Background: Improved preferred gait speed in older adults is associated with increased survival rates. There are inconsistent findings in clinical trials regarding effects of exercise on preferred gait speed, and heterogeneity in interventions in the current reviews and meta-analyses. Objective: to determine the meta-effects of different types or combinations of exercise interventions from randomized controlled trials on improvement in preferred gait speed. Methods: Data sources: A literature search was performed; the following databases were searched for studies from 1990 up to 9 December 2013: PubMed, EMBASE, EBSCO (AMED, CINAHL, ERIC, Medline, PsycInfo, and SocINDEX), and the Cochrane Library. Study eligibility criteria: Randomized controlled trials of exercise interventions for older adults ≥ 65 years, that provided quantitative data (mean/SD) on preferred gait speed at baseline and post-intervention, as a primary or secondary outcome measure in the published article were included. Studies were excluded when the PEDro score was ≤4, or if participants were selected for a specific neurological or neurodegenerative disease, Chronic Obstructive Pulmonary Disease, cardiovascular disease, recent lower limb fractures, lower limb joint replacements, or severe cognitive impairments. The meta-effect is presented in Forest plots with 95 % confidence Study appraisal and synthesis methods: intervals and random weights assigned to each trial. Homogeneity and risk of publication bias were assessed. Results: Twenty-five studies were analysed in this meta-analysis. Data from six types or combinations of exercise interventions were pooled into sub-analyses. First, there is a significant positive meta-effect of resistance training progressed to 70-80 % of 1RM on preferred gait speed of 0.13 [CI 95 % 0.09-0.16] m/s. The difference between intervention- and control groups shows a substantial meaningful change (>0.1 m/s). Secondly, a significant positive meta-effect of interventions with a rhythmic component on preferred gait speed of 0.07 [CI 95 % 0.03-0.10] m/s was found. Thirdly, there is a small significant positive meta-effect of progressive resistance training, combined with balance-, and endurance training of 0.05 [CI 95 % 0.00-0.09] m/s. The other sub-analyses show non-significant small positive meta-affects. Conclusions: Progressive resistance training with high intensities, is the most effective exercise modality for improving preferred gait speed. Sufficient muscle strength seems an important condition for improving preferred gait speed. The addition of balance-, and/or endurance training does not contribute to the significant positive effects of progressive resistance training. A promising component is exercise with a rhythmic component. Keeping time to music or rhythm possibly trains higher cognitive functions that are important for gait. Limitations: The focus of the present meta-analysis was at avoiding as much heterogeneity in exercise interventions. However heterogeneity in the research populations could not be completely avoided, there are probably differences in health status within different studies.
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We designed and held a romantic speed-dating experience at three locations, one in the Netherlands, one in the US, and one in China. We manipulated self-disclosure and tried to predict matches from participants' physiological body reactions.
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Purpose: In long-track speed skating, drafting is a commonly used phenomenon in training; however, it is not allowed in time-trial races. In speed skating, limited research is available on the physical and psychological impact of drafting. The aim of this study was to determine the influence of “skating alone,” “leading,” or “drafting” on physical intensity (heart rate and blood lactate) and perceived intensity (perceived exertion) of speed skaters. Methods: Twenty-two national-level long-track speed skaters with a mean age of 19.3 (2.6) years skated 5 laps, with similar external intensity in 3 different conditions: skating alone, leading, or drafting. Repeated-measures analysis of variance showed differences between the 3 conditions, heart rate (F2,36 = 10.546, P < .001), lactate (F2,36 = 12.711, P < .001), and rating of perceived exertion (F2,36 = 5.759, P < .01). Results: Heart rate and lactate concentration were significantly lower (P < .001) when drafting compared with leading (heart rate Δ = 7 [8] beats·min–1, 4.0% [4.7%]; lactate Δ = 2.3 [2.3] mmol/L, 28.2% [29.9%]) or skating alone (heart rate Δ = 8 [7.1] beats·min–1, 4.6% [3.9%]; lactate Δ = 2.8 [2.5] mmol/L, 33.6% [23.6%]). Rating of perceived exertion was significantly lower (P < .01) when drafting (Δ = 0.8 [1.0], 16.5% [20.9%]) or leading (Δ = 0.5 [0.9], 7.7% [20.5%]) versus skating alone. Conclusions: With similar external intensity, physical intensity, as well as perceived intensity, is reduced when drafting in comparison with skating alone. A key finding of this study is the psychological effect: Skating alone was shown to be more demanding than leading, whereas leading and drafting were perceived to be similar in terms of perceived exertion. Knowledge about the reduction of internal intensity for a drafting skater compared with leading or skating alone can be used by coaches and trainers to optimize training conditions.
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The specificity of training for races is believed to be important for performance development. However, measuring specificity is challenging. This study aimed to develop a method to quantify the specificity of speed skating training for sprint races (i.e., 500 and 1,000 m), and explore the amount of training specificity with a pilot study. On-ice training and races of 10 subelite-to-elite speed skaters were analyzed during 1 season (i.e., 26 weeks). Intensity was mapped using 5 equal zones, between 4 m·s-1 to peak velocity and 50% to peak heart rate. Training specificity was defined as skating in the intensity zone most representative for the race for a similar period as during the race. During the season, eight 500 m races, seven 1,000 m races, and 509 training sessions were analyzed, of which 414 contained heart rate and 375 sessions contained velocity measures. Within-subject analyses were performed. During races, most time was spent in the highest intensity zone (Vz5 and HRz5). In training, the highest velocity zone Vz5 was reached 107 ± 28 times, with 9 ± 3 efforts (0.3 ± 0.1% training) long enough to be considered 500 m specific, 6 ± 5 efforts (0.3 ± 0.3% training) were considered 1,000 m specific. For heart rate, HRz5 was reached 151 ± 89 times in training, 43 ± 33 efforts (1.3 ± 0.9% training) were considered 500 m specific, and 36 ± 23 efforts (3.2 ± 1.7% training) were considered 1,000 m specific. This newly developed method enables the examination of training specificity so that coaches can control whether their intended specificity was reached. It also opens doors to further explore the impact of training specificity on performance development.
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The aim of this observational study was to examine the differences between training variables as intended by coaches and perceived by junior speed skaters and to explore how these relate to changes in stress and recovery. During a 4-week preparatory period, intended and perceived training intensity (RPE) and duration (min) were monitored for 2 coaches and their 23 speed skaters, respectively. The training load was calculated by multiplying RPE by duration. Changes in perceived stress and recovery were measured using RESTQ-sport questionnaires before and after 4 weeks. Results included 438 intended training sessions and 378 executed sessions of 14 speed skaters. A moderately higher intended (52:37 h) versus perceived duration (45:16 h) was found, as skaters performed fewer training sessions than anticipated (four sessions). Perceived training load was lower than intended for speed skating sessions (−532 ± 545 AU) and strength sessions (−1276 ± 530 AU) due to lower RPE scores for skating (−0.6 ± 0.7) or shorter and fewer training sessions for strength (−04:13 ± 02:06 hh:mm). All training and RESTQ-sport parameters showed large inter-individual variations. Differences between intended–perceived training variables showed large positive correlations with changes in RESTQ-sport, i.e., for the subscale’s success (r = 0.568), physical recovery (r = 0.575), self-regulation (r = 0.598), and personal accomplishment (r = 0.589). To conclude, speed skaters that approach or exceed the coach’s intended training variables demonstrated an increased perception of success, physical recovery, self-regulation, and personal accomplishment.
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Objective. Clinicians may use implicit or explicit motor learning approaches to facilitatemotor learning of patients with stroke. Implicit motor learning approaches have shown promising results in healthy populations. The purpose of this study was to assess whether an implicit motor learning walking intervention is more effective compared with an explicit motor learning walking intervention delivered at home regarding walking speed in people after stroke in the chronic phase of recovery. Methods. This randomized, controlled, single-blind trial was conducted in the home environment. The 79 participants, who were in the chronic phase after stroke (age = 66.4 [SD = 11.0] years; time poststroke = 70.1 [SD = 64.3] months; walking speed = 0.7 [SD = 0.3] m/s; Berg Balance Scale score = 44.5 [SD = 9.5]), were randomly assigned to an implicit (n = 38) or explicit (n = 41) group. Analogy learning was used as the implicit motor learning walking intervention, whereas the explicit motor learning walking intervention consisted of detailed verbal instructions. Both groups received 9 training sessions (30 minutes each), for a period of 3 weeks, targeted at improving quality of walking. The primary outcome was walking speed measured by the 10-MeterWalk Test at a comfortable walking pace. Outcomes were assessed at baseline, immediately after intervention, and 1 month postintervention. Results. No statistically or clinically relevant differences between groups were obtained postintervention (between-group difference was estimated at 0.02 m/s [95% CI = −0.04 to 0.08] and at follow-up (between-group difference estimated at −0.02 m/s [95% CI = −0.09 to 0.05]). Conclusion. Implicit motor learning was not superior to explicit motor learning to improve walking speed in people after stroke in the chronic phase of recovery. Impact. To our knowledge, this is the first study to examine the effects of implicit compared with explicit motor learning on a functional task in people after stroke. Results indicate that physical therapists can use (tailored) implicit and explicit motor learning strategies to improve walking speed in people after stroke who are in the chronic phase of recovery.
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Purpose: To examine the test–retest reliability and validity of ten activity trackers for step counting at three different walking speeds. Methods:Thirty-one healthy participants walked twice on a treadmill for 30 min while wearing 10 activity trackers (Polar Loop, GarminVivosmart, Fitbit Charge HR, Apple Watch Sport, Pebble Smartwatch, Samsung Gear S, Misfit Flash, Jawbone Up Move, Flyfit, andMoves). Participants walked three walking speeds for 10 min each; slow (3.2 kmIhj1), average (4.8 kmIhj1), and vigorous (6.4 kmIhj1).To measure test–retest reliability, intraclass correlations (ICC) were determined between the first and second treadmill test. Validity wasdetermined by comparing the trackers with the gold standard (hand counting), using mean differences, mean absolute percentage errors,and ICC. Statistical differences were calculated by paired-sample t tests, Wilcoxon signed-rank tests, and by constructing Bland–Altmanplots. Results: Test–retest reliability varied with ICC ranging from j0.02 to 0.97. Validity varied between trackers and different walkingspeeds with mean differences between the gold standard and activity trackers ranging from 0.0 to 26.4%. Most trackers showed relativelylow ICC and broad limits of agreement of the Bland–Altman plots at the different speeds. For the slow walking speed, the GarminVivosmart and Fitbit Charge HR showed the most accurate results. The Garmin Vivosmart and Apple Watch Sport demonstrated the bestaccuracy at an average walking speed. For vigorous walking, the Apple Watch Sport, Pebble Smartwatch, and Samsung Gear S exhibitedthe most accurate results. Conclusion: Test–retest reliability and validity of activity trackers depends on walking speed. In general,consumer activity trackers perform better at an average and vigorous walking speed than at a slower walking speed.
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Reductions in eating rate have been recommended as potential behavioral strategies to prevent and treat overweight. Unfortunately, eating rate is difficult to modify, due to its highly automatic nature. Training people to eat more slowly in everyday eating contexts, therefore, requires creative and engaging solutions. The present study examines the efficacy of a smart fork that helps people to eat more slowly. This adapted fork records eating speed and delivers vibrotactile feedback if users eat too quickly. In two studies, we tested the acceptability and user experience of the fork (Study 1), and its effect on eating rate and satiety levels in a controlled lab-setting (Study 2).
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Special relativity theory (SRT) has recently gained popularity as a first introduction to “modern” physics thinking in upper level secondary physics education. A central idea in SRT is the absolute speed of light, with light propagating with uniform speed relative to the reference frame of the observer. Previous research suggests that students, building on their prior understandings of light propagation and relative motion, develop misunderstandings of this idea. The available research provides little detail on the reasoning processes underlying these misunderstandings. We therefore studied secondary education students’ preinstructional reasoning about the speed of light in a qualitative study, probing students’ reasoning through both verbal reasoning and drawing. Event diagrams (EDs) were used as a representational tool to support student reasoning. Results show that students productively use EDs to reason with light propagation. In line with previous research, we found two alternative reference frames students could use for uniform light propagation. Most students show a flexibility in their use of reference frame: They not only evaluate light propagation in their preferred frame of reference, but also relative to other frames. Some students experienced conflict between an alternative reference frame and the speed of light and changed their reasoning because of that. This finding suggests promising directions for designing education.
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Restoration of walking capacity, as reflected by walking speed and walking distance, is a primary goal after stroke. Peak aerobic capacity (peak oxygen consumption [V̇O2peak]) is suggested to be correlated with walking capacity after stroke. Although the strength of this correlation is unclear, physical therapy programs often target walking capacity by means of aerobic training. Purpose The purpose of this systematic review was to summarize the available evidence on the correlation between V̇O2peak and walking capacity. Data Sources The databases MEDLINE, CINAHL, EMBASE, Cochrane Library, and SPORTDiscus were searched up to May 2014. Study Selection Cross-sectional studies reporting correlation coefficients between V̇O2peak and walking capacity in stroke were included, along with longitudinal studies reporting these correlation coefficients at baseline. Data Extraction The methodological quality of the studies was assessed using a checklist of 27 items for observational research. Information on study design, stroke severity and recovery, and assessments and outcome of V̇O2peak and walking capacity, as well as the reported correlation coefficients, were extracted. Data Synthesis Thirteen studies involving 454 participants were included. Meta-analyses showed combined correlation coefficients (rɱ) for V̇O2peak and walking speed and for V̇O2peak and walking distance of .42 (95% credibility interval=.31, .54) and .52 (95% credibility interval=.42, .62), respectively. Limitations The studies included in the present review had small sample sizes and low methodological quality. Clinical and methodological diversity challenged the comparability of the included studies, despite statistical homogeneity. Relevant data of 3 studies could not be retrieved. Conclusions The strength of the correlation of V̇O2peak with walking speed was low and moderate for V̇O2peak and walking distance, respectively, indicating that other factors, besides V̇O2peak, determine walking capacity after stroke.
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