Nocturnal motor activity and light exposure: Objective actigraphy-based marks of melancholic and non-melancholic depressive disorder. Brief report

Tonon, André Comiran; Fuchs, Daniel Fernando Paludo; Gomes, William Barbosa; Levandovski, Rosa Maria; Fleck, Marcelo Pio de Almeida; Hidalgo, María Paz Loayza; Alencastro, Luciano da Silva

doi:10.1016/j.psychres.2017.08.025

Cited by 16 publications

(11 citation statements)

References 14 publications

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“…Mundt et al and Zhang et al [ 32 , 33 , 34 ] were able to extract features from voice samples and use them to measure depression symptoms. Actigraphy was used to measure patterns of motor activity and was employed for the development of digital biomarkers [ 35 , 36 , 37 ]. Tonon et al [ 37 ] were able to use light exposure measurements to differentiate between melancholic depression and non-melancholic depression patients.…”

Section: The Digital Revolution In Mental Healthmentioning

confidence: 99%

“…Actigraphy was used to measure patterns of motor activity and was employed for the development of digital biomarkers [ 35 , 36 , 37 ]. Tonon et al [ 37 ] were able to use light exposure measurements to differentiate between melancholic depression and non-melancholic depression patients. Jacobson et al [ 36 ] were able to draw associations between light exposure and depression severity.…”

Section: The Digital Revolution In Mental Healthmentioning

confidence: 99%

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A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Taliaz¹,

Souery

2021

JCM

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Mental health disorders are ambiguously defined and diagnosed. The established diagnosis technique, which is based on structured interviews, questionnaires and data subjectively reported by the patients themselves, leaves the mental health field behind other medical areas. We support these statements with examples from major depressive disorder (MDD). The National Institute of Mental Health (NIMH) launched the Research Domain Criteria (RDoC) project in 2009 as a new framework to investigate psychiatric pathologies from a multidisciplinary point of view. This is a good step in the right direction. Contemporary psychiatry considers mental illnesses as diseases that manifest in the mind and arise from the brain, expressed as a behavioral condition; therefore, we claim that these syndromes should be characterized primarily using behavioral characteristics. We suggest the use of smartphones and wearable devices to passively collect quantified behavioral data from patients by utilizing digital biomarkers of mental disorder symptoms. Various digital biomarkers of MDD symptoms have already been detected, and apps for collecting this longitudinal behavioral data have already been developed. This quantified data can be used to determine a patient’s diagnosis and personalized treatment, and thereby minimize the diagnosis rate of comorbidities. As there is a wide spectrum of human behavior, such a fluidic and personalized approach is essential.

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Section: The Digital Revolution In Mental Healthmentioning

confidence: 99%

Section: The Digital Revolution In Mental Healthmentioning

confidence: 99%

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Taliaz¹,

Souery

2021

JCM

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“…The data derived from activity trackers include patterns of sleep, physical activity, and circadian rest-activity rhythm (RAR). Alterations in these patterns have been found in depression [17][18][19][20][21][22] and contributed to objective differentiation of depression subtypes [23]. Further, these behavioral parameters are easily and passively measurable by actigraphy and do not require any invasive procedure or active participation from individuals.…”

Section: Introductionmentioning

confidence: 96%

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method (Preprint)

Minaeva¹,

Riese²,

Lamers³

et al. 2020

Preprint

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BACKGROUND In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process. We aimed to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined. OBJECTIVE The aim of our study was to examine whether ESM-assessed depression-related affect and behavior can discriminate between depressed and non-depressed individuals, whether actigraphy data can discriminate between depressed and non-depressed individuals on its own, and whether it has added value with respect to the use of ESM. METHODS We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. Performance of the models was assessed with goodness of fit indices, calibration curves, and discriminative ability (AUC - the area under the receiver operating characteristic curve). RESULTS In the development dataset, the discriminative ability was good for the actigraphy model (AUC=.790) and excellent for the ESM (AUC=.991) and combined-domains model (AUC=.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=.648) and excellent for the ESM (AUC=.891) and combined-domains model (AUC=.892). CONCLUSIONS ESM was a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy showed no added value to ESM as a diagnostic predictor, but might still be useful when ESM use is restricted.

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“…For instance, studies have shown that sleep deprivation and jet lag can trigger or aggravate depressive, hypomanic or manic episodes [14]. In addition, studies have shown that discrete patterns of daily activity rhythms can distinguish specific mood disorder subgroups, such as bipolar depression and mania, or nonmelancholic and melancholic depression [15,16]. Notably, lower stability and weakened amplitude in rest-activity rhythms have been associated with greater symptom severity (e.g.…”

Section: Introductionmentioning

confidence: 99%

Validation of the English version of the Mood Rhythm Instrument

et al. 2020

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Background: Disruption of biological rhythms has been linked to the pathophysiology of mental disorders. However, little is known regarding the rhythmicity of mood symptoms due to the lack of validated clinical questionnaires. A better understanding of the rhythmicity of mood symptoms can help identifying individuals whose severity of mood symptoms follows an altered circadian rhythm. The objective of this study was to validate the English version of the Mood Rhythm Instrument (MRhI), a self-reported measure of self-perceived rhythmicity of mood symptoms and behaviours, in a sample of the general population from Canada. Methods: After the translation process, the final English version of the Mood Rhythm Instrument (MRhI-English) was applied on participants recruited at McMaster University and St. Joseph's Healthcare Hamilton campuses. Individuals were also asked to answer the Reduced Morningness-Eveningness Questionnaire (rMEQ). Results: Four hundred one individuals completed the English version of the MRhI and the rMEQ. The MRhI-English presented a Cronbach's alpha of 0.75. The factorial analysis grouped the MRhI-15 items in 3 factors (cognitive, affective and somatic), with affective items having a lower frequency of self-reported 24-h peaks. Comparison between sexes showed that women reported a higher frequency of daily peaks in irritability, anxiety, sadness and talking to friends, while men exhibited peaks more frequently in problem-solving, sexual arousal and motivation to exercise. Conclusions: Our findings suggest that the English version of the MRhI displayed good internal consistency. Future directions will include the use of the MRhI instrument in individuals with mood disorders, aiming to provide a better understanding of the relationship between daily patterns of mood variability and mental health outcomes.

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Nocturnal motor activity and light exposure: Objective actigraphy-based marks of melancholic and non-melancholic depressive disorder. Brief report

Cited by 16 publications

References 14 publications

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method (Preprint)

Validation of the English version of the Mood Rhythm Instrument

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