Digital therapeutics (DTx) show promise in bridging mental healthcare gaps. However, treatment selection often relies on availability and trial-and-error, prolonging suffering and increasing costs. Personalised prediction models could help identify individuals benefiting most from specific DTx.

The aim of this secondary analysis was to establish a machine learning-based prediction model for positive treatment outcomes in patients with depressive or anxiety symptoms after 8 weeks of internet-delivered cognitive behavioural therapy (iCBT).

We analysed a large real-world dataset of patients from the online therapy unit iCBT programme in Saskatchewan, Canada (2013–2021). Clinically significant changes in depressive symptoms or anxiety were measured using the Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder-7 (GAD-7). We trained six prediction models using sociodemographic and mental health-related factors at baseline, compared model performances and calculated Shapley values for feature importance.

Data from 4175 patients using 34 features for prediction, identified by least absolute shrinkage and selection operator regression, showed the Gradient Boosted Model (gbm) and logistic regression (log) performed best, with balanced accuracies of 0.76, 95% CI (0.70 to 0.83) and 0.70, 95% CI (0.63 to 0.77). Shapley values indicated GAD-7 scores at baseline as the most important predictor of clinically significant improvement, along with mental health history and sociodemographic variables.

The gbm and log models achieved comparable accuracy in predicting clinically significant improvement after iCBT, supporting the use of simpler, interpretable methods in clinical practice.

These findings could help improve mental health treatment selection, iCBT assignment, enhance effectiveness and optimise treatment for patients.

NCT05758285.

The recent pandemic caused by SARS-CoV-2 had a profound global impact. While many individuals recovered from COVID-19, some developed long-lasting symptoms that significantly disrupted daily life. The WHO defines this condition as post-COVID-19 condition (PCC). Common symptoms include fatigue, dyspnoea, sleep disturbances and cognitive difficulties. Increasing evidence suggests that PCC is a multifactorial condition, shaped not only by biomedical but also psychological and social factors. This article presents the protocol of the Basel Long COVID Cohort Study (BALCoS), which aims to improve understanding of PCC by capturing clinical, functional and psychosocial aspects through repeated assessments over the course of 1 year.

BALCoS is a prospective, single-site cohort study. Inclusion criteria include either a probable or confirmed history of SARS-CoV-2 infection with persistent symptoms consistent with the WHO definition of PCC, sufficient German language skills and age ≥18 years. At baseline, we collected detailed information on previous SARS-CoV-2 infections, symptom history, reinfections, COVID-19 vaccination status and pre-existing medical conditions. The study includes standardised psychometric assessments, physical performance tests, ecological momentary assessments (EMAs), neurocognitive testing and blood sample collection. Assessments are scheduled at baseline and at 3-month, 6-month and 12-month follow-up. All participants complete psychometric assessments at each time point. Blood samples are only collected at baseline. Neurocognitive testing and physical performance measures are collected at baseline and 12-month follow-up for in-person participants only. Participants who are unable to attend in person complete a remote version of the study, excluding these in-clinic assessments. EMAs are initiated the day after each time point and consist of eight questions over 10 consecutive days. The study is exploratory in nature, with a target sample size of 120 participants. BALCoS is part of the Horizon Europe Long COVID project, a multinational interdisciplinary research consortium integrating mechanistic, clinical and interventional studies.

The study was approved by the Ethics Commission of Northwest and Central Switzerland (BASEC-ID: 2023–00359) and is registered at ClinicalTrials.gov (ID: NCT05781893). All participants provide written informed consent. Study findings will be disseminated through peer-reviewed publications.

NCT05781893.