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Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model

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Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model. / Arndt, Alice; Lutz, Wolfgang; Rubel, Julian; Berger, Thomas; Meyer, Björn; Schröder, Johanna; Späth, Christina; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen.

In: COGN BEHAV THERAPY, Vol. 49, No. 1, 01.2020, p. 22-40.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Arndt, A, Lutz, W, Rubel, J, Berger, T, Meyer, B, Schröder, J, Späth, C, Hautzinger, M, Fuhr, K, Rose, M, Hohagen, F, Klein, JP & Moritz, S 2020, 'Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model', COGN BEHAV THERAPY, vol. 49, no. 1, pp. 22-40. https://doi.org/10.1080/16506073.2018.1556331

APA

Arndt, A., Lutz, W., Rubel, J., Berger, T., Meyer, B., Schröder, J., Späth, C., Hautzinger, M., Fuhr, K., Rose, M., Hohagen, F., Klein, J. P., & Moritz, S. (2020). Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model. COGN BEHAV THERAPY, 49(1), 22-40. https://doi.org/10.1080/16506073.2018.1556331

Vancouver

Arndt A, Lutz W, Rubel J, Berger T, Meyer B, Schröder J et al. Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model. COGN BEHAV THERAPY. 2020 Jan;49(1):22-40. https://doi.org/10.1080/16506073.2018.1556331

Bibtex

@article{5a6083cbf7a749fd8981bf497c5789f7,
title = "Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model",
abstract = "To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived {"}good enough{"} dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.",
author = "Alice Arndt and Wolfgang Lutz and Julian Rubel and Thomas Berger and Bj{\"o}rn Meyer and Johanna Schr{\"o}der and Christina Sp{\"a}th and Martin Hautzinger and Kristina Fuhr and Matthias Rose and Fritz Hohagen and Klein, {Jan Philipp} and Steffen Moritz",
year = "2020",
month = jan,
doi = "10.1080/16506073.2018.1556331",
language = "English",
volume = "49",
pages = "22--40",
journal = "COGN BEHAV THERAPY",
issn = "1650-6073",
publisher = "Taylor and Francis AS",
number = "1",

}

RIS

TY - JOUR

T1 - Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model

AU - Arndt, Alice

AU - Lutz, Wolfgang

AU - Rubel, Julian

AU - Berger, Thomas

AU - Meyer, Björn

AU - Schröder, Johanna

AU - Späth, Christina

AU - Hautzinger, Martin

AU - Fuhr, Kristina

AU - Rose, Matthias

AU - Hohagen, Fritz

AU - Klein, Jan Philipp

AU - Moritz, Steffen

PY - 2020/1

Y1 - 2020/1

N2 - To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived "good enough" dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.

AB - To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived "good enough" dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.

U2 - 10.1080/16506073.2018.1556331

DO - 10.1080/16506073.2018.1556331

M3 - SCORING: Journal article

C2 - 30721109

VL - 49

SP - 22

EP - 40

JO - COGN BEHAV THERAPY

JF - COGN BEHAV THERAPY

SN - 1650-6073

IS - 1

ER -