Kessler RC, Merikangas KR, Wang PS. Prevalence, comorbidity, and service utilization for mood disorders in the United States at the beginning of the twenty-first century. Annu Rev Clin Psychol. 2007;3:137–58.

Article  Google Scholar 

World Health Organization (WHO). International Classification of Diseases, Eleventh Revision (ICD-11). 2019. Available from: https://icd.who.int/browse11. Licensed under Creative Commons Attribution-NoDerivatives 3.0 IGO licence (CC BY-ND 3.0 IGO).

Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021. (GBD 2021) Results. Seattle, United States: Institute of Health Metrics and Evaluation (IHME), 2022. Available from https://vizhub.healthdata.org/gbd-results/

American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed., text rev.). 2022. Available from: https://doi.org/10.1176/appi.books.9780890425787

Marvel CL, Paradiso S. Cognitive and neurological impairment in mood disorders. Psychiatr Clin North Am. 2004;27(1):19–viii.

Article  Google Scholar 

Szmulewicz AG, Valerio MP, Smith JM, Samamé C, Martino DJ, Strejilevich SA. Neuropsychological profiles of major depressive disorder and bipolar disorder during euthymia. A systematic literature review of comparative studies. Psychiatry Res. 2017;248:127–33.

Article  Google Scholar 

Iverson GL, Brooks BL, Langenecker SA, Young AH. Identifying a cognitive impairment subgroup in adults with mood disorders. J Affect Disord. 2011;132(3):360–7.

Article  Google Scholar 

Douglas KM, Gallagher P, Robinson LJ, Carter JD, McIntosh VV, Frampton CM, et al. Prevalence of cognitive impairment in major depression and bipolar disorder. Bipolar Disord. 2018;20(3):260–74.

Article  Google Scholar 

McCaffrey RJ, Ortega A, Haase RF. Effects of Repeated Neuropsychological Assessments. Arch Clin Neuropsychol. 1993;8(6):519–24.

Article  Google Scholar 

Yatham LN, Torres IJ, Malhi GS, Frangou S, Glahn DC, Bearden CE, et al. The International Society for Bipolar Disorders–Battery for Assessment of Neurocognition (ISBD-BANC). Bipolar Disord. 2010;12(4):351–63.

Article  Google Scholar 

Zulueta J, Leow AD, Ajilore O. Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. FOC. 2020;18(2):175–80.

Article  Google Scholar 

Burdick KE, Endick CJ, Goldberg JF. Assessing cognitive deficits in bipolar disorder: Are self-reports valid? Psychiatry Res. 2005;136(1):43–50.

Article  Google Scholar 

Hufford MR. Special Methodological Challenges and Opportunities in Ecological Momentary Assessment. The Science of Real-Time Data Capture: Self-Reports in Health Research. Oxford University Press; 2007. pp. 54–73.

Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.

Article  Google Scholar 

Hidalgo-Mazzei D, Young AH. Psychiatry foretold. Aust N Z J Psychiatry. 2019;53(4):365–6.

Article  Google Scholar 

Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digit Health. 2021;3.

Jameel L, Valmaggia L, Barnes G, Cella M. mHealth technology to assess, monitor and treat daily functioning difficulties in people with severe mental illness: A systematic review. J Psychiatr Res. 2022;145:35–49.

Article  Google Scholar 

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(13):2691–704.

Article  Google Scholar 

Faurholt-Jepsen M, Frost M, Christensen EM, Bardram JE, Vinberg M, Kessing LV. The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial. Psychol Med. 2020;50(5):838–48.

Article  Google Scholar 

Cho CH, Lee T, Kim MG, In HP, Kim L, Lee HJ. Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study. J Med Internet Res. 2019;21(4):e11029.

Article  Google Scholar 

Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ. 2016;4:e2537.

Article  Google Scholar 

Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. Next-Generation Psychiatric Assessment: Using Smartphone Sensors to Monitor Behavior and Mental Health. Psychiatr Rehabil J. 2015;38(3):218–26.

Article  Google Scholar 

Boonstra TW, Nicholas J, Wong QJ, Shaw F, Townsend S, Christensen H. Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions. J Med Internet Res. 2018;20(7):e10131.

Article  Google Scholar 

Stange JP, Zulueta J, Langenecker SA, Ryan KA, Piscitello A, Duffecy J, et al. Let Your Fingers Do the Talking: Passive Typing Instability Predicts Future Mood Outcomes. Bipolar Disord. 2018;20(3):285–8.

Article  Google Scholar 

Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker SA et al. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. J Med Internet Res. 2018;20(7).

Vesel C, Rashidisabet H, Zulueta J, Stange JP, Duffecy J, Hussain F, et al. Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study. J Am Med Inf Assoc. 2020;0(0):1–12.

Google Scholar 

Bennett CC, Ross MK, Baek E, Kim D, Leow AD. Predicting clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics. Pervasive Mob Comput. 2022;83:101598.

Article  Google Scholar 

Mastoras RE, Iakovakis D, Hadjidimitriou S, Charisis V, Kassie S, Alsaadi T, et al. Touchscreen typing pattern analysis for remote detection of the depressive tendency. Sci Rep. 2019;9(1):13414.

Article  Google Scholar 

Ghosh S, Sahu S, Ganguly N, Mitra B, De P, EmoKey. An Emotion-aware Smartphone Keyboard for Mental Health Monitoring. In: 2019 11th International Conference on Communication Systems & Networks (COMSNETS). 2019. pp. 496–9.

Chen MH, Leow A, Ross MK, DeLuca J, Chiaravalloti N, Costa SL, et al. Associations between smartphone keystroke dynamics and cognition in MS. Digit HEALTH. 2022;8:20552076221143230.

Google Scholar 

Ross MK, Demos AP, Zulueta J, Piscitello A, Langenecker SA, McInnis M, et al. Naturalistic smartphone keyboard typing reflects processing speed and executive function. Brain Behav. 2021;11(11):e2363.

Article  Google Scholar 

Ning E, Estabrook R, Tulabandhula T, Zulueta J, Mindy K, Ross S, Kabir et al. Predicting Cognitive Functioning in Mood Disorders through Smartphone Typing Dynamics. Journal of Psychopathology and Clinical Science. In.

Rashidisabet H, Thomas PJ, Ajilore O, Zulueta J, Moore RC, Leow A. A systems biology approach to the digital behaviorome. Curr Opin Syst Biology. 2020;20:8–16.

Article  Google Scholar 

Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med. 2013;10(6):565–76.

Article  Google Scholar 

Orsolini L, Fiorani M, Volpe U. Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers? Int J Mol Sci. 2020;21(20):7684.

Article  Google Scholar 

Van Cauter E, Turek FW, Depression. A Disorder of Timekeeping? Perspect Biol Med. 1986;29(4):510–20.

Article  Google Scholar 

Hühne A, Welsh DK, Landgraf D. Prospects for circadian treatment of mood disorders. Ann Med. 2018;50(8):637–54.

Article  Google Scholar 

Nutt D, Wilson S, Paterson L. Sleep disorders as core symptoms of depression. Dialogues Clin Neurosci. 2008;10(3):329–36.

Article  Google Scholar 

Pinho M, Sehmbi M, Cudney LE, Kauer-Sant’anna M, Magalhães PV, Reinares M, et al. The association between biological rhythms, depression, and functioning in bipolar disorder: a large multi-center study. Acta psychiatrica Scandinavica. 2016;133(2):102–8.

Article  Google Scholar 

Mondin TC, Cardoso T, de Souza A, de Jansen LD, da Silva Magalhães K, Kapczinski PV. Mood disorders and biological rhythms in young adults: A large population-based study. J Psychiatr Res. 2017;84:98–104.

Article  Google Scholar 

Jackson A, Cavanagh J, Scott J. A systematic review of manic and depressive prodromes. J Affect Disord. 2003;74(3):209–17.

Article  Google Scholar 

Fellendorf FT, Hamm C, Dalkner N, Platzer M, Sattler MC, Bengesser SA et al. Monitoring Sleep Changes via a Smartphone App in Bipolar Disorder: Practical Issues and Validation of a Potential Diagnostic Tool. Front Psychiatry. 2021;12.

Pearson O, Uglik-Marucha N, Miskowiak KW, Cairney SA, Rosenzweig I, Young AH, et al. The relationship between sleep disturbance and cognitive impairment in mood disorders: A systematic review. J Affect Disord. 2023;327:207–16.

Article  Google Scholar 

Cabanel N, Schmidt AM, Fockenberg S, Brückmann KF, Haag A, Müller MJ, et al. Evening preference and poor sleep independently affect attentional-executive functions in patients with depression. Psychiatry Res. 2019;281:112533.

Article  Google Scholar 

Conley S, Knies A, Batten J, Ash G, Miner B, Hwang Y, et al. Agreement between actigraphic and polysomnographic measures of sleep in adults with and without chronic conditions: A systematic review and meta-analysis. Sleep Med Rev. 2019;46:151–60.

Article  Google Scholar 

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.

Article  Google Scholar 

Abdullah S, Matthews M, Murnane EL, Gay G, Choudhury T. Towards circadian computing: early to bed and early to rise makes some of us unhealthy and sleep deprived. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: Association for Computing Machinery; 2014. pp. 673–84. (UbiComp ’14).

Borger JN, Huber R, Ghosh A. Capturing sleep–wake cycles by using day-to-day smartphone touchscreen interactions. npj Digit Med. 2019;2(1):1–8.

Article  Google Scholar 

Druijff-van de Woestijne GB, McConchie H, de Kort YAW, Licitra G, Zhang C, Overeem S, et al. Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest-activity patterns. J Sleep Res. 2021;30(5):e13285.

Article  Google Scholar 

King S, Stone JM, Cleare A, Young AH. A systematic review on neuropsychological function in bipolar disorders type I and II and subthreshold bipolar disorders—something to think about. CNS Spectr. 2019;24(1):127–43.

Article  Google Scholar 

Nuño L, Gómez-Benito J, Carmona VR, Pino O. A Systematic Review of Executive Function and Information Processing Speed in Major Depression Disorder. Brain Sci. 2021;11(2):147.

Article