Clinical Nutrition 33 (2014) 106e114
Contents lists available at SciVerse ScienceDirect
Clinical Nutrition
journal homepage: http://www.elsevier.com/locate/clnu
The use of hand grip strength as a predictor of nutrition status in
hospital patients
Anna Flood a, Alexis Chung a, Hayley Parker b, Victoria Kearns b, Therese A. O’Sullivan a, *
a
b
School of Exercise and Health Science, Edith Cowan University, Joondalup, Western Australia, Australia
Dietetics Department, Joondalup Health Campus, Joondalup, Western Australia, Australia
a r t i c l e i n f o
s u m m a r y
Article history:
Received 18 July 2012
Accepted 4 March 2013
Background & aims: Hand grip strength (HGS) has been found to respond to nutrition deprivation and
repletion but few studies have investigated its use as an independent nutrition assessment tool. We
conducted an observational study to determine if HGS can predict nutrition status independently of
other factors.
Methods: The Patient Generated Subjective Global Assessment (PG-SGA) was used to determine nutrition
status. PG-SGA and HGS measures were collected from 217 well nourished and malnourished hospital
patients for cross-sectional analysis. Of the 217, 18 patients had these assessments repeated two weeks
(3 days) later to assess change. Correlation, and multiple linear and binary regression analyses were
conducted.
Results: HGS and PG-SGA score were significantly correlated (r ¼ 0.292, P < 0.01). HGS was a significant
independent predictor of PG-SGA score and category (P < 0.01), accounting for 4% and 9% of variability
respectively. Change-in-HGS was an independent predictor of change-in-PG-SGA score (P ¼ 0.04) and
category (P ¼ 0.06) over two weeks, accounting for 47% and 42% of variability respectively.
Conclusions: Our results suggest that HGS can independently predict nutrition status and change in
nutrition status defined by PG-SGA score and category, although future longer term research is required
to confirm the use of HGS as an early detection tool for malnutrition risk.
Ó 2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Keywords:
Hand grip strength
PG-SGA
Malnutrition
Nutrition status
Hospital
1. Introduction
Malnutrition is a common and ongoing problem among hospital
patients.1 Studies in developed countries report that malnourished
patients account for between 20 and 60 percent of the hospitalised
population.2e4 In this setting, malnutrition has been independently
associated with compromised health, including medical complications, prolonged recovery from illness and surgery, and an
increased rate of mortality.2,5 Identification of hospital patients
who are malnourished or at a high risk of malnutrition is therefore
essential in clinical nutrition best practice. Recognising these patients as early as possible facilitates earlier nutritional intervention
and improved health outcomes.
Non-standard abbreviations: (HGS), hand grip strength; (PG-SGA), patient
generated subjective global assessment; (PAL), physical activity level.
* Corresponding author. School of Exercise and Health Sciences, Edith Cowan
University, Building 19, 270 Joondalup Drive, Joondalup, WA 6027, Australia.
Tel.: þ61 8 6304 5055; fax: þ61 8 6304 5384.
E-mail addresses: aflood@our.ecu.edu.au (A. Flood), achung@our.ecu.edu.au
(A.
Chung),
ParkerH@ramsayhealth.com.au
(H.
Parker),
kearnsv@
ramsayhealth.com.au (V. Kearns), t.osullivan@ecu.edu.au (T.A. O’Sullivan).
A variety of nutrition assessment tools are used in hospitals,
many of which rely on weight or physical assessment.6 One such
tool is the scored Patient Generated Subjective Global Assessment
(PG-SGA), a validated means of triaging patients according to
nutritional status and need for nutritional support using a physical
assessment.7 However, research into the physiological changes in
the malnourished human body suggests that a change in muscle
function may be useful as an early indication of malnutrition.8 A
nutrition assessment tool based on an objective measure of physical function may therefore be valuable,9 particularly in circumstances where weight is not known or accurate physical assessment
is difficult due to a lack of trained staff. Hand grip strength (HGS), a
commonly used tool for assessment of muscle function in clinical
settings,10 has gained considerable attention as an indicator of
nutrition status in recent research.11
A systematic review of HGS as a nutritional marker by Norman
and colleagues suggests that this measure would be a good indicator of nutrition status.11 The review also highlights the potential
monitoring capabilities of HGS to detect improvements in nutritional status following supplementation.11 HGS is a rapid, costeffective and a user friendly tool10 that has high test and re-test
reliability, as well as high inter-rater reliability.12 HGS could
0261-5614/$ e see front matter Ó 2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
http://dx.doi.org/10.1016/j.clnu.2013.03.003
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
therefore provide a number of benefits over existing nutrition
assessment methods, which are more time consuming and require
higher skill levels. Despite the promising evidence and benefits of
HGS as a nutrition assessment tool, to our knowledge there have
been no published articles exploring the potential of HGS to independently predict nutrition status in a standard hospital
population.
To evaluate HGS as a nutrition assessment tool, we have undertaken a study of hospital patients from the Joondalup Health
Campus in Western Australia, with the aim to investigate the
relationship between PG-SGA and HGS values. It was hypothesised
that HGS would show a significant correlation with PG-SGA score as
well as being an independent predictor of PG-SGA score and
nutrition status.
2. Materials and methods
2.1. Participants
This observational study combined cross-sectional and longitudinal methods using both prospective and retrospective data. The
study was conducted at Joondalup Health Campus (JHC), Western
Australia, with approval from the Human Research Ethics Committees of JHC and Edith Cowan University.
The retrospective data included in this study was collected from
medical, surgical and rehabilitation ward patients between January
and June 2011. Complete data sets were retrieved from dietetic
inpatient notes with consent from JHC. The prospective component
of this study was carried out during July and August 2011. All adult
patients referred to the JHC Dietetics Department from medical,
surgical and rehabilitation wards were eligible for inclusion. JHC
dietetic outpatients assessed during these periods were also
included. All prospective data was collected by Accredited Practising Dietitians or by student dietitians with written consent from
the participants. Injury, malformation and severe rheumatoid
arthritis in both hands were considered exclusion criteria, to avoid
potential limitations to HGS measurement. Patients who were
under the age of 18, displayed signs of cognitive impairment or had
severe dementia were also excluded. Exclusion criteria were the
same for the prospective and retrospective data groups.
Cross-sectional analyses were performed on combined prospective and retrospective data, which will be referred to as
‘baseline data’. Longitudinal analyses were performed on duplicate
measures and will be referred to as ‘monitoring data’.
2.2. Nutrition status assessment
Nutrition status was assessed using the PG-SGA,7 determining
the presence and severity of malnutrition in each patient. The PGSGA was completed by trained JHC dietitians and student dietitians
whose competence in using the tool was subjectively assessed as
satisfactory by an experienced JHC dietetics supervisor prior to data
collection. The PG-SGA consists of two sections. The first medicallybased component is reported by the patient, including their weight
history, dietary intake, symptoms, and level of function and activity.
The second clinically-based component requires the assessor to
perform a physical examination of fat stores, muscle tone and fluid
status and to use this information to generate a global assessment
of nutrition status. The PG-SGA produces a score and category.
These results are related13 but independent and for this reason have
been analysed as different independent variables of nutrition status
in this study. The PG-SGA category triages patients nutrition status
into well nourished (category A), moderately malnourished or
suspected malnutrition (B), or severely malnourished (C).7 The total
numerical score is summed from questions in the first component
107
of the assessment which provides a guideline for the need and
urgency of nutrition intervention. A patient achieving a score
greater than nine indicates a critical need for nutrition intervention, and a score between one and nine identifies the need to reassess and monitor the patient.13
Additional information needed to complete the assessment,
including relevant diagnosis, primary disease stage, current use of
steroid medication and body temperature, were obtained from the
patients’ medical notes and dietetic inpatient notes.
2.3. Hand grip strength measurement
HGS was measured using one of three JamarÒ Hydraulic Hand
Dynamometers (Sammons Preston Rolyan, Bolingbrook, Illinois,
2010 model). This tool has been shown to be reliable and valid,10
with high inter-rater reliability.12 Jamar dynamometers require 12
monthly calibration and each device was calibrated in December
2010 prior to the start of data collection in January 2011.
Patients performed the test sitting on a bed or chair in the
posture found to produce the most accurate results14: shoulders
adducted and neutrally rotated, elbow flexed at 90 and wrist
neutrally positioned. The patient’s dominant hand was used for the
assessment and where this was not possible, the patient’s nondominant hand was used and this detail was recorded. Each patient was given a demonstration and then asked to complete a
maximal isometric contraction for 3 s. To minimise variance in
psychomotor motivation,12 standardised encouragement was given
to each subject: “Squeeze as hard as you can, harder, harder, relax”,
saying “relax” at 3 s.
A total of three maximal isometric contractions were required
from each patient with no less than 10 s, and no more than 30 s rest
between tests, and the mean HGS result was calculated. The patients predicted HGS was also calculated as shown in Table 1. Mean
and predicted HGS results were then compared and the patients
percentage of predicted HGS was calculated. From this point forward, ‘HGS’ will refer to percentage of predicted HGS. As difficulties
exist with defining acceptable ranges for aspects such as predicted
HGS, we also examined changes in nutritional status. Monitoring
analysis was conducted using the difference between HGS at
baseline and two weeks and the difference between PG-SGA score
and category at these times. This difference will be referred to as
‘change-in-HGS’ and ‘change-in-PG-SGA’ score or category.
During this study it was identified that one of the dynamometers was reading inaccurately, despite calibration occurring before
commencement of data collection. In response to this, all patients
who had been assessed in the previous week and remained in
hospital had their HGS re-assessed using a calibrated device. Any
patients who could not be re-assessed and data which could not be
identified as collected with the unaffected dynamometers were
excluded from the study.
2.4. Data collection
Eligible patients had PG-SGA and HGS assessments completed at
baseline. Gender, age (years), weight (kg), height (cm), body mass
index (BMI) (kg/m2), mid upper arm circumference (cm), reason for
Table 1
Predictive equations of hand grip strength for adults aged 18 years and over.24
Prediction equations
Left hand
Right hand
(Age 0.16) þ (gender 16.68) þ (BMI 0.29) þ 26.60
(Age 0.18) þ (gender 16.90) þ (BMI 0.23) þ 31.33
Age: in years.
Gender: male ¼ 1, female ¼ 0.
BMI: body mass index as measured by weight in kg/height in m2.
108
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
referral to the dietetics department and past medical history were
collected from dietetic inpatient notes. All diagnoses and total
number of diagnoses at the patient’s most recent admission to JHC
were also recorded, as well as the number of medications taken
which are known to impact on nutrition status.
Patients who remained in hospital for two weeks (3 days) or
returned to an outpatient clinic two weeks (3 days) after their
initial assessment had HGS and PG-SGA assessments repeated. For
those assessed again at two weeks, the number of days between
baseline and repeat HGS and PG-SGA assessments was recorded
from dietetic inpatient notes.
2.5. Physical activity level
HGS and physical activity level (PAL) have been shown to
positively correlate in a population of healthy men and women
aged 40e79 (P < 0.05).15 To investigate its possible confounding
effect on HGS, PAL was assessed in the prospective patients using
the International Physical Activity Questionnaire-short form (IPAQSF). This valid and reliable tool16 includes seven questions assessing
the minutes spent walking, sitting and doing vigorous and moderate intensity activity during the last seven days. The questionnaire was used without change to the order or wording of the
questions. Accumulated hours spent being physically active in the
seven days prior to HGS and PG-SGA assessments were calculated
and used in analysis.
2.6. Statistical methodology
Data was analysed using Predictive Analysis Software by Statistical Package for the Social SciencesÓ (SPSS, version 18, SPSS Inc.
Chicago, IL, USA). HGS, PG-SGA and patient characteristic data are
presented as mean standard deviation (SD). Descriptive statistics
were used to detail the subjects characteristics. Within this, a One
Way Analysis of Variance (ANOVA) was then used to identify significant differences between PG-SGA categories and these variables, including HGS, while Spearman’s rho was used to determine
the correlation between HGS and PG-SGA score, as well as the
change-in-HGS against the change-in-PG-SGA score. The PG-SGA
categories were divided into well nourished (PG-SGA category A)
and malnourished (PG-SGA categories B and C) groups, and will be
referred to as nutrition status from this point on. An independent ttest was used to determine the difference in HGS between nutrition
status groups. For baseline and change-in-HGS, multivariate binary
logistic and linear regression were used to investigate factors predicting nutrition status and PG-SGA score respectively. Nagelkerke
r2 values were used to assess individual contributions to the models
for each included variable.17 Potential confounding factors considered for inclusion in the regression models were age (years),
gender, mid upper arm circumference (cm), number of medications, number of diagnoses and PAL (hours). We did not include
body mass index (BMI) in the model as the equation used to estimate the ‘normal’ HGS of patients includes BMI (Table 1). We also
ran receiving operator characteristic (ROC) curve for observed HGS
and nutritional status sensitivity/specificity. Statistical significance
was established at P < 0.05.
(n ¼ 5), non-compliance with requests (n ¼ 2), confusion (n ¼ 2),
aggression (n ¼ 1), hand malformation (n ¼ 1), and being discharged before collection of informed consent (n ¼ 1). The
remaining 217 patients qualified for inclusion. Of these patients, 52
remained in or returned to hospital over the study period and had
duplicate HGS and PG-SGA assessments taken. Eighteen duplicate
measures were included in the study; 34 were excluded because of
collection of data outside of the two week (3 days) period (n ¼ 22)
and incomplete data collection (n ¼ 12).
At baseline, 45 patients were classified as well nourished (A),
148 as moderately malnourished or at risk of malnutrition (B) and
24 as severely malnourished (C), according to the PG-SGA. The
monitoring population consisted of one well nourished, 15
moderately malnourished or at risk of malnutrition and two
severely malnourished patients. Characteristics of the baseline and
monitoring groups are shown in Table 2.
Patients included in the study were located on medical
(n ¼ 104), rehabilitation (n ¼ 34), surgical (n ¼ 24), orthopaedic
(n ¼ 2) and mental health (n ¼ 1) wards. Four outpatients and five
day therapy patients were also included. The remaining patients
were unspecified inpatients (n ¼ 21) or did not have their location
recorded (n ¼ 23). The main reasons for referral to the dietetics
department were loss of appetite or low oral intake (n ¼ 81), unintentional weight loss (n ¼ 60), underweight or malnutrition
(n ¼ 24) and nutrition education (n ¼ 21). Other reasons for referral
included needing a specialised diet (n ¼ 17), gastrointestinal surgery (n ¼ 8), malnutrition screening tool results (n ¼ 6), bariatric
surgery (n ¼ 5), nutrition impact symptoms (n ¼ 5), nasogastric
tube in situ (n ¼ 3), constipation (n ¼ 2), refusing food (n ¼ 2), oral
nutrition support (n ¼ 2), poor diet choices (n ¼ 1), and high stoma
output (n ¼ 1). Some patients had multiple reasons for referral.
3.2. Baseline results
3. Results
The mean HGS of well nourished (A), moderately malnourished
or at risk of malnutrition (B) and severely malnourished (C) patients differed according to PG-SGA category, with values of
82.1 24.9%, 59.1 23.9% and 51.2 20.1%, respectively, P < 0.001
(Table 2). An inverse relationship existed between HGS and PG-SGA
score, rs ¼ 0.292, P < 0.001 as determined by Spearman’s rho
(Fig. 1). Post hoc tests showed HGS scores for patients in PG-SGA
category A was significantly different to patients in categories B
(P < 0.001) and C (P < 0.001) but scores between categories B and C
were not significantly different (P ¼ 0.285) (Fig. 2).
A number of potential confounding factors were considered in
the relationship between HGS and nutrition status. Of these, age
(r ¼ 0.419, P < 0.001), number of medications (r ¼ 0.305,
P < 0.001), and number of diagnoses (r ¼ 0.176, P ¼ 0.01) were
significantly associated with HGS. Similarly, age (r ¼ 0.182,
P ¼ 0.007), number of medications (r ¼ 0.169, P ¼ 0.013), number of
diagnoses (r ¼ 0.168, P ¼ 0.015) and patient ward (r ¼ 0.177,
P ¼ 0.022) were significantly associated with PG-SGA score. PG-SGA
category was significantly associated with age (F ¼ 22.8, P < 0.001),
mid upper arm circumference (F ¼ 10.9, P < 0.001) and number of
medications (F ¼ 9.8, P < 0.001). Hours spent being physically
active in the seven days prior to assessment was not associated
with HGS (r ¼ 0.136, P ¼ 0.369), PG-SGA score (r ¼ 0.027,
P ¼ 0.860) or category (F ¼ 1.008, P ¼ 0.486).
3.1. Patient characteristics
3.3. HGS predicting PG-SGA score
A total of 294 patients were eligible for entry into the study.
Seventy seven of these patients were subsequently excluded for
reasons including incomplete data collection (n ¼ 54), inability to
complete the HGS assessment (n ¼ 11), hand dynamometer failure
The final linear regression model to predict PG-SGA score
showed HGS was an independent predictor accounting for 4% of the
variability, P ¼ 0.001, b ¼ 0.219, B ¼ 0.049, 95% CI
[0.078, 0.020] (Table 3).
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
109
Table 2
Characteristics of baseline and monitoring patients grouped by PG-SGA category.
PG-SGA categorya
Total
A
Well nourished
Baseline (n)
% Male
PG-SGA score (n ¼ 217)
Actual HGS, kg (n ¼ 217)
% ideal HGSc (n 217)
Age, years (n ¼ 216)
Weight, kg (n ¼ 216)
BMI, kg/m2 (n ¼ 216)
MUAC, cm (n ¼ 62)
Diagnosesd (n ¼ 210)
Medicationse (n ¼ 212)
Physical activity (n ¼ 47)
Hours activef
Monitoring (n)
% Males
Baseline (n ¼ 18)
PG-SGA score
Actual HGS, kg
% ideal HGSc
Two weeksg (n ¼ 18)
PG-SGA score
Actual HGS, kg
% ideal HGSc
Age, years (n ¼ 18)
Weight, kg (n ¼ 18)
BMI, kg/m2 (n ¼ 18)
MUAC, cm (n ¼ 7)
Diagnosesd (n ¼ 18)
Medicationse (n ¼ 18)
Days between
assessments (n ¼ 14)
B
Moderately malnourished
C
Severely malnourished
Mean SD
Mean SD
Mean SD
Mean SD
(217)
35.5
11.4
18.8
63.0
75.1
64.4
23.6
23.9
1.6
4.5
(45)
35.6
4.1
27.2
82.1
62.7
83.6
30.0
27.0
1.3
2.8
(148)
35.8
12.4
17.1
59.1
78.8
61.6
22.8
24.7
1.7
5.2
(24)
33.3
18.5
14.0
51.2
75.0
45.6
16.9
19.2
1.8
3.8
5.8
10.7
25.7
15.2
20.4
6.6
4.1
1.0
3.3
2.0 3.3
(18)
27.8
13.7 4.0
14.5 7.1
54.0 16.2
10.3
15.9
57.2
80.5
53.8
21.1
23.6
1.6
5.1
4.1
7.6
17.6
8.4
11.5
4.0
2.9
0.8
3.0
13.8 1.5
3.4
11.7
24.9
17.4
23.0
7.7
3.6
0.8
2.8
1.4 2.7
(1)
0
8.0 0
13.0 0
59.7 0
14.0
14.0
64.3
77.0
61.9
25.8
28.0
2.0
1.0
0
0
0
0
0
0
0
0
0
15.0 0
4.3
9.5
23.9
11.5
15.7
4.7
3.6
1.1
3.2
3.0
7.6
20.1
19.4
13.4
5.0
3.4
1.1
3.4
Pb
0.973
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.106
<0.001
2.2 3.5
(15)
33.3
2.5 4.5
(2)
0
0.549
13.7 3.4
16.0 6.8
55.1 16.9
20.5 3.5
8.0 1.4
42.6 11.0
0.010
0.394
0.685
3.0
0.7
1.2
6.4
2.5
2.5
0
0
3.5
0.028
0.288
0.317
0.183
0.362
0.615
0.133
0.247
0.339
12.5 1.5
0.029
9.8
16.5
55.6
79.3
55.2
21.1
23.0
1.7
5.4
4.0
8.2
18.8
8.2
11.1
3.6
2.5
1.0
2.6
13.9 1.3
12.0
12.5
65.9
88.5
38.8
16.1
20.0
1.0
2.5
0.753
HGS: hand grip strength; MUAC: mid upper arm circumference; PG-SGA: patient generated subjective global assessment.
a
PG-SGA category (A) well nourished, (B) moderately malnourished or risk of malnutrition, (C) severely malnourished.
b
Significance of difference between well nourished (PG-SGA A) and malnourished (PG-SGA B and C) mean patient characteristics.
c
Percent of calculated ideal HGS.
d
Number of diagnoses at most recent admission to JHC.
e
Number of medications taken by the patient that are relevant to nutrition.
f
Accumulative hours spent physically active, defined by the International Physical Activity Questionnaire, over the seven days prior to assessment.16
g
HGS and PG-SGA assessments taken two weeks (3 days) after baseline assessments.
3.4. HGS predicting nutrition status
Logistic regression modelling showed that HGS was an independent predictor accounting for 9% of the variability, P ¼ <0.001,
B ¼ 0.035, OR ¼ 0.966, 95% CI [0.949, 0.983] (Table 4).
3.5. Monitoring results
The mean HGS and PG-SGA score of the monitoring group was
52.0 17.1 and 13.9 4.0, respectively. After an average of
13.8 1.4 days in hospital, the mean HGS for this group increased
to 57.2 18.6 (P < 0.001) and PG-SGA score decreased (representing improved nutrition status) to 10.3 4.1 (P ¼ 0.09) (Fig. 3).
HGS showed a significant inverse association with PG-SGA score
over this monitoring period (Spearman’s rho, r ¼ 0.672, P ¼ 0.002)
(Fig. 4). HGS and PG-SGA category over this period also had a significant negative association (F ¼ 4.028, P ¼ 0.04).
model HGS alone was an independent predictor accounting for 47%
of the variability (P ¼ 0.003) (Table 5).
3.7. HGS predicting nutrition status over time
The final logistic regression model showed that HGS independently predicted a change in nutrition status over time. Within this
model HGS alone was borderline as a predictor accounting for 42%
of the variability (P ¼ 0.079) (Table 6).
3.8. ROC curve
We ran a ROC curve analysis for observed HGS and nutritional
status sensitivity/specificity and found high discriminatory values
(Fig. 5). The area under the curve is 0.776 with a standard error of
0.039 (P ¼ <0.001) and a 95% confidence interval of 0.698, 0.853.
4. Discussion
3.6. HGS predicting PG-SGA score over time
The final factors in the linear regression model to predict
change-in-PG-SGA score were change-in-HGS, number of medications and number of diagnoses. In combination, these factors
accounted for 53% of the variability in PG-SGA score. Within this
In this observational study of a heterogeneous group of well
nourished and malnourished hospital patients, we found a significant association between HGS and nutrition status, as defined by
PG-SGA score and category. Furthermore, we demonstrated that
baseline HGS may independently predict nutrition status and that
110
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
Table 3
Unstandardised (B) and standardised (b) regression coefficients and change in
squared correlations (R2) for each variable in a regression model predicting PG-SGA
score (n ¼ 217).
r = -0.292
Variable
B [95% CI]
b
P
Change
in R2
Percent ideal HGSa
BMI (kg/m2)
Number
of medicationsb
0.049 [0.078, 0.020]
0.297 [0.405, 0.189]
0.157 [0.064, 0.379]
0.219
0.340
0.090
0.001
<0.001
0.163
0.042
0.109
0.009
BMI: body mass index; HGS: hand grip strength; PG-SGA: patient generated subjective global assessment.
a
Percent of calculated ideal HGS.24
b
Number of medications relevant to nutrition taken by the patient.
Table 4
Outcome (B) and odds ratio (Exp[B]) coefficients and change in Nagelkerke R2 17
from baseline for each variable in a regression model predicting risk of malnutrition (PG-SGA categories B and C) (n ¼ 217).
Fig. 1. Correlation between percent of ideal HGS and PG-SGA score by PG-SGA nutrition status category (n ¼ 217).
HGS: hand grip strength.
PG-SGA: patient generated subjective global assessment.25
Scatter plot showing the overall inverse correlation between percent of ideal hand grip
strength and PG-SGA score in patients classified as well nourished (A, black dot),
moderately malnourished (B, white dot) and severely malnourished (C, grey dot),
where ideal HGS was determined using predictive equations that consider age, gender
and BMI. Spearman’s rho correlation: rs ¼ 0.292, P < 0.001.
Variable
B
Exp [B] [95% CI]
P
Change
in R2
Percent ideal HGSa
BMI (kg/m2)
Number
of medicationsb
0.035
0.198
0.149
0.966 [0.949, 0.983]
0.820 [0.761, 0.885]
1.160 [1.007, 1.337]
< 0.001
< 0.001
0.040
0.093
0.216
0.029
BMI: body mass index; HGS: hand grip strength; PG-SGA: patient generated subjective global assessment.
a
Percent of calculated ideal HGS.24
b
Number of medications relevant to nutrition taken by the patient.
Fig. 2. Mean percent ideal HGS across PG-SGA categories of nutrition status.
The same letters indicate a significant difference between means: A (P < 0.001) B (P < 0.001).
HGS: hand grip strength.
PG-SGA: patient generated subjective global assessment.25
Post Hoc test shows that well nourished patients had a mean percent of ideal hand grip strength significantly different to both moderately and severely malnourished patients,
where ideal hand grip strength was determined using predictive equations that consider age, gender and BMI. The same test shows no significant difference between the mean
percent of ideal hand grip strength of the malnourished patient groups.
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
111
Fig. 3. Change in mean percent ideal HGS (left) and mean PG-SGA score (right) between baseline and two weeks (n ¼ 18).
HGS: hand grip strength.
PG-SGA: patient generated subjective global assessment.25
Patients who remained in or returned to hospital two weeks (3 days) after their initial measurements had their hand grip strength and PG-SGA tests repeated. Left: Plot shows a
mean increase in percent of ideal hand grip strength over the monitoring period of 5.3% (P < 0.001), indicating an increase in muscular strength. Right: Plot shows a mean decrease
in PG-SGA score of 3.6 points over the monitoring period (P ¼ 0.09), indicating improvement in nutritional status.
r = -0.672
Fig. 4. Correlation between change-in-HGS and change-in-PG-SGA score (n ¼ 18).
HGS: hand grip strength.
PG-SGA: patient generated subjective global assessment.25
Patients who remained in or returned to hospital two weeks (3 days) after their initial measurements had their hand grip strength and PG-SGA tests repeated. The difference
between these measurements were calculated and shown as change-in-HGS and change-in-(PG-SGA)score. The scatter plot shows change-in-HGS and change-in-score had a
significant inverse correlation. Spearman’s rho correlation: rs ¼ 0.7672, P ¼ 0.002.
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A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
Table 5
Unstandardised (B) and standardised (b) regression coefficients and change in
squared correlations (r2) for each variable in a regression model predicting changein-PG-SGA score over two weeks (n ¼ 18).
Variable
B [95% CI]
b
P
Change
in R2
Change in percent
ideal HGSa
Number of diagnosesb
Number of medicationsc
0.341 [0.543, 0.140]
0.695
0.003
0.467
1.890 [0.812, 4.592]
0.674 [1.407, 0.060]
0.331
0.447
0.156
0.069
0.076
0.131
BMI: body mass index; HGS: hand grip strength; PG-SGA: patient generated subjective global assessment.
a
Change in percent of calculated ideal HGS over two weeks.24
b
Number of diagnoses at most recent admission to Joondalup Health Campus.
c
Number of medications relevant to nutrition taken by the patient.
Table 6
Outcome (B) and odds ratio (OR) coefficients and change in Nagelkerke R2 a for each
variable in a regression model predicting change in nutrition status categories well
nourished (PG-SGA category A) and malnourished (PG-SGA categories B and C), over
two weeks (n ¼ 18).
Variable
B
O [95% CI]
P
Change in R2
Change in percent
ideal HGSb
BMI (kg/m2)
0.200
1.222 [0.977, 1.528]
0.079
0.415
0.172
0.842 [0.563, 1.259]
0.402
0.048
BMI: body mass index; HGS: hand grip strength; OR: odds ratio; PG-SGA: patient
generated subjective global assessment.
a
Nagelkerke R2 represents the degree of variability of the outcome variable
contributed by the dependent variable.17
b
Change in percent of calculated ideal HGS over two weeks.24
monitoring measures of HGS have the potential to independently
predict change in nutrition status over time. The ability of HGS to
account for variation in nutrition status differed with the dependent variable (PG-SGA score or category) and whether baseline or
monitoring HGS measures were used. Change-in-HGS accounted
for the highest proportion of variability (47%) in relation to changein-PG-SGA score (P ¼ 0.003). The results of the ROC curve plot
suggests that HGS has a fair accuracy as a diagnostic test, with the
area under the curve of 0.776 compared to a perfect test of 1 and a
worthless test of 0.5.
4.1. Potential mechanism
The association demonstrated between HGS and PG-SGA score
and category is likely to be linked to the relationship between
muscle function and nutrition status. In the malnourished state,
skeletal muscle is the body’s preferential fuel source,8 which causes
a loss of protein stores and a resultant decline in muscle strength
and functionality.9 This mechanism may explain why mean HGS
was significantly lower in malnourished patients when compared
to well nourished patients. Additionally, muscle protein stores have
been found to respond rapidly to restored nutrition.11 This could
explain the concomitant increase in HGS and nutrition status seen
in the observational group after two weeks of nutrition intervention. Together these mechanisms may contribute to the ability of
HGS to account for nutrition status variability.
4.2. Clinical significance
The clinical significance of our results depends on the ability of
HGS to predict nutrition status. The maximal predictive ability of
a single HGS measure in our model was 9% of PG-SGA category
variability. In practice, this means that almost 10% of the variation
in nutrition status as determined by the PG-SGA can be predicted
by a single HGS measure. Furthermore, for every 10% increase in
HGS, the odds of the patient being at risk of malnutrition decreases by 29%. The maximal predictive ability of change-in-HGS
alone was 47% of change-in-PG-SGA score. This suggests that
Fig. 5. Observed HGS and nutritional status sensitivity/specificity ROC curve.
HGS: hand grip strength.
ROC: receiver operating characteristic.
The area under the curve is 0.776 with a standard error of 0.039 (P ¼ <0.001) and a 95% confidence interval of 0.698, 0.853.
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
almost half of the variation in nutrition status over time may be
determined by change-in-HGS over the same period. Although
this level of accuracy is not high enough to suggest that HGS can
replace current nutrition assessment tools in practice, it is
important to note that HGS and the PG-SGA measure different
nutrition parameters (nutrition status versus muscle strength).
However, the significant association and predictive capacity of
HGS indicates that this tool could provide valuable information
about a patient’s nutrition status and may play an important role
in clinical practice.
Using HGS as a monitoring tool may have more advantages
than providing earlier information about nutrition status.
Repeating the PG-SGA is a way of monitoring a patient’s nutrition
status; however, this requires an updated patient weight to
determine weight loss, gain, or stabilisation. Weight measurement
can be difficult to obtain in a clinical setting, especially in nonambulatory or critically ill patients. Dietitians can be required to
make assessments without weight or wait for other hospital staff
to obtain these measurements for them, which may result in
inaccurate or delayed assessments. An inaccurate assessment may
result in inappropriate nutrition intervention, and delayed assessments may mean patients wait longer for the nutrition therapy
they require. Using HGS as a monitoring tool however, compares
each successive measurement to the patients initial HGS in order
to determine change-in-HGS and therefore change in nutrition
status. This only requires initial BMI which is used to determine
initial percentage of ideal HGS and, therefore, only requires measurement of body weight initially. HGS may therefore provide an
indication of change in nutrition status when ongoing weight
measurement is not available and weight-reliant assessments such
as the PG-SGA are not possible. Literature also recommends that
the PG-SGA is only repeated every two weeks to allow time for
change to occur.13 In contrast, HGS can be repeated more often and
may therefore provide more frequent updates on the patient’s
nutrition status and direction of change. Although we do not
recommend the use of HGS as the primary measure of nutritional
status of hospital patients, it appears to be a potentially useful tool
for performing rapid assessments of nutritional status, which can
indicate the patient’s direction of change over time. HGS is quick,
easy, non-invasive and objective, with high inter-rater reliability12
which can be performed by any staff member following the set
procedure. In addition to the advantages discussed, HGS may
therefore provide rapid information about a patient’s nutrition
status without the subjectivity and invasiveness of the PG-SGA or
the need for a dietitian.
4.3. Further research
A systematic review by Norman and colleagues suggests that
HGS is an attractive tool for predicting nutrition status because it
reacts quickly to change in nutrition deprivation and restoration.11
If muscle function changes before body composition with
decreasing nutrition and HGS can detect this change, HGS may have
an important role in the early detection of malnutrition. Many tools
used to screen for malnutrition or risk of malnutrition, such as the
Malnutrition Screening Tool, are based on changes to body
composition, particularly weight.18 Although not investigated in
our study, HGS may be able to detect change earlier than current
anthropometry based screening tools. In theory, earlier detection of
malnutrition should lead the way to earlier nutrition intervention
and provide a better outcome for the patient. HGS may also be
useful in the evaluation of the adequacy of existing nutrition
intervention. Further research is required to investigate whether
HGS can detect changes in nutrition status earlier than the PG-SGA,
using comparative measures such as body nitrogen or assessment
113
of fat and non fat mass using techniques such as dual-energy X-ray
absorptiometry. Research into specific populations, such as
oncology patients, may be useful, particularly in regards to development of equations that use HGS to determine predicted nutrition
status or change in nutrition status over time. An equation using
HGS but not relying on weight would be useful in dietetic assessment where weight is not easily obtained. We also suggest that data
on PAL be collected in future studies, despite our results finding no
correlation using the IPAQ-SF tool which was contrary to other
literature.15,19 In our study population, the lack of association may
be due to the tool assessing the past seven days, a time when most
patients would have a low PAL due to illness or hospitalisation over
this period. Therefore an alternative measure of long term PAL may
provide a more representative assessment of physical activity in a
clinical population group such as ours.
4.4. Study strengths and limitations
A comprehensive number of variables collected from patients by
trained staff was a strength of this study. HGS and PG-SGA data
were collected from patients simultaneously which strengthens the
consistency of our data. Our analysis was based on a sample size
(n ¼ 217) that is comparable to similar studies,20e22 and included
male and female hospital patients aged 20e99 years from a variety
of wards. However, approximately a quarter of patients were
excluded from our study due to problems with positioning, arthritis
in their hands, or dementia/delirium. This therefore limits the
generalization of our results, which may not be directly transferable
to the wider hospital population. Although patients in our study
were positioned in the posture recommended by The American
Society of Hand Therapists for each HGS assessment which has high
intra-test and inter-test reliability,23 there is no consensus on
assessment protocol for HGS.11 This limits the comparison between
our study and others using different methodology. Standard reference values for HGS are also lacking and a variety of equations have
been used throughout the literature. It is important to state that the
equations used are only estimates of ideal HGS and may have
intrinsic error. The size of the monitoring sample was limited in our
study (n ¼ 18) which is likely to have affected the ability of our
results to reach significance.
5. Conclusion
The association between HGS and nutrition status we observed
in our study indicates the potential of HGS to provide valuable information to health care professionals working with malnourished
patients. Our research contributes to the literature in this field by
demonstrating that HGS may independently predict nutrition status, as determined by PG-SGA score and category, which confirms
our hypothesis. Although we do not recommend that HGS should
replace existing nutrition assessment tools in a clinical setting at
this stage, we do suggest that HGS should be further investigated as
a malnutrition screening and monitoring tool, as it may provide
information about a patient’s nutrition status earlier than existing
methods.
Statement of authorship
H.P. and V.K. were responsible for the conception and design of
this study, and overseeing of data collection. A.F and A.C were
responsible for data collection and interpretation of the results. A.C
carried out the statistical analysis and A.F. drafted the manuscript.
T.O. provided methodology guidance and statistical support as well
as mentoring of A.F and A.C. All authors were involved in editing the
manuscript.
114
A. Flood et al. / Clinical Nutrition 33 (2014) 106e114
Sources of funding
No funding was provided for any component of this study.
Conflict of interest
The authors declare that they have no conflict of interest
regarding this article.
Acknowledgements
We would like to thank the JHC Dietetic Department for assistance with data collection, and the JHC patients who kindly
participated in the research. We would also like to thank Kimberley
Voo for statistical support, and Nicholas Flood and Sarah Cox for
assistance with manuscript editing.
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