Daily Self-Monitoring of Symptoms and Skills Learning in Patients With Borderline Personality Disorder Through a Mobile Phone App: Protocol for a Pragmatic Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Patient self-monitoring via mobile phones during psychotherapy can enhance and provide an overview of psychotherapeutic progress by graphically displaying current and previous symptom scores, providing feedback to the patient, delivering psychoeducative material, and providing timely data to the therapist or treatment team. OBJECTIVE: This study will aim to assess the effects of using a mobile phone to self-monitor symptoms and acquire coping skills instead of using pen and paper during psychotherapy in patients with borderline personality disorder (BPD). Dialectical behavior therapy will be performed to treat BPD. The primary outcome is the mean time needed to learn coping skills directed at emotion regulation; the secondary outcome is changes in the BPD symptom score as measured by the Zanarini Rating Scale for Borderline Personality Disorder. METHODS: This study is a pragmatic, multicenter randomized controlled trial. Participants were recruited through five public general psychiatric outpatient treatment facilities in Denmark. Patients are randomly assigned, on a 1:1 basis, to either the mobile phone condition (using the Monsenso mDiary mobile app) or pen-and-paper condition. Patients will complete several self-report questionnaires on symptom severity; assessments by trained raters on BPD severity will be performed as well. Survival analysis with a shared frailty model will be used to assess the primary outcome. RESULTS: Recruitment began in June 2017 and was completed in February 2019 after 80 participants were recruited. The study ended in February 2020. It is expected that the benefits of mobile phone-based self-report compared to the pen-and-paper method will be demonstrated for skill learning speed and registration compliance. To our knowledge, this is the first trial exploring the impact of cloud-based mobile registration in BPD treatment. CONCLUSIONS: This trial will report on the effectiveness of mobile phone-based self-monitoring during psychiatric treatment. It has the potential to contribute to evidence-based clinical practice since apps are already in use clinically. TRIAL REGISTRATION: ClinicalTrials.gov NCT03191565; https://clinicaltrials.gov/ct2/show/NCT03191565. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17737.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".