Hybrid Experimental Designs for Intervention Development: What, Why, and How

Abstract

Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)—a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).

Optimizing adaptive stepped-care interventions to change adults’ health behaviors: A systematic review

“…This factor could have two levels (yes/no) and groups in the “yes” level would add a coach for nonresponders at the midpoint, while groups in the “no” level would not. Additionally, there are emerging methods for hybrid experimental designs – combining stepped care adaptations with dynamic adaptations for interventions that adapt at multiple timescales [11]. As health behavior intervention science progresses, we expect that more designs will be used to optimize interventions in various ways.…”

“…Aspects of a decision rule to be tested could also include when to tailor the intervention and/or what type of treatment or modality of treatment delivery should be used in different cases. Trials of adaptive interventions can test the efficacy of multiple embedded interventions [11]. They often include options for further exploratory analyses beyond the main research questions, though, similar to other experimental designs, power may be a concern due to small sample sizes in some cells (e.g., when testing moderation of treatment effects).…”

A pilot multiple‐baseline study of a mobile cognitive behavioral therapy for the treatment of eating disorders in university students

“…Research has shown that stepped-care models for students with EDs, as compared to traditional approaches, may result in modest cost reductions and less need for in-person therapy (Kass et al, 2017). In addition, research using multiphase optimization strategy, sequential multiple assignment randomized trial, or hybrid experimental designs could help identify intervention components, tailoring and decision variables, and best practices for integrating human-delivered and digitaldelivered components (Collins et al, 2007;Nahum-Shani et al, 2022).…”

“…Research has shown that stepped‐care models for students with EDs, as compared to traditional approaches, may result in modest cost reductions and less need for in‐person therapy (Kass et al, 2017). In addition, research using multiphase optimization strategy, sequential multiple assignment randomized trial, or hybrid experimental designs could help identify intervention components, tailoring and decision variables, and best practices for integrating human‐delivered and digital‐delivered components (Collins et al, 2007; Nahum‐Shani et al, 2022). Areas of interest would include using early rapid response to identify who may be most likely to need more intensive treatment, refining and streamlining intervention modules, tailoring the use of therapist sessions, and personalizing the digital intervention to individual clients.…”

Mobile Interventions: An Untapped Potential for Education

“…This can be applied to mobile intervention research as well by asking which student characteristics explain the heterogeneity of causal effects revealed by microrandomized trials and personalizing the intervention accordingly. In mHealth research, hybrid experimental designs have been developed that can help identify which students respond strongly to the intervention and which do not, and subsequently adapt the intervention (for an overview, see Nahum-Shani et al, 2022). For instance, students who do not respond desirably to arousalreducing prompts would be switched to arousal-increasing prompts and vice versa.…”