{"id":57891,"date":"2026-08-17T16:04:16","date_gmt":"2026-08-17T19:04:16","guid":{"rendered":"https:\/\/condor.fabbricaweb.com.br\/en\/?page_id=57891"},"modified":"2026-08-18T09:52:50","modified_gmt":"2026-08-18T12:52:50","slug":"actigraphy","status":"publish","type":"page","link":"https:\/\/condor.fabbricaweb.com.br\/pt\/actigraphy\/","title":{"rendered":"Actigraphy: How It Works, What It Measures, and When to Use It"},"content":{"rendered":"<p><!--\n  PAGINA PILLAR: \/en\/actigraphy\/ \u2014 v1 (ago 2026)\n  Estrategia: pillar page p\/ head term \"actigraphy\" (5.400\/mes US) + AEO\/LLM.\n  Regras da casa: claims com fonte verificada; sem exclusividade; specs de\n  concorrentes fora; tom editorial neutro-tecnico; Sarabun #0283C8.\n  DOIs verificados via Crossref\/PubMed em 2026-08-15.\n--><\/p>\n<style>@import url('https:\/\/fonts.googleapis.com\/css2?family=Sarabun:ital,wght@0,400;0,600;0,700;0,800;1,400&display=swap');.condor-page{font-family:'Sarabun',-apple-system,'Segoe UI',sans-serif;color:#212121;line-height:1.75;font-size:17.5px;max-width:860px;margin:0 auto;padding:8px 4px}.condor-page a{color:#0283C8;text-decoration:none;border-bottom:1px solid rgba(2,131,200,.3)}.condor-page a:hover{border-bottom-color:#0283C8}.condor-page .kicker{font-size:13px;font-weight:700;letter-spacing:2px;text-transform:uppercase;color:#0283C8;margin:0 0 14px}.condor-page h1{font-size:38px;line-height:1.18;font-weight:800;color:#111;margin:0 0 18px;letter-spacing:-.015em}.condor-page .standfirst{font-size:20px;line-height:1.6;color:#555;margin:0 0 18px;font-weight:400}.condor-page .byline{font-size:14px;color:#888;margin:0 0 44px;padding-bottom:24px;border-bottom:1px solid #E3E3E3}.condor-page .byline b{color:#555;font-weight:600}.condor-page h2{font-size:26px;font-weight:700;color:#111;margin:52px 0 16px;letter-spacing:-.01em;line-height:1.3}.condor-page h3{font-size:19px;font-weight:700;color:#111;margin:32px 0 10px}.condor-page p{margin:0 0 18px}.condor-page 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li::marker{color:#0283C8;font-weight:700}.condor-page .table-wrap{overflow-x:auto;margin:28px 0 10px;border:1px solid #E3E3E3;border-radius:8px}.condor-page table{width:100%;border-collapse:collapse;font-size:14.5px;margin:0;background:#fff}.condor-page thead th{background:#fff;color:#111;font-weight:700;text-align:left;padding:14px 16px;font-size:13.5px;border-bottom:2px solid #0283C8}.condor-page tbody th{text-align:left;padding:12px 16px;font-weight:600;color:#333;border-bottom:1px solid #F0F0F0;font-size:14px}.condor-page tbody td{padding:12px 16px;border-bottom:1px solid #F0F0F0;color:#444;vertical-align:top}.condor-page tbody tr:last-child td,.condor-page tbody tr:last-child th{border-bottom:none}.condor-page .note{font-size:13.5px;color:#888;margin:10px 0 28px;line-height:1.6}.condor-page .cta{margin:48px 0 8px;padding:30px 34px;border:2px solid #0283C8;border-radius:10px}.condor-page .cta h2{margin:0 0 10px;font-size:22px}.condor-page .cta p{margin-bottom:20px;color:#555}.condor-page .btn{display:inline-block;background:#0283C8;color:#fff !important;font-weight:700;padding:13px 30px;border-radius:6px;border-bottom:none !important;font-size:16px}.condor-page .refs{margin:44px 0 0;padding-top:26px;border-top:1px solid #E3E3E3}.condor-page .refs h2.refs-title{font-size:14px;letter-spacing:1.5px;text-transform:uppercase;color:#888;margin:0 0 16px;font-weight:700}.condor-page .refs ol{padding-left:20px}.condor-page .refs li{font-size:13.5px;color:#777;margin-bottom:10px;line-height:1.6}.condor-page .updated{font-size:13px;color:#aaa;margin-top:28px;font-style:italic}<\/style>\n<div class=\"condor-page\" style=\"max-width:860px;margin:0 auto;padding:24px 20px 40px;font-family:'Sarabun',sans-serif;line-height:1.75\">\n<p class=\"kicker\">Research Methods Guide<\/p>\n<h1>Actigraphy: how it works, what it measures, and when to use it<\/h1>\n<p class=\"standfirst\">A complete guide to actigraphy for researchers, clinicians and study coordinators \u2014 from accelerometer physics to sleep parameters, scoring algorithms, circadian metrics and the honest limitations. With peer-reviewed sources throughout.<\/p>\n<p class=\"byline\">By the <b>Condor Research Team<\/b> \u00b7 Updated August 2026 \u00b7 All cited claims link to published sources<\/p>\n<div class=\"defbox\" id=\"definition\">\n<p><strong>Actigraphy<\/strong> is a non-invasive method of monitoring rest and activity cycles by continuously recording movement, typically with a wrist-worn accelerometer called an actigraph. Movement data are aggregated into short time windows (epochs) and processed with validated algorithms to estimate sleep parameters, physical activity and circadian rhythm variables over days to months in a person&#8217;s natural environment.<\/p>\n<\/div>\n<div class=\"tldr\">\n<h2>Key facts<\/h2>\n<ul>\n<li>Actigraphy estimates sleep from <strong>movement<\/strong>, using tri-axial accelerometers sampled continuously and summarized in epochs (commonly 30 or 60 seconds).<\/li>\n<li>It is recommended by the American Academy of Sleep Medicine for evaluating <strong>circadian rhythm sleep-wake disorders<\/strong> and for estimating sleep parameters when polysomnography is impractical.<sup>[1]<\/sup><\/li>\n<li>Typical recordings run <strong>1\u20132 weeks, 24 hours a day<\/strong> \u2014 capturing night-to-night variability that a single-night lab study cannot.<\/li>\n<li>Classical scoring algorithms (Cole-Kripke, Sadeh, Oakley) detect sleep with high sensitivity but lower specificity for wake \u2014 actigraphy tends to <strong>overestimate sleep in people who lie still while awake<\/strong>.<sup>[2]<\/sup><\/li>\n<li>Modern research actigraphs also record <strong>light exposure<\/strong>; devices with multi-channel spectral sensors can report melanopic EDI, the CIE S 026 quantity used in circadian research.<sup>[5,6]<\/sup><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"how-it-works\">How does actigraphy work?<\/h2>\n<p>An actigraph contains a <strong>micro-electro-mechanical (MEMS) tri-axial accelerometer<\/strong> that samples motion continuously \u2014 research devices typically sample between 25 and 100 Hz. The raw acceleration signal is reduced to <strong>activity counts<\/strong> per epoch using one of a few well-described methods:<\/p>\n<ul>\n<li><strong>PIM (Proportional Integrating Measure)<\/strong> \u2014 integrates the area under the rectified acceleration curve within each epoch; sensitive to both the amount and intensity of movement.<\/li>\n<li><strong>ZCM (Zero-Crossing Mode)<\/strong> \u2014 counts how many times the signal crosses a threshold near zero; sensitive to movement frequency.<\/li>\n<li><strong>TAT (Time Above Threshold)<\/strong> \u2014 accumulates the time the signal spends above a set threshold; sensitive to movement duration.<\/li>\n<\/ul>\n<p>A scoring algorithm then classifies each epoch as <strong>sleep or wake<\/strong> based on the pattern of activity counts in and around it. Because the classification is movement-based, the choice of epoch length, count method and algorithm all influence the final sleep estimates \u2014 which is why methods sections should report all three. (For a deeper treatment, see our guides to <a href=\"\/en\/epoch-length-selection-how-temporal-resolution-impacts-sleep-study-outcomes\/\">epoch length selection<\/a> and <a href=\"\/en\/feature-extraction-in-actigraphy-from-raw-signals-to-meaningful-metrics\/\">feature extraction from raw signals<\/a>.)<\/p>\n<h3 id=\"history\">A brief history<\/h3>\n<p>Actigraphy grew out of 1970s ambulatory monitoring research, and its modern era began when <strong>Webster and colleagues published the first automated sleep-scoring algorithm for wrist activity in 1982<\/strong>.<sup>[8]<\/sup> Four decades of validation studies, algorithm refinements and shrinking hardware later, actigraphy has moved from research curiosity to guideline-supported clinical tool<sup>[1]<\/sup> \u2014 a trajectory reviewed in &#8220;40 years of actigraphy&#8221; by Patterson et&nbsp;al.<sup>[2]<\/sup><\/p>\n<h3 id=\"algorithms\">The scoring algorithms, briefly<\/h3>\n<p>Three classical algorithms dominate the literature, all published and openly described:<\/p>\n<ul>\n<li><strong>Cole-Kripke (1992)<\/strong> \u2014 a weighted sum of activity in the scored epoch and surrounding epochs, validated against polysomnography in adults.<sup>[3]<\/sup><\/li>\n<li><strong>Sadeh (1994)<\/strong> \u2014 a discriminant model using mean activity, variability and counts in a moving window; historically favored in adolescent and young-adult studies.<sup>[4]<\/sup><\/li>\n<li><strong>Oakley \/ Actiware-style (1997)<\/strong> \u2014 a weighted window compared against a sensitivity threshold; the approach used by the widely cited Philips Actiware software. Documented in a technical report rather than a journal article.<sup>[7]<\/sup><\/li>\n<\/ul>\n<p>Machine-learning and deep-learning classifiers are an active research area \u2014 but in a head-to-head comparison of eight algorithms on the same dataset, simple regression-based and heuristic algorithms performed slightly better than the machine-learning and deep-learning models, whose performance seemed to suffer from poor generalization.<sup>[2]<\/sup> That result is one reason the classical algorithms remain the comparability standard across decades of published studies. Our overview of <a href=\"\/en\/sleep-wake-classification-models-from-heuristics-to-machine-learning-approaches\/\">sleep-wake classification models<\/a> covers this evolution, and <a href=\"\/en\/algorithm-validation-in-actigraphy-benchmarking-against-gold-standard-sleep-measures\/\">algorithm validation against gold-standard measures<\/a> explains how agreement is quantified.<\/p>\n<h2 id=\"what-it-measures\">What does actigraphy measure?<\/h2>\n<p>From an epoch-by-epoch sleep\/wake classification plus rest-interval markers, actigraphy software derives the standard sleep parameters:<\/p>\n<div class=\"table-wrap\">\n<table>\n<thead>\n<tr>\n<th scope=\"col\">Parameter<\/th>\n<th scope=\"col\">Definition<\/th>\n<th scope=\"col\">Typical use<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th scope=\"row\">Total sleep time (TST)<\/th>\n<td>Minutes classified as sleep within the rest interval<\/td>\n<td>Primary outcome in most sleep studies<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Sleep onset latency (SOL)<\/th>\n<td>Time from lights-out to first sleep onset<\/td>\n<td>Insomnia phenotyping, intervention response<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Wake after sleep onset (WASO)<\/th>\n<td>Minutes awake between sleep onset and final awakening<\/td>\n<td>Sleep maintenance, fragmentation<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Sleep efficiency (SE)<\/th>\n<td>TST as a percentage of time in bed<\/td>\n<td>Global sleep quality index<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Fragmentation index<\/th>\n<td>Composite of mobile time and short immobility bouts<\/td>\n<td>Restlessness; sensitive in aging and clinical populations<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Because recordings span weeks, actigraphy also yields <strong>circadian rhythm variables<\/strong> that no single-night study can produce:<\/p>\n<ul>\n<li><strong>Non-parametric variables (NPCRA):<\/strong> interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and the timing of the least active 5 hours (L5) and most active 10 hours (M10).<\/li>\n<li><strong>Cosinor analysis:<\/strong> MESOR, amplitude and acrophase of the fitted 24-hour rhythm.<\/li>\n<li><strong>Light-derived metrics:<\/strong> time above illuminance thresholds, timing of light exposure relative to the rest interval and, on spectrally capable devices, melanopic EDI.<sup>[5,6]<\/sup><\/li>\n<\/ul>\n<p>These variables underpin research on <a href=\"\/en\/circadian-biomarker-discovery-using-integrated-wearable-sensor-data\/\">circadian biomarker discovery<\/a> and <a href=\"\/en\/quantifying-sleep-fragmentation-metrics-models-and-clinical-relevance\/\">sleep fragmentation<\/a>.<\/p>\n<h3 id=\"placement\">Where is the device worn?<\/h3>\n<p>The <strong>non-dominant wrist<\/strong> is the standard placement for sleep and circadian applications \u2014 most validation literature, including the classical algorithms above, assumes it. Other placements serve different goals: the <strong>hip<\/strong> is common in physical-activity research (step counts, MVPA), the <strong>ankle<\/strong> in infant studies and restless-legs protocols, and the <strong>chest<\/strong> when actigraphy rides along with cardiac monitors. Placement matters for comparability: counts recorded at different body sites are not interchangeable, and a light sensor reads a different exposure at the wrist than at the chest \u2014 a second, separate source of non-comparability beyond the device-specific counts discussed below. Report placement (including dominant vs non-dominant wrist) in the methods section, and see our guide to <a href=\"\/en\/device-placement-and-wear-compliance-implications-for-data-integrity\/\">device placement and wear compliance<\/a>.<\/p>\n<h2 id=\"vs-psg\">Actigraphy vs polysomnography: which one, when?<\/h2>\n<p>Polysomnography (PSG) remains the gold standard for characterizing sleep architecture \u2014 it records brain activity, eye movements, muscle tone, breathing and oxygenation in a lab or clinic. Actigraphy answers a different question: <strong>what does sleep look like across weeks of ordinary life?<\/strong><\/p>\n<div class=\"table-wrap\">\n<table>\n<thead>\n<tr>\n<th scope=\"col\"><\/th>\n<th scope=\"col\">Actigraphy<\/th>\n<th scope=\"col\">Polysomnography<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th scope=\"row\">Setting<\/th>\n<td>Home, free-living<\/td>\n<td>Sleep laboratory (or attended home setup)<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Duration<\/th>\n<td>1\u20132 weeks typical; months possible<\/td>\n<td>Usually 1 night<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Measures<\/th>\n<td>Sleep\/wake estimates, circadian variables, light, activity<\/td>\n<td>Sleep stages (N1\u2013N3, REM), respiratory events, arousals<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Detects sleep architecture<\/th>\n<td>No \u2014 movement cannot distinguish sleep stages<\/td>\n<td>Yes<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Night-to-night variability<\/th>\n<td>Captured by design<\/td>\n<td>Not captured (single night; first-night effect)<\/td>\n<\/tr>\n<tr>\n<th scope=\"row\">Participant burden and cost<\/th>\n<td>Low per night<\/td>\n<td>High per night<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The 2018 AASM clinical practice guideline supports actigraphy for evaluating circadian rhythm sleep-wake disorders, for supplementing sleep logs in insomnia and hypersomnia work-ups, and for estimating TST when PSG is not feasible \u2014 while PSG remains indicated for suspected sleep-disordered breathing and parasomnias.<sup>[1]<\/sup> In practice the two are complements, not substitutes: many protocols use actigraphy to characterize habitual sleep before and after a PSG night. Our side-by-side analysis of <a href=\"\/en\/breaking-down-actigraphy-vs-polysomnography-a-comprehensive-guide-for-sleep-specialists\/\">actigraphy vs polysomnography<\/a> goes deeper, and <a href=\"\/en\/multimodal-sleep-data-fusion-integrating-actigraphy-psg-and-physiological-signals\/\">multimodal data fusion<\/a> covers protocols that combine both.<\/p>\n<h2 id=\"accuracy\">How accurate is actigraphy?<\/h2>\n<p>Honestly stated: <strong>good at detecting sleep, weaker at detecting quiet wakefulness.<\/strong> Across validation studies, classical algorithms show high sensitivity for sleep (commonly above 90%) but substantially lower specificity for wake, because a motionless awake person looks like a sleeping one to an accelerometer.<sup>[2]<\/sup> The practical consequences:<\/p>\n<ul>\n<li>TST and SE tend to be <strong>overestimated<\/strong> in populations with long quiet wake \u2014 insomnia, older adults, shift workers lying in bed awake.<\/li>\n<li>Agreement with PSG is strongest in healthy sleepers and weakest where wake-in-bed is common; report population-specific validation where it exists.<\/li>\n<li>Accuracy claims should always name the algorithm, epoch length, device and reference standard \u2014 the same hardware can produce different numbers under different scoring settings.<\/li>\n<\/ul>\n<p>Two design choices improve defensibility: pairing actigraphy with a sleep diary (the AASM-recommended practice<sup>[1]<\/sup>), and using devices with <strong><a href=\"\/en\/off-wrist-detection-technology-improving-data-confidence-in-wearable-studies\/\">off-wrist detection<\/a><\/strong> so non-wear is not scored as sleep. When gaps do occur, <a href=\"\/en\/data-loss-and-imputation-strategies-in-actigraphy-studies\/\">principled imputation strategies<\/a> keep the analysis honest.<\/p>\n<h2 id=\"light\">Light measurement: the capability that separates modern actigraphs<\/h2>\n<p>Because circadian physiology responds to light \u2014 particularly short-wavelength light acting on melanopsin-expressing retinal ganglion cells \u2014 modern circadian research increasingly requires <strong>quantifying personal light exposure<\/strong>, not just movement. Since CIE S 026:2018, the reference quantity is <strong>melanopic equivalent daylight illuminance (melanopic EDI)<\/strong>, which weights light by the melanopsin action spectrum.<sup>[5]<\/sup><\/p>\n<p>This is where actigraphs differ most. Single-channel photopic sensors measure brightness as the eye&#8217;s cone system sees it; deriving melanopic EDI requires <strong>spectral information<\/strong> that only multi-channel sensors provide. An independent NIH benchmark of four wearable light sensors found large differences between devices in detection range, accuracy against a criterion spectrometer, and inter-device variability \u2014 differences large enough to change study conclusions about light exposure.<sup>[6]<\/sup> When light is an outcome variable, sensor selection deserves the same scrutiny as algorithm selection. Our guides to <a href=\"\/en\/beyond-sleep-duration-quantifying-circadian-light-exposure-in-real-world-research\/\">quantifying circadian light exposure<\/a> and <a href=\"\/en\/wearable-light-data-quality-control\/\">wearable light data quality<\/a> cover the practical decisions.<\/p>\n<h2 id=\"applications\">What is actigraphy used for?<\/h2>\n<ul>\n<li><strong>Circadian rhythm sleep-wake disorders<\/strong> \u2014 delayed\/advanced sleep phase, irregular sleep-wake rhythm, shift work disorder; the indication with the strongest guideline support.<sup>[1]<\/sup><\/li>\n<li><strong>Insomnia and hypersomnia evaluation<\/strong> \u2014 objective complement to sleep diaries across multiple weeks.<sup>[1]<\/sup><\/li>\n<li><strong>Clinical trials<\/strong> \u2014 objective, low-burden sleep endpoints at scale; actigraphy features in pharmaceutical and behavioral trials as a digital outcome measure. See <a href=\"\/en\/regulatory-considerations-for-wearable-sleep-technologies-in-clinical-trials\/\">regulatory considerations for wearables in trials<\/a>.<\/li>\n<li><strong>Chronobiology and light research<\/strong> \u2014 long-term rhythm stability, light-exposure ecology, seasonal and occupational studies.<\/li>\n<li><strong>Population and remote research<\/strong> \u2014 decentralized protocols where participants never visit a lab; see <a href=\"\/en\/remote-sleep-research-at-scale-designing-decentralized-monitoring-protocols\/\">remote sleep research at scale<\/a>.<\/li>\n<li><strong>Special populations<\/strong> \u2014 pediatrics, older adults, psychiatric and neurodegenerative conditions, where lab studies are burdensome and habitual patterns matter.<\/li>\n<\/ul>\n<h3 id=\"cpt-95803\">Clinical use and reimbursement: CPT 95803<\/h3>\n<p>In the United States, clinical actigraphy has a dedicated Category&nbsp;I billing code \u2014 <strong>CPT&nbsp;95803<\/strong> (&#8220;Actigraphy testing, recording, analysis, interpretation, and report&#8221;). Per the AMA CPT descriptor and its parenthetical instructions, the code requires <strong>a minimum of 72 hours and a maximum of 14 consecutive days of recording<\/strong>, cannot be reported together with home sleep apnea testing or polysomnography codes (95806\u201395811), and cannot be reported more than once in any 14-day period; the AASM guideline recommends recording durations consistent with these requirements.<sup>[1]<\/sup><\/p>\n<p>The code existing does not mean the service is routinely paid. <strong>Several payers classify actigraphy as investigational<\/strong> \u2014 Blue Shield of California&#8217;s medical policy 2.01.73 (effective August 2025), for example, deems actigraphy investigational when used as the sole technique to record and analyze body movement \u2014 and practitioners in billing forums report frequent denials and limited success obtaining payment. Reimbursement also differs between Medicare contractors and between private payers. Before building a clinical workflow around CPT&nbsp;95803, locate the specific medical policy of each payer involved and verify its current position. Research use, by contrast, involves no billing codes at all. This section describes the landscape as of August&nbsp;2026 and is not billing advice.<\/p>\n<h3 id=\"pediatrics\">Actigraphy in children and infants<\/h3>\n<p>Pediatric sleep research leans on actigraphy heavily, because lab studies are burdensome for children and night-to-night variability is high. The method carries specific considerations by age: the <strong>Sadeh algorithm originated in adolescent and young-adult samples<\/strong>,<sup>[4]<\/sup> infant-specific algorithms exist (for example Sazonov et&nbsp;al., validated with ankle placement in infants<sup>[9]<\/sup>), and scoring rules that perform well in adults can misclassify the fragmented, motile sleep of infants. Meltzer et&nbsp;al.&#8217;s review of pediatric actigraphy remains the standard methodological reference \u2014 including the finding that device, algorithm and scoring-rule choices materially change pediatric sleep estimates.<sup>[10]<\/sup> Practical constraints matter too: smaller wrists, school schedules, and parent-reported diaries as the compliance backstop.<\/p>\n<h2 id=\"limitations\">Limitations worth designing around<\/h2>\n<ul>\n<li><strong>No sleep staging.<\/strong> Movement cannot resolve N1\u2013N3 or REM; questions about sleep architecture need PSG or EEG-based wearables.<\/li>\n<li><strong>Quiet wakefulness inflates sleep estimates<\/strong> \u2014 mitigate with diaries, event markers and population-appropriate algorithms.<sup>[2]<\/sup><\/li>\n<li><strong>Counts are device-specific.<\/strong> Activity counts are not directly comparable across manufacturers; switching hardware mid-study introduces a device confound unless bridged deliberately (parallel wear, shared algorithm implementations).<\/li>\n<li><strong>Wear compliance is the real failure mode.<\/strong> Non-wear, water exposure and forgotten devices cost more data than any algorithm choice; see <a href=\"\/en\/device-placement-and-wear-compliance-implications-for-data-integrity\/\">wear compliance and data integrity<\/a>.<\/li>\n<\/ul>\n<p class=\"pull\">Actigraphy&#8217;s value is not that it replaces the sleep lab \u2014 it is that it measures the sleep people actually get, night after night, in the life they actually live.<\/p>\n<h2 id=\"choosing\">Choosing a research actigraph: what to compare<\/h2>\n<p>Specification sheets rarely use the same definitions, so compare on evidence:<\/p>\n<ol>\n<li><strong>Validation trail<\/strong> \u2014 peer-reviewed validations of the device (or its algorithms) in populations like yours, not just manufacturer specifications.<\/li>\n<li><strong>Light sensing<\/strong> \u2014 single photopic channel vs multi-channel spectral sensing with melanopic EDI, and any independent accuracy data.<sup>[6]<\/sup><\/li>\n<li><strong>Data access<\/strong> \u2014 raw data export for reproducible analysis (R, Python, MATLAB), documented formats, no lock-in.<\/li>\n<li><strong>Wear logistics<\/strong> \u2014 battery life at your epoch setting, waterproofing, off-wrist detection, event markers.<\/li>\n<li><strong>Continuity<\/strong> \u2014 if migrating from discontinued hardware, whether legacy files and scoring methods carry over; see our <a href=\"\/en\/actiwatch-replacement\/\">Actiwatch migration guide<\/a>.<\/li>\n<\/ol>\n<p>Condor Instruments builds two research actigraphs \u2014 <a href=\"\/en\/actlumus-actigraph\/\">ActLumus<\/a>, with a 10-channel spectral light sensor reporting melanopic EDI, and <a href=\"\/en\/acttrust-two-actigraph\/\">ActTrust 2<\/a> \u2014 analyzed in <a href=\"\/en\/actstudio-software\/\">ActStudio<\/a>, which implements Cole-Kripke, PIM\/ZCM\/TAT, an Actiware-style scoring algorithm, cosinor and NPCRA, with raw export for independent analysis.<\/p>\n<h2 id=\"faq\">Frequently asked questions<\/h2>\n<h3>What is actigraphy in simple terms?<\/h3>\n<p>It is sleep and activity tracking based on movement. A watch-like device records how much you move, continuously, for days or weeks; software then estimates when you were asleep, when you were awake, and how stable your daily rhythm is.<\/p>\n<h3>What is actigraphy used for?<\/h3>\n<p>Evaluating circadian rhythm sleep-wake disorders, complementing sleep diaries in insomnia and hypersomnia work-ups, estimating sleep in clinical trials, and studying sleep, activity and light exposure in free-living conditions over long periods.<sup>[1]<\/sup><\/p>\n<h3>How accurate is actigraphy compared with polysomnography?<\/h3>\n<p>Validation studies consistently show high sensitivity for detecting sleep (typically above 90%) and lower specificity for detecting wake, so actigraphy tends to overestimate sleep in people who lie still while awake. Accuracy depends on the device, algorithm, epoch length and population, so studies should report all four.<sup>[2]<\/sup><\/p>\n<h3>How long should an actigraphy recording be?<\/h3>\n<p>Most protocols record 7\u201314 consecutive days, 24 hours per day, to capture night-to-night variability and both work and free days. Circadian variables such as interdaily stability benefit from the longer end of that range.<\/p>\n<h3>Is a consumer smartwatch the same as a research actigraph?<\/h3>\n<p>No. Consumer wearables estimate sleep with proprietary, changeable algorithms and rarely expose raw data, which makes methods reproducibility difficult. Research actigraphs provide documented algorithms, raw data export, epoch-level control and published validations \u2014 requirements for peer-reviewed and regulated research.<\/p>\n<h3>Does actigraphy measure light exposure?<\/h3>\n<p>Many research actigraphs include a light sensor. Capabilities vary widely: single-channel sensors record photopic illuminance only, while multi-channel spectral sensors can derive melanopic EDI per CIE S 026 \u2014 the quantity modern circadian research expects. Independent testing has found large between-device differences in light accuracy.<sup>[5,6]<\/sup><\/p>\n<h3>Do I need ethics approval to use actigraphy?<\/h3>\n<p>Actigraphy is non-invasive and low-risk, but it continuously records behavioral data, so research use requires the usual ethics review and informed consent covering data collection, storage and sharing \u2014 typically as expedited\/minimal-risk review.<\/p>\n<div class=\"cta\">\n<h2>Planning an actigraphy study?<\/h2>\n<p>Tell us your protocol \u2014 population, duration, whether light exposure is an outcome. We will send device specifications, validation references and sample data so you can evaluate fit before committing.<\/p>\n<p style=\"margin-bottom:0\"><a class=\"btn\" href=\"\/en\/contato\/\">Talk to our team<\/a><\/p>\n<\/div>\n<div class=\"refs\">\n<h2 class=\"refs-title\">References<\/h2>\n<ol>\n<li>Smith MT, McCrae CS, Cheung J, et&nbsp;al. Use of actigraphy for the evaluation of sleep disorders and circadian rhythm sleep-wake disorders: an American Academy of Sleep Medicine clinical practice guideline. <em>J Clin Sleep Med<\/em>. 2018;14(7):1231\u20131237. <a href=\"https:\/\/doi.org\/10.5664\/jcsm.7230\">doi:10.5664\/jcsm.7230<\/a><\/li>\n<li>Patterson MR, Nunes AAS, Gerstel D, et&nbsp;al. 40 years of actigraphy in sleep medicine and current state of the art algorithms. <em>npj Digital Medicine<\/em>. 2023;6:51. <a href=\"https:\/\/doi.org\/10.1038\/s41746-023-00802-1\">doi:10.1038\/s41746-023-00802-1<\/a><\/li>\n<li>Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep\/wake identification from wrist activity. <em>Sleep<\/em>. 1992;15(5):461\u2013469. <a href=\"https:\/\/doi.org\/10.1093\/sleep\/15.5.461\">doi:10.1093\/sleep\/15.5.461<\/a><\/li>\n<li>Sadeh A, Sharkey KM, Carskadon MA. Activity-based sleep-wake identification: an empirical test of methodological issues. <em>Sleep<\/em>. 1994;17(3):201\u2013207. <a href=\"https:\/\/doi.org\/10.1093\/sleep\/17.3.201\">doi:10.1093\/sleep\/17.3.201<\/a><\/li>\n<li>CIE. <em>CIE S 026:2018 \u2014 System for metrology of optical radiation for ipRGC-influenced responses to light.<\/em> Vienna: CIE, 2018.<\/li>\n<li>Ishihara A, Brychta RJ, LaMunion SR, et&nbsp;al. Performance of wearable light sensors for measuring photopic and melanopic illuminance under laboratory and free-living conditions. <em>Sleep<\/em>. 2026;49(2):zsaf358. <a href=\"https:\/\/doi.org\/10.1093\/sleep\/zsaf358\">doi:10.1093\/sleep\/zsaf358<\/a><\/li>\n<li>Oakley NR. <em>Validation with polysomnography of the Sleepwatch sleep\/wake scoring algorithm used by the Actiwatch activity monitoring system.<\/em> Technical report to Mini-Mitter Co., Inc.; 1997. (Technical report, not a peer-reviewed journal article.)<\/li>\n<li>Webster JB, Kripke DF, Messin S, Mullaney DJ, Wyborney G. An activity-based sleep monitor system for ambulatory use. <em>Sleep<\/em>. 1982;5(4):389\u2013399. <a href=\"https:\/\/doi.org\/10.1093\/sleep\/5.4.389\">doi:10.1093\/sleep\/5.4.389<\/a><\/li>\n<li>Sazonov E, Sazonova N, Schuckers S, Neuman M; CHIME Study Group. Activity-based sleep\u2013wake identification in infants. <em>Physiol Meas<\/em>. 2004;25(5):1291\u20131304. <a href=\"https:\/\/doi.org\/10.1088\/0967-3334\/25\/5\/018\">doi:10.1088\/0967-3334\/25\/5\/018<\/a><\/li>\n<li>Meltzer LJ, Montgomery-Downs HE, Insana SP, Walsh CM. Use of actigraphy for assessment in pediatric sleep research. <em>Sleep Med Rev<\/em>. 2012;16(5):463\u2013475. <a href=\"https:\/\/doi.org\/10.1016\/j.smrv.2011.10.002\">doi:10.1016\/j.smrv.2011.10.002<\/a><\/li>\n<\/ol>\n<\/div>\n<p class=\"updated\">Last reviewed August 2026 by the Condor Research Team. Actiware and Actiwatch are trademarks of Philips Respironics, referenced for algorithm identification. Condor Instruments manufactures the ActLumus and ActTrust actigraphs; product mentions are identified as such.<\/p>\n<\/div>\n<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@graph\": [\n    {\n      \"@type\": \"Article\",\n      \"@id\": \"https:\/\/condor.fabbricaweb.com.br\/en\/actigraphy\/#article\",\n      \"headline\": \"Actigraphy: how it works, what it measures, and when to use it\",\n      \"description\": \"A complete guide to actigraphy for researchers and clinicians: accelerometer principles, sleep parameters, scoring algorithms, circadian metrics, accuracy vs polysomnography, and light measurement.\",\n      \"author\": {\"@type\": \"Organization\", \"name\": \"Condor Instruments\", \"url\": \"https:\/\/condor.fabbricaweb.com.br\"},\n      \"publisher\": {\"@type\": \"Organization\", \"name\": \"Condor Instruments\", \"url\": \"https:\/\/condor.fabbricaweb.com.br\"},\n      \"dateModified\": \"2026-08-15\",\n      \"about\": [\n        {\"@type\": \"Thing\", \"name\": \"Actigraphy\"},\n        {\"@type\": \"Thing\", \"name\": \"Sleep research\"},\n        {\"@type\": \"Thing\", \"name\": \"Circadian rhythm\"}\n      ],\n      \"citation\": [\n        \"https:\/\/doi.org\/10.5664\/jcsm.7230\",\n        \"https:\/\/doi.org\/10.1038\/s41746-023-00802-1\",\n        \"https:\/\/doi.org\/10.1093\/sleep\/15.5.461\",\n        \"https:\/\/doi.org\/10.1093\/sleep\/17.3.201\",\n        \"https:\/\/doi.org\/10.1093\/sleep\/zsaf358\",\n        \"https:\/\/doi.org\/10.1093\/sleep\/5.4.389\",\n        \"https:\/\/doi.org\/10.1088\/0967-3334\/25\/5\/018\",\n        \"https:\/\/doi.org\/10.1016\/j.smrv.2011.10.002\"\n      ]\n    },\n    {\n      \"@type\": \"FAQPage\",\n      \"mainEntity\": [\n        {\n          \"@type\": \"Question\",\n          \"name\": \"What is actigraphy in simple terms?\",\n          \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"It is sleep and activity tracking based on movement. 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