Abstract

BackgroundSelf-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 not available for clinical care.ObjectiveIn this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire—a 10-item, mobile app–based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]).MethodsThe content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model.ResultsA total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, 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%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844).ConclusionsThe ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

Highlights

  • BackgroundBipolar disorder (BD) is a severe mental illness characterized by recurrent depressive, manic, and mixed episodes [1]

  • A total of 99 patients with bipolar disorder (BD) were included in this study and completed an average of 78.1% (SD 18.3%) of the requested Aktibipo Self-rating area under the receiver operating characteristic curve (AUROC) (ASERT) assessments across all patients in this study

  • We evaluated the content and convergent validity, internal structure, and consistency of the questionnaire, using an extensive data set consisting of 99 patients with a mean follow-up duration of almost 2 years, with weekly ASERT assessments and monthly Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale NIMH (MADRS) phone interviews

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Summary

Introduction

BackgroundBipolar disorder (BD) is a severe mental illness characterized by recurrent depressive, manic, and mixed episodes [1]. The existing literature suggests that measurement-based care may be an effective clinical strategy in most psychiatric disorders [3,4,5,6], and monitoring of the clinical course of BD is of high clinical and research interest. Behavioral analysis, including actigraphy and smartphone monitoring, is currently a topic of ongoing research [8,9,10,11], mood assessment is still the primary source of clinical data for BD status. In clinical practice, both clinician-observed instruments and patient-reported instruments are used to assess the course of BD [9,10,12]. Validated, quick, and scalable digital self-report measures that can detect relapse are still not available for clinical care

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