JMIR Publications

ABSTRACT

Background:

Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick and scalable digital self-report measures that can also detect relapse are still missing for clinical care.

Objective:

We aimed to validate the newly developed Aktibipo SElf-RaTing questionnaire (ASERT), a 10-item mobile app-based self-report mood questionnaire, consisting of 4 depression, 4 mania, and 2 non-specific symptom items, each with 5 possible answers. The validation dataset was a subset of the ongoing observational longitudinal AKTIBIPO400 study, aimed at long-term monitoring of mood and activity (via actigraphy), in bipolar disorder (BD) patients. Included were patients with confirmed BD, monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS)).

Methods:

The content validity of ASERT was assessed with principal component analysis and using Cronbach’s alpha for the assessment of internal consistency of each factor. Convergent validity of the depressive or manic items of the ASERT questionnaire with corresponding clinical scale was assessed using linear mixed effect model and linear correlation analyses. Additionally, we investigated the capability of ASERT to distinguish relapse (YMRS≥15, MADRS≥15) from a non-relapse (inter-episode) state (YMRS<15, MADRS<15) using a logistic mixed-effects model.

Results:

Altogether, 99 BD patients were included in the study (mean follow-up=754 days) and completed 78.1% of the requested ASERT assessments (median completion time=24.0 seconds). The ASERT depression items were highly associated with MADRS total scores (P<.001, bootstrap). Similarly, the ASERT mania items were highly associated with YMRS total scores (P<.001, bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (Accuracy=85.0%, Sensitivity=69.9%, Specificity=88.4%, area under the ROC curve AUC=0.880), and mania (Accuracy=87.5%, Sensitivity=64.9%, Specificity=89.9%, AUC=0.844).

Conclusions:

The ASERT questionnaire is a quick and acceptable mood monitoring tool administered via a smartphone application with good capability to detect worsening of clinical symptoms in a long-term monitoring scenario.