Two-way Messaging Therapy for Depression and Anxiety:  Longitudinal Response Trajectories

Hull, Thomas D.; Malgaroli, Matteo; Connolly, Philippa; Feuerstein, Seth; Simon, Naomi M.

doi:10.21203/rs.2.17077/v1

Cited by 1 publication

(2 citation statements)

References 33 publications

(39 reference statements)

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“…This relatively asynchronous form of communication allows for greater flexibility in scheduling for both parties and lowers other otherwise necessary treatment costs (e.g., travel, office space). Recent findings from open trials suggest that messaging therapy may be effective for treatment-seeking populations experiencing depression and anxiety (3,4) and trauma (5). Yet while messaging therapy may be broadly effective and lowers structural barriers to mental health treatment, its rising popularity poses a new challenge: treatment quality assurance.…”

Section: Introductionmentioning

confidence: 99%

“…First, the task was developed to assess in-person communication patterns, not communication by texting. Although messaging platforms offer video-based therapy, many clients opt for a purely message-based treatment paradigm wherein all patient-provider communication is facilitated over SMS messaging (4). To capture FIS skills as conveyed over text messaging, the FIS Task must be translated into a text-based format.…”

Section: Introductionmentioning

confidence: 99%

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Automatic rating of therapist facilitative interpersonal skills in text: A natural language processing application

Zech

Steele

Foley

et al. 2022

Front. Digit. Health

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BackgroundWhile message-based therapy has been shown to be effective in treating a range of mood disorders, it is critical to ensure that providers are meeting a consistently high standard of care over this medium. One recently developed measure of messaging quality–The Facilitative Interpersonal Skills Task for Text (FIS-T)–provides estimates of therapists’ demonstrated ability to convey psychotherapy's common factors (e.g., hopefulness, warmth, persuasiveness) over text. However, the FIS-T's scoring procedure relies on trained human coders to manually code responses, thereby rendering the FIS-T an unscalable quality control tool for large messaging therapy platforms.ObjectiveIn the present study, researchers developed two algorithms to automatically score therapist performance on the FIS-T task.MethodsThe FIS-T was administered to 978 messaging therapists, whose responses were then manually scored by a trained team of raters. Two machine learning algorithms were then trained on task-taker messages and coder scores: a support vector regressor (SVR) and a transformer-based neural network (DistilBERT).ResultsThe DistilBERT model had superior performance on the prediction task while providing a distribution of ratings that was more closely aligned with those of human raters, versus SVR. Specifically, the DistilBERT model was able to explain 58.8% of the variance (R2 = 0.588) in human-derived ratings and realized a prediction mean absolute error of 0.134 on a 1–5 scale.ConclusionsAlgorithms can be effectively used to ensure that digital providers meet a consistently high standard of interactions in the course of messaging therapy. Natural language processing can be applied to develop new quality assurance systems in message-based digital psychotherapy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%