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Related Concept Videos

REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...
Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
Alterations in Respiration II01:30

Alterations in Respiration II

There are numerous types of normal and abnormal respiration. Based on ventilatory movements, breathing patterns are classified as regular, deep, or shallow. Examples include Biot's breathing, Cheyne-Stokes respiration, Kussmaul's breathing, hyperventilation, and hypoventilation. Each pattern is clinically significant and aids in evaluating patients.
In Biot's breathing, the respiratory rate and depth are irregular, alternating between periods of deep gasping and apnea. Common causes include...

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Related Experiment Video

Updated: May 29, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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Published on: February 8, 2019

Rest-activity patterns in patients with delirium.

Miranda van Uitert1, Annemarieke de Jonghe, Swana de Gijsel

  • 1Department of Internal Medicine, Geriatric Section, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. mirandamandjes@gmail.com

Rejuvenation Research
|September 30, 2011
PubMed
Summary
This summary is machine-generated.

Wrist-actigraphs reveal disturbed 24-hour activity patterns in elderly patients with delirium. These devices show differences between delirious and non-delirious patients, aiding in early delirium detection.

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Area of Science:

  • Geriatrics
  • Neurology
  • Sleep Medicine

Background:

  • Delirium is a common yet often underdiagnosed condition in elderly hospital patients.
  • Patients with delirium frequently exhibit disrupted sleep-wake rhythms and fluctuating daily symptoms.
  • The hypoactive subtype of delirium is particularly prone to underdiagnosis.

Purpose of the Study:

  • To pilot a study measuring 24-hour motor patterns in delirious patients using wrist-actigraphy.
  • To systematically review existing literature on 24-hour motor patterns in delirium.

Main Methods:

  • A pilot study involved 9 elderly patients (≥65 years) with hip fractures undergoing surgery.
  • A systematic literature search of MEDLINE and Embase was conducted for studies on motor activity in delirious patients.

Main Results:

  • The pilot study demonstrated severely disturbed 24-hour activity rhythms and impaired sleep parameters in delirious patients.
  • The systematic review identified 10 relevant papers, with most showing significant differences in sleep-wake rhythms between delirious and non-delirious individuals.
  • Some studies successfully classified delirium subtypes based on motor patterns, though not all.

Conclusions:

  • Activity patterns measured by devices can differentiate between delirious and non-delirious patients, and even between delirium subtypes.
  • Further research with larger sample sizes is recommended to validate the utility of activity pattern monitoring for early delirium detection.