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

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression : Mixed-Methods Study. / Rohani, Darius Adam; Tuxen, Nanna; Quemada Lopategui, Andrea; Kessing, Lars Vedel; Bardram, Jakob Eyvind.

In: J M I R Mental Health, Vol. 5, No. 2, e10122, 2018, p. 1-12.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Rohani, DA, Tuxen, N, Quemada Lopategui, A, Kessing, LV & Bardram, JE 2018, 'Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study', J M I R Mental Health, vol. 5, no. 2, e10122, pp. 1-12. https://doi.org/10.2196/10122

APA

Rohani, D. A., Tuxen, N., Quemada Lopategui, A., Kessing, L. V., & Bardram, J. E. (2018). Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study. J M I R Mental Health, 5(2), 1-12. [e10122]. https://doi.org/10.2196/10122

Vancouver

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. J M I R Mental Health. 2018;5(2):1-12. e10122. https://doi.org/10.2196/10122

Author

Rohani, Darius Adam ; Tuxen, Nanna ; Quemada Lopategui, Andrea ; Kessing, Lars Vedel ; Bardram, Jakob Eyvind. / Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression : Mixed-Methods Study. In: J M I R Mental Health. 2018 ; Vol. 5, No. 2. pp. 1-12.

Bibtex

@article{53448e06ffdc44df81134785919fd02f,
title = "Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study",
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.",
author = "Rohani, {Darius Adam} and Nanna Tuxen and {Quemada Lopategui}, Andrea and Kessing, {Lars Vedel} and Bardram, {Jakob Eyvind}",
year = "2018",
doi = "10.2196/10122",
language = "English",
volume = "5",
pages = "1--12",
journal = "J M I R Mental Health",
issn = "2368-7959",
publisher = "J M I R Publications, Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression

T2 - Mixed-Methods Study

AU - Rohani, Darius Adam

AU - Tuxen, Nanna

AU - Quemada Lopategui, Andrea

AU - Kessing, Lars Vedel

AU - Bardram, Jakob Eyvind

PY - 2018

Y1 - 2018

N2 - 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.

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.

U2 - 10.2196/10122

DO - 10.2196/10122

M3 - Journal article

VL - 5

SP - 1

EP - 12

JO - J M I R Mental Health

JF - J M I R Mental Health

SN - 2368-7959

IS - 2

M1 - e10122

ER -

ID: 215922485