Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

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Sleep Quality in Family Caregivers and Matched Non-Caregiving Controls: The REGARDS Study

Innovation in Aging ◽

10.1093/geroni/igab046.2982 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 818-818

Author(s):

Marcela Blinka ◽

Adam Spira ◽

Orla Sheehan ◽

Tansu Cidav ◽

J David Rhodes ◽

...

Keyword(s):

Sleep Quality ◽

Family Caregivers ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Care Recipient ◽

Sleep Onset Latency ◽

Onset Latency ◽

Time In Bed ◽

Regards Study

Abstract The high levels of stress experienced by family caregivers may affect their physical and psychological health, including their sleep quality. However, there are few population-based studies comparing sleep between family caregivers and carefully-matched controls. We evaluated differences in sleep and identified predictors of poorer sleep among the caregivers, in a comparison of 251 incident caregivers and carefully matched non-caregiving controls, recruited from the national REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Incident caregivers and controls were matched on up to seven demographic and health factors (age, sex, race, education level, marital status, self-rated health, and self-reported serious cardiovascular disease history). Sleep characteristics were self-reported and included total sleep time, sleep onset latency, wake after sleep onset, time in bed, and sleep efficiency. Family caregivers reported significantly longer sleep onset latency, before and after adjusting for potential confounders, compared to non-caregiving controls (ps < 0.05). Depressive symptoms in caregivers predicted longer sleep onset latency, greater wake after sleep onset, and lower sleep efficiency. Longer total sleep time in caregivers was predicted by employment status, living with the care recipient, and number of caregiver hours. Employed caregivers and caregivers who did not live with the care recipient had shorter total sleep time and spent less time in bed than non-employed caregivers. Additional research is needed to evaluate whether sleep disturbances contributes to health problems among caregivers.

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The Cycle of Daily Stress and Sleep: Sleep Measurement Matters

Annals of Behavioral Medicine ◽

10.1093/abm/kaaa053 ◽

2020 ◽

Author(s):

Danica C Slavish ◽

Justin Asbee ◽

Kirti Veeramachaneni ◽

Brett A Messman ◽

Bella Scott ◽

...

Keyword(s):

Sleep Disturbances ◽

Multilevel Models ◽

Single Channel ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Diary ◽

Sleep Onset Latency ◽

Daily Stress ◽

Sleep Diaries ◽

Sleep Measurement

Abstract Background Disturbed sleep can be a cause and a consequence of elevated stress. Yet intensive longitudinal studies have revealed that sleep assessed via diaries and actigraphy is inconsistently associated with daily stress. Purpose We expanded this research by examining daily associations between sleep and stress using a threefold approach to assess sleep: sleep diaries, actigraphy, and ambulatory single-channel electroencephalography (EEG). Methods Participants were 80 adults (mean age = 32.65 years, 63% female) who completed 7 days of stressor and sleep assessments. Multilevel models were used to examine bidirectional associations between occurrence and severity of daily stress with diary-, actigraphy-, and EEG-determined sleep parameters (e.g., total sleep time [TST], sleep efficiency, and sleep onset latency, and wake after sleep onset [WASO]). Results Participants reported at least one stressor 37% of days. Days with a stressor were associated with a 14.4-min reduction in actigraphy-determined TST (β = −0.24, p = 0.030), but not with other actigraphy, diary, or EEG sleep measures. Nights with greater sleep diary-determined WASO were associated with greater next-day stressor severity (β = 0.01, p = 0.026); no other diary, actigraphy, or EEG sleep measures were associated with next-day stressor occurrence or severity. Conclusions Daily stress and sleep disturbances occurred in a bidirectional fashion, though specific results varied by sleep measurement technique and sleep parameter. Together, our results highlight that the type of sleep measurement matters for examining associations with daily stress. We urge future researchers to treat sleep diaries, actigraphy, and EEG as complementary—not redundant—sleep measurement approaches.

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507 Clinical Impression of Excessive Daytime Sleepiness (EDS) at initial Sleep Medicine (SM) consultation and its clinical correlates

SLEEP ◽

10.1093/sleep/zsab072.506 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A199-A200

Author(s):

Leon Rosenthal ◽

Raúl Aguilar Roblero

Keyword(s):

Lower Energy ◽

Energy Levels ◽

Sleep Onset ◽

Sleep Time ◽

Clinical Impression ◽

Sleep Onset Latency ◽

Convenience Sample ◽

Time In Bed ◽

Number Of Patients ◽

Cardinal Symptom

Abstract Introduction EDS represents a cardinal symptom in SM. Use of subjective scales are prevalent, which have a modest correlation with the MSLT. While the Clinical Global Impression has been used in research, reports of clinical impression (CI) in medical practice are lacking. We report on the CI of EDS in a convenience sample of patients undergoing initial consultation. Methods Patients reported primary, secondary symptoms and completed the Sleep Wake Activity Inventory (SWAI) prior to Tele-Medicine consultation. A SM physician completed the assessment which included ascertainment of CI of EDS (presence S+ / absence S-). Results There were 39 ♂and 13 ♀. The CI identified 26 patients in each group (S+/S-). Age (52 [14]), BMI (33 [7]), reported time in bed, sleep time, sleep onset latency and # of awakenings did not differ. All identified a primary symptom (S-: 21, S+: 19 reported snoring or a previous Dx of OSA). Sleepiness as a 1ry or 2ry symptom was identified by 0 in the S- and by 13 in the S+ groups. Refreshing quality of sleep differed (χ2 <0.05): un-refreshing sleep was reported by 7 (S-) and by 13 (S+). Naps/week: 0.7 [1.5] and 1.57 [1.5] for the S-, S+ groups respectively (p<0.05). A main effect (p<0.01) was documented on the SWAI. We report on the Sleepiness [SS] and Energy Level [EL] scales (lower scores on the SS reflect higher sleepiness while lower scores on EL denote higher energy). Higher sleepiness (p<0.01) 43 [12] and lower energy levels 24 [6] (p<0.05) were documented on the S+ group (S- 61 [17], and 18 [6] respectively). Available spouse’s Epworth score on 29 patients: S- patients 5.8 [4] and S+ 10.2 [6] (p<0.05). Dx of OSA was identified among all but 1 in the S+ group. Also, Insomnia was diagnosed among 11 (S-) and 19 (S+) patients (p<0.05) despite only 3 and 7 (respectively) identifying it as a presenting symptom. Conclusion While snoring or previous Dx of OSA were prevalent motivations for consultation, sleepiness and insomnia were clinically relevant among a substantial number of patients. Unrefreshing sleep, daytime naps, lower energy, and higher sleepiness were ubiquitous among S+ patients. Support (if any):

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P062 Sleep parameter validation of a home-based ballistocardiograph sleep tracker

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.108 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A41-A42

Author(s):

M Kholghi ◽

I Szollosi ◽

M Hollamby ◽

D Bradford ◽

Q Zhang

Keyword(s):

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Daily Routine ◽

Sleep Onset Latency ◽

Onset Latency ◽

Sleep Monitoring ◽

Significant Discrepancy ◽

Home Based ◽

Wake Detection

Abstract Introduction Consumer home sleep trackers are gaining popularity for objective sleep monitoring. Amongst them, non-wearable devices have little disruption in daily routine and need little maintenance. However, the validity of their sleep outcomes needs further investigation. In this study, the accuracy of the sleep outcomes of EMFIT Quantified Sleep (QS), an unobtrusive and non-wearable ballistocardiograph sleep tracker, was evaluated by comparing it with polysomnography (PSG). Methods 62 sleep lab patients underwent a single clinical PSG and their sleep measures were simultaneously collected through PSG and EMFIT QS. Total Sleep Time (TST), Wake After Sleep Onset (WASO), Sleep Onset Latency (SOL) and average Heart Rate (HR) were compared using paired t-tests and agreement analysed using Bland-Altman plots. Results EMFIT QS data loss occurred in 47% of participants. In the remaining 33 participants (15 females, with mean age of 53.7±16.5), EMFIT QS overestimated TST by 177.5±119.4 minutes (p<0.001) and underestimated WASO by 44.74±68.81 minutes (p<0.001). It accurately measured average resting HR and was able to distinguish SOL with some accuracy. However, the agreement between EMFIT QS and PSG on sleep-wake detection was very low (kappa=0.13, p<0.001). Discussion A consensus between PSG and EMFIT QS was found in SOL and average HR. There was a significant discrepancy and lack of consensus between the two devices in other sleep outcomes. These findings indicate that while EMFIT QS is not a credible alternative to PSG for sleep monitoring in clinical and research settings, consumers may find some benefit from longitudinal monitoring of SOL and HR.

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A Comparative Study of Sleep and Mood Between Young Elite Athletes and Age-Matched Controls

Journal of Physical Activity and Health ◽

10.1123/jpah.2016-0513 ◽

2017 ◽

Vol 14 (6) ◽

pp. 465-473 ◽

Cited By ~ 6

Author(s):

Anette Harris ◽

Hilde Gundersen ◽

Pia Mørk Andreassen ◽

Eirunn Thun ◽

Bjørn Bjorvatn ◽

...

Keyword(s):

Physical Activity ◽

Total Sleep Time ◽

Elite Athletes ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Diary ◽

Sleep Onset Latency ◽

Onset Latency ◽

Social Jetlag ◽

Weekly Training

Background:Sleep and mood have seldom been compared between elite athletes and nonelite athletes, although potential differences suggest that physical activity may affect these parameters. This study aims to explore whether adolescent elite athletes differ from controls in terms of sleep, positive affect (PA) and negative affect (NA).Methods:Forty-eight elite athletes and 26 controls participating in organized and nonorganized sport completed a questionnaire, and a 7-day sleep diary.Results:On school days, the athletes and the controls who participated in organized and nonorganized sport differed in bedtime (22:46, 23:14, 23:42, P < .01), sleep onset (23:03, 23:27, 00:12, P < .01), and total sleep time (7:52, 8:00, 6:50, P < 01). During weekend, the athletes, the controls who participated in organized and nonorganized sport differed in bedtime (23:30, 00:04, 00:49, P < .01), sleep onset (23.42, 00:18, 01:13, P < .01), rise time (9:15, 9:47, 10:55, P < .01), sleep efficiency (95.0%, 94.2%, 90.0%, P < 05), and sleep onset latency (11.8, 18.0, 28.0 minutes, P < .01). Furthermore, the athletes reported less social jetlag (0:53) and higher score for PA (34.3) compared with the controls who participated in nonorganized sport (jetlag: 1:25, P < .05, PA: 29.8, P < .05).Conclusions:An almost dose-response association was found between weekly training hours, sleep, social jetlag and mood in adolescents.

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Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study

JMIR Formative Research ◽

10.2196/16880 ◽

2020 ◽

Vol 4 (6) ◽

pp. e16880

Author(s):

Hirotaka Miyashita ◽

Mitsuteru Nakamura ◽

Akiko Kishi Svensson ◽

Masahiro Nakamura ◽

Shinichi Tokuno ◽

...

Keyword(s):

Pilot Study ◽

Regression Coefficient ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Onset Latency ◽

Inverse Association ◽

Onset Latency ◽

Emotional Status ◽

Nighttime Sleep

Background Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. Results A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. Conclusions Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

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Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study (Preprint)

10.2196/preprints.16880 ◽

2019 ◽

Author(s):

Hirotaka Miyashita ◽

Mitsuteru Nakamura ◽

Akiko Kishi Svensson ◽

Masahiro Nakamura ◽

Shinichi Tokuno ◽

...

Keyword(s):

Pilot Study ◽

Regression Coefficient ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Onset Latency ◽

Inverse Association ◽

Onset Latency ◽

Emotional Status ◽

Nighttime Sleep

BACKGROUND Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. OBJECTIVE The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. METHODS A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. RESULTS A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, <i>P</i>=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, <i>P</i>=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. CONCLUSIONS Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

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0363 Racial/Ethnic Differences in Actigraphy, Questionnaire, and Polysomnography-Measured Indicators of Sleep Health and Sleep Quality: The Multi-Ethnic Study of Atherosclerosis

SLEEP ◽

10.1093/sleep/zsaa056.360 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A138-A139

Author(s):

J Chung ◽

M O Goodman ◽

T Huang ◽

M Wallace ◽

S Bertisch ◽

...

Keyword(s):

Total Sleep Time ◽

Sleep Onset ◽

Well Being ◽

Sleep Time ◽

Lower Probability ◽

Sleep Onset Latency ◽

Sleep Health ◽

Ethnic Study ◽

Sleep Continuity ◽

Racial Ethnic

Abstract Introduction Paradigm shifts in sleep research suggest the importance of considering multi-dimensional sleep health, compared to single metrics, to promote physical and mental well-being and to understand racial/ethnic disparities in sleep. Methods We used data from the Multi-Ethnic Study of Atherosclerosis (MESA; n = 1,740) to create a Sleep Health Score (SHS), including questionnaire (quality, sleepiness); 7-day actigraphy (total sleep time, sleep continuity [sleep maintenance efficiency], timing consistency [midpoint variability], fragmentation, wake after sleep onset, sleep onset latency); and in-home polysomnography (%N3 sleep, %REM sleep, AHI). Sleep parameters were dichotomized based on prior literature or by healthiest quartile(s), with positive values denoting healthier sleep (e.g. Epworth scores < 10). All 11 dichotomized parameters were summed to calculate the SHS (mean=4.9, sd=1.58). We used modified Poisson and linear regression for individual sleep outcomes and the SHS, respectively, adjusting for age and sex. Results The sample was older (mean age=68.28, sd=9.08) and 54% female. SHSs were associated with Black race (β=-0.60 [-0.78, -0.42]) and Hispanic ethnicity (β=-0.40 [-0.59, -0.21]), but not Chinese ethnicity (β=-0.16 [-0.41, 0.08]). Compared to Whites (n=644), Blacks (n=485) showed lower adjusted probability of obtaining favorable levels of: sleep continuity, fragmentation, timing consistency, alertness/sleepiness, and sleep depth (%N3 sleep). Chinese respondents (n=202) had lower probability of obtaining favorable levels of: sleep continuity and timing consistency, but higher probability of quality. Hispanics (n=409) had lower probability of obtaining healthy levels of: sleep continuity, timing consistency, and fragmentation. Neither healthy total sleep time (middle quartiles) nor AHI (<30) differed by race/ethnicity. Conclusion Among MESA-Sleep participants, summary SHSs were lowest in Blacks, followed by Hispanics. Multiple dimensions of sleep - particularly related to continuity and timing consistency - were less favorable across race/ethnic minority groups. A summary SHS may help monitor sleep health across populations, while measurement of specific sleep components may help identify modifiable targets. Support Joon Chung is supported by a T-32 NIH training grant.

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Actigraphy prior to MSLT: nighttime total sleep time predicts sleep onset latency

Journal of Clinical Sleep Medicine ◽

10.5664/jcsm.9528 ◽

2021 ◽

Author(s):

Monica R. Kelly ◽

Michelle R. Zeidler ◽

Sharon DeCruz ◽

Caitlin L. Oldenkamp ◽

Karen R. Josephson ◽

...

Keyword(s):

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Onset Latency ◽

Onset Latency

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Sleep Electroencephalography in Depressive Illness a Collaborative Study by the World Health Organization

The British Journal of Psychiatry ◽

10.1192/bjp.159.4.505 ◽

1991 ◽

Vol 159 (4) ◽

pp. 505-509 ◽

Cited By ~ 41

Author(s):

Julien Mendlewicz ◽

Myriam Kerkhofs

Keyword(s):

Major Depression ◽

Total Sleep Time ◽

Collaborative Study ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Eeg ◽

World Health ◽

Sleep Onset Latency ◽

Depressed Patients ◽

Eeg Abnormalities

Eight WHO research centres from Europe, North America and Asia took part in a WHO study aimed at assessing the reliability and consistency of sleep-EEG abnormalities in major depression. Each centre was asked to include in the study ten depressed patients aged 20–65 years meeting the Research Diagnostic Criteria for a major depressive disorder, and to match them by age and gender with ten controls. There were 67 patients and 66 controls included in the study. Compared with controls, depressed patients showed sleep-continuity disturbances such as increase in sleep-onset latency, and decrease in total sleep time and in sleep efficiency. Stages 2 and 3, as percentages of total sleep time, were reduced in depressed patients, REM latency was shortened and REM density increased. These findings confirm the presence of specific sleep-EEG abnormalities in major depression.

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