The “House of Quality for Behavioral Science”—a user-centered tool to design behavioral interventions

• Published in: Translational behavioral medicine 2019-08, Vol.9 (4), p.810-818
• Main Authors: Mullane, Sarah L, Epstein, Dana R, Buman, Matthew P
• Format: Article
• Language: English
• Subjects: Case studies; Intervention (Psychology); Methods; Optimization theory
• ISSN: 1869-6716; 1613-9860
• DOI: 10.1093/tbm/iby084

Within the behavioral field, a plethora of conceptual frameworks and tools have been developed to improve transition from efficacy to effectiveness trials; however, they are limited in their ability to support new, iterative intervention design decision-making methodologies beyond traditional randomized controlled trial design. Emerging theories suggest that researchers should employ engineering based user-centered design (UCD) methods to support more iterative intervention design decision-making in the behavioral field. We present, an adaptation of a UCD tool used in the engineering field—the Quality Function Deployment “House of Quality” correlation matrix, to support iterative intervention design decision-making and documentation for multicomponent behavioral interventions and factorial trial designs. We provide a detailed description of the adapted tool—“House of Quality for Behavioral Science”, and a step-by-step use-case scenario to demonstrate the early identification of intervention flaws and prioritization of requirements. Four intervention design flaws were identified via the tool application. Completion of the relationship correlation matrix increased requirement ranking variance for the researcher (σ2 = 0.47 to 7.19) and participant (σ2 = 0.56 to 3.89) perspective. Requirement prioritization (ranking) was facilitated by factoring in the strength of the correlation between each perspective and corresponding importance. A correlational matrix tool such as the “House of Quality for Behavioral Science” may provide a structured, UCD approach that balances researcher and participant needs and identifies design flaws for pragmatic behavioral intervention design. This tool may support iterative design decision-making for multicomponent and factorial trial designs.