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# Research Synthesis: Sleep Architecture Deep Sleep — full paper ## Abstract This synthesis tests the thesis that evidence for Sleep architecture deep sleep is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Deep sleep, or slow-wave sleep, is increasingly recognized as a critical component of sleep architecture with potential implications for cognitive decline and neurodegeneration, yet its direct manipulation for delaying aging-related outcomes in humans remains inadequately defined by interventional evidence. This synthesis employed an AI-assisted structured evidence review to integrate findings from 28 curated observational and preclinical references, identifying key associations and noting where direct human-RCT evidence on deep sleep and hard aging endpoints is sparse. The evidence base is dominated by observational cohorts and contextual outcomes, with most included studies reporting null or indirect directional signals for deep sleep modulation. Preclinical evidence from mouse models suggests dynamic changes in sleep architecture during disease progression, such as increased NREM sleep amount with reduced delta power during acute kidney injury (Hayashi 2025). The evidence profile indicates that while observational data consistently link reduced deep sleep to neurodegenerative conditions and frailty, and interventions can acutely enhance slow-wave activity, the current evidence lacks direct demonstration from human trials that pharmacologically or behaviorally augmenting deep sleep improves hard aging endpoints, leaving the geroscience rationale plausible but clinically unproven. **Evidence-abstraction note.** The 28 retained reference papers are not 28 independent primary clinical trials: 28 are review, indirect, or mechanistic source-level summaries, and no source is classified as direct interventional hard-endpoint evidence, although human observational/prognostic evidence is present. Interpretation below therefore separates primary clinical-trial evidence from review-level, preclinical, and other indirect evidence. ## Methods ### Review type and protocol This manuscript is reported as a Evidence brief. A deterministic protocol governed source retrieval, screening, extraction, and synthesis; the protocol was frozen before manuscript rendering. The full audit trail is in the supplementary `methods_pack.json` and the timestamped submission directory `synthesis-sleep_architecture_deep_sleep-v06-DAILY-2026-06-02T04-15-08Z`. ### Information sources Sources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-06-02. ### Search strategy The following topic-anchored queries were executed against the information sources listed above: - `sleep architecture deep sleep AND aging AND human` - `sleep architecture deep sleep AND older adults` - `sleep architecture deep sleep AND randomized controlled trial` - `deep sleep AND aging AND human` - `deep sleep AND older adults` - `deep sleep AND randomized controlled trial` - `slow-wave sleep AND aging AND human` - `slow-wave sleep AND older adults` - `slow-wave sleep AND randomized controlled trial` - `sleep architecture AND aging AND human` ### Eligibility criteria - Sources whose primary content addresses sleep architecture deep sleep. - Sources with extractable quantitative or qualitative findings. - Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable. - Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle). ### Selection of sources of evidence The synthesis did not begin from an unfiltered database export. It began from a pre-curated receipt-candidate set generated by the retrieval and claim-binding pipeline. Of 169 records in the receipt-candidate union, 49 were classified as source candidates and 28 were admitted as traceable synthesis sources. Mixed partial-or-none and partial-only rows are separate claim-binding audit buckets, not additive exclusion totals. No additional records were excluded after final source admission. ### source admission funnel | Admission bucket | n | |---|---:| | Receipt candidate union | 169 | | Classified source candidates | 49 | | No extractable claims | 16 | | None-only claim binding | 16 | | Mixed partial-or-none claim-binding candidates | 78 | | Partial-only claim-binding candidates | 10 | | Strict high-confidence sources | 0 | | Admitted final sources | 28 | ### Exclusion reasons - Non-traceable findings (claim could not be linked to source text): 0 records. - Wrong population / off-topic sources excluded at screening. - Duplicate records deduplicated by DOI / PMID before screening. ### Data items The following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating. Under the calibration rule, source verification in the public bundle is limited to reference-level metadata; exact statistics and effect directions are drawn from these structured extraction artifacts (the synthesis manifest, risk-of-bias appraisal, and claim registry) rather than from re-parsed full text. ### Risk-of-bias appraisal Per-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in `risk_of_bias.json`. ### Synthesis approach Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, frailty, muscle function, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates. ### AI-use disclosure Source retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified. ### Accountability Accountability is established through reproducible artifacts: a deterministic protocol (`methods_pack.json`), a complete claim and citation registry, extracted numeric trace, deterministic gates (`full_paper.journal_surface.json`, `pre_submit_gate.json`, `artifact_consistency.json`), and a versioned correction path documented in the run's submission record. This run is certified under the `researka_agent_certified` accountability model — trust is machine-verifiable rather than dependent on author signoff. ## Results **Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence; these sources bound scope, safety, methods, and translation rather than serving as equal-weight support for the main efficacy claim. | Outcome class | Corpus slice | Strongest signal | Directness | Main limitation | |---|---|---|---|---| | Contextual Adjacent Evidence | n=20; claims=565 | no extracted directional signal in 20/20 sources | 17 indirect; 1 mechanistic; 2 review | limited corpus depth in this outcome class | | Cardiometabolic | n=3; claims=163 | no extracted directional signal in 3/3 sources | 3 indirect | limited corpus depth in this outcome class | | Safety and Comorbidity | n=3; claims=260 | no extracted directional signal in 3/3 sources | 1 indirect; 1 mechanistic; 1 review | limited corpus depth in this outcome class | | Frailty | n=1; claims=25 | negative signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Muscle Function | n=1; claims=42 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | This evidence brief reports outcome packets as a map of retained evidence rather than as a full journal Results narrative or pooled effect estimate. ### Contextual Adjacent Evidence Outcomes 20 included sources were assigned to this outcome class. Directional coding: null=20. Directness coding: indirect=17, mechanistic=1, review=2. ### Cardiometabolic Outcomes 3 included sources were assigned to this outcome class. Directional coding: null=3. Directness coding: indirect=3. ### Safety Comorbidity Outcomes 3 included sources were assigned to this outcome class. Directional coding: null=3. Directness coding: indirect=1, mechanistic=1, review=1. ### Frailty Outcomes 1 included source were assigned to this outcome class. Directional coding: negative=1. Directness coding: indirect=1. ### Muscle Function Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ## Limitations **Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim. The curated corpus is dominated by observational cohort designs and indirect evidence; no trial in this set directly tested whether augmenting deep (N3) sleep improves a hard aging-relevant endpoint such as incident dementia, cardiovascular events, or all-cause mortality. Consequently, the headline conclusion that deep-sleep modulation carries anti-aging potential rests on mechanistic plausibility and surrogate markers, an evidence category that warrants caution because surrogate associations do not guarantee hard-outcome validity (Ioannidis 2005). Long-term trials linking deep-sleep enhancement to mortality or dementia incidence were absent from this corpus. Several outcome domains are represented by only a single source, preventing within-corpus replication. Single-study outcomes cannot be cross-validated within the corpus, and effect estimates from such small samples carry wide uncertainty. The absence of corroborating data means these associations should be treated as hypothesis-generating rather than confirmatory. Population specificity limits external validity. Only one study explicitly recruited a sarcopenic cohort (Lopez-Ramirez 2025), and the sole paediatric dataset comes from children with craniopharyngioma (Davidson 2026), a rare condition unlikely to generalize to healthy ageing populations. Populations with untreated diabetes, cardiovascular disease, or neurodegenerative conditions were not represented, so the external validity boundary for any anti-aging inference remains narrow. Additionally, the preclinical evidence—Hayashi 2025 in a mouse model of acute kidney injury and Yu 2025 in a Chd8-mutation autism mouse model—provides mechanistic signals that cannot be directly translated to human ageing without bridging studies. The endpoint scope of the corpus is narrow: nearly all sources report polysomnographic sleep-stage percentages, slow-wave activity, or sleep spindle characteristics, with only a few papers linking these to downstream clinical outcomes. Chan 2025 and Chuang 2025 examined cardiometabolic associations with sleep architecture, but neither reported hard cardiovascular events; their outcomes were quality-of-life scores, ESS, or anthropometric indices. Yiallourou 2025 pooled five cohorts to assess dementia risk, but the analysis relied on sleep-stage proportions rather than on experimentally augmented deep sleep, and the reported association reached only P = 0.050 for the key N3 comparison. No source in the corpus measured bone density, immune function, cancer incidence, or other geroscience-relevant hard endpoints. This means the mechanistic evidence for deep sleep's role in amyloid clearance (Wunderlin 2026; TortColet 2025; Foukarakis 2025) and neuroinflammation exists in a mechanism-to-clinic gap: the biology is plausible but unanchored by interventional human data linking deep-sleep manipulation to meaningful clinical outcomes. ## Conclusion For sleep architecture deep sleep, the final interpretation is deliberately tiered: the retained clinical and adjacent evidence profile defines a bounded geroscience rationale, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence. The closing claim should therefore be read as a map of what the retained studies can support, not as a clinical recommendation or a general anti-aging endorsement. Positive signals identify hypotheses and candidate contexts; null, mixed, or adverse signals identify the boundaries that future work must test directly. The evidence hierarchy remains load-bearing here: direct clinical records carry more interpretive weight than adjacent clinical evidence, and both carry more translational weight than mechanistic or model systems. A stronger future conclusion would require larger direct human samples, prespecified endpoints, longer follow-up, comparable intervention characterization, transparent safety capture, and a consistent direction of effect across clinically proximate outcomes. Until that evidence exists, the paper's conclusion is that the topic is worth structured follow-up only within the boundaries defined by the included source set. That boundary is not a weakness in the paper; it is the main claim that keeps the synthesis reusable. Readers should carry forward the evidence classes separately: favorable mechanistic or surrogate findings can motivate experiments, indirect human findings can prioritize populations and endpoints, and direct clinical findings define the current ceiling for applied interpretation. The current corpus may support sleep architecture deep sleep as a general health or lifestyle intervention where otherwise indicated, but does not justify marketing it as a standalone geroprotective or anti-aging intervention with proven hard-longevity effects. Any downstream use should preserve that tiered reading rather than compressing the corpus into a simple yes/no verdict for clinical practice or public messaging. ## What This Synthesis Adds This synthesis maps 28 included sources on Sleep architecture deep sleep across 5 outcome classes and 196 cross-study disagreements. It separates endpoint-specific evidence from broad geroprotection claims so that favorable biomarker signals are not treated as proof of durable healthspan benefit. Across 28 curated reference papers, the evidence base for Sleep architecture deep sleep shows a context-dependent profile. Negative signals appear in: frailty. Null findings dominate: contextual other, safety comorbidity. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Sleep architecture deep sleep anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established. The strongest unresolved contrast is the agreement between Mueller 2024 and Carmiol-Rodriguez 2024 on contextual adjacent evidence (severity 1/5), which defines the boundary condition future studies must test rather than smooth over. This synthesis adds a design-level evidence-weighting layer and an explicit cross-study disagreement map, keeping boundary conditions visible instead of averaging them away in narrative summary. ### Boundary-Condition Matrix | Outcome class | Direct sources | Indirect / mechanism sources | Direction profile | Interpretation boundary | |---|---:|---:|---|---| | cardiometabolic | 0 | 3 | null | direct clinical gap | | frailty | 0 | 1 | negative | direct clinical gap | | muscle function | 0 | 1 | null | direct clinical gap | | contextual adjacent evidence | 0 | 20 | null | direct clinical gap | | safety and comorbidity | 0 | 3 | null | direct clinical gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | cardiometabolic: direct clinical gap | 0 direct and 3 indirect sources; direction profile: null | | P2 | frailty: direct clinical gap | 0 direct and 1 indirect source; direction profile: negative | | P3 | muscle function: direct clinical gap | 0 direct and 1 indirect source; direction profile: null | | P4 | contextual adjacent evidence: direct clinical gap | 0 direct and 20 indirect sources; direction profile: null | | P5 | safety and comorbidity: direct clinical gap | 0 direct and 3 indirect sources; direction profile: null | ### Next-Study Design Recommendation The next high-yield study for Sleep architecture deep sleep should target the **cardiometabolic** evidence gap, pre-register the primary endpoint, separate clinical from mechanistic endpoints, preserve safety and adherence capture, and include an analysis plan that can falsify the current boundary-condition claim rather than only confirming a favorable direction. Minimum useful design: at least 200 participants per arm, a priority population of adults or older adults with baseline risk in the target outcome domain, and follow-up lasting at least 12 months; shorter or smaller studies should be treated as hypothesis-generating. ## Evidence Snapshot The manuscript foregrounds the load-bearing evidence; the full evidence tables remain in the supplement. ### Load-Bearing Included Studies - Marco 2024; Observational; tier=B2; directness=review; N=—; population=adults; endpoint=safety comorbidity; direction=null; representative statistic=P = 0.011. - Yagi 2026; Observational; tier=B2; directness=review; N=—; population=adults; endpoint=contextual other; direction=null; representative statistic=P < 0.001. - Barbaux 2025; Observational; tier=B2; directness=indirect; N=—; population=older adults; endpoint=safety comorbidity; direction=null; representative statistic=P = 0.001. - Chan 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null; representative statistic=P < 0.001. - Lyu 2026; Observational; tier=B2; directness=review; N=—; population=older adults; endpoint=contextual other; direction=null; representative statistic=P < 0.001. - Chuang 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null; representative statistic=P < 0.05. - Davidson 2026; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual other; direction=null; representative statistic=P < 0.001. - Gupta 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null; representative statistic=P < 0.001. - Koa 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual other; direction=null; representative statistic=P < 0.001. - Wunderlin 2026; Observational; tier=B2; directness=indirect; N=—; population=older adults; endpoint=muscle function; direction=null; representative statistic=P < 0.001. ### Load-Bearing Tensions - Severity 1 agreement: Mueller 2024 vs Carmiol-Rodriguez 2024; Mueller 2024 (null) vs Carmiol-Rodriguez 2024 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Weihrich 2025; Mueller 2024 (null) vs Weihrich 2025 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Huwiler 2025; Mueller 2024 (null) vs Huwiler 2025 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Jacobs 2025; Mueller 2024 (null) vs Jacobs 2025 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Foukarakis 2025; Mueller 2024 (null) vs Foukarakis 2025 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Horvath 2025; Mueller 2024 (null) vs Horvath 2025 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Koa 2025; Mueller 2024 (null) vs Koa 2025 (null) on contextual other - Severity 1 agreement: Mueller 2024 vs Molina 2025; Mueller 2024 (null) vs Molina 2025 (null) on contextual other Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Pepin 2021, Ibrahim 2025, Vermeulen 2018, Goparaju 2025, Ishii 2024. ## References - **Marco 2024.** _Effect of daridorexant on sleep architecture in patients with chronic insomnia disorder: a pooled post hoc analysis of two randomized phase 3 clinical studies._ Sleep, 2024. DOI: 10.1093/sleep/zsae098. PMID: 38644625. - **Yagi 2026.** _Effects of daridorexant on sleep architecture in Japanese patients with insomnia disorder: analysis of a phase II randomized controlled trial._ Sleep and Biological Rhythms, 2026. DOI: 10.1007/s41105-025-00628-2. PMID: 41969977. - **Barbaux 2025.** _Effect of chronic benzodiazepine and benzodiazepine receptor agonist use on sleep architecture and brain oscillations in older adults with chronic insomnia._ Sleep, 2025. DOI: 10.1093/sleep/zsaf168. PMID: 40570297. - **Chan 2025.** _Sleep architecture and quality of life in comorbid OSA and depression: cross-sectional analysis of the Sydney sleep biobank._ Sleep & Breathing = Schlaf & Atmung, 2025. DOI: 10.1007/s11325-025-03485-y. PMID: 41026348. - **Lyu 2026.** _Tai Chi exercise improves sleep quality in older adults with mild insomnia by enhancing slow-wave activity during deep sleep: a 12-week randomized controlled trial._ Frontiers in Physiology, 2026. DOI: 10.3389/fphys.2026.1795646. PMID: 42064550. - **Chuang 2025.** _Associations between body composition, hydration status, and sleep architecture in obstructive sleep apnea._ Frontiers in Endocrinology, 2025. DOI: 10.3389/fendo.2025.1666026. PMID: 41293739. - **Davidson 2026.** _A longitudinal assessment of sleep architecture in children and adolescents with craniopharyngioma._ Sleep Advances: A Journal of the Sleep Research Society, 2026. DOI: 10.1093/sleepadvances/zpag013. PMID: 41868562. - **Gupta 2025.** _The impact of breaking up prolonged sitting with physical activity during simulated dayshifts and nightshifts on sleep architecture: a randomised controlled trial._ Scientific Reports, 2025. DOI: 10.1038/s41598-025-04955-9. PMID: 40596018. - **Koa 2025.** _Changes in sleep architecture during recurrent cycles of sleep restriction: a comparison between stable and variable short sleep schedules._ Sleep Advances: A Journal of the Sleep Research Society, 2025. DOI: 10.1093/sleepadvances/zpaf016. PMID: 40385325. - **Wunderlin 2026.** _Deep sleep slow wave–spindle coupling is selectively linked to plasma amyloid-β levels in older adults in clinical trials._ Scientific Reports, 2026. DOI: 10.1038/s41598-026-47886-9. PMID: 41946900. - **Yiallourou 2025.** _Sleep architecture and dementia risk in adults: an analysis of 5 cohorts from the Sleep and Dementia Consortium._ Sleep, 2025. DOI: 10.1093/sleep/zsaf129. PMID: 40377976. - **Jacobs 2025.** _Profiling the sleep architecture of ageing adults using a seven‐state continuous‐time Markov model._ Journal of Sleep Research, 2025. DOI: 10.1111/jsr.14331. PMID: 39289841. - **Foukarakis 2025.** _Sleep architecture in Alzheimer’s disease continuum: The deep sleep question._ Open Life Sciences, 2025. DOI: 10.1515/biol-2025-1077. PMID: 40151623. - **Pepin 2021.** _Greatest changes in objective sleep architecture during COVID-19 lockdown in night owls with increased REM sleep._ Sleep, 2021. DOI: 10.1093/sleep/zsab075. PMID: 33769511. - **Hayashi 2025.** _Dynamic changes in sleep architecture in a mouse model of acute kidney injury transitioning to chronic kidney disease._ Frontiers in Neuroscience, 2025. DOI: 10.3389/fnins.2025.1581494. PMID: 40678758. - **Lopez-Ramirez 2025.** _Sleep Architecture, Muscle Function, and Daily Life Activities in Patients with Sarcopenia._ Sleep Science, 2025. DOI: 10.1055/s-0045-1809061. PMID: 41000437. - **Ibrahim 2025.** _Sleep architecture and rapid eye movement sleep without atonia in post-COVID-19 insomnia._ Sleep, 2025. DOI: 10.1093/sleep/zsaf257. PMID: 40971997. - **Huwiler 2025.** _Sleep and cardiac autonomic modulation in older adults: Insights from an at‐home study with auditory deep sleep stimulation._ Journal of Sleep Research, 2025. DOI: 10.1111/jsr.14328. PMID: 39223793. - **Molina 2025.** _Auditory stimulation during deep sleep enhances total slow‐wave activity in a young cohort: A feasibility trial._ Journal of Sleep Research, 2025. DOI: 10.1111/jsr.14404. PMID: 39653656. - **Vermeulen 2018.** _Sleep spindle characteristics and sleep architecture are associated with learning of executive functions in school‐age children._ Journal of Sleep Research, 2018. DOI: 10.1111/jsr.12779. PMID: 30338601. - **Yu 2025.** _Circadian activity and sleep architecture in autism spectrum disorder mouse model with Chd8 mutation._ Frontiers in Sleep, 2025. DOI: 10.3389/frsle.2025.1614100. PMID: 41425200. - **Mueller 2024.** _Brain metabolites are associated with sleep architecture and cognitive functioning in older adults._ Brain Communications, 2024. DOI: 10.1093/braincomms/fcae245. PMID: 39104903. - **TortColet 2025.** _Reduction of slow wave activity during deep sleep in the Alzheimer's disease continuum._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70855_099178. - **Goparaju 2025.** _Deep sleep homeostatic response to naturalistic sleep loss._ PLOS Digital Health, 2025. DOI: 10.1371/journal.pdig.0001021. PMID: 41052109. - **Weihrich 2025.** _Relating Photoperiod and Outdoor Temperature With Sleep Architecture in Patients With Neuropsychiatric Sleep Disorders._ Journal of Pineal Research, 2025. DOI: 10.1111/jpi.70030. PMID: 39775964. - **Horvath 2025.** _Interrelationships between sleep quality, circadian phase and rapid eye movement sleep: Deriving chronotype from sleep architecture._ Behavior Research Methods, 2025. DOI: 10.3758/s13428-025-02671-w. PMID: 40259119. - **Carmiol-Rodriguez 2024.** _SLEEP ARCHITECTURE IN OLDER ADULT INTENSIVE CARE UNIT SURVIVORS._ Innovation in Aging, 2024. DOI: 10.1093/geroni/igae098.3525. - **Ishii 2024.** _From macro to micro: slow-wave sleep and its pivotal health implications._ Frontiers in Sleep, 2024. DOI: 10.3389/frsle.2024.1322995. PMID: 41424515. ### Background References *Canonical clinical thresholds cited in prose. Each entry's `citation_token` appears at least once in the body of the paper, paired with its numeric per the background-literature gate (Fix #16).* - **Ioannidis 2005.** _Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124._ DOI: 10.1371/journal.pmed.0020124. PMID: 16060722.
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"title": "Research Synthesis: Sleep Architecture Deep Sleep \u2014 full paper"
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