INTRODUCTION

The rate of development and complexity of digital health interventions (DHIs) in recent years has to some extent outpaced the methodological development in economic evaluation and costing. Particularly, the choice of cost components included in intervention or program costs of DHIs have received scant attention. The aim of this study was to build a literature-informed checklist of program cost components of DHIs. The checklist was next tested by applying it to an empirical case, Mamma Mia, a DHI developed to prevent perinatal depression.

METHOD

A scoping review with a structured literature search identified peer-reviewed literature from 2010 to 2022 that offers guidance on program costs of DHIs. Relevant guidance was summarized and extracted elements were organized into categories of main cost components and their associated activities following the standard three-step approach, that is, activities, resource use and unit costs.

RESULTS

Of the 3448 records reviewed, 12 studies met the criteria for data extraction. The main cost categories identified were development, research, maintenance, implementation and health personnel involvement (HPI). Costs are largely considered to be context-specific, may decrease as the DHI matures and vary with number of users. The five categories and their associated activities constitute the checklist. This was applied to estimate program costs per user for Mamma Mia Self-Guided and Blended, the latter including additional guidance from public health nurses during standard maternal check-ups. Excluding research, the program cost per mother was more than double for Blended compared with Self-Guided (€140.5 versus €56.6, 2022 Euros) due to increased implementation and HPI costs. Including research increased the program costs to €190.8 and €106.9, respectively. One-way sensitivity analyses showed sensitivity to changes in number of users, lifespan of the app, salaries and license fee.

CONCLUSION

The checklist can help increase transparency of cost calculation and improve future comparison across studies.