In our previous blog contributions for professionals, we have discussed the level of physical activity and deviations in sleep patterns as one of the main symptoms and biomarkers of affective disorders, especially bipolar disorder. At the same time, we presented the possible measures of these parameters. The use of actigraphic sensors seems to be a relatively simple, easy for the patient and, above all, accurate method that reflects the functioning of the circadian system and the activity of the suprachiasmatic nuclei (SCN) [1] [2].
In addition, with the continuous advances and developments in digital technology, smartphones and mobile applications, it is possible to use these devices for subjective patient recordings, objective measurements or to visualize these data using a mobile application. It is digital technologies that can facilitate more objective detection of sleep and movement pattern deviations that can be monitored over the long term, thereby recording the individual disease course of affective disorders or the progress and effectiveness of treatment [3] [4].
The objective data collected through digital technologies can be divided into passive and active.
The passive monitoring of patients’ activity includes mainly actigraphic records of daily and sleep patterns. However, sleep medicine experts recommend supplementing these data with additional information, due to the complexity of actigraphic data analysis. For example, in the form of sleep diaries to accurately determine the onset of sleep and waking [6].
Smartphones and wristbands are also capable of collecting user data. These include location via GPS, social media activity levels, call or text message logs. These types of information are also recommended in research studies to be combined and thus to provide a comprehensive picture of the patient’s current condition [7] [8].
The disadvantage of only passive digital data collection is the lack of contextual information, which in turn can be supplemented by active digital records. Supplementing actigraphic or other objective digital data with subjective assessment of mood symptoms, level of cognitive processes or life events seems to be the most appropriate as a support for a more comprehensive and comprehensible description of the patient’s condition. Psychological questionnaires, self-rating scales or mood diaries are most commonly used for this subjective assessment [9].
There are now a number of studies that combine self-rating scales with actigraphy or other passive measures, including light, temperature, galvanic skin response, and heart rate sensors, to provide multimodal monitoring of the physical and environmental conditions experienced by patients with bipolar disorder in the natural conditions of daily life [5] [10].
Thus, data from mobile apps can facilitate real-time monitoring of patterns of emotions, behavior, biological rhythms, and other situational influences. These approaches have been widely applied to study the underlying features, characteristics, changes in states, and impact of treatment in bipolar disorder.
Findings supported by a systematic research of current digital methods demonstrate visible variations in sleep, particularly longer duration and variability in sleep patterns, lower mean and greater variability in motor activity, and a shift to later peak activity and mid-sleep. The development of new functional analysis tools based on digital data may help to gain deeper insights into the mechanisms and dynamics of bipolar disorder manifestations, while also accounting for the environmental and physiological correlates of the disorder. Digital technology offers great potential for rapid identification, diagnosis, longitudinal follow-up, assessment of clinical status or consideration of the impact of medication [5] [11].
Since 2016, Mindpax has been developing and refining a comprehensive and personalized system for patients with bipolar disorder, combining the above parameters in research studies to capture the current clinical status as accurately as possible. First, we needed to ensure the validity of actigraphy data [12] in bipolar patients and distinguish them from healthy controls. Furthermore, over the years, we have added active digital data via a mood questionnaire (a questionnaire developed by us and validated according to clinical mood scales – ASERT) [13] to the passive digital measurements using an actigraphic sensor. In the current ongoing study, the system is additionally supplemented with the possibilities of daily self-assessment of mood, recording of important life events affecting the patient’s condition, recording of medication intake and visualization of all subjective and objective data. In addition, we provide patients with personalized feedback with therapeutic elements (i.e., digital therapy) after a calibration period of the collected data.
[1] Bauer M, Grof P, Rasgon N, Bschor T, Glenn T, Whybrow PC. Temporal relation between sleep and mood in patients with bipolar disorder. Bipolar Disord. 2006 Apr;8(2):160-7
[2] De Crescenzo, F., Economou, A., Sharpley, A. L., Gormez, A., & Quested, D. J. (2017). Actigraphic features of bipolar disorder: A systematic review and meta-analysis. Sleep Medicine Reviews, 33, 58–69. https://doi.org/10.1016/j.smrv.2016.05.003
[3]Gershon, A., & Eidelman, P. (2015). Inter-episode affective intensity and instability: Predictors of depression and functional impairment in bipolar disorder. Journal of Behavior Therapy and Experimental Psychiatry, 46, 14–18. https://doi.org/10.1016/j.jbtep.2014.07.005
[4]Ducasse, D., Jaussent, I., Guillaume, S., Azorin, J. M., Bellivier, F., Belzeaux, R., … & Albertini, L. (2017). Affect lability predicts occurrence of suicidal ideation in bipolar patients: a two‐year prospective study. Acta Psychiatrica Scandinavica, 135(5), 460-469.
[5] Dunster, G.P., Swendsen, J. & Merikangas, K.R. Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions. Neuropsychopharmacol. 46, 197–208 (2021). https://doi.org/10.1038/s41386-020-00830-5
[6]Ancoli-Israel S, Martin JL, Blackwell T, Buenaver L, Liu L, Meltzer LJ, et al. The SBSM guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med. 2015;13:S4–S38.
[7]Gliddon E, Barnes SJ, Murray G, Michalak EE. Online and mobile technologies for self-management in bipolar disorder: a systematic review. Psychiatr Rehabil J. 2017;40:309–19. https://doi.org/10.1037/prj0000270.
[8]Faurholt-Jepsen M, Frost M, Ritz C, Christensen EM, Jacoby AS, Mikkelsen RL, et al. Daily electronic self-monitoring in bipolar disorder using smartphones – the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trial. Psychol Med. 2015;45:2691–704. https://doi.org/10.1017/S0033291715000410.
[9] Merikangas KR, Swendsen J, Hickie IB, Cui L, Shou H, Merikangas AK, et al. Real-time mobile monitoring of the dynamic associations among motor activity, energy, mood, and sleep in adults with bipolar disorder. JAMA Psychiatry. 2019;76:190–8. https://doi.org/10.1001/jamapsychiatry.2018.3546
[10] Knell G, Gabriel KP, Businelle MS, Shuval K, Wetter DW, Kendzor DE. Ecological momentary assessment of physical activity: validation study. J Med Internet Res. 2017;19:e253.
[11] Orsolini, L., Fiorani, M., & Volpe, U. (2020). Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers? International Journal of Molecular Sciences, 21(20), 7684. https://doi.org/10.3390/ijms21207684
[12] Schneider, J., Bakštein, E., Kolenič, M., Vostatek, P., Correll, C. U., Novák, D., & Španiel, F. (2020). Motor Activity Patterns Can Distinguish Between Inter-Episode Bipolar Disorder Patients and Healthy Controls. CNS Spectrums, 1–32. https://doi.org/10.1017/S1092852920001777
[13]Anýž J, Bakštein E, Dally A, Kolenič M, Hlinka J, Hartmannová T, Urbanová K, Correll CU, Novák D, Španiel F
Validität des Aktibipo-Selbstbewertungsfragebogens zur digitalen Selbsteinschätzung der Stimmungslage und Rückfallerkennung bei Patienten mit bipolarer Störung: Studie zur Validierung des Instruments JMIR Ment Health 2021;8(8):e26348
