Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT

Robberegt, Suzanne J.; Kooiman, Bas E. A. M.; Albers, Casper; Nauta, Maaike; Bockting, Claudi; Stikkelbroek, Yvonne

doi:10.1136/bmjopen-2021-058560

Cited by 5 publications

(8 citation statements)

References 126 publications

(180 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Facilitating a central role for the patient will be crucial, and this may be facilitated by tools such as phone app-based self-monitoring combined with guided app-based personalized intervention modules. 31 The concepts we reviewed may help make good use of such new technological possibilities. Dynamic indicators of resilience may be computed from self-collected time series, while analysis of monitored symptom networks may point to feedbacks that can be targeted to strengthen resilience of the healthy state while reducing vulnerability.…”

Section: Discussionmentioning

confidence: 99%

“…This sounds perhaps unfeasible, but it is not. Facilitating a central role for the patient will be crucial, and this may be facilitated by tools such as phone app–based self-monitoring combined with guided app-based personalized intervention modules . The concepts we reviewed may help make good use of such new technological possibilities.…”

Section: Discussionmentioning

confidence: 99%

“…Bolstering resilience of the healthy state may be done by training specific skills and habits, while the probability of escaping from a particular disorder can be increased by targeting the particular beliefs and behaviors involved in the self-reinforcing feedbacks that stabilize the disorder in a given patient. 31 Obviously, the most promising feedbacks for individualized targeted intervention should depend on how things work in a person. Such personalized webs of causality may be inferred in 2 ways (Figure 3).…”

Section: Planning Interventionmentioning

confidence: 99%

“…The dynamical systems toolbox may also help detect individualized targets for interventions aimed at strengthening resilience of the healthy state or eroding resilience of a specific disorder. Bolstering resilience of the healthy state may be done by training specific skills and habits, while the probability of escaping from a particular disorder can be increased by targeting the particular beliefs and behaviors involved in the self-reinforcing feedbacks that stabilize the disorder in a given patient . Obviously, the most promising feedbacks for individualized targeted intervention should depend on how things work in a person.…”

Section: Planning Interventionmentioning

confidence: 99%

See 3 more Smart Citations

A Dynamical Systems View of Psychiatric Disorders—Practical Implications

Scheffer,

Bockting,

Borsboom

et al. 2024

JAMA Psychiatry

View full text Add to dashboard Cite

ImportanceDynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry.ObservationsEmerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions.Conclusions and RelevanceThose observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Planning Interventionmentioning

confidence: 99%

Section: Planning Interventionmentioning

confidence: 99%

See 2 more Smart Citations

A Dynamical Systems View of Psychiatric Disorders—Practical Implications

Scheffer,

Bockting,

Borsboom

et al. 2024

JAMA Psychiatry

View full text Add to dashboard Cite

show abstract

“…This study describes and critically evaluates the congruency of a novel multimodal data-driven personalization approach in the context of StayFine, a modular guided app-based relapse prevention intervention for 13-to 21-year-old participants in remission of anxiety and/or depressive disorders (41). During StayFine, participants complete six modules.…”

Section: Introductionmentioning

confidence: 99%

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Kooiman,

Robberegt,

Albers

et al. 2023

Front. Psychiatry

View full text Add to dashboard Cite

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1–3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

show abstract

An individual participant data meta-analysis of psychological interventions for preventing depression relapse

Breedvelt,

Karyotaki,

Warren

et al. 2024

Nat. Mental Health

View full text Add to dashboard Cite

Major depressive disorder is a leading cause of disability worldwide; identifying effective strategies to prevent depression relapse is crucial. This individual participant data meta-analysis addresses whether and for whom psychological interventions can be recommended for relapse prevention of major depressive disorder. One- and two-stage individual patient data meta-analyses were conducted on 14 randomized controlled trials (N = 1,720). The relapse risk over 12 months was substantially lower for those who received a psychological intervention versus treatment as usual, antidepressant medication, or evaluation-only control (hazard ratio, 0.60; 95% confidence interval, 0.48–0.74). The number of previous depression episodes moderated the treatment effect, with psychological interventions demonstrating greater efficacy for patients with three or more previous episodes. Our results suggest that adding psychological interventions to current treatment to prevent depression relapse is recommended. For patients at lower risk of relapse, less-intensive approaches may be indicated.

show abstract

Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT

Cited by 5 publications

References 126 publications

A Dynamical Systems View of Psychiatric Disorders—Practical Implications

A Dynamical Systems View of Psychiatric Disorders—Practical Implications

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

An individual participant data meta-analysis of psychological interventions for preventing depression relapse

Contact Info

Product

Resources

About