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Hybrid Exercise Program Enhances Physical Fitness and Reverses Frailty in Older Adults: Insights and Predictions from Machine Learning

  • Original Research
  • Published:
The journal of nutrition, health & aging

An Erratum to this article was published on 20 October 2023

This article has been updated

Abstract

Purpose

The declining physical condition of the older adults is a pressing issue. Wu Qin Xi exercise, despite being low-intensity, is highly effective among older adults. Inspired by its characteristics, we designed a new exercise program for frail older adults, combining strength, endurance, and Wu Qin Xi. Furthermore, we employed machine learning to predict whether frailty can be reversed in older adults after the intervention.

Methods

A total of 181 community-dwelling frail older adults aged 65 years or older participated in this single-center, randomized controlled study, with 54.7% (n=99) being female. The study assessed the effectiveness of several exercise modalities in reversing frailty. The Fried‘s frailty criterion was used to assess the degree of frailty of the subjects. Participants were assigned a three-digit code 001–163 and randomly assigned (1:1:1) by computer to three different groups based on the study participant number: the Wu Qin Xi group (WQX), the strength exercise mixed with endurance exercise training group (SE), and the WQXSE hybrid exercise group incorporated the above two. Body composition and frailty-related physical fitness factors were measured before and after a 24-week intervention. The measurements included Body height, Body mass, Timed Up and Go Test (TUGT), grip strength assessment (GS), 6min walk test (6 min WT), and 10 m maximum walk speed (10 m MWS). Data were analyzed using repeated measures ANOVA to determine group and time interaction effects and machine learning models were used to predict program effectiveness.

Results

A total of 163 participants completed the study, with 53.9% (n=88) of them being female. The two items, 10 m maximum walking speed (10 m MWS) and grip strength, were significantly affected by the interaction of group and time. Compared to the other two groups, the WQXSE group showed the most improvement in the item 10 m MWS. In addition, following 24 weeks of training, 68 (41.7%) of the initially frail older adults had reversed their frailty status. Among them, 19 (36.5%) were in the WQX group, 24 (44.4%) were in the WQXSE group, and 25 (43.9%) were in the SE group. The stacking model exhibited superior performance when compared to other algorithms.

Conclusion

A hybrid exercise regimen comprising the Wu Qin Xi routine and exercises focused on both strength and endurance holds the potential to yield greater improvements in the physical fitness of older adults, as well as reducing frailty. Leveraging a stacking model, it is possible to forecast the likelihood of older adults successfully reversing their frailty status following participation in a prevention exercise program.

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Availability of data and materials: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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References

  1. Palmer A K, Jensen M D. Metabolic changes in aging humans: current evidence and therapeutic strategies[J]. Journal of Clinical Investigation, 2022, 132(16): e158451. doi:https://doi.org/10.1172/JCI158451.

    Google Scholar 

  2. van Rijckevorsel-Scheele J, Willems R C W J, Roelofs P D D M, et al. Effects of health care interventions on quality of life among frail elderly: a systematized review[J]. Clinical interventions in aging, 2019: 643–658.doi:https://doi.org/10.2147/CIA.S190425.

  3. Kumar R, Mohan N, Upadhyay A D, et al. Identification of serum sirtuins as novel noninvasive protein markers for frailty[J]. Aging cell, 2014, 13(6): 975–980. doi:https://doi.org/10.1111/acel.12260.

    Google Scholar 

  4. Hoogendijk E O, Afilalo J, Ensrud K E, et al. Frailty: implications for clinical practice and public health[J]. The Lancet, 2019, 394(10206): 1365–1375. doi:https://doi.org/10.1016/S0140-6736(19)31786-6.

    Google Scholar 

  5. Cacciatore F, Testa G, Galizia G, et al. Clinical frailty and long-term mortality in elderly subjects with diabetes[J]. Acta diabetologica, 2013, 50: 251–260. doi:https://doi.org/10.1007/s00592-012-0413-2.

    Google Scholar 

  6. Fried L P, Tangen C M, Walston J, et al. Frailty in older adults: evidence for a phenotype[J]. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 2001, 56(3): M146–M157. doi: https://doi.org/10.1093/gerona/56.3.M146.

    Google Scholar 

  7. Morley J E, Vellas B, Van Kan G A, et al. Frailty consensus: a call to action[J]. Journal of the American Medical Directors Association, 2013, 14(6): 392–397. doi: https://doi.org/10.1016/j.amda.2013.03.022.

    Google Scholar 

  8. Espinoza S E, Fried L P. Risk factors for frailty in the older adult[J]. Clinical Geriatrics, 2007, 15(6): 37.

    Google Scholar 

  9. Kojima G. Prevalence of frailty in nursing homes: a systematic review and meta-analysis[J]. Journal of the American Medical Directors Association, 2015, 16(11): 940–945. doi: https://doi.org/10.1016/j.jamda.2015.06.025.

    Google Scholar 

  10. Lee D H, Buth K J, Martin B J, et al. Frail patients are at increased risk for mortality and prolonged institutional care after cardiac surgery[J]. Circulation, 2010, 121(8): 973–978. doi: https://doi.org/10.1161/CIRCULATIONAHA.108.841437.

    Google Scholar 

  11. Gilbert T, Neuburger J, Kraindler J, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study[J]. The Lancet, 2018, 391(10132): 1775–1782. doi: https://doi.org/10.1016/S0140-6736(18)30668-8.

    Google Scholar 

  12. Lang P O, Michel J P, Zekry D. Frailty syndrome: a transitional state in a dynamic process[J]. Gerontology, 2009, 55(5): 539–549. doi: https://doi.org/10.1159/000211949.

    Google Scholar 

  13. Kasim N F, van Zanten J V, Aldred S. Tai Chi is an effective form of exercise to reduce markers of frailty in older age[J]. Experimental gerontology, 2020, 135: 110925. doi: https://doi.org/10.1016/j.exger.2020.110925.

    Google Scholar 

  14. Cadore E L, Casas-Herrero A, Zambom-Ferraresi F, et al. Multicomponent exercises including muscle power training enhance muscle mass, power output, and functional outcomes in institutionalized frail nonagenarians[J]. Age, 2014, 36: 773–785. doi: https://doi.org/10.1007/s11357-013-9586-z.

    Google Scholar 

  15. Bray N W, Smart R R, Jakobi J M, et al. Exercise prescription to reverse frailty[J]. Applied physiology, nutrition, and metabolism, 2016, 41(10): 1112–1116. doi: https://doi.org/10.1139/apnm-2016-0226@apnm-vis.issue01.

    Google Scholar 

  16. Cadore E L, Pinto R S, Bottaro M, et al. Strength and endurance training prescription in healthy and frail elderly[J]. Aging and disease, 2014, 5(3): 183. doi: https://doi.org/10.14336/AD.2014.0500183.

    Google Scholar 

  17. XIAOQUAN Z, YANG L I U, WEI Z, et al. The effect of Chinese traditional exercise on cognitive function improvement in the elderly-meta analysis[J]. Archives of Budo, 2021, 17: 307–318.

    Google Scholar 

  18. Lan C, Lai J S, Chen S Y. Tai Chi Chuan: an ancient wisdom on exercise and health promotion[J]. Sports medicine, 2002, 32: 217–224. doi: https://doi.org/10.2165/00007256-200232040-00001.

    Google Scholar 

  19. Zhang B, Cheng C, Ye M, et al. A preliminary study of the effects of medical exercise Wuqinxi on indicators of skin temperature, muscle coordination, and physical quality[J]. Medicine, 2018, 97(34). doi: https://doi.org/10.1097/MD.0000000000012003.

  20. Liao Y Y, Chen I H, Wang R Y. Effects of Kinect-based exergaming on frailty status and physical performance in prefrail and frail elderly: A randomized controlled trial[J]. Scientific reports, 2019, 9(1): 9353. doi: https://doi.org/10.1038/s41598-019-45767-y.

    Google Scholar 

  21. Kim H, Suzuki T, Kim M, et al. Effects of exercise and milk fat globule membrane (MFGM) supplementation on body composition, physical function, and hematological parameters in community-dwelling frail Japanese women: a randomized double blind, placebo-controlled, follow-up trial[J]. PloS one, 2015, 10(2): e0116256. doi: 0.1371/journal.pone.0116256.

    Google Scholar 

  22. McPhee J S, French D P, Jackson D, et al. Physical activity in older age: perspectives for healthy ageing and frailty[J]. Biogerontology, 2016, 17: 567–580. doi: https://doi.org/10.1007/s10522-016-9641-0

    Google Scholar 

  23. Devereux-Fitzgerald A, Powell R, Dewhurst A, et al. The acceptability of physical activity interventions to older adults: A systematic review and meta-synthesis[J]. Social science & medicine, 2016, 158: 14–23. doi: https://doi.org/10.1016/j.socscimed.2016.04.006.

    Google Scholar 

  24. Ehsani A A, Spina R J, Peterson L R, et al. Attenuation of cardiovascular adaptations to exercise in frail octogenarians[J]. Journal of Applied Physiology, 2003, 95(5): 1781–1788. doi: https://doi.org/10.1152/japplphysiol.00194.2003.

    Google Scholar 

  25. Serra-Rexach J A, Bustamante-Ara N, Hierro Villaran M, et al. Short-term, light-to moderate-intensity exercise training improves leg muscle strength in the oldest old: a randomized controlled trial[J]. Journal of the American Geriatrics Society, 2011, 59(4): 594–602. doi: https://doi.org/10.1111/j.1532-5415.2011.03356.x.

    Google Scholar 

  26. Shirwaikar R D, Acharya D, Makkithaya K, et al. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction[J]. Artificial intelligence in medicine, 2019, 98: 59–76. doi: https://doi.org/10.1016/j.artmed.2019.07.008.

    Google Scholar 

  27. Fleckenstein J, Flössel P, Engel T, et al. Individualized exercise in chronic non-specific low back pain: a systematic review with meta-analysis on the effects of exercise alone or in combination with psychological interventions on pain and disability[J]. The Journal of Pain, 2022. doi: https://doi.org/10.1016/j.jpain.2022.07.005.

  28. Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care[J]. Neural computing and applications, 2020, 32(24): 18069–18083. doi: https://doi.org/10.1007/s00521-019-04051-w.

    Google Scholar 

  29. Faul F, Erdfelder E, Lang A G, et al. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences[J]. Behavior research methods, 2007, 39(2): 175–191. doi: https://doi.org/10.3758/BF03193146.

    Google Scholar 

  30. Karavirta L, Häkkinen A, Sillanpää E, et al. Effects of combined endurance and strength training on muscle strength, power and hypertrophy in 40–67-year-old men[J]. Scandinavian journal of medicine & science in sports, 2011, 21(3): 402–411. doi: https://doi.org/10.1111/j.1600-0838.2009.01059.x.

    Google Scholar 

  31. Kallinen M, Sipilä S, Alen M, et al. Improving cardiovascular fitness by strength or endurance training in women aged 76–78 years. A population-based, randomized controlled trial[J]. Age and ageing, 2002, 31(4): 247–254. doi: https://doi.org/10.1093/ageing/31.4.247.

    Google Scholar 

  32. Dent E, Lien C, Lim W S, et al. The Asia-Pacific clinical practice guidelines for the management of frailty[J]. Journal of the American Medical Directors Association, 2017, 18(7): 564–575. doi: https://doi.org/10.1016/j.jamda.2017.04.018.

    Google Scholar 

  33. Ngai S P C, Cheung R T H, Lam P L, et al. Validation and reliability of the Physical Activity Scale for the Elderly in Chinese population[J]. Journal of rehabilitation medicine, 2012, 44(5): 462–465. doi: https://doi.org/10.2340/16501977-0953.

    Google Scholar 

  34. Wickstrom R J, Wang Y C, Wickstrom N E, et al. A new two square agility test for workplace health—reliability, validity and minimal detectable change[J]. Journal of Physical Therapy Science, 2019, 31(10): 823–830. doi: https://doi.org/10.1589/jpts.31.823.

    Google Scholar 

  35. Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785–794. doi: https://doi.org/10.1145/2939672.2939785.

  36. Abhishek L. Optical character recognition using ensemble of SVM, MLP and extra trees classifier[C]//2020 International Conference for Emerging Technology (INCET). IEEE, 2020: 1–4. doi: https://doi.org/10.1109/INCET49848.2020.9154050.

  37. Ke G, Meng Q, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree[J]. Advances in neural information processing systems, 2017, 30.

  38. Friedman J H. Greedy function approximation: a gradient boosting machine[J]. Annals of statistics, 2001: 1189–1232.

  39. Kumar R, Naik S M, Naik V D, et al. Predicting clicks: CTR estimation of advertisements using logistic regression classifier[C]//2015 IEEE international advance computing conference (IACC). IEEE, 2015: 1134–1138. doi: https://doi.org/10.1109/IADCC.2015.7154880.

  40. Breiman L. Random forests[J]. Machine learning, 2001, 45: 5–32. doi: https://doi.org/10.1023/A:1010933404324.

    Google Scholar 

  41. Szostak D, Walkowiak K, Włodarczyk A. Short-term traffic forecasting in optical network using linear discriminant analysis machine learning classifier[C]//2020 22nd International Conference on Transparent Optical Networks (ICTON). IEEE, 2020: 1–4. doi: https://doi.org/10.1109/ICTON51198.2020.9203040.

  42. Lundberg S M, Lee S I. A unified approach to interpreting model predictions[J]. Advances in neural information processing systems, 2017, 30.

  43. Li X, Zhang Y, Tian Y, et al. Exercise interventions for older people with cognitive frailty—a scoping review[J]. BMC geriatrics, 2022, 22(1): 721. doi: https://doi.org/10.1186/s12877-022-03370-3.

    Google Scholar 

  44. SA A A, Vennu V, Alotaibi A D, et al. The effect of a multicomponent exercise programme onelderly adults’ risk of falling in nursing homes: A systematic review[J]. JPMA. The Journal of the Pakistan Medical Association, 2020, 70(4): 699–704. doi: https://doi.org/10.5455/jpma.292007.

    Google Scholar 

  45. Karinkanta S, Heinonen A, Sievänen H, et al. A multi-component exercise regimen to prevent functional decline and bone fragility in home-dwelling elderly women: randomized, controlled trial[J]. Osteoporosis International, 2007, 18: 453–462. doi: https://doi.org/10.1007/s00198-006-0256-1.

    Google Scholar 

  46. Xiao C, Zhuang Y, Kang Y. Effects of Wu Qin xi Qigong exercise on physical functioning in elderly people with knee osteoarthritis: A randomized controlled trial[J]. Geriatrics & Gerontology International, 2020, 20(10): 899–903. doi: https://doi.org/10.1111/ggi.14007.

    Google Scholar 

  47. CAO H, SUN W, XI X, et al. Effects of Wuqinxi on Balance, Walking and Quality of Life for Patients with Parkinson’s Disease[J]. Chinese Journal of Rehabilitation Theory and Practice, 2021: 1087–1092.

  48. Xiao C M, Li J J, Kang Y, et al. Follow-up of a Wuqinxi exercise at home programme to reduce pain and improve function for knee osteoarthritis in older people: a randomised controlled trial[J]. Age and Ageing, 2021, 50(2): 570–575. doi: https://doi.org/10.1093/ageing/afaa179.

    Google Scholar 

  49. Li L, Huang H, Song J, et al. Network meta-analysis of the effects of different types of traditional Chinese exercises on pulmonary function, endurance capacity and quality of life in patients with COPD[J]. Frontiers in Medicine, 2022, 9: 806025. doi: https://doi.org/10.3389/fmed.2022.806025.

    Google Scholar 

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Funding

Funding: Guang Yang obtained «The Fundamental Research Funds for the Central Universities» (Number: 135222026).

Author information

Authors and Affiliations

  1. Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China

    M. Wei, S. He, D. Meng, Guang Yang & Ziheng Wang

  2. AI Group, Intelligent Lancet LLC, Sacramento, CA, 95816, USA

    Ziheng Wang

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  2. S. He
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  3. D. Meng
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  4. Guang Yang
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Contributions

Authors’ contributions: Experimental design, data collection were conducted by GY, WM and SH. Data analysis was performed by ZW and DM. The first draft of the manuscript was written by WM, SH and DM, and all authors have reviewed the manuscript. GY and ZW revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Guang Yang or Ziheng Wang.

Ethics declarations

Competing interests: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethics approval: This study was granted ethical approval by the Ethics Committee of Northeast Normal University (NC2018041103).

Additional information

Clinical trials registration: The trial was registered in ClinicalTrials.gov (NCT05832853).

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Wei, M., He, S., Meng, D. et al. Hybrid Exercise Program Enhances Physical Fitness and Reverses Frailty in Older Adults: Insights and Predictions from Machine Learning. J Nutr Health Aging 27, 894–902 (2023). https://doi.org/10.1007/s12603-023-1991-0

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  • DOI: https://doi.org/10.1007/s12603-023-1991-0

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