Designing and Evaluating an Asynchronous Remote Communication Approach to Behavioral Activation With Clinicians and Adolescents At Risk for Depression
Phase: NA
Conditions: Depression
Sponsor: University of Washington

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Adapting Behavioral Activation to Technology Platform 
 
NCT03783533 
 
7/22/2022 
 
 
IRB Renewed February 14, 2022

Specific Aims 
An estimated 3.1 million adolescents are diagnosed with depression (MDD) each year 
(SAMHSA, 2016), and adolescent onset MDD is associated with chronic physical, mental and 
psychosocial disability (Birmaher et al., 1996). However, over 60% of adolescents with MDD do 
not receive mental health care, and, among those who do, treatment engagement is low 
(SAMHSA, 2016; Olfson et al., 2003). Behavioral Activation (BA) is an evidence-based 
psychosocial intervention (EBPI) for individuals with MDD (Dimidjian et al., 2006). While BA 
holds promise as an effective treatment with adolescents (McCauley et al., 2015, 2016), 
previous research approaches have found that adolescents may be better reached and 
engaged through social media, mobile technologies, and other technology platforms (Boyd, 
2007; Park & Calamaro, 2013). In addition, BA requires frequent interaction from patients over 
time, which can be difficult and costly for clinicians to administer directly. Thus, there is an 
opportunity to improve usability and engagement of EBPIs via new technology-based tools. 
Asynchronous Remote Communities (ARC) is a promising technology-based approach for 
engaging adolescents that capitalizes on the reach of technology while also providing support, 
social interactions, and motivation to engage. ARCs are technology-mediated groups that use 
private online platforms to deliver weekly tasks to participants and gather information about 
perceptions in a format that is lightweight, accessible, usable, and low burden. We aim to test 
the feasibility of an BA+ARC approach in a small feasibility study with adolescents experiencing 
mild to moderate depression. We propose the following specific aims: 
 
Study Aim: Test feasibility and usability with small pilot groups of adolescent and 
clinician target users: Once we have a robust enough prototype of the ARC delivery platform 
for BA, we will conduct a small pilot study with adolescents at-risk for depression and clinicians 
to assess the feasibility and usability of the approach. We will collect data on the feasibility, 
usability, user burden, acceptability, and symptom outcomes. 
 
This research supports the mission of the UW ALACRITY Center by combining expertise in 
psychology and human-centered computing to improve the usability and accessibility of an EPBI 
for a high need population.  
 
Study Protocol 
We aim to test the feasibility of our approach in a small feasibility and usability study with 
clinician and adolescent patient target users. 
 
Participant Recruitment: Adolescents with PHQ-9 scores between 5 and 20 (Mild to Moderately 
Severe Range) who do not report current suicidality (Pine et al., 1999) will be recruited from 
clinician target users’ practice settings. Dr. Jenness has formed recruitment relationships with 
primary care clinics, Seattle Children’s Hospital clinics (e.g., Adolescent Medicine) and school 
health centers. Dr. McCauley is core faculty at the School Mental Health Assessment, 
Research, & Training (SMART) center with contacts at school-based mental health centers in 
Seattle 
 
Testing the Feasibility of the ARC method for Delivering a Behavioral Activation Intervention 
We will test the feasibility of our ARC BA approach previously in a short pilot study with a group 
of 10 clinicians and 10 adolescents at risk for depression (see Bhattacharya et al., 2021). This 
pilot study will involve recruiting a small set of participants and running a second ARC to deliver 
the components of the BA approach using the Slack platform. The primary goal of this pilot will 
be to understand the patient burden and acceptability of the approach. To assess the usability, 
feasibility, and burden of the ARC approach, we will administer the recommended DDBT 

measures including the User Burden Scale (Suh et al., 2016) (developed by Co-PI Kientz) with 
both clinician and adolescent participants and the System Usability Scale with adolescent 
participants (Brooke, 1996). We will also administer all three subscales of the Acceptability of 
Intervention Measure, Intervention Appropriateness Measure, and Feasibility of Intervention 
Measure to assess acceptability of the BA intervention itself (Weiner et al., 2017). Although 
assessing patient outcomes is not the primary goal of this pilot, we will administer the PHQ-9 
(Richardson et al., 2010) pre- and post-study to gather preliminary data indicating improved 
outcomes. To assess engagement with the platform, we will use the built-in analytics tools for 
the Slack application to identify the number of logins and posts per day per participant. We will 
also conduct semi-structured interviews with participants at the end of the ARC to understand 
more details on the acceptability of the approach and to understand which specific aspects and 
features of it were acceptable and get ideas for how the approach can be improved further. As 
this is a pilot analysis, we will examine outcome effect sizes and percentile cut-offs to determine 
acceptability, usability, feasibility of the ARC approach.  
 
Statistical Analysis Plan 
 
For qualitative interviews, the coders first read and inductively coded a part of the data (four 
teen-interview transcripts) line by line and wrote their codes in a cumulative document. The 
coders then met for weekly discussions to prepare a codebook. We distributed the weekly Slack 
data and interview transcripts between three coders and the remainder of the data was coded 
using the final codebook (Appendix C). All coders read and shared memos with each other and 
met for discussions to resolve discrepancies in coding. We conducted affinity modelling 
(Holtzblatt et al., 2004), an open-ended process by which researchers iteratively discuss and 
cluster data, to identify emergent themes. Three main themes emerged from our analysis. The 
research team further iterated on these themes through discussion and while writing. We 
summarize the results focusing on the insights we gained for iterating on our design below and 
a detailed empirical analysis can be found in (Bhattacharya, 2020, Chapter 5). 
 
To provide descriptive characterization of our sample, we calculated average scores of PHQ-8 
pre- and post-study. We calculated an average score for each item of the Acceptability, 
Intervention Appropriateness, and Feasibility of Intervention Measure for the clinician group and 
teen group, respectively. For the User Burden Scale (Suh et al., 2016), we computed average 
scores of teens and clinician groups separately across each of the 6 constructs – physical, 
mental & emotional, time & social, financial, difficulty of use, and privacy. In the IUS, items are 
rated on a Likert scale from 0 (strongly disagree) to 4 (strongly agree), with half of the items 
reverse-scored (Lyon, Pullman, et al., 2020). A total score is normally calculated by multiplying 
the sum of these scores by 2.78 (range: 0-100). Drawing parallels from scoring the system 
usability scale (SUS), above a 68 would be considered above average and anything below 68 is 
below average (Lewis, 2018). 
 
 
References Cited 
 
Avenevoli, S., Swendsen, J., He, J.-P., Burstein, M., & Merikangas, K. R. (2015). Major depression in 
the National Comorbidity Survey–Adolescent Supplement: prevalence, correlates, and 
treatment. Journal of the American Academy of Child & Adolescent Psychiatry, 54(1), 37–44. 
 
Bhattacharya, A., Nagar, R., Jenness, J., Munson, S. A., & Kientz, J. A. (2021). Designing 
Asynchronous Remote Support for Behavioral Activation in Teenagers With Depression: 
Formative Study. JMIR Formative Research, 5(7), e20969. 
 

Birmaher, B., Ryan, N. D., Williamson, D. E., Brent, D. A., & Kaufman, J. (1996). Childhood and 
adolescent depression: a review of the past 10 years. Part II. Journal of the American Academy 
of Child & Adolescent Psychiatry, 35(12), 1575–1583. 
 
Boyd, D. (2007). Why youth (heart) social network sites: The role of networked publics in teenage 
social life. MacArthur foundation series on digital learning–Youth, identity, and digital media 
volume, 119-142. 
 
Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189(194), 4-7. 
 
Chu, B. C., Colognori, D., Weissman, A. S., & Bannon, K. (2009). An Initial Description and Pilot of 
Group Behavioral Activation Therapy for Anxious and Depressed Youth. Cognitive and 
Behavioral Practice, 16(4), 408–419. https://doi.org/10.1016/j.cbpra.2009.04.003 
 
Cuijpers, P., van Straten, A., & Warmerdam, L. (2007). Behavioral activation treatments of depression: 
A meta-analysis. Clinical Psychology Review, 27(3), 318–326. 
https://doi.org/10.1016/j.cpr.2006.11.001 
 
Dimidjian, S., Barrera Jr, M., Martell, C., Muñoz, R. F., & Lewinsohn, P. M. (2011). The origins and 
current status of behavioral activation treatments for depression. Annual Review of Clinical 
Psychology, 7, 1–38. 
 
Dimidjian, S., Hollon, S. D., Dobson, K. S., Schmaling, K. B., Kohlenberg, R. J., Addis, M. E., … Gollan, 
J. K. (2006). Randomized trial of behavioral activation, cognitive therapy, and antidepressant 
medication in the acute treatment of adults with major depression. Journal of Consulting and 
Clinical Psychology, 74(4), 658. 
 
Epstein, D. A., Cordeiro, F., Fogarty, J., Hsieh, G., & Munson, S. A. (2016, May). Crumbs: lightweight 
daily food challenges to promote engagement and mindfulness. In Proceedings of the 2016 CHI 
Conference on Human Factors in Computing Systems (pp. 5632-5644). ACM. 
 
Fixsen, D. L., Blase, K. A., Naoom, S. F., Van Dyke, M., & Wallace, F. (2009). Implementation: The 
missing link between research and practice. NIRN implementation brief, 1, 218-227. 
 
Fritz, T., Huang, E. M., Murphy, G. C., & Zimmermann, T. (2014, April). Persuasive technology in the 
real world: a study of long-term use of activity sensing devices for fitness. In Proceedings of the 
SIGCHI Conference on Human Factors in Computing Systems (pp. 487-496). ACM. 
 
Huh, J., McDonald, D. W., Hartzler, A., & Pratt, W. (2013). Patient moderator interaction in online health 
communities. In AMIA Annual Symposium Proceedings (Vol. 2013, p. 627). American Medical 
Informatics Association. 
 
McCauley, E., Gudmundsen, G., Schloredt, K., Martell, C., Rhew, I., Hubley, S., & Dimidjian, S. (2015). 
The Adolescent Behavioral Activation Program: Adapting Behavioral Activation as a Treatment 
for Depression in Adolescence. Journal of Clinical Child & Adolescent Psychology. 
https://doi.org/10.1080/15374416.2014.979933 
 
McCauley, E., Gudmundsen, G., Schloredt, K., Martell, C., Rhew, I., Hubley, S., & Dimidjian, S. (2016). 
The adolescent behavioral activation program: Adapting behavioral activation as a treatment for 
depression in adolescence. Journal of Clinical Child & Adolescent Psychology, 45(3), 291–304. 

 
MacLeod, H., Oakes, K., Geisler, D., Connelly, K., & Siek, K. (2015, April). Rare world: Towards 
technology for rare diseases. In Proceedings of the 33rd Annual ACM Conference on human 
factors in computing systems (pp. 1145-1154). ACM. 
 
Mental Health Services Administration (SAMHSA).(2016). Mental and substance use disorders. 
 
Mojtabai, R., Olfson, M., & Han, B. (2016). National trends in the prevalence and treatment of 
depression in adolescents and young adults. Pediatrics, e20161878. 
 
Olfson, M., Gameroff, M. J., Marcus, S. C., & Waslick, B. D. (2003). Outpatient treatment of child and 
adolescent depression in the United States. Archives of General Psychiatry, 60(12), 1236–1242. 
 
Park, B. K., & Calamaro, C. (2013). A systematic review of social networking sites: Innovative platforms 
for health research targeting adolescents and young adults. Journal of Nursing Scholarship, 
45(3), 256-264. 
 
Pine, D. S., Cohen, E., Cohen, P., & Brook, J. (1999). Adolescent depressive symptoms as predictors 
of adult depression: moodiness or mood disorder? American Journal of Psychiatry, 156(1), 133–
135. 
 
Richardson, L. P., McCauley, E., Grossman, D. C., McCarty, C. A., Richards, J., Russo, J. E., … Katon, 
W. (2010). Evaluation of the Patient Health Questionnaire-9 Item for detecting major depression 
among adolescents. Pediatrics, 126(6), 1117–1123. 
 
Ritschel, L. A., Ramirez, C. L., Jones, M., & Craighead, W. E. (2011). Behavioral activation for 
depressed teens: A pilot study. Cognitive and Behavioral Practice, 18(2), 281–299. 
 
Simon, G. E., & Ludman, E. J. (2010). Predictors of early dropout from psychotherapy for depression in 
community practice. Psychiatric Services, 61(7), 684–689. 
 
Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing 
generalized anxiety disorder: the GAD-7. Archives of internal medicine, 166(10), 1092-1097. 
 
Suh, H., Shahriaree, N., Hekler, E. B., & Kientz, J. A. (2016, May). Developing and validating the user 
burden scale: A tool for assessing user burden in computing systems. In Proceedings of the 
2016 CHI conference on human factors in computing systems (pp. 3988-3999). ACM. 
 
Tatara, N., Årsand, E., Skrøvseth, S. O., & Hartvigsen, G. (2013). Long-term engagement with a mobile 
self-management system for people with type 2 diabetes. JMIR mHealth and uHealth, 1(1). 
 
Weiner, B. J., Lewis, C. C., Stanick, C., Powell, B. J., Dorsey, C. N., Clary, A. S., ... & Halko, H. (2017). 
Psychometric assessment of three newly developed implementation outcome measures. 
Implementation Science, 12(1), 108.