体动记录仪身体活动指数 Movement Index 在慢性失眠或睡眠障碍研究中的意义

… index (FI), and mean motor activity (MA). We also considered two actigraphic circadian indexes… Using the Youden index, we calculated the quantitative actigraphic criteria that performed …

… the most useful actigraphic sleep parameters to separate insomnia patients from normal sleepers. Using Youden index we calculated the preliminary QAC for each actigraphic sleep …

Actigraphy, a method for inferring sleep/wake patterns based on movement data gathered using actigraphs, is increasingly used in population-based epidemiologic studies because of its ability to monitor activity in natural settings. Using special software, actigraphic data are analyzed to estimate a range of sleep parameters. To date, despite extensive application of actigraphs in sleep research, published literature specifically detailing the methodology for derivation of sleep parameters is lacking; such information is critical for the appropriate analysis and interpretation of actigraphy data. Reporting of sleep parameters has also been inconsistent across studies, likely reflecting the lack of consensus regarding the definition of sleep onset and offset. In addition, actigraphy data are generally underutilized, with only a fraction of the sleep parameters generated through actigraphy routinely used in current sleep research. The objectives of this paper are to review existing algorithms used to estimate sleep/wake cycles from movement data, demonstrate the rules/methods used for estimating sleep parameters, provide clear technical definitions of the parameters, and suggest potential new measures that reflect intraindividual variability. Utilizing original data collected using Motionlogger Sleep Watch (Ambulatory Monitoring Inc., Ardsley, NY), we detail the methodology and derivation of 29 nocturnal sleep parameters, including those both widely and rarely utilized in research. By improving understanding of the actigraphy process, the information provided in this paper may help: ensure appropriate use and interpretation of sleep parameters in future studies; enable the recalibration of sleep parameters to address specific goals; inform the development of new measures; and increase the breadth of sleep parameters used.

… measures of WASO, and only 1 evaluated actigraphic measures of SOL. Accurate … of insomnia. The current study attempted to validate actigraphy with people with insomnia and …

… actigraphic monitoring and daily diaries to assess the sleep of a group of 40 subjects with insomnia … ; (ii) determine the concordance between actigraph and sleep diary measures of …

… This study tested the ecological validity of actigraphy (ACT) for estimating objective sleep … -related arousal index ≥ 15 during on screening PSG. In addition, we excluded insomnia …

Abstract: Background: Sleep disturbance is a common self-reported complaint by PTSD patients. However, there are controversies in documenting objective indices of disrupted sleep in these patients. The aim of the present study was to assess sleep disturbances in veterans with chronic PTSD, using both subjective and objective assessments. Methods: Thirty two PTSD patients with complaints of insomnia were evaluated using the Clinician Administrated PTSD Scale version 1 (CAPS) and completed the Pittsburg Sleep Quality Index (PSQI) for subjective evaluation of their sleep. For objective evaluation, participants underwent two consecutive overnight actigraphic assessments. Total Sleep Time (TST), Sleep Latency (SL), Sleep Efficiency (SE) and Number of Awakening (NWAK) were measured in all participants. Results: Participants underestimated TST (p less than 0.0001), SE (p less than 0.0001) as well as NASO (0.03) in the questionnaire compared to the actigraphic assessment and overestimated SL (p less than 0.0001). Conclusions: Objective sleep parameters do not adversely affect veterans with chronic PTSD. Self-reported sleep disturbance in these patients is not reliable and objective sleep assessments are necessary.

Wake after sleep onset and sleep efficiency derived from actigraphy are common assessments of sleep fragmentation (or continuity). The sleep fragmentation index (SFI), measuring the frequency of sleep-wake transitions, is less understood. This study examined (1) the convergent validity between SFI and other sleep metrics obtained by actigraphy and polysomnography; and (2) associations of SFI with sleep symptoms, obstructive sleep apnea, periodic limb movement index, and cognition (Digit Symbol Coding test). Cross-sectional analysis using logistic and multiple regression analyses adjusted for potential confounders. 1,908 participants in the Multi-Ethnic Study of Atherosclerosis study who underwent 7-day actigraphy and polysomnography. The sample was 53.9% female; age 68.3 ± 9.1 years (mean ± standard deviation); apnea-hypopnea index 19.5 ± 17 events/h; and SFI 20.09 ± 6.99. Higher SFI was associated with older age, male sex, Black race, smoking, body mass index, obstructive sleep apnea, and polysomnography-based metrics of sleep architecture. SFI was strongly correlated with actigraphy-measured sleep efficiency (r = −.75; P < .0001) and wake after sleep onset (r = .63; P < .0001), and modestly correlated with polysomnography-wake after sleep onset, apnea-hypopnea index, and arousal index (rs = 0.23–0.27; Ps < .0001). In adjusted analyses, each standard deviation unit increase in SFI was associated with 1.1–1.4 higher odds of insomnia symptoms, sleepiness, obstructive sleep apnea, an elevated periodic limb movement index, and with lower Digit Symbol Coding test scores (P < .05). The results support the convergent validity between actigraphy-estimated SFI and actigraphy-wake after sleep onset and sleep efficiency. SFI showed modestly stronger associations with clinical symptoms compared to other fragmentation variables, supporting its utility as a marker of sleep continuity. Saleh D, Bertisch SM, Reid M, Lim A, Purcell S, Redline S. Actigraphy-derived sleep fragmentation index: convergent validity and associations with clinical outcomes. J Clin Sleep Med. 2025;21(9):1557–1565.

STUDY OBJECTIVES Actigraphy is a useful tool for estimating sleep, but less accurately distinguishes sleep and wakefulness in patients with insomnia disorder (ID) than in good sleepers. Specific algorithm parameter settings have been suggested to improve the accuracy of actigraphic estimates of sleep onset or nocturnal sleep and wakefulness in ID. However, a direct comparison of how different algorithm parameter settings affect actigraphic estimates of sleep features has been lacking. This study aimed to define the optimal algorithm parameter settings for actigraphic estimates of polysomnographic sleep features in people suffering from ID and matched good sleepers. METHODS We simultaneously recorded actigraphy and polysomnography without sleep diaries during 210 laboratory nights of people with ID (n = 58) and matched controls (CTRL) without sleep complaints (n = 56). We analyzed cross-validation errors using 150 algorithm parameter configurations and Bland-Altman plots of sleep features using the optimal settings. RESULTS Optimal sleep onset latency and total sleep time (TST) errors were lower in CTRL (8.9 ± 2.1 and 16.5 ± 2.1 min, respectively) than in ID (11.7 ± 0.8 and 29.1 ± 3.4 min). The sleep-wake algorithm, a period duration of 5 min, and a wake sensitivity threshold of 40 achieved optimal results in ID and near-optimal results in CTRL. Bland-Altman plots were nearly identical for ID and controls for all common all-night sleep features except for TST. CONCLUSION This systematic evaluation shows that actigraphic sleep feature estimation can be improved by using uncommon parameter settings. One specific parameter setting provides (near-)optimal estimation of sleep onset and nocturnal sleep across ID and controls.

In the past decades, actigraphy has emerged as a promising, cost‐effective, and easy‐to‐use tool for ambulatory sleep recording. Polysomnography (PSG) validation studies showed that actigraphic sleep estimates fare relatively well in healthy sleepers. Additionally, round‐the‐clock actigraphy recording has been used to study circadian rhythms in various populations. To this date, however, there is little evidence that the diagnosis, monitoring, or treatment of insomnia can significantly benefit from actigraphy recordings. Using a case–control design, we therefore critically examined whether mean or within‐subject variability of actigraphy sleep estimates or circadian patterns add to the understanding of sleep complaints in insomnia. We acquired actigraphy recordings and sleep diaries of 37 controls and 167 patients with varying degrees of insomnia severity for up to 9 consecutive days in their home environment. Additionally, the participants spent one night in the laboratory, where actigraphy was recorded alongside PSG to check whether sleep, in principle, is well estimated. Despite moderate to strong agreement between actigraphy and PSG sleep scoring in the laboratory, ambulatory actigraphic estimates of average sleep and circadian rhythm variables failed to successfully differentiate patients with insomnia from controls in the home environment. Only total sleep time differed between the groups. Additionally, within‐subject variability of sleep efficiency and wake after sleep onset was higher in patients. Insomnia research may therefore benefit from shifting attention from average sleep variables to day‐to‐day variability or from the development of non‐motor home‐assessed indicators of sleep quality.

Actigraphy is increasingly used in practice and research studies because of its relative low cost and decreased subject burden. How multiple nights of at‐home actigraphy compare to one independent night of in‐laboratory polysomnography (PSG) has not been examined in people with insomnia. Using event markers (MARK) to set time in bed (TIB) compared to automatic program analysis (AUTO) has not been systematically evaluated. Subjects (n = 30) meeting DSM‐5 criteria for insomnia and in‐laboratory PSG sleep efficiency (SE) of <85% were studied. Subjects were free of psychiatric, sleep or circadian disorders, other chronic conditions and medications that effect sleep. Subjects had an in‐laboratory PSG, then were sent home for 7 nights with Philips Actiwatch Spectrum Plus. Data were analysed using Philips Actiware version 6. Using the mean of seven nights, TIB, total sleep time (TST), SE, sleep‐onset latency (SOL) and wake after sleep onset (WASO) were examined. Compared to PSG, AUTO showed longer TIB and TST and less WASO. MARK only differed from PSG with decreased WASO. Differences between the PSG night and the following night at home were found, with better sleep on the first night home. Actigraphy in people with insomnia over seven nights is a valid indicator of sleep compared to an independent in‐laboratory PSG. Event markers increased the validity of actigraphy, showing no difference in TIB, TST, SE and SOL. AUTO was representative of SE and SOL. Increased SE and TST without increased TIB suggests possible compensatory sleep the first at night home after in‐laboratory PSG.

… Much of the work using actigraphy as a measure of sleep disorders is reviewed earlier in this … a significantly higher nighttime activity index as measured by actigraphy (p<.001) and lower …

Background: Disrupted sleep is common among nursing home patients with dementia and is associated with increased agitation, depression, and cognitive impairment. Detecting and treating sleep problems in this population are therefore of great importance, albeit challenging. Systematic observation and objective recordings of sleep are time-consuming and resource intensive and self-report is often unreliable. Commonly used proxy-rated scales contain few sleep items, which affects the reliability of the raters' reports. The present study aimed to adapt the proxy-rated Sleep Disorder Inventory (SDI) to a nursing home context and validate it against actigraphy. Methods: Cross-sectional study of 69 nursing home patients, 68% women, mean age 83.5 (SD 7.1). Sleep was assessed with the SDI, completed by nursing home staff, and with actigraphy (Actiwatch II, Philips Respironics). The SDI evaluates the frequency, severity, and distress of seven sleep-related behaviors. Internal consistency of the SDI was evaluated by Cronbach's alpha. Spearman correlations were used to evaluate the convergent validity between actigraphy and the SDI. Test performance was assessed by calculating the sensitivity, specificity, and predictive values, and by ROC curve analyses. The Youden's Index was used to determine the most appropriate cut-off against objectively measured sleep disturbance defined as <6 h nocturnal total sleep time (TST) during 8 h nocturnal bed rest (corresponding to SE <75%). Results: The SDI had high internal consistency and convergent validity. Three SDI summary scores correlated moderately and significantly with actigraphically measured TST and wake-after-sleep-onset. A cut-off score of five or more on the SDI summed product score (sum of the products of the frequency and severity of each item) yielded the best sensitivity, specificity, predictive values, and Youden's Index. Conclusion: We suggest a clinical cut-off for the presence of disturbed sleep in institutionalized dementia patients to be a SDI summed product score of five or more. The results suggest that the SDI can be clinically useful for the identification of disrupted sleep when administered by daytime staff in a nursing home context. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03357328.

The purpose of this systematic review is to provide supporting evidence for a clinical practice guideline on the use of actigraphy. The American Academy of Sleep Medicine commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies that compared the use of actigraphy, sleep logs, and/or polysomnography. Statistical analyses were performed to determine the clinical significance of using actigraphy as an objective measure of sleep and circadian parameters. Finally, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence for making recommendations. The literature search resulted in 81 studies that met inclusion criteria; all 81 studies provided data suitable for statistical analyses. These data demonstrate that actigraphy provides consistent objective data that is often unique from patient-reported sleep logs for some sleep parameters in adult and pediatric patients with suspected or diagnosed insomnia, circadian rhythm sleep-wake disorders, sleep-disordered breathing, central disorders of hypersomnolence, and adults with insufficient sleep syndrome. These data also demonstrate that actigraphy is not a reliable measure of periodic limb movements in adult and pediatric patients. The task force provided a detailed summary of the evidence along with the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use considerations. Smith MT, McCrae CS, Cheung J, Martin JL, Harrod CG, Heald JL, Carden KA. Use of actigraphy for the evaluation of sleep disorders and circadian rhythm sleep-wake disorders: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment. J Clin Sleep Med. 2018;14(7):1209–1230.

OBJECTIVE To evaluate the reliability of actigraphy to distinguish the features of estimated daytime and nighttime sleep between patients with central disorders of hypersomnolence and healthy controls. METHODS Thirty-nine drug-naïve patients with Narcolepsy Type 1, twenty-four drug-naïve patients with Idiopathic Hypersomnia, and thirty age- and sex- matched healthy controls underwent seven days of actigraphic and self-report monitoring of sleep/wake behavior. The following variables were examined: estimated time in bed (eTIB), estimated total sleep time, estimated sleep latency (eSOL), estimated sleep efficiency, estimated wake after sleep onset, number of estimated awakenings (eAwk), number of estimated awakenings longer than 5 minutes, estimated sleep motor activity (eSMA), number of estimated naps, mean duration of the longest estimated nap (eNapD), and daytime motor activity. RESULTS All actigraphic parameters significantly differentiated the three groups, except eTIB and eSOL. A discriminant score computed combining actigraphic parameters from nighttime (eSMA, eAwk) and daytime (eNapD) periods showed a wide area under the curve (0.935) and a good balance between positive (95%) and negative predictive (87%) values in Narcolepsy Type 1 cases. CONCLUSION Actigraphy provided a reliable objective measurement of sleep quality and daytime napping behavior able to distinguish central disorders of hypersomnolence and in particular Narcolepsy Type 1. The nycthemeral profile, combined with a careful clinical evaluation, may be an ecological information, useful to track disease course.

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is characterized by abnormal behaviours during REM sleep. Several studies showed that iRBD is a prodromal stage of synucleinopathies. Therefore, identifying iRBD in the general population is of utmost importance. In this study, we explore whether the assessment of rest–activity rhythm features can distinguish patients with iRBD from patients with disorders characterized by other pathological motor activity during sleep and healthy controls.

… aware of a number of pitfalls of actigraphy: (1) validity has not … ) actigraphy is not sufficient for diagnosis of sleep disorders in individuals with motor disorders or high motility during sleep…

… that actigraphy is sensitive in detecting unique sleep patterns associated with specific sleep disorders as well as with other medical or neurobehavioral disorders. Furthermore, …

… There are several studies that have investigated the validity of sleep measurements with an actigraph.In a review from 1995, approved by the American Sleep Disorder Association, it …

BackgroundSleep disturbances are widespread among nursing home (NH) patients and associated with numerous negative consequences. Identifying and treating them should therefore be of high clinical priority. No prior studies have investigated the degree to which sleep disturbances as detected by actigraphy and by the sleep-related items in the Cornell Scale for Depression in Dementia (CSDD) and the Neuropsychiatric Inventory – Nursing Home version (NPI-NH) provide comparable results. Such knowledge is highly needed, since both questionnaires are used in clinical settings and studies use the NPI-NH sleep item to measure sleep disturbances. For this reason, insight into their relative (dis)advantages is valuable.MethodCross-sectional study of 83 NH patients. Sleep was objectively measured with actigraphy for 7 days, and rated by NH staff with the sleep items in the CSDD and the NPI-NH, and results were compared. McNemar's tests were conducted to investigate whether there were significant differences between the pairs of relevant measures. Cohen's Kappa tests were used to investigate the degree of agreement between the pairs of relevant actigraphy, NPI-NH and CSDD measures. Sensitivity and specificity analyses were conducted for each of the pairs, and receiver operating characteristics (ROC) curves were designed as a plot of the true positive rate against the false positive rate for the diagnostic test.ResultsProxy-raters reported sleep disturbances in 20.5% of patients assessed with NPI-NH and 18.1% (difficulty falling asleep), 43.4% (multiple awakenings) and 3.6% (early morning awakenings) of patients had sleep disturbances assessed with CSDD. Our results showed significant differences (p<0.001) between actigraphy measures and proxy-rated sleep by the NPI-NH and CSDD. Sensitivity and specificity analyses supported these results.ConclusionsCompared to actigraphy, proxy-raters clearly underreported NH patients' sleep disturbances as assessed by sleep items in NPI-NH and CSDD. The results suggest that the usefulness of proxy-rater measures of sleep may be questionable and further research is needed into their clinical value. The results highlight the need for NH staff to acquire and act on knowledge about sleep and sleep challenges among NH patients.Trial registrationRegistered at www.clinicaltrials.gov (registration number NCT02238652) on July 7th 2014 (6 months after study initiation).

BACKGROUND Sleep disturbances (SDs) are commonly reported in patients with Crohn's disease (CD). Several survey instruments assessing subjective measures of insufficient sleep have identified SDs in subjects with CD. However, there are limited data on objective measures of SDs in these patients as they relate to disease activity. In this prospective cross-sectional study, we compared objective estimates of sleep obtained using multiday wrist actigraphy in individuals with CD with varying disease activity. METHODS Eighty patients with a diagnosis of CD were recruited to take part in the study. Participants were stratified by disease activity into remission, mild disease, and moderate to severe disease groups using the Harvey-Bradshaw Index and C-reactive protein levels. Participants were excluded on the basis of significant comorbidity (Charlson Comorbidity Index ≥3), a known history of a sleep disorder, or the concomitant use of systemic corticosteroids. Participants completed surveys, including the PROMIS-SD Short Form 8a, the Epworth Sleepiness Scale, and the Women's Health Initiative Insomnia Rating scale, and were provided with an accelerometer that estimated sleep-wake patterns over 7 days. Comparisons of actigraphic sleep parameters were performed between disease activity groups. Multivariate logistic regression analyses were performed using covariates determined a priori to have an association with sleep disturbance in CD through a review of the literature. RESULTS Of the 80 participants enrolled in the study, 72 completed 5 days of actigraphy data: 28 subjects in remission, 22 subjects with mild disease activity, and 22 subjects with moderate to severe disease activity. Self-reported sleep characteristics assessed by questionnaires were similar between groups. By actigraphy, individuals with moderate to severe CD spent a significantly longer time awake after falling asleep compared with subjects with remissive disease or compared with subjects with mild disease (65.8 minutes vs 44.3 minutes and 49.1 minutes, respectively; each P < 0.05). Individuals with moderate to severe CD had significantly lower sleep efficiency compared with those with remissive CD (86.6% vs 89.9%; P = 0.03). In the multivariate analyses, moderate to severe CD disease activity was significantly associated with an increased amount of fragmented sleep (odds ratio [OR], 3.70; 95% confidence interval [CI], 1.23-11.32; P = 0.02; WASO ≥ 60 minutes). Moreover, the use of controlled substances was associated with poor sleep efficiency (OR, 3.86; 95% CI, 1.01-14.7; P = 0.04; SE ≤ 85.5%). CONCLUSIONS This is the first study to objectively quantify disturbed sleep using wrist actigraphy in adults with CD with varying disease activity. Wrist actigraphy may serve as a useful modality for discerning SD in subjects with active vs remissive disease that is not evident with questionnaires alone. Although we determined that disease severity is a significant factor that leads to SDs in CD, larger studies using these objective measures may help determine the contribution of other factors.

BACKGROUND Actigraphy has enabled consecutive observation of individual health conditions such as sleep or daily activity. This study aimed to examine the usefulness of actigraphy in evaluating depressive and/or bipolar disorder symptoms. METHOD A systematic review and meta-analysis was conducted. We selected studies that used actigraphy to compare either patients vs. healthy controls, or pre- vs. post-treatment data from the same patient group. Common actigraphy measurements, namely daily activity and sleep-related data, were extracted and synthesized. RESULTS Thirty-eight studies (n = 3,758) were included in the analysis. Compared with healthy controls, depressive patients were less active (standardized mean difference; SMD=1.27, 95%CI=[0.97, 1.57], P<0.001) and had longer wake after sleep onset (SMD= - 0.729, 95%CI=[- 1.20, - 0.25], p = 0.003). Total sleep time (SMD= - 0.33, 95%CI=[- 0.55, - 0.11], P = 0.004), sleep latency (SMD= - 0.22, 95%CI=[- 0.42, - 0.02], P = 0.032), and wake after sleep onset (SMD= - 0.22, 95%CI=[- 0.39, - 0.04], P = 0.015) were longer in euthymic/remitted patients compared to healthy controls. In pre- and post-treatment comparisons, sleep latency (SMD=- 0.85, 95%CI=[- 1.53, - 0.17], P = 0.015), wake after sleep onset (SMD= - 0.65, 95%CI=[- 1.20, - 0.10], P = 0.022), and sleep efficiency (SMD=0.77, 95%CI=[0.29, 1.24], P = 0.002) showed significant improvement. LIMITATION The sample sizes for each outcome were small. The type of actigraphy devices and patients' illness severity differed across studies. It is possible that hospitalizations and medication influenced the outcomes. CONCLUSION We found significant differences between healthy controls and mood disorders patients for some actigraphy-measured modalities. Specific measurement patterns characterizing each mood disorder/status were also found. Additional actigraphy data linked to severity and/or treatment could enhance the clinical utility of actigraphy.

Background The “first night effect” of polysomnography (PSG) has been studied, however the ability to quantify the level of sleep disruption has been confounded by using PSG on all nights. We used actigraphy to quantify disruption level, and examined characteristics associated with disruption. Methods 778 older men (76.2 ± 5.4 years) from a population-based study at six US centers underwent one night of in-home PSG. Actigraphy was gathered on the PSG night and three subsequent nights. Actigraphically measured total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), and sleep onset latency (SOL) were compared from the PSG night and subsequent nights. Linear regression models were used to examine the association of characteristics and sleep disruption. Results On average, sleep on the PSG night was worse than the following night (p<0.05, TST 21 ± 85 min less, SE 2.3 ± 11.3% less, WASO 4.9 ± 51.8 min more, SOL 6.6 ± 56.2 min more). Compared to sleep two and three nights later, sleep on the PSG night was significantly worse. Characteristics associated with greater sleep disruption on the PSG night, included older age, higher apnea-hypopnea index, worse neuromuscular function, and more depressive symptoms. Minorities and men with excessive daytime sleepiness slept somewhat better on the PSG night. Conclusions Among older men, there was sleep disruption on the PSG night which may lead to an underestimation of sleep time. The increase of sleep on the night after the PSG suggests data from the second monitoring may overestimate sleep.

Kaplan KA, Talbot LS, Gruber J, Harvey AG. Evaluating sleep in bipolar disorder: comparison between actigraphy, polysomnography, and sleep diary. 
Bipolar Disord 2012: 14: 870–879. © 2012 John Wiley & Sons A/S.Published by Blackwell Publishing Ltd.

Rationale: Poor sleep quality is common in the intensive care unit (ICU) and may be associated with adverse outcomes. Hence, ICU‐based efforts to promote sleep are gaining attention, motivating interest in methods to measure sleep in critically ill patients. Actigraphy evaluates rest and activity by algorithmically processing gross motor activity data, usually collected by a noninvasive wristwatch‐like accelerometer device. In critically ill patients, actigraphy has been used as a surrogate measure of sleep; however, its use has not been systematically reviewed. Objectives: To conduct a systematic review of ICU‐based studies that used actigraphy as a surrogate measure of sleep, including its feasibility, validity, and reliability as a measure of sleep in critically ill patients. Methods: We searched PubMed, EMBASE, CINAHL, Proquest, and Web of Science for studies that used actigraphy to evaluate sleep in five or more patients in an ICU setting. Results: Our search yielded 4,869 citations, with 13 studies meeting eligibility criteria. These 13 studies were conducted in 10 countries, and eight (62%) were published since 2008. Across the 13 studies, the mean total sleep time of patients in the ICU, as estimated using actigraphy, ranged from 4.4 to 7.8 hours at nighttime and from 7.1 to 12.1 hours over a 24‐hour period, with 1.4 to 49.0 mean nocturnal awakenings and a sleep efficiency of 61 to 75%. When compared side‐by‐side with other measures of sleep (polysomnography, nurse assessments, and patient questionnaires), actigraphy consistently yielded higher total sleep time and sleep efficiency, fewer nighttime awakenings (vs. polysomnography), and more overall awakenings (vs. nurse assessment and patient questionnaires). None of the studies evaluated the association between actigraphy‐based measures of sleep and outcomes of patients in the ICU. Conclusions: In critically ill patients, actigraphy is being used more frequently as a surrogate measure of sleep; however, because actigraphy only measures gross motor activity, its ability to estimate sleep is limited by the processing algorithm used. Prior ICU‐based studies involving actigraphy were heterogeneous and lacked data regarding actigraphy‐based measures of sleep and patient outcomes. Larger, more rigorous and standardized studies are needed to better understand the role of actigraphy in evaluating sleep and sleep‐related outcomes in critically ill patients.

… sleep stage and actigraphic movement counts, with a higher level of counts per minute recorded in epochs with lighter PSG sleep … , actigraph and PSG estimates of sleep efficiency were …

… an activity count (range: 0–255); each movement above the … with the reference voltage and a count (range: 0–255) is … sleep time and of sleep efficiency derived from all movement …

Objectives: Our count-scaled algorithm automatically scores sleep across 24 hours to process sleep timing, quantity, and quality. The aim of this study was to validate the algorithm against overnight PSG in children to determine the best site placement for sleep. Methods: 28 children (5–8 years) with no history of sleep disturbance wore two types of accelerometers (ActiGraph GT3X+ and Actical) at two sites (left hip, non-dominant wrist) for 24-h. Data were processed using the count-scaled algorithm. PSG data were collected using an in-home Type 2 device. PSG-actigraphy epoch sensitivity (sleep agreement) and specificity (wake agreement) were determined and sleep outcomes compared for timing (onset and offset), quantity [sleep period time (SPT) and total sleep time (TST)], and quality metrics [sleep efficiency and waking after sleep onset (WASO)]. Results: Overall, sensitivities were high (89.1% to 99.5%) and specificities low (21.1% to 45.7%). Sleep offset was accurately measured by actigraphy, regardless of brand or placement site. By contrast, sleep onset agreed with PSG using hip-positioned but not wrist-positioned devices (difference ActiGraph : PSG 21 min, P < .001; Actical : PSG 14 min, P < .001). The ActiGraph at the wrist accurately detected WASO and sleep efficiency, but under (−34 min, P < .001) and overestimated (5.8%, P < .001) these at the hip. The Actical under- and over-estimated these variables respectively at both sites. Results for TST varied ranging from significant differences to PSG of −26 to 21 min (ActiGraph wrist and hip respectively) and 9 min (ns) to 59 min for Actical (wrist and hip respectively). Conclusion: Overall the count-scaled algorithm produced high sensitivity at the expense of low specificity in comparison with PSG. A best site placement for estimates of all sleep variables could not be determined, but overall the results suggested ActiGraph GT3X+ at the hip may be superior for sleep timing and quantity metrics, whereas the wrist may be superior for sleep quality metrics. Both devices placed at the hip performed well for sleep timing but not for sleep quality. Differences are likely linked to freedom of movement of the wrist vs the trunk (hip) during overnight sleep.

… activity count sequences of video and wrist actigraphy with a ground truth observation (‘no … of the proposed video actigraphy method for estimating the sleep efficiency, we analyzed a …

… The minute-by-minute count of the ActiGraph Link and … of total sleeping time, wake after sleep onset, and sleep efficiency of … estimated by body movement and should not be used for the …

… actigraphic movement counts, thus allowing for the use of validated algorithms to estimate sleep … movements and thus improve the estimate of sleep quality. Further improvements of the …

… actigraph overestimated sleep efficiency and total sleep time. Sensitivity of both Fitbit and actigraphy for accurately identifying sleep … mathematically weighs activity counts during the four …

… based on movement, and similarly generate activity counts for each … of sleep efficiency obtained from each of the actigraphy … by wrist actigraphy may be confounded by external motion. …

Introduction Sleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor of various physiological and psychological disorders. Therefore, sleep evaluation is vital for diagnosing or monitoring those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy. Methods For classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG, and they slept overnight with wrist-type actigraphy. We assessed the performance of four proposed machine learning models. Results With HRV-related and raw features of actigraphy, Cohen's kappa was 0.974 (p < 0.001) for classifying sleep stages into five stages: wake (W), REM (Rapid Eye Movement) (R), Sleep N1 (Non-Rapid Eye Movement Stage 1, S1), Sleep N2 (Non-Rapid Eye Movement Stage 2, S2), Sleep N3 (Non-Rapid Eye Movement Stage 3, S3). In addition, our machine learning model for the estimation of sleep efficiency showed an accuracy of 0.86. Discussion Our model demonstrated that automated sleep classification results could perfectly match the PSG results. Since models with acceleration features showed modest performance in differentiating some sleep stages, further research on acceleration features must be done. In addition, the sleep efficiency model demonstrated modest results. However, an investigation into the effects of HRV-derived and acceleration features is required.

… movement event or count, and a smoothing filter is applied to the movement data to implement the expectation that sleep … in this actigraph preserves relative movement amplitude at 8-bit …

Background Monitoring for physical activity becomes popular and actually many devices are available. Some physical activity monitors (PAMs) provide data about sleep quality for the user, but there are scarce data concerning validity and usability of these measurements. This study compared the data of sleep parameters generated by a PAM with the polysomnography (PSG). Methods In 2016, data of 26 patients in two consecutive PSGs as well as in two daytime and nighttime measurements with a PAM according to physical activity and sleep quality were collected. Furthermore, sleep quality, using the Pittsburgh sleep quality index (PSQI), daytime fatigue, using the multidimensional fatigue inventory (MFI-20) and additionally data of a sleep diary were collected. Results There were positive correlations of both methods with respect to total sleep time (TST) (r = 0.76, P < 0.01) and sleep efficiency (r = 0.71, P < 0.01). Data analysis over two nights showed that over 90% of the TST (95% confidence interval (CI) -1.59 to 0.82) and of the sleep efficiency (95% CI -8.28 to 15.51) were within the limits of agreement. The analysis of the PSQI and the sleep efficiency of the PAM showed no significant correlations. The daytime fatigue correlated negatively with the physical activity (r = -0.72, P < 0.01). Conclusion The sleep efficiency and TST measured with the PAM sufficiently reflect the PSG sleep parameters and the subjects’ subjective feelings. At the same time, PAM results are also correlated with the subjectively perceived quality of sleep. Further investigations to assess the long-term results are pending.

… Actiwatch-estimated total sleep time and sleep efficiency were … Generally, actigraph devices record movement using … sensitivity (wake sensitivity at 40 activity counts per epoch), with no …

We explored the associations of actigraphy-derived rest-activity patterns and circadian phase parameters with clinical symptoms and level 1 polysomnography (PSG) results in patients with chronic insomnia to evaluate the clinical implications of actigraphy-derived parameters for PSG interpretation. Seventy-five participants underwent actigraphy assessments and level 1 PSG. Exploratory correlation analyses between parameters derived from actigraphy, PSG, and clinical assessments were performed. First, participants were classified into two groups based on rest-activity pattern variables; group differences were investigated following covariate adjustment. Participants with poorer rest-activity patterns on actigraphy (low inter-day stability and high intra-daily variability) exhibited higher insomnia severity index scores than participants with better rest-activity patterns. No between-group differences in PSG parameters were observed. Second, participants were classified into two groups based on circadian phase variables. Late-phase participants (least active 5-h and most active 10-h onset times) exhibited higher insomnia severity scores, longer sleep and rapid eye movement latency, and lower apnea–hypopnea index than early-phase participants. These associations remained significant even after adjusting for potential covariates. Some actigraphy-derived rest-activity patterns and circadian phase parameters were significantly associated with clinical symptoms and PSG results, suggesting their possible adjunctive role in deriving plans for PSG lights-off time and assessing the possible insomnia pathophysiology.

ABSTRACT The relevance of altered rest-activity rhythm (RAR) and light exposure rhythm (LER) in insomnia patients under natural conditions remains unclear. The aim of this study was to compare the parametric and nonparametric circadian variables of RAR and those of LER under natural conditions between insomnia patients and normal controls (NC) in a community-dwelling setting. The relationship of the nonparametric variables with sleep quality was also explored in both groups. Participants above 18 years old were recruited from three Public Health Centers in a rural area of Korea. Actigraphy (Actiwatch 2; Philips Respironics, Murrysville PA, USA) recording was conducted for 7 days. Subjects were eligible for our study if they had an insomnia disorder (ID) for at least 1 month. Actigraphy data of 78 normal control (NC) subjects (Age, 55.95 ± 13.22 years) and 104 patients with insomnia disorder (ID) (Age, 62.14 ± 12.34 years) were included for the analysis. Acrophases and amplitudes of RAR and LER were estimated using cosinor analysis. Interdaily stability (IS), intradaily variability (IV), and relative amplitude (RA) of these rhythms were determined using nonparametric methods. Parametric cosinor and nonparametric variables of RAR and LER were compared between the NC and ID groups. Generalized linear models (GLMs) were applied to evaluate the main effects of group and each nonparametric variable as well as a group by each variable interaction on the sleep onset latency (SOL), sleep efficiency (SE), and wake after sleep onset (WASO) reflecting sleep quality. Among sleep parameters, the ID group showed significantly lower SE and greater WASO than the NC group. There were no significant differences in the acrophase and amplitude of RAR and LER between the two groups. There were no significant differences in IV, IS, and RA of RAR and LER between the two groups either. GLMs for RAR revealed a significant interaction between the group and IS on the SOL (β = −46.39, p < 0.01), indicating a negative relationship of the IS with SOL in ID unlike its positive relationship in NC. There were no significant main effects of IV on the SOL, SE, and WASO, but significant main effects of RA on the SE and WASO (β = 63.65 and β = −221.43, respectively, p < 0.01). GLMs for LER revealed no significant main effects of IS, IV or RA on the SOL, SE, and WASO, but significant interactions between group and RA on the SE and WASO (β = 56.17 and β = −171.93, respectively, p < 0.05), indicating a stronger positive relationship of the RA with SE in ID compared to NC, and a negative relationship of the RA with WASO in ID, unlike its positive relationship in NC. Although our study did not reveal group differences in circadian variables of RAR and LER, it suggested that the regularity of RAR could be positively associated with sleep initiation, while the robustness of LER could be positively associated with sleep maintenance in insomnia patients.

Background There is a growing population of survivors of colorectal cancer (CRC). Fatigue and insomnia are common symptoms after CRC, negatively influencing health-related quality of life (HRQoL). Besides increasing physical activity and decreasing sedentary behavior, the timing and patterns of physical activity and rest over the 24-h day (i.e. diurnal rest-activity rhythms) could also play a role in alleviating these symptoms and improving HRQoL. We investigated longitudinal associations of the diurnal rest-activity rhythm (RAR) with fatigue, insomnia, and HRQoL in survivors of CRC. Methods In a prospective cohort study among survivors of stage I-III CRC, 5 repeated measurements were performed from 6 weeks up to 5 years post-treatment. Parameters of RAR, including mesor, amplitude, acrophase, circadian quotient, dichotomy index, and 24-h autocorrelation coefficient, were assessed by a custom MATLAB program using data from tri-axial accelerometers worn on the upper thigh for 7 consecutive days. Fatigue, insomnia, and HRQoL were measured by validated questionnaires. Confounder-adjusted linear mixed models were applied to analyze longitudinal associations of RAR with fatigue, insomnia, and HRQoL from 6 weeks until 5 years post-treatment. Additionally, intra-individual and inter-individual associations over time were separated. Results Data were available from 289 survivors of CRC. All RAR parameters except for 24-h autocorrelation increased from 6 weeks to 6 months post-treatment, after which they remained relatively stable. A higher mesor, amplitude, circadian quotient, dichotomy index, and 24-h autocorrelation were statistically significantly associated with less fatigue and better HRQoL over time. A higher amplitude and circadian quotient were associated with lower insomnia. Most of these associations appeared driven by both within-person changes over time and between-person differences in RAR parameters. No significant associations were observed for acrophase. Conclusions In the first five years after CRC treatment, adhering to a generally more active (mesor) and consistent (24-h autocorrelation) RAR, with a pronounced peak activity (amplitude) and a marked difference between daytime and nighttime activity (dichotomy index) was found to be associated with lower fatigue, lower insomnia, and a better HRQoL. Future intervention studies are needed to investigate if restoring RAR among survivors of CRC could help to alleviate symptoms of fatigue and insomnia while enhancing their HRQoL. Trial registration EnCoRe study NL6904 ( https://www.onderzoekmetmensen.nl/ ).

… Sleep patterns of adults with insomnia show substantial night-tonight variability, … actigraphy typically provides data of multiple consecutive 24-h periods, evaluation of restactivity rhythms …

Growing epidemiological evidence points toward an association between fragmented 24‐h rest–activity cycles and cognition in the aged. Alterations in the circadian timing system might at least partially account for these observations. Here, we tested whether daytime rest (DTR) is associated with changes in concomitant 24‐h rest probability profiles, circadian timing and neurobehavioural outcomes in healthy older adults. Sixty‐three individuals (59–82 years) underwent field actigraphy monitoring, in‐lab dim light melatonin onset assessment and an extensive cognitive test battery. Actimetry recordings were used to measure DTR frequency, duration and timing and to extract 24‐h rest probability profiles. As expected, increasing DTR frequency was associated not only with higher rest probabilities during the day, but also with lower rest probabilities during the night, suggesting more fragmented night‐time rest. Higher DTR frequency was also associated with lower episodic memory performance. Moreover, later DTR timing went along with an advanced circadian phase as well as with an altered phase angle of entrainment between the rest–activity cycle and circadian phase. Our results suggest that different DTR characteristics, as reflective indices of wake fragmentation, are not only underlined by functional consequences on cognition, but also by circadian alteration in the aged.

STUDY OBJECTIVES Objective rest-activity rhythm (RAR) disturbances are linked with major disease outcomes. If consumer wearables yield RAR measures comparable to traditional research devices, these popular devices could provide a scalable option for clinical applications of RAR monitoring, e.g., risk factor screening. METHODS We asked convenience sample of participants (analytic n = 23; mean age = 27 years; 80% female) to wear Apple Watch and ActiGraph devices on separate wrists for one week. We derived a time series of activity counts from the raw accelerometer data, then extracted non-parametric (interdaily stability (IS), relative amplitude (RA), intradaily variability (IV)) and extended-cosine (pseudo-F, amplitude, up-mesor, acrophase, down-mesor) RAR variables. Spearman correlation coefficients (R) assessed association. Intraclass Correlation Coefficients (ICCs) assessed both agreement (closeness of values) and consistency (similarity of rankings) of RAR measures from the two devices. RESULTS The devices' RAR measures were highly correlated (Spearman R range: 0.7-0.9, p<.001). Agreement ICCs indicated good reliability for most metrics (ICC = 0.74-0.85), except for amplitude (which is highly dependent on the activity-count's scale; ICC = 0.01). Agreement ICCs had wide confidence intervals, most reached at least the moderate agreement range (e.g., IS agreement ICC = 0.77; 95% CI: 0.47-0.90). Consistency ICCs were higher and exhibited narrower 95% CIs, with estimates in the good-to-excellent range (e.g., IS consistency ICC = 0.84; 95% CI: 0.61-0.92). CONCLUSIONS Good-to-excellent consistency ICCs indicate that these devices yield similar participants rank-orderings on these RAR measures. However, there was systematic disagreement, suggesting absolute values from different devices' measures should not be pooled.

… of the rest–activity rhythm, with a focus on actigraphy. … using rhythmometric procedures to traditional actigraphic studies … study of the rest–activity circadian rhythm using actigraphy and …

ABSTRACT 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.

Sleep-wake disturbances in individuals at clinical high risk (CHR) of psychosis may relate to increased symptom severity and contribute to disease progression. Here, we examined differences in rest-activity rhythms (RAR) measures, derived from actigraphy, and objective sleep outcomes, derived from electroencephalography (EEG), between 12 CHR and 16 healthy comparison (HC) individuals. Further, we examined the relationships between RAR disturbances, objective sleep outcomes and clinical psychosis symptoms (i.e., negative, positive, disorganized, general symptoms). Sleep-wake behaviors were monitored via actigraphy for 3–7 days (CHR: 5.7 ± 1.7 days; HC: 6.3 ± 1.2 days) prior to participants spending a night in the sleep laboratory, which was monitored with EEG. Separate regressions were used to examine the effect of clinical group on RAR measures and objective sleep outcomes after controlling for age and gender. CHR participants were found to be less active, specifically during the evening (17:00–20:00; β = 1.145, SE = 0.362, p = .004) and nighttime (21:00–24:00; β = 1.152, SE = 0.326, p = .002) relative to HC. Further, CHR participants had more fragmented sleep (wake after sleep onset: β = 0.888, SE = 0.395, p = .034) and more hyperarousal during sleep (NREM gamma activity: β = 1.087, SE = 0.348, p = .005), but these sleep disturbances were not related to reduced activity or clinical symptoms, whereas lower nighttime activity was related to more disorganized symptoms (ρ = −.640, p = .025). Thus, increasing activity through behavioral interventions may have additional beneficial effects on CHR clinical symptoms.

… Rest–activity patterns provide a field method to study exposures related to circadian rhythms… Twenty-four-hour rest–activity indices from actigraphy provide a field method to study …

Background: This study aimed to empirically derive subgroups based on both actigraphy- and app-measured rest–activity rhythm (RAR) patterns and investigate the relationship between these profiles and health outcomes, including depression and obesity. Methods: We developed a mobile app, Rhythm, to record human–smartphone interactions and calculate RAR patterns alongside standard actigraphy in 135 participants (mean age: 43.8 ± 12.3 years, 64% women) with and without major depressive disorder and/or obesity. Wrist actigraphy and Rhythm app recorded activity data for at least 4 weeks, totaling 3978 person-days. Person-centered clustering was conducted to identify subgroups based on RAR characteristics, and their associations with clinical outcomes were evaluated using multivariable regression models. Results: Three distinct groups with different RAR patterns were identified based on acrophase, interdaily stability (IS), and intradaily variability (IV), measured by actigraphy and human–smartphone interactions, respectively. The “earlier” group exhibited earlier acrophase both by actigraphy and the app and had lower depressive symptom severity than the other two groups. The “later” group showed a later acrophase and a lower body mass index (BMI) compared to the “earlier” group. The “irregular” group, characterized by higher IV, lower IS, and desynchronized actigraphy- and app-measured acrophase, was associated with higher levels of depressive symptom severity and BMI. Conclusions: Our study highlights the usefulness of human–smartphone interaction patterns in providing a comprehensive understanding of individuals' circadian rhythms beyond standard actigraphy measurements. Identifying distinct RAR profiles based on both actigraphy and app measurements contributes to a better understanding of the associations between circadian disruptions and mental and physical health outcomes.

Importance Given its recurrent nature and burden, major depressive disorder (MDD) warrants reliable methods of relapse prediction. Objective To determine whether actigraphy-derived parameters, measured over 1 to 2 years, are associated with relapse. Design, Setting, and Participants This was an observational cohort study with data collection from July 2016 to January 2019. The setting was multicentric. A referred sample of participants from outpatient psychiatric and primary care clinics across Canada were followed up for 1 to 2 years. Participants had a diagnosis of MDD and Montgomery-Åsberg Depression Rating Scale (MADRS) score less than or equal to 14 at baseline. Exposures Actigraphy-derived parameters measured over 1 to 2 years. Main Outcome and Measures The primary outcome was relapse, defined as any of the following: MADRS score greater than or equal to 22 for 2 consecutive weeks, psychiatric hospitalization, emergence of suicidal intent or behavior, or antidepressant treatment escalation-all adjudicated by an independent panel. Continuous actigraphy data were averaged every 2 weeks. Results From a referred sample of 102 adults, 93 participants (mean [SD] age, 39.1 [12.7] years; 58 female [62%]) contributed approximately 32 000 complete actigraphy days (median, 46 weeks). In Cox models adjusted for age, sex, season, and baseline MADRS score, baseline lower sleep regularity (hazard ratio [HR], 0.46; 95% CI, 0.28-0.74; P = .002), lower relative amplitude (RA; HR, 0.45; 95% CI, 0.29-0.70; P < .001), lower sleep efficiency (HR, 0.57; 95% CI, 0.38-0.85; P = .005), higher wake after sleep onset (HR, 1.77; 95% CI, 1.12-2.80; P = .01), and higher nighttime activity (HR, 1.86; 95% CI, 1.32-2.62; P < .001) were associated with relapse. In time-varying models, greater composite phase deviation (HR, 1.76; 95% CI, 1.04-2.98; P = .04) and lower RA (HR, 0.45; 95% CI, 0.21-0.97; P = .046) were associated with relapse, with RA remaining significant even after adjusting for concurrent MADRS scores (HR, 0.60; 95% CI, 0.36-0.98; P = .04). Actigraphy significantly differentiated individuals experiencing relapse from those with an ultrastable (MADRS score <14 throughout) and unstable (transient MADRS score, 14-22 without relapse) clinical course. Conclusions and Relevance Actigraphy measures of sleep phase variability and daily activity amplitude were associated with depressive relapse, supporting actigraphy as a potential scalable biomarker to identify high-risk individuals and enable timely, personalized relapse prevention in MDD.

Background Evidence mapping is a structured approach used to synthesize the state-of-the-art in an emerging field of research when systematic reviews or meta-analyses are deemed inappropriate. We employed this strategy to summarise knowledge regarding longitudinal ecological monitoring of rest-activity rhythms (RAR) and disease modifiers, course of illness, treatment response or outcome in bipolar disorders (BD). Structure We had two key aims: (1) to determine the number and type of actigraphy studies of in BD that explored data regarding: outcome over time (e.g. relapse/recurrence according to polarity, or recovery/remission), treatment response or illness trajectories and (2) to examine the range of actigraphy metrics that can be used to estimate disruptions of RAR and describe which individual circadian rhythm or sleep–wake cycle parameters are most consistently associated with outcome over time in BD. The mapping process incorporated four steps: clarifying the project focus, describing boundaries and ‘coordinates’ for mapping, searching the literature and producing a brief synopsis with summary charts of the key outputs. Twenty-seven independent studies (reported in 29 publications) were eligible for inclusion in the map. Most were small-scale, with the median sample size being 15 per study and median duration of actigraphy being about 7 days (range 1–210). Interestingly, 17 studies comprised wholly or partly of inpatients (63%). The available evidence indicated that a discrete number of RAR metrics are more consistently associated with transition between different phases of BD and/or may be predictive of longitudinal course of illness or treatment response. The metrics that show the most frequent associations represent markers of the amount, timing, or variability of RAR rather than the sleep quality metrics that are frequently targeted in contemporary studies of BD. Conclusions Despite 50 years of research, use of actigraphy to assess RAR in longitudinal studies and examination of these metrics and treatment response, course and outcome of BD is under-investigated. This is in marked contrast to the extensive literature on case–control or cross-sectional studies of actigraphy, especially typical sleep analysis metrics in BD. However, given the encouraging findings on putative RAR markers, we recommend increased study of putative circadian phenotypes of BD.

People with disrupted circadian rhythms, such as shift workers, have shown a higher risk of hypertension. However, it is unclear whether more subtle differences in diurnal rest–activity rhythms in the population are associated with hypertension. Clarifying the association between the rest–activity rhythm, a modifiable behavioural factor, and hypertension could provide insight into preventing hypertension and possibly cardiovascular diseases. In this study, we investigated the association between rest–activity rhythm characteristics and hypertension in a large representative sample of United States adults. Cross‐sectional data were obtained from the National Health and Nutrition Examination Survey 2011–2014 (N = 6726; mean [range] age 49 [20–79] years; 52% women). Five rest–activity rhythm parameters (i.e., pseudo F statistic, amplitude, mesor, amplitude:mesor ratio, and acrophase) were derived from 24‐h actigraphy data using the extended cosine model. We performed multiple logistic regression to assess the associations between the rest–activity rhythm parameters and hypertension. Subgroup analysis stratified by age, gender, race/ethnicity, body mass index and diabetes status was also conducted. A weakened overall rest–activity rhythm, characterised by a lower F statistic, was associated with higher odds of hypertension (odds ratio quintile 1 versus quintile 5 [OR Q1vs.Q5 ] 1.61, 95% confidence interval [CI] 1.26–2.05; p trend < 0.001). Similar results were found for lower amplitude (OR Q1vs.Q5 1.51, 95% CI 1.13–2.03; p trend = 0.01) and amplitude:mesor ratio (OR Q1vs.Q5 1.34, 95% CI 1.01–1.78; p trend = 0.03). The results were robust to the adjustment of confounders, individual behaviours including physical activity levels and sleep duration and appeared consistent across subgroups. Possible interaction between the rest–activity rhythm and body mass index was found. Our results support an association between weakened rest–activity rhythms and higher odds of hypertension.

BACKGROUND/PURPOSE This prospective observational study investigated the relationship between circadian rest-activity rhythms and functional outcomes in subacute stroke rehabilitation. METHODS A cohort of 70 subacute stroke patients (32.9 % female; mean age 67.1 ± 12.2 years) was assessed. Actigraphy data collected over seven days were used to calculate rest-activity rhythm indicators, including interdaily stability (IS), intradaily variability, relative amplitude, and the 10 most active and five least active continuous hours. Correlations between these indicators and functional outcomes, measured by the Barthel Index (BI) at discharge, were analyzed. RESULTS Significant associations were identified between rest-activity rhythm indicators and functional outcomes. By univariate analysis, IS demonstrated positive correlations with BI scores at admission (r = 0.32, P = 0.007) and at discharge (r = 0.46, P < 0.001), whereas relative amplitude and the 10 most active continuous hours also showed positive correlations with BI scores at both time points. By multivariate analysis, after adjusting for age, sex, BI score, cognition, stroke severity at admission, and other rest-activity rhythm indicators, IS was an independent predictor of discharge BI scores (β = 0.23, P = 0.013). CONCLUSION Circadian rest-activity rhythm indicators are significantly associated with functional recovery in post-stroke patients. These findings highlight the negative impact of circadian disruptions on rehabilitation outcomes and suggest that actigraphy-derived metrics could serve as promising digital biomarkers to guide interventions and enhance outcomes.

Background Wearables have been gaining increasing momentum and have enormous potential to provide insights into daily life behaviors and longitudinal health monitoring. However, to date, there is still a lack of principled algorithmic framework to facilitate the analysis of actigraphy and objectively characterize day-by-day data patterns, particularly in cohorts with sleep problems. Objective This study aimed to propose a principled algorithmic framework for the assessment of activity, sleep, and circadian rhythm patterns in people with posttraumatic stress disorder (PTSD), a mental disorder with long-lasting distressing symptoms such as intrusive memories, avoidance behaviors, and sleep disturbance. In clinical practice, these symptoms are typically assessed using retrospective self-reports that are prone to recall bias. The aim of this study was to develop objective measures from patients’ everyday lives, which could potentially considerably enhance the understanding of symptoms, behaviors, and treatment effects. Methods Using a wrist-worn sensor, we recorded actigraphy, light, and temperature data over 7 consecutive days from three groups: 42 people diagnosed with PTSD, 43 traumatized controls, and 30 nontraumatized controls. The participants also completed a daily sleep diary over 7 days and the standardized Pittsburgh Sleep Quality Index questionnaire. We developed a novel approach to automatically determine sleep onset and offset, which can also capture awakenings that are crucial for assessing sleep quality. Moreover, we introduced a new intuitive methodology facilitating actigraphy exploration and characterize day-by-day data across 49 activity, sleep, and circadian rhythm patterns. Results We demonstrate that the new sleep detection algorithm closely matches the sleep onset and offset against the participants' sleep diaries consistently outperforming an existing open-access widely used approach. Participants with PTSD exhibited considerably more fragmented sleep patterns (as indicated by greater nocturnal activity, including awakenings) and greater intraday variability compared with traumatized and nontraumatized control groups, showing statistically significant (P<.05) and strong associations (|R|>0.3). Conclusions This study lays the foundation for objective assessment of activity, sleep, and circadian rhythm patterns using passively collected data from a wrist-worn sensor, facilitating large community studies to monitor longitudinally healthy and pathological cohorts under free-living conditions. These findings may be useful in clinical PTSD assessment and could inform therapy and monitoring of treatment effects.

Circadian rhythms become less dominant and less regular with chronic-degenerative disease, such that to accurately assess these pathological conditions it is important to quantify not only periodic characteristics but also more irregular aspects of the corresponding time series. Novel data-adaptive techniques, such as singular spectrum analysis (SSA), allow for the decomposition of experimental time series, in a model-free way, into a trend, quasiperiodic components and noise fluctuations. We compared SSA with the traditional techniques of cosinor analysis and intradaily variability using 1-week continuous actigraphy data in young adults with acute insomnia and healthy age-matched controls. The findings suggest a small but significant delay in circadian components in the subjects with acute insomnia, i.e. a larger acrophase, and alterations in the day-to-day variability of acrophase and amplitude. The power of the ultradian components follows a fractal 1/f power law for controls, whereas for those with acute insomnia this power law breaks down because of an increased variability at the 90min time scale, reminiscent of Kleitman’s basic rest-activity (BRAC) cycles. This suggests that for healthy sleepers attention and activity can be sustained at whatever time scale required by circumstances, whereas for those with acute insomnia this capacity may be impaired and these individuals need to rest or switch activities in order to stay focused. Traditional methods of circadian rhythm analysis are unable to detect the more subtle effects of day-to-day variability and ultradian rhythm fragmentation at the specific 90min time scale.

The normal spectrum trait measures of mood instability and impulsivity are implicated in and comprise core symptoms of several psychiatric disorders. A bidirectional relationship between these traits and sleep disturbance and circadian rhythm dysfunction has been hypothesised, although has not been systematically assessed using objective measures in naturalistic settings. We systematically reviewed the literature following PRISMA guidelines, according to a pre-registered protocol (PROSPERO: CRD 42018108213). Peer-reviewed quantitative studies assessing an association between actigraphic variables and any measure of mood instability or impulsivity in participants aged 12-65 years old were included. Studies were critically appraised using the AXIS tool. Twenty-three articles were retained for inclusion. There was significant heterogeneity in the selection and reporting of actigraphic variables and metrics of mood instability and impulsivity. We identified emerging evidence of a positive association between circadian rest-activity pattern disturbance and delayed sleep timing with both mood instability and impulsivity. Evidence for an association with sleep duration, sleep efficiency or sleep quality was inconsistent. Future research should focus on longitudinal intra-individual associations to establish the directionality between these measures and may lead to the development of chronotherapeutic interventions for a number of psychiatric disorders.

Abstract Background Sleep and circadian rhythm disturbances in schizophrenia are common, but incompletely characterized. We aimed to describe and compare the magnitude and heterogeneity of sleep-circadian alterations in remitted schizophrenia and compare them with those in interepisode bipolar disorder. Methods EMBASE, Medline, and PsycINFO were searched for case–control studies reporting actigraphic parameters in remitted schizophrenia or bipolar disorder. Standardized and absolute mean differences between patients and controls were quantified using Hedges’ g, and patient–control differences in variability were quantified using the mean-scaled coefficient of variation ratio (CVR). A wald-type test compared effect sizes between disorders. Results Thirty studies reporting on 967 patients and 803 controls were included. Compared with controls, both schizophrenia and bipolar groups had significantly longer total sleep time (mean difference [minutes] [95% confidence interval {CI}] = 99.9 [66.8, 133.1] and 31.1 [19.3, 42.9], respectively), time in bed (mean difference = 77.8 [13.7, 142.0] and 50.3 [20.3, 80.3]), but also greater sleep latency (16.5 [6.1, 27.0] and 2.6 [0.5, 4.6]) and reduced motor activity (standardized mean difference [95% CI] = −0.86 [−1.22, −0.51] and −0.75 [−1.20, −0.29]). Effect sizes were significantly greater in schizophrenia compared with the bipolar disorder group for total sleep time, sleep latency, and wake after sleep onset. CVR was significantly elevated in both diagnoses for total sleep time, time in bed, and relative amplitude. Conclusions In both disorders, longer overall sleep duration, but also disturbed initiation, continuity, and reduced motor activity were found. Common, modifiable factors may be associated with these sleep-circadian phenotypes and advocate for further development of transdiagnostic interventions that target them.

… for bipolar disorder indicate that disruption of circadian rhythms is … both circadian activity and sleep patterns using actigraphy … to set the analysis periods for the actigraphic sleep data. …

… the extent of this disruption under “real” life situations. Simultaneous wrist actigraphy, diary … profiles are appropriate tools to assess circadian rhythms and sleep patterns in field studies. …

Quantitative and objective evaluation of disease severity and/or drug effect is necessary in clinical practice. Wearable accelerometers such as an actigraph enable long-term recording of a patient's movement during activities and they can be used for quantitative assessment of symptoms due to various diseases. We reviewed some applications of actigraphy with analytical methods that are sufficiently sensitive and reliable to determine the severity of diseases and disorders such as motor and nonmotor disorders like Parkinson's disease, sleep disorders, depression, behavioral and psychological symptoms of dementia (BPSD) for vascular dementia (VD), seasonal affective disorder (SAD), and stroke, as well as the effects of drugs used to treat them. We believe it is possible to develop analytical methods to assess more neurological or psychopathic disorders using actigraphy records.

Sleep-related problems are prevalent in patients with psychotic disorders, yet their contribution to fluctuations in delusional experiences is less clear. This study combined actigraphy and experience-sampling methodology (ESM) to capture the relation between sleep and next-day persecutory symptoms in patients with psychosis and prevailing delusions. Individuals with current persecutory delusions (PD; n = 67) and healthy controls (HC; n = 39) were assessed over 6 consecutive days. Objective sleep and circadian rhythm measures were assessed using actigraphy. Every morning upon awakening, subjective sleep quality was measured using ESM. Momentary assessments of affect and persecutory symptoms were gathered at 10 random time points each day using ESM. Robust linear mixed modeling was performed to assess the predictive value of sleep measures on affect and daytime persecutory symptoms. PD showed significantly lower scores for subjective quality of sleep but significantly higher actigraphic-measured sleep duration and efficiency compared with HC. Circadian rhythm disruption was associated with more pronounced severity of persecutory symptoms in HC. Low actigraphy-derived sleep efficiency was predictive of next-day persecutory symptoms in the combined sample. Negative affect was partly associated with sleep measures and persecutory symptoms. Our results imply an immediate relationship between disrupted sleep and persecutory symptoms in day-to-day life. They also emphasize the relevance of circadian rhythm disruption for persecutory symptoms. Therapeutic interventions that aim to reduce persecutory symptoms could benefit from including modules aimed at improving sleep efficacy, stabilizing sleep-wake patterns, and reducing negative affect. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

We report the relationship between daily rest-activity patterns and trait impulsivity in healthy young adults. The Barratt Impulsiveness Scale was used to identify high and low impulsive individuals among a group of 51 volunteers. Participants' sleep behaviour and circadian rhythm function was assessed using week-long actigraphy. High impulsive individuals displayed phase-delayed patterns of sleep, a decreased total sleep time and sleep efficiency, and disrupted circadian function. Such outcomes were also associated with greater self-reported attention deficit hyperactivity disorder symptoms. The results highlight that sleep and circadian rhythm disturbances may be associated with impulsive traits replicating relationships described in psychiatric illnesses in which impulsivity is a core feature.

Remarkably, the specifics of sleep along the human lineage have been slow to emerge, which is surprising given our distinct mental and behavioral capacity and the importance of sleep for individual health and cognitive performance. Largely due to difficultly of measuring sleep outside a controlled, clinical, and laboratory study in ambulatory individuals, human biologists have yet to undergo a thorough examination of sleep in ecologically diverse settings. Here, I outline the procedures and methods for generating sleep data in a broader ecological context with the goal of facilitating the integration of sleep and circadian analyses into human biology research.

… Circadian rhythms of high amplitude were detected by cosinor analysis for each participant and … Actigraphy served here to quantify circadian rhythmicity in spontaneous wrist movement. …

To review methods for analyzing circadian rest-activity patterns using actigraphy and to discuss their applications in large cohort and population-based studies. We reviewed several widely used approaches, including parametric analysis (i.e., cosinor model and wavelet analysis), nonparametric analysis, data adaptive approach (i.e., empirical mode decomposition), and nonlinear dynamical approach (i.e., fractal analysis). We delved into the specifics of each approach and highlighted their advantages and disadvantages. Various approaches have been developed to study circadian rest-activity rhythms using actigraphy. Features extracted from these approaches have been associated with population health outcomes. Limitations exist in prior research, including inconsistencies due to various available analytical approaches and lack of studies translating findings to the context of the circadian system. Potential future steps are proposed. The review ends with an introduction to an open-source software application—ezActi2—developed to facilitate scalable applications in analyzing circadian rest-activity rhythms.

… First-generation microprocessor-based wrist monitors utilized threshold-motion detectors, … validation samples. Although we tested the accuracy and validity of the algorithms in validation …

Study purpose: The integration of methods to assess daytime physical activity (PA) and sedentary behavior (SB) and nighttime sleep would allow the evaluation of 24‐hour daily activity using a single device. Accelerometer devices used to assess daytime PA have not been substantially validated to evaluate sleep. The objective of this study was to use polysomnography (PSG) to validate a commonly used PA accelerometer worn on both wrists and the hip. Methods: Seventeen participants (50‐75 years) completed a single‐night in‐home PSG recording while concurrently wearing 3 PA accelerometers. Accelerometer devices were worn on each wrist and the hip. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) were compared for each device against PSG. Correlation coefficients estimated measurement agreement. Paired t tests and Bland‐Altman plots assessed measurement differences. Results: Between PSG and devices, mean TST ranged from 361.6 to 403.2 minutes. Mean SE estimates ranged from 86.9% to 96.9%. Mean WASO estimates ranged from 12 to 51.2 minutes. For TST, SE, and WASO hip estimates differed significantly from PSG estimates (paired t tests, TST: P = .03, SE: P < .001, WASO: P< .001). No significant differences were found between wrist accelerometers and PSG estimates of TST, SE, or WASO. Conclusions: PA accelerometer devices worn on either wrist provide valid estimates of TST, WASO, and SE when compared with PSG. Further studies are needed to investigate methods to improve assessment of sleep parameters by PA accelerometer devices to advance device integration and assessment 24‐hour activity in populations.

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research‐grade accelerometers worn on each wrist with concurrent laboratory‐based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5 [7.2] years, body mass index 25.5 [3.7] kg/m2) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1‐night PSG assessment. Sleep estimates (sleep period time window [SPT‐window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open‐source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer‐derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT‐window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08). The mean bias between all accelerometer‐derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT‐window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

ABSTRACT OBJECTIVE: In a device based on midsagittal jaw movements analysis, we assessed a sleep-wake automatic detector as an objective method to measure sleep in healthy adults by comparison with wrist actigraphy against polysomnography (PSG). METHODS: Simultaneous and synchronized in-lab PSG, wrist actigraphy and jaw movements were carried out in 38 healthy participants. Epoch by epoch analysis was realized to assess the ability to sleep-wake distinction. Sleep parameters as measured by the three devices were compared. This included three regularly reported parameters: total sleep time, sleep onset latency, and wake after sleep onset. Also, two supplementary parameters, wake during sleep period and latency time, were added to measure quiet wakefulness state. RESULTS: The jaw movements showed sensitivity level equal to actigraphy 96% and higher specificity level (64% and 48% respectively). The level of agreement between the two devices was high (87%). The analysis of their disagreement by discrepant resolution analysis used PSG as resolver revealed that jaw movements was right (58.9%) more often than actigraphy (41%). In sleep parameters comparison, the coefficient correlation of jaw movements was higher than actigraphy in all parameters. Moreover, its ability to distinct sleep-wake state allowed for a more effective estimation of the parameters that measured the quiet wakefulness state. CONCLUSIONS: Midsagittal jaw movements analysis is a reliable method to measure sleep. In healthy adults, this device proved to be superior to actigraphy in terms of estimation of all sleep parameters and distinction of sleep-wake status.

This study aimed to develop an algorithm for determining sleep/wake states by using chronological data on the amount of physical activity (activity intensity) measured with the FS-750 actigraph, a device that can be worn at the waist, allows for its data to be downloaded at home, and is suitable for use in both sleep research and remote sleep medicine. Participants were 34 healthy young adults randomly assigned to two groups, A (n =17) and B (n =17), who underwent an 8-hour polysomnography (PSG) in the laboratory environment. Simultaneous activity data were obtained using the FS-750 attached at the front waist. Sleep/wake state and activity intensity were calculated every 2 minutes (1 epoch). To determine the central epoch of the sleep/wake states (x), a five-variable linear model was developed using the activity intensity of Group A for five epochs (x-2, x-1, x, x+1, x+2; 10 minutes). The optimal coefficients were calculated using discriminant analysis. The agreement rate of the developed algorithm was then retested with Group B, and its validity was examined. The overall agreement rates for group A and group B calculated using the sleep/wake score algorithm developed were 84.7% and 85.4%, respectively. Mean sensitivity (agreement rate for sleep state) was 88.3% and 90.0% and mean specificity (agreement rate for wakeful state) was 66.0% and 64.9%, respectively. These results confirmed comparable agreement rates between the two groups. Furthermore, when applying an estimation rule developed for the sleep parameters measured by the FS-750, no differences were found in the average values between the calculated scores and PSG results, and we also observed a correlation between the two sets of results. Thus, the validity of these evaluation indices based on measurements from the FS-750 is confirmed. The developed algorithm could determine sleep/wake states from activity intensity data obtained with the FS-750 with sensitivity and specificity equivalent to that determined with conventional actigraphs. The FS-750, which is smaller, less expensive, and able to take measurements over longer periods than conventional devices, is a promising tool for advancing sleep studies at home and in remote sleep medicine.

… Actigraphy is based on the assumption that people move most during wake states with a progressive reduction in motion … Actigraphy has also been validated in pediatric populations 7 ; …

… actigraphs in clinical practice is growing as more clinicians understand the benefits of long-term recordings of sleep/wake … For sleep clinicians, actigraphy can provide useful information …

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual’s own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

There is extensive laboratory research studying the effects of acute sleep deprivation on biological and cognitive functions, yet much less is known about naturalistic patterns of sleep loss and the potential impact on daily or weekly functioning of an individual. Longitudinal studies are needed to advance our understanding of relationships between naturalistic sleep and fluctuations in human health and performance, but it is first necessary to understand the efficacy of current tools for long-term sleep monitoring. The present study used wrist actigraphy and sleep log diaries to obtain daily measurements of sleep from 30 healthy adults for up to 16 consecutive weeks. We used non-parametric Bland-Altman analysis and correlation coefficients to calculate agreement between subjectively and objectively measured variables including sleep onset time, sleep offset time, sleep onset latency, number of awakenings, the amount of wake time after sleep onset, and total sleep time. We also examined compliance data on the submission of daily sleep logs according to the experimental protocol. Overall, we found strong agreement for sleep onset and sleep offset times, but relatively poor agreement for variables related to wakefulness including sleep onset latency, awakenings, and wake after sleep onset. Compliance tended to decrease significantly over time according to a linear function, but there were substantial individual differences in overall compliance rates. There were also individual differences in agreement that could be explained, in part, by differences in compliance. Individuals who were consistently more compliant over time also tended to show the best agreement and lower scores on behavioral avoidance scale (BIS). Our results provide evidence for convergent validity in measuring sleep onset and sleep offset with wrist actigraphy and sleep logs, and we conclude by proposing an analysis method to mitigate the impact of non-compliance and measurement errors when the two methods provide discrepant estimates.

Long-term recording of a person’s activity (actimetry or actigraphy) using devices typically worn on the wrist is increasingly applied in sleep/wake, chronobiological, and clinical research to estimate parameters of sleep and sleep-wake cycles. With the recognition of the importance of light in influencing these parameters and with the development of technological capabilities, light sensors have been introduced into devices to correlate physiological and environmental changes. Over the past two decades, many such new devices have appeared from different manufacturers. One of the aims of this review is to help researchers and clinicians choose the data logger that best fits their research goals. Seventeen currently available light-and-motion recorders entered the analysis. They were reviewed for appearance, dimensions, weight, mounting, battery, sensors, features, communication interface, and software. We found that all devices differed from each other in several features. In particular, six devices are equipped with a light sensor that can measure blue light. It is noteworthy that blue light most profoundly influences the physiology and behavior of mammals. As the wearables market is growing rapidly, this review helps guide future developments and needs to be updated every few years.

… sensors with actigraphy in terms of sleep–wake detection and the sleep quality measurement. The advantage of video and PIR sensors over actigraphy for sleep monitoring would be …

Sleep is a fundamental biological process essential for health and homeostasis. Traditionally investigated through laboratory-based polysomnography (PSG), sleep research has undergone a paradigm shift with the advent of wearable technologies that enable non-invasive, long-term, and real-world monitoring. This review traces the evolution from early analog and actigraphic methods to current multi-sensor and AI-driven wearable systems. We summarize major technological milestones, including the transition from movement-based to physiological and biochemical sensing, and the growing role of edge computing and deep learning in automated sleep staging. Comparative studies with PSG are discussed, alongside the strengths and limitations of emerging devices such as wristbands, rings, headbands, and camera-based systems. The clinical applications of wearable sleep monitors are examined in relation to remote patient management, personalized medicine, and large-scale population research. Finally, we outline future directions toward integrating multimodal biosensing, transparent algorithms, and standardized validation frameworks. By bridging laboratory precision with ecological validity, wearable technologies promise to redefine the gold standard for sleep monitoring, advancing both individualized care and population-level health assessment.

… Abstract—Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations … algorithm is suitable for integration into a wearable device for long-term home monitoring. …

… comparable to actigraphy in sleep/wake studies. The study suggests that the device may be used in long-term monitoring of sleep/wake patterns with similar performance to actigraphy. …

… , low-cost and long-term sleep monitoring. How reliable and … of wrist-based actigraphy in sleep-wake cycle discrimination, … , long-term, and large-scale sleep monitoring remains on the …

Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children’s growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.

… , and impossibility to perform long-term monitoring. This has … In particular, actigraphy and cardiorespiratory signals have … shape of a specific state (sleep/wake)? Dynamic Warping (DW) …

… a long-term monitoring of subjects with potential sleep problems, in particular the ones who suffer from insomnia. In addition, the first night or reverse first night effect of using PSG in a …

… goal of this study was to assess correspondence between actigraphy and PSG using two metrics… actigraphy WASO, presence of chronic primary insomnia and the interaction between …

ABSTRACT Objective/Background: Actigraphy is an inexpensive and objective wrist-worn activity sensor that has been validated for the measurement of sleep onset latency (SOL), number of awakenings (NWAK), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE) in both middle-aged and older adults with insomnia. However, actigraphy has not been evaluated in young adults. In addition, most previous studies compared actigraphy to in-lab polysomnography (PSG), but none have compared actigraphy to more ecologically valid ambulatory polysomnography. Participants: 21 young adults (mean age = 19.90 ± 2.19 years; n = 13 women) determined to have chronic primary insomnia through structured clinical interviews. Methods: Sleep diaries, actigraphy, and ambulatory PSG data were obtained over a single night to obtain measures of SOL, NWAK, WASO, time spent in bed after final awakening in the morning (TWAK), TST, and SE. Results: Actigraphy was a valid estimate of SOL, WASO, TST, and SE, based on significant correlations (r = 0.45 to 0.87), nonsignificant mean differences between actigraphy and PSG, and inspection of actigraphy bias from Bland Altman plots (SOL α = 1.52, WASO α = 7.95, TST α = −8.60, SE α = −1.38). Conclusions: Actigraphy was a valid objective measure of SOL, WASO, TST, and SE in a young adult insomnia sample, as compared to ambulatory PSG. Actigraphy may be a valid alternative for assessing sleep in young adults with insomnia when more costly PSG measures are not feasible.

Consumer activity trackers claiming to measure sleep/wake patterns are ubiquitous within clinical and consumer settings. However, validation of these devices in sleep disorder populations are lacking. We examined 1 night of sleep in 42 individuals with insomnia (mean = 49.14 ± 17.54 years) using polysomnography, a wrist actigraph (Actiwatch Spectrum Pro: AWS) and a consumer activity tracker (Fitbit Alta HR: FBA). Epoch‐by‐epoch analysis and Bland−Altman methods evaluated each device against polysomnography for sleep/wake detection, total sleep time, sleep efficiency, wake after sleep onset and sleep latency. FBA sleep stage classification of light sleep (N1 + N2), deep sleep (N3) and rapid eye movement was also compared with polysomnography. Compared with polysomnography, both activity trackers displayed high accuracy (81.12% versus 82.80%, AWS and FBA respectively; ns) and sensitivity (sleep detection; 96.66% versus 96.04%, respectively; ns) but low specificity (wake detection; 39.09% versus 44.76%, respectively; p = .037). Both trackers overestimated total sleep time and sleep efficiency, and underestimated sleep latency and wake after sleep onset. FBA demonstrated sleep stage sensitivity and specificity, respectively, of 79.39% and 58.77% (light), 49.04% and 95.54% (deep), 65.97% and 91.53% (rapid eye movement). Both devices were more accurate in detecting sleep than wake, with equivalent sensitivity, but statistically different specificity. FBA provided equivalent estimates as AWS for all traditional actigraphy sleep parameters. FBA also showed high specificity when identifying N3, and rapid eye movement, though sensitivity was modest. Thus, it underestimates these sleep stages and overestimates light sleep, demonstrating more shallow sleep than actually obtained. Whether FBA could serve as a low‐cost substitute for actigraphy in insomnia requires further investigation.

In this paper we propose a new machine learning model for classification of nocturnal awakenings in acute insomnia and normal sleep. The model does not require sleep diaries or any other subjective information from the individuals who took part of the study. It is based on nocturnal actigraphy collected from pre-medicated individuals with acute insomnia and normal sleep controls. We have derived dynamical and statistical features from the actigraphy time series data. These features are combined using two machine learning techniques namely Random Forest (RF) and Support Vector Machine (SVM). RF shows better performance (accuracy - 84%) than SVM (73%) in classifying individuals with insomnia from healthy sleepers. The developed model provides a signature of the condition of acute insomnia obtained from actigraphy only and is very promising as a tool to detect the condition in a non-invasive way and without sleep diaries or any other subjective information.

Self-reported sleep difficulties are the primary concern associated with diagnosis and treatment of chronic insomnia. This said, in-home sleep monitoring technology in combination with self-reported sleep outcomes may usefully assist with the management of insomnia. The rapid acceleration in consumer sleep technology capabilities together with their growing use by consumers means that the implementation of clinically useful techniques to more precisely diagnose and better treat insomnia are now possible. This review describes emerging techniques which may facilitate better identification and management of insomnia through objective sleep monitoring. Diagnostic techniques covered include insomnia phenotyping, better detection of comorbid sleep disorders, and identification of patients potentially at greatest risk of adverse outcomes. Treatment techniques reviewed include the administration of therapies (e.g., Intensive Sleep Retraining, digital treatment programs), methods to assess and improve treatment adherence, and sleep feedback to address concerns about sleep and sleep loss. Gaps in sleep device capabilities are also discussed, such as the practical assessment of circadian rhythms. Proof-of-concept studies remain needed to test these sleep monitoring-supported techniques in insomnia patient populations, with the goal to progress towards more precise diagnoses and efficacious treatments for individuals with insomnia.

ABSTRACT Objectives: This study compared subjective (questionnaire) and objective (actigraphy) sleep assessments, and examined agreement between these methods, in vulnerable older adults participating in a Veterans Administration Adult Day Health Care (ADHC) program. Methods: 59 ADHC participants (95% male, mean age = 78 years) completed sleep questionnaires and 72 continuous hours of wrist actigraphy. Linear regression was used to examine agreement between methods and explore discrepancies in subjective/objective measures. Results: Disturbed sleep was common, yet there was no agreement between subjective and objective sleep assessment methods. Compared with objective measures, one-half of participants reported worse sleep efficiency (SE) on questionnaires while one-quarter over-estimated SE. Participants reporting worse pain had a greater discrepancy between subjective and objective SE. Conclusions: Vulnerable older adults demonstrated unique patterns of reporting sleep quality when comparing subjective and objective methods. Additional research is needed to better understand how vulnerable older adults evaluate sleep problems. Clinical Implications: Objective and subjective sleep measures may represent unique and equally important constructs in this population. Clinicians should consider utilizing both objective and subjective sleep measures to identify individuals who may benefit from behavioral sleep treatments, and future research is needed to develop and validate appropriate sleep assessments for vulnerable older adults.

ABSTRACT The purpose of the present work is to examine, on a clinically diverse population of older adults (N = 46) sleeping at home, the performance of two actigraphy-based sleep tracking algorithms (i.e., Actigraphy-based Sleep algorithm, ACT-S1 and Sadeh’s algorithm) compared to manually scored electroencephalography-based PSG (PSG-EEG). ACT-S1 allows for a fully automatic identification of sleep period time (SPT) and within the identified sleep period, the sleep-wake classification. SPT detected by ACT-S1 did not differ statistically from using PSG-EEG (bias = −9.98 min; correlation 0.89). In sleep-wake classification on 30-s epochs within the identified sleep period, the new ACT-S1 presented similar or slightly higher accuracy (83–87%), precision (86–89%) and F1 score (90–92%), significantly higher specificity (39–40%), and significantly lower, but still high, sensitivity (96–97%) compared to Sadeh’s algorithm, which achieved 99% sensitivity as the only measure better than ACT-S1’s. Total sleep times (TST) estimated with ACT-S1 and Sadeh’s algorithm were higher, but still highly correlated to PSG-EEG’s TST. Sleep quality metrics of sleep period efficiency and wake-after-sleep-onset computed by ACT-S1 were not significantly different from PSG-EEG, while the same sleep quality metrics derived by Sadeh’s algorithm differed significantly from PSG-EEG. Agreement between ACT-S1 and PSG-EEG reached was highest when analyzing the subset of subjects with least disrupted sleep (N = 28). These results provide evidence of promising performance of a full-automation of the sleep tracking procedure with ACT-S1 on older adults. Future longitudinal validations across specific medical conditions are needed. The algorithm’s performance may further improve with integrating multi-sensor information.