Interesting new anxiety research on a therapeutic technique, neural circuitry and predicting therapeutic outcomes.
Blended Acceptance and Commitment Therapy Versus Face-to-face Cognitive Behavioral Therapy for Older Adults With Anxiety Symptoms in Primary Care: Pragmatic Single-blind Cluster Randomized Trial.
Witlox M, Garnefski N, Kraaij V, de Waal MWM, Smit F, Bohlmeijer E, Spinhoven P. J Med Internet Res. 2021 Mar 26;23(3):e24366. doi: 10.2196/24366. PMID: 33769293.
- “Anxiety symptoms in older adults are prevalent and disabling but often go untreated. Most trials on psychological interventions for anxiety in later life have examined the effectiveness of face-to-face cognitive behavioral therapy (CBT).
- “This study is the first to examine the effectiveness of a brief blended acceptance and commitment therapy (ACT) intervention for older adults with anxiety symptoms, compared with a face-to-face CBT intervention.”
- “Large reductions in anxiety symptom severity (Cohen d≥0.96) were found in both conditions post treatment, and these were maintained at the 12-month follow-up. The rates of clinically significant changes in anxiety symptoms were also not different for the blended ACT group and CBT group (χ21=0.2, P=.68). Regarding secondary outcomes, long-term effects on positive mental health were significantly stronger in the blended ACT group (B=.27, P=.03, Cohen d=0.29), and treatment satisfaction was significantly higher for blended ACT than CBT (B=3.19, P<.001, Cohen d=0.78).”
Reciprocal control of obesity and anxiety-depressive disorder via a GABA and serotonin neural circuit.
Xia G, Han Y, Meng F, He Y, Srisai D, Farias M, Dang M, Palmiter RD, Xu Y, Wu Q. Mol Psychiatry. 2021 Mar 26. doi: 10.1038/s41380–021–01053-w. Epub ahead of print. PMID: 33767348.
- “The high comorbidity between obesity and mental disorders, such as depression and anxiety, often exacerbates metabolic and neurological symptoms significantly. However, neural mechanisms that underlie reciprocal control of feeding and mental states are largely elusive.”
- “Our results unveil a neural mechanism for reciprocal control of appetite and mental states, which culminates in a novel zonisamide-granisetron cocktail therapy for potential tackling the psychosis-obesity comorbidity.”
Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data.
Bone C, Simmonds-Buckley M, Thwaites R, Sandford D, Merzhvynska M, Rubel J, Deisenhofer AK, Lutz W, Delgadillo J. Lancet Digit Health. 2021 Apr;3(4):e231-e240. doi: 10.1016/S2589–7500(21)00018–2. PMID: 33766287.
- “Common mental disorders can be effectively treated with psychotherapy, but some patients do not respond well and require timely identification to prevent treatment failure. We aimed to develop and validate a dynamic model to predict psychological treatment outcomes, and to compare the model with currently used methods, including expected treatment response models and machine learning models.”
- “In this prediction model development and validation study, we obtained data from two UK studies including patients who had accessed therapy via Improving Access to Psychological Therapies (IAPT) services managed by ten UK National Health Service (NHS) Trusts between March, 2012, and June, 2018, to predict treatment outcomes. In study 1, we used data on patient-reported depression (Patient Health Questionnaire 9 [PHQ-9]) and anxiety (Generalised Anxiety Disorder 7 [GAD-7]) symptom measures obtained on a session-by-session basis (Leeds Community Healthcare NHS Trust dataset; n=2317) to train the Oracle dynamic prediction model using iterative logistic regression analysis.”
- “The performance of the Oracle model was similar to complex (eg, including patient profiling variables) and computationally intensive machine learning models (eg, neural networks based on a multilayer perceptron algorithm, extreme gradient boosting). Furthermore, the predictive accuracy of a more simple dynamic algorithm including only baseline and index-session scores was comparable to more complex algorithms that included additional predictors modelling sample-level and individual-level variability. Overall, the Oracle algorithm significantly outperformed the expected treatment response model (mean AUC 0·80 vs 0·70, p<0·0001]).”
- “Dynamic prediction models using sparse and readily available symptom measures are capable of predicting psychotherapy outcomes with high accuracy.”
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