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 not available for clinical care. Objective In 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]). Methods The 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. Results A 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). Conclusions The 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.

Cyklotymie je charakterizována jako psychická porucha s dlouhodobým střídáním nálad od mírné deprese po mírné elace. Přes spletitý historický vývoj je tato diagnostická jednotka pořád spojována s částečnou kontroverzí, převážně ve smyslu afektivní versus temperamentové poruchy. Zmíněná kontroverze se spolupodílí na nižším zájmu o detailnější zkoumání cyklotymie, což vede k nedostatečným údajům o adekvátních postupech léčby. Dopad na kvalitu života a běžné fungování přitom může být u pacientů trpících touto psychickou poruchou zásadní. V článku jsme se zaměřili na přehled historického vývoje, klinický obraz, diagnostické možnosti včetně digitálních technologií a léčbu cyklotymie.Klíčová slova: cyklotymie, bipolární afektivní porucha, aktigrafie, digitální technologie, stabilizátory nálady. CyclothymiaCyclothymia is characterized as a mental disorder with long-term mood swings from mild depression to mild elation. Despite its complex historical development, this diagnostic unit is still associated with partial controversy, mainly according to affective versus temperament disorders. This controversy contributes to the lower interest of the research field to study cyclothymia, which leads to insufficient data on adequate treatment. But the impact on quality of life and functioning can be significant in patients suffering from cyclothymia. In this article, we focused on an overview of historical developments, the clinical symptomatology, diagnostic options, including digital technologies, and the treatment of cyclothymia.

No 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.