Comparison of time and frequency domain measures of RSA in ambulatory recordings
ANNEBET D.GOEDHART,a SOPHIE VAN DER SLUIS,a JAN H.HOUTVEEN,b GONNEKE WILLEMSEN,a and ECO J.C.DE GEUS a
a Department of Biological Psychology,Vrije Universiteit Amsterdam,The Netherlands b
Departement of Health Psychology,University of Utrecht,Utrecht,The Netherlands
Abstract
The extent to which various measures of ambulatory respiratory sinus arrhythmia (RSA)capture the same information across conditions in different subjects remains unclear.In this study the root mean square of successive differences (RMSSD),peak valley RSA (pvRSA),and high frequency power (HF power)were assessed during ambulatory recording in 84subjects,of which 64were retested after about 3years.We used covariance structure modeling to test the equality of the correlations among three RSA measures over two test days and three conditions (daytime sitting or walking and nighttime sleep)and in groups with low,medium,and high mean heart rate (HR),or low,medium,and high mean respiration rate (RR).Results showed that ambulatory RMSSD,pvRSA,and HF power are highly correlated and that their correlation is stable across time,ambulatory conditions,and a wide range of resting HR and RR values.RMSSD appears to be the most cost-ef?cient measure of RSA.
Descriptors:Heart rate variability,Ambulatory,Parasympathetic,Interbeat interval,Respiration
Measures of heart rate variability (HRV)provide a window on the modulation of heart rate by the autonomic nervous system and have broad applications in both human and animal physi-ology (Berntson et al.,1997;T ask Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,1996).Within-subject studies show that HRV is responsive to changes in psychological state,particular-ly mental load and emotional stress (Allen &Crowell,1989;Kamphuis &Frowein,1985;Langewitz &Ruddel,1989;Mulder,1992;Sakakibara,T akeuchi,&Hayano,1994),and to changes in posture and physical activity (Hat?eld et al.,1998;Houtveen,Groot,&de Geus,2005;Houtveen,Rietveld,&de Geus,2002;Tulppo,Makikallio,T akala,Seppanen,&Huikuri,1996).Between-subjects studies further show that lower levels of HRV independently predict cardiac disease and cardiac mortal-ity (Bigger,Fleiss,Rolnitzky,&Steinman,1993;Dekker et al.,1997,2000;Hayano et al.,1991;Lombardi et al.,1987;Nolan et al.,1998;Saul et al.,1988;Singer et al.,1988;Singh et al.,1998;Tsuji et al.,1996.)
Most research has focused on HRV in the respiratory fre-quency range,also known as respiratory sinus arrhythmia (RSA).RSA is the difference in heart period during the inspi-ratory and expiratory phases of the respiratory cycle.RSA shows
virtually no sensitivity to sympathetic nervous system activity but is affected in a dose–response way by muscarinergic blockers in humans (Martinmaki,Rusko,Kooistra,Kettunen,&Saalasti,2006)or vagal cooling in animals (Katona &Jih,1975).This has led to the use of tonic RSA levels as a proxy for individual dif-ferences in vagal cardiac control (Berntson et al.,1997;T ask Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,1996),al-though not without controversy because of potential confound-ing by individual differences in sensitivity of chemoreceptor and baroreceptor re?exes (Berntson et al.,1997;Houtveen et al.,2002)and by individual differences in respiratory behavior (Grossman &Kollai,1993;Grossman,Wilhelm,&Spoerle,2004;Ritz &Dahme,2006).
RSA can be derived from the interbeat interval (IBI)time series in the time domain by taking the root mean square of differences between successive interbeat intervals (RMSSD;Penttila et al.,2001)or,in the frequency domain,by Fourier analysis (Akselrod et al.,1981,1985)or W avelet analysis (Pichot et al.,1999;Wiklund,Akay,&Niklasson,1997).RSA can also be derived by peak–valley estimation (pvRSA;Katona &Jih,1975)using the time series of IBIs in combination with the res-piration signal.
An important feature of these time-and frequency-domain RSA measures is that they can be reliably measured under nat-uralistic conditions with the use of ambulatory monitoring (de Geus,Willemsen,Klaver,&van Doornen,1995;Houtveen et al.,2005;Wilhelm,Roth,&Sackner,2003).In stress research,ambulatory recording is a huge advantage.Different between-
Preparation of this article was,in part,?nancially supported by NWO/MaGW VIDI-016-065-318.
Address reprint requests to:Annebet D.Goedhart,Vrije Universiteit,Department of Biological Psychology,van der Boechorststraat 1,1081BT,Amsterdam,The Netherlands.E-mail:ad.goedhart@psy.vu.nl.
Psychophysiology,44(2007),203–215.Blackwell Publishing Inc.Printed in the USA.Copyright r 2007Society for Psychophysiological Research DOI:10.1111/j.1469-8986.2006.00490.x
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subjects and within-subject mechanisms may determine cardio-vascular reactivity to arti?cial laboratory stressors than to real-istic stressors,encountered repeatedly at home or in the work setting.Generalization of individual differences in cardiovascu-lar stress reactivity from standardized laboratory situations to actual real-life situations has indeed been shown to be moderate at best(Gerin,Rosofsky,Pieper,&Pickering,1994;Kamarck, Schwartz,Janicki,Shiffman,&Raynor,2003;V an Doornen, Knol,Willemsen,&de Geus,1994).
With regard to potential negative health consequences of stress,ambulatory monitoring may be expected to have higher predictive validity for long-term health outcomes than labora-tory measurements.This has already been shown to be the case for blood pressure,where ambulatory levels are better predictors for cardiovascular morbidity and mortality than laboratory or of?ce measurements(Pickering&Devereux,1987;Verdecchia et al.,1994,1998;Verdecchia,Schillaci,Reboldi,Franklin,& Porcellati,2001).It is reasonable to assume that prolonged re-cording of RSA in naturalistic settings will also have added pre-dictive power over short-term recordings.This assumption, however,remains to be tested empirically.
Testing this assumption will require large-scale ambulatory recording in many thousands of subjects and a rational choice between the various RSA measures is direly needed.Unfortu-nately,the extent to which these measures capture the same in-formation across different ambulatory conditions and different subjects remains unclear.Although time and frequency domain measures are highly correlated under standardized recordings, with r s4.80(Bigger,Fleiss,Steinman,et al.,1992;Byrne& Porges,1993;Grossman,van Beek,&Wientjes,1990;Hayano et al.,1991;Houtveen&Molenaar,2001;Penttila et al.,2001),we may expect them to diverge more strongly under ambulatory recording conditions.In ambulatory recordings,higher ecologic-al validity is balanced by a lack of experimental control over important confounders of RSA.In comparison to laboratory recording,ambulatory settings are characterized by frequent changes in activity and posture,frequent speech,circadian rhythms,temperature variations,and a larger variance in emo-tional state and mental load.The differential sensitivity of the various RSA measures to these within-subject factors is currently unknown.
Previous studies have shown that the sharpest changes in RSA levels arise when going from awake to sleep recording,and that RSA in awake recordings is most sensitive to changes in physical activity and posture(Grossman et al.,2004;Kupper et al.,2004; Vrijkotte,van Doornen,&de Geus,2000).One design to ex-amine the potential differential sensitivity of various RSA meas-ures to these within-subject factors is to compare the structure of the correlations between ambulatory RSA measures across day-time and nighttime recordings,and,during the daytime part of the recording,across sitting activities and activities involving upright physical activity.
Even within these restricted ambulatory conditions,between-subject variance in the mean and range of respiration rate(RR) and heart rate may still be larger than in laboratory testing.This is problematic because individual differences in RR and heart rate both in?uence RSA measures,and such in?uence may be independent of cardiac vagal control(Berntson,Lozano,& Chen,2005;Grossman,Karemaker,&Wieling,1991;Grossman &Kollai,1993).The differential sensitivity of the various RSA measures to these between-subject confounders is currently un-known.It can be addressed by comparing the correlation struc-ture of ambulatory RSA measures across groups of subjects selected to have low,medium,and high mean heart rate during the ambulatory test day,or across groups with low,medium,and high mean RR.
In the present study we test the correlation between RMSSD, peak–valley RSA(pvRSA),and a frequency domain(HF power) measure of RSA in an ambulatory setting.First,reliability will be assessed for each of the measures by looking at short-term with-in-day test–retest correlations and correlations between genetic-ally identical twins.Next,temporal stability will be assessed across an average period of3years.Finally,we will use cova-riance structure modeling to test the equality of the correlations across major changes in ambulatory activity(sleep vs.awake, sitting vs.awake standing/walking)and across groups of subjects with low,medium,and high mean IBI or low,medium,and high mean RR.Based on previous laboratory?ndings and a previous small-scale ambulatory study(Vrijkotte,Riese,&de Geus, 2001),we expect that RMSSD,pvRSA,and HF power will show high correlations over time,across ambulatory activity,and across a wide range of mean heart rate and RR.
Methods
Participants
Participants were all registered in the Netherlands Twin Register (NTR).They came from families that participated in a linkage study searching for genes in?uencing personality and cardiovas-cular disease risk,which is described elsewhere(Boomsma et al., 2000).Out of the1332twins and siblings who returned a DNA sample(buccal swabs)for the linkage study,816also participated in cardiovascular ambulatory monitoring.Reasons for exclusion were pregnancy,heart transplantation,pacemaker and known ischemic heart disease,congestive heart failure,or diabetic neu-ropathy.Of these participants a total of65(20male,45female) participants were tested twice,separated by a minimum of2years and1month and a maximum of4years and8months(mean 3years and4months).RSA measures could not be reliably ob-tained in1participant.Age at the?rst day of testing in the remaining64participants ranged between18and62years (mean530.9,SD59.7).In addition,20randomly selected identical MZ twin pairs(18male,22female)with zygosity con-?rmed by DNA typing,were also included.Average age of the twins was27with a range of18to32.These twins were only tested once.The Ethics Committee of the Vrije Universiteit ap-proved the study protocol and all participants gave written con-sent before entering the study.No payment was made for participation,but all subjects received an annotated review of their ambulatory heart rate recordings.
Ambulatory Recording
Subjects were invited to participate in the study by letter and all participants were subsequently phoned by the researchers,who provided additional information on the study and made an ap-pointment with the participants for24-h ambulatory monitoring. The?rst ambulatory measurement took place during a repre-sentative workday(or a day with representative housekeeping chores for those who were not employed).The second ambula-tory measurement day took place during a comparable(work) day for most of the participants,but17subjects would only participate if the repeated measurement was scheduled on a leisure day.On the day preceding monitoring and on the
204 A.D.Goedhart et al.
monitoring day itself participants were asked to refrain from leisure time exercise or heavy physical work.Participants were visited at home between7:00and10:00a.m.and were?tted with the Vrije Universiteit Ambulatory Monitoring System(VU-AMS46;de Geus et al.,1995;Riese et al.,2003;Willemsen,de Geus,Klaver,van Doornen,&Carroll,1996).The VU-AMS produced an audible alarm approximately every30min(10min randomized)to prompt the participant to?ll out an activity diary.Participants were instructed to write down their physical activity and bodily postures during the last30-min period in chronological order.Diary prompting was disabled during sleep.
The ECG and changes in the thorax impedance(dZ)were recorded continuously using six disposable,pregelled Ag/AgCl electrodes.The?rst ECG/dZ electrode was placed on the ster-num over the?rst rib between the two collarbones.The second ECG electrode was placed at the apex of the heart over the ninth rib on the left lateral margin of the chest approximately3cm under the left nipple.The third ECG electrode is a ground elec-trode and was placed at the lower right abdomen.A second dZ measuring electrode was placed over the tip of the xiphoid com-plex of the sternum.The dZ current electrodes were placed on the back over cervical vertebra C4and between thorax vertebras T8 and T9.Electrode resistance was kept low(below10k O)by cleaning the skin with alcohol and rubbing.
Ambulatory Signal Scoring
Using the activity diary entries in combination with a visual dis-play of the output of an inbuilt vertical accelerometer,the entire 24-h recording was divided into?xed periods.These periods were coded for posture(supine,sitting,standing,walking,bicycling), physical activity(e.g.,desk work,dinner,meetings,watching TV),and physical load(no load,light,intermediate,and heavy). Minimum duration of periods was always5min and maximum duration was always1h.If periods with similar activity and posture lasted more than1h(e.g.,during sleep),they were di-vided into multiple periods of maximally1h.All periods were classi?ed as lying asleep,sitting,or standing/walking based on the dominant posture reported;the exact timing of changes in posture was veri?ed using the accelerometer signal from the am-bulatory device.We then looked at the self-reported activity and physical load to determine whether this period could be classi?ed as sitting with light physical activity(desk work,watching TV, writing,eating,reading,etc.)or sitting interspersed with inter-mediate physical activity(machine operation).The periods inter-spersed with intermediate activity were discarded.For standing/ walking periods we selected only those periods in which the par-ticipants reported no more than light physical load.For each period coded for posture,activity,and physical load we deter-mined the average RMSSD,pvRSA,and HF power.An average of25periods was created per participant.The mean duration of the sleep periods was56min(SD511),of the sitting activities 26min(SD514),and of the standing/walking condition19min (SD512).This procedure allowed us to test the sensitivity of the correlation structure of the three RSA measures to major chang-es in posture and activity.
From the ECG and the dZ we obtained the IBI time series and respiration signal according to the procedures detailed elsewhere (de Geus et al.,1995).Artifact preprocessing was performed on the IBI data.When the IBI deviated more than3SD from the moving mean of a particular period,it was automatically iden-ti?ed as an artifact and accepted or overruled by visual inspec-tion.Because artifacts cannot simply be deleted because the continuity of time would be lost,spuriously short IBIs were summed and missing beats were‘‘created’’by splitting spuriously long IBIs.The mean IBI and RMSSD values were computed from these corrected IBI time series across each of the labelled periods.RMSSD was de?ned as
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Per breath,estimates of pvRSA were obtained by substracting the shortest IBI during heart rate acceleration in the inspirational phase(which was made to include750ms from the following expiration to account for phase shifts)from the longest IBI dur-ing deceleration in the expirational phase(including750ms from the following expiratory pause/inspirational phase).When no phase-related acceleration or deceleration was found,the breath was assigned a pvRSA score of zero.Automatic scoring of RR and pvRSA was checked by visual inspection of the respiratory signal from the entire recording.Breathing cycles that showed irregularities like gasps,breath holding,or coughing were not considered valid and were rejected and removed from further processing.The3%shortest and longest breaths were automat-ically removed from the entire recording before averaging pvRSA across all remaining breaths to a single mean pvRSA for each of the labeled periods.We discarded17.3%of all auto-matically scored breaths.A total of10.3%of these breaths oc-curred in periods in which we could not reliably establish posture and activity or in which signal quality was deemed insuf?cient during visual inspection.A further7%were nonplausible long or short breaths,deviated more than3SD from the mean,or had close to zero amplitude.
Computation of HF power by Fourier analysis,a widely used strategy to asses RSA,assumes that the data show at least weak stationarity(Weber,Molenaar,&van der Molen,1992).Sta-tionarity of time series may be interpreted as having a stable mean and variance over time.In ambulatory studies and/or for analysis of relatively long data segments,the assumption of sta-tionarity is likely to be violated.Therefore,we improve on the usual Fourier approach by using a W avelet decomposition for the computation of HF power that does not have a stationarity as-sumption(Houtveen&Molenaar,2001).Additionally,by using W avelet transformation,much longer ambulatory fragments can be selected for cross-method comparison.Uniformly spaced samples were created by interpolation of the IBI data using a W avelet interpolation algorithm.Next,Discrete W avelet Trans-formation(DWT)was performed using a cardinal cubic spline function as base(see Houtveen&Molenaar,2001,for more information regarding this procedure).This method results in identical power values for stationary relatively short coded pe-riods(e.g.,7min of quiet reading)as compared to Fourier transformation,but it is superior for our relatively longer and nonstationary coded periods(e.g.,?rst hour of sleep).The HF power was computed as the sum of the variances of the
0.125–0.25-Hz and0.25–0.5-Hz windows.Note that the size of
a frequency window always doubles after each W avelet decompo-sition step.Because the DWT(like Fourier)suffers from aliasing effects at both ends,the?rst and last40data points(2.5s)of the time series were excluded from the derivation of the variances. Statistical Analyses
Reliability,heritability and temporal stability.To test the re-liability of RMSSD,pvRSA,and HF power,the short-term
Comparison of ambulatory RSA measures205
within-day test–retest correlations and the correlations between genetically identical twins were assessed.The within-day correl-ations were computed between the second and the third hour of sleep and also between two periods of comparable sitting activ-ities during daytime recordings(e.g.,reading a magazine or newspaper or watching TV).
The MZ correlations and temporal stabilities were separately computed for three main ambulatory conditions(sleep,awake sitting activities,awake standing/walking).When the MZ cor-relation is not unity,this means that environmental in?uences are creating dissimilarity between genetically identical subjects. Measurement error is completely contained within these envi-ronmental in?uences.Hence,the MZ correlation sets an upper limit to measurement error corresponding to[1rMZ]2.Temporal stability was computed as the intraclass correlation between the ?rst measurement and the second measurement that took place after an average period of3years and4months.
Correlations between RMSSD,pvRSA,and HF power.We used covariance structure modeling(Bollen,1989)to test four hypotheses regarding the equality of the correlations among the three measures(RMSSD,pvRSA,and HF power).All modeling was performed in Mplus,version4(Muthe n&Muthe n,2005). LISREL8.53(Jo reskog&So rbom,2001)was used to calculate the standardized residuals.
The?rst hypothesis states that the correlation structure re-mains stable over time,that is,across the two test days.To test this hypothesis,we?rst estimated the full correlation matrix be-tween RMSSD,pvRSA,and HF power in all ambulatory con-ditions on Test Day1and Test Day2.This model,in which all relations between the measures are estimated freely,is saturated in the sense that the number of estimated parameters equals the number of observed statistics.The saturated model therefore?ts the data perfectly,that is,had zero degrees of freedom,and a w2 of exactly zero.Subsequently,the correlations between the three measures(RMSSD,pvRSA,and HF power)collected during sleeping,sitting,and standing/walking on Day1were con-strained to be equal to the symmetric correlations collected on Day2.In addition,the cross-measure cross-test day correlations were?xed to be equal(i.e.,the correlations of the measures col-lected on Test Day1with those collected on Test Day2were?xed to be equal to the correlations of the measures collected on Test Day2with those collected on Test Day1).Figure1illustrates this testing procedure.
Under the saturated baseline model,illustrated in the upper panel of Figure1,all153correlations are estimated freely,as is denoted by all correlations having different indices(i.e.,all cor-relations between measures collected on Test Day1have index A, all correlations between measures collected on Test Day2have index B,all test–retest correlations have index X,and the cross-measure cross-test day correlations have either index C or D). Under the alternative,more restricted,model illustrated in the lower panel of Figure1,the36correlations between the measures collected on Test Day1are constrained to be equal to the36 correlations between the measures collected on Test Day2.In the lower panel of Figure1,these correlations all have index A, whereas correlations with the same numerical extension are ac-tually constrained to be equal(i.e.,correlation A1on Test Day1 is set equal to correlation A1on Test Day2,etc.).In addition,the cross-measure cross-test day correlations are set to be equal. Therefore,in the lower panel of Figure1,the36correlations of the measures collected on Test Day1with the measures collected on Test Day2have the same index C as the36correlations of the measures collected on Test Day2with the measures collected on Test Day1.Again,correlations with the same numerical exten-sion are?xed to be equal.All in all,this resulted in an alternative model with72constraints,and thus72degrees of freedom.
The second hypothesis states that the correlation structure is stable over the three main ambulatory conditions(sleep,awake sitting,awake walking).Here,ambulatory data were available for84subjects,the64subjects that were tested twice and an additional20subjects obtained by randomly selecting one of the twins from the20MZ twins pairs that were used to compute the MZ twin correlations.The testing procedure with respect to the effect of ambulatory condition is illustrated in Figure2.Under the saturated baseline model,illustrated in the upper panel of Figure2,all36correlations are estimated freely,as is denoted by all correlations having different indices.More specifically,all correlations between measures collected in the same ambulatory condition have either index A(sleeping),B(sitting),or C(stand-ing/walking).All test–retest correlations of the same measures collected across different ambulatory conditions have index X (between sleeping and sitting),Y(between sleeping and standing/ walking),or Z(between sitting and standing/walking).All cross-measure cross-condition correlations have indices D,E,F,G,H, and I,respectively.Under the alternative,more restricted model illustrated in the lower panel of Figure2,the correlations be-tween the three measures(RMSSD,pvRSA,and HF power)are constrained across ambulatory conditions(index A),such that correlations with similar numerical extensions are equal(e.g.,all A1s are equal).In addition,the cross-measure cross-condition correlations are constrained(index D),such that correlations with the same numerical extension are equal(e.g.,all D1s are equal).This resulted in an alternative,restricted model with21 constraints,and thus21degrees of freedom.
The third and fourth hypotheses state that the correlation structure is independent of mean IBI and RR,respectively.These hypotheses were tested on the data obtained in the84subjects on the?rst test day.We subdivided this sample?rst into three IBI and next into three RR groups.To do so,mean IBI and RR scores were calculated for each participant across the three am-bulatory conditions.Based on these mean scores,we distin-guished‘‘low,’’‘‘medium,’’and‘‘high’’IBI and RR groups, corresponding to the lowest33%,the medium33%,and the highest33%of the sample.To test whether the correlation ma-trices were equal for the low,medium,and high groups,mul-tigroup analyses were conducted,in which the correlations between the nine measures recorded on Test Day1(three meas-ures collected under three ambulatory conditions)were?rst es-timated freely in all groups(saturated model),and then constrained to be equal across the groups.For instance,in the alternative model for the IBI groups,the36correlations of the low IBI group were constrained to be equal to the36correlations of the medium IBI group,and the36correlations of the high IBI group,resulting in a restricted model with72degrees of freedom. Note that the restrictions concerned the correlations and not the covariances,so the variances of the measures were allowed to differ across groups.
Fit statistics.In testing Hypotheses1to4,the more restricted models are always nested under the saturated model(Bollen, 1989).Normally,the?t of nested models is evaluated by means of a likelihood ratio test.This test is constructed by subtracting the w2value of the less restrained model with more freely
206 A.D.Goedhart et al.
Comparison of ambulatory RSA measures
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estimated parameters,from the w2value of the more restricted model with fewer free parameters.The difference in w2(denoted as w2diff)between the two models follows a w2distribution with the number of degrees of freedom(df)equaling the difference in the number of parameters estimated in the two models.The re-stricted model is considered tenable if its value of the w2good-ness-of-?t statistic is not significantly greater than that of the more lenient model,that is,if the difference in w2is not signif-icant.However,Browne,MacCallum,Kim,Andersen,and Gla-ser(2002)noted that the w2statistic can be markedly in?ated if some measures in the model are highly correlated,for example,if highly reliable measures are used to measure the same or related characteristics several times.In that case,standardized residuals, which are a function of the differences between the observed covariance matrix S and the estimated covariance matrix S,may be(very)small,indicating close?t of the model to the observed data,whereas the w2statistic,and?t indices based on this stat-istic,indicate a poorly?tting model.
We expected that many of the correlations between the de-pendent measures in this study would be larger than.80.Al-though such high correlations are desirable in the sense that they indicate reliable measurement and substantial overlap between measures,the consequence may be that the w2statistic of the restricted models may assume large values in the presence of trivial mis?https://www.doczj.com/doc/f818242127.html,paring the restricted model to the more lenient model using the usual likelihood ratio test may then result in the rejection of a perfectly acceptable model.In view of the above,we chose to evaluate the?t of the restricted model in a different way. If the?t of the restricted model was in itself acceptable,we always considered the constraints to be tenable,that is,the sets of cor-relations to be equal.Although we will report the w2statistic of every tested model to conform to common practice,our main indices of?t were the Comparative Fit Index(CFI)and the standardized residuals(Bollen&Long,1993;Schermelleh-Engel,Moosbrugger,&Mu ller,2003).The CFI is based on the w2statistic,but introduces penalties for every additional pa-rameter estimated(i.e.,favors more parsimonious models)and is relatively unaffected by sample size.The CFI ranges between0 and1.00,with values below.95indicating poor?t,values be-tween.95and.97indicating acceptable?t,and values between .97and1.00indicating good?t.Standardized residuals are standardized differences between the observed covariance matrix S and the estimated covariance matrix S.Ideally,the standard-ized residuals should lie betweenà3and13and show a normal distribution by approximation(this can be evaluated readily us-ing LISREL’s stem leaf plots).In case the CFI is below.95and/ or the standardized residuals are outside the acceptable range (à3and13),the largest(absolute)standardized residual usually indicates the element that is most poorly?tted by the model.To trace these sources of local misspeci?cation,we planned to use the Modi?cation Indices(MIs)supplied by the Mplus program. MIs are calculated for every?xed parameter in the model,and the value of the MIs represents the expected drop in overall w2 (i.e.,improvement in model?t)if the parameter were to be freely estimated.
Missingness.In the comparison across ambulatory condi-tions,complete data during sleep on both days were available for
208 A.D.Goedhart et al.
Figure2.Testing equality of the correlations of the RSA measures across ambulatory conditions.The upper panel shows the Null
model in which all36correlations are estimated freely.The lower panel shows the Alternative model in which correlations with the
same indices are constrained to be equal.
51subjects only,due to ECG or ICG signal loss during sleep.In the presence of missing data,one can use Full Information Maximum Likelihood(FIML)estimation,which uses all avail-able data.However,this method of estimation should only be applied if data are missing(completely)at random(MAR or MCAR;we refer the reader to Schafer&Graham,2002,for a detailed discussion of mechanisms of missingness).The missing-ness in our data was therefore?rst examined with SPSS missing data analysis.When considered across all18measures,missing-ness could be considered completely at random(MCAR),as indicated by the nonsignificance of Little’s MCAR test (w2(80)599.83,n.s.).In both Mplus and LISREL,FIML esti-mation could therefore be used to accommodate missing data so that all available data were used in the model estimation. Results
Means
T able1presents the untransformed means and standard devi-ations for RMSSD,pvRSA,HF power,IBI,and RR across all ambulatory conditions and separately for each ambulatory con-dition(i.e.,sleep,awake sitting,and awake standing/walking). Because the RMSSD,pvRSA,and HF power distributions were skewed,their natural logarithms were used in all further analyses. Repeated measures ANOVA showed a significant effect of ambulatory condition on RMSSD,F(2,51)531.63,p o.001, pvRSA,F(2,50)535.94,p o.001,and HF power, F(2,51)510.48,p o.001,and post hoc testing showed that all three measures decreased significantly from sleep to sitting to standing/walking,all p o.05.There were no significant main ef-fects of test day on the means of RMSSD,pvRSA,and HF power or interaction effects between ambulatory condition and test day.
Short-Term Reliability
Within-day correlations between the second and the third hour of sleep and between two periods of sitting activities all exceeded .85(see T able2)suggesting good to excellent short-term relia-bility for RMSSD,pvRSA,and HF power alike.For compar-ison,short-term reliability of IBI and RR is also given.
The intrapair MZ correlations further con?rmed good reli-ability(see T able3).For all three measures,MZ correlations were highest during sitting activities and lowest during standing/ walking.Even at standing/walking,however,the lowest MZ correlation for pvRSA suggests that measurement error cannot account for more than18%of the variance([1à.58]2).Based on the within-day test–retest or MZ twin correlations,none of the three measures could be favored as the‘‘best,’’that is,most reliable,RSA measure.
Temporal Stability
T able4displays the correlations across the two test days for the three RSA measures,IBI and RR.Temporal stability for RMSSD,pvRSA,and HF power was good when computed across all ambulatory conditions and separately across awake sitting activities.Temporal stability was moderate during stand-ing/walking,potentially because of the low stability of the re-spiratory frequency during these periods of physical activity.For all measures,recordings proved most stable during sleep.As with short-term reliability,none of the three measures seemed to be clearly favored by temporal stability as the best RSA measure. Correlations between RMSSD,pvRSA,and HF Power
T able5presents the full correlation matrix between the three RSA measures in all ambulatory conditions on Test Day1(upper left block)and Test Day2(lower right block)and across test days (lower left block).As can be seen in T able5,many of the cor-relations between the dependent measures in this study were larger than.80,and quite a few exceed.90.This justi?es our approach of evaluating the?t of the restricted models directly besides comparing the?t of the restricted models to the?t of the saturated model.
Effect of test day.The?rst hypothesis states that the corre-lation structure remains stable over time,that is,across the two test days.The?t indices of the alternative model representing this hypothesis are shown in T able6(Model TD).As expected,the difference in w2between the saturated model and the alternative model was significant,w2diff(72)5130.12,p o.001.Yet,both the CFI and the standardized residuals indicated that the?t of the alternative model was good.We therefore conclude that the cor-relations can,to reasonable approximation,be considered equal across the two test days.
Effect of ambulatory conditions.The second hypothesis states that the correlation structure is stable over the three main am-bulatory conditions(sleep,awake sitting,awake standing/walk-ing).T aken that the effect of test day was negligible,the effect of ambulatory condition on the correlations among RMSSD, pvRSA,and HF power was initially tested on data from the ?rst test day only(Model AMBa in T able6).As expected,the difference in w2between the restricted model and the saturated model was significant,w2(21)595.53,p o.001.However,the
Comparison of ambulatory RSA measures209 T able1.Means(SD)of RMSSD,pvRSA,and HF Power Separately for the Two Test Days
N RMSSD(ms)pvRSA(ms)HF(ms2)IBI(ms)RR(bpm) Full recording
Test8447.60(24.06)49.82(22.08)786.22(705.55)803.99(10.44)16.70(1.10) Retest6441.71(20.85)43.95(19.87)618.68(562.17)792.80(83.65)16.63(1.35) Sleep
Test7865.65(33.19)60.86(31.22)1109.05(1097.01)981.49(126.35)16.23(2.00) Retest5760.36(36.95)51.40(25.49)991.11(1163.15)970.61(107.32)16.17(2.15) Sitting
Test8442.00(24.34)50.10(23.97)688.50(686.82)772.67(109.60)17.00(1.25) Retest6439.47(22.67)46.59(24.41)590.30(622.00)791.15(87.17)16.84(1.70) Standing/walking
Test8438.04(920.52)40.35(19.44)590.63(545.34)689.31(97.25)16.87(1.01) Retest6433.43(14.73)36.77(15.64)444.61(344.60)700.33(70.57)16.72(1.25)
CFI was only just below the critical value of.95(CFI5.94),and the standardized residuals were clearly within the acceptable range.We repeated the analysis on the data from the second test day(Model AMBb).Again,the difference in w2between the restricted model and the saturated model was significant, w2(21)573.35,p o.001,but the small standardized residuals and high CFI indicated good?t.T aken together,the analyses across the two days suggest invariance of the correlation struc-ture of RMSSD,pvRSA,and HF power correlations across sleep,sitting,and standing/walking activities.
Effect of mean IBI.Next we tested whether the correlations among RMSSD,pvRSA,and HF power were equal for subjects, with low(707.09?43.90),medium(795.95?21.37),or high (922.71?90.52)mean ambulatory IBI.These three groups dif-fered significantly in their IBI scores,F(2,81)592.77,p o.001. Given that the effect of test day was negligible,the effect of group membership on the correlations among RMSSD,pvRSA,and HF power was tested on the data collected on the?rst test day only.The36correlations within each group were constrained to be equal for the low,medium,and high IBI groups,yielding a restricted model with72degrees of freedom(Model IBI).The?t indices of this restricted model are shown in T able6.Although the difference in w2between the saturated model and the alter-native was significant,w2diff(72)5107.54,p o.001,the CFI and the standardized residuals indicated that the?t of the alternative model was acceptable.We therefore conclude that the correl-ations between the RMSSD,pvRSA,and HF power measures of RSA can be considered approximately equal across groups dis-tinguished with respect to their mean ambulatory heart rate.
Effect of mean RR.Finally,we tested whether the correl-ations among RMSSD,pvRSA,and HF power were equal for subjects,with low(15.55?0.45),medium(16.72?0.25),or high(17.88?0.68)mean ambulatory RR.These three groups differed significantly with respect to their RR scores, F(2,81)5155.09,p o.001.Again,the effect of group member-ship on the correlations among RMSSD,pvRSA,and HF power was tested on the data collected on the?rst test day only as the effect of test day had proven negligible.The constraints imposed on the correlation matrices of the RR groups were analogous to those imposed on the matrices of the IBI groups.That is,the36correlations within each group were constrained to be equal for the low,medium,and high RR groups,yielding a restricted model with72degrees of freedom(Model RR).The?t indices of this restricted model are shown in T able6.Although the model ?tted significantly worse than the saturated model, w2diff(72)5136.67,p o.001,both the CFI and the standardized residuals indicated good?t.We therefore conclude that the cor-relations between the RMSSD,pvRSA,and HF power measures can be considered approximately equal across groups distin-guished with respect to their mean ambulatory RR. Discussion
Cardiovascular psychophysiology aimed at identifying individ-uals at risk for future cardiovascular disease is increasingly rely-ing on ambulatory monitoring under the expectation that this has higher predictive validity for long-term health outcomes than laboratory measurements(Goldstein,Shapiro,&Guthrie,2006; Grossman,2004;Vrijkotte et al.,2000).RSA is a promising measure for large-scale ambulatory studies because it has been linked,both theoretically and empirically,to activity of the para-sympathetic nervous system,that,in turn,is paramount to the electrical stability of the heart(Ando et al.,2005;Hull et al., 1990;Levy&Schwartz,1994;V anoli et al.,1991).
However,RSA can be assessed in many different ways,which clearly differ in terms of costs and whether they are cumbersome to the study participants or require labor-intensive data reduc-tion by the researcher.A full ECG recording(e.g.,the Holter monitor),for instance,is only mildly cumbersome to the patients, who need to wear electrodes and a small portable recording de-vice on the hip.However,this mode of assessment is labor in-tensive to the researcher,who needs to perform repeated Fourier analyses to compute HF power on all stationary5-min segments, which often need visual inspection of the automatically detected erroneous IBIs.In contrast,computation of the RMSSD from the IBI time series obtained from a wrist-watch type HR-re-cording device together with a single elastic recording band around the chest(e.g.,the Polar Sporttester)is much less de-manding of both participant and researcher.The researcher still needs to visually inspect the automatically detected erroneous IBIs,but computation of time domain measures like RMSSD is otherwise straightforward.Additional recording of the RR
210 A.D.Goedhart et al. T able2.Within-Day Correlations(Intraclass Correlations)between the Second and the Third Hour of Sleep and between Two Periods of Light Physical Sitting Activities
N RMSSD(ms)pvRSA(ms)HF(ms2)IBI(ms)RR(bpm) Sleep1500.88nn0.86nn0.89nn0.92nn0.93nn Sitting1550.86nn0.85nn0.87nn0.84nn0.64nn
nn Correlation is significant at.01level.
T able3.Intrapair MZ Twin Correlations
N RMSSD(ms)pvRSA(ms)HF(ms2)IBI(ms)RR(bpm) Full recording200.76nn0.75nn0.76nn0.74nn0.61nn Sleep200.63nn0.69nn0.65nn0.70nn0.84nn Sitting200.81nn0.80nn0.80nn0.82nn0.42n Standing/walking190.63nn0.58nn0.60nn0.61nn0.72nn n Correlation is significant at.05level.
nn Correlation is significant at.01level.
would allow computation of pvRSA,but at the cost of adding another layer of work to the data-reduction phase and an ad-ditional burden on the participant by requiring the wearing of either additional electrodes or respiratory bands.
In this study,we tested whether the assessment of between-subject differences in RSA was sensitive to the method used,that is,whether‘‘high tech’’pvRSA or HF power were superior to ‘‘low tech’’RMSSD.The answer is a resounding no.All three RSA measures were highly correlated among each other and none of them stood out in terms of short-term reliability or tem-poral stability over a period of more than3years.The present ?ndings with respect to the reliability of ambulatory RSA,either de?ned as a within-day short-term retest coef?cient(.85–.89)or as the intrapair resemblance in genetically identical twins(.58–.81),are in line with previous studies that showed similarly high test–retest correlations for the average24-h levels of RMSSD (.67–.89)and HF power(.76–.92)after3to65days in both healthy individuals and cardiac patients(Bigger,Fleiss,Ro-lnitzky,&Steinman,1992;Hohnloser,Klingenheben,Zabel, Schroder,&Just,1992;Kleiger et al.,1991;Sinnreich,Kark, Friedlander,Sapoznikov,&Luria,1998;Stein,Rich,Rottman, &Kleiger,1995).Likewise,the?nding that RMSSD in the full recording is temporally stable(.71)across an average period of 3years and4months is in agreement with the only previous study that had a prolonged test–retest interval and reported long-term stability of.79for the24-h RMSSD level(Pitzalis et al.,1996). The present results contribute to these previous studies by showing similar stability for HF power and pvRSA.In addition, the present study shows that the high intercorrelations of the three RSA measures do not change over prolonged periods of time.This suggests that longitudinal studies of RSA can,for instance,use pvRSA at the?rst wave and RMSSD at the second wave.
In keeping with a large body of literature,the mean values of the three RSA measures increased from standing to sitting and from sitting to sleep(Grossman et al.,2004;Houtveen et al., 2005;Martinmaki et al.,2006).Although temporal stability was good to excellent for sitting and sleep,it was only moderate for standing/walking.One possible explanation might be that it is more dif?cult to arrive at a reliable measure of RSA during standing/walking because the standing/walking periods are rela-tively short.The mean duration of the sleep periods was56min, whereas the mean duration was26min for sitting activities,and 19min for standing/walking.However,if averaging RSA meas-ures across longer periods would yield higher stability than aver-ages across shorter periods,then we would expect the temporal stability of the RSA measures to be highest during sleep.This is not the pattern that is evident from T able3,which shows that correlations during sleep and sitting are comparably high,even though the mean duration of sitting periods was only half the duration of the sleeping periods.Duration per se,therefore,does not seem to explain why RSA measures recorded during walking/standing are less stable than measures obtained in both other conditions.As an alternative explanation,the temporal stability of RSA during standing/walking activities may be lower than that of sitting and sleep,because respiratory behavior in this condition is much more variable.This is directly supported by the lower temporal stability of the respiratory frequency during these periods of physical activity.Based on extensive ambulatory pvRSA data,Grossman et al.(2004)have shown that physical activity needs to be taken into account when interpreting ambu-latory RSA,and our data underscore their warning.
Independent of ambulatory condition,large differences in mean respiratory frequency and heart rate were found.We were concerned that such differences might distort the correlations between the three RSA measures,because RR and heart rate may both distort their relation to cardiac vagal control(Berntson et al.,2005;Grossman et al.,1991;Grossman&Kollai,1993). This concern was greatly mitigated by the data.Differences in mean resting heart rate or mean RR did not affect the correl-ations between the various RSA measures;the RSA measures were as highly correlated in a group with a mean heart rate of83 bpm as in a group with a mean heart rate of65bpm,and as highly correlated in groups with a mean RR of15(range14–16) versus18(range17–20)times per minute.This contradicts the idea that one of these RSA measures is relatively more sensitive than the others to confounders such as individual differences in RR(Grossman,1992)or in heart rate(Berntson et al.,2005).
T aken together,our results suggest that,at least in healthy subjects,neither pvRSA nor HF power provides superior meas-ures of RSA compared to RMSSD.This favors the RMSSD measure as the most cost-ef?cient measure of RSA,because it is most easily obtained with the least effort on the part of the ex-perimenter and the lowest burden for the participant.This is particularly true in comparison to pvRSA,as this measure ne-cessitates additional recording of the ambulatory respiration sig-nal,which in turn requires the wearing of additional electrodes or a vest,thereby adding to the discomfort of the subjects.However, an obvious advantage of the respiration signal is that it allows a number of additional parameters to be computed from ambu-latory recordings,including respiration depth and frequency (de Geus et al.,1995,2005;Grossman,2004;Wilhelm et al., 2003),which are important parameters in themselves.Indeed Ritz and Dahme(2006)recently argued that RSA can be used as a measure of cardiac vagal control only when taking respiratory behavior into account.Likewise,when Fourier or W avelet anal-ysis are used to obtain HF power,heart rate variability at a lower frequency(0.07–0.14Hz)can be measured,which may poten-tially index sympathetic nervous system activity(Malliani, Pagani,Lombardi,&Cerutti,1991).Furthermore,heart rate variability at very low frequencies(0.001–0.07)can be measured, which has been associated with an increased risk for cardiovas-cular disease independent of HF power(Hadase et al.,2004;La Rovere et al.,2003).
Comparison of ambulatory RSA measures211 T able4.Temporal Stability for an Average Period of3Years and4Months
N RMSSD(ms)pvRSA(ms)HF(ms2)IBI(ms)RR(bpm) Full recording640.71nn0.58nn0.76nn0.58nn0.74nn Sleep520.73nn0.72nn0.81nn0.65nn0.91nn Sitting640.70nn0.68nn0.80nn0.66nn0.69nn Standing/walking640.44nn0.44nn0.57nn0.61nn0.37nn nn Correlation is significant at.01level.
212
A.D.Goedhart et al.
T a b l e 5.C o r r e l a t i o n s b e t w e e n R M S S D ,p v R S A ,a n d H F P o w e r S e p a r a t e l y f o r L y i n g d u r i n g S l e e p ,S i t t i n g ,a n d S t a n d i n g /W a l k i n g
F i r s t m e a s u r e m e n t
S e c o n d m e a s u r e m e n t
S l e e p
S i t t i n g
S t a n d i n g /w a l k i n g S l e e p S i t t i n g
S t a n d i n g /w a l k i n g
R M S S D p v R S A
H F R M S S D p v R S A H F R M S S D p v R S A H F R M S S D p v R S A H F R M S S D p v R S A H F
R M S S D p v R S A H F
F i r s t m e a s u r e m e n t S l e e p R M S S D 1.00p v R S A .79n n
1.00H F .94n n
.83n n
1.00
S i t t i n g R M S S D .73n n
.50n n
.66n n
1.00p v R S A .63n n
.59n n
.67n n
.87n n
1.00H F .70n n
.53n n
.73n n
.93n n
.88n n
1.00
S t a n d i n g /w a l k i n g R M S S D .55n n
.32n n
.46n n
.87n n
.72n n
.74n n
1.00p v R S A .52n n
.46n n
.51n n
.76n n
.84n n
.74n n
.76n n
1.00H F .57n n
.39n n
.57n n
.87n n
.78n n
.86n n
.94n n .78n n
1.00
S e c o n d m e a s u r e m e n t S l e e p R M S S D .73n n
.50n n
.71n n
.59n n
.42n n
.57n n
.39n n
.29n
.40n n
1.00p v R S A .58n n
.72n n
.61n n
.36n n
.40n n
.39n n
.21.26.24.77n n
1.00H F .73n n
.60n n
.81n n
.54n n
.51n n
.64n n
.34n
.36n n
.46n n
.96n n .77n n
1.00
S i t t i n g R M S S D .53n n
.24.51n n
.70n n
.65n n
.70n n
.48n n
.46n n
.52n n
.70n n
.53n n
.65n n
1.00p v R S A .45n n
.42n n
.51n n
.52n n
.68n n
.59n n
.34n n
.48n n
.43n n
.58n n
.71n n
.59n n
.81n n
1.00H F .58n n
.39n n
.67n n
.67n n
.70n n
.80n n
.42n n
.50n n
.59n n
.73n n
.58n n
.77n n
.94n n
.81n n
1.00
S t a n d i n g /w a l k i n g R M S S D .45n n
.18.45n n
.60n n
.55n n
.59n n
.44n n
.36n n
.47n n
.67n n
.47n n
.61n n
.92n n
.73n n
.86n n
1.00p v R S A .30n
.28n
.37n n
.41n n
.59n n
.47n n
.28n
.44n n
.35n n
.48n n
.62n n
.50n n
.73n n
.92n n
.71n n
.73n n
1.00H F .54n n
.34n n
.63n n .62n n
.64n n
.75n n .41n n
.45n n
.57n n
.71n n
.52n n
.75n n
.90n n
.74n n
.95n n .94n n
.70n n
1.00
n
C o r r e l a t i o n i s s i g n i f i c a n t a t 0.05l e v e l .n n
C o r r e l a t i o n i s s i g n i f i c a n t a t 0.01l e v e l .
In this study,we used the W avelet approach to obtain HF power rather than Fourier analysis.Although Fourier analysis has been the more common approach so far,we preferred the W avelet approach because,in contrast to Fourier analysis,it is not sensitive to violations of the stationarity assumption.Such violations are likely to occur across longer ambulatory recording periods.To deal with this,many ambulatory studies using Four-ier transformation have divided the recording into smaller pe-riods of,for example,5or10min.This reduces probability of nonstationarity and leads to a convergence of Fourier and W avelet results(Houtveen&Molenaar,2001).Cross-method convergence may be lower in cases of longer periods as in the current study.Therefore,it remains to be demonstrated whether the correlations of HF power to RMSSD and pvRSA also holds when Fourier-based HF estimates are used.W avelet transfor-mation has the additional advantage of allowing precise local-ization of particular RSA events in time by providing a full time-frequency decomposition of the IBI time series(e.g.,see Pichot et al.,1999).In the current study,we have not used this addi-tional time-frequency information because no comparable in-formation can be obtained from RMSSD and pvRSA.
A limitation of this study that is worth mentioning is that we examined the relative behavior of the three ambulatory RSA measures by comparing them to each other across time and ambulatory conditions.True validity testing,however,would require comparison of each of the RSA measures to some future cardiovascular disease endpoint or to an external valida-tion criterion of cardiac vagal control,which would be the most plausible explanation for any cardioprotective effects associated with high levels of RSA.Although a number of studies have shown predictive validity of RMSSD(Dekker et al.,2000;Nolan et al.,1998;Singh et al.,1998;Tsuji et al., 1996)and HF power(Bigger et al.,1993;Singh et al.,1998;Tsuji et al.,1996)for cardiovascular disease,none has done so for pvRSA.However,in these prospective studies,HF and RMSSD were either measured during short periods(Dekker et al.,2000; Singh et al.,1998;Tsuji et al.,1996)or when full24-h ambulatory recording was used in patient samples(Bigger et al.,1993;Nolan et al.,1998).Whether prolonged recording of RSA in naturalistic settings has predictive power in the population at large remains to be established.T aking into account the high correlations among the three measures in this study,it is hard to imagine that one of these measures would exceed the others in predictive power.
To test which of these RSA measures is most closely associ-ated with individual differences in cardiac vagal control,ambu-latory recordings could be made under partial and full parasympathetic blockade.We do not consider such long-term (i.e.,24-h)pharmacological interventions feasible in a true nat-uralistic setting.However,a number of studies in controlled ex-perimental settings have shown that RMSSD,pvRSA,and HF power all respond to muscarinergic blockade by showing a grad-ed,almost linear,decrease with increasing dose(Berntson et al., 1997;Cacioppo et al.,1994;Martinmaki et al.,2006;T ask Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,1996).Combined, these?ndings and the high correlations among the three meas-ures in a realistic ambulatory setting render it unlikely that one of these measures would exceed the others in detecting individual differences in daily cardiac vagal control.In summary,we con-clude that ambulatory RMSSD,pvRSA,and HF power are highly correlated and that their correlation is stable across time, ambulatory conditions,and a wide range of resting RR and HR values.Because the different RSA measurement strategies have varying specific advantages,for instance,providing addi-tional information on RR or on(very)low frequency power, the choice for a specific measure should be based on the exact research questions.In large-scale research that focuses entirely on individual differences in RSA as correlates or predictors of disease risk,RMSSD appears to be the most cost-ef?cient measure.
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Test day1AMBa95.5321.94à1.78 1.53 Test day2AMBb73.3521.95à1.00 1.63 Groups
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(R eceived July19,2006;A ccepted November15,2006)
Comparison of ambulatory RSA measures215
全文1.5倍行距 标题标题标题标题(二号宋体，居中，加粗)【说明：(标题是能反映论文中特定内容的恰当、简明的词语的逻辑组合，应避免使用含义笼统、泛指性很强的词语(一般不超过20字，必要时可加副标题，尽可能不用动宾结构，而用名词性短语，也不用“……的研究”，“基于……”)。】作者11，作者22，作者31，……(四号楷体，居中) (1. 学校院、系名，省份城市邮编；2. 单位名称，省份城市邮编)(五号楷体，居中) 摘要：(小五号黑体，缩进两格)摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容……(小五号楷体) 【说明：摘要应具有独立性和自含性，即不阅读全文，就能获得必要的信息。要使用科学性文字和具体数据，不使用文学性修饰词；不使用图、表、参考文献、复杂的公式和复杂的化学式，非公知公用的符号或术语；不要加自我评价，如“该研究对…有广阔的应用前景”，“目前尚未见报道”等。摘要能否准确、具体、完整地概括原文的创新之处，将直接决定论文是否被收录、阅读和引用。摘要长度200～300字。摘要一律采用第三人称表述，不使用“本文”、“文章”、“作者”、“本研究”等作为主语。】 关键词：(小五号黑体，缩进两格)关键词；关键词；关键词；关键词(小五号楷体，全角分号隔开) 【说明：关键词是为了便于作文献索引和检索而选取的能反映论文主题概念的词或词组，每篇文章标注3～8个关键词，词与词之间用全角分号隔开。中文关键词尽量不用英文或西文符号。注意：关键词中至少有两个来自EI控词表。一般高校数字图书馆均可查到。】 中图分类号：(小五号黑体，缩进两格)TM 344.1(小五号Times New Roman体，加粗)文献标志码：(小五号黑体，前空四格)A(小五号Times New Roman体，加粗) 【说明：请查阅中国图书馆分类法(第4版)(一般要有3位数字，如TM 344.1)】 引言(四号宋体，加粗，顶格) 引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言……(五号宋体，段前前缩进两格) 【说明：引言作为论文的开端，主要回答“为什么研究”这个问题。它简明介绍论文的背景、相关领域的前人研究历史与现状，以及著者的意图与分析依据，包括论文的追求目标、研究范围和理论、技术方案的选取等。引言应言简意赅，不要 收稿日期： 基金项目：省部级以上基金资助项目(必须要有编号) 作者简介：姓名(出生年-)，性别，职称，学位，主要研究方向，(Tel)；(E-mail)。 导师姓名(联系人)，性别，职称，硕(博)士生导师，(Tel)；(E-mail)。
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论文写作指导 英语专业本科生毕业论文写作指导 1. 毕业论文写作概述 毕业论文写作是大学英语专业教学计划中一个不可缺少的部分和实践性环节。《英语教学大纲》明确指出：“毕业论文是考查学生综合能力、评估学业成绩的一个重要方式。”毕业论文写作的优劣是决定学生毕业时可否被授予学士学位的重要依据。 1.1 毕业论文写作的意义
撰写毕业论文不仅具有一般写作的意义，而且有其特殊的作用：即培养科研创新意识、锻炼思维组织能力、训练语言运用能力、激活知识的输出与输入等。 1.2 毕业论文的特点 ? ? ? ? 毕业论文的题材与体裁：专业性与学术性毕业论文的论点和论据：独创性、可靠性和科学性毕业论文的结构：规范性、完整性和一致性毕业论文的语言：准确性、得体性及生动多样
性 1.3 论文的种类 从学位等级的角度来看，学位论文分为学士学位论文、硕士学位论文和博士学位论文。大学英语专业本科生的毕业论文为学士学位论文。 从专业范围来看，理科学生的毕业论文属于自然科学学术论文，文科学生的毕业论文属于人文科学领域的学术论文。 从研究方法来看，毕业论文有以下几种主要类型：分析评论型、描述推展型、文献综合型和自身设计型。分析评论型论文主要是就某位作家、某部作
品、某种语言现象或教学方法进行有理有据的分析，并在此基础上得出自己的结论；描述推展型论文是指首先深入学习研究他人在某一问题上已做出的研究成果，然后进行归纳和介绍，并在此基础上作进一步的研究从而得出自己的结论；撰写文献综合型论文要求作者大量阅读文献，研究学者们在某一方面已获得的学术成果，然后对其进行综合性介绍和可观的评价；在自身设计型论文中，作者主要是根据自己的知识积累、生活感受对某一问题展开讨论，表明自己对这一问题的观点，并加以说明和论证。
医学论文的写作和发表，都是有技巧的。比如在我们写期刊论文的时候，遵守期刊风格是很重要的，对审查也会有帮助。当然，内容是最重要的，但如果杂志编辑和审稿人看到你在遵照期刊风格上的努力，论文被接受的可能性会更大。要决定论文的风格不是一个简单的过程，好在有一些规则可以遵循。 首先，要有全盘的概念，当编辑设立期刊，决定要使用何种风格指南作为标准时，他们会以最适合关注领域的风格为准，比如医学杂志最有可能使用AMA风格，因为AMA风格特别针对医学写作，像是微生物的复数写法规范还有医学学位的缩写标准等。大多数的风格手册都涵盖了基本语法、标点符号、参考文献格式的建议，不过与特别领域相关的风格指南会涵盖更为详细且有用的要点。 当你选定要投稿的目标期刊后，务必要仔细阅读给作者的投稿要求，大部分在最后的地方会有期刊使用的风格信息。当然，每个期刊都会有一些风格例外，有些会直接列在投稿指南中，作者一定要特别注意这个部分。比如说，如果你不确定参考文献要怎么排版，而投稿指南中的信息也不清楚，这时候可以去看期刊近期发表的一两篇论文，了解参考文献部分是怎么排版的；再举一个例子，如果期刊有在线版本，而你想要知道可不可以使用断字符，可以在先前发表的文章使用Ctrl+F搜索单词或片语了解该怎么处理。期刊的风格规则是会改变的，尤其是有新版本的风格指南发行时，所以要注意最新发表的论文。如果你无法找到例子，那么选择一个风格然后保持一致性，如此一来，就算期刊是采用不同的风格，内容编辑也能很快地进行适当修改。 如果你遇到风格指南中对某一项有几种不同的做法选择是，有几个解决办法。如果你是作者或内容编辑，想想哪一个会更贴近读者，考虑语言（英式或美式？）、咨询期刊编辑、或者看一下领域内的其他期刊是怎么处理的，然后再做决定，记得将这些记录下来。举个例子，在某书中“建议使用韦氏第三版新国际词典和最新的主要抄本韦氏大学词典....如果有超过一种的拼法或复数形式，芝加哥风格手册通常采用第一个提及的形式（包括同等形式）来保持一致”。 最后，如果你是期刊内容编辑，你可以做的一件最重要的事情是将这些风格做法另外建档记录，保持一致性，可考虑与作者分享部分的风格规定，帮助论文在接受后的后续出版过程更加快速。如果你是作者，最好的做法是详读期刊给作者的投稿规定，然后将期刊的基本风格守则保存在随手可取得的地方。 讨论是论文的精华部分，是对引言所提出的问题的回答，是将研究结果表象的感性认识升华为本质的理性认识。在讨论中作者通过对研究结果的思考、理论分析和科学推论，阐明事物的内部联系和发展规律，从深度和广度两方面丰富和提高对研究结果的认识。讨论水平的高低取决于作者的理论水平、学术素养以及专业知识的深、广度。讨论的内容大致包括以下几个方面：①简要的概述国内外对本课题的研究近况，以及本研究的结论和结果与国际、国内先进水平相比居于什么地位。②根据研究的目的阐明本研究结果的理论意义和实践意义。 ③着重说明本文创新点所在，以及本研究结果从哪些方面支持创新点。④对本研究的限度、缺点、疑点等加以分析和解释，说明偶然性和必然性。⑤说明本文未能解决的问题，提出今后研究的方向与问题。并不是每篇论文都必须包括以上内容，应从论文的研究目的出发，突出重点，紧扣论题。 讨论是最能体现论文水平的部分，也是写作难度较高的部分。对于初写着来说，要特别注意以下几点：①讨论是作者阐明自己的学术观点，但并不等于是自由论坛，不能泛泛而谈。讨论的内容要从论文的研究结果出发，围绕创新点与结论展开，要做到层次清晰、主次分明，不要在次要问题浪费笔墨冲淡主题。与文献一致处可一笔带过，重点讨论不一致处；引证必要的文献，切忌作文献综述。②实事求是、恰如其分的评价，不乱下结论，切忌推理过分外延。医学中尚有许多尚未阐明的问题，所以推理应非常谨慎，通常冠以“可能”等。③任何研究都有其局限性，如国内的研究结果有待国外验证；体外试验有待于体内试验验证。因此，讨论要坚持一分为二的观点，对于与他人研究结果不一致处要认真分析原因，要抱有虚心追求真理的态度与其他作者商摧，切勿持“唯我正确”的态度。④并非每篇论文都要有讨论，有的短篇可不写。若结果与讨论关系密切则可放在一起写，合称结果与分析等。 科研工作的顺利完成离不开他人的帮助，在正文的最后应向对本研究提供过帮助的人致以谢意。致谢的对象包括：对研究工作提出指导性建议者，论文审阅者，资料提供者，技术
中国核心期刊论文格式要求 2010-04-14 21:08 一、限在“CSSCI检索期刊2003”、“中文核心期刊（学术刊物，非杂志类）北大2004版”期刊发表的论文格式 （一）基本要求 1、您的论文请用WORD等文本编辑软件打好后以附件的形式发到我们的电子信箱lunwen888@https://www.doczj.com/doc/f818242127.html,中，便于我们及时下载进行编辑。来稿以5000字为宜，并附100-200字的内容摘要，3-8个关键词，标题、内容摘要和关键词必须翻译成英文；内容摘要的写作应力求简明扼要，具有相对独立性和完整性，客观反映论文的主要内容和研究方法。摘要的写作应避免出现“本文论述了......对......有重要意义”之类的用语。 2、关键词是反映论文主要内容的名词性术语一般每篇可选3-8个，应尽量从《汉语主题词表》中选用。未被词表收录的新学科、新技术中的重要术语和地区、人物、文献等名称，也可作为关键词标注。关键词应以与正文不同的字体字号编排在摘要下方。多个关键词之间用分号分隔。中英文关键词应一一对应。中文关键词前以“关键词：”或“[关键词]”作为标识；英文关键词前以“Key words：”作为标识。 3、稿件内容力求观点鲜明、选题新颖、逻辑严密、下笔有据、论证充分、结合实际、深人浅出、文字通顺、言简意赅。当然如果您的论文达不到上述的要求，“核心论文发表网”的站长将利用其职业（经济学编辑）特长和同学关系网（编辑联盟）负责帮您编辑，直至发表。 （二）标题 标题应简明、具体、确切，能概括文章的特定内容，符合编制题录、索引和检索的有关原则，一般不超过20个字。必要时可加副标题，用较小字号另行起排。文章标题及作者姓名单位均须翻译成英文。 （三）注明投稿日期 是指您投稿的日期。示例：收稿日期：2004-02-18 （四）基金项目 获得基金资助产出的文章应以“基金项目：”或“[基金项目]”作为标识注明基金项目名称，并在圆括号内注明项目编号。多项基金项目应依次列出，其间以分号隔开。示例：基金项目：国家社会科学规划基金资助项目（2004BJL001）（五）作者简介 作者的姓名、出生年、性别、民族（汉族可省略）、籍贯、职称、学位等作出介绍，其前以“作者简介：”作为标识。一般排在篇首页地脚，置于投稿日期（或基金项目）之后。同一篇文章的其他主要作者简介可以在同一“作者简介：”标识后相继列出，其间以分号隔开。 示例： 作者简介：张三（1960-），男，汉族，福建厦门人，厦门大学会计系教授，博士。 （六）正文 文内标题力求简短、明确，题末不用标点符号（问号、叹号、省略号除外）。层次不宜过多，一般不超过5级。大段落的标题居中排列，可不加序号。层次序号可采用一、（一）、1、（1）、1）；不宜用①，以与注号区别。文中应做到不
论文写作总结 题目： 题目应该覆盖主要目的或者信息，也应该吸引读者，不能太长。并且应该避免附标题。摘要： 用第三人称写，说明文章目的，方法，结果和结论，不应出现“本文”，“我们”“作者”字眼，也不要有“首次”，“最后”，“简单”，“主要”和“次要”等修饰词。 摘要四要素：研究工作的目的，方法，结果，结论 引用别人的话： 单一作者时：某某（1987）提出。。。。。。；某某(1981)的研究发现。。。。。； 几个作者时：国内一些学者（某某，1997；，某某，1984；某某，1845）的研究。。。。。； 一些研究者（某某，1998主张；某某，1874）主张。。。。。 MIT 的Arthur Smith 教授他提醒我尽量不要使用被他称为“投机性”词汇的一些词，如“obviously”，“probably”，“certainly”，“undoubtedly”等。因为使用表示可能性的词汇，这说明你不能无法证明你的观点，而是在进行假设和猜测。可信度自然非常低。 引言部分： 引言部分主要回答为什么研究，介绍论文背景，相关领域研究历史与现状，本文目的意义，创新在什么地方（有待解决的问题） 引言第一句号很重要，应当明确提出这篇文章的目的，并且表示目的很重要。 引言包含的要素（老外写） 1文章的目的；2对目的的证实（为什么整个工作重要）；3背景，其他人已经做了的，怎样去做的，我们以前已经做的；4指导作者：作者应该在文章中看到什么?文章中让人感兴趣的关键点是什么？我们使用了什么，我们使用什么方法来做的？本文采用的基本方法和假设5概括和总结：作者所期望的结论是什么？ 编辑对引言一般意见：引言是否充分反映了当前存在的问题，并阐述了该项研究的必要性？编辑部对参考文献一般意见：参考文献是否遗漏了近期重要文献？ 结果： 不要罗列结果，要分析，结果间要有逻辑联系。 Plant and soil杂志主编提醒注意：引言的最后以：你研究工作的目的和提出一个清楚的假设作为结尾。并且指出，事实上对。。的研究之前没有人做个并不是一个好的理由。因为你的研究在逻辑上很可能是跟随过去的研究。 引言写作注意事项： 1好的引言相当于文章成功一半，最重要是保持鲜明的层次感和极强的逻辑性，层层递进关系。首先：阐述自己研究领域的基本内容，要尽量的简洁明了，不要罗里罗嗦一大堆。一些显而易见的知识要用概括性的而不是叙述性的语言来描述。 2其次：接下来就是引言的重头戏之一：文献的总结回顾。要特别着重笔墨来描写。一方面要把该领域内的过去和现在的状况全面的概括总结出来，不能有丝毫的遗漏，特别是最新的进展和过去经典文献的引用。这是两个最容易出现的问题，应该是我们要极力避免的。 3再次：然后就是分析过去研究的局限性并且阐明自己研究的创新点，这是整个引言的高潮所在，所以更是要慎之又慎。阐明局限要客观。在阐述自己的创新点时，要仅仅围绕过去研究的缺陷性来描述，完整而清晰的描述自己的解决思路，并且文章摊子不要铺的太大。创新
期刊论文写作格式要求指导 标准论文字体格式 标准论文字体格式的第一页： 论文题目(黑体、居中、三号字) (空一行) 作者(宋体、小三) (空一行) [摘要](四号黑体)空一格打印内容(四号宋体，200-300字)……………………… (空一行) [关键词](四号黑体)关键词内容(小四号宋体、每两个关键词之间空两格) 标准论文字体格式的第二页： 目录(居中、四号黑体) (空一行) (空一行) 引言(小四号宋体)…………………………………………………页码(小四号宋体) 一、标题(小四号宋体)……………………………………………………………………………页码(小四号宋体)
1.(小标题)(小四号宋体)………………………………………………………………页码(小四号宋体) (1)(下级标题)(小四号宋体)………………………………………………………页码(小四号宋体) 二、(标题)(小四号宋体)…………………………………………………………………页码(小四号宋体) 1.(小标题)(小四号宋体)……………………………………………………………………页码(小四号宋体) (1)(下级标题)(小四号宋体)…………………………………………………………页码(小四号宋体) 参考文献(小四号宋体)……………………………………………………………………………页码(小四号宋体) 附录(小四号宋体)………………………………………………………………………………………页码(小四号宋体) 致谢语(小四号宋体)………………………………………………页码(小四号宋体) 英文题目、摘要、关键词(小四号宋体)………………………………………………页码(小四号宋体) 引言(居中、四号黑体) (空一行) (空一行)
核心期刊论文发表格式有哪些？中文核心期刊论文格式要求限在“CSSCI检索期刊2003”、“中文核心期刊(学术刊物，非杂志类)北大2004版”期刊发表的论文格式 (一)基本要求 1、您的论文请用WORD等文本编辑软件打好后以附件的形式发到我们的电子信箱中，便于我们及时下载进行编辑。来稿以5000字为宜，并附100-200字的内容摘要，3-8个关键词，标题、内容摘要和关键词必须翻译成英文;内容摘要的写作应力求简明扼要，具有相对独立性和完整性，客观反映论文的主要内容和研究方法。摘要的写作应避免出现“本文论述了......对......有重要意义”之类的用语。 2、关键词是反映论文主要内容的名词性术语一般每篇可选3-8个，应尽量从《汉语主题词表》中选用。未被词表收录的新学科、新技术中的重要术语和地区、人物、文献等名称，也可作为关键词标注。关键词应以与正文不同的字体字号编排在摘要下方。多个关键词之间用分号分隔。中英文关键词应一一对应。中文关键词前以“关键词：”或“[关键词]”作为标识;英文关键词前以“Key words：”作为标识。 3、稿件内容力求观点鲜明、选题新颖、逻辑严密、下笔有据、论证充分、结合实际、深人浅出、文字通顺、言简意赅。当然如果您的论文达不到上述的要求，“核心论文发表网”的站长将利用其职业(经济学编辑)特长和同学关系网(编辑联盟)负责帮您编辑，直至发表。 (二)标题 标题应简明、具体、确切，能概括文章的特定内容，符合编制题录、索引和检索的有关原则，一般不超过20个字。必要时可加副标题，用较小字号另行起排。文章标题及作者姓名单位均须翻译成英文。 (三)注明投稿日期
北大核心期刊论文发表经验汇总 期刊之家网论文能定制积八年经验汇上千案例 抓紧时间下载以免下线无处寻觅………… 发表中文核心期刊及其更高级刊物的作者投稿经验推介，包括期刊之家网对多位期刊论文写作高手的约访。期刊之家网将隔段时间陆续推出经验谈系列，也力图能将各行各业、各类期刊的论文写作经验及知识谈都囊括在内。 一般每隔一空行后为不同作者的经验介绍，请那些投稿经验不丰富的朋友耐心看完，一定会对你发文章大有帮助 期刊之家网友经验谈之（一）：投稿中遇到的问题 我个人认为，现在的中文学报文章都不怎么好： 1、投稿周期长、关系稿多，还收很贵的版面费，不合算； 2、同样影响的外文期刊，不收版面费， 3、要投中文稿件，英文摘要要写好，中文表达要清楚，只要说明白自己做的是什么、为什么这么做、得到了什么结果、同国内外同样工作的比较情况，引用本刊的文章。 期刊之家网友经验谈之（二）：实验出真知，耐心得正果 2004年下半年我进的实验室，先跟在一位师兄后学习了两个月，
之后我就独立做实验了接手了一个方向，是关于三唑类化合物的合成。刚开始真是不顺利——午睡也取消了、通宵也熬了、饭量也降了，一直没作出来，而同实验室的同学已作出了十几个新化合物。那时真是郁闷！！一直到05年春节后，我终于做出来了！做合成的都知道，只要路线打通了，后面的接不同的取代基就很简单了！！等到我做了8个新化合物时，导师又把安排到能源组搞催化剂，同时要求我把这段时间所作的工作总结一下，准备发篇文章！ 对于没发过文章的人来说，难度可想而知了！！首先，看相应的期刊——别人到底是什么格式、该期刊的特点和侧重点等等。那段时间，前后大约一个月，初稿大概花了20天，之间导师让修改了4遍，刚开始是修改自己的表达不够专业，而用的通俗用语；等框架构建起来了，标点、大小写、中英文表达等等这些小问题！当然，这期间没少受导师的批评——基础太差，文献看的太少，动手能力很是欠缺！！不过幸运的是，最终该文也发表了！！ 经过这次发文章之后，自己对写文章不是很恐惧了，而且也了解了其中的程序。但唯感欠缺的是如何准确的给自己的工作定位，能够正确选中所投期刊的档次和种类！！ 首先实验结果要争真实可靠。 其次格式一定要符合所投刊物的要求，这很重要。 语言方面要精炼，表述意思要清楚，整体简洁而思路清晰。 参考文献的引用要准确和适当，中文的期刊，一般要求你的参考文献中的要投的这个刊物的文章的参考文献占一定的比例。
SCI期刊论文写作的方法和步骤 SCI期刊论文写作的方法和步骤 医学论文是传播精神文明，推进科学发展的载体，是医学科研和临床的书面总结、交流和提高医疗技术水平的重要工具。具体来说医学论文是医药卫生科研工作人员通过科学思维运用书面语言，准确概括医学科研过程，客观表达科学实验或临床观测结果的论证文章。医学论文写作时作者对其学术成果与科技信息运用文字、数据、符号、图表等加以表达的创造性思维活动，同时对其进行概括并上升为理论性的文章。医学论文水平的高低，直接影响科研成果的价值和水平，也是科研工作者能力和作风的具体反应，因此撰写医学论文是医学工作者的基本功之一，也是医学科研不可缺少的组成部分。医学论文属于科技论文的一种，由于医学专业的特点，医学论文写作有其惯用格式和特点。文章将就常见的医学论文写作的有关问题进行简单的阐述。 一、准备资料 资料的准备包括直接和间接资料的收集、资料的整理以及资料的评价与取舍等方面。直接资料的主要有①现有的医药卫生工作记录文件，如病案、健康查体记录、专业防治机构的记录；②经常性的统计报表如疫情报告表、医院工作年报表、职业病报告卡、出生与死亡登记；③专门试验研究或进行特殊检查所得的资料；④实际调查所得的资料如流行病、地方病、职业病的调查等。间接资料的有①参考书包括各种医学教科书、专注学术会议论文集；②期刊；③工具书医学辞典、医学百科全书、年鉴、文献索引、主题索引等。而资料收集的方法主要有：①进行相关的调查；②对研究对象进行持续客观的观察；③进行相关的实验研究，以及④文献检索扥更，其中较为重要的文献检索部分，因为必须对本课题研究的国内外学术动态及相关资料由全面的了解，才能为论文的撰写开拓思路，提供理论依据。具体的做法是根据研究课题选择检索工具，确定检索方法，查阅原始文献，检索过程中应特别注意这几方面的内容：在方法上沿用前人研究结论与已不同需要加以说明的，前人对本文所研究的问题存在争议和正在探讨的。资料的整理过程是对资料进行科学综合以及加工过程，整理原始资料时，要重点保存那些能阐明正式研究涉及中提出的假设或观点部分，是原始资料得以系统化、条理化，其中包括对统计数据的检查审核，分组涉及，归纳汇总，以及图表的应用等。在科研实践中，由于各种主客观原因，收集到的资料难免有失偏颇，这就需要对资料的真实性准确性进行分析和比较，以求去芜存菁，去伪存真。对于资料的评价和取舍，一般会出现三种情况：资料可以说明说明研究结果的；资料基本完整，但不够充分的；与原研究结果基本相悖的。对于最后一种情况需认真对待，应仔细寻找原因，检查设计是否考虑周详，实验方法是否妥当，数据是否可靠，若都不存在，则需要改变思路，建立新的假设，切不可为片面追求符合研究结果而随意丢弃原始数据，主观臆造，因
核心期刊论文发表格式与核心期刊论要求 网友经验谈之（一）：中文期刊投稿经验谈 本人认为应注意以下几点： 1、选题得当，突出主题。论文的题目好比人的眼睛，通过题目应让读者猜到你的内容，吸引读者 2、中英文摘要简练。有些作者喜欢把论文里的东西都在摘要里有所展示，这是错误的。作者应在摘要里把你的实验目的、方法、结论以及意义说清就可，语言越简练越好。 3、前言或者绪论。中国人常说文章是"龙头、虎尾、猪肚子"。好文章一般前言都要写的非常好，要求语言既要简练，而且能把你的实验研究的目的、国内外研究现状以及实验的意义都要给读者表达清楚。 4、写好结论。写结论要注意前后呼应，一定和摘要以及文章对应好。注意文章没有讨论过的问题和结论，不要在结论中出现。 好就顶一下 以个人的经验，有以下几条紧要的： 1、选题要小，开掘要深；不要题目很大，内容却很单薄。 2、要有时代感，包括研究古典和理工的文章；你的文章有时代感，编者和读者才会对你的文章敏感，采用率才会高。 3、文笔要流畅；前言不搭后语，编辑和读者难以卒读，自然不会被采用。 4、结构要严谨，先说什么后说什么要摆布清楚；如有小标题，其文字最好整齐一点。 5、给编辑写一封短信，恳求人家斧正，并写清你的基本情况。 我认为: 中文期刊的周期挺长 前言摘要和结论要写好,就要查很多文献
参考文献多引用本期刊的会比较好 文章所研究的内容是目前的热点内容就行,文章内容的深度自己控制,不同的深度选择投不同的学报. 一定表达清楚自己的文章和别人的文章存在那些不同或者创新,哪怕只有一个闪光点. 文字要认真组织,否则审稿的老师不会仔细看. 文章中间的插图一定要认真画. 1. 写作前要读好书、翻阅大量资料、注意学术积累，在这个过程中，还要注重利用网络，特别是一些专业数据库 2."选题新、方法新、资料新"的三新原则（老板教导的） 3."新题新做"和"小题大做" 4. 写作后，要进行校对、调整和仔细琢磨，还要注意新资料的及时补充，最好放几天再看，或请同学来看，这样便于发现问题。 5. 我老板把中国的刊物按地区分为三类，即东部沿海地区、中部地区和西部地区，建议我们多在东部地区刊物上发文章，这些刊物水平高、跟读者联系很好且基本不收版面费。 如果是三人左右的联合署名，中稿几率大于单独署名！这个显示是团体智慧，仁慈的编辑不会轻易枪毙它。呵呵 现在一般综述性的文章在核心期刊以上的刊物中比较难发。试验性质的相对好发！ 如果你的选题是大家都比较熟悉，相对就难发些，你做的方法和结论都要有一定的新意。 如果是研究人员比较少的方向，那就容易发了，呵呵！ 至于格式，一定要符合所投刊物要求。另外就是找你相关专业国内最好的一级期刊，多看些文章， 学学如何写好。 （1）看看你发表的刊物是否接受你投的paper，这点非常重要。 （2）内容要充实，具体，有新意 （3）实验部分要详实，可靠
全文1.5倍行距 标题标题标题标题（二号宋体，居中，加粗） 【说明：标题是能反映论文中特定内容的恰当、简明的词语的逻辑组合，应避免使用含义笼统、泛指性很强的词语 （一 般不超过20字，必要时可加副标题，尽可能不用动宾结构，而用名词性短语，也不用“……的研究”，“基于……” ）o]作者11,作者22,作者31,……（四号楷体,居中） （1.学校院、系名，省份城市邮编；2.单位名称，省份城市邮编）（五号楷体，居中）摘要：（小五号黑体，缩进两格）摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容……（小五号楷体） 【说明：摘要应具有独立性和自含性，即不阅读全文，就能获得必要的信息。要使用科学性文字和具体数据，不使用文学性修饰词；不使用图、表、参考文献、复杂的公式和复杂的化学式，非公知公用的符号或术语；不要加自我评价，如 “该 研究对…有广阔的应用前景”，“目前尚未见报道”等。摘要能否准确、具体、完整地概括原文的创新之处，将直接决定论文是否被收录、阅读和引用。摘要长度200?300字。摘要一律采用第三人称表述，不使用“本文”、“文章”、“作者”、“本研究”等作为主语。】 关键词：（小五号黑体，缩进两格）关键词；关键词；关键词；关键词（小五号楷体，全角分号隔开） 【说明：关键词是为了便于作文献索引和检索而选取的能反映论文主题概念的词或词组，每篇文章标注3?8个关键词， 词与词之间用全角分号隔开。中文关键词尽量不用英文或西文符号。注意：关键词中至少有两个来自EI控词表。一般高校 数字图书馆均可查到。】 中图分类号：（小五号黑体，缩进两格）TM 344.1（小五号Times New Roman体，加粗）文献标志码：（小五号黑体，前空四格）A（小五号Times New Roman体，加粗） 【说明：请查阅中国图书馆分类法（第4版）（一般要有3位数字，如TM 344.1）】 引言（四号宋体，加粗，顶格） 弓I言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引引言引言引言引言引言引言引言引言（五号宋体，段前前缩进两格） 【说明：引言作为论文的开端，主要回答“为什么研究”这个问题。它简明介绍论文的背景、相关领域的前人研究历史与现状，以及著者的意图与分析依据，包括论文的追求目标、研究范围和理论、技术方案的选取等。引言应言简意赅，不要等同于文摘，或成为文摘的注释。引言中不应详述同行熟知的，包括教科书上已有陈述的基本理论、实验方法和基本方程的推导。如果在正文中采用比较专业化的术语或缩写用词时，应先在引言中定义说明。引言一般不超过800字，且不计入章节
论文写作技巧_写作指导 论文写作技巧 当我们对一个问题研究之后,如何将其展现于众人面前是一个重要的工作.在这里我们结合具体的事例,给大家介绍科研的一个重要部分枣论文的一般格式及其注意事项.当然,要写出一篇好的论文,绝不是单单这么一个简要的介绍就够了,还需自己多写,多练. 随着科学技术的发展,越来越多的学者涉及到学术论文的写作领域,那么怎样写学术论文,学术论文写作是怎样要求的,格式如何,下面就介绍一下学术论文的写作,希望能对您论文写作有所帮助. (一)题名(title,topic) 题名又称题目或标题.题名是以最恰当,最简明的词语反映论文中最重要的特定内容的逻辑组合. 论文题目是一篇论文给出的涉及论文范围与水平的第一个重要信息,也是必须考虑到有助于选定关键词不达意和编制题录,索引等二次文献可以提供检索的特定实用信息. 论文题目十分重要,必须用心斟酌选定.有人描述其重要性,用了下面的一句话:论文题目是文章的一半. 对论文题目的要求是:准确得体:简短精炼:外延和内涵恰如其分:醒目. 对这四方面的要求分述如下. 1.准确得体要求论文题目能准确表达论文内容,恰当反映所研究的范围和深度. 常见毛病是:过于笼统,题不扣文.关键问题在于题目要紧扣论文内容,或论文内容民论文题目要互相匹配,紧扣,即题要扣文,文也要扣题.这是撰写论文的基本准则. 2.简短精炼力求题目的字数要少,用词需要精选.至于多少字算是合乎要求,并无统一的硬性规定,一般希望一篇论文题目不要超出20个字,不过,不能由于一味追求字数少而影响题目对内容的恰当反映,在遇到两者确有矛盾时,宁可多用几个字也要力求表达明确. 若简短题名不足以显示论文内容或反映出属于系列研究的性质,则可利用正,副标题的方法解决,以加副标题来补充说明特定的实验材料,方法及内容等信息使标题成为既充实准确又不流于笼统和一般化. 3.外延和内涵要恰如其分外延和内涵属于形式逻辑中的概念.所谓外延,是指一个概念所反映的每一个对象;而所谓内涵,则是指对每一个概念对象特有属性的反映. 命题时,若不考虑逻辑上有关外延和内涵的恰当运用,则有可能出现谬误,至少是不当. 4.醒目论文题目虽然居于首先映入读者眼帘的醒目位置,但仍然存在题目是否醒目的问题,因为题目所用字句及其所表现的内容是否醒目,其产生的效果是相距甚远的. 有人对36种公开发行的医学科持期刊1987年发表的论文的部分标题,作过统计分析,从中筛选100条有错误的标题.在100条有错误的标题中,属于省略不当错误的占20%;属于介词使用不当错误的占12%).在使用介词时产生的错误主要有: ①省略主语枣第一人称代词不达意后,没有使用介词结构,使辅助成分误为主语; ②需要使用介词时又没有使用; ③不需要使用介词结构时使用.属主事的错误的占11%;属于并列关系使用不当错误的占9%;属于用词不当,句子混乱错误的各占9%,其它类型的错误,如标题冗长,文题不符,重复,歧意等亦时有发生. (二)作者姓名和单位(author and department) 这一项属于论文署名问题.署名一是为了表明文责自负,二是记录作用的劳动成果,三是便于读者与作者的联系及文献检索(作者索引).大致分为二种情形,即:单个作者论文和多作者文.后者按署名顺序列为第一作者,第二作者厖.重要的是坚持实事求是的态度,对研究工作与论文撰写实际贡献最大的列为第一作者,贡献次之的,列为第二作者,余类推.注明作者所在单位同样是为了便于读者与作者的联系. 共2页，当前第1页12论文写作技巧相关内容:英语演讲稿的写作技巧英语演讲稿首先开头要开门见山，既要一下子抓住听众又要提出你的观点，中间要用各种方法和所准备的材料说明、支持你的论点，感染听众，然后在结尾加强说明论点或得出结论，结束演讲。 如何增强演讲稿写作的现场感
全文1.５倍行距 标题标题标题标题(二号宋体，居中,加粗)【说明： 标题是能反映论文中特定内容的恰当、简明的词语的逻辑组合,应避免使用含义笼统、泛指性很强的词语(一般不超过20字，必要时可加副标题,尽可能不用动宾结构,而用名词性短语，也不用“……的研究”，“基于……”)。】作者11，作者2２,作者31,……(四号楷体,居中) (1. 学校院、系名,省份城市邮编;2．单位名称,省份城市邮编）(五号楷体,居中) 摘要：(小五号黑体,缩进两格）摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容摘要内容……(小五号楷体） 【说明：摘要应具有独立性和自含性，即不阅读全文，就能获得必要的信息。要使用科学性文字和具体数据,不使用文学性修饰词；不使用图、表、参考文献、复杂的公式和复杂的化学式，非公知公用的符号或术语;不要加自我评价，如“该研究对…有广阔的应用前景”，“目前尚未见报道”等。摘要能否准确、具体、完整地概括原文的创新之处，将直接决定论文是否被收录、阅读和引用。摘要长度200~30０字。摘要一律采用第三人称表述，不使用“本文”、“文章”、“作者”、“本研究”等作为主语。】 关键词:(小五号黑体,缩进两格)关键词；关键词;关键词;关键词(小五号楷体，全角分号隔开) 【说明:关键词是为了便于作文献索引和检索而选取的能反映论文主题概念的词或词组，每篇文章标注3~8个关键词,词与词之间用全角分号隔开。中文关键词尽量不用英文或西文符号。注意:关键词中至少有两个来自EＩ控词表。一般高校数字图书馆均可查到。】 中图分类号:(小五号黑体，缩进两格）ＴＭ3４4.１(小五号Ｔｉmｅs Ｎew Roｍaｎ体，加粗)文献标志码：（小五号黑体，前空四格)A(小五号Tｉmes Ｎｅｗ Romａn体，加粗） 【说明：请查阅中国图书馆分类法(第4版）(一般要有3位数字，如ＴM 344.1)】 引言(四号宋体，加粗,顶格） 引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引言引引言引言引言引言引言引言引言引言……(五号宋体,段前前缩进两格) 【说明:引言作为论文的开端，主要回答“为什么研究”这个问题。它简明介绍论文的背景、相关领域的前人研究历史与现状，以及著者的意图与分析依据，包括论文的追求目标、研究范围和理论、技术方案的选取等。引言应言简意赅,不要等同于文摘，或成为文摘的注释。引言中不应详述同行熟知的,包括教科书上已有陈述的基本理论、实验方法和基本方程的 收稿日期： 基金项目：省部级以上基金资助项目(必须要有编号) 作者简介：姓名(出生年-)，性别，职称，学位，主要研究方向，(Tel)；(E-mail)。 导师姓名(联系人)，性别，职称，硕(博)士生导师，(Tel)；(E-mail)。
中国核心期刊论文格式要求 一、限在“CSSCI检索期刊2003”、“中文核心期刊（学术刊物，非杂志类）北大2004版”期刊发表的论文格式 （一）基本要求 1、您的论文请用WORD等文本编辑软件打好后以附件的形式发到我们的电子信箱lunwen888@https://www.doczj.com/doc/f818242127.html, 中，便于我们及时下载进行编辑。来稿以5000字为宜，并附100-200字的内容摘要，3-8个关键词，标题、内容摘要和关键词必须翻译成英文；内容摘要的写作应力求简明扼要，具有相对独立性和完整性，客观反映论文的主要内容和研究方法。摘要的写作应避免出现“本文论述了......对......有重要意义”之类的用语。 2、关键词是反映论文主要内容的名词性术语一般每篇可选3-8个，应尽量从《汉语主题词表》中选用。未被词表收录的新学科、新技术中的重要术语和地区、人物、文献等名称，也可作为关键词标注。关键词应以与正文不同的字体字号编排在摘要下方。多个关键词之间用分号分隔。中英文关键词应一一对应。中文关键词前以“关键词：”或“[关键词]”作为标识；英文关键词前以“Key words：”作为标识。 3、稿件内容力求观点鲜明、选题新颖、逻辑严密、下笔有据、论证充分、结合实际、深人浅出、文字通顺、言简意赅。当然如果您的论文达不到上述的要求，“核心论文发表网”的站长将利用其职业（经济学编辑）特长和同学关系网（编辑联盟）负责帮您编辑，直至发表。（二）标题 标题应简明、具体、确切，能概括文章的特定内容，符合编制题录、索引和检索的有关原则，一般不超过20个字。必要时可加副标题，用较小字号另行起排。文章标题及作者姓名单位均须翻译成英文。 （三）注明投稿日期 是指您投稿的日期。示例：收稿日期：2004-02-18 （四）基金项目 获得基金资助产出的文章应以“基金项目：”或“[基金项目]”作为标识注明基金项目名称，并在圆括号内注明项目编号。多项基金项目应依次列出，其间以分号隔开。示例：基金项目：国家社会科学规划基金资助项目（2004BJL001） （五）作者简介 作者的姓名、出生年、性别、民族（汉族可省略）、籍贯、职称、学位等作出介绍，其前以“作者简介：”作为标识。一般排在篇首页地脚，置于投稿日期（或基金项目）之后。同一篇文章的其他主要作者简介可以在同一“作者简介：”标识后相继列出，其间以分号隔开。示例： 作者简介：张三（1960-），男，汉族，福建厦门人，厦门大学会计系教授，博士。 （六）正文 文内标题力求简短、明确，题末不用标点符号（问号、叹号、省略号除外）。层次不宜过多，一般不超过5级。大段落的标题居中排列，可不加序号。层次序号可采用一、（一）、1、（1）、1）；不宜用①，以与注号区别。文中应做到不背题，一行不占页，一字不占行。 （七）参考文献 参考文献著录的条目以小于正文的字号编排在文末。其格式为： 专著、论文集、学位论文、研究报告[序号]主要责任者，文献题名[文献类型标识].出版地：出版者，出版年，起止页码（任选）。 示例： [1]周振甫.周易译注[M].北京：中华书局，1991.
SCI期刊论文写作的方法和步骤 一、准备资料 包括各种医学教科书、专注学术会议论文集；②期刊；③工具书医学辞典、医学百科全书、年鉴、文献索引、主题索引等。而资料 收集的方法主要有：①进行相关的调查；②对研究对象进行持续客 观的观察；③进行相关的实验研究，以及④文献检索扥更，其中较 为重要的文献检索部分，因为必须对本课题研究的国内外学术动态 及相关资料由全面的了解，才能为论文的撰写开拓思路，提供理论 依据。具体的做法是根据研究课题选择检索工具，确定检索方法， 查阅原始文献，检索过程中应特别注意这几方面的内容：在方法上 沿用前人研究结论与已不同需要加以说明的，前人对本文所研究的 问题存在争议和正在探讨的。资料的整理过程是对资料进行科学综 合以及加工过程，整理原始资料时，要重点保存那些能阐明正式研 究涉及中提出的假设或观点部分，是原始资料得以系统化、条理化，其中包括对统计数据的检查审核，分组涉及，归纳汇总，以及图表 的应用等。在科研实践中，由于各种主客观原因，收集到的资料难 免有失偏颇，这就需要对资料的真实性准确性进行分析和比较，以 求去芜存菁，去伪存真。对于资料的评价和取舍，一般会出现三种 情况：资料可以说明说明研究结果的；资料基本完整，但不够充分的；与原研究结果基本相悖的。对于最后一种情况需认真对待，应 仔细寻找原因，检查设计是否考虑周详，实验方法是否妥当，数据 是否可靠，若都不存在，则需要改变思路，建立新 的假设，切不可为片面追求符合研究结果而随意丢弃原始数据，主观臆造，因为这种情况往往提示可能有新的发现孕育其中。 二、文章布局 构思是对整个文章的结构思维。主要考虑三个方面：文章从何处说起才能切题，有吸引力；阐述推理等实质性文题如何展开才能全