Circadian rest-activity rhythms predict future increases in depressive symptoms among community-dwelling older men

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled "Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment".

Exploring actigraphy as a digital phenotyping measure: A study on differentiating psychomotor agitation and retardation in depression

INTRODUCTION

Psychomotor activity stands out as a crucial symptom in characterizing behaviors associated with depression. This study aims to explore the potential of actigraphy as a tool for digital phenotyping in characterizing symptoms of psychomotor agitation and retardation, which are clinically challenging dimensions to capture, in patients diagnosed with major depressive episode (MDE) according to DSM-5 criteria.

METHODS

We compared rest-activity circadian rhythm biomarkers measured by the Motion Watch 8 actigraphy between 58 (78.4%) patients with MDE and psychomotor retardation (PMR), and 16 (21.6%) patients with MDE and psychomotor agitation (PMA), according to DSM-5 criteria.

RESULTS

Actigraphy allowed to objectively report PMA through heightened activity over a 24-h period, while PMR manifests as reduced activity during the most active 10 h. Lower rest-activity rhythm (RAR) amplitude in PMR was accompanied by increased irregularities in intra- and inter-day rhythms. Interestingly, actigraphy emerges as an objective tool to measure the characteristics of the active and rest periods, free from the confounding effects of sleep disturbances. Indeed, no differences in sleep disturbances were identified between patients exhibiting psychomotor agitation and those displaying PMR.

CONCLUSION

Digital phenotyping through actigraphy may aid in distinguishing psychomotor retardation and psychomotor agitation allowing for a more precise characterization of the depression phenotype. When integrated with clinical assessment, measurements from actigraphy could offer additional insights into activity rhythms alongside subjective assessments and hold the potential to augment existing clinical decision-making processes in psychiatry.

Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review

BACKGROUND

The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly.

METHOD

The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis.

RESULTS

The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects.

CONCLUSION

Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.

Sleep-wake behavioral characteristics associated with depression symptoms: findings from the Multi-Ethnic Study of Atherosclerosis

STUDY OBJECTIVES

To help prioritize target/groups for experimental intervention studies, we characterized cross-sectional associations between 24-hour sleep-wake measures and depression symptoms, and evaluated if similar sleep-wake-depression relationships existed in people with and without higher insomnia severity.

METHODS

Participants had ≥3 days of actigraphy data (n = 1884; mean age = 68.6/SD = 9.1; 54.1% female). We extracted 18 sleep, activity, timing, rhythmicity, and fragmentation measures from actigraphy. We used individual and multivariable regressions with the outcome of clinically significant depression symptoms (Center for Epidemiologic Studies Depression Scale ≥ 16). We conducted sensitivity analyses in people with higher insomnia severity (top quartile of the Women's Health Initiative Insomnia Rating Scale total score).

RESULTS

From separate models in the overall sample, the odds of having depression symptoms were higher with: later timing (e.g. activity onset time odds ratio [OR]/1 SD = 1.32; 95% confidence interval [CI]: 1.16 to 1.50), lower rhythmicity (e.g. pseudo-F OR/1 SD = 0.75; 95% CI: 0.66 to 0.85), less activity (e.g. amplitude OR/1 SD = 0.83; 95% CI: 0.72 to 0.95), and worse insomnia (OR/1 SD = 1.48, 95% CI: 1.31 to 1.68). In multivariable models conducted among people with lower insomnia severity, later timing, lower rhythmicity, and higher insomnia severity were independent correlates of depression. In people with higher insomnia symptom severity, measures of later timing were most strongly associated with depression symptoms.

CONCLUSIONS

These correlative observations suggest that experimental studies are warranted to test if: broadly promoting 24-hour sleep-wake functioning reduces depression even in people without severe insomnia, and if advancing timing leads to depression symptom reductions in people with insomnia.

Associations of Circadian Rest-Activity Rhythms With Affect and Cognition in Community-Dwelling Stroke Survivors: An Ambulatory Assessment Study

BACKGROUND

Rest-activity rhythm (RAR) is a modifiable behavioral factor associated with affect and cognition. Identifying RAR characteristics associated with affect and cognition among stroke survivors provides insight into preventing poststroke affective and cognitive impairment.

OBJECTIVE

To examine the associations of RAR characteristics with affect and cognition among community-dwelling stroke survivors.

METHODS

Forty participants with mild stroke (mean age = 52.8; 42.5% female; 55% White) reported their affect and cognitive complaints using ecological momentary assessment and wore an accelerometer for 7 consecutive days and completed the National Institutes of Health Toolbox Cognition Battery. RAR characteristics were extracted using parametric and non-parametric approaches. Multivariable linear regressions were used to identify RAR characteristics associated with affect and cognition.

RESULTS

Later onset of rest (B = 0.45; P = .008) and activity (B = 0.36; P = .041) were positively associated with depressed affect. These associations were reversed for cheerful effect (rest onset: B = -0.42; P = .017; activity onset: B = -0.39; P = .033). Cheerful affect was also positively associated with relative amplitude (ie, distinctions in activity levels between rest and activity; B = .39; P = .030). Intra-daily variability (ie, RAR fragmentation; B = 0.35; P = .042) and later onset of activity (B = .36; P = .048) were positively associated with cognitive complaints. Less erratic RAR was positively associated with fluid cognition (B = 0.29; P = .036); RAR fragmentation was positively associated with crystallized cognition (B = 0.39; P = .015).

CONCLUSIONS

We identified RAR correlates of affect and cognition among stroke survivors, highlighting the value of managing RAR and sleep in stroke rehabilitation. Future studies should test whether advancing the onset of rest and activity, promoting a regular active lifestyle, and improving rest and sleep in the nighttime protect stroke survivors from affective and cognitive impairment.

Individual-specific change points in circadian rest-activity rhythm and sleep in individuals tapering their antidepressant medication: an actigraphy study

Group-level studies showed associations between depressive symptoms and circadian rhythm elements, though whether these associations replicate at the within-person level remains unclear. We investigated whether changes in circadian rhythm elements (namely, rest-activity rhythm, physical activity, and sleep) occur close to depressive symptom transitions and whether there are differences in the amount and direction of circadian rhythm changes in individuals with and without transitions. We used 4 months of actigraphy data from 34 remitted individuals tapering antidepressants (20 with and 14 without depressive symptom transitions) to assess circadian rhythm variables. Within-person kernel change point analyses were used to detect change points (CPs) and their timing in circadian rhythm variables. In 69% of individuals experiencing transitions, CPs were detected near the time of the transition. No-transition participants had an average of 0.64 CPs per individual, which could not be attributed to other known events, compared to those with transitions, who averaged 1 CP per individual. The direction of change varied between individuals, although some variables showed clear patterns in one direction. Results supported the hypothesis that CPs in circadian rhythm occurred more frequently close to transitions in depression. However, a larger sample is needed to understand which circadian rhythm variables change for whom, and more single-subject research to untangle the meaning of the large individual differences.

Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study

BACKGROUND

The sleep and circadian rhythm patterns associated with smartphone use, which are influenced by mental activities, might be closely linked to sleep quality and depressive symptoms, similar to the conventional actigraphy-based assessments of physical activity.

OBJECTIVE

The primary objective of this study was to develop app-defined circadian rhythm and sleep indicators and compare them with actigraphy-derived measures. Additionally, we aimed to explore the clinical correlations of these indicators in individuals with insomnia and healthy controls.

METHODS

The mobile app "Rhythm" was developed to record smartphone use time stamps and calculate circadian rhythms in 33 patients with insomnia and 33 age- and gender-matched healthy controls, totaling 2097 person-days. Simultaneously, we used standard actigraphy to quantify participants' sleep-wake cycles. Sleep indicators included sleep onset, wake time (WT), wake after sleep onset (WASO), and the number of awakenings (NAWK). Circadian rhythm metrics quantified the relative amplitude, interdaily stability, and intradaily variability based on either smartphone use or physical activity data.

RESULTS

Comparisons between app-defined and actigraphy-defined sleep onsets, WTs, total sleep times, and NAWK did not reveal any significant differences (all P>.05). Both app-defined and actigraphy-defined sleep indicators successfully captured clinical features of insomnia, indicating prolonged WASO, increased NAWK, and delayed sleep onset and WT in patients with insomnia compared with healthy controls. The Pittsburgh Sleep Quality Index scores were positively correlated with WASO and NAWK, regardless of whether they were measured by the app or actigraphy. Depressive symptom scores were positively correlated with app-defined intradaily variability (β=9.786, SD 3.756; P=.01) and negatively correlated with actigraphy-based relative amplitude (β=-21.693, SD 8.214; P=.01), indicating disrupted circadian rhythmicity in individuals with depression. However, depressive symptom scores were negatively correlated with actigraphy-based intradaily variability (β=-7.877, SD 3.110; P=.01) and not significantly correlated with app-defined relative amplitude (β=-3.859, SD 12.352; P=.76).

CONCLUSIONS

This study highlights the potential of smartphone-derived sleep and circadian rhythms as digital biomarkers, complementing standard actigraphy indicators. Although significant correlations with clinical manifestations of insomnia were observed, limitations in the evidence and the need for further research on predictive utility should be considered. Nonetheless, smartphone data hold promise for enhancing sleep monitoring and mental health assessments in digital health research.

24 h Rest/Activity Rhythms in Older Adults with Memory Impairment: Associations with Cognitive Performance and Depressive Symptomatology

Little is known about links of circadian rhythm alterations with neuropsychiatric symptoms and cognition in memory impaired older adults. Associations of actigraphic rest/activity rhythms (RAR) with depressive symptoms and cognition are examined using function-on-scalar regression (FOSR). Forty-four older adults with memory impairment (mean: 76.84 ± 8.15 years; 40.9% female) completed 6.37 ± 0.93 days of actigraphy, the Beck depression inventory-II (BDI-II), mini-mental state examination (MMSE) and consortium to establish a registry for Alzheimer's disease (CERAD) delayed word recall. FOSR models with BDI-II, MMSE, or CERAD as individual predictors adjusted for demographics (Models A1-A3) and all three predictors and demographics (Model B). In Model B, higher BDI-II scores are associated with greater activity from 12:00-11:50 a.m., 2:10-5:50 p.m., 8:40-9:40 p.m., 11:20-12:00 a.m., higher CERAD scores with greater activity from 9:20-10:00 p.m., and higher MMSE scores with greater activity from 5:50-10:50 a.m. and 12:40-5:00 p.m. Greater depressive symptomatology is associated with greater activity in midafternoon, evening, and overnight into midday; better delayed recall with greater late evening activity; and higher global cognitive performance with greater morning and afternoon activity (Model B). Time-of-day specific RAR alterations may affect mood and cognitive performance in this population.

Rest-Activity Rhythm Characteristics Associated With Depression Symptoms in Stroke Survivors

OBJECTIVE

To examine which 24-hour rest-activity rhythm (RAR) characteristics are associated with depression symptoms in stroke survivors.

DESIGN

Cross-sectional observational study examining associations of RAR characteristics with the presence of depression symptoms adjusting for age, sex, race, and medical comorbidity.

SETTING

Community setting.

PARTICIPANTS

Stroke survivors: (1) recruited locally (N women=35, N men=28) and (2) a nationally representative probability sample (the National Health and Nutrition Examination Survey [NHANES]; N women=156, N men=124).

INTERVENTIONS

None.

MEASUREMENTS

Objective RAR characteristics derived from accelerometer recordings including activity onset/offset times and non-parametric measures of RAR strength (relative amplitude), stability (interdaily stability), and fragmentation (intradaily variability). The presence of depression symptoms was categorized using Patient Health Questionnaire scores.

RESULTS

In both samples, the only RAR characteristic associated with depression symptoms was intradaily variability (fragmentation): local sample, odds ratio=1.96 [95% confidence interval=1.05-3.63]; NHANES sample, odds ratio=1.34, [95% confidence interval=1.01-1.78]). In the NHANES sample, which included both mild and moderate/severe depression, the association between 24-hour sleep-wake fragmentation and depression symptoms was driven by moderate-to-severe cases.

CONCLUSIONS

Stroke survivors with higher levels of RAR fragmentation were more likely to have depression symptoms in both samples. These findings have implications, given prior studies in general samples linking RAR fragmentation with future depression and dementia risk. Research is needed to establish the potential consequences, mechanisms, and modifiability of RAR fragmentation in stroke survivors.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response

Major depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimate = 0.11, F = 7.01, P = 0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression.

Chronobiological parameters as predictors of early treatment response in major depression

BACKGROUND

Alterations in circadian system organization have been related to major depressive disorder manifestations. This study aimed to evaluate chronobiological parameters, such as sleep, levels of 6-sulfatoxymelatonin, and others derived from actimetry as potential predictors of adequate treatment response in MDD.

METHODS

98 adult women with confirmed diagnosis of MDD were included. Participants completed standard questionnaires (Hamilton Depression Rating Scale - HAM-D; Munich Chronotype Questionnaire - MCTQ) at baseline and after 4 weeks of treatment. Urinary samples for assessing 6-sulfatoxymelatonin were collected on the day before and immediately after pharmacological treatment administration, and 28 continuous days of actigraphy data were collected during the protocol. Participants were classified into Responder (R) or Non-responder (NR) to antidepressant treatment in 4 weeks (early responder), which was characterized by a ≥50 % decrease in the HAM-D score.

RESULTS

The following biological rhythms variables significantly predicted a better treatment response in a model controlling for age, sex, and previous treatments: higher levels of activity (M10 - average activity in the 10 most active hours within the 24 h-day) and an earlier center of the 10 most active hours (M10c), as well as lower intradaily variability (IV) of light exposure. Sleep parameters and 6-sulfatoxymelatonin levels did not associate with treatment response prediction.

LIMITATION

Actimetry data were not assessed before changing in the treatment plan.

CONCLUSION

Different patterns in activity and light exposure might be linked to early antidepressant response.

A digital health intervention to stabilize the 24-hour rhythm of sleep, meals, and physical activity for reducing depression among older bereaved spouses: Protocol for a randomized controlled trial

BACKGROUND

Despite the high prevalence of depression and disruption to 24-h sleep-wake routines following the death of a spouse in late-life, no bereavement interventions have been developed to re-entrain a regular sleep-wake routine among older widow(er)s. We describe the rationale and methodology of the NIH-funded WELL Study (Widowed Elders' Lifestyle after Loss), a randomized controlled trial (RCT) comparing the efficacy of a digital health intervention (DHI) to enhanced usual care (EUC) arm for reducing depression symptoms in older spousally-bereaved adults.

METHODS

We will randomize approximately 200 recently bereaved (<12 months) adults aged 60+ years to one of two 12-week interventions: digital monitoring of the timing and regularity of sleep, meals, and physical activity plus weekly motivational health coaching; or enhanced usual care consisting of weekly telephone calls and similar assessment schedules. Participants will complete self-report and clinical assessments at baseline, post-intervention, and 3-, 6-, and 12-months post-intervention, and objective actigraphic assessments of their 24-h rest-activity rhythm (RAR) at baseline and 1-, 2-, and 3-months during the intervention. The primary outcome is change in depression symptoms burden (using the Hamilton Rating Scale for Depression) from pre- to post-intervention and over 12 months of follow-up.

DISCUSSION

WELL Study findings will inform the development of widely generalizable and scalable technology-based interventions to support bereaved spouses in community-based settings. Clinical http://Trials.gov Identifier: NCT04016896.

Association of 24-Hour Activity Pattern Phenotypes With Depression Symptoms and Cognitive Performance in Aging

Importance

Evidence regarding the nature and prevalence of 24-hour activity pattern phenotypes in older adults, especially those related to depression symptoms and cognition, is needed to guide the development of targeted mechanism research and behavioral interventions.

Objectives

To identify subgroups of older adults with similar 24-hour activity rhythm characteristics and characterize associated depression symptoms and cognitive performance.

Design, Setting, and Participants

From January to March 2022, a cross-sectional analysis of the 2011-2014 National Health and Nutrition Examination and Survey (NHANES) accelerometer study was conducted. The NHANES used a multistage probability sample that was designed to be representative of noninstitutionalized adults in the US. The main analysis included participants 65 years or older who had accelerometer and depression measures weighted to represent approximately 32 million older adults.

Exposures

Latent profile analysis identified subgroups with similar 24-hour activity pattern characteristics as measured using extended-cosine and nonparametric methods.

Main Outcomes and Measures

Covariate-adjusted sample-weighted regressions assessed associations of subgroup membership with (1) depression symptoms defined as 9-Item Patient Health Questionnaire (PHQ-9) scores of 10 or greater (PHQ-9) and (2) having at least psychometric mild cognitive impairment (p-MCI) defined as scoring less than 1 SD below the mean on a composite cognitive performance score.

Results

The actual clustering sample size was 1800 (weighted: mean [SD] age, 72.9 [7.3] years; 57% female participants). Clustering identified 4 subgroups: (1) 677 earlier rising/robust (37.6%), (2) 587 shorter active period/less modelable (32.6%), (3) 177 shorter active period/very weak (9.8%), and (4) 359 later settling/very weak (20.0%). The prevalence of a PHQ-9 score of 10 or greater differed significantly across groups (cluster 1, 3.5%; cluster 2, 4.7%; cluster 3, 7.5%; cluster 4, 9.0%; χ2 P = .004). The prevalence of having at least p-MCI differed significantly across groups (cluster 1, 7.2%; cluster 2, 12.0%; cluster 3, 21.0%; cluster 4, 18.0%; χ2 P < .001). Five of 9 depression symptoms differed significantly across subgroups.

Conclusions and Relevance

In this cross-sectional study, findings indicate that approximately 1 in 5 older adults in the US may be classified in a subgroup with weak activity patterns and later settling, and approximately 1 in 10 may be classified in a subgroup with weak patterns and shorter active duration. Future research is needed to investigate the biologic processes related to these behavioral phenotypes, including why earlier and robust activity patterns appear protective, and whether modifying disrupted patterns improves outcomes.

Disrupted sleep and risk of depression in later life: A prospective cohort study with extended follow up and a systematic review and meta-analysis

BACKGROUND

Sleep difficulties increase the risk of current and future depression, but it is unclear if this relationship is causal.

METHODS

Prospective cohort study of a community sample of men aged 70-89 years followed for up to 17 years. Initial assessments occurred between 2001 and 2004. Participants were followed until death or 31 December 2018. Patient Health Questionnaire (PHQ-9) ≥ 10 at subsequent waves of assessments (every 2-3 years) or the recorded diagnosis of a depressive disorder in the Western Australian Data Linkage System marked the onset of depression during follow up. We excluded from follow up men with prevalent depression. The systematic review of longitudinal studies examining the association between disrupted sleep and depression in later life followed PRISMA guidelines.

RESULTS

3441 of 5547 older men reported sleep difficulties at study entry. Current or past depression affected 437 of 5547 participants. Of the 4561 older men free of depression, 2693 reported sleep difficulties. The hazard ratio (HR) of incident depression among participants with sleep problems was 1.67 (95%CI = 1.39-2.00). Statistical adjustments for age, place of birth, education, smoking and physical frailty did not change the effect-size of this association. The systematic review identified another 14 studies, and the meta-analysis yielded an overall risk ratio of depression of 1.82 (95%CI = 1.69-1.97), although the overall quality of available evidence was sub-optimal.

CONCLUSIONS

Disrupted sleep increases the risk of depression in later life and this seems unlikely to be due to reverse causality. Older adults with sleep difficulties are legitimate targets of interventions to prevent depression.

Sleep Deficiency in Obstructive Sleep Apnea

Sleep deficiency in patients with obstructive sleep apnea includes abnormal quality, timing, and duration of sleep, and the presence of other comorbid conditions. These include insomnia, circadian misalignment disorders, and periodic limb movements of sleep. The co-occurrence of these conditions with obstructive sleep apnea likely plays a role in the pathogenesis, clinical presentation, and management of obstructive sleep apnea. Considering these conditions and their treatment in evaluating sleep deficiency in obstructive sleep apnea may help to improve patient outcomes. However, future research is needed to understand the intersection between obstructive sleep apnea and these disorders.

Sleep disorders and non-sleep circadian disorders predict depression: a systematic review and meta-analysis of longitudinal studies

Patients with depression often suffer from sleep disorders and non-sleep circadian disorders. However, whether they precede and predict subsequent depression is unclear. We conducted a meta-analysis of studies on sleep disorders and non-sleep circadian disorders. We found insomnia, hypersomnia, short and long sleep duration, obstructive sleep apnea, restless legs syndrome and eveningness orientation at baseline all led to subsequent depression. Those with propensity to late meal patterns, heightened levels of cortisol in awakening response and low robustness of rest-activity rhythm at baseline had higher risks for later depression. Among insomnia subtypes, difficulty initiating sleep and difficulty maintaining sleep predicted future depression. Notably, persistent insomnia at baseline contributed to more than two-fold risk of incident depression compared to insomnia. Moreover, insomnia symptom numbers showed dose-dependent relationship with the incident depression. In conclusion, different types of sleep disorders and non-sleep circadian disorders were proven to be risk factors of subsequent depression, and mechanisms underlying the relationship between sleep disorders, non-sleep circadian disorders and subsequent depression should be further elucidated in the future.

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Background

Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening.

Objective

The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population.

Methods

This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds.

Results

A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression.

Conclusions

Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

The role of circadian rhythms in Obstructive Sleep Apnea symptoms and novel targets for treatment

Obstructive Sleep Apnea (OSA) is a common disorder that is associated with disability, premature mortality and lost quality of life. Excessive daytime sleepiness and depressive symptoms confer a great portion of the disability and lost quality of life associated with the disorder. While showing robust rates of response and symptoms resolutions, current treatments aimed at correcting the respiratory disturbances are not universally successful and a non-negligible proportion of patients who are correctly using available therapies do not experience symptomatic relief, suggesting that mechanisms beyond the respiratory disturbances may be involved in the pathogenesis of symptoms. A growing body of literature concerning animal and human models suggests that the sleep and respiratory disturbances commonly seen in OSA, namely sleep fragmentation, partial sleep deprivation, intermittent hypoxia, can promote shifts in circadian rhythms ultimately leading to misalignment between sleep-wake rhythms and the internal clock, as well as desynchrony amongst peripheral clocks and peripheral and central clock. This manuscript reviews the current evidence in support of a circadian disturbance underlying OSA symptomatology and proposes new applications for existing chronotherapeutic interventions with the potential for improving symptoms and quality of life for those patients that do not find symptomatic relief with currently available treatments.

Rest-activity rhythms and white matter microstructure across the lifespan

STUDY OBJECTIVES

The purpose of this study was to examine how rest-activity (RA) rhythm stability may be associated with white matter microstructure across the lifespan in healthy adults free of significant cardiovascular risk.

METHODS

We analyzed multi-shell diffusion tensor images from 103 healthy young and older adults using tract-based spatial statistics (TBSS) to examine relationships between white matter microstructure and RA rhythm stability. RA measures were computed using both cosinor and non-parametric methods derived from 7 days of actigraphy data. Fractional anisotropy (FA) and mean diffusivity (MD) were examined in this analysis. Because prior studies have suggested that the corpus callosum (CC) is sensitive to sleep physiology and RA rhythms, we also conducted a focused region of interest analysis on the CC.

RESULTS

Greater rest-activity rhythm stability was associated with greater FA across both young and older adults, primarily in the CC and anterior corona radiata. This effect was not moderated by age group. While RA measures were associated with sleep metrics, RA rhythm measures uniquely accounted for the variance in white matter integrity.

CONCLUSIONS

This study strengthens existing evidence for a relationship between brain white matter structure and RA rhythm stability in the absence of health risk factors. While there are differences in RA stability between age groups, the relationship with brain white matter was present across both young and older adults. RA rhythms may be a useful biomarker of brain health across both periods of adult development.

Circadian activity rhythm in Parkinson's disease: findings from the PHASE study

OBJECTIVE

Circadian disruptions in Parkinson's disease (PD) are characterized as amplitude reduction rather than as phase shift; however, large-scale studies evaluating circadian rhythms between PD patients and non-PD older adults have not been performed. The present study aimed to compare the circadian activity rhythm (CAR) between PD patients and non-PD older adults.

METHODS

In this cross-sectional study on 157 PD outpatients and 1111 community-dwelling older adults (controls), physical activity was measured using actigraphy at 1-min intervals over 6 days in PD patients and 2 days in non-PD older adults. Data were base-10 log-transformed and regretted to the sigmoidally transformed cosine curve.

RESULTS

The mean amplitude (log counts/min) and acrophase were 1.85 (SD, 0.52) and 14:19 (SD, 1:15), respectively, in the controls (n = 1111); 1.42 (0.48) and 14:24 (1:20), respectively, in the early-stage (Hoehn-Yahr I and II) PD patients (n = 95); and 1.23 (0.54) and 13:41 (1:56), respectively, in the late-stage (Hoehn-Yahr III-V) PD patients (n = 62). Multivariable analysis revealed significantly lower amplitude in the early-stage and late-stage PD groups than in the controls. The acrophase significantly advanced in the late-stage PD group than in the controls. With the advancement of PD stage, amplitude and peak significantly decreased; trough increased; acrophase and active offset advanced; and robustness weakened.

CONCLUSIONS

Compared with non-PD older adults, PD patients exhibited a phase advance in CAR, along with amplitude reduction. With an advanced stage of PD, a phase advance in CAR also occurred, along with amplitude reduction and weakened robustness.

Circadian characteristics of the rest-activity rhythm, executive function, and glucose fluctuations in young adults with type 1 diabetes

Circadian alignment is an important element in individual health, and one behavioral marker, rest-activity rhythm, could influence self-management in young adults with type 1 diabetes (T1D). Little is known about the rest-activity rhythms, executive function, and glycemia among young adults with type 1 diabetes (T1D). The purpose of this study was to evaluate parametric and nonparametric circadian characteristics of the rest-activity rhythm and the associations between these variables, sleep-wake behavior, executive function, and glycemia among young adults with T1D. Young adults with T1D, recruited from diabetes clinics, wore wrist actigraphs and a continuous glucose monitor (CGM) concurrently for 6-14 days. Participants completed a 3-minute Trail Making Test on paper and electronic questionnaires - 8-item PROMIS v1.0 Emotional Distress Scale, 17-item Diabetes Distress Scale, including twice-daily Pittsburgh sleep diaries. Cosinor and nonparametric analyses were used to compute the rest-activity rhythm parameters, and linear regression modeling procedures were performed to determine the associations among the study variables. The sample included 46 young adults (mean age 22.3 ± 3.2; 32.6% male; 84.8% non-Hispanic White, HbA1c mean 7.2 ± 1.1%, BMI mean 27.0 ± 4.4 kg/m²). A number of parametric associations were observed between a stronger rhythm, better objective sleep-wake characteristics, and less daytime sleepiness. Nonparametric circadian parameters were significantly associated with several outcomes: a stronger rhythm adherence (higher inter-daily stability) with better objective sleep-wake characteristics, better executive function, lower diabetes distress, less hyperglycemia risk, and more time spent in hypoglycemia/hypoglycemia risk; and a more robust rhythm (higher relative amplitude) with better objective sleep-wake characteristics and more time spent in hypoglycemia/higher hypoglycemia risk. Future work should be directed at designs that test causality, such as interventions directed at the strength and stability of rest-activity rhythms, for the potential to improve glucoregulation and other diabetes outcomes.

Multidimensional sleep health domains in older men and women: an actigraphy factor analysis

The multidimensional sleep health framework emphasizes that sleep can be characterized across several domains, with implications for developing novel sleep treatments and improved prediction and health screening. However, empirical evidence regarding the domains and representative measures that exist in actigraphy-assessed sleep is lacking. We aimed to establish these domains and representative measures in older adults by examining the factor structure of 28 actigraphy-derived sleep measures from 2,841 older men from the Osteoporotic Fractures in Men Sleep Study and, separately, from 2,719 older women from the Study of Osteoporotic Fractures. Measures included means and standard deviations of actigraphy summary measures and estimates from extended cosine models of the raw actigraphy data. Exploratory factor analyses revealed the same five factors in both sexes: Timing (e.g. mean midpoint from sleep onset to wake-up), Efficiency (e.g. mean sleep efficiency), Duration (e.g. mean minutes from sleep onset to wake-up), Sleepiness/Wakefulness (e.g. mean minutes napping and amplitude of rhythm), and Regularity (e.g. standard deviation of the midpoint). Within each sex, confirmatory factor analyses confirmed the one-factor structure of each factor and the entire five-factor structure (Comparative Fit Index and Tucker-Lewis Index ≥ 0.95; Root Mean Square Error of Approximation 0.08-0.38). Correlation magnitudes among factors ranged from 0.01 to 0.34. These findings demonstrate the validity of conceptualizing actigraphy sleep as multidimensional, provide a framework for selecting sleep health domains and representative measures, and suggest targets for behavioral interventions. Similar analyses should be performed with additional measures of rhythmicity, other age ranges, and more racially/ethnically diverse samples.

Irregular sleep-wake patterns in older adults with current or remitted depression

BACKGROUND

Disturbed sleep and irregular sleep-wake patterns have been associated with poor outcomes in older adults. Sleep regularity however has not been studied in a sample with current or remitted major depression.

METHODS

138 participants (63.8±8.6 years; n=27 current major depression, n=64 remitted, and n=47 healthy controls) were monitored using wrist-worn actigraphy. The Sleep Regularity Index (SRI), sleep-wake fragmentation and stability, sleep onset and offset timing, number of awakenings and measures from cosinor analysis were computed.

RESULTS

Compared with controls, older adults with current depression had lower SRI (p < 0.01), lower relative amplitude (p < 0.05), and higher activity during sleeping and post-midnight hours (p < 0.05). Older adults with remitted depression displayed lower activity during the day (p < 0.05), showed reduced average activity and lower amplitude than controls. Total sleep time, sleep timing, and number of awakenings did not differ between groups. All groups differed significantly in self-reported sleep quality and depression severity.

LIMITATIONS

Longitudinal studies which examine how sleep-wake patterns change based on depressive episode recency, severity and how medications may influence these patterns are needed.

CONCLUSIONS

Older adults with current or remitted major depression do not differ from controls on traditional sleep metrics but do report poor quality sleep and show differences in sleep regularity and rest-activity patterns. Reducing the risk of poor outcomes in both groups may be aided by interventions that help promote sleep regularity and increased activity.

Rest-activity rhythms, daytime symptoms, and functional performance among people with heart failure

Sleep disturbance and decreased daytime activity are well-described among people with chronic heart failure (HF) who suffer from disabling daytime symptoms and poor function. Alterations in the circadian rhythmicity of rest-activity may also be associated with these outcomes. However, little is known about the associations between rest-activity rhythms (RARS), symptoms, and functional performance or the extent to which they are explained by sleep characteristics among people with HF. The purpose of this study is to evaluate parametric and non-parametric circadian characteristics of RARs and the associations between these variables, daytime symptoms, and functional performance among patients with stable heart failure (HF). We recruited adults with stable HF from HF disease management programs. Participants wore wrist actigraphs for 3 d, completed one night of unattended polysomnography and the Six Minute Walk Test, and reported daytime symptoms and physical function. We performed cosinor, non-parametric, and spectral analyses to evaluate the rest-activity rhythms and computed bivariate correlations between the rest-activity rhythm, demographics, daytime symptoms, and functional performance. We conducted multiple regression analysis to examine how RARs contribute to daytime symptoms and functional performance after controlling for insomnia and covariates. The sample included 135 participants [Mean age = 60.6 (16.1) y, n = 88 (65.2%) male]. Older age, greater comorbidity, and poorer New York Heart Association (NYHA) Class, and more EEG arousals were associated with greater intra-daily variability of the RAR. More robust rhythmicity represented by the circadian quotient was associated with better NYHA class and less sleep fragmentation. A higher circadian quotient was significantly associated with lower fatigue, depression, and sleepiness, and better functional performance after controlling for insomnia and clinical and demographic characteristics. Circadian parameters of rest-activity are associated with symptoms and functional performance among people with HF independent of insomnia or sleep disordered breathing. Interventions targeted at improving the stability and strength of rest-activity rhythms may improve symptom and functional outcomes for these patients.

A composite measure of sleep health is associated with symptoms of depression among Japanese female hospital nurses

BACKGROUND

Individual dimensions of sleep health, including satisfaction, sleepiness/alertness, timing, efficiency, and duration, are associated with depression. We investigated whether a composite sleep health score is associated with symptoms of depression among Japanese female hospital nurses.

METHODS

Participants were nurses (n = 2482, all women, age 31.2 ± 8.9 years) working at three general hospitals in Tokyo, Japan. A cross-sectional survey, conducted in 2015, assessed self-reported sleep and symptoms of depression. Sleep health was categorized as "good" or "poor" across five dimensions: satisfaction, daytime sleepiness, mid-sleep time, efficiency, and duration. A composite sleep health score was calculated by summing the number of "poor" dimensions. Depression was defined by depressed mood, loss of interest, or at least one of those symptoms ("depression symptoms"). Associations between sleep health and symptoms of depression were evaluated with multivariate logistic regression analyses, adjusting for sociodemographic factors and hypnotic medication use.

RESULTS

In multivariate logistic regression analyses, sleep health symptoms of poor satisfaction, efficiency, and duration were significantly associated with depressed mood; daytime sleepiness and poor efficiency were significantly associated with loss of interest; and poor satisfaction, daytime sleepiness, mid-sleep time, and efficiency were significantly associated with having at least one depressive symptom. The composite sleep health score was associated in a graded fashion with greater odds of depression symptoms.

CONCLUSION

Individual and composite sleep health scores were associated with symptoms of depression. Assessing composite measures of multidimensional sleep health may help to better understand the well-known associations between poor sleep and depression and lead to improved intervention strategies.

Characterizing Behavioral Activity Rhythms in Older Adults Using Actigraphy

Wrist actigraphy has been used to assess sleep in older adult populations for nearly half a century. Over the years, the continuous raw activity data derived from actigraphy has been used for the characterization of factors beyond sleep/wake such as physical activity patterns and circadian rhythms. Behavioral activity rhythms (BAR) are useful to describe individual daily behavioral patterns beyond sleep and wake, which represent important and meaningful clinical outcomes. This paper reviews common rhythmometric approaches and summarizes the available data from the use of these different approaches in older adult populations. We further consider a new approach developed in our laboratory designed to provide graphical characterization of BAR for the observed behavioral phenomenon of activity patterns across time. We illustrate the application of this new approach using actigraphy data collected from a well-characterized sample of older adults (age 60+) with osteoarthritis (OA) pain and insomnia. Generalized additive models (GAM) were implemented to fit smoothed nonlinear curves to log-transformed aggregated actigraphy-derived activity measurements. This approach demonstrated an overall strong model fit (R2 = 0.82, SD = 0.09) and was able to provide meaningful outcome measures allowing for graphical and parameterized characterization of the observed activity patterns within this sample.

Moderators of Response to Cognitive Behavior Therapy for Major Depression in Patients With Heart Failure

OBJECTIVE

Although cognitive behavior therapy (CBT) is efficacious for major depression in patients with heart failure (HF), approximately half of patients do not remit after CBT. To identify treatment moderators that may help guide treatment allocation strategies and serve as new treatment targets, we performed a secondary analysis of a randomized clinical trial. Based on evidence of their prognostic relevance, we evaluated whether clinical and activity characteristics moderate the effects of CBT.

METHODS

Participants were randomized to enhanced usual care (UC) alone or CBT plus enhanced UC. The single-blinded outcomes were 6-month changes in Beck Depression Inventory total scores and remission (defined as a Beck Depression Inventory ≤ 9). Actigraphy was used to assess daily physical activity patterns. We performed analyses to identify the specific activity and clinical moderators of the effects of CBT in 94 adults (mean age = 58, 49% female) with HF and major depressive disorder.

RESULTS

Patients benefited more from CBT (versus UC) if they had the following: more medically severe HF (i.e., a higher New York Heart Association class or a lower left ventricular ejection fraction), more stable activity patterns, wider active periods, and later evening settling times. These individual moderator effects were small (|r| = 0.10-0.21), but combining the moderators yielded a medium moderator effect size (r = 0.38; 95% CI = 0.20-0.52).

CONCLUSIONS

These findings suggest that increasing the cross-daily stability of activity patterns, and prolonging the daily active period, might help increase the efficacy of CBT. Given moderating effects of HF severity measures, research is also needed to clarify and address factors in patients with less severe HF that diminish the efficacy of CBT.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov identifier: NCT01028625.

Impact of Poor Sleep on Physical and Mental Health in Older Women

Many aspects of sleep and circadian rhythms change as people age. Older adults usually experience decrease in sleep duration and efficiency, increase in sleep latency and fragmentation, high prevalence of sleep disorders, and weakened rest-activity rhythms. Research evidence suggests that women are more likely to report aging-related sleep problems. This review presents epidemiologic and clinical evidence on the relationships between sleep deficiency and physical and mental outcomes in older women, explores potential mechanisms underlying such relationships, points out gaps in the literature that warrant future investigations, and considers implications in clinical and public health settings.

Rest-activity rhythm profiles associated with manic-hypomanic and depressive symptoms

Rest-activity rhythm (RAR) disturbances are associated with mood disorders. But there remains a need to identify the particular RAR profiles associated with psychiatric symptom dimensions. Establishing such profiles would support the development of tools that track the 24-h sleep-wake phenotypes signaling clinical heterogeneity. We used data-driven clustering to identify RAR profiles in 145 adults aged 36-82 years (mean = 60, standard deviation = 9). Then we evaluated psychiatric symptom dimensions (including positive and negative affect, depressive, manic-hypomanic, panic-agoraphobic, and substance use symptoms) associated with these empirically-derived RAR profiles. Clustering identified three sub-groups characterized, on average, by: (1) earlier and more robust RARs ("earlier/robust," n = 55, 38%); (2) later and irregular RARs ("later/irregular," n = 31, 21%); and (3) later RARs and a narrower active period ("later/narrower," n = 59, 41%). Compared with the "earlier/robust" group: the "later/irregular" group had higher levels of lifetime manic-hypomanic symptoms (β (standard error) = 0.80 (0.22) higher standardized symptom units, p = 0.0004) and lifetime depression symptoms (β (standard error) = 0.73 (0.21) higher standardized symptom units, p = 0.0009); the "later/narrower" group had more lifetime depression symptoms (β (standard error) = 0.48 (0.18) higher standardized symptom units, p = 0.0076). These associations persisted after adjustments for sleep continuity and duration, suggesting that RARs are distinct behavioral correlates of clinical heterogeneity. Longitudinal studies are needed to confirm whether RAR characteristics contribute to the risk of manic and/or depressive episodes, and whether they reflect the consequences of psychiatric disturbance (e.g., on quality of life or disability). Opportunities to monitor or intervene on objectively-assessed RARs could facilitate better mental health related outcomes.

Which Sleep Health Characteristics Predict All-Cause Mortality in Older Men? An Application of Flexible Multivariable Approaches

Study Objectives

Sleep is multidimensional, with domains including duration, timing, continuity, regularity, rhythmicity, quality, and sleepiness/alertness. Individual sleep characteristics representing these domains are known to predict health outcomes. However, most studies consider sleep characteristics in isolation, resulting in an incomplete understanding of which sleep characteristics are the strongest predictors of health outcomes. We applied three multivariable approaches to robustly determine which sleep characteristics increase mortality risk in the osteoporotic fractures in men sleep study.

Methods

In total, 2,887 men (mean 76.3 years) completed relevant assessments and were followed for up to 11 years. One actigraphy or self-reported sleep characteristic was selected to represent each of seven sleep domains. Multivariable Cox models, survival trees, and random survival forests were applied to determine which sleep characteristics increase mortality risk.

Results

Rhythmicity (actigraphy pseudo-F statistic) and continuity (actigraphy minutes awake after sleep onset) were the most robust sleep predictors across models. In a multivariable Cox model, lower rhythmicity (hazard ratio, HR [95%CI] =1.12 [1.04, 1.22]) and lower continuity (1.16 [1.08, 1.24]) were the strongest sleep predictors. In the random survival forest, rhythmicity and continuity were the most important individual sleep characteristics (ranked as the sixth and eighth most important among 43 possible sleep and non-sleep predictors); moreover, the predictive importance of all sleep information considered simultaneously followed only age, cognition, and cardiovascular disease.

Conclusions

Research within a multidimensional sleep health framework can jumpstart future research on causal pathways linking sleep and health, new interventions that target specific sleep health profiles, and improved sleep screening for adverse health outcomes.

An Aggregate Measure of Sleep Health Is Associated With Prevalent and Incident Clinically Significant Depression Symptoms Among Community-Dwelling Older Women

Objectives

Sleep can be characterized along multiple dimensions. We investigated whether an aggregate measure of sleep health was associated with prevalent and incident clinically significant depression symptoms in a cohort of older women.

Methods

Participants were older women (mean age 80.1 years) who completed baseline (n = 6485) and follow-up (n = 3806) visits, approximately 6 years apart, in the Study of Osteoporotic Fractures (SOF). Self-reported sleep over the past 12 months was categorized as "good" or "poor" across 5 dimensions: satisfaction with sleep duration, daytime sleepiness, mid-sleep time, sleep onset latency, and sleep duration. An aggregate measure of sleep health was calculated by summing the number of "poor" dimensions. Clinically significant depression symptoms were defined as a score ≥6 on the Geriatric Depression Scale. Relationships between sleep health and depression symptoms were evaluated with multivariate logistic regression, adjusting for health measures and medications.

Results

Individual sleep health dimensions of sleep satisfaction, daytime sleepiness, mid-sleep time, and sleep onset latency were significantly associated with prevalent depression symptoms (odds ratios [OR] = 1.26-2.69). Sleep satisfaction, daytime sleepiness, and sleep onset latency were significantly associated with incident depression symptoms (OR = 1.32-1.79). The number of "poor" sleep health dimensions was associated in a gradient fashion with greater odds of prevalent (OR = 1.62-5.41) and incident (OR = 1.47-3.15) depression symptoms.

Conclusion

An aggregate, multidimensional measure of sleep health was associated with both prevalent and incident clinically-significant depression symptoms in a gradient fashion. Future studies are warranted to extend these findings in different populations and with different health outcomes.

Variation in actigraphy-estimated rest-activity patterns by demographic factors

Rest-activity patterns provide an indication of circadian rhythmicity in the free-living setting. We aimed to describe the distributions of rest-activity patterns in a sample of adults and children across demographic variables. A sample of adults (N = 590) and children (N = 58) wore an actigraph on their nondominant wrist for 7 days and nights. We generated rest-activity patterns from cosinor analysis (MESOR, acrophase and magnitude) and nonparametric circadian rhythm analysis (IS: interdaily stability; IV: intradaily variability; L5: least active 5-hour period; M10: most active 10-hour period; and RA: relative amplitude). Demographic variables included age, sex, race, education, marital status, and income. Linear mixed-effects models were used to test for demographic differences in rest-activity patterns. Adolescents, compared to younger children, had (1) later M10 midpoints (β = 1.12 hours [95% CI: 0.43, 1.18] and lower M10 activity levels; (2) later L5 midpoints (β = 1.6 hours [95% CI: 0.9, 2.3]) and lower L5 activity levels; (3) less regular rest-activity patterns (lower IS and higher IV); and 4) lower magnitudes (β = -0.95 [95% CI: -1.28, -0.63]) and relative amplitudes (β = -0.1 [95% CI: -0.14, -0.06]). Mid-to-older adults, compared to younger adults (aged 18-29 years), had (1) earlier M10 midpoints (β = -1.0 hours [95% CI: -1.6, -0.4]; (2) earlier L5 midpoints (β = -0.7 hours [95% CI: -1.2, -0.2]); and (3) more regular rest-activity patterns (higher IS and lower IV). The magnitudes and relative amplitudes were similar across the adult age categories. Sex, race and education level rest-activity differences were also observed. Rest-activity patterns vary across the lifespan, and differ by race, sex and education. Understanding population variation in these patterns provides a foundation for further elucidating the health implications of rest-activity patterns across the lifespan.

Rest-activity rhythm and sleep characteristics associated with depression symptom severity in strained dementia caregivers

Depression is associated with disturbances to sleep and the 24-h sleep-wake pattern (known as the rest-activity rhythm: RAR). However, there remains a need to identify the specific sleep/RAR correlates of depression symptom severity in population subgroups, such as strained dementia caregivers, who are at elevated risk for major depressive disorder. We assessed the cross-sectional associations of sleep/RARs with non-sleep depression symptom severity among 57 (mean age: 74 years, standard deviation: 7.4) strained dementia caregivers who were currently without clinical depression. We derived sleep measures from polysomnography and actigraphy, modelled RARs using a sigmoidally transformed cosine curve and measured non-sleep depression symptom severity using the Hamilton Depression Rating Scale (HRDS) with sleep items removed. The following sleep-wake measures were associated with greater depression symptom severity (absolute Spearman's correlations ranged from 0.23 to 0.32): more time awake after sleep onset (WASO), higher RAR middle level (mesor), relatively shorter active periods (alpha), earlier evening settling time (down-mesor) and less steep RARs (beta). In multivariable analysis, high WASO and low RAR beta were associated independently with depression symptom severity. Predicted non-sleep HDRS means (95% confidence intervals) in caregivers with and without these characteristics were: normal WASO/beta = 3.7 (2.3-5.0), high WASO/normal beta = 5.5 (3.5-7.6), normal WASO/low beta = 6.3 (3.6-8.9) and high WASO/low beta = 8.1 (5.3-10.9). Thus, in our sample of strained caregivers, greater sleep fragmentation (WASO) and less sustained/sharply segregated resting and active periods (low RAR beta) correlate uniquely with depression symptom severity. Longitudinal studies are needed to establish whether these independent sleep-wake correlates of depression symptoms explain heightened depression risk in dementia caregivers.

Sex Differences in the Relationship Between Depressive Symptoms and Actigraphic Assessments of Sleep and Rest-Activity Rhythms in a Population-Based Sample

OBJECTIVE

Depression is often associated with disruptions in sleep and circadian rhythms. We aimed to confirm these relationships via actigraphic assessment in a large, population-based sample and test whether sex moderates these relationships.

METHODS

A total of 418 participants (age = 35-85 years, mean [standard deviation] = 57.04 [11.47]) completed questionnaires and 1 week of actigraphy, used to calculate sleep and rest-activity statistics including mesor (mean activity level), amplitude (height of rhythm), and acrophase (time of day that rhythm peaks).

RESULTS

Depressive symptoms, assessed via Center for Epidemiologic Studies Depression Scale, were associated with disrupted sleep and rest-activity rhythms. Furthermore, men demonstrated longer sleep onset latency (SOL, B = -13.28, p < .001), longer wake time after sleep onset (B = -6.26, p < .01), lower sleep efficiency (B = 5.91, p < .001), and lower total sleep time (TST, B = 33.16, p < .001) than women. Sex moderated the relationship between depression and SOL, TST, mesor, and amplitude; sex-stratified models revealed that higher depression scores were associated with greater SOL (B = 1.05, p < .001) and less TST (B = -0.87, p < .10) for women with higher depressive symptoms, but lower mesor (B = -1.75, p < .01) and amplitude (B = -1.94, p < .01) for men with higher depressive symptoms.

CONCLUSIONS

Depressive symptoms were related to disrupted sleep continuity and rest-activity rhythms in this population-based sample; however, these relationships differed by sex. Women with greater depressive symptoms exhibited difficulty with sleep continuity, whereas men with greater depressive symptoms demonstrated disruption throughout the 24-hour rhythm.

Opportunities for clinical applications of rest-activity rhythms in detecting and preventing mood disorders

PURPOSE OF REVIEW

Rest-activity rhythm (RAR) measurements may aid in the detection of depression risk and serve as an important target for depression prevention. This review evaluates the strength of current evidence supporting these potential applications.

RECENT FINDINGS

Depression is associated with lower activity levels, that is less regularly patterned, and potentially shifted earlier or later in the day. Specific RAR patterns (combinations of several RAR characteristics) in patients with clinical depression may be unique or partially shared across disorders. Longitudinal research is limited but provides initial evidence that multiple distinct RAR patterns are associated with the risk of developing depression symptoms.

SUMMARY

RAR measures provide a comprehensive and objective assessment of depression's behavioral manifestations, and therefore may be useful as monitoring tool, providing additional information to help clinicians tailor behavioral treatments to specific patients. RARs also appear to contribute to depression risk and may be an important target for depression prevention. But research has not established valid predictive metrics using RARs to diagnose depression or detect depression risk. Future research should prioritize establishing the specific RAR patterns related to depression risk in high-risk groups, and should seek to place this risk within the known psychosocial and neurobiological risk architecture of depression.

Seasonal and meteorological associations with depressive symptoms in older adults: A geo-epidemiological study

BACKGROUND

Given increased social and physiological vulnerabilities, older adults may be particularly susceptible to environmental influences on mood. Whereas the impact of season on mood is well described for adults, studies rarely extend to elders or include objective weather data. We investigated the impact of seasonality and meteorological factors on risk of current depressive symptoms in older adults.

METHODS

We used data on 8027 participants from the first wave of The Irish Longitudinal Study of Ageing, a population-representative cohort of adults aged 50+. Depressive symptoms were recorded using the Centre for Epidemiological Studies Depression Scale. Season was defined according to the World Meteorological Organisation. Data on climate over the preceding thirty years, and temperature and rain over the preceding month, were provided by the Irish Meteorological Service and linked using Geographic Information Systems techniques to participant's geo-coded locations at a resolution of one kilometre.

RESULTS

The highest levels of depressive symptoms were reported in winter and the lowest in spring (mean 6.56 [CI95% 6.09, 7.04] vs. 5.81 [CI95%: 5.40, 6.22]). In fully adjusted linear regression models, participants living in areas with higher levels of rainfall in the preceding and/or current calendar month had greater depressive symptoms (0.04 SE 0.02; p=0.039 per 10mm additional rainfall per month) while those living in areas with sunnier climates had fewer depressive symptoms (-2.67 SE 0.88; p=0.003 for every additional hour of average annual daily sunshine).

LIMITATIONS

This was a cross-sectional analysis thus causality cannot be inferred; monthly rain and temperature averages were available only on a calendar month basis while monthly local levels of sunshine data were not available.

CONCLUSIONS

Environmental cues may influence mood in older adults and thus have relevance for the recognition and treatment of depression in this age group.

Latent activity rhythm disturbance sub-groups and longitudinal change in depression symptoms among older men

Activity rhythm disturbances and depression often co-occur among older adults. However, little is known about how activity rhythm disturbances themselves co-occur, or how disturbances to multiple aspects of the activity rhythm relate to depression over time. In this study, we performed a Latent Class Analysis to derive sub-groups of older men [total n = 2933, mean age = 76.28, standard deviation (SD) = 5.48] who shared similar patterns of activity rhythm disturbances (defined as extreme values of modeled activity rhythm parameters). We found eight sub-groups with distinct combinations of activity rhythm disturbances: one had all normative activity rhythm parameters (32.09%), one had only lower activity (10.06%), three had earlier activity (totaling 26.96%) and three had later activity (totaling 30.89%). Groups with similar timing were distinguished depending on whether the relative length of the active period was shorter and/or if the activity rhythm had lesser amplitude/robustness. We next examined whether the derived activity rhythm sub-groups were associated with different rates of change in depression symptom levels over an average of 5.5 (0.52 SD) follow-up years. The sub-group with lower activity only had faster increases in depressive symptoms over time (compared with the group with normative rhythm parameters), but this association was accounted for by adjustments for concurrently assessed health status covariates. Independent of these covariates, we found that four activity rhythm disturbance sub-groups experienced faster depressive symptom increases (compared with the normative sub-group): These included all three sub-groups that had later activity timing and one sub-group that had earlier activity timing plus a shorter active period and a dampened rhythm. Low activity rhythm height/robustness with normal timing therefore may mark depression risk that is attributable to co-occurring disease processes; in contrast, having late or combined early/compressed/dampened activity rhythms may independently contribute to depression symptom development. Our findings suggest that activity rhythm-related depression risk is heterogeneous, and may be detected when multiple aspects of rhythm timing are delayed or when early timing is accompanied by compressed/dampened activity rhythms. Future studies should consider how distinct combinations of altered activity rhythm timing and height/robustness develop and conjointly determine health risks. Further research is also needed to determine whether/how activity rhythms can be modified to improve depression outcomes.

Depressive Symptoms are the Main Predictor for Subjective Sleep Quality in Patients with Mild Cognitive Impairment--A Controlled Study

Objective

Controlled data on predictors of subjective sleep quality in patients with memory complaints are sparse. To improve the amount of comprehensive data on this topic, we assessed factors associated with subjective sleep quality in patients from our memory clinic and healthy individuals.

Methods

Between February 2012 and August 2014 patients with mild cognitive impairment (MCI) and subjective cognitive decline (SCD) from our memory clinic and healthy controls were recruited. Apart from a detailed neuropsychological assessment, the subjective sleep quality, daytime sleepiness and depressive symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS) and the Beck Depression Inventory (BDI-II).

Results

One hundred fifty eight consecutive patients (132 (84%) MCI patients and 26 (16%) SCD patients) and 75 healthy controls were included in the study. Pairwise comparison of PSQI scores showed that non-amnestic MCI (naMCI) patients (5.4±3.5) had significantly higher PSQI scores than controls (4.3±2.8, p = .003) Pairwise comparison of PSQI subscores showed that naMCI patients (1.1±0.4) had significantly more “sleep disturbances” than controls (0.9±0.5, p=.003). Amnestic MCI (aMCI) (0.8±1.2, p = .006) and naMCI patients (0.7±1.2, p = .002) used “sleep medication” significantly more often than controls (0.1±0.6) Both, aMCI (11.5±8.6, p<.001) and naMCI (11.5±8.6, p<.001) patients showed significantly higher BDI-II scores than healthy controls (6.1±5.3). Linear regression analysis showed that the subjective sleep quality was predicted by depressive symptoms in aMCI (p<.0001) and naMCI (p<.0001) patients as well as controls (p<.0001). This means, that more depressive symptoms worsened subjective sleep quality. In aMCI patients we also found a significant interaction between depressive symptoms and global cognitive function (p = .002)

Discussion

Depressive symptoms were the main predictor of subjective sleep quality in MCI patients and controls, but not in SCD patients. Better global cognitive function ameliorated the negative effect of depressive symptoms on the subjective sleep quality in aMCI patients.