Kirkland E, Schumann SO, Schreiner A, Heincelman M, Zhang J, Marsden J, et al. Patient demographics and clinic type are Associated with Patient Engagement within a remote monitoring program. Telemed J E Health. 2021;27(8):843–50.

Article  PubMed  PubMed Central  Google Scholar 

Coa KI, Wiseman KP, Higgins B, Augustson E. Associations between Engagement and Outcomes in the SmokefreeTXT Program: a growth mixture modeling analysis. Nicotine Tob Res. 2019;21(5):663–9.

Article  PubMed  Google Scholar 

Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who uses mobile phone health apps and does Use Matter? A secondary data Analytics Approach. J Med Internet Res. 2017;19(4):e125.

Article  PubMed  PubMed Central  Google Scholar 

Chandrasekaran R, Katthula V, Moustakas E. Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US adults: insights from a National Survey. J Med Internet Res. 2020;22(10):e22443.

Article  PubMed  PubMed Central  Google Scholar 

Jaana M, Pare G. Comparison of Mobile Health Technology Use for Self-Tracking between older adults and the General Adult Population in Canada: cross-sectional survey. JMIR Mhealth Uhealth. 2020;8(11):e24718.

Article  PubMed  PubMed Central  Google Scholar 

Lee J, Turchioe MR, Creber RM, Biviano A, Hickey K, Bakken S. Phenotypes of engagement with mobile health technology for heart rhythm monitoring. JAMIA Open. 2021;4(2):ooab043.

Article  PubMed  PubMed Central  Google Scholar 

Ross EL, Jamison RN, Nicholls L, Perry BM, Nolen KD. Clinical integration of a smartphone app for patients with Chronic Pain: retrospective analysis of predictors of benefits and patient Engagement between Clinic visits. J Med Internet Res. 2020;22(4):e16939.

Article  PubMed  PubMed Central  Google Scholar 

Yang Q, Hatch D, Crowley MJ, Lewinski AA, Vaughn J, Steinberg D, et al. Digital phenotyping self-monitoring behaviors for individuals with type 2 diabetes Mellitus: Observational Study using latent class growth analysis. JMIR Mhealth Uhealth. 2020;8(6):e17730.

Article  PubMed  PubMed Central  Google Scholar 

Bilderbeck AC, Atkinson LZ, McMahon HC, Voysey M, Simon J, Price J, et al. Psychoeducation and online mood tracking for patients with bipolar disorder: a randomised controlled trial. J Affect Disord. 2016;205:245–51.

Article  PubMed  Google Scholar 

Depp CA, Mausbach B, Granholm E, Cardenas V, Ben-Zeev D, Patterson TL, et al. Mobile interventions for severe mental illness: design and preliminary data from three approaches. J Nerv Ment Dis. 2010;198(10):715–21.

Article  PubMed  PubMed Central  Google Scholar 

Lieberman DZ, Kelly TF, Douglas L, Goodwin FK. A randomized comparison of online and paper mood charts for people with bipolar disorder. J Affect Disord. 2010;124(1–2):85–9.

Article  PubMed  Google Scholar 

Whybrow PC, Grof P, Gyulai L, Rasgon N, Glenn T, Bauer M. The electronic assessment of the longitudinal course of bipolar disorder: the chronoRecord software. Pharmacopsychiatry. 2003;36(Suppl3):244–S9.

Google Scholar 

McKnight RF, Bilderbeck AC, Miklowitz DJ, Hinds C, Goodwin GM, Geddes JR. Longitudinal mood monitoring in bipolar disorder: course of illness as revealed through a short messaging service. J Affect Disord. 2017;223:139–45.

Article  PubMed  Google Scholar 

Bopp JM, Miklowitz DJ, Goodwin GM, Stevens W, Rendell JM, Geddes JR. The longitudinal course of bipolar disorder as revealed through weekly text messaging: a feasibility study. Bipolar Disord. 2010;12(3):327–34.

Article  PubMed  PubMed Central  Google Scholar 

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.

PubMed  PubMed Central  Google Scholar 

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(4):641–50.

Article  PubMed  Google Scholar 

Scott J, Vaaler AE, Fasmer OB, Morken G, Krane-Gartiser K. A pilot study to determine whether combinations of objectively measured activity parameters can be used to differentiate between mixed states, mania, and bipolar depression. Int J Bipolar Disord. 2017;5(1):5.

Article  PubMed  PubMed Central  Google Scholar 

Grunerbl A, Muaremi A, Osmani V, Bahle G, Ohler S, Troster G, et al. Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform. 2015;19(1):140–8.

Article  PubMed  Google Scholar 

Faurholt-Jepsen M, Vinberg M, Frost M, Debel S, Margrethe Christensen E, Bardram JE, et al. Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder. Int J Methods Psychiatr Res. 2016;25(4):309–23.

Article  PubMed  PubMed Central  Google Scholar 

Ryan KA, Babu P, Easter R, Saunders E, Lee AJ, Klasnja P, et al. A smartphone app to monitor Mood symptoms in bipolar disorder: development and usability study. JMIR mental health. 2020;7(9):e19476.

Article  PubMed  PubMed Central  Google Scholar 

Gideon J, Provost EM, McInnis M. Mood State Prediction from Speech of varying Acoustic Quality for individuals with bipolar disorder. Proc IEEE Int Conf Acoust Speech Signal Process. 2016;2016:2359–63.

PubMed  PubMed Central  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):10.

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  PubMed  PubMed Central  Google Scholar 

Palmius N, Tsanas A, Saunders KEA, Bilderbeck AC, Geddes JR, Goodwin GM, et al. Detecting Bipolar Depression from Geographic Location Data. IEEE Trans Biomed Eng. 2017;64(8):1761–71.

Article  CAS  PubMed  Google Scholar 

Ortiz A, Maslej MM, Husain I, Daskalakis J, Mulsant BH. Apps and gaps in bipolar disorder: a systematic review on electronic monitoring for episode prediction. J Affect Disord. 2021;295:1190–200.

Article  PubMed  Google Scholar 

Moitra E, Gaudiano BA, Davis CH, Ben-Zeev D. Feasibility and acceptability of post-hospitalization ecological momentary assessment in patients with psychotic-spectrum disorders. Compr Psychiatry. 2017;74:204–13.

Article  PubMed  PubMed Central  Google Scholar 

Rotondi AJ, Eack SM, Hanusa BH, Spring MB, Haas GL. Critical design elements of e-health applications for users with severe mental illness: singular focus, simple architecture, prominent contents, explicit navigation, and inclusive hyperlinks. Schizophr Bull. 2015;41(2):440–8.

Article  PubMed  Google Scholar 

Arean PA, Hallgren KA, Jordan JT, Gazzaley A, Atkins DC, Heagerty PJ, et al. The Use and Effectiveness of Mobile apps for Depression: results from a fully remote clinical trial. J Med Internet Res. 2016;18(12):e330.

Article  PubMed  PubMed Central  Google Scholar 

Owen JE, Jaworski BK, Kuhn E, Makin-Byrd KN, Ramsey KM, Hoffman JE. mHealth in the Wild: using Novel Data to examine the Reach, Use, and impact of PTSD Coach. JMIR Ment Health. 2015;2(1):e7.

Article  PubMed  PubMed Central  Google Scholar 

Torous J, Staples P, Slaters L, Adams J, Sandoval L, Onnela JP, et al. Characterizing Smartphone Engagement for Schizophrenia: results of a Naturalist Mobile Health Study. Clin Schizophr Relat Psychoses; 2017.

Ben-Zeev D, Scherer EA, Gottlieb JD, Rotondi AJ, Brunette MF, Achtyes ED, et al. mHealth for Schizophrenia: Patient Engagement with a mobile phone intervention following Hospital Discharge. JMIR Ment Health. 2016;3(3):e34.

Article  PubMed  PubMed Central  Google Scholar 

Ortiz A, Hintze A, Burnett R, Gonzalez-Torres C, Unger S, Yang D, et al. Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study. BMC Psychiatry. 2022;22(1):288.

Article  PubMed  PubMed Central  Google Scholar 

Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington. VA:American Psychiatric Association; 2013.

First M, Williams J, Karg R. RL S. Structured clinical interview for DSM-5, Research Version (SCID-5). Arlington, VA: American Psychiatric Association; 2015.

Google Scholar 

Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–35.

Article  CAS  PubMed  Google Scholar 

Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382–9.

Article  CAS  PubMed  Google Scholar 

Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: the day reconstruction method. Science. 2004;306(5702):1776–80.

Article  CAS  PubMed  Google Scholar 

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Altman EG, Hedeker D, Peterson JL, Davis JM. The Altman Self-Rating Mania Scale. Biol Psychiatry. 1997;42(10):948–55.

Article  CAS  PubMed