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Study Title and Description

Effects of caffeine on heart rate and QT variability during sleep.



Key Questions Addressed
1 For [population], is caffeine intake above [exposure dose], compared to intakes [exposure dose] or less, associated with adverse effects on cardiovascular outcomes?
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Primary Publication Information
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TitleData
Title Effects of caffeine on heart rate and QT variability during sleep.
Author M Bonnet,M Tancer,T Uhde,VK Yeragani,
Country
Year 2005
Numbers

Secondary Publication Information
There are currently no secondary publications defined for this study.


Extraction Form: Cardiovascular Design
Design Details
Question... Follow Up Answer Follow-up Answer
What outcome is being evaluated in this paper? Cardiovascular
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What is the objective of the study (as reported by the authors)? In this study, we sought to examine spectral powers of beat-to-beat HR and QT intervals after the administration of caffeine to normal young adults during sleep.
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Provide a general description of the methods as reported by the authors. Information should be extracted based on relevance to the SR (i.e., caffeine related methods) Subjects Subjects were healthy 18- to 39-year-old males and females. Potential subjects were solicited from research referrals and from advertisements in the local newspapers for participants in sleep research. Individuals considered further completed a screening questionnaire that indicated that they had normal sleep with rare daytime naps and no current history of night work. Selected subjects did not consume excessive caffeine (more than 250 mg of caffeine per day) and had not used psychoactive medications within the previous year. Potential subjects who had histories strongly suggestive of circadian desynchrony (e.g., shift workers), sleep apnea, or periodic leg movements were excluded. We selected subjects who were moderate caffeine users. Our definition of ‘‘moderate’’ was anything less than 250 mg per day based on a questionnaire response from subjects asking for caffeinated coffee, tea, and soft drink consumption on an average day. Design Subjects were scheduled for an adaptation night followed by two consecutive nights on the following days. Subjects identified on the adaptation night with an apnea/hypopnea index greater than 10 or a periodic leg movement arousal index greater than 10 were disqualified. On one of the two laboratory nights, subjects received a placebo (sugar pill) 30 min prior to going to bed. On the other night (counterbalanced across subjects), subjects received caffeine, 400 mg, 30 min prior to going to bed. Subjects completed tests and questionnaires at their individual computer workstation in their room under technician observation via video monitors. Meals and breaks were scheduled in another area of the laboratory, which was also within technician observation. Subjects performed computer tests, completed a Minnesota Multiphasic Personality Inventory (MMPI) and a sleep history, and were fed the same menu of food prepared at the laboratory during the day. Caffeinated beverages were not available. Subjects usually did not leave the laboratory during the day and did not engage in vigorous activity. During each day, all subjects remained at the laboratory. Immediately after awakening each morning, subjects had a 20-min waking metabolic observation that was also used to collect HR data. Starting 2 hr after awakening, subjects had four research sleep tests at 2-hr intervals. Following each sleep test, subjects had a 20-min waking metabolic observation that was also used to collect HR data. Between sleep test observations, subjects performed psychomotor performance tests and mood evaluations. Throughout each night and the 20-min daytime sessions, ECG data were recorded through a Grass Braintree system running Gamma software (version 4) at a sampling rate of 500 samples/s. After collection, the ECG and time data were visualized and checked for artifacts with the Gamma software. We took 256 s of data from wake stage before sleep, stage II of NREM (nonrapid eye movement), and REM periods for placebo as well as caffeine conditions. We used a peak detection algorithm to identify the R-R intervals (in milliseconds) from the ECG. QT variability All these analyses were conducted on 256-s segments of data sampled at 500 Hz. This was performed on a PC using Solaris Desktop Unix software (Sunsoft, Mountain View, CA), which uses a graphical interface of digitized ECG where the time of the ‘‘R’’ wave is obtained using a peak detection algorithm. Then the operator provides the program with the beginning and the end of the QTwave template. This algorithm finds the QT interval for each beat using the time-stretch model. If the operator chooses a longer QT template, all the QT intervals will be biased accordingly. This algorithm’s purpose is mainly to study QT variability and not the mean QT. The HR (beats per minute: bpm) time series were sampled at 4Hz using the technique of Berger et al. [1986]. We used HR time series free of ventricular premature beats and noise. We then detrended the data by using the best-fit line prior to the computation of spectral analyses. Spectral Analysis HR time series (256 s of data at 4Hz=1,024 points, during each wake and each sleep stage) were subjected to spectral analyses, and the power spectrum was computed with the Blackman–Tukey method [Berger et al., 1989]. The powers were integrated in the bands of LF and HF regions. Statistical Analysis We used BMDP statistical package (Berkeley, CA) to perform all the analyses. First, we used repeated measures analysis of variance (ANOVA) to compare placebo and caffeine conditions for wake, stage II NREM, and REM periods (with two levels of repeated measures) followed by post hoc t tests for significant effects on ANOVA to identify differences, especially between REM and other stages. We used a probability value of .05 for significance to avoid type II error.
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How many outcome-specific endpoints are evaluated? 3
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What is the (or one of the) endpoint(s) evaluated? (Each endpoint listed separately) Heart rate
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List additional health endpoints (separately). 2 Heart rate variability (total power [TP], low-frequency power [LF]; high frequency power [HF], LF/HF ratio)
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List additional health endpoints (separately).3 QT variability (a log value of QT variance corrected for mean QT interval divided by the HR variance corrected for mean HR; QTvi)
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List additional health endpoints (separately).4
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List additional health endpoints (separately).5
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List additional health endpoints (separately).6
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Clinical, physiological, other Physiological
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What is the study design? Controlled Trial
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Randomized or Non-Randomized? RCT
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What were the diagnostics or methods used to measure the outcome? Objective
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Optional: Name of Method or short description ECG data were recorded through a Grass Braintree system running Gamma software (version 4) at a sampling rate of 500 samples/s.
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Caffeine (general) Caffeine (general)
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Coffee, Chocolate, energy drink, gum, medicine/supplement, soda, tea, other?
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Measured or self reported? Measured
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Children, adolescents, adults, or pregnant included? Adults
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What was the reference, comparison, or control group(s)? (e.g. high vs low consumption, number of cups, etc.) Subjects served as their own controls (placebo)
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What were the listed confounders or modifying factors as stated by the authors? (e.g. multi-variable components of models.  Copy from methods) None
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What conflicts of interest were reported? No information provided
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Refid 16184581
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What were the sources of funding? No information provided
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Results & Comparisons

No Results found.