Skip to main content
Journal of Sport and Health Science logoLink to Journal of Sport and Health Science
. 2020 Jun 19;10(3):290–295. doi: 10.1016/j.jshs.2020.06.009

Handgrip strength and health outcomes: Umbrella review of systematic reviews with meta-analyses of observational studies

Pinar Soysal a,, Christopher Hurst b,, Jacopo Demurtas c, Joseph Firth d, Reuben Howden e, Lin Yang f, Mark A Tully g, Ai Koyanagi h,i, Petre Cristian Ilie j, Guillermo F López-Sánchez k, Lukas Schwingshackl l, Nicola Veronese m,, Lee Smith n,
PMCID: PMC8167328  PMID: 32565244

Highlights

  • We carried out an umbrella review of systematic reviews with meta-analyses of observational studies on handgrip strength and all health outcomes.

  • Three outcomes (lower all-cause mortality, lower cardiovascular mortality, and lower risk of disability) were found to have highly suggestive evidence.

  • One outcome (chair rise performance over time) was found to have suggestive evidence.

  • Five outcomes (walking speed, inability to balance, hospital admissions, cardiac death, and mortality in those with chronic kidney disease) were found to have weak evidence.

Keywords: Handgrip strength, Health outcomes, Meta-analysis, Umbrella review

Abstract

Purpose

The aim of the present study was to assess both the credibility and strength of evidence arising from systematic reviews with meta-analyses of observational studies on handgrip strength and health outcomes.

Methods

An umbrella review of systematic reviews with meta-analyses of observational studies was conducted. We assessed meta-analyses of observational studies based on random-effect summary effect sizes and their p values, 95% prediction intervals, heterogeneity, small-study effects, and excess significance. We graded the evidence from convincing (Class I) to weak (Class IV).

Results

From 504 articles returned in a search of the literature, 8 systematic reviews were included in our review, with a total of 11 outcomes. Overall, nine of the 11 of the outcomes reported nominally significant summary results (p < 0.05), with 4 associations surviving the application of the more stringent p value (p < 10−6). No outcome presented convincing evidence. Three associations showed Class II evidence (i.e., highly suggestive): (1) higher handgrip values at baseline were associated with a minor reduction in mortality risk in the general population (n = 34 studies; sample size = 1,855,817; relative risk = 0.72, 95% confidence interval (95%CI): 0.67–0.78), (2) cardiovascular death risk in mixed populations (n = 15 studies; relative risk = 0.84, 95%CI: 0.78–0.91), and (3) incidence of disability (n = 7 studies; relative risk = 0.76, 95%CI: 0.66–0.87).

Conclusion

The present results show that handgrip strength is a useful indicator for general health status and specifically for early all-cause and cardiovascular mortality, as well as disability. To further inform intervention strategies, future research is now required to fully understand mechanisms linking handgrip strength scores to these health outcomes.

Graphical Abstract

Image, graphical abstract

1. Introduction

A decline in physical function is a natural phenomenon that is associated with aging.1 Such a decline is a public health concern because it has been shown to be associated with increased risk of falls,2 health care use,3 level of dependency,4 and premature mortality.5 Indeed, for many independent older adults, everyday tasks, such as climbing stairs, require functioning close to maximal capacity, meaning that further decline could increase their risk of becoming dependent on a carer.6 One widely employed measure of physical functioning is handgrip strength. The handgrip strength test is commonly used to evaluate the integrated performance of the muscles by determining the maximal grip force that can be produced in 1 muscular contraction, which further serves as a marker for general muscle strength.7 Handgrip strength is a valid measure of physical function and has been widely employed in observational research and clinical settings.8, 9, 10, 11 Importantly, 1 study found that dynamometer-determined handgrip strength could be a useful instrument in geriatric practice to identify the “oldest old” patients (i.e., those aged over 75 years) at risk of disability.12

In recent years there has been an exponential increase in the literature investigating associations between handgrip strength and health outcomes (e.g., depression,13 cognitive function,14 suicidal ideation,15 mobility limitations,16 falls,17 cardiovascular disease,18 diabetes,19 renal outcomes,20 osteoporotic factors,21 multimorbidity,22 and mortality23); consequently, there has been an increase in systematic reviews with meta-analyses. However, to date, most systematic reviews have focused on a single disease end-point, and there has not been a systematic evaluation of the relationships between handgrip strength and diverse physical and mental health outcomes. Moreover, the strength and reliability of the evidence presented in the literature is unclear.

To address the breadth of the literature of physiological measurements and outcomes, an increasing emphasis has been placed on “umbrella reviews” (i.e., the syntheses of existing systematic reviews with meta-analyses in order to capture the breadth of outcomes associated with a given exposure).

Given this situation, the aim of the present study was to carry out an umbrella review of existing systematic reviews with meta-analyses of handgrip strength and all health outcomes in order to systematically assess the quality and strength of the evidence across all health outcomes and to identify those studies with the strongest evidence.

2. Methods

This umbrella review was registered in PROSPERO:

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=158547.

2.1. Data sources and searches

We conducted an umbrella review24 by first searching several databases (MEDLINE, Scopus, and Embase) from inception until 20 November 2019. The following search terms were used: (“meta-analysis”[ptyp] OR “metaanaly*”[tiab] OR “meta-analy*”[tiab] OR “systematic review”[ptyp] OR “systematic review” [tiab]) AND “handgrip” [tiab]). In addition, we hand-searched the reference lists of eligible articles.

2.2. Study selection

In this umbrella review, we included systematic reviews and/or meta-analyses of observational studies that investigate the relationship between handgrip strength and any health outcome. Specific inclusion criteria included the following: (1) meta-analyses or systematic reviews containing sufficient data for a meta-analysis (as defined by the authors) that measured handgrip strength and ascertained health outcomes using self-report (e.g., depression questionnaires), observed (e.g., clinical diagnoses), or objective (e.g., biomarkers and mortality) criteria; (2) case control studies or cohort studies (retrospective and prospective cohorts); and (3) meta-analyses of cohort studies that investigated the association between handgrip strength with any health-related outcome (e.g., cardiovascular disease, cancer, death, obesity/overweight, mental illness, diabetes, and metabolic diseases). Studies had to report these outcomes as odds ratio, relative risk (RR), hazard ratio, or continuous data. Two authors (PS and CH) independently performed title and abstract screening in couples. Disagreements were resolved through consensus with another independent author (LS).

2.3. Data extraction

Four independent investigators (PS, LS, CH, and NV) extracted in pairs the following information for each article: first author name, year of publication, journal, the number of included studies and the total number of participants included in the studies reviewed, the inclusion criteria for the studied populations, the measures by which handgrip strength was captured, how handgrip strength was categorized, the effect sizes used in the review, the subgroupings used in the meta-analysis, the study design (case control, retrospective, and prospective), the number of cases and controls for each study, and health outcomes.

We then extracted the study-specific estimated RR for health outcomes (RR, odds ratio, hazard ratio, standardized mean difference), along with the 95% confidence interval (95%CI), and the number of cases for each study by subjects and controls. If 2 reviews covered the same association, we included the review with the largest number of studies.

2.4. Risk of bias assessment

Two authors (PS and CH) independently rated the methodological quality of the included systematic reviews using “A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR 2)”,25,26 which ranks the quality of a meta-analysis in one of 4 categories ranging from “critically low” to “high” according to 16 predefined items. The review is ranked as high quality if it has no or 1 noncritical weakness (the systematic review provides an accurate and comprehensive summary of the results of the available studies that address the question of interest). The review is ranked as moderate quality if it has more than 1 noncritical weakness (the systematic review has more than 1 noncritical weakness but no critical flaws; it may provide an accurate summary of the results of the available studies that were included in the review). The review is ranked as low quality if it has 1 critical flaw with or without noncritical weaknesses (the review has a critical flaw and may not provide an accurate and comprehensive summary of the available studies that address the question of interest). Finally, the review is ranked as critically low quality if it has more than 1 critical flaw with or without noncritical weaknesses (the review has more than 1 critical flaw and should not be relied on to provide an accurate and comprehensive summary of the available studies).26 For further reading relating to the AMSTAR 2 and what constitutes a critical flaw or a critical weakness, and so on, we refer the reader to the following reference.26

2.5. Statistical analysis

For each meta-analysis, we estimated the summary effect size and its 95%CI through random-effects models.27 We also estimated the prediction interval and its 95%CI, which further accounts for between-study effects and estimates the certainty of the association if a new study addresses that same association.28 Between-study association was estimated with the I² metric; values of 50% or greater are indicative of high heterogeneity, while values above 75% suggest very high heterogeneity.29

In addition, we calculated the evidence of small-study effects (i.e., whether small studies would have inflated effect sizes compared to larger ones). To this end, we used the regression asymmetry test developed by Egger and co-workers.30 A p value of less than 0.10, with more conservative effects in larger studies than in random-effects meta-analysis, was considered as indicative of small-study effects.21 Finally, we applied Ioannidis's excess of significance test to evaluate whether there was an excess of studies reporting statistically significant results.31

2.6. Grading the evidence

We used the credibility assessment criteria, which are based on established tools for observational evidence as summarized previously.24,32, 33, 34, 35 We classified evidence from meta-analyses of observational studies with nominally statistically significant summary results (p < 0.05) into 4 categories (Classes I, II, III, and IV). Associations were considered to be convincing (Class I) if they had (1) a statistical significance of p value of less than 10–6, (2) included more than 1000 cases (or more than 20,000 participants for continuous outcomes), (3) had the largest component study reporting a significant result (p < 0.05), (4) had a 95% prediction interval that excluded the null, (5) did not have large heterogeneity (I² < 50%), and (6) showed no evidence of small study effects (p > 0.10) or of excess significance bias (p > 0.10). Highly suggestive (Class II) evidence was assigned to associations that (1) reported a significance of p values of less than 0.001, (2) included more than 1000 cases (or more than 20,000 participants for continuous outcomes), and (3) had the largest component study reporting a statistically significant result (p < 0.05). Suggestive (Class III) evidence was assigned to associations that reported a significance of a p value of less than 0.01 with more than 1000 cases (or more than 20,000 participants for continuous outcomes). Weak (Class IV) evidence was assigned to the remaining significant associations with a p value of less than 0.05.

Due to the inherent limitations of case control studies in examining temporal associations, we had planned to provide the classification of evidence for Class I and Class II based on the following order: (1) meta-analyses of prospective studies and (2) meta-analyses of prospective and retrospective case control studies. However, no outcome had these characteristics.

3. Results

3.1. Literature review

Our search identified 20 potentially eligible reviews. Of the 20 reviews, eight were deemed to be eligible for our umbrella review. The 8 reviews had 11 different outcomes that were included in our umbrella review.

3.2. Meta-analyses of observational studies

The median number of studies of meta-analyses that included observational studies for each outcome was 8 (range 4–34), the median number of participants was 23,064 (range 2775–1,855,817), and the median number of cases was 1823 (Table 1).

Table 1.

Health outcomes and evidence class reported in included meta-analyses of observational studies.

Studya Population Outcome Study design Number of studies Cases Sample size Effect size Mean ES (95%CI) p I2 Small study effects Excess significance bias Largest study significant 95%PI Class of evidence
García-Hermoso et al. (2018)43 General population All-cause mortality Cohort 34 57,854 1,855,817 RR 0.72 (0.67–0.78) 2.04E–18 83.5 Yes No Yes 0.52 to 1.00 II
Chainani et al. (2016)44 Mixed CVD mortality Cohort/clinical trials 15 2183 29,105 RR 0.84 (0.78–0.91) 0.00001 84.3 No No Yes 0.67 to 1.07 II
Vermeulen et al. (2011)45 Mixed Disability Cohort 7 1136 5201 RR 0.76 (0.66–0.87) 0.00009 89.9 Yes Yes Yes 0.50 to 1.16 II
Hardy et al. (2013)46 Adults aged 50 years or older Chair rise performance Cohort 8 NA 10,098 β 0.93 (0.65–1.21) 6.20E–11 91.0 No NA Yes –0.02 to 1.88 III
Hardy et al. (2013)46 Adults aged 50 years or older Walking speed Cohort 8 NA 7261 β 0.89 (0.61–1.17) 5.37E–10 88.4 No NA Yes –0.03 to 1.82 IV
Hardy et al. (2013)46 Adults aged 50 years or older Inability to balance Cohort 8 NA 11,318 OR 0.94 (0.92–0.98) 1.58E–09 76.2 No NA Yes 0.88 to 1.00 IV
Pavasini et al. (2019)47 Patients with cardiac disease Cardiac death Cohort/clinical trials 6 3000 23,435 OR 0.83 (0.74–0.94) 0.01 52.1 No Yes Yes 0.59 to 1.17 IV
Pavasini et al. (2019)47 Patients with cardiac disease Hospital admission for HF Cohort/clinical trials 4 125 23,064 OR 0.88 (0.82–0.95) 0.01 14.3 No No Yes 0.71 to 1.10 IV
Hwang et al. (2019)48 Patients with CKD undergoing dialysis Mortality Cohort 10 589 2775 RR 0.92 (0.87–0.98) 0.02 70.3 Yes No Yes 0.85 to 1.19 IV
Denk et al. (2018)49 Adults aged 50 years or older Hip fracture Case-control 12 1462 28,579 RR 1.32 (0.97–1.79) 0.08 90.8 No Yes Yes 0.50 to 3.47 NS
García-Hermoso et al. (2018)50 Healthy youth and adults Cancer mortality Cohort 10 8887 1,297,163 RR 0.97 (0.92–1.07) 0.28 18.9 No No No 0.88 to 1.07 NS

Notes: Associations were considered to be convincing (Class I) if they (1) had a statistical significance of p < 106, (2) included more than 1000 cases (or more than 20,000 participants for continuous outcomes), (3) had the largest component study reporting a significant result (p < 0.05), (4) had a 95%PI that excluded the null, (5) did not have large heterogeneity (I² < 50%), and (6) showed no evidence of small study effects (p > 0.10) or of excess significance bias (p > 0.10). Highly suggestive (Class II) evidence was assigned to associations that (1) reported a significance of p < 0.001, (2) included more than 1000 cases (or more than 20,000 participants for continuous outcomes), and (3) had the largest component study reporting a statistically significant result (p < 0.05). Suggestive (Class III) evidence was assigned to associations that reported a significance of p < 0.01 with more than 1000 cases (or more than 20,000 participants for continuous outcomes). Weak (Class IV) evidence was assigned to the remaining significant associations with p < 0.05.

a

Please refer to the Supplementary Table 1 for reference list of included studies in the umbrella review.

Abbreviations: 95%CI =  95% confidence interval; 95%PI = 95% prediction interval; CKD = chronic kidney disease; CVD = cardiovascular disease; ES = effect size; HF = heart failure; NA = not applicable; NS = not statistically significant; OR = odds ratio; RR = relative risk.

The majority of the meta-analyses included studies on the general population or in adults older than 50 years, followed by patients with cardiovascular disease. Overall, nine of the 11 outcomes reported nominally significant summary results (p < 0.05), with 4 associations surviving to the application of the more stringent p value (p < 10−6) (Table 1). Heterogeneity among studies was high in nine of the 11 of the outcomes included, with seven having an I2 of 75% or greater. Only 2 associations presented 95% prediction intervals excluding the null value. Evidence for excess statistical significance was present in five of 41 outcomes, and small-study effects were seen in three of 11 outcomes. Bias was present in three of the outcomes included. The largest study, in terms of participants for each outcome, was statistically significant in all the associations, except one.

Based on the above criteria, no outcome presented convincing evidence. However, 3 associations showed Class II evidence (i.e., highly suggestive): higher handgrip values at baseline, were associated with a minor reduction in mortality risk in the general population (n = 34 studies; sample size = 1,855,817; RR = 0.72, 95%CI: 0.67–0.78); cardiovascular death in mixed populations (e.g., diabetes, general, and other conditions) (n = 15 studies; RR = 0.84, 95%CI: 0.78–0.91), and incidence of disability (n = 7 studies; RR = 0.76, 95%CI: 0.66–0.87) (Table 1). The other outcomes were ranked as suggestive (association between higher handgrip values and chair rise performance over time) or weak (5 outcomes), with only 2 associations not statistically significant (i.e., the association between handgrip strength and incident hip fracture or cancer mortality) (Table 1).

3.3. Quality assessment

Based on scores derived from using the AMSTAR 2 tool, a total of four of the meta-analyses included in our review scored “critically low” and four scored “low” (Supplementary Table 1). Notably, most studies did not include a list of excluded studies (n = 8) or report the source of funding for the included studies (n = 7). Moreover, it should be noted that 1 study did not include a systematic review.

4. Discussion

In this umbrella review of 8 meta-analyses and 11 health outcomes investigating associations between handgrip strength and all health outcomes, a total of 3 outcomes (lower all-cause mortality, lower cardiovascular mortality, and lower risk of disability) were found to have highly suggestive evidence. One outcome (chair rise performance over time) was found to have suggestive evidence. Five outcomes (walking speed, inability to balance, hospital admissions, cardiac death, and mortality in those with chronic kidney disease) were found to have weak evidence. Importantly, 2 associations were found to be nonsignificant (incident hip fracture and cancer mortality). Taken together, these findings suggest that handgrip strength is a useful indicator for general health status, early all-cause mortality, cardiovascular mortality, disability, and leg power (chair rise performance).

Several mechanisms may explain the relationship between handgrip strength and early mortality. First, early life factors, such as participation in sufficient levels of physical activity, influence handgrip strength,36 and childhood levels of physical activity and handgrip strength have been shown to track into adulthood.37,38 Importantly, maintaining adequate levels of physical activity and function over the entire life course likely yields the greatest benefit to health, owing to the reduction of any prolonged exposure from unhealthy behaviors. Next, strength is related to muscle mass and muscle mass is used a protein reserve during cases of trauma.39 Finally, other genetic contributions may be at play that result in muscle dystrophy and early mortality.40

When considering the relationship between handgrip strength and disability and leg power, this may be explained by sarcopenia (a progressive reduction in muscle strength and mass, absolute and relative to body size, commonly occurring with aging).41 Sarcopenia is associated with a decline in physical function and an increase in disability.8 Next, the handgrip strength test is not just a pure measure of strength; and those with joint disorders, who will likely have increased risk of disability and lower leg power, may perform worse when carrying out this task.8

Umbrella reviews provide top-tier evidence and important insights, but there are a number of limitations to our review that should be considered. The meta-analyses contained studies that differed in their designs, populations, and other characteristics. However, we applied an I2of less than 50% as one of the criteria for Class I evidence (convincing) to assign the best evidence grade only to robust associations. Next, meta-analyses have inherent limitations:42 their findings depend on which estimates are selected from each primary study and how they are applied in the meta-analysis. Finally, all the meta-analyses included in our review scored low or critically low when appraised through the use of the AMSTAR 2 tool, suggesting that future meta-analyses in this area will require more accurate reporting of methods and will also need to incorporate more robust discussions around findings.

5. Conclusion

Our results show that handgrip strength is a useful indicator for general health status, early all-cause mortality, cardiovascular mortality, disability, and leg power (chair rise performance). Future research is needed to fully understand the mechanisms linking handgrip strength scores to these health outcomes and further inform intervention strategies.

Authors’ contributions

PS, CH, NV, and LeeS conceived the idea, wrote the protocol, extracted the data, analyzed the data, and wrote the original draft; JD, JF, RH, LY, MAT, AK, PCI, GFLS, and LS contributed to the protocol and the drafting of the manuscript. All authors have read and approved the final version of the manuscript, and agree with the order of the presentation of the authors.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Peer review under responsibility of Shanghai University of Sport.

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.jshs.2020.06.009.

Contributor Information

Nicola Veronese, Email: ilmannato@gmail.com.

Lee Smith, Email: lee.smith@anglia.ac.uk.

Appendix. Supplementary materials

Download video file (1.1MB, mp4)
mmc2.zip (26.8KB, zip)

References

  • 1.Runge M., Rittweger J., Russo C.R., Schiessl H., Felsenberg D. Is muscle power output a key factor in the age-related decline in physical performance? A comparison of muscle cross section, chair-rising test and jumping power. Clin Physiol Funct Imaging. 2004;24:335–340. doi: 10.1111/j.1475-097X.2004.00567.x. [DOI] [PubMed] [Google Scholar]
  • 2.Singh D.K.A., Shahar S., Vanoh D., Kamaruzzaman S.B., Tan M.P. Diabetes, arthritis, urinary incontinence, poor self-rated health, higher body mass index and lower handgrip strength are associated with falls among community-dwelling middle-aged and older adults: Pooled analyses from two cross-sectional Malaysian datasets. Geriatr Gerontol Int. 2019;19:798–803. doi: 10.1111/ggi.13717. [DOI] [PubMed] [Google Scholar]
  • 3.Cheng Y., Goodin A.J., Pahor M., Manini T., Brown J.D. Healthcare utilization and physical functioning in older adults in the United States. J Am Geriatr Soc. 2020;68:266–271. doi: 10.1111/jgs.16260. [DOI] [PubMed] [Google Scholar]
  • 4.Meskers C.G.M., Reijnierse E.M., Numans S.T. Association of handgrip strength and muscle mass with dependency in (instrumental) activities of daily living in hospitalized older adults—The EMPOWER Study. J Nutr Health Aging. 2019;23:232–238. doi: 10.1007/s12603-019-1170-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Eekhoff E.M.W., van Schoor N.M., Biedermann J.S. Relative importance of four functional measures as predictors of 15-year mortality in the older Dutch population. BMC Geriatr. 2019;19:92. doi: 10.1186/s12877-019-1092-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Rikli R.E., Jones C.J. Development and validation of a functional fitness test for community-residing older adults. J Aging Phys Act. 1999;7:129–161. [Google Scholar]
  • 7.Leong D.P., Teo K.K., Rangarajan S. Reference ranges of handgrip strength from 125,462 healthy adults in 21 countries: A prospective urban rural epidemiologic (PURE) study. J Cachexia Sarcopenia Muscle. 2016;7:535–546. doi: 10.1002/jcsm.12112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Giampaoli S. Hand-grip strength predicts incident disability in non-disabled older men. Age Ageing. 1999;28:283–288. doi: 10.1093/ageing/28.3.283. [DOI] [PubMed] [Google Scholar]
  • 9.Rantanen T. Midlife hand grip strength as a predictor of old age disability. JAMA. 1999;281:558. doi: 10.1001/jama.281.6.558. [DOI] [PubMed] [Google Scholar]
  • 10.Rantanen T., Avlund K., Suominen H., Schroll M., Frändin K., Pertti E. Muscle strength as a predictor of onset of ADL dependence in people aged 75 years. Aging Clin Exp Res. 2002;14(Suppl. 3):S10–S15. [PubMed] [Google Scholar]
  • 11.Onder G., Penninx B.W, Ferrucci L., Fried L.P., Guralnik J.M., Pahor M. Measures of physical performance and risk for progressive and catastrophic disability: Results from the Women's Health and Aging Study. J Gerontol A Biol Sci Med Sci. 2005;60:74–79. doi: 10.1093/gerona/60.1.74. [DOI] [PubMed] [Google Scholar]
  • 12.Taekema D.G., Gussekloo J., Maier A.B., Westendorp R.G.J., de Craen A.J.M. Handgrip strength as a predictor of functional, psychological and social health. A prospective population-based study among the oldest old. Age Ageing. 2010;39:331–337. doi: 10.1093/ageing/afq022. [DOI] [PubMed] [Google Scholar]
  • 13.Smith L., White S., Stubbs B. Depressive symptoms, handgrip strength, and weight status in US older adults. J Affect Disord. 2018;238:305–310. doi: 10.1016/j.jad.2018.06.016. [DOI] [PubMed] [Google Scholar]
  • 14.Yang L., Koyanagi A., Smith L. Hand grip strength and cognitive function among elderly cancer survivors. PloS One. 2018;13 doi: 10.1371/journal.pone.0197909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Cao C., Liu Q., Yang L. Handgrip strength is associated with suicidal thoughts in men: Cross-sectional analyses from NHANES. Scand J Med Sci Spor. 2020;30:92–99. doi: 10.1111/sms.13559. [DOI] [PubMed] [Google Scholar]
  • 16.Sallinen J., Stenholm S., Rantanen T., Heliövaara M., Sainio P., Koskinen S. Hand-grip strength cut points to screen older persons at risk for mobility limitation. J Am Geriatr Soc. 2010;58:1721–1726. doi: 10.1111/j.1532-5415.2010.03035.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Hoda W., Samia A.R., Ahmed M. Handgrip strength and falls in community-dwelling Egyptian seniors. Adv Aging Res. 2013;2:37618. doi: 10.4236/aar.2013.24016. [DOI] [Google Scholar]
  • 18.Celis-Morales C.A., Welsh P., Lyall D.M. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: Prospective cohort study of half a million UK Biobank participants. BMJ. 2018;361:k1651. doi: 10.1136/bmj.k1651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Karvonen-Gutierrez C.A., Peng Q., Peterson M., Duchowny K., Nan B., Harlow S. Low grip strength predicts incident diabetes among mid-life women: The Michigan study of women's health across the nation. Age Ageing. 2018;47:685–691. doi: 10.1093/ageing/afy067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Chang Y.T., Wu H.L., Guo H.R. Handgrip strength is an independent predictor of renal outcomes in patients with chronic kidney diseases. Nephrol Dial Transpl. 2011;26:3588–3595. doi: 10.1093/ndt/gfr013. [DOI] [PubMed] [Google Scholar]
  • 21.Cheung C.L., Tan K.C., Bow C.H., Soong C.S., Loong C.H., Kung A.W. Low handgrip strength is a predictor of osteoporotic fractures: Cross-sectional and prospective evidence from the Hong Kong Osteoporosis Study. Age (Dordr) 2012;34:1239–1248. doi: 10.1007/s11357-011-9297-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Cheung C.L., Nguyen U.S., Au E., Tan K.C., Kung A.W. Association of handgrip strength with chronic diseases and multimorbidity. Age (Dordr) 2013;35:929–941. doi: 10.1007/s11357-012-9385-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Kim G.R., Sun J., Han M., Park S., Nam C.M. Impact of handgrip strength on cardiovascular, cancer and all-cause mortality in the Korean longitudinal study of ageing. BMJ Open. 2019;9 doi: 10.1136/bmjopen-2018-027019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Ioannidis J.P.A. Integration of evidence from multiple meta-analyses: A primer on umbrella reviews, treatment networks and multiple treatments meta-analyses. CMAJ Can Med Assoc J. 2009;181:488–493. doi: 10.1503/cmaj.081086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Wells G, Shea B, O'Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses. Available at: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. [accessed 12.06.2020]
  • 26.Shea B.J., Reeves B.C., Wells G. AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008. doi: 10.1136/bmj.j4008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Lau J. Quantitative synthesis in systematic reviews. Ann Intern Med. 1997;127:820–826. doi: 10.7326/0003-4819-127-9-199711010-00008. [DOI] [PubMed] [Google Scholar]
  • 28.Higgins J.P.T., Thompson S.G., Spiegelhalter D.J. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc. 2009;172:137–159. doi: 10.1111/j.1467-985X.2008.00552.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ioannidis J.P.A., Patsopoulos N.A., Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ. 2007;335:914–916. doi: 10.1136/bmj.39343.408449.80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Egger M., Smith G.D., Schneider M., Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–634. doi: 10.1136/bmj.315.7109.629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ioannidis J.P.A. Clarifications on the application and interpretation of the test for excess significance and its extensions. J Math Psychol. 2013;57:184–187. [Google Scholar]
  • 32.Veronese N., Solmi M., Caruso M.G. Dietary fiber and health outcomes: An umbrella review of systematic reviews and meta-analyses. Am J Clin Nutr. 2018;107:436–444. doi: 10.1093/ajcn/nqx082. [DOI] [PubMed] [Google Scholar]
  • 33.Veronese N., Demurtas J., Celotto S. Is chocolate consumption associated with health outcomes? An umbrella review of systematic reviews and meta-analyses. Clin Nutr. 2019;38:1101–1108. doi: 10.1016/j.clnu.2018.05.019. [DOI] [PubMed] [Google Scholar]
  • 34.Smith L., Luchini C., Demurtas J. Telomere length and health outcomes: An umbrella review of systematic reviews and meta-analyses of observational studies. Ageing Res Rev. 2019;51:1–10. doi: 10.1016/j.arr.2019.02.003. [DOI] [PubMed] [Google Scholar]
  • 35.Veronese N., Demurtas J., Pesolillo G. Magnesium and health outcomes: An umbrella review of systematic reviews and meta-analyses of observational and intervention studies. Eur J Nutr. 2020;59:263–272. doi: 10.1007/s00394-019-01905-w. [DOI] [PubMed] [Google Scholar]
  • 36.Kim S.H., Lim B.O., An K.O. Association of physical activity and handgrip strength among Korean elderly. Asian J Kinesiol. 2019;21:16–21. [Google Scholar]
  • 37.Trudeau F., Shephard R.J., Arsenault F., Laurencelle L. Tracking of physical fitness from childhood to adulthood. Can J Appl Physiol. 2003;28:257–271. doi: 10.1139/h03-020. [DOI] [PubMed] [Google Scholar]
  • 38.Smith L., Gardner B., Aggio D., Hamer M. Association between participation in outdoor play and sport at 10 years old with physical activity in adulthood. Prev Med. 2015;74:31–35. doi: 10.1016/j.ypmed.2015.02.004. [DOI] [PubMed] [Google Scholar]
  • 39.Buckner S.L., Dankel S.J., Bell Z.W., Abe T., Loenneke J.P. The association of handgrip strength and mortality: What does it tell us and what can we do with it? Rejuvenation Res. 2019;22:230–234. doi: 10.1089/rej.2018.2111. [DOI] [PubMed] [Google Scholar]
  • 40.Salzberg D.C., Mann J.R., McDermott S. Differences in race and ethnicity in muscular dystrophy mortality rates for males under 40 years of age, 2006–2015. Neuroepidemiology. 2018;50:201–206. doi: 10.1159/000488244. [DOI] [PubMed] [Google Scholar]
  • 41.Santilli V., Bernetti A., Mangone M., Paoloni M. Clinical definition of sarcopenia. Clin Cases Miner Bone Metab. 2014;11:177–180. [PMC free article] [PubMed] [Google Scholar]
  • 42.Ioannidis J.P.A. The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses. Milbank Q. 2016;94:485–514. doi: 10.1111/1468-0009.12210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.García-Hermoso A, Cavero-Redondo I, Ramírez-Vélez R. Muscular strength as a predictor of all-cause mortality in an apparently healthy population: A systematic review and meta-analysis of data from approximately 2 million men and women. Arch Phys Med Rehabil. 2018;99:2100–2113. doi: 10.1016/j.apmr.2018.01.008. e5. [DOI] [PubMed] [Google Scholar]
  • 44.Chainani V, Shaharyar S, Dave K. Objective measures of the frailty syndrome (hand grip strength and gait speed) and cardiovascular mortality: A systematic review. Int J Cardiol. 2016;215:487–493. doi: 10.1016/j.ijcard.2016.04.068. [DOI] [PubMed] [Google Scholar]
  • 45.Vermeulen J, Neyens JC, van Rossum E, Spreeuwenberg MD, de Witte LP. Predicting ADL disability in community-dwelling elderly people using physical frailty indicators: A systematic review. BMC Geriatr. 2011;11:33. doi: 10.1186/1471-2318-11-33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Hardy R, Cooper R, Aihie Sayer A. Body mass index, muscle strength and physical performance in older adults from eight cohort studies: The HALCyon Programme. PLoS One. 2013;8:e56483. doi: 10.1371/journal.pone.0056483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Pavasini R, Serenelli M, Celis-Morales CA. Grip strength predicts cardiac adverse events in patients with cardiac disorders: An individual patient pooled meta-analysis. Heart. 2019;105:834–841. doi: 10.1136/heartjnl-2018-313816. [DOI] [PubMed] [Google Scholar]
  • 48.Hwang SH, Lee DH, Min J, Jeon JY. Handgrip strength as a predictor of all-cause mortality in patients with chronic kidney disease undergoing dialysis: A meta-analysis of prospective cohort studies. J Ren Nutr Off J Counc Ren Nutr Natl Kidney Found. 2019;29:471–479. doi: 10.1053/j.jrn.2019.01.002. [DOI] [PubMed] [Google Scholar]
  • 49.Denk K, Lennon S, Gordon S, Jaarsma RL. The association between decreased hand grip strength and hip fracture in older people: A systematic review. Exp Gerontol. 2018;111:1–9. doi: 10.1016/j.exger.2018.06.022. [DOI] [PubMed] [Google Scholar]
  • 50.García-Hermoso A, Ramírez-Vélez R, Peterson MD. Handgrip and knee extension strength as predictors of cancer mortality: A systematic review and meta-analysis. Scand J Med Sci Sports. 2018;28:1852–1858. doi: 10.1111/sms.13206. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Download video file (1.1MB, mp4)
mmc2.zip (26.8KB, zip)

Articles from Journal of Sport and Health Science are provided here courtesy of Shanghai University of Sport

RESOURCES