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1. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Ranjan, Yatharth, Rashid, Zulqarnain, Stewart, Callum, Conde, Pauline, Sankesara, Heet, Laiou, Petroula, Matcham, Faith, White, Katie M, Oetzmann, Carolin, Lamers, Femke, Siddi, Sara, Simblett, Sara, Vairavan, Srinivasan, Myin-Germeys, Inez, Mohr, David C., Wykes, Til, Haro, Josep Maria, Annas, Peter, Penninx, Brenda WJH, Narayan, Vaibhav A, Hotopf, Matthew, Dobson, Richard JB, and consortium, RADAR-CNS

Subjects
Statistics - Applications, Quantitative Biology - Quantitative Methods
Abstract

Objective: This study aimed to explore the associations between depression severity and wearable-measured circadian rhythms, accounting for seasonal impacts and quantifying seasonal changes in circadian rhythms.Materials and Methods: Data used in this study came from a large longitudinal mobile health study. Depression severity (measured biweekly using the 8-item Patient Health Questionnaire [PHQ-8]) and behaviors (monitored by Fitbit) were tracked for up to two years. Twelve features were extracted from Fitbit recordings to approximate circadian rhythms. Three nested linear mixed-effects models were employed for each feature: (1) incorporating the PHQ-8 score as an independent variable; (2) adding the season variable; and (3) adding an interaction term between season and the PHQ-8 score. Results: This study analyzed 10,018 PHQ-8 records with Fitbit data from 543 participants. Upon adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced activity, irregular behaviors, and delayed rhythms. Notably, the negative association with daily step counts was stronger in summer and spring than in winter, and the positive association with the onset of the most active continuous 10-hour period was significant only during summer. Furthermore, participants had shorter and later sleep, more activity, and delayed circadian rhythms in summer compared to winter. Discussion and Conclusions: Our findings underscore the significant seasonal impacts on human circadian rhythms and their associations with depression and indicate that wearable-measured circadian rhythms have the potential to be the digital biomarkers of depression.

Published
2023

2. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Author

Sun, Shaoxiong, Folarin, Amos A., Zhang, Yuezhou, Cummins, Nicholas, Garcia-Dias, Rafael, Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Laiou, Petroula, Sankesara, Heet, Matcham, Faith, Leightley, Daniel, White, Katie M., Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Simblett, Sara, Nica, Raluca, Rintala, Aki, Mohr, David C., Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda W. J. H., Vairavan, Srinivasan, Narayan, Vaibhav A., Annas, Peter, Hotopf, Matthew, and Dobson, Richard J. B.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. From 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.

Published
2022
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3. Exploring the dynamic relationships between nocturnal heart rate, sleep disruptions, anxiety levels, and depression severity over time in recurrent major depressive disorder

Author

Condominas, Elena, Sanchez-Niubo, Albert, Domènech-Abella, Joan, Haro, Josep Maria, Bailon, Raquel, Giné-Vázquez, Iago, Riquelme, Gemma, Matcham, Faith, Lamers, Femke, Kontaxis, Spyridon, Laporta, Estela, Garcia, Esther, Peñarrubia Maria, Maria Teresa, White, Katie M., Oetzmann, Carolin, Annas, Peter, Hotopf, Matthew, Penninx, Brenda W.J.H., Narayan, Vaibhav A., Folarin, Amos, Leightley, Daniel, Cummins, Nicholas, Ranjan, Yathart, de Girolamo, Giovanni, Preti, Antonio, Simblett, Sara, Wykes, Til, Myin-Germeys, Inez, Dobson, Richard, and Siddi, Sara

Published
2025
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4. Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Vairavan, Srinivasan, Qian, Linglong, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Sankesara, Heet, Matcham, Faith, White, Katie M, Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Simblett, Sara, Rintala, Aki, Mohr, David C, Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Annas, Peter, Hotopf, Matthew, Dobson, Richard JB, and consortium, RADAR-CNS

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of daily walking in real-world scenarios and their relationships with depression are yet to be fully explored. This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. In this study, we used two ambulatory datasets: a public dataset with 71 elder adults' 3-day acceleration signals collected by a wearable device, and a subset of an EU longitudinal depression study with 215 participants and their phone-collected acceleration signals (average 463 hours per participant). We detected participants' gait cycles and force from acceleration signals and extracted 20 statistics-based daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period corresponding to the self-reported depression score. The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets. Daily-life gait features could significantly improve the goodness of fit of evaluating depression severity relative to laboratory gait patterns and demographics, which was assessed by likelihood-ratio tests in both datasets. This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The gait cadence of faster steps in daily-life walking has the potential to be a biomarker for evaluating depression severity, which may contribute to clinical tools to remotely monitor mental health in real-world settings.

Published
2022
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5. Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Matcham, Faith, Oetzmann, Carolin, Lamers, Femke, Siddi, Sara, Simblett, Sara, Rintala, Aki, Mohr, David C, Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Pennix, Brenda WJH, Narayan, Vaibhav A, Annas, Peter, Hotopf, Matthew, and Dobson, Richard JB

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluetooth devices count (NBDC). The continuous NBDC data can partially reflect individuals' behaviors and status, such as social connections and interactions, working status, mobility, and social isolation and loneliness, which were found to be significantly associated with depression by previous survey-based studies. This paper aims to explore the NBDC data's value in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). The data used in this paper included 2,886 bi-weekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the UK as part of the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. A number of significant associations were found between Bluetooth features and depressive symptom severity. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics, R2= 0.526, and root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE = 4.547).

Published
2021
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6. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

Author

Zhang, Yuezhou, Pratap, Abhishek, Folarin, Amos A., Sun, Shaoxiong, Cummins, Nicholas, Matcham, Faith, Vairavan, Srinivasan, Dineley, Judith, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, White, Katie M., Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Rambla, Carla Hernández, Simblett, Sara, Nica, Raluca, Mohr, David C., Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda W. J. H., Annas, Peter, Narayan, Vaibhav A., Hotopf, Matthew, and Dobson, Richard J. B.

Published
2023
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8. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author

Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia, Haro, Josep Maria, Horsfall, Melany, Ivan, Alina, Lavelle, Grace, Li, Qingqin, Lombardini, Federica, Mohr, David C., Narayan, Vaibhav A., Oetzmann, Carolin, Penninx, Brenda W. J. H., Bruce, Stuart, Nica, Raluca, Simblett, Sara K., Wykes, Til, Brasen, Jens Christian, Myin-Germeys, Inez, Rintala, Aki, Conde, Pauline, Dobson, Richard J. B., Folarin, Amos A., Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Cummins, Nick, Manyakov, Nikolay V., Vairavan, Srinivasan, and Hotopf, Matthew

Published
2022
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9. Pre- and post-COVID 19 outbreak relationship between physical activity and depressive symptoms in Spanish adults with major depressive disorder: a secondary analysis of the RADAR-MDD cohort study.

Author

Ibáñez, Delia, Condominas, Elena, Haro, Josep Maria, Vázquez, Iago Giné, Bailón, Raquel, Garcia, Esther, Kontaxis, Spyridon, Peñarrubia-Maria, Maria Teresa, Arranz, Belen, Llaosa-Scholten, Raúl, Gardeñes, Lluisa, Hotopf, Matthew, Matcham, Faith, Lamers, Femke, Penninx, Brenda W. J. H., Annas, Peter, Folarin, Amos, Narayan, Vaibhav, Lima, Rodrigo Antunes, and Siddi, Sara

Subjects
COVID-19 pandemic, PHYSICAL activity, MENTAL depression, SPANIARDS, DISEASE relapse, SEDENTARY behavior
Abstract

Aim: To evaluate the longitudinal association of sedentary behavior, light and moderate-to-vigorous physical activity (MVPA) participation with depressive symptoms and whether their possible association changed depending on the pandemic phase. Methods: This longitudinal study conducted secondary analysis from the Spanish cohort of the Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-8). Sedentary behavior and physical activity were estimated via wrist-worn devices. Linear mixed models evaluated the longitudinal associations of sedentary behavior and physical activity (light and moderate-to-vigorous intensities) with depressive symptoms. Results: In total, 95 participants (67.5% women, 53.0 [±10.5] years of age on average) were monitored pre-COVID-19 and included in the analyses. Pre-COVID-19, 73.7% of participants presented depression, and, on average, participated in 13.2 (±1.08) hours/day of sedentary behavior, 2.42 (±0.90) hours/day of light physical activity and 23.6 (±19.80) minutes/day of MVPA. Considering all the observations (from November 2019 to October 2020), an additional hour/day of sedentary behavior was longitudinally associated with higher depressive symptoms [ β std = 0.06, 95% confidence interval (CI) 0.10 to 0.47], whereas an additional hour/day in light physical activity was associated with lower depressive symptoms (β std = −0.06, 95% CI −0.59 to −0.15). Time in MVPA was not associated with depressive symptomatology. The association of sedentary behavior and light physical activity with depressive symptoms was significant only during pre-COVID-19 and COVID-19 relaxation periods, whereas during the strictest periods of the pandemic with regards to the restrictions (lockdown and de-escalation), the association was not observed. Conclusion: Sedentary behavior and light physical activity were longitudinally associated with depressive symptoms in participants with a history of MDD. The incorporation of light physical activity should be stimulated in adults with a history of MDD. Neither sedentary behavior nor light physical activity were associated with depressive symptoms during the most restrictive COVID-19 phases, whereas sedentary behavior (positively) and light physical activity (negatively) were associated with depressive symptoms in persons with MDD before and after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Generating normative data from web-based administration of the Cambridge Neuropsychological Test Automated Battery using a Bayesian framework.

Author

Wragg, Elizabeth, Skirrow, Caroline, Dente, Pasquale, Cotter, Jack, Annas, Peter, Lowther, Milly, Backx, Rosa, Barnett, Jenny, Cree, Fiona, Kroll, Jasmin, and Cormack, Francesca

Subjects
STATISTICAL models, CROSS-sectional method, CROWDSOURCING, COGNITIVE testing, RESEARCH funding, SEX distribution, STATISTICAL sampling, FISHER exact test, EPISODIC memory, INTERNET, AGE distribution, DESCRIPTIVE statistics, ODDS ratio, AGING, NEUROPSYCHOLOGICAL tests, NEUROPSYCHOLOGY, CONCEPTUAL structures, COMPARATIVE studies, CONFIDENCE intervals, DATA analysis software, HEALTH outcome assessment, EDUCATIONAL attainment, REGRESSION analysis, ALGORITHMS
Abstract

Introduction: Normative cognitive data can distinguish impairment from healthy cognitive function and pathological decline from normal ageing. Traditional methods for deriving normative data typically require extremely large samples of healthy participants, stratifying test variation by pre-specified age groups and key demographic features (age, sex, education). Linear regression approaches can provide normative data from more sparsely sampled datasets, but non-normal distributions of many cognitive test results may lead to violation of model assumptions, limiting generalisability. Method: The current study proposes a novel Bayesian framework for normative data generation. Participants (n = 728; 368 male and 360 female, age 18-75 years), completed the Cambridge Neuropsychological Test Automated Battery via the research crowdsourcing website Prolific.ac. Participants completed tests of visuospatial recognition memory (Spatial Working Memory test), visual episodic memory (Paired Associate Learning test) and sustained attention (Rapid Visual Information Processing test). Test outcomes were modelled as a function of age using Bayesian Generalised Linear Models, which were able to derive posterior distributions of the authentic data, drawing from a wide family of distributions. Markov Chain Monte Carlo algorithms generated a large synthetic dataset from posterior distributions for each outcome measure, capturing normative distributions of cognition as a function of age, sex and education. Results: Comparison with stratified and linear regression methods showed converging results, with the Bayesian approach producing similar age, sex and education trends in the data, and similar categorisation of individual performance levels. Conclusion: This study documents a novel, reproducible and robust method for describing normative cognitive performance with ageing using a large dataset. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Methods for Neuroscience Drug Development: Guidance on Standardization of the Process for Defining Clinical Outcome Strategies in Clinical Trials

Author

Domingo, Silvia Zaragoza, primary, Alonso, Jordi, additional, Ferrer, Montserrat, additional, Acosta, Maria T, additional, Alphs, Larry, additional, Annas, Peter, additional, Balabanov, Pavel, additional, Butlen-Ducuing, Florence, additional, Berger, Anna-Karin, additional, Bishop, Kim I, additional, Dorffner, Georg, additional, Edgar, Chris, additional, de Gracia Blanco, Manuel, additional, Harel, Brian, additional, Harrison, John, additional, Horan, William P, additional, Jaeger, Judith, additional, Kottner, Jan, additional, Pinkham, Amy, additional, Tinoco, Daniella, additional, Vance, Monika, additional, and Yavorsky, Christian, additional

Published
2024
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12. Methods for Neuroscience Drug Development: Guidance on Standardization of the Process for Defining Clinical Outcome Strategies in Clinical Trials

Author

Zaragoza Domingo, Silvia, Alonso, Jordi, Ferrer, Montse, Acosta, Maria T., Alphs, Larry, Annas, Peter, Balabanov, Pavel, Berger, Anna-Karin, Bishop, Kim I., Butlen-Ducuing, Florence, Dorffner, Georg, Edgar, Chris, de Gracia Blanco, Manuel, Harel, Brian, Harrison, John, Horan, William P., Jaeger, Judith, Kottner, Jan, Pinkham, Amy, Tinoco, Daniella, Vance, Monika, and Yavorsky, Christian

Published
2024
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13. Wearable-derived sleep features predict relapse in Major Depressive Disorder.

Author

Matcham, Faith, primary, Carr, Ewan, additional, Meyer, Nicholas, additional, White, Katie M, additional, Oetzmann, Carolin, additional, Leightley, Daniel, additional, Lamers, Femke, additional, Siddi, Sara, additional, Cummins, Nick, additional, Annas, Peter, additional, de Girolamo, Giovanni, additional, Haro, Josep Maria, additional, Lavelle, Grace, additional, Li, Qingqin, additional, Lombardini, Federica, additional, Mohr, David C, additional, Narayan, Vaibhav, additional, Penninx, Brenda, additional, Coromina, Marta, additional, Alacid, Gemma Riquelme, additional, Simblett, Sara, additional, Nica, Raluca, additional, Wykes, Til, additional, Brasen, Jens Christian, additional, Myin-Germeys, Inez, additional, Dobson, Richard JB, additional, Folarin, Amos A, additional, Ranjan, Yatharth, additional, Rashid, Zulqarnain, additional, DIneley, Jude, additional, Vairavan, Srinivasan, additional, and Hotopf, Matthew, additional

Published
2023
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14. The Impact of COVID-19 Lockdown on Adults with Major Depressive Disorder from Catalonia: A Decentralized Longitudinal Study

Author

Lavalle, Raffaele, Condominas, Elena, Haro, Josep Maria, Giné-Vázquez, Iago, Bailon, Raquel, Laporta, Estela, Garcia, Ester, Kontaxis, Spyridon, Alacid, Gemma Riquelme, Lombardini, Federica, Preti, Antonio, Peñarrubia-Maria, Maria Teresa, Coromina, Marta, Arranz, Belén, Vilella, Elisabet, Rubio-Alacid, Elena, Radar-Mdd Spain, Matcham, Faith, Lamers, Femke, Hotopf, Matthew, Penninx, Brenda W. J. H., Annas, Peter, Narayan, Vaibhav, Simblett, Sara K., Siddi, Sara, The Radar-Cns Consortium, Psychiatry, APH - Digital Health, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and Amsterdam Neuroscience - Complex Trait Genetics

Subjects
Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health, SARS-CoV-2, depression, anxiety, lockdown, quarantine, remote measurement technology, decentralized study, Spain
Abstract

The present study analyzes the effects of each containment phase of the first COVID-19 wave on depression levels in a cohort of 121 adults with a history of major depressive disorder (MDD) from Catalonia recruited from 1 November 2019, to 16 October 2020. This analysis is part of the Remote Assessment of Disease and Relapse-MDD (RADAR-MDD) study. Depression was evaluated with the Patient Health Questionnaire-8 (PHQ-8), and anxiety was evaluated with the Generalized Anxiety Disorder-7 (GAD-7). Depression’s levels were explored across the phases (pre-lockdown, lockdown, and four post-lockdown phases) according to the restrictions of Spanish/Catalan governments. Then, a mixed model was fitted to estimate how depression varied over the phases. A significant rise in depression severity was found during the lockdown and phase 0 (early post-lockdown), compared with the pre-lockdown. Those with low pre-lockdown depression experienced an increase in depression severity during the “new normality”, while those with high pre-lockdown depression decreased compared with the pre-lockdown. These findings suggest that COVID-19 restrictions affected the depression level depending on their pre-lockdown depression severity. Individuals with low levels of depression are more reactive to external stimuli than those with more severe depression, so the lockdown may have worse detrimental effects on them.

Published
2023
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15. Generating normative data from web-based administration of the Cambridge Neuropsychological Test Automated Battery (CANTAB) using a Bayesian framework (Preprint)

Author

Wragg, Elizabeth, primary, Skirrow, Caroline, additional, Dente, Pasquale, additional, Cotter, Jack, additional, Annas, Peter, additional, Lowther, Milly, additional, Backx, Rosa, additional, Barnett, Jenny, additional, Cree, Fiona, additional, Kroll, Jasmin, additional, and Cormack, Francesca, additional

Published
2023
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16. Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis

Author

Siddi, Sara, Giné-Vázquez, Iago, Bailon, Raquel, Matcham, Faith, Lamers, Femke, Kontaxis, Spyridon, Laporta, Estela, Garcia, Esther, Arranz, Belen, Dalla Costa, Gloria, Guerrero, Ana Isabel, Zabalza, Ana, Buron, Mathias Due, Comi, Giancarlo, Leocani, Letizia, Annas, Peter, Hotopf, Matthew, Penninx, Brenda W J H, Magyari, Melinda, Sørensen, Per S, Montalban, Xavier, Lavelle, Grace, Ivan, Alina, Oetzmann, Carolin, White, Katie M, Difrancesco, Sonia, Locatelli, Patrick, Mohr, David C, Aguiló, Jordi, Narayan, Vaibhav, Folarin, Amos, Dobson, Richard J B, Dineley, Judith, Leightley, Daniel, Cummins, Nicholas, Vairavan, Srinivasan, Ranjan, Yathart, Rashid, Zulqarnain, Rintala, Aki, Girolamo, Giovanni De, Preti, Antonio, Simblett, Sara, Wykes, Til, Pab Members, Null, Myin-Germeys, Inez, Haro, Josep Maria, On Behalf Of The Radar-Cns Consortium, Null, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Complex Trait Genetics, and APH - Digital Health

Subjects
stress, COVID-19, SARS-CoV-2, major depressive disorder, multiple sclerosis, depression severity, heart rate, social activity, physical activity, decentralized, General Medicine
Abstract

Changes in lifestyle, finances and work status during COVID-19 lockdownsmay have led to biopsychosocial changes in people with pre-existing vulnerabilities such as MajorDepressive Disorders (MDDs) and Multiple Sclerosis (MS). Methods: Data were collected as a part the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program.We analyzed the following data from long-term participants in a decentralized multinational study:J. Clin. Med. 2022, 11, 7163. https://doi.org/10.3390/jcm11237163 https://www.mdpi.com/journal/jcm J. Clin. Med. 2022, 11, 7163 2 of 20 symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state,steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. Results: Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall,depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods. Conclusions: Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity

Published
2022
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18. The association between persistent cognitive difficulties and depression and functional outcomes in people with Major Depressive Disorder

Author

Matcham, Faith, primary, Simblett, Sara, additional, Leightley, Daniel, additional, Dalby, Maria, additional, Siddi, Sara, additional, Haro, Josep Maria, additional, Lamers, Femke, additional, Penninx, Brenda, additional, Bruce, Stuart, additional, Nica, Raluca, additional, Zorbas, Spyros, additional, Gilpin, Gina, additional, White, Katie M, additional, Oetzmann, Carolin, additional, Annas, Peter, additional, Brasen, Jens Christian, additional, Narayan, Vaibhav, additional, Hotopf, Matthew, additional, and Wykes, Til, additional

Published
2022
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19. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility

Author

Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Vairavan, Srinivasan, Bendayan, Rebecca, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Sankesara, Heet, Matcham, Faith, White, Katie M, Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Vilella, Elisabet, Simblett, Sara, Rintala, Aki, Bruce, Stuart, Mohr, David C, Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Annas, Peter, Hotopf, Matthew, Dobson, Richard JB, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Complex Trait Genetics, and APH - Digital Health

Subjects
Psychiatry and Mental health, mHealth, location data, depression, medical informatics, modeling, mobile health, mental health, mobility, dynamic structural equation modeling
Abstract

Background The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. Objective We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. Results This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=−0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, P Conclusions Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Published
2022
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20. Longitudinal relationships between depressive symptom severity and phone-measured mobility: dynamic structural equation modeling study

Author

Universitat Rovira i Virgili, Zhang, Yuezhou; Folarin, Amos A; Sun, Shaoxiong; Cummins, Nicholas; Vairavan, Srinivasan; Bendayan, Rebecca; Ranjan, Yatharth; Rashid, Zulqarnain; Conde, Pauline; Stewart, Callum; Laiou, Petroula; Sankesara, Heet; Matcham, Faith; White, Katie M; Oetzmann, Carolin; Ivan, Alina; Lamers, Femke; Siddi, Sara; Vilella, Elisabet; Simblett, Sara; Rintala, Aki; Bruce, Stuart; Mohr, David C; Myin-Germeys, Inez; Wykes, Til; Haro, Josep Maria; Penninx, Brenda WJH; Narayan, Vaibhav A; Annas, Peter; Hotopf, Matthew; Dobson, Richard JB; RADAR-CNS consortium, Universitat Rovira i Virgili, and Zhang, Yuezhou; Folarin, Amos A; Sun, Shaoxiong; Cummins, Nicholas; Vairavan, Srinivasan; Bendayan, Rebecca; Ranjan, Yatharth; Rashid, Zulqarnain; Conde, Pauline; Stewart, Callum; Laiou, Petroula; Sankesara, Heet; Matcham, Faith; White, Katie M; Oetzmann, Carolin; Ivan, Alina; Lamers, Femke; Siddi, Sara; Vilella, Elisabet; Simblett, Sara; Rintala, Aki; Bruce, Stuart; Mohr, David C; Myin-Germeys, Inez; Wykes, Til; Haro, Josep Maria; Penninx, Brenda WJH; Narayan, Vaibhav A; Annas, Peter; Hotopf, Matthew; Dobson, Richard JB; RADAR-CNS consortium

Published
2022

22. Long-term Participant Retention and Engagement Patterns in an App and Wearable-based Multinational Remote Digital Depression Study

Author

Dobson, Richard, primary, Zhang, Yuezhou, additional, Pratap, Abhishek, additional, Folarin, Amos, additional, Sun, Shaoxiong, additional, Cummins, Nicholas, additional, Matcham, Faith, additional, Vairavan, Srinivasan, additional, Dineley, Judith, additional, Ranjan, Yatharth, additional, Rashid, Zulqarnain, additional, Conde, Pauline, additional, Stewart, Callum, additional, White, Katie, additional, Oetzmann, Carolin, additional, Ivan, Alina, additional, Lamers, Femke, additional, Siddi, Sara, additional, Rambla, Carla, additional, Simblett, Sara, additional, Nica, Raluca, additional, Mohr, David, additional, Myin-Germeys, Inez, additional, Wykes, Til, additional, Haro, Josep, additional, Penninx, Brenda, additional, Annas, Peter, additional, Narayan, Vaibhav, additional, and Hotopf, Matthew, additional

Published
2022
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23. Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder

Author

Matcham, Faith, primary, Carr, Ewan, additional, White, Katie M, additional, Leightley, Daniel, additional, Lamers, Femke, additional, Siddi, Sara, additional, Annas, Peter, additional, de Girolamo, Giovanni, additional, Haro, Josep Maria, additional, Horsfall, Melany, additional, Ivan, Alina, additional, Lavelle, Grace, additional, Li, Qingqin, additional, Lombardini, Federica, additional, Mohr, David C, additional, Narayan, Vaibhav A, additional, Penninx, Brenda WJH, additional, Oetzmann, Carolin, additional, Coromina, Marta, additional, Simblett, Sara, additional, Weyer, Janice, additional, Wykes, Til, additional, Zorbas, Spyros, additional, Brasen, Jens Christian, additional, Myin-Germeys, Inez, additional, Conde, Pauline, additional, Dobson, Richard JB, additional, Folarin, Amos, additional, Ranjan, Yatharth, additional, Rashid, Zulqarnain, additional, Cummins, Nick, additional, DIneley, Jude, additional, Vairavan, Srinivasan, additional, and Hotopf, Matthew, additional

Published
2022
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24. Additional file 3 of Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author

Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia, Haro, Josep Maria, Horsfall, Melany, Ivan, Alina, Lavelle, Grace, Li, Qingqin, Lombardini, Federica, Mohr, David C., Narayan, Vaibhav A., Oetzmann, Carolin, Penninx, Brenda W. J. H., Bruce, Stuart, Nica, Raluca, Simblett, Sara K., Wykes, Til, Brasen, Jens Christian, Myin-Germeys, Inez, Rintala, Aki, Conde, Pauline, Dobson, Richard J. B., Folarin, Amos A., Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Cummins, Nick, Manyakov, Nikolay V., Vairavan, Srinivasan, and Hotopf, Matthew

Abstract

Additional file 3. Operationalisation of depression definitions.

Published
2022
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25. The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

Author

Laiou, Petroula, Kaliukhovich, Dzmitry A, Folarin, Amos A, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Sun, Shaoxiong, Zhang, Yuezhou, Matcham, Faith, Ivan, Alina, Lavelle, Grace, Siddi, Sara, Lamers, Femke, Penninx, Brenda WJH, Haro, Josep Maria, Annas, Peter, Cummins, Nicholas, Vairavan, Srinivasan, Manyakov, Nikolay V, Narayan, Vaibhav A, Dobson, Richard JB, Hotopf, Matthew, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and APH - Digital Health

Subjects
Depressive Disorder, Major, Recurrence, Surveys and Questionnaires, Humans, Health Informatics, Smartphone, United Kingdom
Abstract

Background Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms. Objective The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD. Methods We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse–Major Depressive Disorder study. The participants were recruited from three study sites: King’s College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables. Results Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI −0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day). Conclusions Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.

Published
2022
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26. Additional file 5 of Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author

Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia, Haro, Josep Maria, Horsfall, Melany, Ivan, Alina, Lavelle, Grace, Li, Qingqin, Lombardini, Federica, Mohr, David C., Narayan, Vaibhav A., Oetzmann, Carolin, Penninx, Brenda W. J. H., Bruce, Stuart, Nica, Raluca, Simblett, Sara K., Wykes, Til, Brasen, Jens Christian, Myin-Germeys, Inez, Rintala, Aki, Conde, Pauline, Dobson, Richard J. B., Folarin, Amos A., Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Cummins, Nick, Manyakov, Nikolay V., Vairavan, Srinivasan, and Hotopf, Matthew

Abstract

Additional file 5. Between-site stratification.

Published
2022
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27. Additional file 1 of Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author

Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia, Haro, Josep Maria, Horsfall, Melany, Ivan, Alina, Lavelle, Grace, Li, Qingqin, Lombardini, Federica, Mohr, David C., Narayan, Vaibhav A., Oetzmann, Carolin, Penninx, Brenda W. J. H., Bruce, Stuart, Nica, Raluca, Simblett, Sara K., Wykes, Til, Brasen, Jens Christian, Myin-Germeys, Inez, Rintala, Aki, Conde, Pauline, Dobson, Richard J. B., Folarin, Amos A., Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Cummins, Nick, Manyakov, Nikolay V., Vairavan, Srinivasan, and Hotopf, Matthew

Abstract

Additional file 1. Schedule of introduction of different RADAR-MDD assessments.

Published
2022
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28. Additional file 4 of Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author

Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia, Haro, Josep Maria, Horsfall, Melany, Ivan, Alina, Lavelle, Grace, Li, Qingqin, Lombardini, Federica, Mohr, David C., Narayan, Vaibhav A., Oetzmann, Carolin, Penninx, Brenda W. J. H., Bruce, Stuart, Nica, Raluca, Simblett, Sara K., Wykes, Til, Brasen, Jens Christian, Myin-Germeys, Inez, Rintala, Aki, Conde, Pauline, Dobson, Richard J. B., Folarin, Amos A., Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Cummins, Nick, Manyakov, Nikolay V., Vairavan, Srinivasan, and Hotopf, Matthew

Abstract

Additional file 4. Reasons for withdrawal.

Published
2022
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29. Challenges in the analysis of mobile health data from smartphones and wearable devices to predict depression symptom severity

Author

Sun, Shaoxiong, Folarin, Amos A., Zhang, Yuezhou, Cummins, Nicholas, Dias, Rafael Garcia, Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Laiou, Petroula, Sankesara, Heet, Matcham, Faith, Leightley, Daniel, White, Katie M., Oetzmann, Carolin, Ivan, Alina, Lamers, Femke, Siddi, Sara, Simblett, Sara, Nica, Raluca, Rintala, Aki, Mohr, David C., Myin-Germeys, Inez, Wykes, Til, Haro, Josep Maria, Penninx, Brenda W. J. H., Vairavan, Srinivasan, Narayan, Vaibhav A., Annas, Peter, Hotopf, Matthew, and Dobson, Richard J. B.

Subjects
FOS: Biological sciences, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)
Abstract

Challenges exist for the analysis of mobile health data. These include: (i) maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; (ii) distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening participants at high risks; (iii) understanding the heterogeneity with which depression manifests itself in behavioural patterns quantified by the passive features. We extracted 21 features reflecting mobility, sleep, and phone use from 479 participants with MDD. We investigated the impact of the number of days of available data on feature quality using intraclass correlation coefficients and Bland-Altman analysis. We then investigated the nature of the correlation between the 8-item Patient Health Questionnaire depression scale (PHQ-8; measured every 14 days) and the features using the pooled correlation coefficient, repeated measures correlation coefficient, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioural difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that 8 (range: 2-12) days were needed for reliable calculation of most features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, while features such as awake duration after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioural difference between periods of depression and no depression.

Published
2022
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30. Additional file 2 of Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Author

Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia, Haro, Josep Maria, Horsfall, Melany, Ivan, Alina, Lavelle, Grace, Li, Qingqin, Lombardini, Federica, Mohr, David C., Narayan, Vaibhav A., Oetzmann, Carolin, Penninx, Brenda W. J. H., Bruce, Stuart, Nica, Raluca, Simblett, Sara K., Wykes, Til, Brasen, Jens Christian, Myin-Germeys, Inez, Rintala, Aki, Conde, Pauline, Dobson, Richard J. B., Folarin, Amos A., Stewart, Callum, Ranjan, Yatharth, Rashid, Zulqarnain, Cummins, Nick, Manyakov, Nikolay V., Vairavan, Srinivasan, and Hotopf, Matthew

Abstract

Additional file 2. The North Wind and the Sun.

Published
2022
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31. Investigating the impact of coronavirus 2 lockdown on adults with a recent history of recurrent major depressive disorder: A multi-centre study using remote measurement technology

Author

Leightley, Daniel, Lavelle, Grace, White, Katie, Sun, Shaoxiong, Matcham, Faith, Ivan, Alina, Oetzmann, Carolin, Penninx, Brenda WJH., Lamers, Femke, Siddi, Sara, Mario Haro, Josep, Germeys, Inez Myin, Bruce, Stuart, Nica, Raluca, Wickersham, Alice, Annas, Peter, C. Mohr, David, Simblett, Sara, Wykes, Til, Cummins, Nicholas, Folarin, Amos, Conde, Pauline, Ranjan, Yatharth, Dobson, Richard, Narayan, Viabhav, and Hotopf, Matthew

Abstract

Background: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness, Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown – pre-, during and post – in adults with a recent history of MDD across multiple centres. Methods: This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n=252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. Results: In our sample of 252 participants, 48% (n=121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (-12.16; 95% CI -18.39 to -5.92; p < 0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p < 0.001, respectively) as compared to those who were not. Conclusions: We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid -19 pandemic.

Published
2021

32. Further validation of the THINC‐it tool and extension of the normative data set in a study of n = 10.019 typical controls.

Author

Dalby, Maria, Annas, Peter, and Harrison, John E.

Subjects
*REFERENCE values, *MENTAL depression, *STATISTICAL reliability, *COGNITIVE testing, *COGNITIVE ability
Abstract

Introduction: We report further validation and normative data for the THINC‐Integrated Tool (THINC‐it), a measure of cognitive function designed for use with individuals living with Major Depressive Disorder, but which is finding use in further psychiatric and neurological diseases. THINC‐it comprises four objective computerised cognitive tests based on traditional psychological paradigms and a version of the Perceived Deficits Questionnaire assessment. Methods: Sample size of n = 10.019 typical control study participants were tested on one to two occasions to further validate the reliability of THINC‐it. Temporal reliability was assessed across 120–180 days. Results: Test‐retest reliability correlations varied between r = 0.50 and 0.72 for the component measures and r = 0.75 (95% confidence intervals 0.74, 0.76) for the THINC‐it composite score. Normative data categorised by Age, Sex and Years of Education were calculated and the effect on task performance was reported. Discussion: Our analysis confirms previously reported levels of reliability and validates previously reported normative data values. [ABSTRACT FROM AUTHOR]

Published
2022
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34. Additional file 1 of Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology

Author

Leightley, Daniel, Lavelle, Grace, White, Katie M., Sun, Shaoxiong, Matcham, Faith, Ivan, Alina, Oetzmann, Carolin, Penninx, Brenda W. J. H., Lamers, Femke, Siddi, Sara, Haro, Josep Mario, Myin-Germeys, Inez, Bruce, Stuart, Nica, Raluca, Wickersham, Alice, Annas, Peter, Mohr, David C., Simblett, Sara, Wykes, Til, Cummins, Nicholas, Folarin, Amos Akinola, Conde, Pauline, Ranjan, Yatharth, Dobson, Richard J. B., Narayan, Viabhav A., and Hotopf, Mathew

Subjects
Hardware_MEMORYSTRUCTURES, Data_FILES
Abstract

Additional file 1 : Supplement A: Missing Data.

Published
2021
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35. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study.

Author

Matcham, Faith, primary, Leightley, Daniel, additional, Siddi, Sara, additional, Lamers, Femke, additional, White, Katie M, additional, Annas, Peter, additional, de Girolamo, Giovanni, additional, Difrancesco, Sonia, additional, Haro, Josep Maria, additional, Horsfall, Melany, additional, Ivan, Alina, additional, Grace, Lavelle, additional, Li, Qingqin, additional, Lombardini, Federica, additional, Mohr, David C, additional, Narayan, Vaibhav A, additional, Oetzmann, Carolin, additional, Penninx, Brenda BWJH, additional, Bruce, Stuart, additional, Raluca, Nica, additional, Simblett, Sara K, additional, Wykes, Til, additional, Brasen, Jens C, additional, Myin-Germeys, Inez, additional, Rintala, Aki, additional, Conde, Pauline, additional, Dobson, Richard JB, additional, Folarin, Amos A, additional, Stewart, Callum, additional, Ranjan, Yatharth, additional, Rashid, Zulqarnain, additional, Cummins, Nick, additional, Manyakov, Nikolay V, additional, Vairavan, Srinivasan, additional, and Hotopf, Matthew, additional

Published
2021
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36. A Framework for Recruiting into a Remote Measurement Technologies (RMTs) study: Experiences from a major depressive disorder cohort

Author

Oetzmann, Carolin, primary, White, Katie, additional, Ivan, Alina, additional, Julie, Jessica, additional, Leightley, Daniel, additional, Lavelle, Grace, additional, Lamers, Femke, additional, Siddi, Sara, additional, Annas, Peter, additional, Garcia, Sara Arranz, additional, Haro, Josep Maria, additional, Mohr, David C, additional, Penninx, Brenda WJH, additional, Simblett, Sara, additional, Wykes, Til, additional, Narayan, Vaibhav A, additional, Hotopf, Matthew, additional, and Matcham, Faith, additional

Published
2021
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39. A twin study of the genetics of fear conditioning

Author

Hettema, John M., Annas, Peter, Neale, Michael C., Kendler, Kenneth S., and Fredrikson, Mats

Subjects
Twins, Fear -- Genetic aspects, Health, Psychology and mental health
Published
2003

41. Home stay reflects symptoms severity in major depressive disorder: A multicenter observational study using geolocation data from smartphones

Author

Laiou, Petroula, primary, Kaliukhovich, Dzmitry A., additional, Folarin, Amos, additional, Ranjan, Yatharth, additional, Rashid, Zulqarnain, additional, Conde, Pauline, additional, Stewart, Callum, additional, Sun, Shaoxiong, additional, Zhang, Yuezhou, additional, Matcham, Faith, additional, Ivan, Alina, additional, Lavelle, Grace, additional, Siddi, Sara, additional, Lamers, Femke, additional, Penninx, Brenda W.J.H., additional, Haro, Josep Maria, additional, Annas, Peter, additional, Cummins, Nicholas, additional, Vairavan, Srinivasan, additional, Manyakov, Nikolay V., additional, Narayan, Vaibhav A., additional, Dobson, Richard, additional, and Hotopf, Matthew, additional

Published
2021
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43. Parental history, aversive exposure and the development of snake and spider phobia in women

Author

Fredrikson, Mats, Annas, Peter, and Wik, Gustav

Subjects
Phobias -- Genetic aspects, Women -- Psychological aspects, Imprinting (Psychology) -- Research, Psychology and mental health
Abstract

Parental history and experiential factors in the development of snake and spider phobia were studied. Phobic women (DSM-IV, n = 158) reported on family history of animal phobia and whether direct (being frightened by the phobic object) or indirect (seeing someone else being frightened by and/or being warned of the phobic object) fear exposure predated phobia development. Fifty-nine mothers (37%) and 11 fathers (7%) had snake or spider phobia, which is higher than the upper 95% confidence interval in the population (Fredrikson, Annas, Fischer & Wik, Behavior Research and Therapy, 34, 33-39). Lifetime Relative Risk, RR, of animal phobia in probands' mothers and fathers as a function of at least one phobic grandparent was 3.3 and 13.7 respectively. Indirect fear exposures were more common in snake (45%) than spider (27%) phobics (RR = 1.4). Indirect fear exposures were more common among probands with a positive parental history, the RRs being 3.6 and 2.1 as a function of maternal and paternal family history. Direct exposures were unrelated to parental history. The familial resemblance and transmission of specific phobia could be experiential in origin mediated by indirect exposures or of hereditary origin mediated by genetic factors. It may represent genetically facilitated learning and exemplify imprinting in humans.

Published
1997

44. Gender and age differences in the prevalence of specific fears and phobias

Author

Fredrikson, Mats, Annas, Peter, Fischer, Hakan, and Wik, Gustav

Subjects
Sex differences -- Psychological aspects, Age -- Psychological aspects, Phobias -- Demographic aspects, Psychology and mental health
Abstract

Point prevalence of specific fears and phobias was determined in 704 respondents of 1000 randomly selected adults aged 18-70 yr. A phobia for lightning, enclosed spaces, darkness, flying, heights, spiders, snakes, injections, dentists and/or injuries was defined if subjects reported a fear that was out of conscious control, interfered with life and lead to the avoidance of the feared object [American Psychiatric Association, 1994. Diagnostic and statistical manual of mental disorders (4th edn). Washington, DC: American Psychiatric Press.] Fear intensity was assessed using visual analogue scales. A factor analysis generally supported the classification of fears and phobias into: (1) situational phobias (lightning, enclosed spaces, darkness, flying and heights); (2) animal phobias (spiders and snakes); and (3) mutilation phobias (injections, dentists, injuries). Total point prevalence of any specific phobia was 19.9% (26.5% for females and 12.4% for males). In total, 21.2% women and 10.9% men met criterias for any single specific phobia. Multiple phobias was reported by 5.4% of the females and 1.5% of the males. Animal phobia had a prevalence of 12.1% in women and 3.3% in men. Point prevalence of situational phobia was 17.4% in women and 8.5% in men. For mutilation phobia no gender difference was observed, being presented in 3.2% of the women and 2.7% of the men. Women as compared to men gave higher fear ratings for all objects and situations. Inanimate object fears and phobias were more common in older than younger individuals. Animal fears were more intense in younger than in older individuals. Fear of flying increased and fear of injections decreased as a function of age in women but not in men. Thus, specific fears and phobias are heterogeneous with respect to sex and age distribution.

Published
1996

46. Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology.

Author

Leightley, Daniel, Lavelle, Grace, White, Katie M., Sun, Shaoxiong, Matcham, Faith, Ivan, Alina, Oetzmann, Carolin, Penninx, Brenda W. J. H., Lamers, Femke, Siddi, Sara, Haro, Josep Mario, Myin-Germeys, Inez, Bruce, Stuart, Nica, Raluca, Wickersham, Alice, Annas, Peter, Mohr, David C., Simblett, Sara, Wykes, Til, and Cummins, Nicholas

Subjects
MENTAL depression, COVID-19, ADULTS, STAY-at-home orders, MENTAL health
Abstract

Background: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown – pre-, during and post – in adults with a recent history of MDD across multiple centres. Methods: This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. Results: In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (− 12.16; 95% CI − 18.39 to − 5.92; p < 0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p < 0.001, respectively) as compared to those who were not. Conclusions: We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid − 19 pandemic. [ABSTRACT FROM AUTHOR]

Published
2021
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48. Different genetic factors underlie fear conditioning and episodic memory

Author

Fredrikson, Mats, Annas, Peter, Hettema, John M., Fredrikson, Mats, Annas, Peter, and Hettema, John M.

Abstract

ObjectiveFear conditioning seems to account for the acquisition of post-traumatic stress disorder, whereas conscious recall of events in aftermath of trauma reflects episodic memory. Studies show that both fear conditioning and episodic memory are heritable, but no study has evaluated whether they reflect common or separate genetic factors. To this end, we studied episodic memory and fear conditioning in 173 healthy twin pairs using visual stimuli predicting unconditioned electric shocks.MethodsFear conditioning acquisition and extinction was determined using conditioned visual stimuli predicting unconditioned mild electric shocks, whereas electrodermal activity served as the fear learning index. Episodic memory was evaluated using cued recall of pictorial stimuli unrelated to conditioning. We used multivariate structural equation modeling to jointly analyze memory performance and acquisition as well as extinction of fear conditioning.ResultsBest-fit twin models estimated moderate genetic loadings for conditioning and memory measures, with no genetic covariation between them.ConclusionIndividual differences in fear conditioning and episodic memory reflect distinct genetically influenced processes, suggesting that the genetic risk for learning-induced anxiety disorders includes at least two memory-related genetic factors. These findings are consistent with the facts that the two separate learning forms are distant in their evolutionary development, involve different brain mechanisms, and support that genetically independent memory systems are pivotal in the development and maintenance of syndromes related to fear learning., Funding: Swedish Brain Foundation

Published
2015
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49. Lithium as a Treatment for Alzheimer's Disease : A Systematic Review and Meta-Analysis

Author

Matsunaga, Shinji, Kishi, Taro, Annas, Peter, Basun, Hans, Hampel, Harald, Iwata, Nakao, Matsunaga, Shinji, Kishi, Taro, Annas, Peter, Basun, Hans, Hampel, Harald, and Iwata, Nakao

Abstract

Background: This is the first meta-analysis of randomized placebo-controlled trials testing lithium as a treatment for patients with Alzheimer's disease (AD) and individuals with mild cognitive impairment (MCI). Methods: The primary outcome measure was efficacy on cognitive performance as measured through the Alzheimer's Disease Assessment Scale cognitive subscale or the Mini-Mental State Examination. Other outcome measures were drug discontinuation rate, individual side effects, and biological markers (phosphorylated tau 181, total tau, and amyloid-beta(42)) in cerebrospinal fluid (CSF). Results: Three clinical trials including 232 participants that met the study's inclusion criteria were identified. Lithium significantly decreased cognitive decline as compared to placebo (standardized mean difference = -0.41, 95% confidence interval = -0.81 to -0.02, p = 0.04, I-2 = 47%, 3 studies, n = 199). There were no significant differences in the rate of attrition, discontinuation due to all causes or adverse events, or CSF biomarkers between treatment groups. Conclusions: The results indicate that lithium treatment may have beneficial effects on cognitive performance in subjects with MCI and AD dementia.

Published
2015
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