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Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

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Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression. / Lutz, Wolfgang; Arndt, Alice; Rubel, Julian; Berger, Thomas; Schröder, Johanna; Späth, Christina; Meyer, Björn; Greiner, Wolfgang; Gräfe, Viola; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Nolte, Sandra; Löwe, Bernd; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen.

in: J MED INTERNET RES, Jahrgang 19, Nr. 6, 09.06.2017, S. e206.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Lutz, W, Arndt, A, Rubel, J, Berger, T, Schröder, J, Späth, C, Meyer, B, Greiner, W, Gräfe, V, Hautzinger, M, Fuhr, K, Rose, M, Nolte, S, Löwe, B, Hohagen, F, Klein, JP & Moritz, S 2017, 'Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression', J MED INTERNET RES, Jg. 19, Nr. 6, S. e206. https://doi.org/10.2196/jmir.7367

APA

Lutz, W., Arndt, A., Rubel, J., Berger, T., Schröder, J., Späth, C., Meyer, B., Greiner, W., Gräfe, V., Hautzinger, M., Fuhr, K., Rose, M., Nolte, S., Löwe, B., Hohagen, F., Klein, J. P., & Moritz, S. (2017). Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression. J MED INTERNET RES, 19(6), e206. https://doi.org/10.2196/jmir.7367

Vancouver

Lutz W, Arndt A, Rubel J, Berger T, Schröder J, Späth C et al. Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression. J MED INTERNET RES. 2017 Jun 9;19(6):e206. https://doi.org/10.2196/jmir.7367

Bibtex

@article{92f43376e07c4e26ac2485069d5b1b1a,
title = "Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression",
abstract = "BACKGROUND: Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment.OBJECTIVE: The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects.METHODS: We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression.RESULTS: Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04).CONCLUSIONS: These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources.",
keywords = "Journal Article",
author = "Wolfgang Lutz and Alice Arndt and Julian Rubel and Thomas Berger and Johanna Schr{\"o}der and Christina Sp{\"a}th and Bj{\"o}rn Meyer and Wolfgang Greiner and Viola Gr{\"a}fe and Martin Hautzinger and Kristina Fuhr and Matthias Rose and Sandra Nolte and Bernd L{\"o}we and Fritz Hohagen and Klein, {Jan Philipp} and Steffen Moritz",
year = "2017",
month = jun,
day = "9",
doi = "10.2196/jmir.7367",
language = "English",
volume = "19",
pages = "e206",
journal = "J MED INTERNET RES",
issn = "1438-8871",
publisher = "Journal of medical Internet Research",
number = "6",

}

RIS

TY - JOUR

T1 - Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

AU - Lutz, Wolfgang

AU - Arndt, Alice

AU - Rubel, Julian

AU - Berger, Thomas

AU - Schröder, Johanna

AU - Späth, Christina

AU - Meyer, Björn

AU - Greiner, Wolfgang

AU - Gräfe, Viola

AU - Hautzinger, Martin

AU - Fuhr, Kristina

AU - Rose, Matthias

AU - Nolte, Sandra

AU - Löwe, Bernd

AU - Hohagen, Fritz

AU - Klein, Jan Philipp

AU - Moritz, Steffen

PY - 2017/6/9

Y1 - 2017/6/9

N2 - BACKGROUND: Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment.OBJECTIVE: The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects.METHODS: We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression.RESULTS: Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04).CONCLUSIONS: These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources.

AB - BACKGROUND: Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment.OBJECTIVE: The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects.METHODS: We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression.RESULTS: Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04).CONCLUSIONS: These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources.

KW - Journal Article

U2 - 10.2196/jmir.7367

DO - 10.2196/jmir.7367

M3 - SCORING: Journal article

C2 - 28600278

VL - 19

SP - e206

JO - J MED INTERNET RES

JF - J MED INTERNET RES

SN - 1438-8871

IS - 6

ER -