Exploring personalized psychotherapy for depression: A system dynamics approach

The need for systems thinking to advance Alzheimer's disease research

Despite extensive research efforts to mechanistically understand late-onset Alzheimer's disease (LOAD) and other complex mental health disorders, curative treatments remain elusive. We emphasize the multiscale multicausality inherent to LOAD, highlighting the interplay between interconnected pathophysiological processes and risk factors. Systems thinking methods, such as causal loop diagrams and systems dynamic models, offer powerful means to capture and study this complexity. Recent studies developed and validated a causal loop diagram and system dynamics model using multiple longitudinal data sets, enabling the simulation of personalized interventions on various modifiable risk factors in LOAD. The results indicate that targeting factors like sleep disturbance and depressive symptoms could be promising and yield synergistic benefits. Furthermore, personalized interventions showed significant potential, with top-ranked intervention strategies differing significantly across individuals. We argue that systems thinking approaches can open new prospects for multifactorial precision medicine. In future research, systems thinking may also guide structured, model-driven data collection on the multiple interactions in LOAD's complex multicausality, facilitating theory development and possibly resulting in effective prevention and treatment options.

Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics

Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.

Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art

Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.