Supporting smartphone-based behavioral activation

Bardram, Jakob E.; Rohani, Darius Adam; Tuxen, Nanna; Faurholt‐Jepsen, Maria; Kessing, Lars Vedel

doi:10.1145/3123024.3125617

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…The ACs consist of 7 distinct classes to cover all types of activities and have been previously presented [ 15 , 16 ]. They were developed in close collaboration with a psychologist by the approach presented in Mørch and Rosenberg and Lejuez et al [ 20 , 21 ] combined with the primary activities introduced in the American Time Use Survey (ATUS) by the Bureau of Labor Statistics [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

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

Rohani¹,

Tuxen²,

Lopategui³

et al. 2018

JMIR Ment Health

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BackgroundBehavioral 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.ObjectiveThe 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.MethodsWe 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.ResultsAcross 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).ConclusionsThe 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.

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Section: Methodsmentioning

confidence: 99%

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

Rohani¹,

Tuxen²,

Lopategui³

et al. 2018

JMIR Ment Health

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“…Self-tracking technologies are systems that make use of personal data collection about the sufferers. A majority (19) of papers present novel designs [11,13,17,59,60,60,61,75,77,81,88,89,110,115,120,130,157,163,179], whereas nine [8,15,56,58,90,111,117,148,159] are concerned with methodologies for developing self-tracking technologies attending to the specificities of affective disorders.…”

Section: Analysis Of Reviewed Work System Functionsmentioning

confidence: 99%

“…Regarding reasons for collecting data and sharing with others, seven systems [13,17,60,61,110,115,163] are aimed at encouraging the patient to adhere to a particular course of treatment, a medication prescription or a set of healthy routines, such as exercising. These are often crucial components of any therapy for affective disorders.…”

Section: Analysis Of Reviewed Work System Functionsmentioning

confidence: 99%

HCI and Affective Health

Sanches

Janson

Karpashevich

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

151

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In the last decade, the number of articles on HCI and health has increased dramatically. We extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of SIGCHI conference proceedings. 72 of these were published in the last two years. A systematic analysis of this growing body of literature revealed that most innovation happens in automated diagnosis, and self-tracking, although there are innovative ideas in tangible interfaces. We noted an overemphasis on data production without consideration of how it leads to fruitful interventions. Moreover, we see a need to promote ethical practices for involvement of people living with affective disorders. Finally, although only 16 studies evaluate technologies in a clinical context, several forms of support and intervention illustrate how rich insights are gained from evaluations with real patients. Our findings highlight potential for growth in the design space of affective health technologies.

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“…The inter-categorization agreement was assessed by Cohen's kappa ( ). к The ACs consist of six (+1: 'Other') distinct classes to cover all types of activities and have been previously presented [13,14]. They were developed in close collaboration with a psychologist on the basis of the approach presented in Mørch & Rosenberg and Lejuez et al [18,19] 1.…”

Section: Participants and Proceduresmentioning

confidence: 99%

“…Recent research has suggested using mobile technology to support BA (e.g., [13][14][15]). This can have several potential benefits.…”

Section: Introductionmentioning

confidence: 99%

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

Rohani¹,

Tuxen²,

Lopategui³

et al. 2018

Preprint

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Background: Behavioral Activation (BA) 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 BA. Objective: The objectives of this paper were to 1) automatically identify behavioral patterns through statistical analysis of the paper-based activity schedules, and 2) determine whether it is feasible to move the BA therapy format to a digital solution. Methods: We collected activity schedules from seven patients, 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 seven 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 non-parametric 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 ANCOVA, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm ( = .265, p = .0288). Furthermore, a second-order trend indicated that ρ two hours of daily social activity was the optimal for the patients (β 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 help to assist, the therapist in a future application.

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Supporting smartphone-based behavioral activation

Cited by 8 publications

References 12 publications

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

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

HCI and Affective Health

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

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