Assessment of patient preferences in digital health trials: procedure development and implementation in a preference-based trial

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: 88.7% completion rate in first assessment step (110/124 participants)

• Primary Outcome: Feasibility and applicability of standardised digital preference assessment procedure

• Nnt Or Sensitivity: 91.7% of students and 88.7% of cancer patients successfully indicated preferences in first step

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility demonstrated with automated digital delivery, minimal resource requirements, high completion rates across different populations Highly applicable to Australian digital health initiatives. Aligns with TGA digital therapeutics framework and could enhance patient-centred care in PBS-funded digital health programs. Relevant for cancer care coordination under Medicare Benefits Schedule Adult cancer patients in digital health interventions, potentially broader chronic disease populations experiencing fatigue or sleep disturbances

Abstract

OBJECTIVES: To develop and evaluate a standardised, fully automated assessment procedure for patient treatment preferences suitable for use in randomised controlled trials (RCTs) with remote or digital health interventions. DESIGN: Multiphase developmental study followed by implementation in a fully randomised preference trial (FRPT). SETTING: University and outpatient cancer care settings in Switzerland; implementation in RCTs with a remote digital health intervention. PARTICIPANTS: 50 medical students for pilot testing; 11 patients with cancer for qualitative testing; 124 adult patients with cancer experiencing fatigue or sleep problems in an FRPT with a remote digital health intervention. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome: feasibility and applicability of a standardised digital preference assessment procedure. SECONDARY OUTCOMES: distribution and strength of treatment preferences, reasons for preference. RESULTS: A procedure with three items for assessing treatment preferences was developed and refined based on pilot testing, stakeholder meetings and patient interviews. The stepwise procedure ensured that study participants with an initially unclear preference were finally able to report their preference. The majority of students (91.7%) indicated their preference already in the first assessment step. Similar in the RCT phase, 110 of 124 participants (88.7%) indicated their preference in the first step. Preference drivers included interest, perceived ease and conceptual appeal in addition to expected treatment benefits. CONCLUSIONS: Our fully automated standardised preference assessment with three items appears feasible in trials with digital health interventions. Future trials with two active treatment conditions should consider implementation of such a preference assessment to evaluate non-specific treatment effects driven by the match of patients' preferences with the respective treatment. TRIAL REGISTRATION NUMBERS: DRKS00035596; DRKS00035541; SNCTP000006209.

References

1. 1.Barth, J., Thomae, A. V., Tietjen, A. K., & Witt, C. M. (2026). Assessment of patient preferences in digital health trials: procedure development and implementation in a preference-based trial. BMJ Open. https://doi.org/10.1136/bmjopen-2025-115137