Whole-body cryotherapy : empirical evidence and theoretical perspectives

Whole-body cryotherapy (WBC) involves short exposures to air temperatures below –100°C. WBC is increasingly accessible to athletes, and is purported to enhance recovery after exercise and facilitate rehabilitation postinjury. Our objective was to review the efficacy and effectiveness of WBC using empirical evidence from controlled trials. We found ten relevant reports; the majority were based on small numbers of active athletes aged less than 35 years. Although WBC produces a large temperature gradient for tissue cooling, the relatively poor thermal conductivity of air prevents significant subcutaneous and core body cooling. There is weak evidence from controlled studies that WBC enhances antioxidant capacity and parasympathetic reactivation, and alters inflammatory pathways relevant to sports recovery. A series of small randomized studies found WBC offers improvements in subjective recovery and muscle soreness following metabolic or mechanical overload, but little benefit towards functional recovery. There is evidence from one study only that WBC may assist rehabilitation for adhesive capsulitis of the shoulder. There were no adverse events associated with WBC; however, studies did not seem to undertake active surveillance of predefined adverse events. Until further research is available, athletes should remain cognizant that less expensive modes of cryotherapy, such as local ice-pack application or cold-water immersion, offer comparable physiological and clinical effects to WBC.

Psychological predictors of injury among elite athletes

Objectives: To establish injury rates among a population of elite athletes, to provide normative data for psychological variables hypothesised to be predictive of sport injuries, and to establish relations between measures of mood, perceived life stress, and injury characteristics as a precursor to introducing a psychological intervention to ameliorate the injury problem. Methods: As part of annual screening procedures, athletes at the Queensland Academy of Sport report medical and psychological status. Data from 845 screenings (433 female and 412 male athletes) were reviewed. Population specific tables of normative data were established for the Brunel mood scale and the perceived stress scale. Results: About 67% of athletes were injured each year, and about 18% were injured at the time of screening. Fifty percent of variance in stress scores could be predicted from mood scores, especially for vigour, depression, and tension. Mood and stress scores collectively had significant utility in predicting injury characteristics. Injury status (current, healed, no injury) was correctly classified with 39% accuracy, and back pain with 48% accuracy. Among a subset of 233 uninjured athletes (116 female and 117 male), five mood dimensions (anger, confusion, fatigue, tension, depression) were significantly related to orthopaedic incidents over the preceding 12 months, with each mood dimension explaining 6–7% of the variance. No sex differences in these relations were found. Conclusions: The findings support suggestions that psychological measures have utility in predicting athletic injury, although the relatively modest explained variance highlights the need to also include underlying physiological indicators of allostatic load, such as stress hormones, in predictive models.

Accelerometer data reduction : a comparison of four reduction algorithms on select outcome variables

Purpose Accelerometers are recognized as a valid and objective tool to assess free-living physical activity. Despite the widespread use of accelerometers, there is no standardized way to process and summarize data from them, which limits our ability to compare results across studies. This paper a) reviews decision rules researchers have used in the past, b) compares the impact of using different decision rules on a common data set, and c) identifies issues to consider for accelerometer data reduction. Methods The methods sections of studies published in 2003 and 2004 were reviewed to determine what decision rules previous researchers have used to identify wearing period, minimal wear requirement for a valid day, spurious data, number of days used to calculate the outcome variables, and extract bouts of moderate to vigorous physical activity (MVPA). For this study, four data reduction algorithms that employ different decision rules were used to analyze the same data set. Results The review showed that among studies that reported their decision rules, much variability was observed. Overall, the analyses suggested that using different algorithms impacted several important outcome variables. The most stringent algorithm yielded significantly lower wearing time, the lowest activity counts per minute and counts per day, and fewer minutes of MVPA per day. An exploratory sensitivity analysis revealed that the most stringent inclusion criterion had an impact on sample size and wearing time, which in turn affected many outcome variables. Conclusions These findings suggest that the decision rules employed to process accelerometer data have a significant impact on important outcome variables. Until guidelines are developed, it will remain difficult to compare findings across studies

Conducting accelerometer-based activity assessments in field-based research

Purpose The purpose of this review is to address important methodological issues related to conducting accelerometer-based assessments of physical activity in free-living individuals. Methods We review the extant scientific literature for empirical information related to the following issues: product selection, number of accelerometers needed, placement of accelerometers, epoch length, and days of monitoring required to estimate habitual physical activity. We also discuss the various options related to distributing and collecting monitors and strategies to enhance compliance with the monitoring protocol. Results No definitive evidence exists currently to indicate that one make and model of accelerometer is more valid and reliable than another. Selection of accelerometer therefore remains primarily an issue of practicality, technical support, and comparability with other studies. Studies employing multiple accelerometers to estimate energy expenditure report only marginal improvements in explanatory power. Accelerometers are best placed on hip or the lower back. Although the issue of epoch length has not been studied in adults, the use of count cut points based on 1-min time intervals maybe inappropriate in children and may result in underestimation of physical activity. Among adults, 3–5 d of monitoring is required to reliably estimate habitual physical activity. Among children and adolescents, the number of monitoring days required ranges from 4 to 9 d, making it difficult to draw a definitive conclusion for this population. Face-to-face distribution and collection of accelerometers is probably the best option in field-based research, but delivery and return by express carrier or registered mail is a viable option. Conclusion Accelerometer-based activity assessments requires careful planning and the use of appropriate strategies to increase compliance.

Clinical use of objective measures of physical activity

With measurement of physical activity becoming more common in clinical practice, it is imperative that healthcare professionals become more knowledgeable about the different methods available to objectively measure physical activity behaviour. Objective measures do not rely on information provided by the patient, but instead measure and record the biomechanical or physiological consequences of performing physical activity, often in real time. As such, objective measures are not subject to the reporting bias or recall problems associated with self-report methods. The purpose of this article was to provide an overview of the different methods used to objectively measure physical activity in clinical practice. The review was delimited to heart rate monitoring, accelerometers and pedometers since their small size, low participant burden and relatively low cost make these objective measures appropriate for use in clinical practice settings. For each measure, strengths and weakness were discussed; and whenever possible, literature-based examples of implementation were provided.

Intervening to reduce workplace sitting time : how and when do changes to sitting time occur?

Objective To investigate how and when changes in workplace sitting time occurred following a workplace intervention to inform evaluation of intervention success. Method The 4-week Stand Up Comcare study (June–September 2011) aimed to reduce workplace sitting time via regularly interrupting and replacing sitting time throughout the day. Activity monitor (activPAL3) workplace data from control (n=22) and intervention participants (n=21) were analysed. Differences in the number and usual duration of sitting bouts were used to evaluate how change occurred. To examine when change occurred, intervention effects were compared by hour since starting work and hour of the workday. Change in workplace activity (sitting, standing, stepping) was examined to further inform alignment with intervention messages. Individual variability was examined in how and when the change occurred. Results Overall, behavioural changes aligned with intervention aims. All intervention participants reduced total workplace sitting time, though there was wide individual variability observed (range −29 to −262 min per 8 h workday). On average, intervention participants reduced number of sitting bouts (−4.6 bouts (95% CI −10.1 to 1.0), p=0.106) and usual sitting bout duration (−5.6 min (95% CI −9.8 to −1.4, p=0.011)) relative to controls. Sitting time reductions were observed across the workday, though intervention effects varied by hour of the day (p=0.015). The intervention group successfully adopted the Stand Up and Sit Less intervention messages across the day. Conclusion These analyses confirmed that this workplace intervention successfully modified sitting behaviour as intended (ie, fewer and shorter sitting bouts, with changes occurring throughout the day).

Physical inactivity remains the greatest public health problem of the 21st century : evidence, improved methods and solutions using the '7 investments that work' as a framework

In 2009, BJSM's first editorial argued that ‘Physical inactivity is the greatest public health problem of the 21st century’.1 The data supporting that claim have not yet been challenged. Now, 5 years after BJSM published its first dedicated ‘Physical Activity is Medicine’ theme issue (http://bjsm.bmj.com/content/43/1.toc) we are pleased to highlight 23 new contributions from six countries. This issue contains an analysis of the cost of physical inactivity from the US Centre for Diseases Control.2 We also report the cost-effectiveness of one particular physical activity intervention for adults.3

Measuring and influencing physical activity with Smartphone technology : a systematic review

Background Rapid developments in technology have encouraged the use of smartphones in physical activity research, although little is known regarding their effectiveness as measurement and intervention tools. Objective This study systematically reviewed evidence on smartphones and their viability for measuring and influencing physical activity. Data Sources Research articles were identified in September 2013 by literature searches in Web of Knowledge, PubMed, PsycINFO, EBSCO, and ScienceDirect. Study Selection The search was restricted using the terms (physical activity OR exercise OR fitness) AND (smartphone* OR mobile phone* OR cell phone*) AND (measurement OR intervention). Reviewed articles were required to be published in international academic peer-reviewed journals, or in full text from international scientific conferences, and focused on measuring physical activity through smartphone processing data and influencing people to be more active through smartphone applications. Study Appraisal and Synthesis Methods Two reviewers independently performed the selection of articles and examined titles and abstracts to exclude those out of scope. Data on study characteristics, technologies used to objectively measure physical activity, strategies applied to influence activity; and the main study findings were extracted and reported. Results A total of 26 articles (with the first published in 2007) met inclusion criteria. All studies were conducted in highly economically advantaged countries; 12 articles focused on special populations (e.g. obese patients). Studies measured physical activity using native mobile features, and/or an external device linked to an application. Measurement accuracy ranged from 52 to 100 % (n = 10 studies). A total of 17 articles implemented and evaluated an intervention. Smartphone strategies to influence physical activity tended to be ad hoc, rather than theory-based approaches; physical activity profiles, goal setting, real-time feedback, social support networking, and online expert consultation were identified as the most useful strategies to encourage physical activity change. Only five studies assessed physical activity intervention effects; all used step counts as the outcome measure. Four studies (three pre–post and one comparative) reported physical activity increases (12–42 participants, 800–1,104 steps/day, 2 weeks–6 months), and one case-control study reported physical activity maintenance (n = 200 participants; >10,000 steps/day) over 3 months. Limitations Smartphone use is a relatively new field of study in physical activity research, and consequently the evidence base is emerging. Conclusions Few studies identified in this review considered the validity of phone-based assessment of physical activity. Those that did report on measurement properties found average-to-excellent levels of accuracy for different behaviors. The range of novel and engaging intervention strategies used by smartphones, and user perceptions on their usefulness and viability, highlights the potential such technology has for physical activity promotion. However, intervention effects reported in the extant literature are modest at best, and future studies need to utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better explore the physical activity measurement and intervention capabilities of smartphones.

Validation of a 24-h physical activity recall in indigenous and non-indigenous Australian adolescents

The purpose of this study was to evaluate the concurrent validity of a modified version of the widely used previous day physical activity recall (PDPAR24) self-report instrument in a diverse sample of Australian adolescents comprising Aboriginal and Torres Strait Islanders (A&TSI) and non-indigenous high school students. A sample of 63 A&TSI and 59 non-indigenous high school students (N = 122) from five public secondary schools participated in the study. Participants completed the PDPAR-24 after wearing a seated electronic pedometer on the previous day. Significant positive correlations were observed between the self-reported physical activity variables (mean MET level, blocks of vigorous activity, and blocks of moderate-to-vigorous physical activity) and 24-h step counts. Validity coefficients (rho) ranged from 0.29 to 0.34 (p<0.05). A significant inverse correlation was observed for self-reported screen time and 24-h step count (rho = -0.19, p<0.05). Correlations for A&TSI students were equal to or greater than those observed for non-indigenous students. The PDPAR-24 instrument is a quick, unobtrusive, and cost-effective assessment tool. that would be useful for evaluating physical activity and sedentary behaviour in population-based studies. (C) 2006 Sports Medicine Australia.

Tracking of physical activity in young children

The purpose of this study was to determine whether physical activity behavior tracks during early childhood. Forty-seven children (22 males, 25 females) aged 3-4 yr at the beginning of the study were followed over a 3-yr period. Heart rates were measured at least 2 and up to 4 d . yr(-1) with a Quantum XL Telemetry heart rate monitor. Physical activity was quantified as the percentage of observed minutes between 3:00 and 6:00 p.m. during which heart rate was 50% or more above individual resting heart rate (PAHR-50 Index). Tracking of physical activity was analyzed using Pearson and Spearman correlations. Yearly PAHR-50 index tertiles were created and examined for percent agreement and Cohen's kappa. Repeated measures ANOVA was used to calculate the intraclass correlation coefficient across the 3 yr of the study. Spearman rank order correlations ranged from 0.57 to 0.66 (P < 0.0001). Percent agreement ranged from 49% to 62%. The intraclass R for the 3 yr was 0.81. It was concluded that physical activity behavior tends to track during early childhood.

Comparison of accelerometer cut points for predicting activity intensity in youth

The absence of comparative validity studies has prevented researchers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents. PURPOSE This study aimed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard. METHODS A total of 206 participants between the ages of 5 and 15 yr completed 12 standardized activity trials. Trials consisted of sedentary activities (lying down, writing, computer game), lifestyle activities (sweeping, laundry, throw and catch, aerobics, basketball), and ambulatory activities (comfortable walk, brisk walk, brisk treadmill walk, running). During each trial, participants wore an ActiGraph GT1M, and VO 2 was measured breath-by-breath using the Oxycon Mobile portable metabolic system. Physical activity intensity was estimated using five independently developed cut points: Freedson/Trost (FT), Puyau (PU), Treuth (TR), Mattocks (MT), and Evenson (EV). Classification accuracy was evaluated via weighted κ statistics and area under the receiver operating characteristic curve (ROC-AUC). RESULTS Across all four intensity levels, the EV (κ = 0.68) and FT (κ = 0.66) cut points exhibited significantly better agreement than TR (κ = 0.62), MT (κ = 0.54), and PU (κ = 0.36). The EV and FT cut points exhibited significantly better classification accuracy for moderate-to vigorous-intensity physical activity (ROC-AUC = 0.90) than TR, PU, or MT cut points (ROC-AUC = 0.77-0.85). Only the EV cut points provided acceptable classification accuracy for all four levels of physical activity intensity and performed well among children of all ages. The widely applied sedentary cut point of 100 counts per minute exhibited excellent classification accuracy (ROC-AUC = 0.90). CONCLUSIONS On the basis of these findings, we recommend that researchers use the EV ActiGraph cut points to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents. Copyright © 2011 by the American College of Sports Medicine.

The feasibility of a home-based moderate-intensity physical activity intervention in obese children and adolescents

Objective To explore the feasibility of conducting a 10-week home-based physical activity (PA) programme and evaluate the changes in insulin sensitivity (S I) commensurate with the programme in obese young people. Design Open-labelled intervention. Setting Home-based intervention with clinical assessments at a tertiary paediatric hospital. Subjects 18 obese (body mass index (BMI)>International Obesity Task Force age and sex-specifi c cut-offs) children and adolescents (8-18 years, 11 girls/7 boys) were recruited. 15 participants (nine girls/six boys, mean±SE age 11.8±0.6 years, BMI-SD scores (BMI-SDS) 3.5±0.1, six prepubertal/nine pubertal) completed the intervention. Intervention The programme comprised biweekly home visits over 10 weeks with personalised plans implemented aiming to increase moderate-intensity PA. Pedometers and PA diaries were used as self-monitoring tools. The goals were to (1) teach participants behavioural skills related to adopting and maintaining an active lifestyle and (2) increase daily participation in PA. Outcome measures Mean steps/day were assessed. SI assessed by the frequently sampled intravenous glucose tolerance test and other components of the insulin resistance syndrome were measured. Results Mean steps/day increased significantly from 10 363±927 (baseline) to 13 013±1131 (week 10) (p<0.05). S I was also significantly increased, despite no change in BMI-SDS, and remained so after an additional 10-week follow-up. Conclusions The results suggest that such a homebased PA programme is feasible. S I improved without changes in BMI-SDS. More rigorous evaluations of such programmes are warranted.

Effects of physical activity intervention in youth : a review

Effects of physical activity interventions in youth: A review. International SportMed Journal. Vol.2 No.5 2001. The purpose of this paper is to review the peer-reviewed literature pertinent to physical activity interventions for children and adolescents. In order to provide a more quantitative conclusion regarding the effectiveness of these interventions, a meta-analytic approach was utilized in which effect sizes (the efficacy of each intervention or magnitude of the intervention effect was expressed as a standardized effect size, which represents the influence of the treatment or intervention on the dependent variable) from each study are pooled to provide a global estimate of effectiveness. A search of the relevant peer-reviewed literature was conducted using several computer-based databases, including MEDLINE, PYSCHLIT, SOCIAL SCIENCE INDEX, and SPORTS DISCUS. Manual searches were also made using the reference lists from recovered articles. Applying strict criteria for quality of design and assessment of physical activity, 10 studies were located, yielding a total of 44 effect sizes. The mean effect size was 0.47 (95% C.I. 0.28 – 0.66) suggesting that interventions have produced moderate increases in physical activity behavior. Effect sizes ranged from –0.61 to 2.5. Interventions focusing on increasing the amount of physical activity performed during regular physical education were more effective than those targeting overall levels of physical activity. Interventions were almost entirely school-based. Accordingly, the development and evaluation of community-based approaches for promoting physical activity among young people, especially older adolescents, remains an urgent priority for future research.

Every step counts : promoting activity among obese adolescents

In separate articles, two projects are described. The first describes a community project in Rockhampton to encourage people to walk more often and the second, a project to encourage more walking in obese adolescents