Using a Mobile Diary App in the Treatment of Borderline Personality Disorder: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Borderline personality disorder (BPD) is a disorder characterized by difficulties with regulating emotions and impulsive behavior. Long-term monitoring of progress during BPD psychotherapy constitutes a challenge using paper and pencil registration. Hence, a mobile app assessing emotions and progress in treatment may be useful. OBJECTIVE: The aim of this study was to examine the feasibility of using the mDiary app as an adjunct to dialectical behavior therapy (DBT) for the treatment of BPD. METHODS: A total of 9 focus group interviews were conducted and analyzed according to the grounded theory approach. Furthermore, the usability of the mDiary app was examined using the System Usability Scale (SUS). The app was implemented in a standard DBT program as an adjunct to DBT. In total, 16 patients (age range 19-41 years) and 23 therapists (age range 25-64 years) from 5 Danish public outpatient psychiatric treatment facilities participated in the study. RESULTS: Overall, patients were satisfied with the mDiary app, as it was "easy to use" and "always there." Inside-out innovation, meaning new work tasks generated during implementation and communication of modifications needed in the app, was found to influence the perceived usability negatively among the interviewed therapists. The patients rated the usability as high (mean SUS score 81.2, SD 9.9), whereas therapists rated the mDiary app at an average level (mean 68.3, SD 14.3). Older age of the users correlated with lower usability ratings on the SUS score (Pearson r=-0.60). CONCLUSIONS: The mDiary app was considered as an acceptable and relevant way of registering DBT diary data for both patients and therapists generating increased long-term overview. Older users were overall more reluctant to accept this new technology in clinical practice. Time to align expectations among involved parties needs to be set aside when implementing this new approach to patient monitoring. Here, the focus should be on the realistic use of resources and expected impact on present clinical work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".