Agile Science: Behavior Change
We started to develop agile science for behavioral interventions. As such, we have the most concrete specification for supporting behavioral interventions.
The agile science process for behavior change focuses on creating useful and usable behavior change interventions and corresponding usable evidence for making decisions. It borrows from agile software development, user-centered design, lean start-up, and data science and modeling techniques for simulating complexity.
The process starts with creating many variations of plausibly useful behavior change interventions for a “niche,” i.e. specified people, places, and times. This is followed by optimizing those behavior change interventions for that targeted niche.
Optimization tests whether interventions produce the desired real-world success, with definitions of success and failure called optimization criteria. The methods of this phase are inspired by Linda Collins et al's MultiPhase Optimization Strategy and other optimization methods. If interventions are useful for a given niche, they are repurposed for others who might benefit from them.
If behavior change intervention components are useful for a given niche, they are repurposed for other niches. This occurs via:
The agile science process for behavior change focuses on creating useful and usable behavior change interventions and corresponding usable evidence for making decisions. It borrows from agile software development, user-centered design, lean start-up, and data science and modeling techniques for simulating complexity.
The process starts with creating many variations of plausibly useful behavior change interventions for a “niche,” i.e. specified people, places, and times. This is followed by optimizing those behavior change interventions for that targeted niche.
Optimization tests whether interventions produce the desired real-world success, with definitions of success and failure called optimization criteria. The methods of this phase are inspired by Linda Collins et al's MultiPhase Optimization Strategy and other optimization methods. If interventions are useful for a given niche, they are repurposed for others who might benefit from them.
If behavior change intervention components are useful for a given niche, they are repurposed for other niches. This occurs via:
- modularizing an intervention to its smallest, meaningful, and self-contained element,
- engaging in a science of matchmaking that systematically studies the decision policies used to match interventions with other people, places, and times (what we call a "niche").
- within a clinical context, taking advantage of implementation science methods, such as pragmatic clinical trials, to iteratively improve upon processes within real-world contexts.
Adaptation to Digital Therapeutics
Digital therapeutics are highly regulated technologies and, thus, require some degree of adaptation of this basic process to be appropriate for this research arena (particularly the addition of a clear clinical trial testing phase and then on-going monitoring). To address this, we developed the Digital Therapeutics Real-World Evidence Framework. Pasted below is the abstract to the published paper, followed by the key figure. For more information please visit this website: https://www.jmir.org/2024/1/e49208/
ABSTRACT: Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.
ABSTRACT: Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.
