TY - JOUR

T1 - Capitalizing on natural language processing (NLP) to automate the evaluation of coach implementation fidelity in guided digital cognitive-behavioral therapy (GdCBT)

AU - Zainal, Nur Hani

AU - Eckhardt, Regina

AU - Rackoff, Gavin N.

AU - Fitzsimmons-Craft, Ellen E.

AU - Rojas-Ashe, Elsa

AU - Barr Taylor, Craig

AU - Funk, Burkhardt

AU - Eisenberg, Daniel

AU - Wilfley, Denise E.

AU - Newman, Michelle G.

N1 - Publisher Copyright: © The Author(s), 2025. Published by Cambridge University Press.

PY - 2025/4/2

Y1 - 2025/4/2

N2 - Background As the use of guided digitally-delivered cognitive-behavioral therapy (GdCBT) grows, pragmatic analytic tools are needed to evaluate coaches' implementation fidelity. Aims We evaluated how natural language processing (NLP) and machine learning (ML) methods might automate the monitoring of coaches' implementation fidelity to GdCBT delivered as part of a randomized controlled trial. Method Coaches served as guides to 6-month GdCBT with 3,381 assigned users with or at risk for anxiety, depression, or eating disorders. CBT-trained and supervised human coders used a rubric to rate the implementation fidelity of 13,529 coach-to-user messages. NLP methods abstracted data from text-based coach-to-user messages, and 11 ML models predicting coach implementation fidelity were evaluated. Results Inter-rater agreement by human coders was excellent (intra-class correlation coefficient 980-.992). Coaches achieved behavioral targets at the start of the GdCBT and maintained strong fidelity throughout most subsequent messages. Coaches also avoided prohibited actions (e.g. reinforcing users' avoidance). Sentiment analyses generally indicated a higher frequency of coach-delivered positive than negative sentiment words and predicted coach implementation fidelity with acceptable performance metrics (e.g. area under the receiver operating characteristic curve [AUC] = 74.48%). The final best-performing ML algorithms that included a more comprehensive set of NLP features performed well (e.g. AUC = 76.06%). Conclusions NLP and ML tools could help clinical supervisors automate monitoring of coaches' implementation fidelity to GdCBT. These tools could maximize allocation of scarce resources by reducing the personnel time needed to measure fidelity, potentially freeing up more time for high-quality clinical care.

AB - Background As the use of guided digitally-delivered cognitive-behavioral therapy (GdCBT) grows, pragmatic analytic tools are needed to evaluate coaches' implementation fidelity. Aims We evaluated how natural language processing (NLP) and machine learning (ML) methods might automate the monitoring of coaches' implementation fidelity to GdCBT delivered as part of a randomized controlled trial. Method Coaches served as guides to 6-month GdCBT with 3,381 assigned users with or at risk for anxiety, depression, or eating disorders. CBT-trained and supervised human coders used a rubric to rate the implementation fidelity of 13,529 coach-to-user messages. NLP methods abstracted data from text-based coach-to-user messages, and 11 ML models predicting coach implementation fidelity were evaluated. Results Inter-rater agreement by human coders was excellent (intra-class correlation coefficient 980-.992). Coaches achieved behavioral targets at the start of the GdCBT and maintained strong fidelity throughout most subsequent messages. Coaches also avoided prohibited actions (e.g. reinforcing users' avoidance). Sentiment analyses generally indicated a higher frequency of coach-delivered positive than negative sentiment words and predicted coach implementation fidelity with acceptable performance metrics (e.g. area under the receiver operating characteristic curve [AUC] = 74.48%). The final best-performing ML algorithms that included a more comprehensive set of NLP features performed well (e.g. AUC = 76.06%). Conclusions NLP and ML tools could help clinical supervisors automate monitoring of coaches' implementation fidelity to GdCBT. These tools could maximize allocation of scarce resources by reducing the personnel time needed to measure fidelity, potentially freeing up more time for high-quality clinical care.

KW - anxiety

KW - depression

KW - digital mental health intervention

KW - eating disorders

KW - guided internet-delivered cognitive-behavioral therapy

KW - implementation fidelity

KW - machine learning

KW - natural language processing

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=105001856822&partnerID=8YFLogxK

U2 - 10.1017/S0033291725000340

DO - 10.1017/S0033291725000340

M3 - Journal articles

C2 - 40170669

AN - SCOPUS:105001856822

VL - 55

JO - Psychological Medicine

JF - Psychological Medicine

SN - 0033-2917

M1 - e106

ER -