Literature DB >> 18761026

Circadian rhythm of wrist temperature in normal-living subjects A candidate of new index of the circadian system.

J A Sarabia1, M A Rol, P Mendiola, J A Madrid.   

Abstract

Most circadian rhythms are under the control of a major pacemaker located in the hypothalamic suprachiasmatic nucleus. Some of these rhythms, called marker rhythms, serve to characterize the timing of the internal temporal order. A marker rhythm, (e.g., one used in chronotherapy) has to be periodic and easy to measure over long periods using non-invasive methods. The most frequent reference variables for human chronotherapy include salivary melatonin or cortisol, urinary 6-sulfatoximelatonin, actimetry and core body temperature (CBT). Recent evidence suggests that sleepiness may be more closely linked to increased peripheral skin temperature than to a core temperature drop, and that distal skin temperature seems to be correlated and phase-advanced with respect to CBT, suggesting that heat loss from the extremities may drive the circadian CBT rhythm. The aim of the present study was to evaluate whether the wrist skin temperature rhythm could be used as a possible index of the human circadian system. To this end, wrist skin temperature (WT1), as determined by a wireless data logger in healthy normal living subjects, was correlated with sleep-wake diaries and oral temperature (OT) recordings. WT and sleep habits were studied in 99 university students. Each subject wore a wireless iButton sensor attached to the inner side of a sport wristband. Our results show that the WT rhythm exhibits an inverse phase relationship with OT, and it is phase-advanced by 60 min with respect to OT. WT started to increase in association to bed time and dropped sharply after awakening. A secondary WT increase, independent of feeding, was observed in the early afternoon. In conclusion, WT wireless recording can be considered a reliable procedure to evaluate circadian rhythmicity, and an index to establish and follow the effects of chronotherapy in normal living subjects.

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Year:  2008        PMID: 18761026     DOI: 10.1016/j.physbeh.2008.08.005

Source DB:  PubMed          Journal:  Physiol Behav        ISSN: 0031-9384


  67 in total

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Authors:  A Roebuck; V Monasterio; E Gederi; M Osipov; J Behar; A Malhotra; T Penzel; G D Clifford
Journal:  Physiol Meas       Date:  2013-12-17       Impact factor: 2.833

2.  Chronotype variation drives night-time sentinel-like behaviour in hunter-gatherers.

Authors:  David R Samson; Alyssa N Crittenden; Ibrahim A Mabulla; Audax Z P Mabulla; Charles L Nunn
Journal:  Proc Biol Sci       Date:  2017-07-12       Impact factor: 5.349

3.  Ontogeny and aging of the distal skin temperature rhythm in humans.

Authors:  H Batinga; A Martinez-Nicolas; M Zornoza-Moreno; M Sánchez-Solis; E Larqué; M T Mondéjar; M Moreno-Casbas; F J García; M Campos; M A Rol; J A Madrid
Journal:  Age (Dordr)       Date:  2015-03-27

4.  Differences in daily rhythms of wrist temperature between obese and normal-weight women: associations with metabolic syndrome features.

Authors:  M D Corbalán-Tutau; J A Madrid; J M Ordovás; C E Smith; F Nicolás; M Garaulet
Journal:  Chronobiol Int       Date:  2011-05       Impact factor: 2.877

5.  Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: A randomized, crossover trial.

Authors:  C Bandín; F A J L Scheer; A J Luque; V Ávila-Gandía; S Zamora; J A Madrid; P Gómez-Abellán; M Garaulet
Journal:  Int J Obes (Lond)       Date:  2014-10-14       Impact factor: 5.095

Review 6.  Sensors Capabilities, Performance, and Use of Consumer Sleep Technology.

Authors:  Massimiliano de Zambotti; Nicola Cellini; Luca Menghini; Michela Sarlo; Fiona C Baker
Journal:  Sleep Med Clin       Date:  2020-01-03

7.  Developing Biomarker Arrays Predicting Sleep and Circadian-Coupled Risks to Health.

Authors:  Janet M Mullington; Sabra M Abbott; Judith E Carroll; Christopher J Davis; Derk-Jan Dijk; David F Dinges; Philip R Gehrman; Geoffrey S Ginsburg; David Gozal; Monika Haack; Diane C Lim; Madalina Macrea; Allan I Pack; David T Plante; Jennifer A Teske; Phyllis C Zee
Journal:  Sleep       Date:  2016-04-01       Impact factor: 5.849

8.  Physical activity, and not fat mass is a primary predictor of circadian parameters in young men.

Authors:  Hannah R Tranel; Elizabeth A Schroder; Jonathan England; W Scott Black; Heather Bush; Michael E Hughes; Karyn A Esser; Jody L Clasey
Journal:  Chronobiol Int       Date:  2015-06-23       Impact factor: 2.877

9.  A new integrated variable based on thermometry, actimetry and body position (TAP) to evaluate circadian system status in humans.

Authors:  Elisabet Ortiz-Tudela; Antonio Martinez-Nicolas; Manuel Campos; María Ángeles Rol; Juan Antonio Madrid
Journal:  PLoS Comput Biol       Date:  2010-11-11       Impact factor: 4.475

10.  Circadian rhythmicity as a predictor of weight-loss effectiveness.

Authors:  C Bandín; A Martinez-Nicolas; J M Ordovás; J A Madrid; M Garaulet
Journal:  Int J Obes (Lond)       Date:  2013-11-15       Impact factor: 5.095

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