Cheyenne McIntyre

Predicting Coping Strategies and Support Preferences By: Cheyenne McIntyre Supervised by: Dr. Andrew Cooper

Introduction: Mood Disorders

• Mood disorders have a high prevalence in Canada’s population • Traditional psychotherapy delivery changed by lockdowns

Introduction: Mental Health apps

1. Wasil et al., 2020 2. Giota & Kleftaras, 2014

• Apps are marketed to help people cope with intense emotions • Mental health apps are relatively novel treatment options [1] • Overcome common barriers to treatment [2]

Introduction: Emotion Regulation

• One way to conceptualize mental wellness is through adaptive emotion regulation (ER) • People cope in multiple different ways [3]

3.

Aldao & Nolen-Hoeksema, 2012b

Emotion Regulation Strategies Cognitive Reappraisal

Self-Criticism

Acceptance

Hiding Expressions

Rumination

Experiential Suppression

• Preferences for treatment matters

Introduction: Treatment Preferences

• We are unsure of the rationale people use to choose apps • Is there a relationship between the ER strategies people use and what intervention they may choose?

Research Questions

Research Question 1 What emotion regulation (ER) strategies do individuals report using during recent, intense emotionally challenging experiences?

Research Question 2 What type of mental health app will individuals find most appealing for helping with emotion regulation issues?

Research Question 3 Do individuals differ in their preferences for ER interventions based on their self-identified coping strategy usage?

Methodology

Study Design • Cross-Sectional • Sample: Undergraduate PSYA02 students (no inclusion or exclusion criteria) • Recruited from SONA • N = 125

Willingness to use apps

RECALL: Anger

RECALL: Anxiety

RECALL: Sadness

ER Strategy Usage

Vignette

ER Strategy Usage • ER strategies: acceptance, cognitive reappraisal, self-criticism, hiding expressions, experiential suppression, rumination • Participants rate their use of these strategies on a Likert-type scale

A-App*

R-App*

• Focuses on improving users’ approach to feeling internal experiences from a more neutral perspective • Similar to Calm and Headspace • Specific features: • Mindfulness practice • Daily guided meditation • Body awareness

• Focuses on identifying and reevaluating thoughts associated with negative emotions • Similar to MoodNotes and CBT Thought Diary • Specific features: • Journaling • Thought tracking

Choice Between Interventions -100

-75

Strongly prefer A-app

-50

-25

0

25

50

75

100

Strongly prefer R-app

Expectancy of Treatment Outcome [ETO] (Adapted) • Questionnaire assessing strength of treatment preference [4] • Included a few more questions to adapt to the purpose of this study • Sample item: “How successful do you think that the A-app will be in reducing your intense emotions?”

4. Holder et al., 2019

Hypotheses & Analyses

Research Question 1 What emotion regulation (ER) strategies do individuals report using during recent, intense emotionally challenging experiences?

Hypothesis 1a Participants will report using more positive ER strategies than negative ER strategies in their recollection of intense emotional events [5].

Hypothesis 1b Participants will report greater use of acceptance [3].

3. Aldao & Nolen-Hoeksema, 2012b; Dixon-Gordon et al., 2015 5. Aldao & Nolen-Hoeksema, 2012a; Dixon-Gordon et al., 2015; Hiekkaranta et al., 2021

Research Question 2 What type of mental health app will individuals find most appealing for helping with emotion regulation issues?

Hypothesis 2 Due to the reach mindfulness-based apps have, participants will find this type of app more appealing to help any difficulties in emotion regulation [1].

1. Wasil et al., 2020

Research Question 3 Do individuals differ in their preferences for ER interventions based on their self-identified coping strategy usage?

Hypothesis 3* Participants’ self-reported ER strategy usage will predict their preferred app.

Results

Hypothesis 1a

Hypothesis 1b

Participants reported using more negative ER strategies across all scenarios, t(124) = -2.64, p = .012, d = 0.24, CI [-0.35, -0.06]

Participants reported greater use of acceptance across all scenarios, t(124) = -7.64, p = <.001, d = 0.68, CI [-0.86, -0.50])

ER Strategy Use Across Scenarios

Hypothesis 2 There was no strong preference for either app, t(123) = -0.145, p = .443.

However, there was an interesting trend…

Hypothesis 3 Certain aspects of ER strategy use significantly predicted app preference*

Primary Model For this regression model, we looked at composite reappraisal and composite acceptance as predictors of final MH app preference.

The global model was not significant, F(2, 121) = 2.85, p = .062).

However, when looking at the bootstrap model, there is a trend suggesting composite acceptance is predictive of lower scores on the MH app preference variable, t = -1.92, p = .036, CI [-29.70, -2.01]

This meant greater use of acceptance is indicative of a stronger preference towards the A-app.

AApp

Discussion & Conclusion

Overall, these findings suggest…

In any given situation, people use multiple coping strategies to regulate their intense emotions.

People tend to implement acceptance to a greater degree than cognitive reappraisal.

Coping strategy usage may predict preferences for MH apps.

Improving MH App Outcomes • Apps should capitalize on what people already report doing • Apps should be constructed using empirical research (see Wasil et al., 2020)

Thank you!

References Aldao, A., & Nolen-Hoeksema, S. (2012a). When are adaptive strategies most predictive of psychopathology? Journal of Abnormal Psychology, 121(1), 276-281. http://dx.doi.org/10.1037/a0023598 Aldao, A., & Nolen-Hoeksema, S. (2012b). The influence of context on the implementation of adaptive emotion regulation strategies. Behaviour Research and Therapy, 50(7-8), 493-501. http://dx.doi.org/10.1016/j.brat.2012.04.004 Cheavens, J. S., Strunk, D. R., Lazarus, S. A., & Goldstein, L. A. (2012). The compensation and capitalization models: A test of two approaches to individualizing the treatment of depression. Behaviour Research and Therapy, 50(11), 699-706. http://dx.doi.org/10.1016/j.brat.2012.08.002 Dixon-Gordon, K. L., Aldao, A., & De Los Reyes, A. (2015). Emotion regulation in context: Examining the spontaneous use of strategies across emotional intensity and type of emotion. Personality and Individual Differences, 86, 271-276. http://dx.doi.org/10.1016/j.paid.2015.06.011 Giota, K. G., & Kleftaras, G. (2014). Mental health apps: Innovations, risks and ethical considerations. E-Health Telecommunication Systems and Networks, 3, 19-23. http://dx.doi.org/10.4236/etsn.2014.33003 Hiekkaranta, A. P., Kirtley, O. J., Lafit, G., Decoster, J., Derom, C., de Hert, M., Gülöksüz, S., Jacobs, N., Menne-Lothmann, C., Rutten, B. P. F., Thiery, E., van Os, J., van Winkel, R., Wichers, M., & Myin-Germeys, I. (2021). Emotion regulation in response to daily negative and positive events in youth: The role of event intensity and psychopathology. Behaviour Research and Therapy, 144. http://dx.doi.org/10.1016/j.brat.2021.103916 Holder, N., Holliday, R., Wiblin, J., LePage, J., & Surís, A. (2019). Predcitors of dropout from a randomized clinical trial of cognitive processing therapy for female veterans with military sexual trauma-related PTSD. Psychiatry Research, 276, 87-93. https://doi.org/10.1016/j.psychres.2019.04.022 Wasil, A. R., Gillespie, S., Patel, R., Petre, A., Venturo-Conerly, K. E., Shingleton, R. M., Weisz, J. R., & DeRubeis, R. J. (2020).

Reassessing evidence-based content in popular smartphone apps for depression and anxiety: Developing and applying useradjusted analyses. Journal of Consulting and Clinical Psychology, 88(11), 983– 993. https://doi.org/10.1037/ccp0000604