Circadian Amplitude

Khan, Abdul Haseeb; Tian, Xiao-Li

doi:10.1007/978-3-030-22009-9_1072

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…After developing the cosinor model, we will extract the following parametric rhythmic parameters for each participant: midline estimating statistic of rhythm (MESOR), amplitude, and acrophase. MESOR is the rhythm-adjusted mean [ 36 ], the amplitude presents the difference between the equilibrium position and the peak point of the rhythm oscillation [ 92 ], and the acrophase presents the timing of the high values recurring in each cycle of the rhythm [ 36 ]. Since the amplitude can present the strength of the rhythm [ 93 ], while comparing the diurnal (12-hour period) and circadian (24-hour period) rhythms to determine which rhythm has more strength, we will compare the amplitude.…”

Section: Methodsmentioning

confidence: 99%

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Ahmed,

Hasan,

Islam

et al. 2024

JMIR Res Protoc

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Background Understanding a student’s depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. Objective The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. Methods Through a countrywide study, we collected 2952 students’ raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. Results After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days’ app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. Conclusions Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage–based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms’ capability to find subtle differences. International Registered Report Identifier (IRRID) DERR1-10.2196/51540

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Section: Methodsmentioning

confidence: 99%

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Ahmed,

Hasan,

Islam

et al. 2024

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

“…Circadian rhythms are almost 24-h oscillations found in various physiological processes in the human brain and body that are regulated by the suprachiasmatic nucleus (SCN) in the hypothalamus (Colwell, 2011). There are a few ways to represent the circadian rhythm, and one of them is the circadian amplitude, the difference in level between the peak and valley is the amplitude of the circadian rhythm (Khan & Tian, 2021), where a specific measurement (hormone level, activity, or a certain protein) is measured and its variation portrayed over time (Khan & Tian, 2021). Some of the main risk factors for dysregulation of circadian rhythm is exposure to altered light, caused by night work or forced sleep deprivation, which can directly influence the expression of clock genes and interfere with biological processes (James et al, 2017).…”

mentioning

confidence: 99%

Circadian rhythm alterations affecting the pathology of neurodegenerative diseases

Canever

Queiroz

SOARES

et al. 2023

Journal of Neurochemistry

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The circadian rhythm is a nearly 24‐h oscillation found in various physiological processes in the human brain and body that is regulated by environmental and genetic factors. It is responsible for maintaining body homeostasis and it is critical for essential functions, such as metabolic regulation and memory consolidation. Dysregulation in the circadian rhythm can negatively impact human health, resulting in cardiovascular and metabolic diseases, psychiatric disorders, and premature death. Emerging evidence points to a relationship between the dysregulation circadian rhythm and neurodegenerative diseases, suggesting that the alterations in circadian function might play crucial roles in the pathogenesis and progression of neurodegenerative diseases. Better understanding this association is of paramount importance to expand the knowledge on the pathophysiology of neurodegenerative diseases, as well as, to provide potential targets for the development of new interventions based on the dysregulation of circadian rhythm. Here we review the latest findings on dysregulation of circadian rhythm alterations in Parkinson's disease, Alzheimer's disease, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, spinocerebellar ataxia and multiple‐system atrophy, focusing on research published in the last 3 years.image

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Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study (Preprint)

Ahmed,

Hasan,

Islam

et al. 2023

Preprint

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BACKGROUND Understanding a student’s depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. OBJECTIVE The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. METHODS Through a countrywide study, we collected 2952 students’ raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. RESULTS After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days’ app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. CONCLUSIONS Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage–based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms’ capability to find subtle differences. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/51540

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Circadian Amplitude

Cited by 3 publications

References 55 publications

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Circadian rhythm alterations affecting the pathology of neurodegenerative diseases

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study (Preprint)

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