Major Depressive Disorder (MDD) is a heterogeneous disorder with a considerable symptomatic overlap with other psychiatric and somatic disorders. This study aims at providing evidence for association ...of a set of serum and urine biomarkers with MDD. We analyzed urine and serum samples of 40 MDD patients and 47 age- and sex-matched controls using 40 potential MDD biomarkers (21 serum biomarkers and 19 urine biomarkers). All participants were of Caucasian origin. We developed an algorithm to combine the heterogeneity at biomarker level. This method enabled the identification of correlating biomarkers based on differences in variation and distribution between groups, combined the outcome of the selected biomarkers, and calculated depression probability scores (the “bio depression score”). Phenotype permutation analysis showed a significant discrimination between MDD and euthymic (control) subjects for biomarkers in urine (P < .001), in serum (P = .02) and in the combined serum plus urine result (P < .001). Based on this algorithm, a combination of 8 urine biomarkers and 9 serum biomarkers were identified to correlate with MDD, enabling an area under the curve (AUC) of 0.955 in a Receiver Operating Characteristic (ROC) analysis. Selection of either urine biomarkers or serum biomarkers resulted in AUC values of 0.907 and 0.853, respectively. Internal cross-validation (5-fold) confirmed the association of this set of biomarkers with MDD.
•This study investigates biomarker panels for Major Depressive Disorder (MDD).•The biomarker panels were assessed in serum as well as in urine.•A new method was used to combine results of multiple biomarkers into a single score.•This scoring method is based on differences in variation and distribution.•A panel of 9 serum and 8 urine biomarkers was identified to correlate with MDD.
Background
Psychosocial eHealth interventions for people with cancer are promising in reducing distress; however, their results in terms of effects and adherence rates are quite mixed. Developing ...interventions with a solid evidence base while still ensuring adaptation to user wishes and needs is recommended to overcome this. As most models of eHealth development are based primarily on examining user experiences (so-called bottom-up requirements), it is not clear how theory and evidence (so-called top-down requirements) may best be integrated into the development process.
Objective
This study aims to investigate the integration of top-down and bottom-up requirements in the co-design of eHealth applications by building on the development of a mobile self-compassion intervention for people with newly diagnosed cancer.
Methods
Four co-design tasks were formulated at the start of the project and adjusted and evaluated throughout: explore bottom-up experiences, reassess top-down content, incorporate bottom-up and top-down input into concrete features and design, and synergize bottom-up and top-down input into the intervention context. These tasks were executed iteratively during a series of co-design sessions over the course of 2 years, in which 15 people with cancer and 7 nurses (recruited from 2 hospitals) participated. On the basis of the sessions, a list of requirements, a final intervention design, and an evaluation of the co-design process and tasks were yielded.
Results
The final list of requirements included intervention content (eg, major topics of compassionate mind training such as psychoeducation about 3 emotion systems and main issues that people with cancer encounter after diagnosis such as regulating information consumption), navigation, visual design, implementation strategies, and persuasive elements. The final intervention, Compas-Y, is a mobile self-compassion training comprising 6 training modules and several supportive functionalities such as a mood tracker and persuasive elements such as push notifications. The 4 co-design tasks helped overcome challenges in the development process such as dealing with conflicting top-down and bottom-up requirements and enabled the integration of all main requirements into the design.
Conclusions
This study addressed the necessary integration of top-down and bottom-up requirements into eHealth development by examining a preliminary model of 4 co-design tasks. Broader considerations regarding the design of a mobile intervention based on traditional intervention formats and merging the scientific disciplines of psychology and design research are discussed.