TY  - JOUR
AU  - Rohani, Darius Adam
AU  - Tuxen, Nanna
AU  - Quemada Lopategui, Andrea
AU  - Kessing, Lars Vedel
AU  - Bardram, Jakob Eyvind
PY  - 2018
DA  - 2018/06/28
TI  - Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study
JO  - JMIR Ment Health
SP  - e10122
VL  - 5
IS  - 2
KW  - activities
KW  - behavior
KW  - behavioral activation
KW  - bipolar disorder
KW  - circadian rhythm
KW  - depression
KW  - hourly planning
KW  - psychotherapy
KW  - statistics
AB  - Background: Behavioral activation is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance behavioral activation. Objective: The objectives of this paper were to (1) automatically identify behavioral patterns through statistical analysis of the paper-based activity diaries, and (2) determine whether it is feasible to move the behavioral activation therapy format to a digital solution. Methods: We collected activity diaries from seven patients with bipolar depression, covering in total 2,480 hours of self-reported activities. A pleasure score, on a 1-10 rating scale, was reported for each activity. The activities were digitalized into 6 activity categories, and statistical analyses were conducted. Results: Across all patients, movement-related activities were associated with the highest pleasure score followed by social activities. On an individual level, through a nonparametric Wilcoxon Signed-Rank test, one patient had a statistically significant larger amount of spare time activities when feeling bad (z=–2.045, P=.041). Through a within-subject analysis of covariance, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm (ρ=.265, P=.029). Furthermore, a second-order trend indicated that two hours of daily social activity was optimal for the patients (β2=–0.08, t (63)=–1.22, P=.23). Conclusions: The data-driven statistical approach was able to find patterns within the behavioral traits that could assist the therapist in as well as help design future technologies for behavioral activation. 
SN  - 2368-7959
UR  - http://mental.jmir.org/2018/2/e10122/
UR  - https://doi.org/10.2196/10122
UR  - http://www.ncbi.nlm.nih.gov/pubmed/29954726
DO  - 10.2196/10122
ID  - info:doi/10.2196/10122
ER  -