Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression
Standard
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, Vol. 19, No. 6, 09.06.2017, p. e206.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
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 -