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

Multidimensional Sleep Profiles via Machine learning and Risk of Dementia and Cardiovascular Disease

Importance

Sleep health comprises several dimensions such as duration and fragmentation of sleep, circadian activity, and daytime behavior. Yet, most research has focused on individual sleep characteristics. Studies are needed to identify sleep profiles incorporating multiple dimensions and to assess how different profiles may be linked to adverse health outcomes.

Objective

To identify actigraphy-based 24-hour sleep/circadian profiles in older men and to investigate whether these profiles are associated with the incidence of dementia and cardiovascular disease (CVD) events over 12 years.

Design

Data came from a prospective sleep study with participants recruited between 20032005 and followed until 2015-2016.

Setting

Multicenter population-based cohort study.

Participants

Among the 3,135 men enrolled, we excluded 331 men with missing or invalid actigraphy data and 137 with significant cognitive impairment at baseline, leading to a sample of 2,667 participants.

Exposures

Leveraging 20 actigraphy-derived sleep and circadian activity rhythm variables, we determined sleep/circadian profiles using an unsupervised machine learning technique based on multiple coalesced generalized hyperbolic mixture modeling.

Main Outcomes and Measures

Incidence of dementia and CVD events.

Results

We identified three distinct sleep/circadian profiles: active healthy sleepers (AHS; n=1,707 (64.0%); characterized by normal sleep duration, higher sleep quality, stronger circadian rhythmicity, and higher activity during wake periods), fragmented poor sleepers (FPS; n=376 (14.1%); lower sleep quality, higher sleep fragmentation, shorter sleep duration, and weaker circadian rhythmicity), and long and frequent nappers (LFN; n=584 (21.9%); longer and more frequent naps, higher sleep quality, normal sleep duration, and more fragmented circadian rhythmicity). Over the 12-year follow-up, compared to AHS, FPS had increased risks of dementia and CVD events (Hazard Ratio (HR)=1.35, 95% confidence interval (CI)=1.02-1.78 and HR=1.32, 95% CI=1.08-1.60, respectively) after multivariable adjustment, whereas LFN showed a marginal association with increased CVD events risk (HR=1.16, 95% CI=0.98-1.37) but not with dementia (HR=1.09, 95%CI=0.86-1.38).

Conclusion and Relevance

We identified three distinct multidimensional profiles of sleep health. Compared to healthy sleepers, older men with overall poor sleep and circadian activity rhythms exhibited worse incident cognitive and cardiovascular health. These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes.

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.

Stability and Volatility of Human Rest-Activity Rhythms: Insights from Very Long Actograms (VLAs)

Importance

Wrist-worn activity monitors provide biomarkers of health by non-obtrusively measuring the timing and amount of rest and physical activity (rest-activity rhythms, RARs). The morphology and robustness of RARs vary by age, gender, and sociodemographic factors, and are perturbed in various chronic illnesses. However, these are cross-sectionally derived associations from recordings lasting 4-10 days, providing little insights into how RARs vary with time.

Objective

To describe how RAR parameters can vary or evolve with time (~months).

Design Setting and Participants

48 very long actograms ("VLAs", ≥90 days in duration) were identified from subjects enrolled in the STAGES (Stanford Technology, Analytics and Genomics in Sleep) study, a prospective cross-sectional, multi-site assessment of individuals > 13 years of age that required diagnostic polysomnography to address a sleep complaint. A single 3-year long VLA (author GD) is also described.

Exposures/Intervention

None planned.

Main Outcomes and Measures

For each VLA, we assessed the following parameters in 14-day windows: circadian/ultradian spectrum, pseudo-F statistic ("F"), cosinor amplitude, intradaily variability, interdaily stability, acrophase and estimates of "sleep" and non-wearing.

Results

Included STAGES subjects (n = 48, 30 female) had a median age of 51, BMI of 29.4kg/m2, Epworth Sleepiness Scale score (ESS) of 10/24 and a median recording duration of 120 days. We observed marked within-subject undulations in all six RAR parameters, with many subjects displaying ultradian rhythms of activity that waxed and waned in intensity. When appraised at the group level (nomothetic), averaged RAR parameters remained remarkably stable over a ~4 month recording period. Cohort-level deficits in average RAR robustness associated with unemployment or high BMI (>29.4) also remained stable over time.

Conclusions and Relevance

Through an exemplary set of months-long wrist actigraphy recordings, this study quantitatively depicts the longitudinal stability and dynamic range of human rest-activity rhythms. We propose that continuous and long-term actigraphy may have broad potential as a holistic, transdiagnostic and ecologically valid monitoring biomarker of changes in chronobiological health. Prospective recordings from willing subjects will be necessary to precisely define contexts of use.

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-Hour Rest-Activity Rhythm in Middle-Aged and Older Persons with Depression

Changes in rest or active states were clinically observed in persons with depression. However, the association between symptoms of depression and 24 h rest-activity rhythm (RAR) components that can be measured using wearable devices was not clarified. This preliminary cross-sectional study aimed to clarify the 24 h RAR components associated with symptoms of depression in middle-aged and older persons. Participants were recruited from among inpatients and outpatients requiring medical treatment at Akita University Hospital for the group with depression and from among healthy volunteers living in Akita prefecture, Japan, for the healthy control group. To assess RAR parameters including inter-daily stability (IS), intra-daily variability (IV), relative amplitude (RA), and average physical activity level for the most active 10 h span (M10) or for the least active 5 h span (L5), all the participants were instructed to wear an Actiwatch Spectrum Plus device on their non-dominant wrist for seven days. Twenty-nine persons with depression and 30 controls were included in the analysis. The results of a binomial regression analysis showed that symptoms of depression were significantly associated with a high IS value (odds ratio [OR], 1.20; 95% confidence interval [95% CI], 1.01-1.44; p = 0.04) and a low M10 value (OR, 0.85; 95% CI, 0.74-0.96; p = 0.01). Our findings suggest potential components of 24 h RAR are associated with depression.

The bidirectional association of 24-h activity rhythms and sleep with depressive symptoms in middle-aged and elderly persons

BACKGROUND

In older populations disturbed 24-h activity rhythms, poor sleep, and depressive symptoms are often lingering and co-morbid, making treatment difficult. To improve insights into these commonly co-occurring problems, we assessed the bidirectional association of sleep and 24-h activity rhythms with depressive symptoms in middle-aged and elderly persons.

METHODS

In 1734 participants (mean age: 62.3 ± 9.3 years, 55% women) from the prospective Rotterdam Study, 24-h activity rhythms and sleep were estimated with actigraphy (mean duration: 146 ± 19.6 h), sleep quality with the Pittsburgh Sleep Quality Index, and depressive symptoms with the Center for Epidemiological Studies Depression scale. Repeated measures were available for 947 participants (54%) over a median follow-up of 6 years (interquartile range = 5.6-6.3). Linear-mixed models were used to assess temporal associations of 24-h activity rhythms and sleep with depressive symptoms in both directions.

RESULTS

High 24-h activity rhythm fragmentation (IV) (B = 1.002, 95% confidence interval (CI) = 0.641-1.363), long time in bed (TIB) (B = 0.111, 95% CI = 0.053-0.169), low sleep efficiency (SE) (B = -0.015, 95% CI = -0.020 to -0.009), long sleep onset latency (SOL) (B = 0.009, 95% CI = 0.006-0.012), and low self-rated sleep quality (B = 0.112, 95% CI = 0.0992-0.124) at baseline were associated with increasing depressive symptoms over time. Conversely, more depressive symptoms at baseline were associated with an increasing 24-h activity rhythm fragmentation (B = 0.002, 95% CI = 0.001-0.003) and TIB (B = 0.009, 95% CI = 0.004-0.015), and a decreasing SE (B = -0.140, 95% CI = -0.196 to -0.084), SOL (B = 0.013, 95% CI = 0.008-0.018), and self-rated sleep quality (B = 0.193, 95% CI = 0.171-0.215) over time.

CONCLUSION

This study demonstrates a bidirectional association of 24-h activity rhythms, actigraphy-estimated sleep, and self-rated sleep quality with depressive symptoms over a time frame of multiple years in middle-aged and elderly persons.

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.

Actigraphy-derived sleep health profiles and mortality in older men and women

STUDY OBJECTIVES

To identify actigraphy sleep health profiles in older men (Osteoporotic Fractures in Men Study; N = 2640) and women (Study of Osteoporotic Fractures; N = 2430), and to determine whether profile predicts mortality.

METHODS

We applied a novel and flexible clustering approach (Multiple Coalesced Generalized Hyperbolic mixture modeling) to identify sleep health profiles based on actigraphy midpoint timing, midpoint variability, sleep interval length, maintenance, and napping/inactivity. Adjusted Cox models were used to determine whether profile predicts time to all-cause mortality.

RESULTS

We identified similar profiles in men and women: High Sleep Propensity [HSP] (20% of women; 39% of men; high napping and high maintenance); Adequate Sleep [AS] (74% of women; 31% of men; typical actigraphy levels); and Inadequate Sleep [IS] (6% of women; 30% of men; low maintenance and late/variable midpoint). In women, IS was associated with increased mortality risk (Hazard Ratio [HR] = 1.59 for IS vs. AS; 1.75 for IS vs. HSP). In men, AS and IS were associated with increased mortality risk (1.19 for IS vs. HSP; 1.22 for AS vs. HSP).

CONCLUSIONS

These findings suggest several considerations for sleep-related interventions in older adults. Low maintenance with late/variable midpoint is associated with increased mortality risk and may constitute a specific target for sleep health interventions. High napping/inactivity co-occurs with high sleep maintenance in some older adults. Although high napping/inactivity is typically considered a risk factor for deleterious health outcomes, our findings suggest that it may not increase risk when it occurs in combination with high sleep maintenance.

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 Effect of Bright Light Treatment on Rest-Activity Rhythms in People with Dementia: A 24-Week Cluster Randomized Controlled Trial

Bright light treatment is an effective way to influence circadian rhythms in healthy adults, but previous research with dementia patients has yielded mixed results. The present study presents a primary outcome of the DEM.LIGHT trial, a 24-week randomized controlled trial conducted at nursing homes in Bergen, Norway, investigating the effects of a bright light intervention. The intervention consisted of ceiling-mounted LED panels providing varying illuminance and correlated color temperature throughout the day, with a peak of 1000 lx, 6000 K between 10 a.m. and 3 p.m. Activity was recorded using actigraphs at baseline and after 8, 16, and 24 weeks. Non-parametric indicators and extended cosine models were used to investigate rest-activity rhythms, and outcomes were analyzed with multi-level regression models. Sixty-one patients with severe dementia (median MMSE = 4) were included. After 16 weeks, the acrophase was advanced from baseline in the intervention group compared to the control group (B = -1.02, 95%; CI = -2.00, -0.05). There was no significant difference between the groups on any other rest-activity measures. When comparing parametric and non-parametric indicators of rest-activity rhythms, 25 out of 35 comparisons were significantly correlated. The present results indicate that ambient bright light treatment did not improve rest-activity rhythms for people with dementia.

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

Background

In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process.

Objective

This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined.

Methods

We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]).

Results

In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892).

Conclusions

ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted.

Level and timing of physical activity during normal daily life in depressed and non-depressed individuals

Engaging in physical activity is known to reduce depressive symptoms. However, little is known which behavioral factors are relevant, and how patterns of activity change during depressive episodes. We expected that compared to controls, in depressed individuals the level of activity would be lower, the amplitude of 24-h-actigraphy profiles more dampened and daytime activities would start later. We used 14-day continuous-actigraphy data from participants in the Netherlands Study of Depression and Anxiety (NESDA) who participated in an ambulatory assessment study. Participants with a depression diagnosis in the past 6 months (n = 58) or its subsample with acute depression (DSM diagnosis in the past 1 month, n = 43) were compared to controls without diagnoses (n = 63). Depression was diagnosed with a diagnostic interview. Actigraphy-derived variables were activity mean levels (MESOR), the difference between peak and mean level (amplitude) and the timing of the activity peak (acrophase), which were estimated with cosinor analysis. Compared to the control group, both depression groups (total: B = -0.003, p = 0.033; acute: B = -0.004, p = 0.005) had lower levels of physical activity. Amplitude was also dampened, but in the acute depression group only (total: B = -0.002, p = 0.065; acute: B = -0.003, p = 0.011). Similarly, the timing of activity was marginally significant towards a later timing of activity in the acute, but not total depression group (total: B = 0.206, p = 0.398; acute: B = 0.405, p = 0.084). In conclusion, our findings may be relevant for understanding how different aspects of activity (level and timing) contribute to depression. Further prospective research is needed to disentangle the direction of the association between depression and daily rest-activity rhythms.

The associations between various sleep-wake disturbances and depression in community-dwelling older adults- the Yilan study, Taiwan

Objectives: Given the close relationship between sleep-wake disturbances and depression, an in-depth investigation of such a relationship is imperative. The present study aims at elucidating the relationship between various sleep-wake disturbances and depression in older adults and at examining the influence of co-occurring anxiety on such associations.Method: A community-based survey using the cohort from the Yilan Study in Taiwan was conducted from August 2013 to November 2016. Adults aged 65 and older were randomly selected to participate in the study. The Hospital Depression and Anxiety Scale was used to measure clinical depressive and anxiety symptoms. Insomnia and daytime sleepiness were defined through the Athens Insomnia Scale and the Epworth Sleepiness Scale, respectively. Furthermore, the use of hypnotics, subjective sleep duration and sleep-wake scheduling were evaluated. Their relationship with depression was examined through logistic regression analyses.Results: There were 2620 participants surveyed and 247 (9.4%) had depression. Before controlling for anxiety, insomnia (OR: 1.78, 95% CI: 1.23-2.55), daytime sleepiness (OR: 1.79, 95% CI: 1.27-2.53), and long sleepers (OR: 1.77, 95% CI: 1.24-2.53) have a higher likelihood for depression in the multivariable regression analysis. However, when including anxiety into the multivariable regression model, only those with daytime sleepiness and long sleepers had an elevated risk for depression. Therefore, the association between insomnia and depression turned to be statistically non-significant.Conclusion: In older adults, various sleep-wake disturbances differ in their relationship with depression. In addition, daytime sleepiness and long sleep duration were mostly characteristic of depression when co-occurring anxiety was considered.

Subgrouping Poor Sleep Quality in Community-Dwelling Older Adults with Latent Class Analysis - The Yilan Study, Taiwan

The manifestation of older adults with poor sleep quality is heterogeneous. Using data-driven classifying methods, the study aims to subgroup community-dwelling older adults with poor sleep quality. Adults aged 65 and older participated in the Yilan study. Poor sleep quality was defined using the Pittsburgh Sleep Quality Index. Latent class analysis with the 7 subscores of the Pittsburgh Sleep Quality Index as the indicators was used to generate empirical subgroups. Differences in comorbidity patterns between subgroups were compared. A total of 2622 individuals, of which 1011 (38.6%) had Pittsburgh Sleep Quality Index -defined poor sleep quality, participated. Three groups for poor sleep quality were specified in the latent class analysis: High Insomnia (n = 191, 7.3%), Mild Insomnia (n = 574, 21.9%), and High Hypnotics (n = 246, 9.4%). The High Insomnia and Mild Insomnia groups shared similar profiles but different severities in the 7 domains of the Pittsburgh Sleep Quality Index. In contrast, the High Hypnotics group had the lowest Pittsburgh Sleep Quality Index total scores and insomnia severity but had similar mental and physical comorbid patterns as the High Insomnia group. This finding suggests that poor sleep quality in community-dwelling older adults had various feature-based subgroups. It also implicates the development of group-centered interventions.

24-h Activity Rhythms and Health in Older Adults

Purpose of Review

Circadian rhythms, including 24-h activity rhythms, change with age. Disturbances in these 24-h activity rhythms at older age have also been implied in various diseases. This review evaluates recent findings on 24-h activity rhythms and disease in older adults.

Recent Findings

Growing evidence supports that 24-h activity rhythm disturbances at older age are related to the presence and/or progression of disease. Longitudinal and genetic work even suggests a potential causal contribution of disturbed 24-h activity rhythms to disease development. Interventional studies targeting circadian and 24-h activity rhythms demonstrate that 24-h rhythmicity can be improved, but the effect of improving 24-h rhythmicity on disease risk or progression remains to be shown.

Summary

Increasing evidence suggests that 24-h activity rhythms are involved in age-related diseases. Further studies are needed to assess causality, underlying mechanisms, and the effects of treating disturbed 24-h activity rhythms on age-related disease.

Using Chronobiological Phenotypes to Address Heterogeneity in Bipolar Disorder

Bipolar disorder (BD) is a neuropsychiatric mood disorder characterized by recurrent episodes of mania and depression in addition to disruptions in sleep, energy, appetite, and cognitive functions-rhythmic behaviors that typically change on daily cycles. BD symptoms can also be provoked by seasonal changes, sleep, and/or circadian disruption, indicating that chronobiological factors linked to the circadian clock may be a common feature in the disorder. Research indicates that BD exists on a clinical spectrum, with distinct subtypes often intersecting with other psychiatric disorders. This heterogeneity has been a major challenge to BD research and contributes to problems in diagnostic stability and treatment outcomes. To address this heterogeneity, we propose that chronobiologically related biomarkers could be useful in classifying BD into objectively measurable phenotypes to establish better diagnoses, inform treatments, and perhaps lead to better clinical outcomes. Presently, we review the biological basis of circadian time keeping in humans, discuss the links of BD to the circadian clock, and pre-sent recent studies that evaluated chronobiological measures as a basis for establishing BD phenotypes. We conclude that chronobiology may inform future research using other novel techniques such as genomics, cell biology, and advanced behavioral analyses to establish new and more biologically based BD phenotypes.

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.

Treating insomnia in depression: Insomnia related factors predict long-term depression trajectories

OBJECTIVE

Insomnia and major depressive disorders (MDD) often co-occur, and such comorbidity has been associated with poorer outcomes for both conditions. However, individual differences in depressive symptom trajectories during and after treatment are poorly understood in comorbid insomnia and depression. This study explored the heterogeneity in long-term depression change trajectories, and examined their correlates, particularly insomnia-related characteristics.

METHOD

Participants were 148 adults (age M ± SD = 46.6 ± 12.6, 73.0% female) with insomnia and MDD who received antidepressant pharmacotherapy, and were randomized to 7-session Cognitive Behavioral Therapy for Insomnia or control conditions over 16 weeks with 2-year follow-ups. Depression and insomnia severity were assessed at baseline, biweekly during treatment, and every 4 months thereafter. Sleep effort and beliefs about sleep were also assessed.

RESULTS

Growth mixture modeling revealed three trajectories: (a) Partial-Responders (68.9%) had moderate symptom reduction during early treatment (p value < .001) and maintained mild depression during follow-ups. (b) Initial-Responders (17.6%) had marked symptom reduction during treatment (p values < .001) and low depression severity at posttreatment, but increased severity over follow-up (p value < .001). (c) Optimal-Responders (13.5%) achieved most gains during early treatment (p value < .001), continued to improve (p value < .01) and maintained minimal depression during follow-ups. The classes did not differ significantly on baseline measures or treatment received, but differed on insomnia-related measures after treatment began (p values < .05): Optimal-Responders consistently endorsed the lowest insomnia severity, sleep effort, and unhelpful beliefs about sleep.

CONCLUSIONS

Three depression symptom trajectories were observed among patients with comorbid insomnia and MDD. These trajectories were associated with insomnia-related constructs after commencing treatment. Early changes in insomnia characteristics may predict long-term depression outcomes. (PsycINFO Database Record

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.

[Diagnosis and comorbidities of Circadian Rhythm Sleep Disorders]

Circadian rhythm sleep disorders (CRSD) result from a disturbed endogenous clock (intrinsic CRSD) or from a misalignment between the biological clock and an imposed environment (extrinsic CRSD). Among intrinsic CRSD, one distinguishes the delayed sleep-wake phase disorder, the advanced sleep-wake phase disorder, the irregular sleep-wake rhythm disorder and the non-24-hour sleep-wake rhythm disorder. Shift work disorder, jet lag disorder and circadian sleep-wake disorder not otherwise specified are extrinsic CRSD. Prevalences of the different CRSD remain largely unknown. Some CRSD are particularly frequent such as sleep delayed phase syndrome in adolescents. Overall, CRSD are probably under-diagnosed. CRSD generate insomnia and excessive daytime somnolence. A biological clock dysfunction has to be evoked in case of insomnia or sleepiness. Furthermore, as CRSD can overlap with other sleep disorders, their diagnosis and treatment are essential. CRSD cause significant mental, physical or socio-professional sufferings. They are frequently associated with comorbidities, mainly neurodevelopmental, psychiatric and neurodegenerative disorders. Regarding neurodevelopmental comorbidities, therapy using a chronobiological approach is complementary to the usual clinical care. It helps to limit the significant impact of CRSD on quality of live, daytime functioning, social interactions and neurocognitive difficulties in the children. In psychiatry, sleep disorders and circadian rhythms sleep-wake disorders are a factor of vulnerability, of suicidal risk, of relapse and pharmacoresistance. Thus, diagnosis of CRSD associated with a psychiatric disorder is of major importance. Treatment using a chronobiological approach reinforcing the entrainment of the sleep-wake cycle is complementary to usual treatments. Sleep disorders and circadian sleep-wake rhythm disorders can be a preclinical sign of Alzheimer's and Parkinson's disease. In the elderly, a beginning neurodegenerative disorder can be associated with a CRSD and complaints of sleepiness, nocturnal awakenings and/or irregular sleep-wake cycles. Patients affected by neurogenerative disorders are particularly vulnerable for having CRSD. Data from different studies suggest that CRSD participate in pathophysiology of Alzheimer's disease. Even though treatment of CRSD associated with neurodegenerative disorders is entirely part of the treatment strategy, it remains uncertain to which extend this treatment may impact disease progression.

Rest-activity rhythms characteristics and seasonal changes in seasonal affective disorder

Identifying objectively measurable seasonal changes in 24-h activity patterns (rest-activity rhythms or RARs) that occur in seasonal affective disorder (SAD) could help guide research and practice towards new monitoring tools or prevention targets. We quantified RARs from actigraphy data using non-parametric and extended cosine based approaches, then compared RARs between people with SAD and healthy controls in the summer (n = 70) and winter seasons (n = 84). We also characterized the within-person seasonal RAR changes that occurred in the SAD (n = 19) and control (n = 26) participants who contributed repeated measures. Only controls had significant winter increases in RAR fragmentation (intra-daily variability; in controls mean winter-summer changes (log scale) = 0.05, 0.21 standard deviation, p = 0.03). In SAD participants only, estimated evening settling times (down-mesor) were an average of 30 min earlier in the winter compared with the summer (1-h standard deviation, p = 0.045). These RAR characteristics correlated with greater fatigue (Spearman r = 0.36) but not depression symptom severity. Additional research is needed to ascertain why healthy controls, but not people with SAD, appear to have increased RAR fragmentation in the winter. People with SAD lacked this increase in RAR fragmentation, and instead had earlier evening setting in the winter. Prospective and intervention studies with greater temporal resolution are warranted to ascertain how these seasonal behavioral differences relate to fatigue pathophysiology in SAD. Future research is needed to determine whether extending the winter active period, even in relatively fragmented bouts, could help reduce the fatigue symptoms common in SAD.

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.

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.

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.

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.