The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.
Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN)
Abstract
In this study, a computer-based feedback, decision and clinical problem-solving system for clinical practice will be described - the Trier Treatment Navigator (TTN). The paper deals with the underlying research concepts related to personalized pre-treatment recommendations for drop-out risk and optimal treatment strategy selection as well as personalized adaptive recommendations during treatment. The development sample consisted of 1234 patients treated with cognitive behavioral therapy (CBT). Modern statistical machine learning techniques were used to develop personalized recommendations. Drop-out analyses resulted in seven significant predictors explaining 12.0% of variance. The prediction of optimal treatment strategies resulted in differential prediction models substantially improving effect sizes and reliable improvement rates. The dynamic failure boundary reliably identified patients with a higher risk for no improvement or deterioration and indicated the usage of clinical problem-solving tools in risk areas. The probability to be reliably improved for patients identified as at risk for treatment failure was about half of the probability for other patients (35% vs. 62.15%; χ 2df=1 = 82.77, p < .001). Results related to the computer-based feedback system are discussed with regard to the implication for clinical applications as well as clinical training and future research possibilities.
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“First, based on the patient's initial characteristics and a dataset of already treated patients, the TTN predicts the optimal treatment strategy (i.e., problem-solving, motivation-oriented or mixed) for the individual patient.”
From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Lutz et al., 2019
In the citing paper
“When the alert of one of these areas is activated, the therapist can access the CSTs that guide how to address that particular area (e.g., a clinical guideline on how to work on motivation to change).”
From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Lutz et al., 2019
In the citing paper
“The TTN offers personalized clinical recommendations, helps monitor the process and provides clinical support tools (CSTs) for patients who are not improving as expected.”
From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Lutz et al., 2019
In the citing paper
“Second, during treatment, the feedback system comprises an expected treatment response (i.e., predicted trajectory of progress for the patient) and a dynamic failure boundary informed by specific patient's pre-treatment features and the experienced change progress.”
From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Lutz et al., 2019
In the citing paper
“Additionally, the TTN estimates the patient dropout probability and offers the results of the psychometrical initial assessment.”
From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Lutz et al., 2019
In the citing paper
“When a patient is not progressing as expected, the TTN generates an alert signal, and CSTs are activated according to the assessment of critical areas that might hinder the change process (i.e., suicidality risk, motivation to change and therapy goals, therapeutic alliance, social support and critical life events, emotionand self-regulation).”
From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Lutz et al., 2019
In the citing paper
“Additionally, two of the studies suffered from potential bias in their loss to follow-up [82,83].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [82]
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Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531
Psychopathological networks: Theory, methods and practice
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Self-citationBickman, L. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01065-8 · Cited by 184
A Scoping Review of Machine Learning in Psychotherapy Research
Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1808729 · Cited by 179
Routine Outcome Monitoring (ROM) and Feedback: Research Review and Recommendations
Barkham, M., de Jong, K., Delgadillo, J., & Lutz, W. (2023) · Psychotherapy Research · doi:10.1080/10503307.2023.2181114 · Cited by 210
Self-citation
Metadata
- Authors
- Lutz, W., Rubel, J. A., Schwartz, B., Schilling, V., & Deisenhofer, A.-K.
- Affiliations
- Universität Trier (DE)
- Corresponding author
- Lutz, Wolfgang
- Venue
- Behaviour Research and Therapy
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 120
- Pages
- 103438
- ISSN
- 0005-7967 (ISSN-L)
- OpenAlex
- W2956805281
- Cited by
- 196
- References
- 54
- Citations by year
- 2026: 18 · 2025: 26 · 2024: 39 · 2023: 25 · 2022: 22 · 2021: 34 · 2020: 25 · 2019: 7
- FWCI
- 24.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Behaviour Research and Therapy (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (LU 660/8-1, LU 660/10-1)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- personalized feedback, cognitive behavioral therapy, treatment strategy selection, clinical problem solving, computer-based decision support, treatment failure prediction, clinical teaching, machine learning prediction models
- MeSH terms
- Clinical Decision-Making, Machine Learning, Adjustment Disorders, Adolescent, Adult, Aged, Anxiety Disorders, Mental Disorders, Feedback, Female, Humans, Male, Middle Aged, Patient Dropouts, Personality Disorders, Problem Solving, Surveys and Questionnaires, Stress Disorders, Post-Traumatic, Cognitive Behavioral Therapy, Mood Disorders, Decision Support Systems, Clinical, Young Adult, Precision Medicine
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