Subjective Measures of Workload and Sleep in Australian Army Recruits; Potential Utility as Monitoring Tools

ABSTRACT Introduction Subjective measures may offer practitioners a relatively simple method to monitor recruit responses to basic military training (BMT). Yet, a lack of agreement between subjective and objective measures may presents a problem to practitioners wishing to implement subjective monitoring strategies. This study therefore aims to examine associations between subjective and objective measures of workload and sleep in Australian Army recruits. Materials and Methods Thirty recruits provided daily rating of perceived exertion (RPE) and differential RPE (d-RPE) for breathlessness and leg muscle exertion each evening. Daily internal workloads determined via heart rate monitors were expressed as Edwards training impulse (TRIMP) and average heart rate. External workloads were determined via global positioning system (PlayerLoadTM) and activity monitors (step count). Subjective sleep quality and duration was monitored in 29 different recruits via a customized questionnaire. Activity monitors assessed objective sleep measures. Linear mixed-models assessed associations between objective and subjective measures. Akaike Information Criterion assessed if the inclusion of d-RPE measures resulted in a more parsimonious model. Mean bias, typical error of the estimate (TEE) and within-subject repeated measures correlations examined agreement between subjective and objective sleep duration. Results Conditional R2 for associations between objective and subjective workloads ranged from 0.18 to 0.78, P < 0.01, with strong associations between subjective measures of workload and TRIMP (0.65–0.78), average heart rate (0.57–0.73), and PlayerLoadTM (0.54–0.68). Including d-RPE lowered Akaike Information Criterion. The slope estimate between objective and subjective measures of sleep quality was not significant. A trivial relationship (r = 0.12; CI −0.03, 0.27) was observed between objective and subjective sleep duration with subjective measures overestimating (mean bias 25 min) sleep duration (TEE 41 min). Conclusions Daily RPE offers a proxy measure of internal workload in Australian Army recruits; however, the current subjective sleep questionnaire should not be considered a proxy measure of objective sleep measures.

Implementing Physical Activity Studies During COVID-19 and Winter Storms: Lessons Learned

Patient recruitment and retention are challenging for longitudinal studies. Stay-at-home restrictions for the Galveston and Houston regions in 2020 for COVID-19 and in 2021 for the Winter Storms shut down elective healthcare activities and created additional recruitment barriers during the implementation of a 12-month study examining the physical function of older adults receiving a total knee arthroplasty. This presentation describes recruitment and retention strategies during natural disasters. Ten participants started the study during the pandemic and 6 remained through the winter storms (3 withdrew, 1 no showed). Physical activity monitors were distributed and collected through mail, patient reported outcomes were completed online or over the phone, clinician-initiated measures were only collected when clinics were open, and efforts were made to minimize staff burden and follow evolving hospital guidelines. Most importantly, regular communication and follow-up with participants, research team, and department personnel created a sense of community.

Effect of long-haul transmeridian travel on recovery and performance in international level swimmers

This study holistically examined the effects of long-haul transmeridian travel (LHTT) on physiological, perceptual, sleep and performance markers in nine international level swimmers preparing for the 2019 FINA World Long Course Championships in Gwangju, South Korea. Baseline (BL) measurements were taken over two days during the week before a long-haul eastward flight across eight time-zones. Following the flight, measurements were taken over a six-day holding camp in Japan (C1-C6), and over four days at the competition venue in Gwangju before the Championships commenced (PR1-PR4). Salivary cortisol (sCort), immunoglobulin A (sIgA), alpha-amylase (sAA) concentrations and perceptual measures via the Liverpool John Moore's University Jetlag Questionnaire were assessed. Sleep was monitored using wrist activity monitors and self-report sleep diaries. Performance was assessed via squat jump (SJ), countermovement jump (CMJ) and a 4 × 100 m swim test. Participants perceived themselves to be significantly more fatigued and jet lagged than BL for five- and nine-days post-travel, respectively. Morning sCort decreased by 70% on C1 and remained significantly lower than BL until C6 ( p < 0.05). Sleep ratings improved significantly in comparison to BL from C5 onwards ( p < 0.05). Compared with BL, there was no significant change in swim performance or SJ height following travel; however, there was a 3.8 cm improvement ( p < 0.001) in CMJ height on C5. It took ten days for elite swimmers to perceive themselves recovered from jet lag following LHTT in an eastward direction across eight time-zones. LHTT did not negatively affect sleep or physical performance in the swimmers in comparison to BL.

Exercise in Obesity—the Role of Technology in Health Services: Can This Approach Work?

Purpose of Review Physical activity (PA) is an important strategy to prevent and treat obesity. Electronic health (eHealth) interventions, such as wearable activity monitors and smartphone apps, may promote adherence to regular PA and successful weight loss. This review highlights the evidence for eHealth interventions in promoting PA and reducing weight. Recent Findings Wearables can increase PA and are associated with moderate weight loss in middle/older-aged individuals, with less convincing effects long-term (> 1 year) and in younger people. Data for interventions such as mobile phone applications, SMS, and exergaming are less robust. Investigations of all eHealth interventions are often limited by complex, multi-modality study designs, involving concomitant dietary modification, making the independent contribution of each eHealth intervention on body weight challenging to assess. Summary eHealth interventions may promote PA, thereby contributing to weight loss/weight maintenance; however, further evaluation is required for this approach to be adopted into routine clinical practice.

A technology-enhanced weight-loss program in multiple-cat households: a randomized controlled trial

Objectives The objectives of this study were to determine whether a technology-enhanced weight-loss program, using a home pet health technology ecosystem, is an effective tool in feline weight-loss management in multiple-cat households and to evaluate its impact on cat behavior. Methods The study was a prospective parallel unmasked block-randomized controlled trial comparing two weight loss intervention groups: (1) traditional group with dietary restriction alone (n = 9); (2) technology group that used dietary restriction, digital scales, smart feeders, activity monitors and pet treat cameras (n = 6). A 12-week weight-loss program of client-owned indoor-only two- or three-cat households with at least one overweight cat was conducted in Canada and the USA. Owner impressions of the technology, weight loss rates, smart feeder data, activity monitor data and health-related quality of life (HRQoL) were assessed. Results The study was completed by 9/15 traditional group and 6/10 technology group cats. Dropouts were mainly due to owner issues unrelated to the study. The pet health technology ecosystem received favorable reviews (six responders). Smart feeders and home scales were perceived as valuable additions, while activity monitors and pet treat cameras were valued lower. The average weekly weight-loss rate (percent loss of initial body weight) was higher ( P = 0.036) in the technology group (0.694%) than in the traditional group (0.175%). Although not associated with weight-loss rates, technology group cats trended toward grazing feeding patterns and decreased daily activity counts, while HRQoL increased, on average, for all cats. Conclusions and relevance This introductory investigation suggests that a technology-enhanced weight-loss program would be accepted by cat owners and may deliver advantageous outcomes in multiple-cat households, providing an effective and practical tool in feline weight-loss strategies that will continue to evolve as new technologies become available. It also illustrates the potential value of data gathered from home monitoring devices and digital diaries, providing deeper insights into pet behavior.

Integration of Consumer-Based Activity Monitors into Clinical Practice for Children with Type 1 Diabetes: A Feasibility Study

Current technology commonly utilized in diabetes care includes continuous glucose monitors (CGMs) and insulin pumps. One often overlooked critical component to the human glucose response is daily physical activity habits. Consumer-based activity monitors may be a valid way for clinics to collect physical activity data, but whether or not children with type 1 diabetes (T1D) would wear them or use the associated mobile application is unknown. Therefore, the purpose of this study was to test the feasibility of implementing a consumer-based accelerometer directly into ongoing care for adolescents managing T1D. Methods: Adolescents with T1D were invited to participate in this study and instructed to wear a mobile physical activity monitor while also completing a diet log for a minimum of 3 days. Clinical compliance was defined as the number of participants who were compliant with all measures while also having adequate glucose recordings using either a CGM, insulin pump, or on the diet log. Feasibility was defined as >50% of the total sample reaching clinical compliance. Results: A total of 57 children and teenagers between the ages of 7 and 19 agreed to participate in this study and were included in the final analysis. Chi-square results indicated significant compliance for activity tracking (p < 0.001), diet logs (p = 0.04), and overall clinical compliance (p = 0.04). Conclusion: More than half the children in this study were compliant for both activity monitoring and diet logs. This indicates that it is feasible for children with T1D to wear a consumer-based activity monitor while also recording their diet for a minimum of three days.

183 Designing Research for Beef Cattle Production in Extensive Environments

Designing research for beef cattle production in rangeland environments is an ongoing challenge for researchers worldwide. Specifically, creating study designs that mirror actual production environments yet have enough observations for statistical inference is a challenge that often hinders researchers in efforts to publish their observations. Numerous journals will accept “case study” or observational results that lack valid statistical inference. However, these journals are limited in number and often lack impact. Approaches are available to gain statistical inference by creating multiple observations within a common group of animals. Approaches to increasing statistical observations will be discussed in this presentation. Modeling animal behavior and performance on extensive rangeland landscapes is commonly practiced in wildlife ecology and, more recently, has been published in Animal Science journals. Additionally, new technology has made it possible to apply treatments (e.g., supplementation studies) to individual animals on extensive environments where large, diverse herds/flocks of cattle/sheep are managed as a single group. Use of individual animal identification (EID) and feed intake technology has opened a wide range of research possibilities for beef cattle production systems research in rangeland environments. Likewise, global positioning system (GPS) collars and activity monitors have created the opportunity to evaluate animal grazing behavior in remote and extensive landscapes. The use of multiple regression models to evaluate resource use in extensive environments will, in turn, help managers optimize beef cattle production and the sustainable use of forage/rangeland resources. Embracing new technologies such as GPS, activity monitors, EID tags, and feed intake monitors combined with multiple regression modeling tools will aid in designing and publishing beef cattle production research in extensive rangeland environments.

The use of accelerometer-based wearable activity monitors in clinical settings: current practice, barriers, enablers, and future opportunities

Background Wearable activity monitors (WAMs, e.g. Fitbits and research accelerometers) show promise for helping health care professionals (HCPs) measure and intervene on patients’ activity patterns. This study aimed to describe the clinical use of WAMs within South Australia, barriers and enablers, and future opportunities for large-scale clinical use. Methods A descriptive qualitative study was undertaken using semi-structured interviews. Participants were HCPs with experience using WAMs in South Australian clinical settings. Commencing with participants identified through the research team’s professional networks, snowball recruitment continued until all identified eligible HCPs had been invited. Semi-structured interviews were used to explore the research aims, with quantitative data analysed descriptively, and qualitative data analysed thematically. Results 18 participants (physiotherapists n = 8, exercise physiologists n = 6, medical consultants n = 2, and research personnel recommended by medical consultants n = 2), represented 12 discrete “hubs” of WAM use in clinical practice, spanning rehabilitation, orthopaedics, geriatrics, intensive care, and various inpatient-, outpatient-, community-based hospital and private-practice settings. Across the 12 hubs, five primarily used Fitbits® (various models), four used research-grade accelerometers (e.g. GENEActiv, ActivPAL and StepWatch accelerometers), one used Whoop Bands® and another used smartphone-based step counters. In three hubs, WAMs were used to observe natural activity levels without intervention, while in nine they were used to increase (i.e. intervene on) activity. Device selection was typically based on ease of availability (e.g. devices borrowed from another department) and cost-economy (e.g. Fitbits® are relatively affordable compared with research-grade devices). Enablers included device characteristics (e.g. accuracy, long battery life, simple metrics such as step count) and patient characteristics (e.g. motivation, rehabilitation population, tech-savvy), whilst barriers included the HCPs’ time to download and interpret the data, multidisciplinary team attitudes and lack of protocols for managing the devices. Conclusions At present, the use of WAMs in clinical practice appears to be fragmented and ad hoc, though holds promise for understanding patient outcomes and enhancing therapy. Future work may focus on developing protocols for optimal use, system-level approaches, and generating cost-benefit data to underpin continued health service funding for ongoing/wide-spread WAM use.