/biom.12278.

    Article  Google Scholar 

  102. 102.

    Gershon A, Ram N, Johnson SL, Harvey AG, Zeitzer JM. Daily actigraphy profiles distinguish depressive and interepisode states in bipolar disorder. Clin Psychol Sci. 2016;4:641–50. https://doi.org/10.1177/2167702615604613.

    Article  Google Scholar 

  103. 103.

    Goldsmith J, Liu X, Jacobson JS, Rundle A. New insights into activity patterns in children, found using functional data analyses. Med Sci Sports Exerc. 2016;48:1723.

  104. 104.

    Goldsmith J, Zipunnikov V, Schrack J. Generalized multilevel function-on-scalar regression and principal component analysis. Biometrics. 2015;71:344–53. https://doi.org/10.1186/s12888-017-1574-x.

    Article  Google Scholar 

  105. 105.

    Bai J, Sun Y, Schrack JA, Crainiceanu CM, Wang MC. A two-stage model for wearable device data. Biometrics. 2018;74:744–52. https://doi.org/10.1111/biom.12781.

    Article  Google Scholar 

  106. 106.

    Di J, Spira A, Bai J, Urbanek J, Leroux A, Wu M, et al. Joint and individual representation of domains of physical activity. Sleep, Circadian Rhythmicity Stat Biosci. 2019;11:371–402. https://doi.org/10.1007/s12561-019-09236-4.

    Article  Google Scholar 

  107. 107.

    Gonzalez R, Gonzalez SD, McCarthy MJ. Using chronobiological phenotypes to address heterogeneity in bipolar disorder. Mol Neuropsychiatry. 2020;5:72–84. https://doi.org/10.1159/000506636.

    Article  Google Scholar 

  108. 108.

    Benard V, Etain B, Vaiva G, Boudebesse C, Yeim S, Benizri C, et al. Sleep and circadian rhythms as possible trait markers of suicide attempt in bipolar disorders: an actigraphy study. J Affect Disord. 2019;244:1–8. https://doi.org/10.1016/j.jad.2018.09.054.

    CAS  Article  Google Scholar 

  109. 109.

    Hwang JY, Choi JW, Kang SG, Hwang SH, Kim SJ, Lee YJ. Comparison of the effects of quetiapine XR and lithium monotherapy on actigraphy-measured circadian parameters in patients with bipolar II depression. J Clin Psychopharmacol. 2017;37:351–4. https://doi.org/10.1097/jcp.0000000000000699.

    CAS  Article  Google Scholar 

  110. 110.

    Goldstein TR, Merranko J, Krantz M, Garcia M, Franzen P, Levenson J, et al. Early intervention for adolescents at-risk for bipolar disorder: a pilot randomized trial of interpersonal and social rhythm therapy (IPSRT). J Affect Disord. 2018;235:348–56. https://doi.org/10.1016/j.jad.2018.04.049.

    Article  Google Scholar 

  111. 111.

    Melo PR, Goncalves BS, Menezes AA, Azevedo CV. Circadian activity rhythm in pre-pubertal and pubertal marmosets (Callithrix jacchus) living in family groups. Physiol Behav. 2016;155:242–9. https://doi.org/10.1016/j.physbeh.2015.12.023.

    CAS  Article  Google Scholar 

  112. 112.

    Hensch T, Wozniak D, Spada J, Sander C, Ulke C, Wittekind DA, et al. Vulnerability to bipolar disorder is linked to sleep and sleepiness. Transl Psychiatry. 2019;9:294. https://doi.org/10.1038/s41398-019-0632-1.

    Article  Google Scholar 

  113. 113.

    Henriksen TE, Skrede S, Fasmer OB, Schoeyen H, Leskauskaite I, Bjorke-Bertheussen J, et al. Blue-blocking glasses as additive treatment for mania: a randomized placebo-controlled trial. Bipolar Disord. 2016;18:221–32. https://doi.org/10.1111/bdi.12390.

    Article  Google Scholar 

  114. 114.

    Scott J, Hidalgo-Mazzei D, Strawbridge R, Young A, Resche-Rigon M, Etain B, et al. Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative. Int J Bipolar Disord. 2019;7:20. https://doi.org/10.1186/s40345-019-0156-x.

    Article  Google Scholar 

Download references

Acknowledgements

The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the paper; and decision to submit the paper for publication.

Author information

Affiliations

Authors

Contributions

Drs. GD, JS, and KRM all contributed to the drafting and revision of the paper. All authors critically revised the paper for important intellectual content and approved the final version.

Corresponding author

Correspondence to Kathleen Ries Merikangas.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dunster, G.P., Swendsen, J. & Merikangas, K.R. Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions. Neuropsychopharmacol. 46, 197–208 (2021). https://doi.org/10.1038/s41386-020-00830-5

Download citation

Further reading

Search

Quick links