TY  - JOUR
A1  - Schwartz, Brian
A1  - Rubel, Julian A.
A1  - Deisenhofer, Anne-Katharina
A1  - Lutz, Wolfgang
T1  - Movement-based patient-therapist attunement in psychological therapy and its association with early change
T2  - Digital Health
N2  - Objective: Attunement is a novel measure of nonverbal synchrony reflecting the duration of the present moment shared by two interaction partners. This study examined its association with early change in outpatient psychotherapy.
Methods: Automated video analysis based on motion energy analysis (MEA) and cross-correlation of the movement time-series of patient and therapist was conducted to calculate movement synchrony for N = 161 outpatients. Movement-based attunement was defined as the range of connected time lags with significant synchrony. Latent change classes in the HSCL-11 were identified with growth mixture modeling (GMM) and predicted by pre-treatment covariates and attunement using multilevel multinomial regression.
Results: GMM identified four latent classes: high impairment, no change (Class 1); high impairment, early response (Class 2); moderate impairment (Class 3); and low impairment (Class 4). Class 2 showed the strongest attunement, the largest early response, and the best outcome. Stronger attunement was associated with a higher likelihood of membership in Class 2 (b = 0.313, p = .007), Class 3 (b = 0.251, p = .033), and Class 4 (b = 0.275, p = .043) compared to Class 1. For highly impaired patients, the probability of no early change (Class 1) decreased and the probability of early response (Class 2) increased as a function of attunement.
Conclusions: Among patients with high impairment, stronger patient-therapist attunement was associated with early response, which predicted a better treatment outcome. Video-based assessment of attunement might provide new information for therapists not available from self-report questionnaires and support therapists in their clinical decision-making.
KW  - Motor mimicry
KW  - nonverbal synchrony
KW  - early response
KW  - growth mixture modeling
KW  - motion energy analysis
Y1  - 2022
UR  - https://ubt.opus.hbz-nrw.de/frontdoor/index/index/docId/2024
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:385-1-20246
VL  - 2022
IS  - Band 8
PB  - SAGE Publishing
ER  -