a novel model to predict cutaneous finger blood flow via finger and rectal temperatures

A Novel Model to Predict Cutaneous Finger Blood Flowvia Finger and Rectal Temperatures

ANDRES E. CARRILLO,* STEPHEN S. CHEUNG,� AND ANDREAS D. FLOURIS*,�

*FAME Laboratory, Institute of Human Performance and Rehabilitation, Centre for Research and Technology Thessaly, Trikala, Greece;�Department of Kinesiology, Brock University, St. Catharines, Ontario, Canada; �Department of Research and Technology Development, Biomnic

Ltd., Trikala, Greece

Address for correspondence: Andreas D. Flouris, FAME Laboratory, Institute of Human Performance and Rehabilitation, Centre for Research and

Technology Thessaly, Karies, Trikala 42100, Greece. E-mail: [email protected]

Received 8 June 2011; accepted 19 September 2011.

ABSTRACT

Objectives: To generate a model that predicts fingertip blood

flow (BFf) and to cross-validate it in another group of subjects.

Methods: We used fingertip temperature (Tf), forearm

temperature minus Tf (TFor-f), rectal temperature (Tre), and their

changes across time (lagT) to estimate BFf. Ten participants (six

male, four female) were randomly divided into ‘‘model’’ and

‘‘validation’’ groups. We employed a passive hot–cold water

immersion protocol during which each participant’s core

temperature increased and decreased by 0.5�C above ⁄ below

baseline during hot ⁄ cold conditions, respectively. A hierarchical

multiple linear regression analysis was introduced to generate

models using temperature indicators and lagT (independent

variables) obtained from the model group to predict BFf

(dependent variable).

Results: Mean BFf (109.5 ± 158.2 PU) and predicted BFf (P-BFf)

(111.4 ± 136.7 PU) in the model group calculated using the

strongest (R2 = 0.766, p < 0.001) prediction model [P-BFf =

Tf · 19.930 + lag4Tf · 74.766 + lag4Tre · 124.255 – 447.474] were

similar (p = 0.6) and correlated (r = 0.880, p < 0.001). Auto-

regressive integrated moving average time-series analyses

demonstrated a significant association between P-BFf and BFf

(R2 = 0.381; Ljung–Box statistic = 8.097; p < 0.001) in the

validation group.

Conclusions: We provide a model that predicts BFf via two

practical temperature indicators that can be implemented in both

clinical and field settings.

Key words: finger perfusion, skin temperature, core temperature,

forearm temperature, cold-induced vasodilation

Abbreviations used : ARIMA, auto-regressive integrative moving

average; BFf, fingertip blood flow; P-BFf, predicted fingertip blood

flow; PU, perfusion units; R2, coefficient of variation; Tf, fingertip

temperature; TF or -f, forearm temperature minus fingertip

temperature; lagT, temperature lag; Tre, rectal temperature.

Please cite this paper as: Carrillo, Cheung, and Flouris (2011). A Novel Model to Predict Cutaneous Finger Blood Flow Via finger and Rectal Temperatures.

Microcirculation 18(8), 670–676.

INTRODUCTION

Thermoregulatory measurements are widely important in

both clinical and field settings [4,12], and are necessary

components for various research and bioengineering appli-

cations [6,7]. However, the high equipment cost and the

extensive requirements from participants necessary for

accurate measurements introduce complications that may

prevent practical use. In this light, measurements from

practical and inexpensive techniques have been used to

estimate data recorded from direct techniques. For exam-

ple, measurements of skin-surface temperature gradients

(forearm temperature minus fingertip temperature [TFor-f])

have been associated with fingertip blood flow (BFf) in

steady-state conditions [17]. To our knowledge, however,

no studies have attempted to predict BFf during dynamic

fluctuations in thermal balance, which may introduce com-

plications with acquiring accurate blood flow predictions.

It is well known that the measured fingertip temperature

(Tf) is a slow indicator of what occurs deeper in the tissue

[2]. Hence, problems arise when Tf measurements are

solely used to represent BFf changes within the microcircu-

latory vessels of the finger. Interpretation of the results may

be inaccurate particularly when a strong thermal stimulus

is introduced that induces an immediate change in BFf,

which is associated with a delayed Tf response. For

example, cold-induced vasodilation is commonly reported

as a cyclical increase in BFf during exposure to cold,

and has been assessed often by our lab [8] and others

[3,13,16] through Tf data. Given the delayed Tf response,

DOI:10.1111/j.1549-8719.2011.00136.x

Original Article

670 ª 2011 John Wiley & Sons Ltd, Microcirculation, 18, 670–676

measurements of Tf that define the occurrence of cold-

induced vasodilation should be complemented by estimated

measurements of BFf when direct measurements are

unavailable or inappropriate, such as during field testing or

athletic competition. It is important therefore to address

the commonly reported delays in Tf that introduce incon-

sistencies regarding the timing of BFf estimations [14], to

extend the link between Tf and BFf beyond the simple posi-

tive relationship previously reported [17].

Our aims in this study were to generate a model that

provides an accurate prediction of BFf via practical and

inexpensive indicators, and to cross-validate it in an inde-

pendent group of subjects. The use of Tf as an indicator of

BFf was deemed vital a priori. In addition, we examined

the potential predictive efficacy of TFor-f due to its reported

association with BFf [17]. While we previously reported

that rectal temperature (Tre) alone was not associated with

reflex alterations in BFf, when used in combination with

peripheral skin temperatures, we have found that it shows

a strong association with BFf [9]. As such, the predictive

efficacy of Tre was also examined. More importantly, to

correct for the response delay of the three chosen indica-

tors (i.e., Tf, TFor-f, and Tre) to reflect fluctuations in BFf,

their changes across time were also evaluated as potential

valuable indicators. Multiplying the three chosen indicators

by their changes across time (e.g., Tf · [Tf at time t minus

Tf at time t ) 1]) converts their expected small and slow

alterations into the large and rapid fluctuations usually

observed in BFf. We speculated that a combination of the

three temperature indicators and their changes across time

would allow for an accurate estimation of BFf.

MATERIALS AND METHODS

Participants and ProceduresThe experimental protocol conformed to the standards set

by the Declaration of Helsinki, and was approved by the

ethical review board at Dalhousie University. Ten partici-

pants (six males, four females; age: 25.8 ± 5.3 years; height:

173.9 ± 6.8 cm; weight: 76.6 ± 15.0 kg; percent body fat:

15.3 ± 9.9%) volunteered for this study. The sample was

randomly divided into ‘‘model’’ (five males, three females)

and ‘‘validation’’ (one male, one female) groups. One-way

ANOVA showed no differences in age, height, weight, or

percent body fat between the model and validation groups

(p > 0.05). All participants were screened for Raynaud’s

phenomenon and high blood pressure. Female subjects

completed testing during the early follicular phase (days 1–

6) of their menstrual cycle, and were screened for preg-

nancy prior to participation. Following a full explanation

of procedures, written informed consent was obtained from

all participants. During a familiarization session conducted

three days prior to testing, participants were given a

detailed verbal description of the protocol, followed by

extensive familiarization with all data collection procedures

and instruments. Anthropometric measurements were also

obtained at this time.

Participants were instructed to abstain from alcohol and

caffeine for the 12 hours prior to data collection, as well as

physical activity and excessive stressors, such as exposure to

extreme hot or cold temperatures for the 48 hours prior to

participation. Immediately prior to data collection, all par-

ticipants changed into appropriate water immersion attire.

Males wore a regular one-piece swim suit and females wore

either a one (full)- or two-piece swim suit. To normalize

testing conditions at baseline all participants were asked to

sit for 15 minutes in a ventilated and air-conditioned ther-

moneutral environment (ambient temperature: 25�C; rela-

tive humidity: 40%). To record a wide range of BFf, Tf,

TFor-f, and Tre values without the application of local stim-

uli, we employed a thermal manipulation protocol exten-

sively used by our group to assess peripheral adaptations to

abrupt changes in thermal homeostasis [7,9,11]. Partici-

pants entered a water tank maintained at 42�C water tem-

perature and passively rested until their Tre increased by

0.5�C above baseline. Thereafter, they exited the water tank

and entered a different water bath maintained at 12�C

water temperature until their Tre was decreased by 0.5�C

below baseline. Ad libitum water intake was permitted dur-

ing data collection. During baseline, participants relaxed in

a semisupine position, while during water immersion they

remained in the same position and were immersed up to

the upper part of their chest, slightly below the level of the

clavicle. Their arms during baseline and water immersion

were supported at the level of the heart and were not

immersed in the water at any time.

Blood Flow and Temperature MeasurementsFinger blood flow (BFf) was measured using laser-Doppler

velocimetry (PeriFlux System 5000, main control unit;

PF5010 LDPM, function unit; Perimed, Stockholm, Swe-

den) at the pulp of the index finger on the right hand. A

standard calibration device (PF 1000; Perimed) was used to

adjust the laser-Doppler flowmeter readings to coincide

with the readings obtained with Perimed’s Motility Stan-

dard. The probe (PR 407 small straight probe; Perimed)

was held in place with a plastic mini holder (diameter:

5 mm; PH 07-5; Perimed) and double-sided adhesive strips

(PF 105-3; Perimed). The BFf was expressed in PU and was

sampled at 32 Hz. These data were then used to provide

one-minute mean BFf values.

For the measurement of finger (Tf) and forearm temper-

ature, a ceramic chip skin thermistor (MA-100; Thermo-

metrics, Edison, NJ, USA) was securely placed next to the

Doppler probe on the pad of the right index finger and on

the posterior aspect of the forearm midway between the

Cutaneous Finger Blood Flow Prediction

ª 2011 John Wiley & Sons Ltd, Microcirculation, 18, 670–676 671

elbow and the wrist joints, respectively. Both skin thermis-

tors were held in place using surgical tape (3M Transpore

Tape, 3M, London, Canada). Rectal temperature (Tre) was

measured using a self-inserted flexible probe (2 mm in

diameter [Mon-A-Therm Core; Mallinkrodt Medical, St.

Louis, MO, USA]) to a depth of 15 cm beyond the anal

sphincter. Values for Tf, forearm temperature, and Tre were

recorded throughout baseline and during water immersion

at eight-second intervals using a data logger (Smartreader 8

Plus; ACR, Vancouver, BC, Canada) interfaced with a com-

puter for continuous monitoring by the investigators.

These values were used to provide one-minute mean values

for each variable.

Statistical AnalysisThe prediction model was created using data from the

model group. As expected, initial assessment of BFf, Tf,

TFor-f, and Tre data revealed a delay in Tf, TFor-f, and Tre

during the hot and cold sessions compared with fluctua-

tions in BFf. To account for the delay, dummy variables

were calculated representing the difference between the cur-

rent Tf, TFor-f, or Tre and the Tf, TFor-f, or Tre collected at

either one, or two, or three, or four, or five minutes before

(Tf example for one-minute change: Tf at time t minus Tf

at time t ) 1). These data are henceforth referred to as

‘‘lag’’ data (lagT). A hierarchical multiple linear regression

analysis was introduced to generate models using Tf, lagTf,

TFor-f, lagTFor-f, Tre, and lagTre data (independent variables)

obtained from the model group to predict BFf (dependent

variable). Similar to a previous study [9], Tre was not

highly associated with BFf (R2 = 0.062, F1,403 = 31.87,

p < 0.001). Thus, Tf and TFor-f were used separately as

independent variables, and the BFf was used as the depen-

dent variable in all models, while lagTf (i.e., lag1T f, lag2Tf,

lag3Tf, lag4Tf, and lag5Tf), lagTFor-f (i.e., lag1TFor-f, lag2TFor-f,

lag3TFor-f, lag4TFor-f, and lag5TFor-f), and lagTre (i.e., lag1Tre,

lag2Tre, lag3Tre, lag4Tre, and lag5Tre) were inserted in each

model iteratively as independent variables. This procedure

allowed for the optimal lagTf, lagTFor-f, and lagTre determina-

tion through the assessment of change in the coefficient of

determination (R2). Paired sample t-tests and Pearson’s

product moment correlation coefficients were employed to

assess differences and associations between the measured

BFf and the predicted BFf (P-BFf).

Validation of the prediction model was performed using

data from the validation group via ARIMA analysis, given

the time series nature of the data. This is because common

techniques are limited by the lack of independence amongst

sequential data points within a time series. This limitation

does not influence ARIMA that can mathematically

describe the association between two variables across time

and has been successfully employed to identify associations

between time series variables in thermal physiology [8,10].

ARIMA combines three statistical processes, autoregression,

integration ⁄ differencing, and moving averages, to mathe-

matically describe the association in a disturbance in one

time series (in this case, P-BFf [independent variable]) and

the possible associated perturbations in a second time series

(BFf [dependent variable]). Using specific model building

procedures, an appropriate ARIMA (a,d,q) model can be

specified based on the calculated autoregression (a), differ-

encing (d), and moving averages (q) integers. Given the

stringent nature of an ARIMA analysis, data must be col-

lected in time series with adequate resolution to construct

a viable model. Therefore, ARIMA was used to determine

whether changes in BFf across time were associated with

similar fluctuations in P-BFf. The overall integrity of the

ARIMA model was determined using the Ljung–Box test in

which a probability level of p > 0.05 implies that the model

is correctly specified, while a probability level of p < 0.05

suggests that there is structure in the observed series, which

is not accounted for by the model. Paired sample t-tests

and Pearson’s product moment correlation coefficients

were employed to assess differences and associations

between BFf and P-BFf, respectively. All statistical analyses

were completed using PASW Statistics (version 18; SPSS,

Inc., Chicago, IL, USA) statistical software package with

the level of significance set at p < 0.05 except in the AR-

IMA Ljung–Box test.

RESULTS

Preanalysis screening procedures were conducted to deter-

mine whether the model-group data satisfied the assump-

tions of hierarchical multiple linear regression analysis.

Assessment of residuals detected no violation of normality,

linearity, and homoscedasticity between P-BFf and errors of

prediction (p > 0.05). Results of the two hierarchical multi-

ple regression analyses are presented in Tables 1 and 2,

demonstrating that the model providing the strongest pre-

diction incorporated Tf, lag4Tf, and lag5Tre, while the second

strongest prediction came from the model incorporating

Tf, lag5Tf, and lag5Tre. However, cross-validation results for

these models (see below) demonstrated that calculated P-

BFf was statistically different from BFf (p = 0.048 and

0.036, respectively; Table 3). Thus, the prediction model

that most appropriately satisfied the selection criteria was

the model incorporating Tf, lag4Tf, and lag4Tre:

P� BFf ¼ Tf � 19:930þlag4 Tf � 74:766þlag4 Tre

� 124:255� 447:474

The above prediction model can generate negative

values. However, given that negative blood flow is not

plausible, all negative values must be recorded as zero.

A.E. Carrillo et al.


Paired-sample t test results revealed no mean difference

between BFf and P-BFf (109.5 ± 158.2 vs. 111.4 ± 136.7

PU, respectively; p = 0.6). The correlation coefficient

between BFf and P-BFf is shown in Table 1.

Cross-validation of the five strongest models for the

prediction of P-BFf was conducted using data from the

validation group. Table 3 outlines the ARIMA-specific

Ljung–Box statistic and R2 values associated with each pre-

Table 1. Results derived from the hierarchical regression analysis conducted in the model group for the prediction of cutaneous finger blood

flow via Tf, lagTf, and lagTre

IndVariables R2 SEE t P-BFf (mean ± SD) BFf (mean ± SD) r*

Tf, lag1Tf, lag1Tre 0.697 86.1 )0.599 115.9 ± 127.6 113.6 ± 155.9 0.842

Tf, lag1Tf, lag2Tre 0.707 85.1 )0.570 114.5 ± 129.2 112.3 ± 156.6 0.847

Tf, lag1Tf, lag3Tre 0.722 83.5 )0.586 113.7 ± 131.7 111.4 ± 157.8 0.856

Tf, lag1Tf, lag4Tre 0.744 80.4 )0.627 111.8 ± 134.0 109.5 ± 158.2 0.870

Tf, lag1Tf, lag5Tre 0.754 78.6 )0.598 109.0 ± 135.0 106.7 ± 158.0 0.875

Tf, lag2Tf, lag2Tre 0.725 82.4 )0.515 114.3 ± 131.3 112.3 ± 156.6 0.857

Tf, lag2Tf, lag3Tre 0.740 80.8 )0.504 113.3 ± 133.8 111.4 ± 157.8 0.865

Tf, lag2Tf, lag4Tre 0.760 77.8 )0.539 111.4 ± 136.0 109.5 ± 158.2 0.877

Tf, lag2Tf, lag5Tre 0.769 76.2 )0.499 108.6 ± 136.8 106.7 ± 158.0 0.882

Tf, lag3Tf, lag3Tre 0.746 79.8 )0.484 113.2 ± 134.5 111.4 ± 157.8 0.868

fT, lag3Tf, lag4Tre 0.764 77.1 )0.516 111.3 ± 136.5 109.5 ± 158.2 0.879

Tf, lag3Tf, lag5Tre 0.772 75.7 )0.466 108.4 ± 137.3 106.7 ± 158.0 0.883

Tf, lag4Tf, lag4Tre 0.766 76.7 )0.525 111.4 ± 136.7 109.5 ± 158.2 0.880

Tf, lag4Tf, lag5Tre 0.773 75.6 )0.465 108.4 ± 137.4 106.7 ± 158.0 0.883

Tf, lag5Tf, lag5Tre 0.772 75.8 )0.502 108.6 ± 137.1 106.7 ± 158.0 0.883

Tf = finger skin temperature; Tre = rectal temperature; lagT = temperature lag; R2 = regression analysis coefficient of determination; SEE = stan-

dard error of the estimate; t = paired sample t-test statistic; P-BFf = predicted fingertip blood flow (PU); BFf = measured fingertip blood flow (PU);

r = correlation coefficient.

*Pearson’s correlation coefficient significant at p < 0.001.

Table 2. Results derived from the hierarchical regression analysis conducted in the model group for the prediction of cutaneous finger blood

flow via TFor-f, lagTFor-f, and lagTre

IndVariables R2 SEE t P-BFf (mean ± SD) BFf (mean ± SD) r*

TFor-f, lag1TFor-f, lag1Tre 0.516 98.8 )0.466 104.5 ± 99.1 102.4 ± 141.5 0.726















Tf = finger skin temperature; Tre = rectal temperature; lagT = temperature lag; R2 = regression analysis coefficient of determination; SEE = stan-

dard error of the estimate; t = paired sample t-test statistic; P-BFf = predicted fingertip blood flow (PU); BFf = measured fingertip blood flow (PU);

r = correlation coefficient.

*Pearson’s correlation coefficient significant at p < 0.001.



diction model for BFf. All models passed the Ljung–Box

test (p > 0.05) confirming that they were correctly specified

in the ARIMA process. The highest R2 value was observed

in the model incorporating Tf, lag4Tf, and lag4Tre. The

increased efficacy of this model was confirmed also through

the more typical paired t test and Pearson’s product–

moment correlation coefficient. These results suggest that

perturbations in BFf were systematically followed by similar

perturbations in the P-BFf calculated through Tf, lag4Tf, and

lag4Tre. This is illustrated in Figure 1, which presents the

fluctuations in BFf and the P-BFf (calculated through Tf,

lag4Tf, and lag4Tre) during the thermal protocol in the model

and the validation groups. Indeed, it becomes apparent that

the prediction model is very responsive following closely

the large and rapid fluctuations observed in BFf.

DISCUSSION

In this study, we generated a model that provides an accu-

rate prediction of fingertip blood flow via practical and

inexpensive indicators, and also cross-validated it in an

independent group of subjects. Our results show that fin-

gertip blood flow can be accurately predicted through fin-

gertip temperature, the difference between current fingertip

temperature and that recorded four minutes before, as well

as the difference between current rectal temperature and

that recorded four minutes before. The resulting prediction

model provides an effective, easy, and cost-efficient alterna-

tive for the estimation of fingertip blood flow that only

requires a two-site temperature measurement.

Skin temperature gradients have been validated for the

estimation of blood flow and vasomotor tone [17]. Our

model provides a complementary approach to predicting

changes in fingertip blood flow with the consideration of

the expected lag using a two-site temperature measurement

from the finger and the core. During certain clinical situa-

tions such as the preoperative period, the evaluation of fin-

ger blood flow is useful for the determination of

thermoregulatory responses that have been reported to be

disrupted with anesthesia [19]. Furthermore, finger blood

flow evaluations may also provide useful information dur-

ing the assessment of thermoregulatory adaptations in

applied settings during which actual measurements are not

plausible. For example, a recent study evaluated the train-

ability of cold-induced vasodilation responses from highly

trained mountaineers prior to and following a three-week

expedition [5]. Thus, estimates for finger blood flow have a

Table 3. Results and comparisons of the five strongest models predicting cutaneous finger blood flow in the validation group

IndVariables P-BFf (mean ± SD) BFf (mean ± SD) r t R2 Ljung–Box statistic

Tf, lag2Tf, lag5Tre 112.3 ± 165.7 97.4 ± 138.7 0.884* )1.750 0.254 13.931

Tf, lag3Tf, lag5Tre 111.5 ± 162.1 97.4 ± 138.7 0.905* )1.868 0.339 8.143

Tf, lag4Tf, lag4Tre 111.7 ± 154.1 99.8 ± 138.0 0.917* )1.801 0.381 8.097

Tf, lag4Tf, lag5Tre 111.0 ± 157.5 97.4 ± 138.7 0.920* )2.004� 0.308 13.944

Tf, lag5Tf, lag5Tre 111.0 ± 153.2 97.4 ± 138.7 0.925* )2.134� 0.380 7.009

Tf = finger skin temperature; Tre = rectal temperature; lagT = temperature lag; t = paired sample t-test statistic; P-BFf = predicted fingertip blood

flow (PU); BFf = measured fingertip blood flow (PU); r = correlation coefficient; R2 = ARIMA coefficient of determination.

*Pearson’s coefficient significant at p < 0.001.�Paired sample t-test significant at p < 0.05.

Figure 1. Fluctuations in measured blood flow and the blood flow

predicted through Tf, lag4Tf, and lag4Tre during the thermal protocol in

the model and the validation groups. The validation group data have

been expanded to improve clarity.



wide range of use and should be applied when actual mea-

surements are difficult to implement.

Vasodilation and vasoconstriction of the small arteries in

peripheral body parts are key functions that regulate ther-

mal stability [8,18]. Cold-induced vasodilation has been

reported to occur, at least in part, from the dynamic struc-

tural characteristics of arteriovenous anastomoses that are

capable of complete closure or large amounts of blood

transfer when the vessels are open [1]. A number of meth-

ods are available to quantify the amount of vasodilation,

but because of the invasiveness and difficulty of direct mea-

surement, the majority of used methods are indirect [2].

The most commonly used method to measure cold-

induced vasodilation is finger temperature that represents a

delayed indicator of what is actually occurring in the dee-

per tissues [14]. The quantification of cold-induced vasodi-

lation determined via finger temperatures is well described

[2]. Nevertheless, the difference in timing between finger

blood blow and skin temperature introduces some ambigu-

ity that may hinder interpretation. Thus, additional estima-

tions of blood flow reported with finger temperature data

may provide further insight into thermoregulation research

such as with cold-induced vasodilation.

Despite a previously reported association between TFor-f

and BFf [17], our results suggest that accounting for the

delay in skin and rectal temperatures by incorporating the

change in Tf and Tre provides a stronger prediction of

BFf. Furthermore, our results confirm a previous study

[9] reporting that Tre alone is not associated with reflex

alterations in BFf, but when used in combination with

peripheral skin temperatures, it shows a strong association

with BFf. More importantly, our cross-validation results

confirm that multiplying the skin surface temperature

indicators by their changes across time converts their

expected small and slow alterations into the large and

rapid fluctuations usually observed in BFf. Therefore, the

predictive efficacy of the change across time of different

skin surface temperatures should be evaluated to correct

for the response delay of these thermal indicators to

reflect fluctuations in BFf.

To record a wide range of BFf, Tf, TFor-f, and Tre values

without the application of local stimuli, data collection was

conducted in a stable thermoneutral ambient air tempera-

ture environment. Therefore, our prediction model should

be validated also in a changing air temperature environ-

ment. Furthermore, hot water immersion increases skin

blood flow faster than dry heat [15]. Local effects of heat

transfer, however, were eliminated during water immersion,

given that the arms were supported at the level of the heart

and were not immersed in the water. Nevertheless, the

generated model should be validated in conditions with

varying rates of heat transfer to ensure the usefulness of

this measurement in clinical and other settings. Finally,

although our study was conducted in a relatively small

group of subjects, our recordings were prolonged, resulting

in a vast amount of data to be used to create and validate

our model. Indeed, even after data clustering into one-min-

ute mean values, the total number of cases in our model

group was 403. According to strict criteria [20], the mini-

mum number of observations for a multiple linear regres-

sion model incorporating three independent variables

(predictors) is 74, which represents only 18% of the avail-

able data in our study. Therefore, although generating a

model from 403 cases is not the same as having 403 sub-

jects, the methodology used in the present study adheres to

the appropriate statistical protocols.

In conclusion, we provide a model that accurately pre-

dicts fingertip blood flow via two practical and inexpensive

temperature indicators: current fingertip temperature and

its difference with that recorded four minutes before, as

well as the difference between current rectal temperature

and that recorded four minutes before. The prediction is

accurate and provides a simple cost-efficient alternative

that can be implemented in both clinical and field settings,

as well as for various research and bioengineering applica-

tions where thermoneutral ambient air remains relatively

stable, and when the actual measurement of finger blood

flow is not plausible.

PERSPECTIVE

The high equipment cost and the extensive requirements

from participants necessary for accurate measurement of

fingertip blood flow introduce complications that may pre-

vent practical use. In this paper we provide a model that

accurately predicts fingertip blood flow via two practical

and inexpensive temperature indicators: current fingertip

temperature and its difference with that recorded four min-

utes before, as well as the difference between current rectal

temperature and that recorded four minutes before. The

model provides a simple cost-efficient alternative that can

be implemented in both clinical and field settings.

ACKNOWLEDGMENTS

We are grateful to our participants for their effort and per-

severance. The authors report no conflict of interest. This

work was supported in part by funding from the European

Union 7th Framework Program (FP7-PEOPLE-IRG-2008

Grant No. 239521), and a Discovery Grant (227912-07, S.S.

Cheung) from the Natural Science and Engineering

Research Council of Canada.



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a novel model to predict cutaneous finger blood flow via finger and rectal temperatures

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