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Cognitive Moderation of CBT: Disorder-Specific or Transdiagnostic Predictors of Treatment Response

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Abstract

Cognitive vulnerability research has focused on cognitive variables that are hypothesized to confer risk to specific disorders within the mood and anxiety spectrum, while transdiagnostic research has emphasized common risk factors across disorders. The purpose of the present study was to test specific versus common cognitive predictors of treatment response across three treatment groups. Participants (N = 373) with major depressive disorder (MDD; N = 187, panic disorder with/without agoraphobia (PD/A; N = 85), and obsessive compulsive disorder (OCD; N = 101) completed measures of cognitive vulnerability (performance-oriented dysfunctional attitudes, anxiety sensitivity, and obsessive beliefs) and disorder-specific symptom measures at pre- and post CBT treatment. Based on latent difference score analysis, pre-treatment performance-oriented dysfunctional attitudes alone predicted improvement in depressive symptoms in the MDD group; pre-treatment anxiety sensitivity alone predicted reductions in anxious arousal symptoms in the PD/A group; and pre-treatment obsessive beliefs alone predicted change in OCD symptoms in the OCD group. These findings provide support for disorder-specific cognitive factors in the prediction of CBT treatment outcomes and provide guidance towards ways in which current CBT approaches may benefit from augmentation or adjustment.

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Notes

  1. An analysis of variance found that age significantly differed according to diagnostic groups, F(2, 326) = 14.63 p < .001. Post-hoc comparisons using Tukey’s HSD test indicated that the mean age for the OCD group was significantly lower than for the PD/A or MDD group, p < .05. A Chi-Square test indicated that the diagnostic groups did not differ according to gender, χ2 (2) = 2.57, p = 0.28.

  2. Each variable was evaluated for longitudinal measurement invariance. A confirmatory factor analysis (CFA) was first conducted for each measure, and all items were retained. Measurement invariance was evaluated before proceeding with the LDS analysis, testing the null hypothesis of weak (i.e., equal factor loadings over time) and strong (i.e., equal measurement intercepts over time) longitudinal measurement invariance.

  3. Proportional Change Model: Latent change is proportional to the latent score from the previous time point.E[ΔVariable(t)n] = β x E[Variable (t − 1)n]; αs x E[ss,n] = 0.

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Authors and Affiliations

  1. Department of Psychiatry, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, M4N 3M5, Toronto, ON, Canada

    Danielle E. Katz, Lance L. Hawley & Neil A. Rector

  2. Centre for Addiction and Mental Health, Toronto, ON, Canada

    Judith M. Laposa & Leanne Quigley

  3. Department of Psychiatry, University of Toronto, Toronto, ON, Canada

    Judith M. Laposa, Lance L. Hawley & Neil A. Rector

Authors
  1. Danielle E. Katz
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  2. Judith M. Laposa
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  3. Lance L. Hawley
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  4. Leanne Quigley
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  5. Neil A. Rector
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Corresponding author

Correspondence to Neil A. Rector.

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Danielle Katz, Judith Laposa, Lance Hawley, Leanne Quigley, and Neil Rector declare that they have no conflict of interest.

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Informed consent was obtained from all individual participants included in the study.

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Appendices

Appendix 1

LDS Reciprocal Models

Bidirectional relationships between symptom measures and cognitive variables.

MDD Sample

Equation: Reciprocal model examining the relationship of QIDS and DAS-Pft:

$${\text{E}}[\Delta {\text{QIDS}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{QIDS}}}} \times {\text{E}}\left[ {{\text{QIDS}}_{\text{n}}} \right]+ \gamma \times {\text{E}}\left[ {{\text{DAS{-}Pft}}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}} \right]$$
$${\text{E}}[\Delta {\text{DAS{-}Pft}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{DAS{-}Pft}}}} \times {\text{E}}\left[ {{\text{DAS{-}Pft}}}{{\left( {{\text{t}}-{\text{1}}} \right)}_{\text{n}}} \right]+{\gamma _{{\text{QIDS}}}} \times {\text{E}}\left[ {{\text{QIDS}}{{\left( {{\text{t}}-{\text{1}}} \right)}_{\text{n}}}} \right]$$

Equation: Reciprocal model examining the relationship of QIDS and OBQ:

$${\text{E}}[\Delta {\text{QIDS}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{QIDS}}}} \times {\text{E}}\left[ {{\text{QIDS}}_{\text{n}}} \right]+\gamma \times {\text{E}}\left[ {{\text{OBQ}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$
$${\text{E}}[\Delta {\text{OBQ}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{OBQ}}}} \times {\text{E}}\left[ {{\text{OBQ }}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]+{\gamma _{{\text{QIDS}}}} \times {\text{E}}\left[ {{\text{QIDS}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$

Equation: Reciprocal model examining the relationship of QIDS and ASI total score:

$${\text{E}}[\Delta {\text{QIDS}}{\left( {\text{t}} \right)_{\text{n}}}]={\beta _{{\text{QIDS}}}} \times {\text{E}}\left[ {{\text{QIDS}}_{\text{n}}} \right]+\gamma \times {\text{E}}\left[ {{\text{ASI}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$
$${\text{E}}[\Delta {\text{ASI}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{ASI}}}} \times {\text{E}}\left[ {{\text{ASI}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]+ {\gamma _{{\text{QIDS}}}} \times {\text{E}}\left[ {{\text{QIDS}}{{\left( {{\text{t}} -{\text{1}}} \right)}_{\text{n}}}} \right]$$

OCD Sample

Equation: Reciprocal model examining the relationship of YBOCS and DAS-Pft:

$${\text{E}}[\Delta {\text{YBOCS}}{\left( {\text{t}} \right)_{\text{n}}}]={\beta _{{\text{YBOCS}}}} \times {\text{E}}\left[ {{\text{YBOCS}}_{\text{n}}} \right]+\gamma \times {\text{E}}\left[ {{\text{DAS{-}Pft}}{{\left( {{\text{t}} -{\text{1}}} \right)}_{\text{n}}}} \right]$$
$${\text{E}}[\Delta {\text{DAS{-}Pft}} {\left( {\text{t}} \right)_{\text{n}}}]={\beta _{{\text{DAS{-}Pft}}}} \times {\text{E}}\left[ {{\text{DAS{-}Pft}}{{\left( {{\text{t}} -{\text{1}}} \right)}_{\text{n}}}} \right]+{\gamma _{{\text{YBOCS}}}} \times {\text{E}}\left[ {{\text{YBOCS}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$

Equation: Reciprocal model examining the relationship of YBOCS and OBQ:

$${\text{E}}[\Delta {\text{YBOCS}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{YBOCS}}}} \times {\text{E}}\left[ {{\text{YBOC}}{{\text{S}}_{\text{n}}}} \right]+ \gamma \times {\text{E}}\left[ {{\text{OBQ}}{{\left( {{\text{t}}-{\text{1}}} \right)}_{\text{n}}}} \right]$$
$${\text{E}}[\Delta {\text{OBQ}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{OBQ}}}} \times {\text{E}}\left[ {{\text{OBQ}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]+ {\gamma _{{\text{YBOCS}}}} \times {\text{E}}\left[ {{\text{YBOCS}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$

Equation: Reciprocal model examining the relationship of YBOCS and ASI total score:

$${\text{E}}[\Delta {\text{YBOCS}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{YBOCS}}}} \times {\text{E}}\left[ {{\text{YBOCS}}_{\text{n}}} \right]+\gamma \times {\text{E}}\left[ {{\text{ASI}}{{\left( {{\text{t}}- {\text{1}}} \right)}_{\text{n}}}} \right]$$
$${\text{E}}[\Delta {\text{ASI}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{ASI}}}} \times {\text{E}}\left[ {{\text{ASI}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right] +{\gamma _{{\text{YBOCS}}}} \times {\text{E}}\left[ {{\text{YBOCS}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$

PD/A Sample

Equation: Reciprocal model examining the relationship of DASS-A and DAS-Pft:

$${\text{E}}[\Delta {\text{DASS{-}A}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{DASS{-}A}}}} \times {\text{E}}\left[ {{\text{DASS{-}A}}_{\text{n}}}\right]+\gamma \times {\text{E}}\left[ {{\text{DAS{-}Pft}}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}} \right]$$
$${\text{E}}[\Delta {\text{DAS{-}Pft}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{DAS{-}Pft}}}} \times {\text{E}}\left[ {{\text{DAS{-} Pft}}}{{\left( {{\text{t}}- {\text{1}}} \right)}_{\text{n}}} \right]+\gamma _{{\text{DASS{-}A}}} \times {\text{E}}\left[ {{\text{DASS{-}A}}}{{\left( {{\text{t}} -{\text{1}}} \right)}}_{\text{n}} \right]$$

Equation: Reciprocal model examining the relationship of DASS-A and OBQ:

$${\text{E}}[\Delta {\text{DASS{-}A}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{DASS{-}A}}}} \times {\text{E}}\left[ {{\text{DASS{-}A}}}_{\text{n}} \right]+\gamma \times {\text{E}}\left[ {{\text{OBQ}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]$$
$${\text{E}}[\Delta {\text{OBQ}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{OBQ}}}} \times {\text{E}}\left[ {{\text{OBQ}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}}} \right]+{\gamma _{{\text{DASS{-}A}}}} \times {\text{E}}\left[ {{\text{DASS{-}A}}}{{\left( {{\text{t}} - {\text{1}}} \right)}}_{\text{n}} \right]$$

Equation: Reciprocal model examining the relationship of DASS-A and ASI physical subscale:

$${\text{E}}[\Delta {\text{DASS{-}A}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{DASS{-}A}}}} \times {\text{E}}\left[ {{\text{DASS{-}A}}}_{\text{n}} \right]+\gamma \times {\text{E}}\left[ {{\text{ASI{-}P}}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}} \right]$$
$${\text{E}}[\Delta {\text{ASI{-}P}}{\left( {\text{t}} \right)_{\text{n}}}]= {\beta _{{\text{ASI{-}P}}}} \times {\text{E}}\left[ {{\text{ASI{-} P}}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}} \right]+{\gamma _{{\text{DASS{-}A}}}} \times {\text{E}}\left[ {{\text{DASS{-}A}}}{{\left( {{\text{t}} - {\text{1}}} \right)}_{\text{n}}} \right]$$

Appendix 2

See Fig. 4.

Fig. 4
figure 4

a SEM pathways for the “Reciprocal Model” for the MDD Sample. b SEM pathways for the “Reciprocal Model” for the OCD sample. c SEM pathways for the “Reciprocal Model” for the PD/A sample

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Katz, D.E., Laposa, J.M., Hawley, L.L. et al. Cognitive Moderation of CBT: Disorder-Specific or Transdiagnostic Predictors of Treatment Response. Cogn Ther Res 43, 803–818 (2019). https://doi.org/10.1007/s10608-019-10009-y

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