scholarly journals Metabolome-Wide Association Study of the Relationship Between Habitual Physical Activity and Plasma Metabolite Levels

2019 ◽  
Vol 188 (11) ◽  
pp. 1932-1943 ◽  
Author(s):  
Ming Ding ◽  
Oana A Zeleznik ◽  
Marta Guasch-Ferre ◽  
Jie Hu ◽  
Jessica Lasky-Su ◽  
...  
Keyword(s):  
Physical Activity ◽  
Multiple Testing ◽  
Tail Probability ◽  
Habitual Physical Activity ◽  
Self Report ◽  
Health Study ◽  
Plasma Metabolites ◽  
Cardiometabolic Health ◽  
Physically Active ◽  
Plasma Metabolite

Abstract We identified plasma metabolites associated with habitual physical activity among 5,197 US participants from the Nurses’ Health Study (NHS), Nurses’ Health Study II (NHS II), and the Health Professionals Follow-up Study (HPFS). Physical activity was assessed every 2–4 years via self-report questionnaires. Blood was collected in the NHS in 1989–1990, in NHS II during 1996–1999, and in the HPFS during 1993–1995. Metabolic profiling was conducted by liquid chromatography–mass spectrometry. Our study included 337 known metabolites, with 256 of them classified as lipids. We corrected for multiple testing by controlling the tail probability of the proportion of false positives (TPPFP) and accounted for correlated tests using bootstrapping. Physical activity was significantly associated with 20 metabolites after correction for multiple testing (TPPFP < 0.05), and positive associations were found for most of the metabolites, including 2 amino acids (citrulline and glycine), 4 cholesteryl esters (C18:2, C18:1, C16:0, C18:3), 8 phosphocholines (PCs) (C36:4 PC-A, C34:3 PC plasmalogen, C36:3 PC plasmalogen, C34:2 PC plasmalogen, C36:2 PC) and lysophosphatidylcholines (C18:2, C20:5, C18:1), and 3 phosphatidylethanolamines (PEs) (C38:3 PE plasmalogen) and lysophosphatidylethanolamines (C18:2, C18:1). We independently replicated the 20 metabolites among 2,305 women in the Women’s Health Initiative using 1993 data, and half of the metabolites were replicated. Our study may help identify biomarkers of physical activity and provide insight into biological mechanisms underlying the beneficial effect of being physically active on cardiometabolic health.

scholarly journals Association of physical activity on memory interference: Boston Puerto Rican Health Study

2021 ◽  
Vol 11 (2) ◽  
pp. 256-260
Author(s):  
Paul D. Loprinzi ◽  
Lindsay K. Crawford ◽  
Tammy Scott ◽  
Katherine L. Tucker
Keyword(s):  
Physical Activity ◽  
Puerto Rican ◽  
Proactive Interference ◽  
Retroactive Interference ◽  
Memory Task ◽  
Habitual Physical Activity ◽  
Self Report ◽  
Health Study ◽  
Memory Interference ◽  
The Difference

Background: The objective of this study was to evaluate the association between habitual physical activity engagement on memory interference. The present analysis used cross-sectional data from the Boston Puerto Rican Health Study (n=1,241; mean age= 57.2; 72.1% female). Methods: Physical activity was evaluated via self-report. Memory interference was evaluated using a word-list paradigm. The memory task included learning a list of 16 words (List A; 5 trials), followed by a distractor list (List B), and then an immediate recall of List A. Proactive interference occurs when preceding stimuli (e.g., Trial 1 and Trial 5 of List A) interferes with performance on a subsequent stimuli (List B). Retroactive interference occurs when subsequent stimuli (List B) interferes with the recall of previously encoded stimuli (Trial 5). Results: For proactive interference, there was no association between physical activity and the difference between performance on List B and Trial 1 of List A (β=0.00001; P=0.96). Similarly, for retroactive interference, there was no association between physical activity and the difference between the short delay recall and Trial 5 of List A (β=0.0002; P=0.50). Conclusion: The present study did not observe an association between habitual physical activity on attenuating memory interference.

scholarly journals Relationship between the intention–behavior gap and self-efficacy for physical activity during childhood

2018 ◽  
Vol 23 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Tsunenori Isa ◽  
Yuya Ueda ◽  
Ryo Nakamura ◽  
Shogo Misu ◽  
Rei Ono
Keyword(s):  
Physical Activity ◽  
High Activity ◽  
Self Efficacy ◽  
Self Report ◽  
Physically Active ◽  
Education Interventions ◽  
Relationship Of ◽  
The Relationship ◽  
Low Activity ◽  
Actual Participation

This study investigated the relationship of a gap between the intent to be physically active and actual participation in physical activity (‘intention–behavior gap’) and self-efficacy for physical activity during childhood. A self-report questionnaire was used to collect information from 946 children from the fourth and sixth grades in Japan on self-efficacy, intention, and physical activity. Children with an intention–behavior gap (high intent–low activity or low intent–high activity) had higher self-efficacy scores than those with low intent and low activity (27.66 or 27.65 vs. 21.69; p < .001). They had lower self-efficacy scores than those with high intent and high activity (27.66 or 27.65 vs. 30.56; p < .001). Children with an intention–behavior gap had lower self-efficacy for physical activity than those who intended to be and were physically active. Such children may benefit from education interventions that focus on improving self-efficacy.

scholarly journals Physical activity and prodromal features of Parkinson disease

Neurology ◽  
2019 ◽  
Vol 93 (23) ◽  
pp. e2157-e2169 ◽  
Author(s):  
Katherine C. Hughes ◽  
Xiang Gao ◽  
Samantha Molsberry ◽  
Linda Valeri ◽  
Michael A. Schwarzschild ◽  
...  
Keyword(s):  
Physical Activity ◽  
Parkinson Disease ◽  
Bodily Pain ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Health Study ◽  
Older Individuals ◽  
Sleep Behavior ◽  
Physically Active ◽  
Baseline Physical Activity

ObjectiveTo investigate the relationship between physical activity and prodromal features of Parkinson disease that often precede the clinical diagnosis.MethodsIncluded are participants in 2 well-established cohorts: the Nurses' Health Study and the Health Professionals Follow-up Study. Physical activity was assessed using validated questionnaires at baseline (1986) and every 2 years until 2008. Prodromal features (e.g., constipation, hyposmia, and probable REM sleep behavior disorder [pRBD]) were assessed in 2012–2014.ResultsThe multivariable-adjusted odds ratio (OR) for having ≥3 prodromal features vs none comparing the highest to the lowest quintile were 0.65 (95% confidence interval [CI] 0.53–0.79; ptrend = 0.0006) for baseline physical activity and 0.52 (95% CI 0.35–0.76; ptrend = 0.009) for cumulative average physical activity. Considering each feature independently, baseline physical activity was associated with lower odds of constipation (OR 0.78, 95% CI 0.73–0.83; ptrend < 0.0001), excessive daytime sleepiness (OR 0.72, 95% CI 0.60–0.86; ptrend = 0.002), depressive symptoms (OR 0.82, 95% CI 0.69–0.97; ptrend = 0.13), and bodily pain (OR 0.81, 95% CI 0.68–0.96; ptrend = 0.03). Similar or stronger associations were observed for cumulative average physical activity, which, in addition, was associated with pRBD (OR 0.85, 95% CI 0.77–0.95; ptrend = 0.02). In contrast, neither hyposmia nor impaired color vision was associated with physical activity. Early life physical activity was associated with constipation and, in men only, with the co-occurrence of ≥3 features.ConclusionsThe reduced prevalence of prodromal features associated with Parkinson disease in older individuals who were more physically active in midlife and beyond is consistent with the hypothesis that high levels of physical activity may reduce risk of Parkinson disease.

scholarly journals What they say and what they do: comparing physical activity across the USA, England and the Netherlands

2018 ◽  
Vol 72 (6) ◽  
pp. 471-476 ◽  
Author(s):  
Arie Kapteyn ◽  
James Banks ◽  
Mark Hamer ◽  
James P Smith ◽  
Andrew Steptoe ◽  
...  
Keyword(s):  
Physical Activity ◽  
The Netherlands ◽  
Age Groups ◽  
The Self ◽  
Self Report ◽  
Objective Measures ◽  
Accelerometer Data ◽  
Physically Active ◽  
Self Reports ◽  
The Usa

BackgroundPhysical activity (PA) is important for maintaining health, but there are fundamental unanswered questions on how best it should be measured.MethodsWe measured PA in the Netherlands (n=748), the USA (n=540) and England (n=254), both by a 7 day wrist-worn accelerometer and by self-reports. The self-reports included a global self-report on PA and a report on the frequency of vigorous, moderate and mild activity.ResultsThe self-reported data showed only minor differences across countries and across groups within countries (such as different age groups or working vs non-working respondents). The accelerometer data, however, showed large differences; the Dutch and English appeared to be much more physically active than Americans h (For instance, among respondents aged 50 years or older 38% of Americans are in the lowest activity quintile of the Dutch distribution). In addition, accelerometer data showed a sharp decline of PA with age, while no such pattern was observed in self-reports. The differences between objective measures and self-reports occurred for both types of self-reports.ConclusionIt is clear that self-reports and objective measures tell vastly different stories, suggesting that across countries people use different response scales when answering questions about how physically active they are.

Automatic Regulation Used in Sport and Exercise Research

2017 ◽  
Author(s):  
Amanda L. Rebar
Keyword(s):  
Physical Activity ◽  
Physical Activity Behavior ◽  
Sport Performance ◽  
Self Report ◽  
Automatic Regulation ◽  
Physically Active ◽  
Self Identity ◽  
Automatic Associations ◽  
Activity Behavior ◽  
Sport And Exercise

Much of our sport and physical activity behavior is regulated by processes occurring outside of conscious awareness. In contrast, most sport and physical activity research focuses on processes that are easily accessible by conscious introspection. More and more, however, research is demonstrating that automatic regulation is instrumental to our understanding of how to get people to maintain a physically active lifestyle and how to get the most out of people’s sports performance potential. Automatic regulation is the influence on our thoughts and actions that result from the mental network of associations we use to make sense of the world around us. Habits are automatic associations of cues with behavioral responses. Automatic evaluations are automatic associations of cues as being good or bad. Automatic schemas are automatic associations of cues with actual or ideal self-identity. These processes have been assessed with implicit measures by making indirect inferences from self-report or response latency tasks. Emerging research demonstrates that automatic associations influence sport performance and physical activity behavior, but further work is still needed to establish which type of automatic regulation is responsible for these influences and how automatic regulation and reflective processes interact to impact movement.

Mujeres (In)Activas: An Exploratory Study of Physical Activity among Adolescent Latinas

2005 ◽  
Vol 14 (1) ◽  
pp. 95-105 ◽  
Author(s):  
Katherine M. Jamieson ◽  
Kaori Araki ◽  
Yong Chul Chung ◽  
Sun Yong Kwon ◽  
Lisa Riggioni ◽  
...  
Keyword(s):  
Physical Activity ◽  
Family Background ◽  
Local Communities ◽  
Self Report ◽  
Immigrant Communities ◽  
Physically Active ◽  
Work Related ◽  
The Social ◽  
Women's Lives ◽  
Report Measure

Recently, a significant growth in immigrant populations has influenced the social, cultural, and political landscape of many local communities. Understanding such changes in U.S. and local demography are central to effective efforts toward reducing physical inactivity, and associated health risks and diseases. In part to document the ways that physical activity currently fits into particular women’s lives, and as critique of the essentialized notions of immigrant communities as deficient in their health standards, we set out to investigate just how physically active Latinas in local communities were. The research was guided by the following two questions: 1) What are the social conditions under which adolescent Latinas make choices about physical activity? 2) To what extent are adolescent Latinas involved in physical activity? Centering on these two questions we administered questionnaires that measured current physical activity involvement, and individual and family background factors. Survey data indicate that Latina physical activity scores increase when home and work related physical activity is included in a self-report measure.

Physical activity and multiple sclerosis: a meta-analysis

2005 ◽  
Vol 11 (4) ◽  
pp. 459-463 ◽  
Author(s):  
Robert W Motl ◽  
Edward McAuley ◽  
Erin M Snook
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Meta Analysis ◽  
Self Report ◽  
Weighted Mean ◽  
Physically Active ◽  
Manual Search ◽  
Relapsing Remitting ◽  
Quantitative Synthesis ◽  
The Difference

Using meta-analytic procedures, this study involved a quantitative synthesis of the difference in physical activity among individuals with multiple sclerosis (MS) compared with nondiseased and diseased populations and then examined factors (i.e., moderators) that explain variation in the overall difference in physical activity. We searched MEDLINE, PsycINFO and Current Contents Plus using the key words physical activity, exercise and physical fitness in conjunction with multiple sclerosis; conducted a manual search of bibliographies of the retrieved papers; and contacted study authors about additional studies. Overall, 53 effects were retrieved from 13 studies with 2360 MS participants and yielded a weighted mean effect size (ES) of -0.60 (95% CI= -0.44,-0.77). The weighted mean ES was heterogenous, Q=1164.11, df=52, p<0.0001. There were larger effects with objective versus self-report measures of physical activity, nondiseased versus diseased populations and primary progressive versus relapsing-remitting MS. The cumulative evidence suggests that individuals with MS are less physically active than nondiseased, but not diseased, populations.

scholarly journals Factors Associated with Sustained Exergaming: Longitudinal Investigation

10.2196/13335 ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. e13335 ◽  
Author(s):  
Erin Kathleen O'Loughlin ◽  
Tracie A Barnett ◽  
Jennifer J McGrath ◽  
Mia Consalvo ◽  
Lisa Kakinami
Keyword(s):  
Physical Activity ◽  
Video Game ◽  
Self Report ◽  
Behavioral Correlates ◽  
Physically Active ◽  
Video Game Play ◽  
Factors Associated ◽  
Longitudinal Investigation ◽  
Grade 5 ◽  
Viable Approach

Background Exergaming is technology-driven physical activity (PA) which, unlike traditional video game play, requires that participants be physically active to play the game. Exergaming may have potential to increase PA and decrease sedentary behavior in youth, but little is known about sustained exergaming. Objective The objectives of this study were to describe the frequency, correlates, and predictors of sustained exergaming. Methods Data were available in AdoQuest (2005-11), a longitudinal investigation of 1843 grade 5 students in Montréal, Canada. This analysis used data from grade 9 (2008-09) and 11 (2010-11). Participants at Time 1 (T1; mean age 14 years, SD 0.8 ) who reported past-week exergaming (n=186, 19.1% of AdoQuest sample) completed mailed self-report questionnaires at Time 2 (T2; mean age 16 years, SD 0.8). Independent sociodemographic, psychological, and behavioral correlates (from T2)/predictors (from T1 or earlier) were identified using multivariable logistic regression. Results Of 186 exergamers at T1, 81 (44%) reported exergaming at T2. Being female and having higher introjected regulation (ie, a type of PA motivation indicative of internalizing PA as a behavior) were independent correlates. None of the predictors investigated were associated with sustained exergaming. Conclusions Almost half of grade 9 exergamers sustained exergaming for 2 years. Exergaming may be a viable approach to help adolescents engage in and sustain PA during adolescence. Sex and PA motivation may be important in the sustainability of exergaming.

Abstract P332: Socio-environmental Influences on Youth Total Sedentary Time: Results From the Hispanic Community Health Study / Study of Latino Youth

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Melawhy L Garcia ◽  
Sheila F Castaneda ◽  
Linda C Gallo ◽  
Maria Lopez-Gurrola ◽  
Krista M Perreira ◽  
...  
Keyword(s):  
Physical Activity ◽  
Environmental Factors ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Latino Youth ◽  
Self Report ◽  
Health Study ◽  
Future Research ◽  
Linear Regression Models ◽  
Sampling Weights

Introduction: Sedentary behavior (SED) is associated with higher obesity and cardiometabolic risk in youth, independent of physical activity. Studies showing the association between screen time and SED among Hispanics/Latinos, have primarily focused on Mexican-origin Hispanics. Additional research is needed to examine other socio-environmental factors that can influence SED among diverse Hispanics/Latinos. This cross-sectional study examined the home-, neighborhood-, and school- environment to identify factors associated with total sedentary time among youth. Methods: Data from 1,104 youth ages 8-16 years and 728 caregivers (mean age 43.1 ± 8.2 years) from four U.S. cities, who participated in the Study of Latino Youth (2012-2014), were examined. Associations between socio-environmental factors (measured by self-report) and total sedentary time (measured by one-week Actical accelerometry) were examined in linear regression models that included MVPA minutes/day, demographic covariates, and accounted for the complex survey design and sampling weights. Results: Mean sedentary time was 10.1 ± 1.8 hours/day. Home environment factors, such as electronics in the bedroom and parent limit setting, were not associated with total sedentary time. Presence of barriers to physical activity in the neighborhood (e.g., muggings, gangs) was associated with 13.4 more minutes of sedentary time per day. Attending a school that never/rarely compared to sometimes offered after school physical activity opportunities was associated with more sedentary time (B=38.0 minutes/day; 95% Confidence Interval: 13.5-62.4). Conclusions: The study findings highlight the need for future research to investigate other sources of sedentary behavior in the home for interventionist to focus on specific SED-based strategies to decrease sedentary time among youth. Minimizing barriers by identifying safe places to be active in participant’s neighborhoods may also support youth to spend less time indoors where sedentary time is prevalent.

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