Adherence With Online Therapy vs Face-to-Face Therapy and With Online Therapy vs Care as Usual: Secondary Analysis of Two Randomized Controlled Trials

Sonia Lippke, Lingling Gao, Franziska Maria Keller, Petra Becker, Alina Dahmen, Sonia Lippke, Lingling Gao, Franziska Maria Keller, Petra Becker, Alina Dahmen

Abstract

Background: Adherence to internet-delivered interventions targeting mental health such as online psychotherapeutic aftercare is important for the intervention's impact. High dropout rates limit the impact and generalizability of findings. Baseline differences may be putting patients at risk for dropping out, making comparisons between online with face-to-face (F2F) therapy and care as usual (CAU) necessary to examine.

Objective: This study investigated adherence to online, F2F, and CAU interventions as well as study dropout among these groups and the subjective evaluation of the therapeutic relationship. Sociodemographic, social-cognitive, and health-related variables were considered.

Methods: In a randomized controlled trial, 6023 patients were recruited, and 300 completed the baseline measures (T1), 144 completed T2 (retention 44%-52%), and 95 completed T3 (retention 24%-36%). Sociodemographic variables (eg, age, gender, marital status, educational level), social-cognitive determinants (eg, self-efficacy, social support), health-related variables (eg, depressiveness), and expectation towards the treatment for patients assigned to online or F2F were measured at T1.

Results: There were no significant differences between the groups regarding dropout rates (χ21=0.02-1.06, P≥.30). Regarding adherence to the treatment condition, the online group outperformed the F2F and CAU conditions (P≤.01), indicating that patients randomized into the F2F and CAU control groups were much more likely to show nonadherent behavior in comparison with the online therapy groups. Within study groups, gender differences were significant only in the CAU group at T2, with women being more likely to drop out. At T3, age and marital status were also only significant in the CAU group. Patients in the online therapy group were significantly more satisfied with their treatment than patients in the F2F group (P=.02; Eta²=.09). Relationship satisfaction and success satisfaction were equally high (P>.30; Eta²=.02). Combining all study groups, patients who reported lower depressiveness scores at T1 (T2: odds ratio [OR] 0.55, 95% CI 0.35-0.87; T3: OR 0.56, 95% CI 0.37-0.92) were more likely to be retained, and patients who had higher self-efficacy (T2: OR 0.57, 95% CI 0.37-0.89; T3: OR 0.52, 95% CI 0.32-0.85) were more likely to drop out at T2 and T3. Additionally, at T3, the lower social support that patients reported was related to a higher likelihood of remaining in the study (OR 0.68, 95% CI 0.48-0.96). Comparing the 3 intervention groups, positive expectation was significantly related with questionnaire completion at T2 and T3 after controlling for other variables (T2: OR 1.64, 95% CI 1.08-2.50; T3: OR 1.59, 95% CI 1.01-2.51).

Conclusions: While online interventions have many advantages over F2F variants such as saving time and effort to commute to F2F therapy, they also create difficulties for therapists and hinder their ability to adequately react to patients' challenges. Accordingly, patient characteristics that might put them at risk for dropping out or not adhering to the treatment plan should be considered in future research and practice. Online aftercare, as described in this research, should be provided more often to medical rehabilitation patients.

Trial registration: ClinicalTrials.gov NCT04989842; https://ichgcp.net/clinical-trials-registry/NCT04989842.

Keywords: care as usual; dropout; face-to-face therapy; medical rehabilitation; online therapy; psychotherapeutic aftercare; retention.

Conflict of interest statement

Conflicts of Interest: None declared.

©Sonia Lippke, Lingling Gao, Franziska Maria Keller, Petra Becker, Alina Dahmen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.11.2021.

Figures

Figure 1
Figure 1
CONSORT (Consolidated Standards of Reporting Trials) flowchart for the Curriculum Hannover. CAU: care as usual, F2F: face-to-face, ONL: online.
Figure 2
Figure 2
Mean scores for improvement expectation during therapy among patients who completed the questionnaires and those who dropped out at T2, in the face-to-face (F2F) and combined online groups (ONL1+ONL2).

References

    1. Lecomte T, Potvin S, Corbière M, Guay S, Samson C, Cloutier B, Francoeur A, Pennou A, Khazaal Y. Mobile apps for mental health issues: meta-review of meta-analyses. JMIR Mhealth Uhealth. 2020 May 29;8(5):e17458. doi: 10.2196/17458. v8i5e17458
    1. Wu A, Scult MA, Barnes ED, Betancourt JA, Falk A, Gunning FM. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. NPJ Digit Med. 2021 Feb 11;4(1):20. doi: 10.1038/s41746-021-00386-8. doi: 10.1038/s41746-021-00386-8.10.1038/s41746-021-00386-8
    1. Zarski A, Berking M, Reis D, Lehr D, Buntrock C, Schwarzer R, Ebert DD. Turning good intentions into actions by using the Health Action Process Approach to predict adherence to internet-based depression prevention: secondary analysis of a randomized controlled trial. J Med Internet Res. 2018 Jan 11;20(1):e9. doi: 10.2196/jmir.8814. v20i1e9
    1. Erbe D, Eichert H, Riper H, Ebert DD. Blending face-to-face and internet-based interventions for the treatment of mental disorders in adults: systematic review. J Med Internet Res. 2017 Sep 15;19(9):e306. doi: 10.2196/jmir.6588. v19i9e306
    1. Lindhiem O, Bennett CB, Rosen D, Silk J. Mobile technology boosts the effectiveness of psychotherapy and behavioral interventions: a meta-analysis. Behav Modif. 2015 Nov 17;39(6):785–804. doi: 10.1177/0145445515595198. 0145445515595198
    1. 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 Feb 15;263:413–419. doi: 10.1016/j.jad.2019.11.167.S0165-0327(19)32606-0
    1. Karin E, Dear BF, Heller GZ, Crane MF, Titov N. "Wish you were here": examining characteristics, outcomes, and statistical solutions for missing cases in web-based psychotherapeutic trials. JMIR Ment Health. 2018 Apr 19;5(2):e22. doi: 10.2196/mental.8363. v5i2e22
    1. Skea ZC, Newlands R, Gillies K. Exploring non-retention in clinical trials: a meta-ethnographic synthesis of studies reporting participant reasons for drop out. BMJ Open. 2019 Jun 03;9(6):e021959. doi: 10.1136/bmjopen-2018-021959. bmjopen-2018-021959
    1. Dahmen A, Gao L, Keller F, Lehr D, Becker P, Lippke S. Wirksamkeit webbasierter psychotherapeutische Nachsorge nach psychosomatischer Rehabilitation ? ein Test in zwei Randomized Controlled Trials. Die Rehabilitation (forthcoming) 2021:1.
    1. Deutsche Rentenversicherung. [2021-10-01]. .
    1. Brown M, O'Neill N, van Woerden H, Eslambolchilar P, Jones M, John A. Gamification and adherence to web-based mental health interventions: a systematic review. JMIR Ment Health. 2016 Aug 24;3(3):e39. doi: 10.2196/mental.5710. v3i3e39
    1. Buyruk Genç A, Amanvermez Y, Zeren SG, Erus SM. Early separations: Dropout from online and face-to-face counseling. Pegem Eğitim ve Öğretim Dergisi. 2019 Sep 18;9(4):1001–1030. doi: 10.14527/pegegog.2019.032.
    1. Marks IM, Kenwright M, McDonough M, Whittaker M, Mataix-Cols D. Saving clinicians' time by delegating routine aspects of therapy to a computer: a randomized controlled trial in phobia/panic disorder. Psychol Med. 2004 Jan 14;34(1):9–17. doi: 10.1017/s003329170300878x.
    1. Beatty L, Binnion C. A systematic review of predictors of, and reasons for, adherence to online psychological interventions. Int J Behav Med. 2016 Dec;23(6):776–794. doi: 10.1007/s12529-016-9556-9.10.1007/s12529-016-9556-9
    1. Weightman M. Digital psychotherapy as an effective and timely treatment option for depression and anxiety disorders: Implications for rural and remote practice. J Int Med Res. 2020 Jun;48(6):300060520928686. doi: 10.1177/0300060520928686.
    1. Karin E, Crane MF, Dear BF, Nielssen O, Heller GZ, Kayrouz R, Titov N. Predictors, outcomes, and statistical solutions of missing cases in web-based psychotherapy: methodological replication and elaboration study. JMIR Ment Health. 2021 Feb 5;8(2):e22700. doi: 10.2196/22700.
    1. Linardon J, Fuller-Tyszkiewicz M. Attrition and adherence in smartphone-delivered interventions for mental health problems: A systematic and meta-analytic review. J Consult Clin Psychol. 2020 Jan;88(1):1–13. doi: 10.1037/ccp0000459.2019-66487-001
    1. Cai Z, Fan X, Du J. Gender and attitudes toward technology use: A meta-analysis. Computers & Education. 2017 Feb;105:1–13. doi: 10.1016/j.compedu.2016.11.003. doi: 10.1016/j.compedu.2016.11.003.
    1. Retzer L, Reindl R, Zauter S, Richter K. Bevorzugen Frauen Face-to-Face-Beratung bei Insomnie? Somnologie. 2021 Mar 02;25(2):151–154. doi: 10.1007/s11818-020-00292-3.
    1. Al-Asadi AM, Klein B, Meyer D. Posttreatment attrition and its predictors, attrition bias, and treatment efficacy of the anxiety online programs. J Med Internet Res. 2014 Oct 14;16(10):e232. doi: 10.2196/jmir.3513. v16i10e232
    1. Schmidt ID, Forand NR, Strunk DR. Predictors of dropout in internet-based cognitive behavioral therapy for depression. Cognit Ther Res. 2019 Jun 16;43(3):620–630. doi: 10.1007/s10608-018-9979-5.
    1. Schwarzer R, Lippke S, Luszczynska A. Mechanisms of health behavior change in persons with chronic illness or disability: the Health Action Process Approach (HAPA) Rehabil Psychol. 2011 Aug;56(3):161–70. doi: 10.1037/a0024509.2011-14571-001
    1. Davies F, Shepherd HL, Beatty L, Clark B, Butow P, Shaw J. Implementing web-based therapy in routine mental health care: systematic review of health professionals' perspectives. J Med Internet Res. 2020 Jul 23;22(7):e17362. doi: 10.2196/17362. v22i7e17362
    1. Kobelt A, Nickel L, Grosch EV, Lamprecht F, Künsebeck HW. [Participation in psychosomatic outpatient care after in-patient rehabilitation] Psychother Psychosom Med Psychol. 2004 Feb;54(2):58–64. doi: 10.1055/s-2003-812612.
    1. Kobelt A, Grosch E. Indikation zur ambulanten Nachsorge (Curriculum Hannover) in der Psychosomatischen Rehabilitation. Psychotherapeut. 2005 Sep;50(5):340–346. doi: 10.1007/s00278-005-0438-x.
    1. Faul F, Erdfelder E, Lang A, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods. 2007 May;39(2):175–191. doi: 10.3758/BF03193146.
    1. NCSS Statistical Software. [2021-10-01].
    1. Rabung S, Harfst T, Kawski S, Koch U, Wittchen H, Schulz H. [Psychometric analysis of a short form of the "Hamburg Modules for the Assessment of Psychosocial Health" (HEALTH-49)] Z Psychosom Med Psychother. 2009;55(2):162–79. doi: 10.13109/zptm.2009.55.2.162.
    1. Mittag O, Meyer T, Glaser-Möller N, Matthis C, Raspe H. [Predicting gainful employment in a population sample of 4225 statutory pension insurance members covering a prognostic period of five years using a brief subjective prognostic employment scale (SPE Scale)] Gesundheitswesen. 2006 May;68(5):294–302. doi: 10.1055/s-2006-926781.
    1. Tuomi K, Ilmarinen J, Jahkola A, Katajarinne L, Tulkki A. Work Ability Index, 2nd revised edition. Helsinki, Finland: Finnish Institute of Occupational Health; 1998.
    1. Schulte D. Messung der Therapieerwartung und Therapieevaluation von Patienten (PATHEV) Zeitschrift für Klinische Psychologie und Psychotherapie. 2005 Jun 1;34(3):176–187. doi: 10.1026/1616-3443.34.3.176.
    1. Nübling R, Kraft M, Henn J, Kriz D, Lutz W, Schmidt J, Wittmann W, Bassler M. [Testing the psychometric properties of the Helping Alliance Questionnaire (HAQ) in different health care settings] Psychother Psychosom Med Psychol. 2017 Nov 30;67(11):465–476. doi: 10.1055/s-0043-111083.
    1. Schmidt J, Nübling R. ZUF-8. Fragebogen zur Messung der Patientenzufriedenheit. In: Brähler E, Schumacher J, Strauß B, editors. Diagnostische Verfahren in der Psychotherapie. Göttingen, Germany: Hogrefe; 2002. pp. 392–96.
    1. Andersson G, Cuijpers P, Carlbring P, Riper H, Hedman E. Guided internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis. World Psychiatry. 2014 Oct;13(3):288–95. doi: 10.1002/wps.20151. doi: 10.1002/wps.20151.
    1. Mehta VS, Parakh M. Web based interventions in psychiatry: an overview. Int J Ment Health Psychiatry. 2015;01(03):1. doi: 10.4172/2471-4372.1000108.
    1. Lippke S, Dahmen A, Gao L, Guza E, Nigg CR. To what extent is internet activity predictive of psychological well-being? PRBM. 2021 Feb;Volume 14:207–219. doi: 10.2147/prbm.s274502.

Source: PubMed

3
購読する