• This record comes from PubMed

Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models

. 2023 May 17 ; 11 (1) : 18. [epub] 20230517

Status PubMed-not-MEDLINE Language English Country Germany Media electronic

Document type Journal Article

Grant support
R21 MH123849 NIMH NIH HHS - United States
1R21MH123849 NIH HHS - United States

Links

PubMed 37195477
PubMed Central PMC10192477
DOI 10.1186/s40345-023-00297-5
PII: 10.1186/s40345-023-00297-5
Knihovny.cz E-resources

BACKGROUND: Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence. METHODS: Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes. RESULTS: Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed "perfect" adherence; 37.1% showed "good" adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence. CONCLUSIONS: Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement.

See more in PubMed

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. doi: 10.1089/tmj.2020.0535. PubMed DOI PMC

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. doi: 10.1093/ntr/nty073. PubMed DOI PMC

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. doi: 10.2196/jmir.5604. PubMed DOI PMC

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. doi: 10.2196/22443. PubMed DOI PMC

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. doi: 10.2196/24718. PubMed DOI PMC

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. doi: 10.1093/jamiaopen/ooab043. PubMed DOI PMC

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. doi: 10.2196/16939. PubMed DOI PMC

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. doi: 10.2196/17730. PubMed DOI PMC

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. doi: 10.1016/j.jad.2016.06.064. PubMed DOI

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. doi: 10.1097/NMD.0b013e3181f49ea3. PubMed DOI PMC

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. doi: 10.1016/j.jad.2009.10.019. PubMed DOI

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. PubMed

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. doi: 10.1016/j.jad.2017.07.029. PubMed DOI

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. doi: 10.1111/j.1399-5618.2010.00807.x. PubMed DOI PMC

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 PMC

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. doi: 10.1177/2167702615604613. PubMed DOI PMC

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. doi: 10.1186/s40345-017-0076-6. PubMed DOI PMC

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. doi: 10.1109/JBHI.2014.2343154. PubMed DOI

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. doi: 10.1002/mpr.1502. PubMed DOI PMC

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. doi: 10.2196/19476. PubMed DOI PMC

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 PMC

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. doi: 10.2196/jmir.9775. PubMed DOI PMC

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. doi: 10.1111/bdi.12637. PubMed DOI PMC

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. doi: 10.1109/TBME.2016.2611862. PubMed DOI PMC

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. doi: 10.1016/j.jad.2021.08.140. PubMed DOI

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. doi: 10.1016/j.comppsych.2017.01.018. PubMed DOI PMC

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. doi: 10.1093/schbul/sbt194. PubMed DOI PMC

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. doi: 10.2196/jmir.6482. PubMed DOI PMC

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. doi: 10.2196/mental.3935. PubMed DOI PMC

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. PubMed

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. doi: 10.2196/mental.6348. PubMed DOI PMC

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. doi: 10.1186/s12888-022-03923-1. PubMed DOI PMC

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

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

Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–35. doi: 10.1192/bjp.133.5.429. PubMed DOI

Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382–9. doi: 10.1192/bjp.134.4.382. PubMed DOI

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. doi: 10.1126/science.1103572. PubMed DOI

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. doi: 10.1046/j.1525-1497.2001.016009606.x. PubMed DOI PMC

Altman EG, Hedeker D, Peterson JL, Davis JM. The Altman Self-Rating Mania Scale. Biol Psychiatry. 1997;42(10):948–55. doi: 10.1016/S0006-3223(96)00548-3. PubMed DOI

de Zambotti M, Rosas L, Colrain IM, Baker FC. The Sleep of the Ring: Comparison of the OURA Sleep Tracker Against Polysomnography. Behav Sleep Med. 2017:1–15. PubMed PMC

Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19(6):716–23. doi: 10.1109/TAC.1974.1100705. DOI

Schwarz G. Estimating the dimension of a model. The annals of statistics. 1978:461–4.

Muthén LK, Muthén BO. Mplus User’s Guide. 8th2007.

Torous J, Lipschitz J, Ng M, Firth J. Dropout rates in clinical trials of smartphone apps for depressive symptoms: a systematic review and meta-analysis. J Affect Disord. 2020;263:413–9. doi: 10.1016/j.jad.2019.11.167. PubMed DOI

Hekler EB, Klasnja P, Traver V, Hendriks M. Realizing effective behavioral management of health: the metamorphosis of behavioral science methods. IEEE Pulse. 2013;4(5):29–34. doi: 10.1109/MPUL.2013.2271681. PubMed DOI

Simmons LA, Wolever RQ, Bechard EM, Snyderman R. Patient engagement as a risk factor in personalized health care: a systematic review of the literature on chronic disease. Genome Med. 2014;6(2):16. doi: 10.1186/gm533. PubMed DOI PMC

King DK, Toobert DJ, Portz JD, Strycker LA, Doty A, Martin C, et al. What patients want: relevant health information technology for diabetes self-management. Health and Technology. 2012;2(3):147–57. doi: 10.1007/s12553-012-0022-7. DOI

Chen C, Haddad D, Selsky J, Hoffman JE, Kravitz RL, Estrin DE, et al. Making sense of mobile health data: an open architecture to improve individual- and population-level health. J Med Internet Res. 2012;14(4):e112. doi: 10.2196/jmir.2152. PubMed DOI PMC

Bauer M, Glenn T, Alda M, Grof P, Sagduyu K, Bauer R, et al. Comparison of pre-episode and pre-remission states using mood ratings from patients with bipolar disorder. Pharmacopsychiatry. 2011;44(Suppl 1):49–53. doi: 10.1055/s-0031-1273765. PubMed DOI

Bauer M, Glenn T, Grof P, Pfennig A, Rasgon NL, Marsh W, et al. Self-reported data from patients with bipolar disorder: frequency of brief depression. J Affect Disord. 2007;101(1–3):227–33. doi: 10.1016/j.jad.2006.11.021. PubMed DOI

Ortiz A, Alda M. The perils of being too stable: mood regulation in bipolar disorder. J Psychiatry Neurosci. 2018;43(6):363–5. doi: 10.1503/jpn.180183. PubMed DOI PMC

Ortiz A, Grof P. Electronic monitoring of self-reported mood: the return of the subjective? Int J Bipolar Disord. 2016;4(1):28. doi: 10.1186/s40345-016-0069-x. PubMed DOI PMC

Depp CA, Harmell AL, Savla GN, Mausbach BT, Jeste DV, Palmer BW. A prospective study of the trajectories of clinical insight, affective symptoms, and cognitive ability in bipolar disorder. J Affect Disord. 2014;152–154:250–5. doi: 10.1016/j.jad.2013.09.020. PubMed DOI PMC

Španiel F, Hrdlička J, Novák T, Kožený J, Höschl C, Mohr P, et al. Effectiveness of the information technology-aided program of relapse prevention in schizophrenia (ITAREPS): a randomized, controlled, double-blind study. J Psychiatr Pract. 2012;18(4):269–80. doi: 10.1097/01.pra.0000416017.45591.c1. PubMed DOI

Jethwani K, Kvedar J, Kvedar J. Behavioral phenotyping: a tool for personalized medicine. Per Med. 2010;7(6):689–93. doi: 10.2217/pme.10.62. PubMed DOI

Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, et al. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med. 2015;5(3):335–46. doi: 10.1007/s13142-015-0324-1. PubMed DOI PMC

Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-Time adaptive interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior support. Ann Behav Med. 2018;52(6):446–62. doi: 10.1007/s12160-016-9830-8. PubMed DOI PMC

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...