McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia—an overview. JAMA Psychiat. 2020;77(2):201–10.

Kotzeva A, et al. Socioeconomic burden of schizophrenia: a targeted literature review of types of costs and associated drivers across 10 countries. J Med Econ. 2023;26(1):70–83.

Article  PubMed  Google Scholar 

Keepers GA, et al. The American Psychiatric Association practice guideline for the treatment of patients with schizophrenia. Focus (Am Psychiatr Publ). 2020;18(4):493–7.

PubMed  Google Scholar 

Taipale H, et al. Representation and outcomes of individuals with schizophrenia seen in everyday practice who are ineligible for randomized clinical trials. JAMA Psychiat. 2022;79(3):210–8.

Article  Google Scholar 

Cai L, Huang J. Schizophrenia and risk of dementia: a meta-analysis study. Neuropsychiatr Dis Treat. 2018;14:2047–55.

Article  PubMed  PubMed Central  Google Scholar 

Abou Kassm S, et al. Metabolic syndrome among older adults with schizophrenia spectrum disorder: Prevalence and associated factors in a multicenter study. Psychiatry Res. 2019;275:238–46.

Article  PubMed  Google Scholar 

Hoertel N, et al. A comprehensive model of predictors of quality of life in older adults with schizophrenia: results from the CSA study. Soc Psychiatry Psychiatr Epidemiol. 2021;56(8):1411–25.

Article  PubMed  Google Scholar 

Figueroa CA, Aguilera A. The need for a mental health technology revolution in the COVID-19 pandemic. Front Psychiatry. 2020;11:523.

Article  PubMed  PubMed Central  Google Scholar 

Torous J, et al. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health. 2016;3(2):e16.

Article  PubMed  PubMed Central  Google Scholar 

Onnela JP. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology. 2021;46(1):45–54.

Article  PubMed  Google Scholar 

Chia AZR, Zhang MWB. Digital phenotyping in psychiatry: a scoping review. Technol Health Care. 2022;30(6):1331–42.

Article  PubMed  Google Scholar 

Benoit J, et al. Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses. Harv Rev Psychiatry. 2020;28(5):296–304.

Article  PubMed  Google Scholar 

Jacobson NC, Summers B, Wilhelm S. Digital biomarkers of social anxiety severity: digital phenotyping using passive smartphone sensors. J Med Internet Res. 2020;22(5):e16875.

Article  PubMed  PubMed Central  Google Scholar 

Jacobson NC, Weingarden H, Wilhelm S. Using digital phenotyping to accurately detect depression severity. J Nerv Ment Dis. 2019;207(10):893–6.

Article  PubMed  Google Scholar 

Ettore E, et al. Digital phenotyping for differential diagnosis of major depressive episode: narrative review. JMIR Ment Health. 2023;10:e37225.

Article  PubMed  PubMed Central  Google Scholar 

Saccaro LF, et al. Portable technologies for digital phenotyping of bipolar disorder: a systematic review. J Affect Disord. 2021;295:323–38.

Article  PubMed  Google Scholar 

Iliescu R, et al. Smartphone ownership and use of mental health applications by psychiatric inpatients. Psychiatry Res. 2021;299:113806.

Article  PubMed  Google Scholar 

Naslund JA, Aschbrenner KA. Technology use and interest in digital apps for mental health promotion and lifestyle intervention among young adults with serious mental illness. J Affect Disord Rep. 2021;1(6):100227.

Article  Google Scholar 

•• Fulford D, et al. Smartphone sensing of social interactions in people with and without schizophrenia. J Psychiatr Res. 2021;137:613–20. This paper provides an innovative use of digital phenotyping combined with methodological rigor.

Depp CA, et al. GPS mobility as a digital biomarker of negative symptoms in schizophrenia: a case control study. NPJ Digit Med. 2019;2(1):108.

Wang R, et al. Predicting symptom trajectories of schizophrenia using mobile sensing. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2017;1(3):Article 110.

Bartolomeo LA, Raugh IM, Strauss GP. The positivity offset theory of anhedonia in schizophrenia: evidence for a deficit in daily life using digital phenotyping. Psychol Med. 2023;1–9.

He-Yueya J, et al. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. NPJ Schizophr. 2020;6(1):35.

Article  PubMed  PubMed Central  Google Scholar 

Henson P, et al. Towards clinically actionable digital phenotyping targets in schizophrenia. NPJ Schizophr. 2020;6(1):13.

Article  PubMed  PubMed Central  Google Scholar 

Buck B, et al. Relationships between smartphone social behavior and relapse in schizophrenia: A preliminary report. Schizophr Res. 2019;208:167–72.

Article  PubMed  PubMed Central  Google Scholar 

Castañeda-Babarro A, Arbillaga-Etxarri A, Gutiérrez-Santamaría B, Coca A. Physical activity change during COVID-19 confinement. Int J Environ Res Public Health. 2020;17(18):6878.

Article  PubMed  PubMed Central  Google Scholar 

Luther L, et al. Examining potential barriers to mHealth implementation and engagement in schizophrenia: phone ownership and symptom severity. J Technol Behav Sci. 2022;7(1):13–22.

Article  PubMed  Google Scholar 

Gay K, et al. Digital technology use among individuals with schizophrenia: results of an online survey. JMIR Mental Health. 2016;3(2): e15.

Article  PubMed  PubMed Central  Google Scholar 

Lopez-Morinigo JD, Barrigón ML, Porras-Segovia A, Ruiz-Ruano VG, Martínez AS, Escobedo-Aedo PJ, Alonso SS, Iturralde LM, Lorenzo LM, Artés-Rodríguez A, David AS. Use of ecological momentary assessment through a passive smartphone-based app (eB2) by patients with schizophrenia: acceptability study. J Med Internet Res. 2021;23(7): e26548.

Article  PubMed  PubMed Central  Google Scholar 

Ranjan T, Melcher J, Keshavan M, Smith M, Torous J. Longitudinal symptom changes and association with home time in people with schizophrenia: an observational digital phenotyping study. Schizophr Res. 2022 May 1;243:64-9.

• Ben-Zeev D, et al. CrossCheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr Rehabil J. 2017;40(3):266–75. This paper offers an impressive example of large-scale digital phenotyping research and is of special note as the resulting dataset has been used to conduct many important secondary analyses.

Raugh IM, et al. Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. J Psychiatr Res. 2021;138:436–43.

Article  PubMed  PubMed Central  Google Scholar 

Buck B, et al. The relationship between appraisals of auditory verbal hallucinations and real-time affect and social functioning. Schizophr Res. 2022;250:112–9.

Article  PubMed  Google Scholar 

Raugh IM, et al. Geolocation as a digital phenotyping measure of negative symptoms and functional outcome. Schizophr Bull. 2020;46(6):1596–607.

Article  PubMed  PubMed Central  Google Scholar 

Strauss GP, et al. Validation of accelerometry as a digital phenotyping measure of negative symptoms in schizophrenia. Schizophrenia (Heidelb). 2022;8(1):37.

Article  PubMed  Google Scholar 

Cohen AS, et al. Digital phenotyping of negative symptoms: the relationship to clinician ratings. Schizophr Bull. 2021;47(1):44–53.

Article  PubMed  Google Scholar 

Hor K, Taylor M. Suicide and schizophrenia: a systematic review of rates and risk factors. J Psychopharmacol. 2010;24(4 Suppl):81–90.

Article  PubMed  PubMed Central  Google Scholar 

Coid JW, et al. Paranoid ideation and violence: meta-analysis of individual subject data of 7 population surveys. Schizophr Bull. 2016;42(4):907–15.

Article  PubMed  PubMed Central  Google Scholar 

Ascher-Svanum H, et al. The cost of relapse and the predictors of relapse in the treatment of schizophrenia. BMC Psychiatry. 2010;10:2.

Article  PubMed  PubMed Central  Google Scholar 

Frueh BC, et al. Patients’ reports of traumatic or harmful experiences within the psychiatric setting. Psychiatr Serv. 2005;56(9):1123–33.

Article  PubMed  Google Scholar 

Lecomte T, et al. Predicting and preventing symptom onset and relapse in schizophrenia-a metareview of current empirical evidence. J Abnorm Psychol. 2019;128(8):840–54.

Article  PubMed  Google Scholar 

Meyer N, et al. P029 Sleep and circadian rhythm disturbances and relapse in schizophrenia: a digital phenotyping study. BMJ Open Respir Res. 2019;6(Suppl 1):A17.

Google Scholar 

Cohen A, et al. Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study. Schizophrenia (Heidelb). 2023;9(1):6.

Article  PubMed  Google Scholar 

Di Capite S, Upthegrove R, Mallikarjun P. The relapse rate and predictors of relapse in patients with first-episode psychosis following discontinuation of antipsychotic medication. Early Interv Psychiatry. 2018;12(5):893–9.

Article  PubMed  Google Scholar 

Afonso P, et al. Treatment adherence and quality of sleep in schizophrenia outpatients. Int J Psychiatry Clin Pract. 2014;18(1):70–6.

Article  CAS  PubMed  Google Scholar 

Khurshid KA. Comorbid insomnia and psychiatric disorders: an update. Innov Clin Neurosci. 2018;15(3–4):28.

PubMed  PubMed Central  Google Scholar 

Reeve S, Sheaves B, Freeman D. The role of sleep dysfunction in the occurrence of delusions and hallucinations: a systematic review. Clin Psychol Rev. 2015;1(42):96–115.

Article