@Article{info:doi/10.2196/10122,
author="Rohani, Darius Adam
and Tuxen, Nanna
and Quemada Lopategui, Andrea
and Kessing, Lars Vedel
and Bardram, Jakob Eyvind",
title="Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study",
journal="JMIR Ment Health",
year="2018",
month="Jun",
day="28",
volume="5",
number="2",
pages="e10122",
keywords="activities; behavior; behavioral activation; bipolar disorder; circadian rhythm; depression; hourly planning; psychotherapy; statistics",
abstract="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 ($\rho$=.265, P=.029). Furthermore, a second-order trend indicated that two hours of daily social activity was optimal for the patients ($\beta$2=--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. ",
issn="2368-7959",
doi="10.2196/10122",
url="http://mental.jmir.org/2018/2/e10122/",
url="https://doi.org/10.2196/10122",
url="http://www.ncbi.nlm.nih.gov/pubmed/29954726"
}