Quantifying the association between psychotherapy content and clinical outcomes using deep learning
Open accessAbstract
Importance: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for measuring the delivery of psychotherapy. Objective: To use a deep learning model applied to a large-scale clinical data set of cognitive behavioral therapy (CBT) session transcripts to generate a quantifiable measure of treatment delivered and to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes. Design, Setting, and Participants: All data were obtained from patients receiving internet-enabled CBT for the treatment of a mental health disorder between June 2012 and March 2018 in England. Cognitive behavioral therapy was delivered in a secure online therapy room via instant synchronous messaging. The initial sample comprised a total of 17 572 patients (90 934 therapy session transcripts). Patients self-referred or were referred by a primary health care worker directly to the service. Exposures: All patients received National Institute for Heath and Care Excellence-approved disorder-specific CBT treatment protocols delivered by a qualified CBT therapist. Main Outcomes and Measures: Clinical outcomes were measured in terms of reliable improvement in patient symptoms and treatment engagement. Reliable improvement was calculated based on 2 severity measures: Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7), corresponding to depressive and anxiety symptoms respectively, completed by the patient at initial assessment and before every therapy session (see eMethods in the Supplement for details). Results: Treatment sessions from a total of 14 899 patients (10 882 women) aged between 18 and 94 years (median age, 34.8 years) were included in the final analysis. We trained a deep learning model to automatically categorize therapist utterances into 1 or more of 24 feature categories. The trained model was applied to our data set to obtain quantifiable measures of each feature of treatment delivered. A logistic regression revealed that increased quantities of a number of session features, including change methods (cognitive and behavioral techniques used in CBT), were associated with greater odds of reliable improvement in patient symptoms (odds ratio, 1.11; 95% CI, 1.06-1.17) and patient engagement (odds ratio, 1.20, 95% CI, 1.12-1.27). The quantity of nontherapy-related content was associated with reduced odds of symptom improvement (odds ratio, 0.89; 95% CI, 0.85-0.92) and patient engagement (odds ratio, 0.88, 95% CI, 0.84-0.92). Conclusions and Relevance: This work demonstrates an association between clinical outcomes in psychotherapy and the content of therapist utterances. These findings support the principle that CBT change methods help produce improvements in patients' presenting symptoms. The application of deep learning to large clinical data sets can provide valuable insights into psychotherapy, informing the development of new treatments and helping standardize clinical practice.
AI snapshot
Written from the paper’s full text.
TL;DR
A deep learning model categorized therapist utterances from approximately 90,000 hours of internet-enabled CBT and found that greater quantities of CBT change methods were associated with higher odds of reliable improvement and engagement, while nontherapy-related content was associated with lower odds. The study supports key CBT principles and demonstrates large-scale psychotherapy content quantification, but it is observational and cannot establish causality or session quality.
Key findings
- In 90,934 transcripts from 17,572 patients (reliable improvement rate 63.4%; IAPT engagement rate 87.3%), “change methods” was positively associated with reliable improvement (OR, 1.11; 95% CI, 1.06-1.17) and with first-session IAPT engagement (OR, 1.20; 95% CI, 1.12-1.27).
- Nontherapy-related “other” content was negatively associated with reliable improvement (OR, 0.89; 95% CI, 0.85-0.92) and engagement (OR, 0.88; 95% CI, 0.84-0.92); “hello” and “goodbye” were also negatively associated with improvement.
- Other positive associations with reliable improvement included therapeutic praise (OR, 1.21; 95% CI, 1.15-1.27), planning for the future (OR, 1.12; 95% CI, 1.06-1.19), perceptions of change (OR, 1.11; 95% CI, 1.06-1.16), set agenda (OR, 1.08; 95% CI, 1.02-1.14), elicit feedback (OR, 1.06; 95% CI, 1.02-1.11), give feedback (OR, 1.05; 95% CI, 1.00-1.10), and review homework (OR, 1.04; 95% CI, 1.00-1.09). Negative associations included therapeutic empathy (OR, 0.84; 95% CI, 0.81-0.88), risk check (OR, 0.85; 95% CI, 0.81-0.89), and bridge (OR, 0.95; 95% CI, 0.91-0.98).
- First-session engagement was positively associated with change methods, elicit feedback, set homework, arrange next session, therapeutic thanks, and formulation; it was negatively associated with other, hello, therapeutic empathy, Socratic questioning, bridge, and planning for the future.
Methods
- Quality improvement study using internet-enabled CBT data from June 2012 to March 2018; outcomes were reliable improvement (PHQ-9/GAD-7) and IAPT engagement (attending ≥2 treatment sessions).
- A research psychologist annotated 290 therapy session transcripts using 24 feature categories informed by the CBT competences framework and Revised Cognitive Therapy Scale; a deep learning model was trained to classify all therapist utterances, with interrater reliability κ = 0.54.
- Multivariable logistic regressions examined reliable improvement (n = 13,073) and first-session IAPT engagement (n = 14,899); predictors included mean words per feature plus demographics, starting PHQ-9/GAD-7 scores, medication status, long-term condition, number of sessions, and session duration; continuous predictors were scaled and centered, with significance at P < .05 two-tailed.
Limitations
- The paper states it cannot determine whether a therapeutic feature was applied appropriately or whether the therapist adhered to the CBT protocol; the model measures association across sessions rather than quality of an individual session.
- The paper states the model does not assess how treatment was received by patients, results reveal association rather than causation, long-term outcomes are not addressed, and other patient factors are not included; for large data sets, ORs and confidence intervals should be considered more informative than statistical significance alone.
- Evident from the text: the design is observational, and interrater reliability was moderate (κ = 0.54).
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 28 cited sources checked · 0 supported · 0 flagged
- Findingown data
Increases in therapeutic empathy were negatively associated with reliable improvement (OR, 0.84; 95% CI, 0.81-0.88).
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92], “hello” [OR, 0.92; 95% CI, 0.88-0.96], and “goodbye” [OR, 0.95; 95% CI, 0.91-0.99]), along with “therapeutic empathy” (OR, 0.84; 95% CI, 0.81-0.88), “risk check” (OR, 0.85; 95% CI, 0.81-0.89), and “bridge” (OR, 0.95; 95% CI, 0.91-0.98) were negatively associated with improvement.”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Findingown data
Increased quantities of CBT change methods were positively associated with reliable improvement in patient symptoms.
“Increased quantities of CBT change methods were positively associated with reliable improvement in patient symptoms”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Findingown data
Increases in the 'hello' feature were negatively associated with reliable improvement (OR, 0.92; 95% CI, 0.88-0.96).
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92], “hello” [OR, 0.92; 95% CI, 0.88-0.96], and “goodbye” [OR, 0.95; 95% CI, 0.91-0.99]), along with “therapeutic empathy” (OR, 0.84; 95% CI, 0.81-0.88), “risk check” (OR, 0.85; 95% CI, 0.81-0.89), and “bridge” (OR, 0.95; 95% CI, 0.91-0.98) were negatively associated with improvement.”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Findingown data
Increases in bridge were negatively associated with reliable improvement (OR, 0.95; 95% CI, 0.91-0.98).
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92], “hello” [OR, 0.92; 95% CI, 0.88-0.96], and “goodbye” [OR, 0.95; 95% CI, 0.91-0.99]), along with “therapeutic empathy” (OR, 0.84; 95% CI, 0.81-0.88), “risk check” (OR, 0.85; 95% CI, 0.81-0.89), and “bridge” (OR, 0.95; 95% CI, 0.91-0.98) were negatively associated with improvement.”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Findingown data
Increases in risk check were negatively associated with reliable improvement (OR, 0.85; 95% CI, 0.81-0.89).
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92], “hello” [OR, 0.92; 95% CI, 0.88-0.96], and “goodbye” [OR, 0.95; 95% CI, 0.91-0.99]), along with “therapeutic empathy” (OR, 0.84; 95% CI, 0.81-0.88), “risk check” (OR, 0.85; 95% CI, 0.81-0.89), and “bridge” (OR, 0.95; 95% CI, 0.91-0.98) were negatively associated with improvement.”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Findingown data
Increases in the 'goodbye' feature were negatively associated with reliable improvement (OR, 0.95; 95% CI, 0.91-0.99).
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92], “hello” [OR, 0.92; 95% CI, 0.88-0.96], and “goodbye” [OR, 0.95; 95% CI, 0.91-0.99]), along with “therapeutic empathy” (OR, 0.84; 95% CI, 0.81-0.88), “risk check” (OR, 0.85; 95% CI, 0.81-0.89), and “bridge” (OR, 0.95; 95% CI, 0.91-0.98) were negatively associated with improvement.”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Findingown data
The quantity of nontherapy-related content showed a negative association with reliable improvement.
“the quantity of nontherapy-related content showed a negative association”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Rests on an assumption: The 'other' feature category represents nontherapy-related content.
- Findingown data
Increases in the 'other' feature category were negatively associated with reliable improvement (OR, 0.89; 95% CI, 0.85-0.92).
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92], “hello” [OR, 0.92; 95% CI, 0.88-0.96], and “goodbye” [OR, 0.95; 95% CI, 0.91-0.99]), along with “therapeutic empathy” (OR, 0.84; 95% CI, 0.81-0.88), “risk check” (OR, 0.85; 95% CI, 0.81-0.89), and “bridge” (OR, 0.95; 95% CI, 0.91-0.98) were negatively associated with improvement.”
Quote in the paperSubstantial claim- Rests on an assumption: The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
- Rests on an assumption: The 'other' feature category represents nontherapy-related content.
- Findingown data
Change methods was associated with increased odds of IAPT engagement (OR, 1.20; 95% CI, 1.12-1.27).
““change methods” (OR, 1.20; 95% CI, 1.12-1.27)”
- Also stated in: § Discussion · plain
“The results revealed a positive association between the quantity of CBT change method-related content and both reliable improvement and IAPT engagement.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
Agenda setting is positively associated with reliable improvement.
“The results show that agenda setting is also positively associated with reliable improvement.”
Quote in the paperSubstantial claim - Findingown data
Goal setting in the first session is positively associated with improvement.
“By contrast, goal setting in the first session was positively associated with improvement”
Quote in the paperSubstantial claim - Findingown data
Total number of treatment sessions was associated with increased odds of reliable improvement (OR, 1.22; 95% CI, 1.17-1.27).
“total number of treatment sessions (OR, 1.22; 95% CI, 1.17-1.27)”
Quote in the paperNot a substantial claim - Findingown data
Starting PHQ-9 score was associated with reduced odds of IAPT engagement (OR, 0.87; CI, 0.82-0.92).
“starting PHQ-9 score (OR, 0.87; CI, 0.82-0.92)”
Quote in the paperNot a substantial claim - Findingown data
Therapeutic praise was positively associated with improvement.
“Here, we found that “therapeutic praise” was positively associated with improvement”
Quote in the paperSubstantial claim - Findingown data
Bridge was associated with reduced odds of IAPT engagement (OR, 0.94; 95% CI, 0.90-0.98).
““bridge” (OR, 0.94; 95% CI, 0.90-0.98)”
Quote in the paperNot a substantial claim - Findingown data
Set homework was associated with increased odds of IAPT engagement (OR, 1.09; 95% CI, 1.03-1.16).
““set homework” (OR, 1.09; 95% CI, 1.03-1.16)”
- Also stated in: § Discussion · plain
“setting homework in the first session was associated with increased engagement.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
Arrange next session was associated with increased odds of IAPT engagement (OR, 1.17; 95% CI, 1.10-1.24).
“” arrange next session” (OR, 1.17; 95% CI, 1.10-1.24)”
Quote in the paperNot a substantial claim - Findingreasoning
Session content related to discussing perceptions of change is positively associated with improvement.
“Session content related to planning for the future after therapy and discussing perceptions of change was also positively associated with improvement.”
Quote in the paperSubstantial claim - Findingown data
Formulation was associated with increased odds of IAPT engagement (OR, 1.10; 95% CI, 1.04-1.17).
““formulation” (OR, 1.10; 95% CI, 1.04-1.17)”
Quote in the paperNot a substantial claim - Findingown data
Therapeutic thanks was associated with increased odds of IAPT engagement (OR, 1.13; 95% CI, 1.06-1.20).
““therapeutic thanks” (OR, 1.13; 95% CI, 1.06-1.20)”
Quote in the paperNot a substantial claim - Findingown data
Presence of a long-term medical condition was associated with reduced odds of reliable improvement (OR, 0.72; 95% CI, 0.66-0.88).
“the presence of a long-term medical condition (OR, 0.72; 95% CI, 0.66-0.88)”
Quote in the paperNot a substantial claim - Findingown data
Starting PHQ-9 score was associated with reduced odds of reliable improvement (OR, 0.95; 95% CI, 0.91-0.99).
“Starting PHQ-9 score (OR, 0.95; 95% CI, 0.91-0.99)”
Quote in the paperNot a substantial claim - Findingown data
Patient age was associated with increased odds of reliable improvement (OR, 1.16; 95% CI, 1.12-1.22).
“patient age (OR, 1.16; 95% CI, 1.12-1.22)”
Quote in the paperNot a substantial claim - Findingown data
Nontherapy-related content 'hello' was associated with reduced odds of IAPT engagement (OR, 0.93; 95% CI, 0.88-0.99).
““hello” OR, 0.93; 95% CI, 0.88-0.99”
Quote in the paperNot a substantial claim - Findingown data
Being prescribed psychotropic medication is negatively associated with reliable improvement.
“being prescribed psychotropic medication were negatively associated.”
Quote in the paperNot a substantial claim - Findingown data
Starting GAD-7 score was associated with increased odds of reliable improvement (OR, 1.29; 95% CI, 1.23-1.34).
“Patient variables of starting GAD-7 score (OR, 1.29; 95% CI, 1.23-1.34)”
Quote in the paperNot a substantial claim - Findingown data
Socratic questioning was associated with reduced odds of IAPT engagement (OR, 0.94; 95% CI, 0.89-0.99).
““Socratic questioning” (OR, 0.94; 95% CI, 0.89-0.99)”
Quote in the paperNot a substantial claim - Findingown data
Increased content related to reviewing homework is positively associated with symptom improvement.
“Increased content related to reviewing homework was positively associated with symptom improvement”
Quote in the paperSubstantial claim - Findingown data
Longer session durations were associated with reduced odds of reliable improvement (OR, 0.95; 95% CI, 0.91-0.99).
“longer session durations (OR, 0.95; 95% CI, 0.91-0.99)”
Quote in the paperNot a substantial claim - Findingreasoning
Risk checking showed a strong negative association with reliable improvement.
“Risk checking also showed a strong negative association with reliable improvement.”
Quote in the paperSubstantial claim - Findingown data
Therapeutic empathy was associated with reduced odds of IAPT engagement (OR, 0.93; 95% CI, 0.88-0.97).
““therapeutic empathy” (OR, 0.93; 95% CI, 0.88-0.97)”
Quote in the paperNot a substantial claim - Findingown data
Planning for the future was associated with reduced odds of IAPT engagement (OR, 0.93; 95% CI, 0.89-0.96).
““planning for the future” (OR, 0.93; 95% CI, 0.89-0.96)”
Quote in the paperNot a substantial claim - Findingown data
Nontherapy-related content 'other' was associated with reduced odds of IAPT engagement (OR, 0.88; 95% CI, 0.84-0.92).
““other” OR, 0.88; 95% CI, 0.84-0.92”
Quote in the paperNot a substantial claim - Findingown data
Elicit feedback was associated with increased odds of IAPT engagement (OR, 1.09; 95% CI, 1.03-1.16).
““elicit feedback” (OR, 1.09; 95% CI, 1.03-1.16)”
Quote in the paperSubstantial claim - Findingown data
Therapeutic empathy showed a negative association with improvement.
“whereas “therapeutic empathy” showed a negative association.”
Quote in the paperSubstantial claim - Findingown data
Not being prescribed medication was associated with increased odds of reliable improvement (OR, 1.23; 95% CI, 1.06-1.41).
“not being prescribed medication (OR, 1.23; 95% CI, 1.06-1.41)”
Quote in the paperNot a substantial claim - Findingown data
Session content related to planning for the future after therapy is positively associated with improvement.
“Session content related to planning for the future after therapy and discussing perceptions of change was also positively associated with improvement.”
Quote in the paperSubstantial claim - Findingown data
The quantity of CBT change method-related content is positively associated with reliable improvement.
“The results revealed a positive association between the quantity of CBT change method-related content and both reliable improvement and IAPT engagement.”
Quote in the paperSubstantial claim - Findingown data
Patient age was associated with increased odds of IAPT engagement (OR, 1.07; CI, 1.02-1.13).
“Patient age (OR, 1.07; CI, 1.02-1.13)”
Quote in the paperNot a substantial claim - Findingown data
Being prescribed and taking medication was associated with increased odds of IAPT engagement (OR, 1.20; 95% CI, 1.01-1.47).
“being prescribed and taking medication (OR, 1.20; 95% CI, 1.01-1.47)”
Quote in the paperNot a substantial claim - Findingown data
Not being prescribed medication was associated with increased odds of IAPT engagement (OR, 1.21; 95% CI, 1.02-1.44).
“not being prescribed medication (OR, 1.21; 95% CI, 1.02-1.44)”
Quote in the paperNot a substantial claim - Findingown data
Duration of the first session was associated with increased odds of IAPT engagement (OR, 1.26; CI, 1.20-1.33).
“duration of the first session (OR, 1.26; CI, 1.20-1.33)”
Quote in the paperNot a substantial claim - Findingnone given
We developed a method of objectively quantifying psychotherapy using a deep learning approach to automatically categorize therapist utterances from approximately 90,000 hours of IECBT.
“We developed a method of objectively quantifying psychotherapy using a deep learning approach to automatically categorize therapist utterances from approximately 90 000 hours of IECBT.”
Quote in the paperSubstantial claim - Findingnone given
This data set represents an accumulation of knowledge from more than 90,000 hours of CBT.
“this data set represents an accumulation of knowledge from more than 90 000 hours of CBT.”
Quote in the paperSubstantial claim - Study design and conductown data
A deep learning model was trained to automatically categorize therapist utterances from approximately 90,000 hours of internet-enabled cognitive behavior therapy (CBT).
“In this quality improvement study, a deep learning model was trained to automatically categorize therapist utterances from approximately 90 000 hours of internet-enabled cognitive behavior therapy (CBT).”
- Also stated in: § Therapy Feature Categories · plain
“A deep learning model (see eMethods in the Supplement) was trained on the annotated utterances and then used to automatically classify all utterances in the full data set into 1 or more of 24 feature categories.”
Quote in the paperSubstantial claim - Also stated in: § Therapy Feature Categories · plain
- Study design and conductcitation
The interrater reliability for annotating therapy session transcripts was κ = 0.54, considered moderate agreement.
“The interrater reliability was κ = 0.54 (a value of 0.4-0.6 is considered moderate agreement, with zero equaling chance agreement24; eTable 3 in the Supplement).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [24] Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977; 33(1): 159- 174. doi: 10.2307/2529310 843571 source
Not checked in the free run.
- Interpretationreasoning
Increased empathy is likely to be indicative of a patient reporting a greater number of problems.
“Rather than playing a causal role in outcomes, we believe increased empathy is likely to be indicative of a patient reporting a greater number of problems.”
Quote in the paperSubstantial claim - Interpretationreasoning
The increased occurrence of both planning for the future and discussing perceptions of change is likely to be reflective of treatment progressing well.
“As such, the increased occurrence of both features is likely to be reflective of treatment progressing well.”
Quote in the paperSubstantial claim - Study design and conductcitation
Clinical audit studies within the IAPT framework do not require additional patient consent or ethical approval.
“clinical audit studies within the IAPT framework do not require additional patient consent or ethical approval.20”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20] N.I.C.E. Principles for Best Practice in Clinical Audit. Radcliffe Medical Press; 2002. http://www.uhbristol.nhs.uk/files/nhs-ubht/best_practice_clinical_audit.pdf. Accessed January 31, 2019.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The quantity of risk checks will increase if a patient confirms that they feel at risk.
“The quantity of risk checks will increase if a patient confirms that they feel at risk”
Quote in the paperSubstantial claim - Interpretationown data
When aggregated across sessions, an excessive or disproportionate amount of time spent on nontherapeutic aspects may reduce the quantity of active intervention.
“our results indicate that, when aggregated across sessions, an excessive or disproportionate amount of time spent on such nontherapeutic aspects may reduce the quantity of active intervention.”
Quote in the paperSubstantial claim - Premisecitation
Only 3.5% of psychotherapy randomized clinical trials use adequate treatment integrity procedures.
“only 3.5% of psychotherapy randomized clinical trials use adequate treatment integrity procedures.9”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9] Perepletchikova F, Treat TA, Kazdin AE. Treatment integrity in psychotherapy research: analysis of the studies and examination of the associated factors. J Consult Clin Psychol. 2007; 75(6): 829- 841. doi: 10.1037/0022-006X.75.6.829 18085901 source
Not checked in the free run.
- Interpretationown data
Placing patients' experiences within a cognitive behavioral framework early in therapy is beneficial.
“suggesting that placing patients’ experiences within a cognitive behavioral framework early in therapy is beneficial.”
Quote in the paperSubstantial claim - Interpretationown data
Goal setting in the first session supports the goal-directed nature of CBT.
“supporting the goal-directed nature of CBT.27”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [27] Beck JS. Cognitive Behavior Therapy: Basics and Beyond. 2nd ed. New York, NY: Guilford Press; 2011.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
The findings accord with evidence that out-of-session homework is important in determining outcomes in CBT.
“these findings accord with evidence that out-of-session homework is important in determining outcomes in CBT.26”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [26] Kazantzis N, Whittington C, Zelencich L, Kyrios M, Norton PJ, Hofmann SG. Quantity and quality of homework compliance: a meta-analysis of relations with outcome in cognitive behavior therapy. Behav Ther. 2016; 47(5): 755- 772. doi: 10.1016/j.beth.2016.05.002 27816086 source
Not checked in the free run.
- Interpretationreasoning
Planning for a future most likely occurs when patients are close to completing treatment and/or have moved toward improvement.
“similarly, planning for a future most likely occurs when patients are close to completing treatment and/or have moved toward improvement.”
Quote in the paperSubstantial claim - Interpretationreasoning
Increased risk-checking content is likely to be reflective of patients with more complex problems who report more thoughts of self-harm.
“We believe this is likely to be reflective of patients with more complex problems who report more thoughts of self-harm.”
Quote in the paperNot a substantial claim - Premisecitation
The Improving Access to Psychological Therapies (IAPT) program in England includes no objective measure of treatment integrity for CBT.
“Despite its widespread use, the Improving Access to Psychological Therapies (IAPT) program in England includes no objective measure of treatment integrity for CBT”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9] Perepletchikova F, Treat TA, Kazdin AE. Treatment integrity in psychotherapy research: analysis of the studies and examination of the associated factors. J Consult Clin Psychol. 2007; 75(6): 829- 841. doi: 10.1037/0022-006X.75.6.829 18085901 source
Not checked in the free run.
- Study design and conductnone given
The results only reveal the presence of an association between therapy content and outcomes.
“We emphasize that our results only reveal the presence of an association between therapy content and outcomes”
Quote in the paperSubstantial claim - Study design and conductnone given
The model provides a measure of the association between features and outcomes across sessions rather than measuring the quality of an individual session.
“It should be noted that the model provides a measure of the association between features and outcomes across sessions rather than measuring the quality of an individual session.”
Quote in the paperSubstantial claim - Study design and conductown data
The study is unable to address the association between therapy content and long-term improvements in symptoms due to limited outcomes measures.
“Given the limited outcomes measures available, we are also unable to address the association between therapy content and long-term improvements in symptoms.”
Quote in the paperNot a substantial claim - Interpretationcitation
Monitoring may help reverse the lower improvement rates observed in more experienced therapists.
“Monitoring may help reverse the lower improvement rates observed in more experienced therapists.34”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] Shapiro DA, Shapiro D. Meta-analysis of comparative therapy outcome studies: a replication and refinement. Psychol Bull. 1982; 92(3): 581- 604. doi: 10.1037/0033-2909.92.3.581 7156259
Not checked in the free run.
- Interpretationcitation
The results accord with previous work investigating treatment outcomes in a sample of approximately 3000 patients receiving IECBT.
“These results accord with previous work investigating treatment outcomes in a sample of approximately 3000 patients receiving IECBT.32”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] Catarino A, Bateup S, Tablan V,. Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety. BJPsych Open. 2018; 4(5): 411- 418. doi: 10.1192/bjo.2018.57 30294451 PMC6171334
Not checked in the free run.
- Interpretationreasoning
Deep learning allows extraction of knowledge from more than 90,000 hours of CBT to provide valuable insights into therapy that were previously unavailable to an individual therapist.
“Deep leaning allows us to extract this knowledge to provide valuable insights into therapy that were previously unavailable to an individual therapist.”
Quote in the paperSubstantial claim
- May combine two claims. 17
- Classified as probably not a substantial claim. 28
May combine two claims.
Findingown dataThe initial sample comprised 90,934 session transcripts from 17,572 patients, with a reliable improvement rate of 63.4% and IAPT engagement rate of 87.3%.
“The initial sample comprised a total of 90 934 session transcripts taken from 17 572 patients, with a reliable improvement rate of 63.4% and IAPT engagement rate of 87.3%.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationBoth studies report a positive association between GAD-7 scores and reliable improvement.
“Both studies report a positive association between GAD-7 scores and reliable improvement.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] Catarino A, Bateup S, Tablan V,. Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety. BJPsych Open. 2018; 4(5): 411- 418. doi: 10.1192/bjo.2018.57 30294451 PMC6171334
Not checked in the free run.
May combine two claims.
Findingown dataFactors specific to CBT and factors common to most psychotherapies are associated with increased odds of reliable improvement in patient symptoms.
“We find that factors specific to CBT, as well as factors common to most psychotherapies, are associated with increased odds of reliable improvement in patient symptoms.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNeither planning for the future nor discussing perceptions of change was significantly associated with outcomes in the first treatment session.
“Consistent with this, neither feature was significantly associated with outcomes in the first treatment session (eTable 5 in the Supplement).”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataMulticollinearity analyses revealed that variance inflation factors were smaller than 2 for all predictor variables, confirming that regression models were not affected by multicollinearity.
“Multicollinearity analyses revealed that variance inflation factors were smaller than 2 for all predictor variables, confirming that regression models were not affected by the presence of multicollinearity.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationnone givenA discussion of perceptions of change is only likely to occur following some degree of change.
“A discussion of perceptions of change is only likely to occur following some degree of change”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductcitationReliable improvement was calculated based on the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7).
“Reliable improvement was calculated based on 2 severity measures: Patient Health Questionnaire (PHQ-9)21 and Generalized Anxiety Disorder 7-item scale (GAD-7),22 corresponding to depressive and anxiety symptoms respectively, completed by the patient at initial assessment and before every therapy session”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [21] Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001; 16(9): 606- 613. doi: 10.1046/j.1525-1497.2001.016009606.x 11556941 PMC1495268 source
Not checked in the free run.
- cites [22] Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006; 166(10): 1092- 1097. doi: 10.1001/archinte.166.10.1092 16717171 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningRather than the quantity of conversation, it is the therapeutic nature of conversation and/or the dosage of therapy delivered in a session that is associated with improvement in patient symptoms.
“this suggests that rather than the quantity of conversation, it is the therapeutic nature of conversation and/or the dosage of therapy delivered in a session that is associated with improvement in patient symptoms.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenCases with missing start or end PHQ-9 or GAD-7 scores (n = 1338) were excluded from the analysis.
“Cases with missing start or end PHQ-9 or GAD-7 scores (n = 1338) were excluded from the analysis.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationThe relative effects of different psychotherapeutic interventions appear similar.
“the relative effects of different psychotherapeutic interventions appear similar.10”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Barth J, Munder T, Gerger H,. Comparative efficacy of seven psychotherapeutic interventions for patients with depression: a network meta-analysis. PLoS Med. 2013; 10(5): e1001454. doi: 10.1371/journal.pmed.1001454 23723742 PMC3665892 source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductcitationA total of 24 feature categories were defined, informed by the CBT competences framework and the Revised Cognitive Therapy Scale.
“We defined a total of 24 feature categories (Box), informed by the CBT competences framework18 and the Revised Cognitive Therapy Scale.23”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [18] Roth AD, Pilling S. Using an evidence-based methodology to identify the competences required to deliver effective cognitive and behavioural therapy for depression and anxiety disorders.. Behav Cogn Psychother. 2008; 36(February): 129- 147. doi: 10.1017/S1352465808004141 source
Not checked in the free run.
- cites [23] Blackburn I, James IA, Milne DL,. The revised cognitive therapy scale (CTS-R): psychometric properties. Behav Cogn Psychother. 2001; 29: 431- 446. doi: 10.1017/S1352465801004040 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe final analysis for IAPT engagement included 14,899 patients.
“making a total of 14 899 patients, at caseness.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductcitationClinical outcomes were defined according to IAPT guidelines and measured as reliable improvement and IAPT engagement.
“Clinical outcomes were defined according to IAPT guidelines19 and were measured in terms of reliable improvement and IAPT engagement and included as binary measures (ie, 0 or 1).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] Clark DM. Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: the IAPT experience. Int Rev Psychiatry. 2011; 23(4): 318- 327. doi: 10.3109/09540261.2011.606803 22026487 PMC3212920 source
Not checked in the free run.
May combine two claims.
PremisecitationThe results of such interventions are difficult to transfer to real-world psychotherapy and require sample sizes larger than typically used.
“the results of such interventions are difficult to transfer to real-world psychotherapy13 and require sample sizes larger than typically used.14”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [13] Doss BD, Atkins DC. Investigating treatment mediators when simple random assignment to a control group is not possible. Clin Psychol Sci Pract. 2006; 13: 321- 336. doi: 10.1111/j.1468-2850.2006.00045.x source
Not checked in the free run.
- cites [14] Bell EC, Marcus DK, Goodlad JK. Are the parts as good as the whole? a meta-analysis of component treatment studies. J Consult Clin Psychol. 2013; 81(4): 722- 736. doi: 10.1037/a0033004 23688145 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningAn extended period focused on risk is likely to cause a deviation in the structure of the session and a subsequent reduction in the dosage of active therapy delivered.
“An extended period focused on risk is also likely to cause a deviation in the structure of the session and a subsequent reduction in the dosage of active therapy delivered.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataThe final analysis for reliable improvement included 13,073 patients.
“leaving a total of 13 073 patients (at a clinical caseness threshold and engaged in treatment) in the analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
PremisecitationTo determine the most effective components of CBT and whether CBT works via the mechanisms proposed by the approach, quantifiable measures of treatment delivered need to be obtained in a natural clinical context and be gathered from a sufficiently large enough sample to draw meaningful conclusions.
“To determine the most effective components of CBT and whether CBT works via the mechanisms proposed by the approach,15 quantifiable measures of treatment delivered need to be obtained in a natural clinical context and be gathered from a sufficiently large enough sample to draw meaningful conclusions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] Lorenzo-Luaces L, German RE, DeRubeis RJ. It’s complicated: the relation between cognitive change procedures, cognitive change, and symptom change in cognitive therapy for depression. Clin Psychol Rev. 2015; 41: 3- 15. doi: 10.1016/j.cpr.2014.12.003 25595660 PMC10080251 source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe positive association between CBT change method-related content and outcomes supports the key principles underlying CBT and provides validation for CBT as a treatment.
“This finding supports the key principles underlying CBT and provides validation for CBT as a treatment (ie, modifying cognitive and behavioral factors produces improvements in patient symptoms).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThe final treatment session was excluded because outcome measures are taken prior to the commencement of each treatment session.
“The final treatment session was excluded because outcome measures are taken prior to the commencement of each treatment session.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningPatients provide written informed consent allowing the service to use their anonymized data for audit purposes and to support research, including academic publications.
“When registering to use the Ieso service, patients provide written informed consent as part of a privacy policy agreement, allowing the service to use their anonymized data for audit purposes and to support research, including academic publications.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataCases with a mean of fewer than 50 patient words were excluded (n = 16), and sessions with a total of fewer than 50 patient words were excluded (n = 121).
“Cases with a mean of fewer than 50 patient words were excluded (n = 16)”
- Also stated in: § Statistical Analysis · plain
“Sessions with a total of fewer than 50 patient words were excluded (n = 121)”
Quote in the paperNot a substantial claim- Also stated in: § Statistical Analysis · plain
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationWhether treatments work through specific factors (eg, CBT change methods) or factors common to most psychotherapies (eg, therapeutic alliance) remains a core issue in the field.
“whether treatments work through specific factors (eg, CBT change methods) or factors common to most psychotherapies (eg, therapeutic alliance) remains a core issue in the field.11,12”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [11] Blagys MD, Hilsenroth MJ. Distinctive activities of cognitive-behavioral therapy: a review of the comparative psychotherapy process literature. Clin Psychol Rev. 2002; 22(5): 671- 706. doi: 10.1016/S0272-7358(01)00117-9 12113201 source
Not checked in the free run.
- cites [12] Mulder R, Murray G, Rucklidge J. Common versus specific factors in psychotherapy: opening the black box. Lancet Psychiatry. 2017; 4(12): 953- 962. doi: 10.1016/S2215-0366(17)30100-1 28689019 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationInternet-enabled CBT has been shown to be clinically effective for the treatment of depression.
“Internet-enabled CBT has been shown to be clinically effective for the treatment of depression16 and is currently deployed within IAPT.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [16] Kessler D, Lewis G, Kaur S,. Therapist-delivered internet psychotherapy for depression in primary care: a randomised controlled trial. Lancet. 2009; 374(9690): 628- 634. doi: 10.1016/S0140-6736(09)61257-5 19700005 source
Not checked in the free run.
May combine two claims.
InterpretationcitationGiving and eliciting feedback helps both the therapist and patient develop a greater understanding of key issues and possibly strengthens the therapeutic alliance.
“The results also support the principle that giving and eliciting feedback helps both the therapist and patient develop a greater understanding of key issues and possibly strengthens the therapeutic alliance.27”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Beck JS. Cognitive Behavior Therapy: Basics and Beyond. 2nd ed. New York, NY: Guilford Press; 2011.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
PremisecitationStudies commonly use observational coding methods to investigate the association between treatment delivered and outcomes.
“Studies commonly use observational coding methods (eg, ratings/transcription of recorded therapeutic conversations) to investigate the association between treatment delivered and outcomes.5”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Perepletchikova F. On the topic of treatment integrity. Clin Psychol (New York). 2011; 18(2): 148- 153. doi: 10.1111/j.1468-2850.2011.01246.x 21769167 PMC3137485
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationSuch monitoring could help arrest therapist drift.
“Such monitoring could help arrest therapist drift, ie, the failure to deliver treatments a therapist has been trained to deliver, which may be one of the biggest factors contributing to poor delivery of treatment.33”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33] Waller G. Evidence-based treatment and therapist drift. Behav Res Ther. 2009; 47(2): 119- 127. doi: 10.1016/j.brat.2008.10.018 19036354 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenSome aspects of therapy (eg, change methods) are typically initiated by the therapist and appear likely to play a causal role.
“although some aspects of therapy (eg, change methods) are typically initiated by the therapist and appear likely to play a causal role.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenOther patient factors not included are likely to play a role in determining outcomes.
“In addition, other patient factors not included are likely to play a role in determining outcomes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationHomework in CBT is used to help patients practice skills learned in therapy and generalize these skills to the real world.
“Homework in CBT is used to help patients practice skills learned in therapy and generalize these skills to the real world.25”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [25] Alford BA, Beck AT. Cognitive therapy of delusional beliefs. Behav Res Ther. 1994; 32(3): 369- 380. doi: 10.1016/0005-7967(94)90134-1 8192636 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThis approach represents an important step in developing a data-driven understanding of mental health treatment and in improving the efficacy of psychotherapy.
“we believe this approach represents an important step in developing a data-driven understanding of mental health treatment and in improving the efficacy of psychotherapy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrevious work indicates therapeutic alliance may be reflective of a change in symptoms.
“although previous work indicates therapeutic alliance may be reflective of a change in symptoms.31”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [31] Strunk DR, Brotman MA, DeRubeis RJ. The process of change in cognitive therapy for depression: predictors of early inter-session symptom gains. Behav Res Ther. 2010; 48(7): 599- 606. doi: 10.1016/j.brat.2010.03.011 20362978 PMC2878902
Not checked in the free run.
May combine two claims.
Study design and conductnone givenIt is not possible to determine whether a therapeutic feature is applied in an appropriate manner or whether a therapist adheres to the CBT protocol.
“A limitation of our approach is that it is not possible to determine whether a therapeutic feature is applied in an appropriate manner or whether a therapist adheres to the CBT protocol.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThis work represents a first step toward a practicable approach for quality controlled behavioral health care.
“We believe that this work represents a first step toward a practicable approach for quality controlled behavioral health care.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe model does not assess how the treatment was received by patients.
“In addition, the model does not assess how the treatment was received by patients.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCommon factors, such as therapeutic alliance, are thought to play a role in all psychotherapeutic treatments and show a moderate association with outcomes.
“Common factors, such as therapeutic alliance, are thought to play a role in all psychotherapeutic treatments29 and show a moderate association with outcomes.30”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [29] Wampold BE. How important are the common factors in psychotherapy? an update. World Psychiatry. 2015; 14(3): 270- 277. doi: 10.1002/wps.20238 26407772 PMC4592639 source
Not checked in the free run.
- cites [30] Martin DJ, Garske JP, Davis MK. Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review. J Consult Clin Psychol. 2000; 68(3): 438- 450. doi: 10.1037/0022-006X.68.3.438 10883561 source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductown dataThe logistic regression model for IAPT engagement was adjusted for symptom severity, patient sex, age, medication status, presence of long-term condition, and session duration.
“Adjusted for symptom severity, patient sex, age, medication status, presence of long-term condition, and session duration.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataIncreased praise may be reflective of a patient responding well to treatment.
“Similarly, increased praise may be reflective of a patient responding well to treatment.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataThe logistic regression model for reliable improvement was adjusted for total number of sessions, symptom severity, patient sex, age, medication status, presence of long-term condition, and session duration.
“Adjusted for total number of sessions, symptom severity, patient sex, age, medication status, presence of long-term condition, and session duration.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The mean number of words for each therapy feature is a valid measure of the quantity of that feature delivered in therapy.
“Using the output of the model, the mean number of words for each feature, averaged across all sessions, was calculated for each case.”
The paper relies on this without support.
The exclusion of cases with fewer than 50 patient words does not introduce bias into the association estimates.
“Cases with a mean of fewer than 50 patient words were excluded (n = 16)”
“Sessions with a total of fewer than 50 patient words were excluded (n = 121)”
The paper relies on this without support.
The sample of patients receiving IECBT is representative of the broader population of patients receiving CBT for common mental health disorders.
“The findings support the key principles underlying CBT as a treatment”
The paper relies on this without support.
The 'other' feature category represents nontherapy-related content.
“increases in nontherapy-related content (“other” [OR, 0.89; 95% CI, 0.85-0.92]”
The paper relies on this without support.
The mean number of words per feature in therapist utterances is a valid measure of the quantity of therapeutic content delivered.
“mean number of words per feature across treatment”
The paper relies on this without support.
The deep learning model accurately categorizes therapist utterances into the feature categories.
“automatically categorize therapist utterances”
The paper relies on this without support.
Reliable improvement in PHQ-9/GAD-7 scores is a valid indicator of clinical improvement.
“reliable improvement in patient symptoms”
The paper relies on this without support.
IAPT engagement is a valid indicator of patient engagement.
“IAPT engagement”
The paper relies on this without support.
The association between therapy content and outcomes is not confounded by unmeasured variables.
“Adjusted for total number of sessions, symptom severity, patient sex, age, medication status, presence of long-term condition, and session duration.”
The paper relies on this without support.
The first treatment session is representative of the therapy content.
“first treatment session”
The paper relies on this without support.
The 90,000 hours of IECBT are representative of CBT practice.
“approximately 90 000 hours of IECBT”
The paper relies on this without support.
A typical IAPT therapist may accrue approximately 30,000 therapy hours throughout a career.
“approximately 30 000 therapy hours”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering collection and assessment of psychiatric scale data and other effectiveness outcomes.
“The studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering the collection and assessment of psychiatric scale data and other effectiveness outcomes (29, 76, 79, 85–88, 92).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [86]
Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe relevant studies highlighted the importance of analyzing the large datasets produced by online mental healthcare interventions and the efficiency and accuracy of AI algorithms, which in some cases were comparable to or better than human assessors.
“As such, the relevant studies highlighted the importance of analyzing the large datasets produced by online mental healthcare interventions and the efficiency and accuracy of AI algorithms which in some cases were comparable to or better than human assessors (6, 19, 28, 29, 31, 33, 83, 85, 86, 92–94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [86]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe studies reporting on the use of ML algorithms found significant success in outcome prediction and feature detection to support online therapy delivery.
“Additionally, the studies reporting on the use of ML algorithms found significant success in outcome prediction and feature detection to support online therapy delivery (6, 19, 28, 29, 31, 33, 83, 85, 86, 92–94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [86]
The Methods (Therapy Feature Categories; page unavailable) describe a deep learning model that classified therapist utterances in internet-enabled CBT, and the Results (Factors Associated With Reliable Improvement Across Treatment; page unavailable) link several detected features with improvement. The paper analyzes associations with outcomes; it does not report that the model successfully predicted patient outcomes. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCBT is relatively simple to operationalize.
“This makes CBT relatively simple to operationalize (86).”
From Waiting for a digital therapist: three challenges on the path to psychotherapy delivered by artificial intelligence (2023) · cites this paper as [86]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Results give a different confidence limit for long-term conditions
MinorThe Results give an upper 95% confidence limit of 0.88 for a long-term medical condition, while Table 1 gives 0.80.
“the presence of a long-term medical condition (OR, 0.72; 95% CI, 0.66-0.88)”
“| Yes | 3632 (27.8) | NA | 0.72 (0.66-0.80) | −6.55 | <.001 |”
Results give a different odds ratio for "other" content
MinorThe Results report an odds ratio of 0.89, while Table 1 gives 0.88 for the same feature.
““other” [OR, 0.89; 95% CI, 0.85-0.92]”
“| Other | 121.1 (81) | 96.0 | 0.88 (0.85-0.92) | −5.82 | <.001 |”
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910 · see citing works
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765 · see citing works
(1962) · Medical Entomology and Zoology · Cited by 4,856 · see citing works
Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review
Martin, D. J., Garske, J. P., & Davis, M. K. (2000) · Journal of Consulting and Clinical Psychology · doi:10.1037//0022-006x.68.3.438 · Cited by 3,355 · see citing works
(2011) · Cited by 2,032 · see citing works
How important are the common factors in psychotherapy? An update
Wampold, B. E. (2015) · World Psychiatry · doi:10.1002/wps.20238 · Cited by 1,728 · see citing works
Cognitive therapy: Nature and relation to behavior therapy
Beck, A. T. (1970) · Behavior Therapy · doi:10.1016/s0005-7894(70)80030-2 · Cited by 863 · see citing works
Clark, D. M. (2011) · International Review of Psychiatry · doi:10.3109/09540261.2011.606803 · Cited by 699 · see citing works
The Lancet Psychiatry Commission on psychological treatments research in tomorrow's science
Holmes, E. A., Ghaderi, A., Harmer, C. J., Ramchandani, P. G., Cuijpers, P., Morrison, A. P., Roiser, J. P., Bockting, C. L. H., O'Connor, R. C., Shafran, R., et al. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30513-8 · Cited by 624 · see citing works
Meta-analysis of comparative therapy outcome studies: a replication and refinement.
Shapiro, D. A., & Shapiro, D. (1982) · Psychological Bulletin · doi:10.1037/0033-2909.92.3.581 · Cited by 590 · see citing works
Barth, J., Munder, T., Gerger, H., Nüesch, E., Trelle, S., Znoj, H., Jüni, P., & Cuijpers, P. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001454 · Cited by 569 · see citing works
The revised cognitive therapy scale (CTS-R): psychometric properties.
Blackburn, I.-M., James, I. A., Milne, D. L., Baker, C., Standart, S., Garland, A., & Reichelt, F. K. (2001) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s1352465801004040 · Cited by 568 · see citing works
Perepletchikova, F., Treat, T. A., & Kazdin, A. E. (2007) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.75.6.829 · Cited by 533 · see citing works
Measuring and improving the quality of mental health care: a global perspective
Kilbourne, A. M., Beck, K., Spaeth‐Rublee, B., Ramanuj, P., O'Brien, R. W., Tomoyasu, N., & Pincus, H. A. (2018) · World Psychiatry · doi:10.1002/wps.20482 · Cited by 457 · see citing works
The Integrative Power of Cognitive Therapy
Alford, B. A., Beck, A. T., & Jones, J. V. (1997) · Journal of Cognitive Psychotherapy · doi:10.1891/0889-8391.11.4.309 · Cited by 400 · see citing works
Kessler, D., Lewis, G., Kaur, S., Wiles, N., King, M., Weich, S., Sharp, D. J., Araya, R., Hollinghurst, S., & Peters, T. J. (2009) · The Lancet · doi:10.1016/s0140-6736(09)61257-5 · Cited by 387 · see citing works
Evidence-based treatment and therapist drift
Waller, G. (2009) · Behaviour Research and Therapy · doi:10.1016/j.brat.2008.10.018 · Cited by 359 · see citing works
Johnsen, T. J., & Friborg, O. (2015) · Psychological Bulletin · doi:10.1037/bul0000015 · Cited by 331 · see citing works
Kazantzis, N., Whittington, C., Zelencich, L., Kyrios, M., Norton, P. J., & Hofmann, S. G. (2016) · Behavior Therapy · doi:10.1016/j.beth.2016.05.002 · Cited by 280 · see citing works
Roth, A. D., & Pilling, S. (2008) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s1352465808004141 · Cited by 260 · see citing works
Waller, G., & Turner, H. (2016) · Behaviour Research and Therapy · doi:10.1016/j.brat.2015.12.005 · Cited by 256 · see citing works
Distinctive activities of cognitive–behavioral therapy
Blagys, M. D., & Hilsenroth, M. J. (2002) · Clinical Psychology Review · doi:10.1016/s0272-7358(01)00117-9 · Cited by 222 · see citing works
Common versus specific factors in psychotherapy: opening the black box
Mulder, R., Murray, G., & Rucklidge, J. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30100-1 · Cited by 198 · see citing works
Are the parts as good as the whole? A meta-analysis of component treatment studies.
Bell, E. C., Marcus, D. K., & Goodlad, J. K. (2013) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0033004 · Cited by 192 · see citing works
Lorenzo-Luaces, L., German, R. E., & DeRubeis, R. J. (2015) · Clinical Psychology Review · doi:10.1016/j.cpr.2014.12.003 · Cited by 190 · see citing works
Strunk, D. R., Brotman, M. A., & DeRubeis, R. J. (2010) · Behaviour Research and Therapy · doi:10.1016/j.brat.2010.03.011 · Cited by 169 · see citing works
On the topic of treatment integrity.
Perepletchikova, F. (2011) · Clinical Psychology Science and Practice · doi:10.1111/j.1468-2850.2011.01246.x · Cited by 121 · see citing works
Cognitive therapy of delusional beliefs.
Alford, B. A., & Beck, A. T. (1994) · Behaviour Research and Therapy · doi:10.1016/0005-7967(94)90134-1 · Cited by 90 · see citing works
Catarino, A., Bateup, S., Tablan, V., Innes, K., Freer, S., Richards, A., Stott, R., Hollon, S. D., Chamberlain, S. R., Hayes, A., et al. (2018) · BJPsych Open · doi:10.1192/bjo.2018.57 · Cited by 40 · see citing works
Self-citationInvestigating Treatment Mediators When Simple Random Assignment to a Control Group is Not Possible.
Doss, B. D., & Atkins, D. C. (2006) · Clinical Psychology: Science and Practice · doi:10.1111/j.1468-2850.2006.00045.x · Cited by 24 · see citing works
Inside the Black Box: The Importance of Monitoring Treatment Implementation
Essock, S. M., Covell, N. H., & Weissman, E. M. (2004) · Schizophrenia Bulletin · doi:10.1093/oxfordjournals.schbul.a007107 · Cited by 12 · see citing works
Investigating treatment mediators when simple random assignment to a control group is not possible.
Doss (2006) · Clinical Psychology Science and Practice · doi:10.1111/cpsp.2006.13.issue-4 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 34 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The promise of machine learning in predicting treatment outcomes in psychiatry
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 · see citing works
An overview of and recommendations for more accessible digital mental health services
Lattie, E. G., Stiles-Shields, C., & Graham, A. K. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-021-00003-1 · Cited by 446 · see citing works
Illuminating the dark spaces of healthcare with ambient intelligence
Haque, A., Milstein, A., & Fei-Fei, L. (2020) · Nature · doi:10.1038/s41586-020-2669-y · Cited by 274 · see citing works
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 225 · see citing works
Malgaroli, M., Hull, T. D., Zech, J. M., & Althoff, T. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02592-2 · Cited by 217 · see citing works
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 · see citing works
Huibers, M. J. H., Lorenzo‐Luaces, L., Cuijpers, P. J., & Kazantzis, N. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.607508 · Cited by 125 · see citing works
Individuals with depression express more distorted thinking on social media
Bathina, K. C., ten Thij, M., Lorenzo‐Luaces, L., Rutter, L. A., & Bollen, J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01050-7 · Cited by 137 · see citing works
Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview
Zhou, S., Zhao, J., & Zhang, L. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.811665 · Cited by 128 · see citing works
Grodniewicz, J. P., & Hohol, M. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1190084 · Cited by 100 · see citing works
Our Digital Moment: Innovations and Opportunities in Digital Mental Health Care
Gratzer, D., Torous, J. B., Lam, R. W. M., Patten, S. B., Kutcher, S., Chan, S. R., Vigo, D., PAJER, K. A., & Yatham, L. N. (2020) · The Canadian Journal of Psychiatry · doi:10.1177/0706743720937833 · Cited by 86 · see citing works
Assessing the accuracy of automatic speech recognition for psychotherapy
Miner, A. S., Haque, A., Fries, J., Fleming, S. L., Wilfley, D. E., Wilson, G. T., Milstein, A. S., Jurafsky, D., Arnow, B. A., Agras, W. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0285-8 · Cited by 82 · see citing works
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
Pavlopoulos, A., Rachiotis, T., & Maglogiannis, I. G. (2024) · Applied Sciences · doi:10.3390/app14199068 · Cited by 81 · see citing works
Gutierrez, G., Stephenson, C., Eadie, J., Asadpour, K., & Alavi, N. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1356773 · Cited by 71 · see citing works
“(85, 86, 91)] focused on the content of the therapy program and the utterances of the participants and found several factors that could promote and predict higher effectiveness and engagement.”
BackgroundResultInfluentialvan Heerden, A. C., Pozuelo, J. R., & Kohrt, B. A. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.1253 · Cited by 72 · see citing works
Eccleston, C., Fisher, E., Liikkanen, S., Sarapohja, T., Stenfors, C., Jääskeläinen, S. K., Rice, A. S. C., Mattila, L., Blom, T., & Bratty, J. R. (2022) · Pain · doi:10.1097/j.pain.0000000000002617 · Cited by 64 · see citing works
Nilsén, P., Svedberg, P., Nygren, J., Frideros, M., Johansson, J., & Schueller, S. M. (2022) · Implementation Research and Practice · doi:10.1177/26334895221112033 · Cited by 62 · see citing works
Ewbank, M. P., Cummins, R., Tablan, V., Catarino, A., Buchholz, S., & Blackwell, A. D. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1788740 · Cited by 39 · see citing works
Application of machine learning in measurement of ageing and geriatric diseases: a systematic review
Das, A., & Dhillon, P. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-04477-x · Cited by 38 · see citing works
Mechanisms of Change in Exposure Therapy for Anxiety and Related Disorders: A Research Agenda
Benito, K., Pittig, A., Abramowitz, J. S., Arch, J. J., Chavira, D. A., de Kleine, R. A., Nadai, A. S. D., Hermans, D., Hofmann, S. G., Hoyer, J., et al. (2024) · Clinical Psychological Science · doi:10.1177/21677026241240727 · Cited by 37 · see citing works
Leehr, E. J., Roesmann, K., Böhnlein, J., Dannlowski, U., Gathmann, B., Herrmann, M. J., Junghöfer, M., Schwarzmeier, H., Seeger, F. R., Siminski, N., et al. (2021) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2021.102448 · Cited by 35 · see citing works
Artificial Intelligence: A Game-Changer for Mental Health Care
Dakanalis, A., Wiederhold, B. K., & Riva, G. (2024) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2023.0723 · Cited by 32 · see citing works
The impact of artificial intelligence on the tasks of mental healthcare workers: A scoping review
Verboom, A. D. P. R., Damion, D. E. V., dos Santos, N. R., & de Graaf, J. W. (2023) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2023.100008 · Cited by 41 · see citing works
Palmer, C. E., Marshall, E., Millgate, E., Warren, G., Ewbank, M., Cooper, E., Lawes, S., Bouazzaoui, M., Smith, A., Hutchins-Joss, C., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/69351 · Cited by 42 · see citing works
Self-citationBuckman, J. E. J., Stott, J. C. H., Main, N., Antonie, D., Singh, S., Naqvi, S. A., Aguirre, E., Wheatley, J., Cirkovic, M., Leibowitz, J., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721004773 · Cited by 29 · see citing works
Developing a natural language-based AI-chatbot for social work training: an illustrative case study
Chan, C., & Li, F. (2023) · China Journal of Social Work · doi:10.1080/17525098.2023.2176901 · Cited by 27 · see citing works
Cuijpers, P. J., Harrer, M., & Furukawa, T. A. (2026) · World Psychiatry · doi:10.1002/wps.70002 · Cited by 31 · see citing works
Leung, Y. W., Wouterloot, E., Adikari, A., Hirst, G., Silva, D. D., Wong, J., Bender, J. L., Gancarz, M., Gratzer, D., Alahakoon, D., et al. (2021) · JMIR Research Protocols · doi:10.2196/21453 · Cited by 29 · see citing works
HabitWorks: Development of a CBM-I Smartphone App to Augment and Extend Acute Treatment
Beard, C., Ramadurai, R., McHugh, R. K., Pollak, J. P., & Björgvinsson, T. (2021) · Behavior Therapy · doi:10.1016/j.beth.2020.04.013 · Cited by 29 · see citing works
A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
Martí-Puig, P., Capra, C., Vega, D., Llunas, L., & Solé‐Casals, J. (2022) · Sensors · doi:10.3390/s22134790 · Cited by 26 · see citing works
Picariello, F., Hulme, K., Seaton, N., Hudson, J. L., Norton, S., Wroe, A. L., & Moss-Morris, R. E. (2024) · Psychological Medicine · doi:10.1017/s0033291723003756 · Cited by 23 · see citing works
Seaton, N., Moss-Morris, R. E., Norton, S., Hulme, K., & Hudson, J. L. (2022) · BJPsych Open · doi:10.1192/bjo.2022.59 · Cited by 22 · see citing works
Imel, Z. E., Tanana, M., Soma, C. S., Hull, T. D., Pace, B. T., Stanco, S. C., Creed, T. A., Moyers, T. B., & Atkins, D. C. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.52590 · Cited by 24 · see citing works
AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients
Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., Gambiraža, M., Dropuljić, B., Mijić, I., Henigsberg, N., & Jovanović, T. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.782866 · Cited by 25 · see citing works
Machine-Learning-Based Prediction of Client Distress From Session Recordings
Kuo, P. B., Tanana, M., Goldberg, S. B., Caperton, D. D., Narayanan, S. S., Atkins, D. C., & Imel, Z. E. (2023) · Clinical Psychological Science · doi:10.1177/21677026231172694 · Cited by 23 · see citing works
Zantvoort, K., Scharfenberger, J., Boß, L., Lehr, D., & Funk, B. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00148-z · Cited by 19 · see citing works
Hudon, A., Beaudoin, M., Phraxayavong, K., Dellazizzo, L., Potvin, S., & Dumais, A. (2022) · Health Informatics Journal · doi:10.1177/14604582221142442 · Cited by 17 · see citing works
Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study
Sadeh‐Sharvit, S., & Hollon, S. D. (2020) · JMIR Mental Health · doi:10.2196/20646 · Cited by 17 · see citing works
The rise of artificial intelligence for cognitive behavioral therapy: A bibliometric overview
Vanhée, L., Andersson, G., Garcia, D., & Sikström, S. (2025) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.70033 · Cited by 14 · see citing works
A Scoping Review of Arabic Natural Language Processing for Mental Health
Alasmari, A. (2025) · Healthcare · doi:10.3390/healthcare13090963 · Cited by 15 · see citing works
Serious video games and psychological support: A depression intervention among young cancer patients
Khan, S., Abbasi, A. Z., Kazmi, S. F., Ting, D. H., Rehman, U., Hlavacs, H., & Arslan, F. S. (2022) · Entertainment Computing · doi:10.1016/j.entcom.2022.100479 · Cited by 13 · see citing works
Artificial intelligence, machine learning and mental health
Delgadillo, J., & Atzil–Slonim, D. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91497-0.00177-6 · Cited by 14 · see citing works
Fox, S. T., Janda, M., & Hubbard, R. E. (2022) · Aging Clinical and Experimental Research · doi:10.1007/s40520-022-02305-7 · Cited by 12 · see citing works
Miner, A. S., Fleming, S. L., Haque, A., Fries, J., Althoff, T., Wilfley, D. E., Agras, W. S., Milstein, A. S., Hancock, J. T., Asch, S. M., et al. (2022) · npj Mental Health Research · doi:10.1038/s44184-022-00020-9 · Cited by 17 · see citing works
Zhang, X., Tanana, M., Weitzman, L. M., Narayanan, S., Atkins, D. C., & Imel, Z. E. (2022) · Psychotherapy · doi:10.1037/pst0000460 · Cited by 11 · see citing works
Malins, S., Figueredo, G. P., Jilani, T. A., Long, Y., Andrews, J. A., Rawsthorne, M., Manolescu, C., Clos, J., Higton, F., Waldram, D., et al. (2022) · JMIR Medical Informatics · doi:10.2196/38168 · Cited by 10 · see citing works
Current and Future Approaches to Pediatric Anxiety Disorder Treatment
Zugman, A., Winkler, A. M., Qamar, P., & Pine, D. S. (2024) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20231037 · Cited by 14 · see citing works
Tasca, A. N., Carlucci, S., Wiley, J. C., Holden, M., El-Roby, A., & Tasca, G. A. (2022) · Psychotherapy Research · doi:10.1080/10503307.2022.2156306 · Cited by 10 · see citing works
Depressed individuals express more distorted thinking on social mediaPreprint
Bathina, K. C., ten Thij, M., Lorenzo‐Luaces, L., Rutter, L. A., & Bollen, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02800 · Cited by 10 · see citing works
Hudon, A., Beaudoin, M., Phraxayavong, K., Dellazizzo, L., Potvin, S., & Dumais, A. (2021) · JMIR Mental Health · doi:10.2196/22651 · Cited by 9 · see citing works
Predicting treatment outcomes in improving access to psychological therapy (IAPT) services
Verbist, I. L., Huey, D., & Bennett, H. (2021) · Mental Health Review Journal · doi:10.1108/mhrj-04-2021-0037 · Cited by 10 · see citing works
Affective Computing in Psychotherapy
Khanna, R., Robinson, N., O'Donnell, M. L., Eyre, H. A., & Smith, E. (2022) · Advances in Psychiatry and Behavioral Health · doi:10.1016/j.ypsc.2022.05.006 · Cited by 9 · see citing works
Malins, S., Moghaddam, N. G., Morriss, R. K., Schroder, T. A., Brown, P., & Boycott, N. (2020) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.2543 · Cited by 9 · see citing works
Zhu, E., Wang, J., Zhou, G., Li, C., Chen, F., Ju, K., Chen, L., Yin, Y., Chen, Y., Zhang, Y., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-04150-7 · Cited by 8 · see citing works
Socratic questioning put into clinical practice
Carona, C., Handford, C., & Fonseca, A. (2020) · BJPsych Advances · doi:10.1192/bja.2020.77 · Cited by 7 · see citing works
Rollmann, I., Gebhardt, N., Stahl-Toyota, S., Simon, J. J., Sutcliffe, M., Friederich, H., & Nikendei, C. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1055868 · Cited by 7 · see citing works
Abinaya, M., & Vadivu, G. (2024) · IEEE Access · doi:10.1109/access.2024.3407885 · Cited by 6 · see citing works
Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning
Yao, L., Wang, Z., Gu, H., Zhao, X., Chen, Y., & Liu, L. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.947081 · Cited by 6 · see citing works
Using technology to understand how therapist variables are associated with clinical outcomes in IAPT
Bateup, S., Palmer, C. R., & Catarino, A. (2020) · The Cognitive Behaviour Therapist · doi:10.1017/s1754470x20000252 · Cited by 6 · see citing works
Self-citationMachine Learning–Based Evaluation of Suicide Risk Assessment in Crisis Counseling Calls
Imel, Z. E., Pace, B. T., Pendergraft, B., Pruett, J., Tanana, M., Soma, C. S., Comtois, K. A., & Atkins, D. C. (2024) · Psychiatric Services · doi:10.1176/appi.ps.20230648 · Cited by 5 · see citing works
Implementation of Real-Time Medical and Health Data Mining System Based on Machine Learning
Wang, P., & Li, J. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/7011205 · Cited by 5 · see citing works
Gao, Y., Xiong, J., Shen, C., & Jia, X. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac3d16 · Cited by 5 · see citing works
Zech, J. M., Steele, R. J., Foley, V. K., & Hull, T. D. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.917918 · Cited by 5 · see citing works
Artificial Intelligence to Support Human-Provided Mental Health Treatment
Soma, C. S., Kuo, P. B., Mehta, M., Srikumar, V., Imel, Z. E., & Atkins, D. C. (2026) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-061724-075336 · Cited by 6 · see citing works
Sadeh‐Sharvit, S., Idan, O., Fowler, L. A., Fitzsimmons‐Craft, E. E., Firebaugh, M., Smith, A. C., Graham, A. K., Goel, N. J., Flatt, R. E., Balantekin, K. N., et al. (2022) · Eating Disorders · doi:10.1080/10640266.2022.2110698 · Cited by 4 · see citing works
Hitchcock, C., Funk, J., Cummins, R., Patel, S. D., Catarino, A., Takano, K., Dalgleish, T., & Ewbank, M. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.134 · Cited by 4 · see citing works
Self-citationBartlett, J. W., Fisher, E., Liikkanen, S., Turunen, J., Skog, M., & Eccleston, C. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.882980 · Cited by 4 · see citing works
Language as a fingerprint: Self-supervised learning of user encodings using transformers
Rocca, R., & Yarkoni, T. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.123 · Cited by 4 · see citing works
Manikandan, S. (2023) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.4.823.51356 · Cited by 4 · see citing works
Editorial: Promoting Quality Psychotherapy: It Is Not the Process but the Outcome That Matters!
Walkup, J. T., Parkhurst, J. T., & Lavigne, J. V. (2020) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2020.05.004 · Cited by 4 · see citing works
Artificial intelligence-based psychotherapy: focusing on common psychotherapeutic factors
Giotakos, O. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1710715 · Cited by 3 · see citing works
Lossio-Ventura, J. A., Frank, S., Ringlein, G., Bonson, K., Olszko, A., Knobel, A., Pine, D. S., Freeman, J. B., Benito, K., Jangraw, D. C., et al. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf127 · Cited by 3 · see citing works
Miner, A. S., Fleming, S. L., Haque, A., Fries, J., Althoff, T., Wilfley, D. E., Agras, W. S., Milstein, A. S., Hancock, J. T., Ash, S. M., et al. (2022) · medRxiv · doi:10.1101/2022.04.24.22274227 · Cited by 3 · see citing works
Liu, X.-Y., Pan, M., Gao, X., Fu, Z., Liu, L., Li, H., Wang, Y., & Qian, Q. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-091352 · Cited by 2 · see citing works
“It’s like having that supervisor in the room”: Examining AI as a reflective partner
Matthews, E. B., Lerman, D., Beach, N., Wiczyk, D., & Goldkind, L. (2025) · Psychotherapy Research · doi:10.1080/10503307.2025.2569047 · Cited by 2 · see citing works
Artificial Intelligence-generated Feedback as a Training Tool for Enhancing Psychotherapy Skill
Young, J. N., Schruff, M. A., Mudryk, E. P., & Bentley, J. P. (2026) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-026-00603-9 · Cited by 2 · see citing works
Positive psychology 4.0: a science of consequence for a world that can no longer wait
van Zyl, L. E. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1726065 · Cited by 2 · see citing works
Andrews, J. A., Rawsthorne, M., Manolescu, C., McFaul, M. B., French, B., Rye, E., McNaughton, R., Baliousis, M., Smith, S., Biswas, S., et al. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2022.100044 · Cited by 2 · see citing works
Lakeman, R. (2024) · Australian and New Zealand Journal of Family Therapy · doi:10.1002/anzf.1601 · Cited by 2 · see citing works
Mylläri, S., Saarni, S. E., Joffe, G., Ritola, V., Stenberg, J., & Rosenström, T. H. (2025) · Psychotherapy Research · doi:10.1080/10503307.2025.2473921 · Cited by 2 · see citing works
Malins, S., Figueredo, G. P., Saxon, D. H., Horton, K., Clos, J., Trimble, T., Fatehi, K., Waldram, D., Higton, F., Hardy, G. E., et al. (2026) · Digital Health · doi:10.1177/20552076251413316 · Cited by 1 · see citing works
Artificial Intelligence for Serious Illness Communication: Proactive Approaches to Mitigating Harm
Tarbi, E. C., Durieux, B. N., Kwok, A., Rizzo, D. M., & Lindvall, C. J. (2025) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhaf024 · Cited by 3 · see citing works
Jones, L. A., Barber, C., Clements-Brod, N., Davies, J., DelPozo‐Baños, M., Hughes, A., Iveson, M. H., Jain, D., John, A. M., Maguire, A., et al. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1760116 · Cited by 1 · see citing works
Malins, S., Saxon, D. H., Figueredo, G. P., Clos, J., Trimble, T., Fatehi, K., Waldram, D., Boycott, N., Frost, A., Sales, C., et al. (2026) · Behaviour Research and Therapy · doi:10.1016/j.brat.2026.105152 · Cited by 1 · see citing works
Hudon, A., Aird, M., & Haye-Caty, N. L. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3040066 · Cited by 1 · see citing works
Augmenting Mental Healthcare Using Artificial Intelligence Advancements
Theodore, S. J., Jr., Lodhi, A. R., Upadhyay, P., & Yadav, S. P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-4203-9.ch008 · Cited by 1 · see citing works
Ethical Considerations of Using VR in Mental Health Training
Cachia, A., Camilleri, V., Dingli, A., Galea, M., Grech, P., Sammut, A., & Scerri, J. (2021) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-7888-9.ch006
Tracy, D. K., Joyce, D. W., Albertson, D. N., & Shergill, S. S. (2020) · The British Journal of Psychiatry · doi:10.1192/bjp.2020.5
Hu, B. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/7239191
Évaluation des résultats des thérapies comportementales et cognitives
Cottraux, J. (2020) · Les Psychothérapies Cognitives et Comportementales · doi:10.1016/b978-2-294-76778-4.00026-0
Thérapie cognitive de la dépression et nouvelles technologies
Cottraux, J. A. (2022) · Elsevier eBooks · doi:10.1016/b978-2-294-77763-9.00009-9
Vance, A. L. A., Winther, J., & Shoorcheh, E. (2022) · Springer eBooks · doi:10.1007/978-3-031-14080-8_10
Vyas, S., & Kumari, A. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9554-7_6
Huang, Y., Li, L., Dong, W., Dong, Y., Huang, Y., & Liu, H. (2024) · doi:10.2196/preprints.65435
Jell, G., Wroe, A. L., Saadiya, A., Causier, C., Palmer, E., Dosanjh, L., & McKechnie, V. (2025) · The Cognitive Behaviour Therapist · doi:10.1017/s1754470x25100159
Kopelovich, S. L., Brown, P., & Hardy, K. V. (2025) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.20250019
Digital Innovation in Mental Health
Gillett, G., Rigby, D., & Gadelrab, R. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009028967.011
Das, A., & Dhillon, P. (2024) · Figshare · doi:10.6084/m9.figshare.26652729
Abichi, M. C., Davis, A., Proudfoot, H., Norton, S., Moss-Morris, R. E., & Hudson, J. L. (2026) · BJPsych Open · doi:10.1192/bjo.2025.10957
- Authors
- Ewbank, M. P., Cummins, R., Tablan, V., Bateup, S., Catarino, A., Martin, A. J., & Blackwell, A. D.
- Affiliations
- ieso Digital Health (UK) Ltd. (United Kingdom) (GB)
- Corresponding author
- Ewbank, Michael P.
- Venue
- JAMA Psychiatry
- Publisher
- American Medical Association (AMA)
- Published
- 2020-01-01
- Volume
- 77
- Issue
- 1
- Pages
- 35
- ISSN
- 2168-622X (ISSN-L)
- PMC
- PMC6707006
- OpenAlex
- W2969401291
- Semantic Scholar
- 2c28755adfb8ef67337a98b07dd4c80f96e82e83
- Cited by
- 108
- References
- 34
- Citations by year
- 2026: 16 · 2025: 14 · 2024: 15 · 2023: 12 · 2022: 25 · 2021: 12 · 2020: 14
- FWCI
- 14.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- JAMA Psychiatry (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- cognitive behavioral therapy, session transcripts, online therapy, treatment engagement, symptom improvement, depression, anxiety, intervention fidelity, deep learning, PHQ-9, GAD-7, mental illness, primary care referral, patient-reported outcomes
- MeSH terms
- Deep Learning, Adolescent, Adult, Aged, Mental Disorders, Female, Humans, Language, Male, Middle Aged, Psychiatric Status Rating Scales, Surveys and Questionnaires, Cognitive Behavioral Therapy, Treatment Outcome, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review