Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: Springer Country of Publication: Germany NLM ID: 101132995 Publication Model: Electronic Cited Medium: Internet ISSN: 1720-8319 (Electronic) Linking ISSN: 15940667 NLM ISO Abbreviation: Aging Clin Exp Res Subsets: MEDLINE
    • Publication Information:
      Publication: <2013-> : Berlin : Springer
      Original Publication: Milano, Italy : Editrice Kurtis, c2002-
    • Subject Terms:
      Hand Strength*/physiology ; Accelerometry*/instrumentation ; Accelerometry*/methods ; Wrist*/physiology; Humans ; Male ; Female ; Aged ; Middle Aged ; Wearable Electronic Devices ; Aged, 80 and over ; Machine Learning
    • Abstract:
      Introduction: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices.
      Methods: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults.
      Results: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females).
      Conclusions: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.
      (© 2024. The Author(s).)
    • References:
      Ups J Med Sci. 1996;101(3):273-85. (PMID: 9055391)
      Sci Rep. 2018 Aug 28;8(1):12975. (PMID: 30154500)
      Gerontology. 2023;69(5):533-540. (PMID: 36592622)
      Nutr Hosp. 2016 Nov 29;33(6):1305-1311. (PMID: 28000457)
      Front Med (Lausanne). 2020 Nov 09;7:609359. (PMID: 33240913)
      BMJ Open Sport Exerc Med. 2015 Jul 8;1(1):e000013. (PMID: 27900119)
      PLoS One. 2015 Nov 16;10(11):e0142533. (PMID: 26569414)
      JAMA. 1999 Feb 10;281(6):558-60. (PMID: 10022113)
      Diabetes. 2017 Aug;66(8):2310-2315. (PMID: 28411266)
      Calcif Tissue Int. 2023 Mar;112(3):297-307. (PMID: 36436030)
      Clin Pharmacol Ther. 2018 Jul;104(1):42-52. (PMID: 29205294)
      Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1848-1851. (PMID: 34891647)
      PLoS One. 2019 May 21;14(5):e0216891. (PMID: 31112585)
      JMIR Mhealth Uhealth. 2019 Jun 19;7(6):e13084. (PMID: 31219048)
      Proc Hum Factors Ergon Soc Annu Meet. 2017 Sep;61(1):1031-1035. (PMID: 29276370)
      PLoS Med. 2016 Feb 02;13(2):e1001953. (PMID: 26836780)
      J Phys Ther Sci. 2019 Jan;31(1):75-78. (PMID: 30774209)
      Clin Interv Aging. 2019 Oct 01;14:1681-1691. (PMID: 31631989)
      PLoS One. 2013 Apr 23;8(4):e61691. (PMID: 23626718)
      Biomed Res Int. 2019 Sep 23;2019:1042834. (PMID: 31662962)
      J Appl Gerontol. 2017 Feb;36(2):127-155. (PMID: 26753803)
      Sensors (Basel). 2016 Jun 02;16(6):. (PMID: 27271621)
      J Clin Med. 2021 Jun 12;10(12):. (PMID: 34204622)
      J Rehabil Med. 2015 Oct 5;47(9):830-5. (PMID: 26181670)
      Sensors (Basel). 2017 Jun 03;17(6):. (PMID: 28587188)
      Healthc Inform Res. 2017 Jan;23(1):4-15. (PMID: 28261526)
      Ergonomics. 2017 Jul;60(7):957-966. (PMID: 27616303)
      J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56. (PMID: 11253156)
      J Appl Physiol (1985). 2014 Oct 1;117(7):738-44. (PMID: 25103964)
      Br J Gen Pract. 2007 Apr;57(537):268-70. (PMID: 17394728)
      J Electromyogr Kinesiol. 2005 Aug;15(4):358-66. (PMID: 15811606)
      PLoS One. 2017 Feb 1;12(2):e0169649. (PMID: 28146576)
      BMC Geriatr. 2018 May 3;18(1):103. (PMID: 29724191)
      J Am Geriatr Soc. 2017 Jul;65(7):1427-1433. (PMID: 28221668)
      PLoS One. 2014 Dec 04;9(12):e113637. (PMID: 25474696)
      J Rehabil Assist Technol Eng. 2018 Nov 18;5:2055668318793587. (PMID: 31191951)
      J Aging Phys Act. 2018 Jan 1;26(1):128-135. (PMID: 28595019)
      BMC Geriatr. 2021 Oct 26;21(1):600. (PMID: 34702174)
      Front Physiol. 2018 Jun 28;9:743. (PMID: 30002629)
      Ergonomics. 2020 Aug;63(8):1010-1026. (PMID: 32202214)
    • Contributed Indexing:
      Keywords: Accelerometry; Frailty; Hand grip strength; Older adults; Pre-frailty; Wearable sensors; Wrist-band devices
    • Publication Date:
      Date Created: 20240508 Date Completed: 20240508 Latest Revision: 20240511
    • Publication Date:
      20240511
    • Accession Number:
      PMC11078825
    • Accession Number:
      10.1007/s40520-024-02757-z
    • Accession Number:
      38717552