Evaluating the Feasibility of Using the Multiphase Optimization Strategy Framework to Assess Implementation Strategies for Digital Mental Health Applications Activations A Proof of Concept Study

Ayla Aydin ^1* Wouter Van Ballegooijen ¹ Ilja Cornelisz ¹ Anne Etzelmueller ^2,3

• ¹ VU Amsterdam, Amsterdam, Netherlands
• ² HelloBetter, Berlin, Germany
• ³ Technical University of Munich, Munich, Bavaria, Germany

Background: Despite the effectiveness and potential of digital mental health interventions ( DMHIs) in routine care, their uptake remains low. In Germany, digital mental health applications (DiGA), certified as low-risk medical devices, can be prescribed by healthcare professionals (HCPs) to support the treatment of mental health conditions. The objective of this proof-of-concept study was to evaluate the feasibility of using the Multiphase Optimization Strategy (MOST) framework when assessing implementation strategies. Methods: We tested the feasibility of the MOST by employing a 2 4 exploratory retrospective factorial design on existing data. We assessed the impact of the implementation strategies (calls, online meetings, arranged and walk-in on-site meetings) individually and in combination, on the number of DiGA activations in a non-randomized design. Data from N = 24,817 HCPs were analyzed using non-parametric tests. Results: The results primarily demonstrated the feasibility of applying the MOST to a non-randomized setting. Furthermore, analyses indicated significant differences between the groups of HCPs receiving specific implementation strategies (χ2 (15) = 1665.2, p < .001, ϵ 2 = 0.07). Combinations of implementation strategies were associated with significantly more DiGA activations. For example, combinations of arranged and walk-in onsite meetings showed higher activation numbers (e.g., Z = 10.60, p < 0.001, χ2 = 1665.24) compared to those receiving other strategies. We found a moderate positive correlation between the number of strategies used and activation numbers (r = 0.30, p < 0.001). Discussion and Limitations: These findings support the feasibility of using the MOST to evaluate implementation strategies in digital mental health care. It also gives an exploratory example on how to conduct factorial designs with information on implementation strategies. However, limitations such as non-random assignment, underpowered analysis, and varying approaches to HCPs affect the robustness and generalizability of the results. Despite these limitations, the results demonstrate that the MOST is a viable method for assessing implementation strategies, highlighting the importance of planning and optimizing strategies before their implementation. By addressing these limitations, healthcare providers and policymakers can enhance the adoption of digital health innovations, ultimately improving access to mental health care for a broader population.