The Effect of Physical Activity Interventions on Glycosylated Haemoglobin (HbA1c) in Non-diabetic Populations: A Systematic Review and Meta-analysis

Cavero‐Redondo, Iván; Peleteiro, Bárbara; Álvarez‐Bueno, Celia; Artero, Enrique G.; Garrido‐Miguel, Miriam; Martínez‐Vizcaíno, Vicente

doi:10.1007/s40279-018-0861-0

Cited by 24 publications

(20 citation statements)

References 63 publications

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“…46 Another systematic review and meta-analysis supported a decrease in HbA1c levels in favor of the physically active group (effect size 0.32; 95% CI 0.01 to 0.62) noting substantial heterogeneity (I 2 =63.2%; p=0.008). 48 Our MR analysis found no effect of average PA, vigorous PA and SB on glycemic traits. This seems plausible because it is unlikely that PA will have a significant effect on these glycemic traits in the normal range in non-diabetic individuals.…”

Section: Discussionmentioning

confidence: 54%

Association of physical activity and sedentary behavior with type 2 diabetes and glycemic traits: a two-sample Mendelian randomization study

Meisinger

Linseisen

Leitzmann

et al. 2020

BMJ Open Diab Res Care

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IntroductionObservational studies suggest that physical activity lowers and sedentary behavior increases the risk of type 2 diabetes. Despite of some supportive trial data for physical activity, it is largely unresolved whether these relations are causal or due to bias.ObjectiveWe investigated the associations between accelerometer-based physical activity and sedentary behavior with type 2 diabetes and several glycemic traits using two-sample Mendelian randomization analysis.Research design and methodsSingle nucleotide polymorphisms (SNPs) associated at p<5×10−8 with accelerometer-based physical activity average accelerations, vigorous physical activity (fraction of accelerations >425 milligravities), and sedentary behavior (metabolic equivalent task ≤1.5) in a genome-wide analysis of the UK Biobank served as instrumental variables.OutcomesType 2 diabetes, hemoglobin A1c (HbA1c), fasting glucose, homeostasis model assessment of beta-cell function (HOMA-B), and homeostasis model assessment of insulin resistance (HOMA-IR).ResultsPhysical activity and sedentary behavior were unrelated to type 2 diabetes, HbA1c, fasting glucose, HOMA-B, and HOMA-IR. The inverse variance weighted ORs per SD increment for the association between average accelerations and vigorous physical activity with type 2 diabetes were 1.00 (95% CI 0.94 to 1.07, p=0.948) and 0.83 (95% CI 0.56 to 1.23, p=0.357), respectively. These results were confirmed by sensitivity analyses using alternative MR-methods to test the robustness of our findings.ConclusionsBased on these results, genetically predicted objectively measured average or vigorous physical activity and sedentary behavior is not associated with type 2 diabetes risk or with glycemic traits in the general population. Further research is required to deepen the understanding of the biological pathways of physical activity.

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Section: Discussionmentioning

confidence: 54%

Association of physical activity and sedentary behavior with type 2 diabetes and glycemic traits: a two-sample Mendelian randomization study

Meisinger

Linseisen

Leitzmann

et al. 2020

BMJ Open Diab Res Care

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“…Low number of daily step counts have been associated with all-cause mortality in some longitudinal studies 41 and results from pooled population studies show clear dose-response effects of PA to overall mortality. 42 In patients with type 2 diabetes, several studies have now shown that increasing step counts can significantly decrease HbA1c levels. For instance, a 10 000 steps per day walking prescription increased steps and decreased HbA1c in patients with type 2 diabetes.…”

Section: Pa Measurementioning

confidence: 99%

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

et al. 2020

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IntroductionDepression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual’s behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.Methods and analysisIn a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18–75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.Ethics and disseminationThe Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.Trial registration numberNCT03490253; pre-results.

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“…On the other hand, exercise is an efficacious non-pharmacological intervention for type 2 diabetes [12]. Even in non-diabetic subjects, physical activity interventions are associated with reduced HbA1c [13]. We have recently shown that acute bouts of aerobic or combined (aerobic and resistance) exercise can reduce glucose variability in patients with type 2 diabetes [14-15].…”

Section: Introductionmentioning

confidence: 99%

Effect of exercise on glucose variability in healthy subjects: randomized crossover trial

Figueira¹,

Umpierre²,

Bock³

et al. 2019

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The aim of this study was to evaluate the acute effect of aerobic (AER) and eccentric (ECC) exercise on glucose variability, correlating it with circulating markers of inflammation and oxidative stress in healthy subjects. Sixteen healthy subjects (32 ± 12 years old) wore a continuous glucose monitoring system for three days. Participants randomly performed single AER and ECC exercise sessions. Glucose variability was evaluated by glucose variance (VAR), glucose coefficient of variation (CV%) and glucose standard deviation (SD). Blood samples were collected to evaluate inflammatory and oxidative stress markers. When compared with the pre-exercise period of 0-6 h, all the indices of glucose variability presented comparable reductions 12-18 h after both exercises (∆AER: VAR= 151.5, ∆CV% = 0.55 and ∆SD = 3.1 and ECC: ∆VAR = 221.2 , ∆CV% = 3.7 and ∆SD = 6.5). Increased interleukin-6 (IL-6) levels after AER (68.5%) and ECC (30.8%) (P<0.001) were observed, with no differences between sessions (P = 0.459). Uric acid levels were increased after exercise sessions (3% in AER and 4% in ECC, P = 0.001). In conclusion, both AER and ECC exercise sessions reduced glucose variability in healthy individuals. Inflammatory cytokines, such as IL-6, and stress oxidative markers might play a role in underlying mechanisms modulating the glucose variability responses to exercise (clinicalTrials.gov NCT02262208).

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The Effect of Physical Activity Interventions on Glycosylated Haemoglobin (HbA1c) in Non-diabetic Populations: A Systematic Review and Meta-analysis

Abstract: PROSPERO CRD42016050991.

Cited by 24 publications

References 63 publications

Association of physical activity and sedentary behavior with type 2 diabetes and glycemic traits: a two-sample Mendelian randomization study

Association of physical activity and sedentary behavior with type 2 diabetes and glycemic traits: a two-sample Mendelian randomization study

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

Effect of exercise on glucose variability in healthy subjects: randomized crossover trial

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