Optimizing Behavioral Strategy Selection during Cognitive Behavior Therapy for Procrastination using Machine Learning
Year: 2026
Type: preprint
Abstract: Objective: To determine whether machine-learning models using baseline self-report data couldpersonalize selection between two behavioral strategies embedded within cognitive-behavioraltherapy (CBT) for procrastination.Methods: Participants were 256 Polish-speaking university students aged 18–26 years (M=22.4;73.0% women) who completed active treatment in two preregistered randomized trials(development sample, N=173; external validation sample, N=83). Participants received fiveweekly group CBT sessions with identical psychoeducation and cognitive restructuring butdifferent behavioral modules: Realistic Planning and Timely Beginning (RPT) or Working TimeRestriction (WTR). Separate partial least-squares regression models used 483 baselinedemographic and self-report items to predict posttreatment Aitken ProcrastinationInventory–Revised (API-R) scores under each strategy. Predicted between-strategy differencesand Youden-index cutoffs generated recommendations: RPT, WTR, Either, or Neither. Becausetreatment assignment was randomized, outcomes could be compared between participants whoseassigned treatment matched versus mismatched their model-derived recommendation.Results: In the development trial, matched participants had lower posttreatment API-R scoresthan mismatched participants (p=5.6×10−24; d=1.90) and higher likely-response rates(p=1.8×10−15; RR=4.6). In the validation trial, each treatment-specific model explained 9% ofoutcome variance. Matched participants again had lower API-R scores (p=.009; d=.53) andhigher response rates (p=.04; RR=1.3). Differences remained at six months (p=.033; d=.40).Conclusion: Together, these findings indicate that personalization of procrastination treatmentcan be achieved by modeling differential response to specific behavioral components rather thanentire treatment packages, supporting precision intervention in procrastination and beyond.Further, explorative examination of predictors suggests that individuals may benefit from amechanism-informed, but not strictly mechanism-deterministic, precision framework.
Source: PsyArXiv (OSF Preprints)
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204870541