James D. Herbert

Acceptance and Mindfulness in Cognitive Behavior Therapy: Understanding and Applying the New Therapies

Contributors vi Foreword Gerald C. Davison, PhD viii Part I New Developments in the Behavior Therapy Tradition: Perspectives on Mindfulness and Psychological Acceptance 1 1 The Evolution of Cognitive Behavior Therapy: The Rise of Psychological Acceptance and Mindfulness 3 James D. Herbert and Evan M. Forman 2 Cognitive Therapy 26 David J. A. Dozois and Aaron T. Beck 3 Mindfulness-Based Cognitive Therapy 57 David M. Fresco, Jessica J. Flynn, Douglas S. Mennin, and Emily A. P. Haigh 4 Metacognitive Therapy 83 Adrian Wells 5 Emotional Schema Therapy: A Bridge Over Troubled Waters 109 Robert L. Leahy 6 Mindfulness-Based Stress Reduction 132 Paul G. Salmon, Sandra E. Sephton, and Samuel J. Dreeben 7 Dialectical Behavior Therapy 164 Clive J. Robins and M. Zachary Rosenthal 8 Behavioral Activation in the Context of Third Wave Therapies 193 Christopher R. Martell and Jonathan Kanter 9 Integrative Behavioral Couple Therapy: An Acceptance-Based Approach to Improving Relationship Functioning 210 Meghan M. McGinn, Lisa A. Benson, and Andrew Christensen 10 Understanding Acceptance and Commitment Therapy in Context: A History of Similarities and Differences With Other Cognitive Behavior Therapies 233 Kelly G. Wilson, Michael J. Bordieri, Maureen K. Flynn, Nadia N. Lucas, and Regan M. Slater Part II Integration and Synthesis 265 11 Mindfulness and Acceptance: The Perspective of Cognitive Therapy 267 Stefan G. Hofmann, Julia A. Glombiewski, Anu Asnaani, and Alice T. Sawyer 12 Mindfulness and Acceptance: The Perspective of Acceptance and Commitment Therapy 291 Michael Levin and Steven C. Hayes 13 Mindfulness and Acceptance in Cognitive Behavior Therapy: What s New? 317 Marvin R. Goldfried Author Index 337 Subject Index 353

Differential Programming Needs of College Students Preferring Web-Based Versus In-Person Physical Activity Programs

ABSTRACT College students report several barriers to exercise, highlighting a need for university-based programs that address these challenges. In contrast to in-person interventions, several web-based programs have been developed to enhance program engagement by increasing ease of access and lowering the necessary level of commitment to participate. Unfortunately, web-based programs continue to struggle with engagement and less-than-ideal outcomes. One explanation for this discrepancy is that different intervention modalities may attract students with distinctive activity patterns, motivators, barriers, and program needs. However, no studies have formally evaluated intervention modality preference (e.g., web-based or in-person) among college students. The current study sought to examine the relationship between intervention modality preference and physical activity programming needs. Undergraduate students (n = 157) enrolled in psychology courses at an urban university were asked to complete an online survey regarding current activity patterns and physical activity program preferences. Participants preferring web-based physical activity programs exercised less (p = .05), were less confident in their abilities to exercise (p = .01), were less likely to endorse the maintenance stage of change (p < .01) and perceived more barriers to exercising (p < .01) than those who preferred in-person programming. Findings suggest that students preferring web-based programming may require programs that enhance self-efficacy by fostering goal-setting and problem-solving skills. A user-centered design approach may enhance the engagement (and therefore effectiveness) of physical activity promotion programs for college students.

Application of Machine Learning to Predict Dietary Lapses During Weight Loss

Background: Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a “lapse.” There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction. Methods: The current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers. Results: WEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance. Conclusions: The predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.