Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Currently accepted at: JMIR Mental Health

Date Submitted: Feb 14, 2018
Open Peer Review Period: Feb 15, 2018 - May 29, 2018
Date Accepted: May 29, 2018
(closed for review but you can still tweet)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/10122

The final accepted version (not copyedited yet) is in this tab.

The final, peer-reviewed published version of this preprint can be found here:

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

Rohani DA, Tuxen N, Quemada Lopategui A, Kessing LV, Bardram JE

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

JMIR Ment Health 2018;5(2):e10122

DOI: 10.2196/10122

PMID: 29954726

PMCID: 6043733

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

  • Darius Adam Rohani; 
  • Nanna Tuxen; 
  • Andrea Quemada Lopategui; 
  • Lars Vedel Kessing; 
  • Jakob Eyvind Bardram

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 (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.


 Citation

Please cite as:

Rohani DA, Tuxen N, Quemada Lopategui A, Kessing LV, Bardram JE

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

JMIR Mental Health. (forthcoming/in press)

DOI: 10.2196/10122

URL: https://preprints.jmir.org/preprint/10122

PMID: 29954726

PMCID: 6043733

Per the author's request the PDF is not available.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.