Oliver James Peacock

Background: Characterizing interindividual differences in physical activity within schools and understanding potential drivers of these differences may support the development of improved strategies to increase in-school physical activity. Methods: Anonymized physical activity data from a school-specific wrist-worn device were obtained from 17,043 pupils and 2555 staff from 165 primary schools in England. Additional school information included UK government school data, area-level deprivation, and estimated playground area. Descriptive statistics were calculated, and data visualizations were used to explore within- and between-school differences. Multilevel models were used to explore the associations between children’s in-school physical activity and school variables, with pupils nested within schools, adjusted for individual characteristics. Results: There was large variation in physical activity between schools. Some schools accumulated twice the amount of physical activity within the school day compared with other schools, with physical activity ranging from 4253 to 8356 steps per school day or 16 to 39 minutes of moderate to vigorous physical activity per school day. School-level data, such as playground space and government school ratings, did not explain between-school variance. Although boys were generally more active than girls in most schools, some schools had more equitable physical activity, and in a small number of schools (∼5%), girls were more active than boys. Conclusions: There is considerable heterogeneity in physical activity levels between English primary schools. Technology-enabled surveillance from wearable devices can be used to identify schools needing greater support and facilitate learning from those with higher and more equitable physical activity among pupils.

Background: Wearable devices have emerged as a new technology for monitoring physical activity over time. Conventional approaches to wearable physical activity data have tended to ignore temporal changes and, instead, have typically analysed summative measures and/or snapshots (e.g., averages over a specific period). In this report, we aimed to develop a novel statistical method to analyse longitudinal physical activity data accounting for the temporal structure in the data. Methods: This research used secondary data from the Multidimensional Individualised Physical Activity (MIPACT) randomized controlled trial. Physical activity data over the 12-week intervention for 80 participants (28 women) aged between 43 and 70 years old met the criteria for inclusion in this analysis. We modelled the temporal dynamic of each participant using a Trend Locally Stationary Wavelet model, and we introduced the Time in Reference Region of Variability (TIRRV) to assess individual changes relative to baseline. Results: The analysis of wearable physical activity data poses an important challenge for traditional statistical methods, which often fail to account for dependency between sequential data points and varying characteristics. In this work we demonstrate the effectiveness of a Trend Locally Stationary Wavelet model (TLSW) approach in analysing hourly resolution data from a 12-week intervention, enhancing the understanding of physical activity data, and providing meaningful insights at both individual and group levels. The TLSW considers the time dependency and structure of the data, enabling detailed trend and point-wise confidence intervals analysis. In addition to trends, the newly-developed TIRRV represents a baseline-informed metric to assess the success of individuals and groups over time. The application of these methods produce robust and readily understandable insights about the effect of interventions. Conclusions: The TLSW-based approach is a novel method for analysing physical activity collected using high-resolution wearable technology. The TLSW trends robustly characterize individual and group behaviour over extended periods of time. This novel approach enables researchers, clinicians, and patients to understand temporal changes in device-measured physical activity data in a way that was not possible previously.

Purpose: Physical activity is a key component of cardiac rehabilitation (CR). Despite the widely reported benefits of CR, uptake in the United Kingdom is still low. Alternative home-based and technology-facilitated delivery models are needed to improve CR uptake and physical activity. This study set out to explore patient and clinician views of personalised, multidimensional physical activity feedback and its potential use within CR. Methods: We developed graphics for the presentation of personalised multidimensional physical activity feedback from data collected through wrist-worn monitors. Thirteen cardiac patients and nine healthcare professionals recruited from South West England wore research grade physical activity monitors for seven days. Participants then attended semi-structured interviews during which personalised physical activity feedback was provided. Interviews were audio-recorded, transcribed, and analysed thematically. Results: Two main themes were derived from the data which covered: 1) the perceived value of multidimensional physical activity feedback, and 2) support needed to facilitate understanding. Within the first theme, participants acknowledged that multidimensional physical activity feedback was useful for monitoring progress, goal setting, and increasing self-awareness of physical activity behaviour among both patients and clinicians. Within theme two, the need for more guidance and support from clinicians to aid patient understanding and reassurance was highlighted, particularly for those with very low physical activity levels. Conclusions: Multidimensional physical activity feedback delivered using a technology-enabled approach was perceived as acceptable among patients and clinicians. This study provides insights into the potential novel use of technology-enabled physical activity feedback to support and expand the delivery of CR.

Phosphate is integral to numerous metabolic processes, several of which strongly predict exercise performance (i.e., cardiac function, oxygen transport, and oxidative metabolism). Evidence regarding phosphate loading is limited and equivocal, at least partly because studies have examined sodium phosphate supplements of varied molar mass (e.g., mono/di/tribasic, dodecahydrate), thus delivering highly variable absolute quantities of phosphate. Within a randomized cross-over design and in a single-blind manner, 16 well-trained cyclists (age 38 ± 16 years, mass 74.3 ± 10.8 kg, training 340 ± 171 min/week; mean ± SD) ingested either 3.5 g/day of dibasic sodium phosphate (Na₂HPO₄: 24.7 mmol/day phosphate; 49.4 mmol/day sodium) or a sodium chloride placebo (NaCl: 49.4 mmol/day sodium and chloride) for 4 days prior to each of two 30-km time trials, separated by a washout interval of 14 days. There was no evidence of any ergogenic benefit associated with phosphate loading. Time to complete the 30-km time trial did not differ following ingestion of sodium phosphate and sodium chloride (3,059 ± 531 s vs. 2,995 ± 467 s). Accordingly, neither absolute mean power output (221 ± 48 W vs. 226 ± 48 W) nor relative mean power output (3.02 ± 0.78 W/kg vs. 3.08 ± 0.71 W/kg) differed meaningfully between the respective intervention and placebo conditions. Measures of cardiovascular strain and ratings of perceived exertion were very closely matched between treatments (i.e., average heart rate 161 ± 11 beats per minute vs. 159 ± 12 beats per minute; Δ2 beats per minute; and ratings of perceived exertion 18 [14–20] units vs. 17 [14–20] units). In conclusion, supplementing with relatively high absolute doses of phosphate (i.e., >10 mmol daily for 4 days) exerted no ergogenic effects on trained cyclists completing 30-km time trials.

Ketone ingestion can alter metabolism but effects on exercise performance are unclear, particularly with regard to the impact on intermittent-intensity exercise and team-sport performance. Nine professional male rugby union players each completed two trials in a double-blind, randomized, crossover design. Participants ingested either 90 ± 9 g carbohydrate (CHO; 9% solution) or an energy matched solution containing 20 ± 2 g CHO (3% solution) and 590 mg/kg body mass β-hydroxybutyrate monoester (CHO + BHB-ME) before and during a simulated rugby union-specific match-play protocol, including repeated high-intensity, sprint and power-based performance tests. Mean time to complete the sustained high-intensity performance tests was reduced by 0.33 ± 0.41 s (2.1%) with CHO + BHB-ME (15.53 ± 0.52 s) compared with CHO (15.86 ± 0.80 s) placebo (p = .04). Mean time to complete the sprint and power-based performance tests were not different between trials. CHO + BHB-ME resulted in blood BHB concentrations that remained >2 mmol/L during exercise (p < .001). Serum lactate and glycerol concentrations were lower after CHO + BHB-ME than CHO (p < .05). Coingestion of a BHB-ME with CHO can alter fuel metabolism (attenuate circulating lactate and glycerol concentrations) and may improve high-intensity running performance during a simulated rugby match-play protocol, without improving shorter duration sprint and power-based efforts.

Background: Increasing physical activity (PA) behavior remains a public health priority, and wearable technology is increasingly being used to support behavior change efforts. Using wearables to capture and provide comprehensive, visually persuasive, multidimensional feedback with real-time support may be a promising way of increasing PA in inactive individuals. Objective: This study aims to explore whether a 6-week self-monitoring intervention using composite web-based multidimensional PA feedback with real-time daily feedback supports increased PA in adults. Methods: A 6-week, mixed methods, 2-armed exploratory randomized controlled trial with 6-week follow-up was used, whereby low to moderately active (PA level [PAL] <2.0) adults (mean age 51.3 years, SD 8.4 years; women 28/51, 55%) were randomly assigned to receive the self-monitoring intervention (36/51, 71%) or waiting list control (15/51, 29%). Assessment of PA across multiple health-harnessing PA dimensions (eg, PAL, weekly moderate to vigorous intensity PA, sedentary time, and steps), psychosocial cognitions (eg, behavioral regulation, barrier self-efficacy, and habit strength), and health were made at the prerandomization baseline at 6 and 12 weeks. An exploratory analysis of the mean difference and CIs was conducted using the analysis of covariance model. After the 12-week assessment, intervention participants were interviewed to explore their views on the program. Results: There were no notable differences in any PA outcome immediately after the intervention; however, at 12 weeks, moderate-to-large effects were observed with a mean difference in PAL of 0.09 (95% CI 0.02-0.15; effect size [Hedges g] 0.8), daily moderate-intensity PA of 24 (95% CI 0-45; Hedges g=0.6) minutes, weekly moderate-to-vigorous intensity PA of 195 (95% CI 58-331; Hedges g=0.8) minutes, and steps of 1545 (95% CI 581-2553; Hedges g=0.7). Descriptive analyses suggested that the differences in PA at 12 weeks were more pronounced in women and participants with lower baseline PA levels. Immediately after the intervention, there were favorable differences in autonomous motivation, controlled motivation, perceived competence for PA, and barrier self-efficacy, with the latter sustained at follow-up. Qualitative data implied that the intervention was highly informative for participants and that the real-time feedback element was particularly useful in providing tangible, day-to-day behavioral support. Conclusions: Using wearable trackers to capture and present sophisticated multidimensional PA feedback combined with discrete real-time support may be a useful way of facilitating changes in behavior. Further investigation into the ways of optimizing the use of wearables in inactive participants and testing the efficacy of this approach via a robust study design is warranted. Trial Registration: ClinicalTrials.gov NCT02432924; https://clinicaltrials.gov/ct2/show/NCT02432924

BACKGROUND Increasing physical activity (PA) behavior remains a public health priority, and wearable technology is increasingly being used to support behavior change efforts. Using wearables to capture and provide comprehensive, visually persuasive, multidimensional feedback with real-time support may be a promising way of increasing PA in inactive individuals. OBJECTIVE This study aims to explore whether a 6-week self-monitoring intervention using composite web-based multidimensional PA feedback with real-time daily feedback supports increased PA in adults. METHODS A 6-week, mixed methods, 2-armed exploratory randomized controlled trial with 6-week follow-up was used, whereby low to moderately active (PA level [PAL] <2.0) adults (mean age 51.3 years, SD 8.4 years; women 28/51, 55%) were randomly assigned to receive the self-monitoring intervention (36/51, 71%) or waiting list control (15/51, 29%). Assessment of PA across multiple health-harnessing PA dimensions (eg, PAL, weekly moderate to vigorous intensity PA, sedentary time, and steps), psychosocial cognitions (eg, behavioral regulation, barrier self-efficacy, and habit strength), and health were made at the prerandomization baseline at 6 and 12 weeks. An exploratory analysis of the mean difference and CIs was conducted using the analysis of covariance model. After the 12-week assessment, intervention participants were interviewed to explore their views on the program. RESULTS There were no notable differences in any PA outcome immediately after the intervention; however, at 12 weeks, moderate-to-large effects were observed with a mean difference in PAL of 0.09 (95% CI 0.02-0.15; effect size [Hedges g] 0.8), daily moderate-intensity PA of 24 (95% CI 0-45; Hedges g=0.6) minutes, weekly moderate-to-vigorous intensity PA of 195 (95% CI 58-331; Hedges g=0.8) minutes, and steps of 1545 (95% CI 581-2553; Hedges g=0.7). Descriptive analyses suggested that the differences in PA at 12 weeks were more pronounced in women and participants with lower baseline PA levels. Immediately after the intervention, there were favorable differences in autonomous motivation, controlled motivation, perceived competence for PA, and barrier self-efficacy, with the latter sustained at follow-up. Qualitative data implied that the intervention was highly informative for participants and that the real-time feedback element was particularly useful in providing tangible, day-to-day behavioral support. CONCLUSIONS Using wearable trackers to capture and present sophisticated multidimensional PA feedback combined with discrete real-time support may be a useful way of facilitating changes in behavior. Further investigation into the ways of optimizing the use of wearables in inactive participants and testing the efficacy of this approach via a robust study design is warranted. CLINICALTRIAL ClinicalTrials.gov NCT02432924; https://clinicaltrials.gov/ct2/show/NCT02432924

Low physical activity is a major public health problem. New cost-effective approaches that stimulate meaningful long-term changes in physical activity are required, especially within primary care settings. It is becoming clear that there are various dimensions to physical activity with independent health benefits. Advances in technology mean that it is now possible to generate multidimensional physical activity ‘profiles’ that provide a more complete representation of physical activity and offer a variety of options that can be tailored to the individual. Mi-PACT is a randomised controlled trial designed to examine whether personalised multidimensional physical activity feedback and self-monitoring alongside trainer-supportive sessions increases physical activity and improves health outcomes in at-risk men and women. We aim to recruit 216 patients from within primary care aged 40 to 70 years and at medium or high risk of cardiovascular disease and/or type II diabetes mellitus. Adopting an unequal allocation ratio (intervention: control) of 2:1, participants will be randomised to one of two groups, usual care or the intervention. The control group will receive usual care from their general practitioner (GP) and standardised messages about physical activity for health. The intervention group will receive physical activity monitors and access to a web-based platform for a 3-month period to enable self-monitoring and the provision of personalised feedback regarding the multidimensional nature of physical activity. In addition, this technology-enabled feedback will be discussed with participants on 5 occasions during supportive one-to-one coaching sessions across the 3-month intervention. The primary outcome measure is physical activity, which will be directly assessed using activity monitors for a 7-day period at baseline, post intervention and at 12 months. Secondary measures (at these time-points) include weight loss, fat mass, and markers of metabolic control, motivation and well-being. Results from this study will provide insight into the effects of integrated physical activity profiling and self-monitoring combined with in-person support on physical activity and health outcomes in patients at risk of future chronic disease. ISRCTN18008011 Trial registration date: 31 July 2013