Clinical trial NCT05633186: Sustainable Upscaling of Depression Prevention
Official title: Sustainable Upscaling of Depression Prevention, Finding the Optimal Balance Between Investment and Benefit (SPRINT)
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Depression
Keywords: Upscaling
Depression prevention
Internet interventions
costs
implementation
Lead sponsor: VU University of Amsterdam
Sponsor class: OTHER
First posted: 2022-12-01
Enrollment: 307
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: FACTORIAL
Intervention model description: All participants receive access to the Moodbuster Life intervention with no or a maximum of 3 additional components. The investigators will use a full-factorial design to study the additional effect of each component either alone or in interaction. Participants are randomized to one of all possible combinations of the 3 components. In our study, with 3 additional components, this means that there are 8 conditions (2x2x2).
Primary purpose: PREVENTION

Brief summary: Research shows that online unguided self-help interventions focused on psycho-education, skills training and lifestyle can prevent mild mood complaints from turning into a full-blown depression. These encouraging results are found even though the adherence to these types of interventions is generally low.

With this project, the investigators examine whether effectiveness and adherence to online unguided self-help interventions can be increased by additional motivational guidance elements. This is examined by adding three additional components to the intervention: 1) A coach who provides online feedback once a week to provide support. 2) Mobile application to monitor mood and related factors and to receive automated personalized messages, 3) Content based on the principles of motivational interviewing. A secondary aim is to compare the additional effects of the individual components against the additional costs.

Detailed description: Given the substantial prevalence rate of Major Depression and its extreme burden among the general population, depression prevention is a high priority on the Dutch public health agenda. The aim of the Depression Prevention Program of the Dutch Ministry of Health, Welfare and Sport (Meerjarenprogramma (MJP, VWS 2017) entails a decrease in major depression prevalence of 30% by the year 2030. One solution to the problem is to offer online self-help interventions focusing on psycho-education, skills-training and lifestyle with the aim to improve mood. These interventions have proven to be effective and can prevent mood problems to sustain and/or worsen (van Zoonen et al., 2014). Self-help interventions are easily accessible and acceptable, and they can reach a population at low costs and on a large scale (Riper et al. 2010).

Still, while online self-help interventions can be effective (Karyotaki et al., 2017), engagement barriers exist, adherence rates are generally low, and integration into daily life routines is difficult to achieve (Karyotaki et al., 2015), which may jeopardize the potential population health impact of these interventions. From this perspective there is a clear optimization need of evidence-based online self-help interventions to increase their impact on the general population. One way to increase adherence and engagement, and subsequently the effectiveness of such interventions, is to administer the intervention with the help of (motivational) guidance elements. Guided interventions are known to increase adherence, engagement and effectiveness of interventions and can be operationalized in various ways (Mohr, Cuijpers \& Lehman, 2011; Kelders, 2017). Examples for types of guidance are human coaches, computerized coaches, chat support functions, personalized messages, and many more. While those motivational guidance elements can help the self-help interventions effectiveness, they come with higher costs as they need, for example, an infrastructure of therapists or coaches. It is therefore of high value to find the optimal balance between the effectiveness of the intervention and the necessary support components to establish a product with the potential to be implemented at scale.

The first objective of this study is to examine whether the effectiveness of an online self-help intervention ("Moodbuster Life") for adults who want to improve their mood can be optimized by three different motivational guidance components. The motivational components are: 1) A coach who provides online feedback once a week to provide support. 2) Mobile application to monitor mood and related factors and to receive automated personalized messages, 3) Content based on the principles of motivational interviewing.

Secondary aims are (1) to investigate whether adherence to the online self-help intervention can be improved by three different motivational components and (2) to compare the additional effects of one component against additional costs defined as extra time investment (in the platform and beyond) and financial costs (service costs, costs incurred by participants).

Eligibility criteria: Inclusion Criteria:

* Aged 18 years or older
* Mild to moderate depression as defined by a score between 5 and 15 on the Patient Health Questionnaire - 9 (PHQ-9)
* Adequate written proficiency in the Dutch language
* Have a valid email address and computer with internet access
* In possession of a smartphone

Exclusion Criteria:

* Current risk for suicide according to the PHQ-9 questionnaire (question 9, score of 1 or higher)
* Currently receiving psychological treatment for depression or another psychiatric disorder in primary or specialized mental health care
* Currently having a psychiatric disorder

Primary outcomes:
measure: Mood improvement
timeFrame: 6 weeks
Secondary outcomes:
measure: Adherence to the online self-help intervention
timeFrame: 5 weeks
measure: Anxiety Symptoms
timeFrame: 6 weeks
measure: Problem Solving Skills
timeFrame: 6 weeks
measure: Behavioral activation
timeFrame: 6 weeks
measure: Worrying
timeFrame: 6 weeks
measure: Physical Activity
timeFrame: 6 weeks
measure: Motivation for following the self-help intervention
timeFrame: 6 weeks
measure: Satisfaction with the self-help intervention
timeFrame: 6 weeks
measure: Intervention engagement
timeFrame: 6 weeks
measure: Technical Alliance
timeFrame: 6 weeks
Other outcomes:
measure: Costs for each component
timeFrame: 5 weeks
measure: Time investment
timeFrame: 6 weeks
Locations:
city: Amsterdam
country: Netherlands
facility: Vrije Universiteit
Study officials:
affiliation: Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit, 1081BT Amsterdam, The Netherlands
name: Heleen Riper, Prof.dr.
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit, 1081BT Amsterdam, The Netherlands
name: Annet Kleiboer, dr.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Buntrock C, Ebert DD, Lehr D, Smit F, Riper H, Berking M, Cuijpers P. Effect of a Web-Based Guided Self-help Intervention for Prevention of Major Depression in Adults With Subthreshold Depression: A Randomized Clinical Trial. JAMA. 2016 May 3;315(17):1854-63. doi: 10.1001/jama.2016.4326.
citation: Simon GE, VonKorff M, Rutter C, Wagner E. Randomised trial of monitoring, feedback, and management of care by telephone to improve treatment of depression in primary care. BMJ. 2000 Feb 26;320(7234):550-4. doi: 10.1136/bmj.320.7234.550.
citation: Batterham PJ, Calear AL. Preferences for Internet-Based Mental Health Interventions in an Adult Online Sample: Findings From an Online Community Survey. JMIR Ment Health. 2017 Jun 30;4(2):e26. doi: 10.2196/mental.7722.
citation: Buntrock C, Ebert D, Lehr D, Riper H, Smit F, Cuijpers P, Berking M. Effectiveness of a web-based cognitive behavioural intervention for subthreshold depression: pragmatic randomised controlled trial. Psychother Psychosom. 2015;84(6):348-58. doi: 10.1159/000438673. Epub 2015 Sep 24.
citation: Hassouni, A. E., Hoogendoorn, M., van Otterlo, M., Eiben, A. E., Muhonen, V., & Barbaro, E. (2018). A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions. arXiv preprint arXiv:1804.03592.
citation: Karyotaki E, Kleiboer A, Smit F, Turner DT, Pastor AM, Andersson G, Berger T, Botella C, Breton JM, Carlbring P, Christensen H, de Graaf E, Griffiths K, Donker T, Farrer L, Huibers MJ, Lenndin J, Mackinnon A, Meyer B, Moritz S, Riper H, Spek V, Vernmark K, Cuijpers P. Predictors of treatment dropout in self-guided web-based interventions for depression: an 'individual patient data' meta-analysis. Psychol Med. 2015 Oct;45(13):2717-26. doi: 10.1017/S0033291715000665. Epub 2015 Apr 17.
citation: Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, Mackinnon A, Meyer B, Botella C, Littlewood E, Andersson G, Christensen H, Klein JP, Schroder J, Breton-Lopez J, Scheider J, Griffiths K, Farrer L, Huibers MJ, Phillips R, Gilbody S, Moritz S, Berger T, Pop V, Spek V, Cuijpers P. Efficacy of Self-guided Internet-Based Cognitive Behavioral Therapy in the Treatment of Depressive Symptoms: A Meta-analysis of Individual Participant Data. JAMA Psychiatry. 2017 Apr 1;74(4):351-359. doi: 10.1001/jamapsychiatry.2017.0044.
citation: Kelders, S. M. (2015, June). Involvement as a working mechanism for persuasive technology. In International Conference on Persuasive Technology (pp. 3-14). Springer, Cham.
citation: Kranzler HR, McKay JR. Personalized treatment of alcohol dependence. Curr Psychiatry Rep. 2012 Oct;14(5):486-93. doi: 10.1007/s11920-012-0296-5.
citation: Mohr DC, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011 Mar 10;13(1):e30. doi: 10.2196/jmir.1602.
citation: Riper H, Andersson G, Christensen H, Cuijpers P, Lange A, Eysenbach G. Theme issue on e-mental health: a growing field in internet research. J Med Internet Res. 2010 Dec 19;12(5):e74. doi: 10.2196/jmir.1713.
citation: van Zoonen K, Buntrock C, Ebert DD, Smit F, Reynolds CF 3rd, Beekman AT, Cuijpers P. Preventing the onset of major depressive disorder: a meta-analytic review of psychological interventions. Int J Epidemiol. 2014 Apr;43(2):318-29. doi: 10.1093/ije/dyt175.
citation: Warmerdam L, Riper H, Klein M, van den Ven P, Rocha A, Ricardo Henriques M, Tousset E, Silva H, Andersson G, Cuijpers P. Innovative ICT solutions to improve treatment outcomes for depression: the ICT4Depression project. Stud Health Technol Inform. 2012;181:339-43.