Adaptive Smartphone-based Behavioural Activation for Treating Depression

Background

Of all registered mental diseases in Europe, depression has the highest (and growing) prevalence of 6.9% [1]. The high incidence imposes significant adverse consequences, including increased mortality, and lower well-being [2]. Despite the overall concern, the current treatment plan is still the same throughout time [3] with pharmacotherapy as the most used intervention [4]. This is of concern since pharmacotherapy is associated with severe side effects [5]. The limited number of clinical personnel in a growing patient group demands an entirely different approach.
To address this, several research groups have developed a mobile application for depressed behavior. S. Watts et al. [6] created the “get happy program” app where weekly Cognitive Behavioral therapy (CBT) lessons were delivered, involving a fictive female depressed character and how she was dealing with her symptoms. CACHET developed the MONARCA system [7]. It is a personal monitoring system, which uses the patient’s phone to collect activity data (e.g., GPS, accelerometer, phone calls), and self-assessment data (e.g., mood, sleep, level of activity). The data is presented on a web-portal for the clinical personnel to monitor the progress, as well as on the phone to self-educate the patients.

Project Objectives

This Ph.D. project aims to develop the next generation of a mobile application for depressed behavior that provides personalized recommendations to the user based on prior monitored activities and mood.
To achieve such an application we performed a workshop with patients to create a smartphone application that investigates how depression and behavior relates. The result was named Moribus (Latin for behaviour) with screenshots shown in the figure below. [8-9].

The study is still active and can be found here.
The findings from Moribus, together with several user-centered design workshops, will yield the final version named: MUBS. It will be designed as a recommender system to elicit behavioral change in the subject, and hopefully, prevent the user entering a depressive state. The first workshop has already been conducted, and some low-fi sketches have been developed, as seen in the figure below.

Below is a video of the project being pitched at DTU Compute.

References

1. Wittchen, Hans-Ulrich, and Frank Jacobi. "Size and burden of mental disorders in Europe—a critical review and appraisal of 27 studies." European Neuropsychopharmacology 15.4 (2005): 357-376.
2. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. Hay PJ, editor. PLoS Med. 2013;10(11):e1001547. PMID: 24223526
3. Gautam S, Jain A, Gautam M, Vahia VN, Grover S. Clinical Practice Guidelines for the management of Depression. Indian J Psychiatry. Wolters Kluwer -- Medknow Publications; 2017;59(Suppl 1): S34–S50. PMID: 28216784
4. Robinson WD, Geske JA, Prest LA, Barnacle R. Depression treatment in primary care. J Am Board Fam Pr. American Board of Family Medicine; 2005;18(2):79–86. PMID: 15798136
5. Khawam EA, Laurencic G, Malone DA. Side effects of antidepressants: An overview [Internet]. Cleve Clin J Med. 2006. p. 351–361. PMID: 16610395
6. Watts, Sarah, et al. "CBT for depression: a pilot RCT comparing mobile phone vs. computer." BMC Psychiatry 13.1 (2013): 1.
7. Bardram, Jakob E., et al. "The MONARCA self-assessment system: a persuasive personal monitoring system for bipolar patients." Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. ACM, 2012.
8. Rohani DA, Tuxen N, Kessing L V, Bardram JE. Designing for Hourly Activity Sampling in Behavioral Activation. In: Proceedings of the 11th International Conference on Pervasive Computing Technologies for Healthcare. ; 2017:1162-1170.
9. Bardram JE, Rohani DA, Tuxen N, Faurholt-jepsen M, Kessing L V. Supporting Smartphone- based Behavioral Activation : A Simulation Study. UbiComp ’17 Proc 2017 ACM Int Jt Conf Pervasive Ubiquitous Comput Proc 2017 ACM Int Symp Wearable Comput. 2017:830-843. doi:10.1145/3123024.3125617.