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Erschienen in: BMC Public Health 1/2020

Open Access 01.12.2020 | Research article

Factors associated with low handgrip strength in older people: data of the Study of Chronic Diseases (Edoc-I)

verfasst von: Cledir de Araújo Amaral, Thatiana Lameira Maciel Amaral, Gina Torres Rego Monteiro, Maurício Teixeira Leite de Vasconcellos, Margareth Crisóstomo Portela

Erschienen in: BMC Public Health | Ausgabe 1/2020

Abstract

Background

Handgrip strength (HGS) is an important health biomarker whose low scores have been shown to be associated with the morbimortality. This study aimed to analyze the factors associated with low HGS in older people in Rio Branco, Acre, Brazil.

Methods

The study was carried out with data from the Study of Chronic Diseases (EDOC-I) – Older People, a cross-sectional household PAPI probability sample survey performed with 1016 people aged over 60 residing in Rio Branco in 2014. The low HGS was defined by the 20th percentile of the maximum HGS by sex and age group. Associations between variables of health status (psychological and physical) and low HGS, by sex, were estimated using logistic regression, expressed by adjusted ORs (aOR).

Results

Older individuals had lower median HGS than younger individuals (− 6.0 kg among men and − 2.6 kg among women). Women aged over 80 had, on average, the lower quintile of HGS compared to women of the previous age groups. Factors independently associated with low HGS in men and women, respectively, were low weigh in body mass index [(aOR = 2.80; 95%CI: 1.19, 6.61) and (aOR = 2.61; 95%CI: 1.46, 4.66)], anemia [(aOR = 4.15; 95%CI: 2.09, 8.21) and (aOR = 1.80; 95%CI: 1.06, 3.06)] and diabetes as a risk factor in men (aOR 1.95; 95%CI: 1.00, 3.81). There was a higher chance of low HGS in men with partners (aOR = 2.44; 95%CI: 1.32, 4.51), smokers or former smokers (aOR = 3.25; 95%CI: 1.25, 8.44), with current self-assessment of health worse than the 12 previous months (aOR = 2.21; 95%CI: 1.14, 4.30) and dependence in activities of daily living (aOR = 2.92; 95%CI: 1.35, 6.30). Only among women, there was an increased chance of low HGS associated with altered waist-to-hip ratio (aOR = 1.79; 95%CI: 1.02, 3.12), insomnia (aOR = 1.83; 95%CI: 1.10, 3.03) and physical activity from displacement/occupation (aOR = 1.75; 95%CI: 1.08, 2.84).

Conclusion

Factors associated with low HGS are not the same between sexes, and the inclusion of HGS as a component of health assessment seems to be a promising strategy for disease prevention and health promotion.
Hinweise

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Abkürzungen
IADL
Instrumental Activities of Daily Living
ADL
Activities of Daily Living
DC
Demographic Census
GDS
Geriatric Depression Scale
EDOC-I
Study of Chronic Diseases - Older People
HGS
Handgrip Strength
HDL-cholesterol
High Density Lipoproteins-Cholesterol
IBGE
Brazilian Institute of Geography and Statistics
CI
Confidence Interval
BMI
Body Mass Index
OR
Odds Ratio
aOR
Adjusted Odds Ratio
WHR
Waist-to-Hip Ratio
χ2
Pearson’s Chi-square test

Background

Population aging is a worldwide phenomenon accompanied by the incidence of physical limitations that result in decreased quality of life and increased health care costs. These limitations pose higher risks of falls, institutionalization, comorbidities, and premature mortality. The loss of muscle mass and strength contributes significantly to physical incapacities in aging [1].
Musculoskeletal functionality plays an important role in health and disease, and it is influenced by age [2]. In the aging process, the capacity of the locomotor system and the secretory function of myosin, which act on the metabolism, is reduced, as well as the function of the muscular tissue and other tissues and organs [3], causing a reduction of muscle strength. The evaluation of such strength gains importance. Therefore, as an indicator of muscular quality and functionality, low muscle strength represents a significant public health problem [4].
Handgrip strength (HGS) is a means of measuring muscle strength that has been used to evaluate important health outcomes in older people [5]. Low HGS is associated with sarcopenia and plays an important role in the definition of the frailty phenotype [6]. It is also associated with falls, reduced functional autonomy, and musculoskeletal complaints [7]. The association between reduced HGS and the presence of depression [8], insomnia, diabetes, hypertension, cardiovascular diseases, multimorbidity [4, 7], and mortality [1] has also been demonstrated.
Further, a relation of HGS with indicators of social inequalities, demographic and behavioral conditions has been reported [4].
HGS as a means of tracking diseases and health problems favors the adoption of disease protection and health promotion actions in order to minimize the impacts of morbidity and mortality of the population. However, more knowledge needs to be consolidated to understand the relationship between HGS and morbidity, considering the possibility of these relationships to vary across different population groups. Only then, its use as a health biomarker can be ratified, and conditions of its applicability can be consistently defined.
In order to contribute to the construction of this knowledge, the objective of this study was to analyze the factors associated with low handgrip strength in older people in the City of Rio Branco, Acre, the northern region of Brazil.

Methods

Study setting

This is a secondary analysis of data from the Study of Chronic Diseases in Older People (Estudo das Doenças Crônicas em Idosos – EDOC-I), a household survey conducted between April and September 2014, with older people (60 years old or older) living in urban and rural areas of Rio Branco City, Acre State, Brazil. Rio Branco is located in the Western Brazilian Amazon. The Brazilian Institute of Geography and Statistics (IBGE) estimated that, on July 1, 2014, the older population (over 60 years old) corresponded to about 10% of the adult population (over 18 years old), with older women representing 53.5% of the total older population [9].

Patients/sample

Individuals with compromises that hindered communication or the understanding of the questions were excluded from the research population. Cluster sampling plans were selected in two stages, Census Enumeration Area (CEA) and household. The selection of the CEAs was made with a probability that was proportional to their number and private households in the 2010 Demographic Census (CD2010) of the IBGE. Households were selected by systematic sampling with random starts and distinct intervals. All older people present in the selected households were interviewed.

Sampling

The EDOC-I included 1016 older people interviewed. However, 50 subjects of the effective sample did not have HGS measurement, resulting in a sub-sample that had its sample weights corrected and recalibrated to produce estimates for 23,416 older people. Further details of the sampling plan of the EDOC-I, calculation, and calibration of the weights of the sample and subsamples are found in Amaral et al. [9].

Measurements

Household interviews were conducted with the study participants who answered a structured questionnaire covering socioeconomic, demographic, lifestyle, and health aspects. The trained interviewers evaluated anthropometric measurements and clinical conditions.

Dependent variable

Handgrip Strength (HGS) was measured, in kilograms (kg), using a SAEHAN SH5001® brand hydraulic hand dynamometer with a resolution of 2 kg, following procedures adopted by the American Society of Hand Therapists previously showed [10]. Measurements were obtained in standardized conditions, with the participants in the seated position, elbow at 90°, and the handle adjusted to the second position. After receiving explanation of the procedures and after familiarizing then with the instrument, they applied the maximum grip strength for 3 to 5 s. The procedure was performed three times with each hand alternately, with an interval of one minute between each measurement. The maximum HGS was identified considering the highest HGS value among six measures, regardless of the individual hand dominance.

Covariates – sociodemographic

Physical activity was analyzed in three aspects: physical activity in displacement - considering active the individual who moved to school or to work walking or using a bicycle whose time to go and return was more than 10 min; occupational physical activity - considering active the subject who reported carrying weights or walking intensely in their jobs or who did the house cleaning alone or, in case of receiving help, being the ones responsible for the most massive part of the cleaning; and physical activity in leisure - considering active those who reported practicing exercises or sports in the last three months with a minimum duration of 150 min per week in the case of moderate activities, or 75 min a week in vigorous activities.
Smoking was initially defined into three categories, including current smokers (those who smoke every day), former smokers (those who used to smoke every day), and non-smokers. Since it was not identified any significant difference between the two first groups, they were aggregated and compared to non-smokers.
Only current alcohol use was considered, although data were also collected on abusive use (binge drinking) in the last 30 days. Due to the low frequency of abusive use reported, the analysis was based on user vs. non-user.

Covariates – clinical factors

The investigation of functional independence was based on Katz’s Modified Activities of Daily Living (ADL) scale and the Instrumental Activities of Daily Living (IADL) scale, as described previously [9]. Older people scoring less than five points in the ADL scale was considered as dependent in this analysis, as well as those who needed help for at least one of the activities addressed in the IADL scale.
The Geriatric Depression Scale (GDS-15) was used to assess the presence of depression, considering scores above five points as suggestive of depression [11].
In the definition of morbidities, musculoskeletal complaints were considered based on the self-report of the presence of “much” or “very much” pain in the joints or limbs, in the back, neck or shoulders, or based on reports of diagnoses of arthritis, arthrosis, tendonitis, and repetitive strain injury or osteoporosis. Cardiovascular events were defined by the occurrence of cerebrovascular accident, infarction or angina, heart failure, and arrhythmias or atrial fibrillation. Insomnia (trouble sleeping) was also identified by the self-report of its occurrence (possible answers: “yes” or “already had” or “no”).
Other morbidities were defined based on laboratory test results of blood samples: anemia (hemoglobin ≤13 mg/dL in males, or ≤ 12 mg/dL in females) [12]; diabetes (glycemia ≥126 mg/dL) [13]; hypercholesterolemia (total cholesterol ≥190 mg/dL); altered HDL-cholesterol (< 40 mg/dL in males, or < 50 mg/dL in females), and hypertriglyceridemia (triglycerides ≥150 mg/dL) [14]. The use of medication to control serum levels was also considered.
Dyslipidemia was defined according to the lipid fraction that was altered, triglycerides ≥150 mg/dL, LDL-cholesterol ≥160 mg/dL, HDL-cholesterol < 40 mg/dL in males, or < 50 mg/dL, in females [14].
For the definition of the metabolic syndrome, a combination of the presence of at least three components of glycemia ≥110 mg/dL, systolic blood pressure ≥ 130 mmHg and/or diastolic ≥85 mmHg, triglycerides ≥150 mg/dL, HDL-cholesterol < 40 mg/dL in males, or < 50 mg/dL in females, and abdominal circumference > 102 cm in males, and > 88 cm, in females [15].
Hypertension was identified based on the mean of the second and third blood pressure measurements (systolic blood pressure ≥ 140 mmHg and/or diastolic ≥90 mmHg) or based on the use of hypotensive medication [16].
The waist circumference, waist-to-hip ratio (WHR) and body mass index (BMI) were obtained as the mean of two repeated measures. In the case of waist circumference, measures > 102 cm in men and > 88 cm in women were considered very high. WHR, in turn, was considered high when values were ≥ 1.0 in men and ≥ 0.85 in women [17]. BMI was obtained as the ratio between weight (kg) and the height squared (m2). Specific cutoff points for BMI to older people were: BMI < 22 for low weight; BMI between 22 and 27 for eutrophic weight; and BMI > 27 for overweight [18].

Statistical analysis

Descriptive statistics were obtained focusing on measures of central tendency (mean and median) and 1st quintile of the Maximum HGS stratified by sex and age group (60–69, 70–79, and 80 or over). According to Fried [19], the 20th percentile of the Maximum HGS by sex and age group was adopted for the definition of low HGS and normal HGS, from which a description of the population was made through sociodemographic variables, life habits, and health conditions focusing on measures of absolute (observed n and expanded N for the population) and relative frequency, estimating the differences in the proportions among subjects classified with low HGS by the Pearson Chi-square test.
Thus, the Odds Ratio (logistic regression) models were used to estimate the associations between low HGS and health variables. Bivariate and multivariate analyses by sex estimated the magnitudes of association. The multivariate models, adjusted for covariables (sociodemographic, life habits, and health conditions) that were associated with the outcome of p ≤ 0.20 in the bivariate analysis, were defined using the Enter method, with their respective confidence intervals at 95% (95% CI) by the Wald statistic. The models that best fit the data were determined using the Hosmer-Lemeshow test, the Akaike criterion, and the percent concordant. The significance level of 5% was adopted. Besides, in all the analyses the effect of the complex sample design and the weights of the observations were taken into account using the proc. survey routines of the statistical package SAS® version 9.3.

Results

The mean handgrip strength in older people was 27.2 kg, and the mean difference between one age group and another, for example, 60–69 to 70–79 and 70–79 to ≥80, was − 4.1 kg, being these differences greater among men. The 20th percentile of HGS per age group in males ranged from 28.8 kg in the sexagenarians to 20.8 kg in the octogenarians. Among women, the 20th percentile of HGS ranged from 18.7 kg among 60–69 years old to 12 kg among the oldest women, with a stronger difference between the ages of 70–79 and 80 or more (Table 1).
Table 1
Mean, median and 20th percentile of the maximum HGS among older people
Variables
Overall
Men
Women
Mean
Median
P20
Mean
Median
P20
Mean
Median
P20
Total
27.2
25.0
18.9
33.6
33.1
25.0
21.6
20.6
16.8
Age (age group)
 60–69 years old
29.4
27.4
20.0
36.4
36.6
28.8
23.3
21.9
18.7
 70–79 years old
25.5
23.9
18.0
31.5
30.6
24.7
20.4
19.6
16.1
  ≥ 80 years old
21.3
20.3
15.2
25.9
24.6
20.8
17.4
16.7
12.0
In general, the prevalence of low HGS was 16.7% in men and 17.8% in women. At the intersection of the low HGS with sociodemographic and life habits variables, it was found the association with occupational physical activity in both men and women. There were also borderline associations of the level of HGS with physical activity in leisure among women and smoking among men (Table 2).
Table 2
Prevalence of Low HGS by sex, according to sociodemographic characteristics and life habits among older people
Variables
Overall
Men
Women
Low HGS (≤P20)
Low HGS (≤P20)
Low HGS (≤P20)
n
N
%
χ2
n
N
%
χ2
n
N
%
χ2
Total
169
4054
17.3
66
1822
16.7
103
2232
17.8
Age (age group)
   
0.592
   
0.751
   
0.517
 60–69 years old
78
2228
16.6
 
31
1061
16.9
 
47
1166
16.4
 
 70–79 years old
58
1168
17,5
 
21
468
15.2
 
37
700
19.4
 
  ≥ 80 years old
33
658
19,7
 
14
292
19.0
 
19
365
20.4
 
Referred skin color
   
0.705
   
0.509
   
0.810
 White
43
1023
18.3
 
18
493
19.7
 
25
530
17.1
 
 Non-white
126
3031
17.0
 
48
1329
15.8
 
78
1702
18.0
 
Marital status a
   
0.122
   
0.087
   
0.587
 With partner
53
1323
14.7
 
28
736
13.7
 
25
587
16.2
 
 Without partner
115
2712
19.0
 
38
1086
20.0
 
77
1626
18.4
 
Education
   
0.045
   
0.155
   
0.137
 Illiterate
71
1678
20.7
 
27
733
19.5
 
44
944
21.8
 
 Elementary School
80
1955
17.3
 
34
960
17.7
 
46
995
16.9
 
 High School
18
421
11.0
 
5
129
8.0
 
13
293
13.2
 
Physical activity in leisure a
   
0.007
   
0.100
   
0.056
 Active
6
155
6.9
 
3
88
7.6
 
3
67
6.0
 
 Sedentary
161
3857
18.6
 
61
1693
17.7
 
100
2164
19.4
 
Occupational Physical Activity or in Displacement
   
0.007
   
0.052
   
0.014
 Active
57
1415
13.3
 
19
531
12.2
 
38
884
14.1
 
 Sedentary
112
2638
20.6
 
47
1291
19.7
 
65
1347
21.6
 
Screen time per day a
   
0.984
   
0.974
   
0.962
 Up to 3 h
119
2927
17.1
 
47
1334
16.3
 
72
1592
17.8
 
 More than 3 h
46
1034
17.1
 
16
413
16.4
 
30
620
17.7
 
Smoking
   
0,073
   
0.065
   
0.417
 Non-smoker
20
500
11.7
 
9
258
9.9
 
11
242
14.4
 
 Smoker, ex-smoker
149
3553
18.6
 
57
1564
18.9
 
92
1989
18.4
 
Alcohol consumption a
   
0,193
   
0.234
   
0.690
 No
152
3580
17.8
 
55
1497
11.0
 
97
2083
14.7
 
 Yes
9
255
11.8
 
6
182
17.4
 
3
73
18.0
 
amissing; n = number of observations in the sample; N = population inference based on the sample weights; % = estimated prevalence based on weighted frequencies; χ 2 = p-value of the Pearson Chi-square test
Regarding the analysis of associations between HGS (low vs. normal) and health/clinic conditions (Table 3), associations with the variables BMI, self-assessment of health in relation to people of the same age, IADL, and anemia were statistically significant in both sexes. Specifically, among women, it was observed that those who had insomnia had a significantly higher proportion of low HGS (23.0%) than those without insomnia (14.4%). Only, among men, the prevalence of low HGS was significantly higher in those who assessed their current health worse than it was 12 months before the interview, those with dependence in ADL, and those with diabetes. Additionally, borderline associations (0.05 < p < 0.10) of HGS were identified with self-assessment of health and hypertriglyceridemia, among men, and with metabolic syndrome, among women.
Table 3
Prevalence of Low HGS by sex, according to health conditions among older people
Variables
Overall
Men
Women
Low HGS (≤P20)
Low HGS (≤P20)
Low HGS (≤P20)
n
N
%
χ2
n
N
%
χ2
n
N
%
χ2
Waist circumference a
   
0.652
   
0.249
   
0.956
 Normal
106
2627
17.5
 
57
1561
17.6
 
49
1066
17.2
 
 Altered
60
1362
16.4
 
9
261
13.2
 
51
1101
17.5
 
Waist-to-hip ratio a
   
0.200
   
0.629
   
0.187
 Normal
65
1647
15.4
 
39
1082
15.9
 
26
565
14.6
 
 Altered
100
2319
18.5
 
26
717
18.0
 
74
1602
18.8
 
BMI*
   
< 0.001
   
0.035
   
< 0.001
 Low weight
41
926
31.8
 
16
389
30.9
 
25
537
32.4
 
 Eutrophic
56
1400
15.8
 
26
771
15.9
 
30
629
15.8
 
 Overweight
67
1599
14.2
 
23
625
13.5
 
44
974
14.6
 
Self-assessment of health
   
0.080
   
0.077
   
0.540
 Very Good /Good /Regular
135
3231
16.4
 
54
1483
15.4
 
81
1749
17.3
 
 Bad /Very bad
34
822
22.3
 
12
339
26.6
 
22
483
20.0
 
Assessment of current health in relation to that of the previous 12 months a
   
0.011
   
0.006
   
0.398
 Better /Equal
101
2391
15.1
 
38
1040
13.3
 
63
1352
16.8
 
 Worse
68
1662
22.2
 
28
782
26.0
 
40
880
19.7
 
Assessment of health in comparison with people of the same age a
   
< 0.001
   
0.005
   
0.020
 Better /Equal
119
2814
15.2
 
46
1227
14.1
 
73
1587
16.2
 
 Worse
44
1090
27.5
 
17
507
28.4
 
27
583
26.8
 
Dependence in ADL a
   
0.035
   
< 0.001
   
0.830
 No
139
3338
16.3
 
49
1363
14.5
 
90
1975
17.9
 
 Yes
29
692
24.5
 
17
459
32.4
 
12
233
16.6
 
Dependence on IADL a
   
< 0.001
   
< 0.001
   
0.032
 No
59
1492
12.3
 
24
693
11.3
 
35
799
13.3
 
 Yes
109
2537
22.8
 
42
1129
24.0
 
67
1408
21.9
 
Depression in the GDS*
   
0.094
   
0.246
   
0.269
 No
104
2506
15.9
 
44
1213
15.4
 
60
1293
20.5
 
 Yes
64
1514
20.3
 
21
576
20.1
 
43
938
16.4
 
Insomnia a
   
0.024
   
0.347
   
0.015
 No
97
2310
15.0
 
45
1225
15.5
 
52
1085
14.4
 
 Yes
72
1744
21.9
 
21
597
19.9
 
51
1147
23.0
 
Musculoskeletal complaints
   
0.733
   
0.701
   
0.335
 No
70
1722
16.8
 
41
1099
17.4
 
29
623
15.7
 
 Yes
99
2332
17.7
 
25
723
15.8
 
74
1609
18.8
 
Cardiovascular event
   
0.203
   
0.509
   
0.171
 No
125
2992
16.5
 
50
1368
16.0
 
75
1625
16.9
 
 Yes
44
1061
20.2
 
16
454
19.2
 
28
607
21.1
 
Anemia a
   
< 0.001
   
< 0.001
   
0.022
 No
107
2573
13.8
 
35
1010
11.9
 
72
1563
15.4
 
 Yes
56
1355
30.3
 
30
791
35.1
 
26
564
25.3
 
Diabetes a
       
0.046
   
0.191
 No
138
3277
17.0
 
51
1393
15.3
 
87
1884
18.4
 
 Yes
28
712
18.1
 
14
407
24.0
 
14
305
13.6
 
Hypertension a
   
0.190
   
0.195
   
0.733
 No
48
1165
20.0
 
22
585
21.1
 
26
580
18.9
 
 Yes
120
2867
16.5
 
43
1216
15.3
 
77
1651
17.6
 
Hypercholesterolemia a
   
0.261
   
0.875
   
0.107
 No
86
2056
18.3
 
39
1039
16.8
 
47
1017
20.1
 
 Yes
78
1891
15.7
 
26
762
16.3
 
52
1129
15.3
 
Altered HDL cholesterol a
   
0.090
   
0.404
   
0.176
 No
105
2579
15.7
 
45
1269
15.7
 
60
1311
15.8
 
 Yes
58
1349
19.8
 
20
532
19.6
 
38
817
19.9
 
Hypertriglyceridemia a
   
0.033
   
0.054
   
0.287
 No
103
2480
19.5
 
47
1256
19.9
 
56
1224
19.1
 
 Yes
62
1491
14.1
 
18
544
12.0
 
44
946
15.6
 
Dyslipidemia a
   
0.280
   
0.265
   
0.563
 No
38
951
20.4
 
22
595
20.7
 
16
356
19.9
 
 Yes
127
3020
16.2
 
43
1206
15.1
 
84
1814
17.0
 
Metabolic syndrome a
   
0.080
   
0.386
   
0.173
 No
91
2213
18.9
 
35
989
18.5
 
56
1224
19.2
 
 Yes
71
1690
14.8
 
29
790
14.8
 
42
900
14.9
 
amissing; n = number of observations in the sample; N = population inference based on weights and sampling design; % = estimated prevalence based on weighted frequencies; χ2 = p-value of the Pearson chi-square test
Table 4 presents the results of the non-adjusted and adjusted logistic regression analyses, identifying the crude and independent effects of the factors associated with the occurrence of low HGS among older men and women. The odds of low HGS were significantly and consistently higher among older people with low weight (men: aOR = 2.80; 95%CI: 1.19, 6.61; women: aOR = 2.61; 95%CI: 1.46, 4.66) and with anemia (men: aOR = 4.15; 95%CI: 2.09, 8.21; women: aOR = 1.80; 95%CI: 1.06,3.06).
Table 4
Logistic regression of the low HGS (≤ P20) with independent variables, by sex, among older people
Variables
Men
Women
OR (CI95%) Crude
OR (CI95%) Adjusted
OR (CI95%) Crude
OR (CI95%) Adjusted
Marital status (With vs. Without partner)
1.57 (0.91, 2.70)
2.44 (1.32, 4.51)
Smoking (Smoker /Ex-smoker vs. Non-smoker)
2.11 (0.89, 4.97)
3.25 (1.25, 8.44)
Assessment of current health in relation to that of the previous 12 months (Worse vs. Better /Equal)
2.28 (1.23, 4.21)
2.21 (1.14, 4.30)
ADL (Dependent vs. Independent)
2.83 (1.63, 4.93)
2.92 (1.35, 6.30)
BMI
 Low weight
2.38 (1.07, 5.27)
2.80 (1.19, 6.61)
2.56 (1.49, 4.33)
2.61 (1.46, 4.66)
 Eutrophic
1
1
1
1
 Overweight
0.83 (0.44, 1.57)
1.09 (0.50, 2.38)
0.91 (0.91, 1.32)
0.86 (0.58, 1.26)
Anemia (Yes vs. No)
4.01 (2.14, 7.51)
4.15 (2.09, 8.21)
1.86 (1.08, 3.22)
1.80 (1.06, 3.06)
Diabetes (Yes vs. No)
1.75 (0.98, 3.12)
1.95 (1.00, 3.81)
0.70 (0.40, 1.23)
0.53 (0.28, 1.01)
Insomnia (Yes vs. No)
1.77 (1.09, 2.89)
1.83 (1.10, 3.03)
Waist-to-hip ratio (Altered vs. Normal)
1.35 (0.85, 2.14)
1.79 (1.02, 3.12)
Physical activity in displacement /occupational physical activity (Yes vs. No)
1.67 (1.09, 2.56)
1.75 (1.08, 2.84)
p-value (Wald)
 
< 0.001
 
< 0.001
 % Concordance
 
76.0
 
67.4
% Concordance is a fitness quality criterion of the logistic regression model
The presence of diabetes was found to be significantly associated with low HGS as a risk factor among men (aOR = 1.95; 95%CI: 1.00, 3.81), having a partner (aOR = 2.44; 95%CI: 1.32, 4.51); being a smoker or ex-smoker (aOR = 3.25; 95%CI: 1.25, 8.44); current self-assessment of health as worse than that of the previous 12 months (aOR = 2.21; 95%CI: 1.14, 4.30); and presenting dependence in activities of daily living (aOR = 2.92; CI95%: 1.35, 6.30) were associated with low HGS in men. Among women, there were higher odds of occurrence of low HGS associated with altered WHR (aOR = 1.79; 95%CI: 1.02, 3.12), insomnia (aOR = 1.83; 95%CI: 1.10, 3.03) and physical activity in displacement/occupational physical activity (aOR = 1.75; 95%CI: 1.08, 2.84).

Discussion

This work confirms the significant decrease of HGS in older men and women, generally indicating a steeper decline in men [4, 20], as well as an intensification of this decline with age in older women. It points out the consistency, in both sexes, of the association of low HGS with low weight and anemia. It also shows the extent to which sociodemographic and behavioral factors, as well as health conditions, are differently associated with low HGS among older men and women.
The option to define the low strength by the lower quintile of the HGS distribution by sex and also by age group (60, 70 and 80 years old or older) was justified by the identification, both among men and women, of a strong correlation of HGS with age [10], which allowed some neutralization of the effect of this variable in the analyses. Differently from the option adopted here, the use of the 20th percentile of HGS adjusted by sex and BMI has been recommended to define low HGS, especially in studies about the frailty phenotype [19]. However, if the 20th percentile of HGS of the entire older population had been adopted, disregarding age ranges, more than 50% of men and women over 80 would be classified as having low HGS, and only a small number of youngest-old would be identified as ‘weak’, which would probably lead to important changes in the results presented here.
BMI is used as a criterion for the definition of nutritional status, and the associations between low weight and low HGS shown here agree with well-established evidence [21]. Hand dynamometry has been recognized as a useful marker of functionality as well as an objective marker of malnutrition [22, 23].
Studies with inpatients revealed that HGS is a reliable measure for the prediction of malnutrition, resulting in more extended hospitalization, clinical complications, and death [23]. Functional losses related to malnutrition can be recovered after protein uptake, and hand dynamometry captures these changes with improved strength levels more rapidly than the BMI [24], which is an alternative for the BMI between the criteria for assessing nutritional status in geriatric patients [25].
Anemia has a multifactorial etiology and, in the older people, contributes to morbidities, physical performance reduction, and the increase in the number of falls, frailty, dementia, hospitalization, and mortality [26]. Identifying the association of HGS with anemia, consistently among men and women, in the general population, ratifies previous findings where reduced hemoglobin levels were linked to low HGS and other criteria of frailty and physical disabilities in the elderly community [27].
This study also confirms that the effect of anemia on the occurrence of low HGS is independent of other sociodemographic, anthropometric, and clinical factors [28]. Although a possible link between reduced HGS and the multiple etiological factors of anemia beyond the scope of the present study is recognized, a plausible explanation for the findings is that the reduction of hemoglobin levels decreases the oxygen consumption capacity of the muscles, leading to tissue hypoxia that promotes the decline of physiological reserve [29].
The relationship between low HGS and diabetes had already been reported in a previous study conducted in Rio Branco among adults aged 18–96, where men with weak HGS presented odds of occurrence of self-reported disease four times higher than those with normal HGS [7]. In this sense, this research contributes to confirming this relationship, considering diabetes diagnosed in the laboratory.
The literature predominantly establishes positive association between the level of HGS and the prevalence of diabetes among men and women [30, 31]. Regarding the association of HGS with the incidence of diabetes, prospective studies are contradictory [32, 33]. The association of HGS with diabetes may be justified by its intimate connection with muscle mass, which plays an important role in the use of blood glucose, also due to its size and responsiveness to insulin [34].
Other explanatory factors of the variation in the occurrence of low HGS had significant effects only in one sex or the other.
Among men, having a partner resulted in a risk factor for low HGS. Although the mechanisms of these associations are unclear, the marital situation is likely a proxy of something that is closely related to HGS, which may reflect accommodation greater preference for household habits, sedentarism, among others. However, in the same sense, it was reported that being married was associated with lower HGS [4] among men aged 72, although a protective effect of marriage on strength has been found among young and middle-aged adults [4, 35]. Studies are needed to explore better the relationships between HGS and marital status in different age groups, since this relationship is little-known.
The smoking history was another factor that was independently associated with the occurrence of low HGS only among men. Smoking had a high prevalence in the study population, especially among women, and the finding that this habit among them did not related to the low HGS is remarkable. Therefore, this is worthy of the exploration in future studies. The relationships of smoking with detrimental health effects are already widely recognized, and it has already been identified in previous studies that smoking men have reduced HGS compared to non-smoking peers [36]. As a possible explanation for the mechanisms underlying muscle reduction among smokers, a review has gathered evidence that the constituents of circulating cigarette smoke seem to play an important role in this process, since they induce loss of muscle mass, reduce oxygen supply, and impair mitochondrial function [37].
The decline in physiological reserve caused by aging leads to a loss of functional independence - a central aspect of the health of older people. The literature has broadly established a relationship between low HGS and dependence in ADL among men and women [3840] but has been less consistent in establishing the relationship with IADL [39, 40]. In any case, the measurement of HGS as a useful tool in the identification of people at risk of future functional decline has been sustained [41]. The findings of this study only confirm, among men, the association between low HGS and dependence on ADL, regardless of other factors.
Furthermore, the assessment of the current health as worse than 12 previous months ago was relevant to the explanation of the variation in the occurrence of low HGS among men. Although self-assessment of health is an indicator of objective physical and mental health conditions, its use in HGS studies is unknown. However, parallel relations support the consideration of the variable and greater exploitation of its effects in different contexts [42]. It is worth mentioning that this study explored self-assessment with three distinct but correlated indicators, as well as the finding that self-assessment of health is a construct that differs between the sexes, with unequal health profiles of men and women being influenced by their perceptions [20].
Among women, in addition to reduced BMI, the presence of anemia and absence of diabetes were independently associated with increased chances of low HGS, altered WHR score, insomnia, and inadequate physical activity in displacement and/or occupational activity.
WHR is an indirect measure of central adiposity, and the accumulation of visceral fat is responsible for the concentration of inflammatory mediators that can result in sarcopenia and frailty, which may explain the association of altered WHR and reduced HGS among women [43].
Although insomnia was considered based on self-report, its association with low HGS finds resonance in other studies [44, 45], although it has presented a relation only among women. A recent study with middle-aged and older people demonstrated a quadratic relationship in which both reduced hours of sleep in both sexes and excessive sleep in women were associated with a steeper decline in HGS over four years of follow-up. One possible explanation on the relationship between sleep and HGS is in the circadian clock, where sleep hours and sleep quality act in musculoskeletal physiology, regulating and being influenced by sleep, by genetic mechanisms and inflammatory processes, which are also associated with loss of strength [45].
Among the limitations of the present research, it must be emphasized a possible attenuation of the associations due to the survival effect. Also, the punctual diagnosis of the diseases, both those defined by physical examinations, such as hypertension, and those resulting from clinical laboratory tests from blood dosage that could falsify results, was somehow mitigated by broad guidance on the protocols to carry out the examinations and evaluations. On the other hand, as a strong point of this study, it should be noted that its results are inferential to the older population of the capital of the state of Acre and that laboratory and clinical measures were used to define the diseases, which allowed to inform people who were unaware of the presence of certain diseases, either by limitation or lack of access to health services or even by a lack of awareness of the need for such care.

Conclusion

Factors associated with low HGS are not equal between the sexes. Among older men, low HGS was associated with low weight (BMI), anemia, diabetes, having a partner, having smoking history, negative self-assessment of current health compared to the previous 12 months, and dependence in ADL. Among older women, low HGS was associated with low weight (BMI), anemia, diabetes (surprisingly, a protective effect was observed here), altered waist-to-hip ratio, insomnia, and insufficient physical activity in displacement or occupational tasks.
The findings reinforce HGS as a health biomarker in older people of both sexes and support its use as a viable strategy which can easily be incorporated into both rehabilitation and primary health care, not only for the screening of the low strength as an indicator of health problems in older people but as a measure, along with other criteria, for monitoring health throughout life, thus allowing promising early intervention actions in disease prevention and health promotion.

Acknowledgments

No applicable.
The basic research project, EDOC, was approved by the Research Ethics Committee (REC) of the Federal University of Acre under the number 17543013.0.0000.5010, all participants signed the Informed Consent Term. This study, specifically, was also approved by the REC of the Sérgio Arouca National School of Public Health, number 50895015.2.0000.5240.
Not applicable.

Competing interests

The authors declare that they have no competing interests.
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Literatur
1.
Zurück zum Zitat Tieland M, Trouwborst I, Clark BC. Skeletal muscle performance and ageing. J Cachexia Sarcopenia Muscle. 2018;9:3–19.CrossRefPubMed Tieland M, Trouwborst I, Clark BC. Skeletal muscle performance and ageing. J Cachexia Sarcopenia Muscle. 2018;9:3–19.CrossRefPubMed
2.
Zurück zum Zitat Sayer AA, Kirkwood TBL. Grip strength and mortality: a biomarker of ageing? Lancet. 2015;386:226–7.CrossRefPubMed Sayer AA, Kirkwood TBL. Grip strength and mortality: a biomarker of ageing? Lancet. 2015;386:226–7.CrossRefPubMed
3.
Zurück zum Zitat Pedersen BK. Muscle as a secretory organ. Compr Physiol. 2013;3:1337–62.PubMed Pedersen BK. Muscle as a secretory organ. Compr Physiol. 2013;3:1337–62.PubMed
4.
Zurück zum Zitat Sternäng O, Reynolds CA, Finkel D, Ernsth-Bravell M, Pedersen NL, Aslan AKD. Factors associated with grip strength decline in older adults. Age Ageing. 2015;44:269–74.CrossRefPubMed Sternäng O, Reynolds CA, Finkel D, Ernsth-Bravell M, Pedersen NL, Aslan AKD. Factors associated with grip strength decline in older adults. Age Ageing. 2015;44:269–74.CrossRefPubMed
5.
Zurück zum Zitat Bohannon RW. Hand-grip dynamometry predicts future outcomes in aging adults. J Geriatr Phys Ther. 2008;31:3–10.CrossRefPubMed Bohannon RW. Hand-grip dynamometry predicts future outcomes in aging adults. J Geriatr Phys Ther. 2008;31:3–10.CrossRefPubMed
6.
Zurück zum Zitat Wilson D, Jackson T, Sapey E, Lord JM. Frailty and sarcopenia: the potential role of an aged immune system. Ageing Res Rev. 2017;36:1–10.CrossRefPubMed Wilson D, Jackson T, Sapey E, Lord JM. Frailty and sarcopenia: the potential role of an aged immune system. Ageing Res Rev. 2017;36:1–10.CrossRefPubMed
7.
Zurück zum Zitat Amaral CA, Portela MC, Muniz PT, Farias ES, Araújo TS, Souza OF. Association of handgrip strength with self-reported diseases in adults in Rio Branco, acre state, Brazil: a population-based study. Cad Saúde Pública. 2015;31:1313–25.CrossRef Amaral CA, Portela MC, Muniz PT, Farias ES, Araújo TS, Souza OF. Association of handgrip strength with self-reported diseases in adults in Rio Branco, acre state, Brazil: a population-based study. Cad Saúde Pública. 2015;31:1313–25.CrossRef
8.
Zurück zum Zitat Lino VTS, Rodrigues NCP, O’Dwyer G, MK de N A, Mattos IE, Portela MC. Handgrip strength and factors associated in poor elderly assisted at a primary care unit in Rio de Janeiro, Brazil. PLoS ONE. 2016;11:e0166373.CrossRefPubMedPubMedCentral Lino VTS, Rodrigues NCP, O’Dwyer G, MK de N A, Mattos IE, Portela MC. Handgrip strength and factors associated in poor elderly assisted at a primary care unit in Rio de Janeiro, Brazil. PLoS ONE. 2016;11:e0166373.CrossRefPubMedPubMedCentral
9.
Zurück zum Zitat Amaral TLM, Amaral CA, Portela MC, Monteiro GTR, Vasconcellos MTL. Study of chronic diseases (Edoc): methodological aspects. Rev Saúde Pública. 2019;53:8. Amaral TLM, Amaral CA, Portela MC, Monteiro GTR, Vasconcellos MTL. Study of chronic diseases (Edoc): methodological aspects. Rev Saúde Pública. 2019;53:8.
10.
Zurück zum Zitat Amaral CA, Amaral TLM, Monteiro GTR, Vasconcellos MTL, Portela MC. Hand grip strength: reference values for adults and elderly people of Rio Branco, acre, Brazil. PLos One. 2019;14:e0211452.CrossRefPubMedPubMedCentral Amaral CA, Amaral TLM, Monteiro GTR, Vasconcellos MTL, Portela MC. Hand grip strength: reference values for adults and elderly people of Rio Branco, acre, Brazil. PLos One. 2019;14:e0211452.CrossRefPubMedPubMedCentral
11.
Zurück zum Zitat Almeida OP, Almeida SA. Reliability of the Brazilian version of the geriatric depression scale (GDS) short form. Arq Neuropsiquiatr. 1999;57:421–6.CrossRefPubMed Almeida OP, Almeida SA. Reliability of the Brazilian version of the geriatric depression scale (GDS) short form. Arq Neuropsiquiatr. 1999;57:421–6.CrossRefPubMed
13.
Zurück zum Zitat American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37:S81–90.CrossRef American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37:S81–90.CrossRef
14.
Zurück zum Zitat Faludi AA, MC de O I, JFK S, APM C, Bianco HT, Afiune Neto A, et al. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose – 2017. Arq Bras Cardiol. 2017;109:1–76.PubMed Faludi AA, MC de O I, JFK S, APM C, Bianco HT, Afiune Neto A, et al. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose – 2017. Arq Bras Cardiol. 2017;109:1–76.PubMed
15.
Zurück zum Zitat Sociedade Brasileira de Hipertensão, Sociedade Brasileira de Cardiologia, Sociedade Brasileira de Endocrinologia e Metabologia, Sociedade Brasileira de Diabetes, Associação Brasileira para Estudos da Obesidade. I Diretriz Brasileira de Diagnóstico e Tratamento da Síndrome Metabólica. Arq Bras Cardiol. 2005;84:3–28. Sociedade Brasileira de Hipertensão, Sociedade Brasileira de Cardiologia, Sociedade Brasileira de Endocrinologia e Metabologia, Sociedade Brasileira de Diabetes, Associação Brasileira para Estudos da Obesidade. I Diretriz Brasileira de Diagnóstico e Tratamento da Síndrome Metabólica. Arq Bras Cardiol. 2005;84:3–28.
16.
Zurück zum Zitat Malachias MVB, M a. M G, Nobre F, Alessi A, Feitosa AD, Coelho EB, et al. 7th Brazilian guideline of arterial hypertension: chapter 2 - diagnosis and classification. Arq Bras Cardiol. 2016;107:7–13.PubMed Malachias MVB, M a. M G, Nobre F, Alessi A, Feitosa AD, Coelho EB, et al. 7th Brazilian guideline of arterial hypertension: chapter 2 - diagnosis and classification. Arq Bras Cardiol. 2016;107:7–13.PubMed
18.
Zurück zum Zitat Lipschitz DA. Screening for nutritional status in the elderly. Prim Care. 1994;21:55–67.PubMed Lipschitz DA. Screening for nutritional status in the elderly. Prim Care. 1994;21:55–67.PubMed
19.
Zurück zum Zitat Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults evidence for a phenotype. J Gerontol Ser A. 2001;56:M146–57.CrossRef Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults evidence for a phenotype. J Gerontol Ser A. 2001;56:M146–57.CrossRef
20.
21.
Zurück zum Zitat Oumi M, Miyoshi M, Yamamoto T. Ultrastructural changes and glutathione depletion in the skeletal muscle induced by protein malnutrition. Ultrastruct Pathol. 2001;25:431–6.CrossRefPubMed Oumi M, Miyoshi M, Yamamoto T. Ultrastructural changes and glutathione depletion in the skeletal muscle induced by protein malnutrition. Ultrastruct Pathol. 2001;25:431–6.CrossRefPubMed
22.
Zurück zum Zitat Bharadwaj S, Ginoya S, Tandon P, Gohel TD, Guirguis J, Vallabh H, et al. Malnutrition: laboratory markers vs nutritional assessment. Gastroenterol Rep. 2016;4:272–80. Bharadwaj S, Ginoya S, Tandon P, Gohel TD, Guirguis J, Vallabh H, et al. Malnutrition: laboratory markers vs nutritional assessment. Gastroenterol Rep. 2016;4:272–80.
23.
Zurück zum Zitat Gaikwad NR, Gupta SJ, Samarth AR, Sankalecha TH. Handgrip dynamometry: a surrogate marker of malnutrition to predict the prognosis in alcoholic liver disease. Ann Gastroenterol Q Publ Hell Soc Gastroenterol. 2016;29:509–14. Gaikwad NR, Gupta SJ, Samarth AR, Sankalecha TH. Handgrip dynamometry: a surrogate marker of malnutrition to predict the prognosis in alcoholic liver disease. Ann Gastroenterol Q Publ Hell Soc Gastroenterol. 2016;29:509–14.
24.
Zurück zum Zitat Schlüssel MM, Anjos LA, Kac G. Hand grip strength test and its use in nutritional assessment. Rev Nutr. 2008;21:233–5.CrossRef Schlüssel MM, Anjos LA, Kac G. Hand grip strength test and its use in nutritional assessment. Rev Nutr. 2008;21:233–5.CrossRef
25.
Zurück zum Zitat Kizilarslanoglu MC, Kilic MK, Gokce D, Sakalar T, Ulger Z. Is it possible using handgrip strength instead of body mass index in MNA-SF test to assess the nutritional status of geriatric patients? J Nutr Health Aging. 2017;21:579–84.CrossRefPubMed Kizilarslanoglu MC, Kilic MK, Gokce D, Sakalar T, Ulger Z. Is it possible using handgrip strength instead of body mass index in MNA-SF test to assess the nutritional status of geriatric patients? J Nutr Health Aging. 2017;21:579–84.CrossRefPubMed
26.
Zurück zum Zitat Milagres CS, Franceschini SDCC, Priore SE, Lima LM, Ribeiro AQ. Prevalência e etiologia da anemia em idosos: uma revisão integral. Med Ribeirao Preto Online. 2015;48:99.CrossRef Milagres CS, Franceschini SDCC, Priore SE, Lima LM, Ribeiro AQ. Prevalência e etiologia da anemia em idosos: uma revisão integral. Med Ribeirao Preto Online. 2015;48:99.CrossRef
27.
Zurück zum Zitat Silva JC, ZV de M, Silva C, S de B M, Guariento ME, Neri AL, et al. Understanding red blood cell parameters in the context of the frailty phenotype: interpretations of the FIBRA (Frailty in Brazilian Seniors) study. Arch Gerontol Geriatr. 2014;59:636–41.CrossRefPubMed Silva JC, ZV de M, Silva C, S de B M, Guariento ME, Neri AL, et al. Understanding red blood cell parameters in the context of the frailty phenotype: interpretations of the FIBRA (Frailty in Brazilian Seniors) study. Arch Gerontol Geriatr. 2014;59:636–41.CrossRefPubMed
28.
Zurück zum Zitat Cecchi F, Pancani S, Vannetti F, Boni R, Castagnoli C, Paperini A, et al. Hemoglobin concentration is associated with self-reported disability and reduced physical performance in a community dwelling population of nonagenarians: the Mugello study. Intern Emerg Med. 2017;12:1167–73.CrossRefPubMedPubMedCentral Cecchi F, Pancani S, Vannetti F, Boni R, Castagnoli C, Paperini A, et al. Hemoglobin concentration is associated with self-reported disability and reduced physical performance in a community dwelling population of nonagenarians: the Mugello study. Intern Emerg Med. 2017;12:1167–73.CrossRefPubMedPubMedCentral
29.
30.
31.
Zurück zum Zitat Peterson MD, McGrath R, Zhang P, Markides KS, Snih SA, Wong R. Muscle weakness is associated with diabetes in older Mexicans: the Mexican health and aging study. J Am Med Dir Assoc. 2016;17:933–8.CrossRefPubMedPubMedCentral Peterson MD, McGrath R, Zhang P, Markides KS, Snih SA, Wong R. Muscle weakness is associated with diabetes in older Mexicans: the Mexican health and aging study. J Am Med Dir Assoc. 2016;17:933–8.CrossRefPubMedPubMedCentral
32.
Zurück zum Zitat Leong DP, Teo KK, Rangarajan S, Lopez-Jaramillo P, Avezum A Jr, Orlandini A, et al. Prognostic value of grip strength: findings from the prospective urban rural epidemiology (PURE) study. Lancet. 2015;386:266–73.CrossRefPubMed Leong DP, Teo KK, Rangarajan S, Lopez-Jaramillo P, Avezum A Jr, Orlandini A, et al. Prognostic value of grip strength: findings from the prospective urban rural epidemiology (PURE) study. Lancet. 2015;386:266–73.CrossRefPubMed
33.
Zurück zum Zitat Li JJ, Wittert GA, Vincent A, Atlantis E, Shi Z, Appleton SL, et al. Muscle grip strength predicts incident type 2 diabetes: population-based cohort study. Metabolism. 2016;65:883–92.CrossRefPubMed Li JJ, Wittert GA, Vincent A, Atlantis E, Shi Z, Appleton SL, et al. Muscle grip strength predicts incident type 2 diabetes: population-based cohort study. Metabolism. 2016;65:883–92.CrossRefPubMed
34.
Zurück zum Zitat Arvandi M, Strasser B, Meisinger C, Volaklis K, Gothe RM, Siebert U, et al. Gender differences in the association between grip strength and mortality in older adults: results from the KORA-age study. BMC Geriatr. 2016;16. Arvandi M, Strasser B, Meisinger C, Volaklis K, Gothe RM, Siebert U, et al. Gender differences in the association between grip strength and mortality in older adults: results from the KORA-age study. BMC Geriatr. 2016;16.
35.
Zurück zum Zitat Guralnik JM, Butterworth S, Patel K, Mishra G, Kuh D. Reduced midlife physical functioning among never married and childless men: evidence from the 1946 British birth cohort study. Aging Clin Exp Res. 2009;21:174–81.CrossRefPubMedPubMedCentral Guralnik JM, Butterworth S, Patel K, Mishra G, Kuh D. Reduced midlife physical functioning among never married and childless men: evidence from the 1946 British birth cohort study. Aging Clin Exp Res. 2009;21:174–81.CrossRefPubMedPubMedCentral
36.
Zurück zum Zitat Al-Obaidi S, Al-Sayegh N, Nadar M. Smoking impact on grip strength and fatigue resistance: implications for exercise and hand therapy practice. J Phys Act Health. 2014;11:1025–31.CrossRefPubMed Al-Obaidi S, Al-Sayegh N, Nadar M. Smoking impact on grip strength and fatigue resistance: implications for exercise and hand therapy practice. J Phys Act Health. 2014;11:1025–31.CrossRefPubMed
37.
Zurück zum Zitat Degens H, Gayan-Ramirez G, van Hees HWH. Smoking-induced skeletal muscle dysfunction. From evidence to mechanisms. Am J Respir Crit Care Med. 2015;191:620–5.CrossRefPubMed Degens H, Gayan-Ramirez G, van Hees HWH. Smoking-induced skeletal muscle dysfunction. From evidence to mechanisms. Am J Respir Crit Care Med. 2015;191:620–5.CrossRefPubMed
38.
Zurück zum Zitat Kim M-J, Yabushita N, Kim M-K, Matsuo T, Okuno J, Tanaka K. Alternative items for identifying hierarchical levels of physical disability by using physical performance tests in women aged 75 years and older. Geriatr Gerontol Int. 2010;10:302–10.CrossRefPubMed Kim M-J, Yabushita N, Kim M-K, Matsuo T, Okuno J, Tanaka K. Alternative items for identifying hierarchical levels of physical disability by using physical performance tests in women aged 75 years and older. Geriatr Gerontol Int. 2010;10:302–10.CrossRefPubMed
39.
Zurück zum Zitat Taekema DG, Gussekloo J, Maier AB, Westendorp RGJ, Craen D, AJ M. Handgrip strength as a predictor of functional, psychological and social health. A prospective population-based study among the oldest old. Age Ageing. 2010;39:331–7.CrossRefPubMed Taekema DG, Gussekloo J, Maier AB, Westendorp RGJ, Craen D, AJ M. Handgrip strength as a predictor of functional, psychological and social health. A prospective population-based study among the oldest old. Age Ageing. 2010;39:331–7.CrossRefPubMed
40.
Zurück zum Zitat Yang M, Ding X, Luo L, Hao Q, Dong B. Disability associated with obesity, dynapenia and dynapenic-obesity in Chinese older adults. J Am Med Dir Assoc. 2014;15:150.e11–6.CrossRef Yang M, Ding X, Luo L, Hao Q, Dong B. Disability associated with obesity, dynapenia and dynapenic-obesity in Chinese older adults. J Am Med Dir Assoc. 2014;15:150.e11–6.CrossRef
41.
Zurück zum Zitat Kim M, Tanaka K. A multidimensional assessment of physical performance for older Japanese people with community-based long-term care needs. Aging Clin Exp Res. 2014;26:269–78.CrossRefPubMed Kim M, Tanaka K. A multidimensional assessment of physical performance for older Japanese people with community-based long-term care needs. Aging Clin Exp Res. 2014;26:269–78.CrossRefPubMed
42.
Zurück zum Zitat Sousa-Santos AR, Afonso C, Moreira P, Padrão P, Santos A, Borges N, et al. Weakness: the most frequent criterion among pre-frail and frail older Portuguese. Arch Gerontol Geriatr. 2018;74:162–8.CrossRefPubMed Sousa-Santos AR, Afonso C, Moreira P, Padrão P, Santos A, Borges N, et al. Weakness: the most frequent criterion among pre-frail and frail older Portuguese. Arch Gerontol Geriatr. 2018;74:162–8.CrossRefPubMed
43.
Zurück zum Zitat Castillo C, Carnicero JA, de la Torre MÁ, Amor S, Guadalupe-Grau A, Rodríguez-Mañas L, et al. Nonlinear relationship between waist to hip ratio, weight and strength in elders: is gender the key? Biogerontology. 2015;16:685–92.CrossRefPubMed Castillo C, Carnicero JA, de la Torre MÁ, Amor S, Guadalupe-Grau A, Rodríguez-Mañas L, et al. Nonlinear relationship between waist to hip ratio, weight and strength in elders: is gender the key? Biogerontology. 2015;16:685–92.CrossRefPubMed
44.
Zurück zum Zitat Auyeung TW, Kwok T, Leung J, Lee JSW, Ohlsson C, Vandenput L, et al. Sleep Duration and Disturbances Were Associated With Testosterone Level, Muscle Mass, and Muscle Strength--A Cross-Sectional Study in 1274 Older Men. J Am Med Dir Assoc. 2015;16:630.e1–6.CrossRef Auyeung TW, Kwok T, Leung J, Lee JSW, Ohlsson C, Vandenput L, et al. Sleep Duration and Disturbances Were Associated With Testosterone Level, Muscle Mass, and Muscle Strength--A Cross-Sectional Study in 1274 Older Men. J Am Med Dir Assoc. 2015;16:630.e1–6.CrossRef
45.
Zurück zum Zitat Wang TY, Wu Y, Wang T, Li Y, Zhang D. A prospective study on the association of sleep duration with grip strength among middle-aged and older Chinese. Exp Gerontol. 2018;103:88–93.CrossRefPubMed Wang TY, Wu Y, Wang T, Li Y, Zhang D. A prospective study on the association of sleep duration with grip strength among middle-aged and older Chinese. Exp Gerontol. 2018;103:88–93.CrossRefPubMed
Metadaten
Titel
Factors associated with low handgrip strength in older people: data of the Study of Chronic Diseases (Edoc-I)
verfasst von
Cledir de Araújo Amaral
Thatiana Lameira Maciel Amaral
Gina Torres Rego Monteiro
Maurício Teixeira Leite de Vasconcellos
Margareth Crisóstomo Portela
Publikationsdatum
01.12.2020
Verlag
BioMed Central
Erschienen in
BMC Public Health / Ausgabe 1/2020
Elektronische ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-020-08504-z

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