Effectiveness of pedometer-based walking programmes in improving some modifiable risk factors of stroke among community-dwelling older adults: a systematic review, theoretical synthesis and meta-analysis.

BACKGROUND: Pedometer-based walking programs hold promise as a health promotion strategy for stroke prevention in community-dwelling older adults, particularly when targeted at physical activity-related modifiable risk factors. The question arises: What is the effectiveness of pedometer-based walking program interventions in improving modifiable stroke risk factors among community-dwelling older adults? METHOD: Eight databases were searched up to December 2nd, 2023, following the Preferred Reporting Items for Systematic Review and Meta-Analysis protocol. Inclusion criteria focused on randomized controlled trials (RCTS) involving community-dwelling older adults and reported in English. Two independent reviewers utilized Physiotherapy Evidence Database (PEDro) tool to extract data, assess eligibility, evaluate study quality, and identify potential bias. Standardized mean difference (SMD) was employed as summary statistics for primary -physical activity level -and secondary outcomes related to cardiovascular function (blood pressure) and metabolic syndrome, including obesity (measured by body mass index and waist circumference), fasting blood sugar, glycated hemoglobin, high-density lipoprotein cholesterol (HDL-C), and triglycerides. A random-effects model was used to generate summary estimates of effects. RESULTS: The review analyzed eight studies involving 1546 participants aged 60-85 years, with 1348 successfully completing the studies. Across these studies, pedometer-based walking programs were implemented 2-3 times per week, with sessions lasting 40-60 minutes, over a duration of 4-26 weeks. The risk of bias varied from high to moderate. Our narrative synthesis revealed positive trends in HDL-C levels, fasting blood sugar, and glycated hemoglobin, suggesting improved glycemic control and long-term blood sugar management. However, the impact on triglycerides was only marginal. Primary meta-analysis demonstrated significantly improved physical activity behavior (SMD=0.44,95%CI:0.26, 0.61,p=<0.00001;I2=0%;4 studies; 532 participants) and systolic blood pressure (SMD=-0.34,95%CI:-0.59,-0.09;p=<0.008;I2=65%,2 studies;249 participants), unlike diastolic blood pressure (SMD=0.13,95%CI:-0.13,-0.38,p=0.33; I2=91%; 2 studies; 237 participants). Interventions based on social cognitive, self-efficacy, and self-efficiency theory(ies), and social cognitive theory applied in an ecological framework, were linked to successful physical activity behavior outcomes. CONCLUSION: Pedometer-based walking programs, utilizing interpersonal health behavior theory/ecological framework, enhance physical activity behavior and have antihypertensive effects in community-dwelling older adults. While they do not significantly affect diastolic blood pressure, these programs potentially serve as a primary stroke prevention strategy aligning with global health goals. TRIAL REGISTRATION: Registration Number: INPLASY202230118.

Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study.

BACKGROUND: This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. OBJECTIVE: We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. METHODS: We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. RESULTS: Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. CONCLUSIONS: Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

Improving physical movement during stroke rehabilitation: investigating associations between sleep measured by wearable actigraphy technology, fatigue, and key biomarkers.

BACKGROUND: Sleep disturbance and fatigue are common in individuals undergoing inpatient rehabilitation following stroke. Understanding the relationships between sleep, fatigue, motor performance, and key biomarkers of inflammation and neuroplasticity could provide valuable insight into stroke recovery, possibly leading to personalized rehabilitation strategies. This study aimed to investigate the influence of sleep quality on motor function following stroke utilizing wearable technology to obtain objective sleep measurements. Additionally, we aimed to determine if there were relationships between sleep, fatigue, and motor function. Lastly, the study aimed to determine if salivary biomarkers of stress, inflammation, and neuroplasticity were associated with motor function or fatigue post-stroke. METHODS: Eighteen individuals who experienced a stroke and were undergoing inpatient rehabilitation participated in a cross-sectional observational study. Following consent, participants completed questionnaires to assess sleep patterns, fatigue, and quality of life. Objective sleep was measured throughout one night using the wearable Philips Actiwatch. Upper limb motor performance was assessed on the following day and saliva was collected for biomarker analysis. Correlation analyses were performed to assess the relationships between variables. RESULTS: Participants reported poor sleep quality, frequent awakenings, and difficulties falling asleep following stroke. We identified a significant negative relationship between fatigue severity and both sleep quality (r=-0.539, p = 0.021) and participants experience of awakening from sleep (r=-0.656, p = 0.003). A significant positive relationship was found between grip strength on the non-hemiplegic limb and salivary gene expression of Brain-derived Neurotrophic Factor (r = 0.606, p = 0.028), as well as a significant negative relationship between grip strength on the hemiplegic side and salivary gene expression of C-reactive Protein (r=-0.556, p = 0.048). CONCLUSION: The findings of this study emphasize the importance of considering sleep quality, fatigue, and biomarkers in stroke rehabilitation to optimize recovery and that interventions may need to be tailored to the individual. Future longitudinal studies are required to explore these relationships over time. Integrating wearable technology for sleep and biomarker analysis can enhance monitoring and prediction of outcomes following stroke, ultimately improving rehabilitation strategies and patient outcomes.

Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use.

GOAL AND AIMS: To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement. FOCUS TECHNOLOGY: Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X. REFERENCE TECHNOLOGY: Polysomnography. SAMPLE: Sixty-three participants (36 female) aged 20-68. DESIGN: Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message. CORE ANALYTICS: Detection and timing accuracy for the sleep onset times and wake times. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods. CORE OUTCOMES: Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency. IMPORTANT ADDITIONAL OUTCOMES: With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit. CORE CONCLUSIONS: Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized ß ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy.

STUDY OBJECTIVES: The aim of this study was to develop a sleep staging classification model capable of accurately performing on different wearable devices. METHODS: Twenty-three healthy participants underwent a full-night type I polysomnography and used two device combinations: (A) flexible single-channel electroencephalogram (EEG) headband + actigraphy (n = 12) and (B) rigid single-channel EEG headband + actigraphy (n = 11). The signals were segmented into 30-second epochs according to polysomnographic stages (scored by a board-certified sleep technologist; model ground truth) and 18 frequency and time features were extracted. The model consisted of an ensemble of bagged decision trees. Bagging refers to bootstrap aggregation to reduce overfitting and improve generalization. To evaluate the model, a training dataset under 5-fold cross-validation and an 80-20% dataset split was used. The headbands were also evaluated without the actigraphy feature. Participants also completed a usability evaluation (comfort, pain while sleeping, and sleep disturbance). RESULTS: Combination A had an F1-score of 98.4% and the flexible headband alone of 97.7% (error rate for N1: combination A = 9.8%; flexible headband alone = 15.7%). Combination B had an F1-score of 96.9% and the rigid headband alone of 95.3% (error rate for N1: combination B = 17.0%; rigid headband alone = 27.7%); in both, N1 was more confounded with N2. CONCLUSIONS: We developed an accurate sleep classification model based on a single-channel EEG device, and actigraphy was not an important feature of the model. Both headbands were found to be useful, with the rigid one being more disruptive to sleep. Future research can improve our results by applying the developed model in a population with sleep disorders. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging; URL: https://clinicaltrials.gov/study/NCT04943562; Identifier: NCT04943562. CITATION: Melo MC, Vallim JRS, Garbuio S, et al. Validation of a sleep staging classification model for healthy adults based on 2 combinations of a single-channel EEG headband and wrist actigraphy. J Clin Sleep Med. 2024;20(6):983-990.

Wrist-worn actigraphy in agitated late-stage dementia patients: A feasibility study on digital inclusion.

BACKGROUND: Wrist-worn actigraphy can be an objective tool to assess sleep and other behavioral and psychological symptoms in dementia (BPSD). We investigated the feasibility of using wearable actigraphy in agitated late-stage dementia patients. METHODS: Agitated, late-stage Alzheimer's dementia care home residents in Greater London area (n = 29; 14 females, mean age ± SD: 80.8 ± 8.2; 93.1% White) were recruited to wear an actigraphy watch for 4 weeks. Wearing time was extracted to evaluate compliance, and factors influencing compliance were explored. RESULTS: A high watch-acceptance (96.6%) and compliance rate (88.0%) was noted. Non-compliance was not associated with age or BPSD symptomatology. However, participants with "better" cognitive function (R = 0.42, p = 0.022) and during nightshift (F1.240, 33.475 = 8.075, p = 0.005) were less compliant. Female participants were also marginally less compliant (F1, 26 = 3.790, p = 0.062). DISCUSSIONS: Wrist-worn actigraphy appears acceptable and feasible in late-stage agitated dementia patients. Accommodating the needs of both the patients and their carers may further improve compliance.

Wearable Sensors to Monitor Physical Activity in Heart Failure Clinical Trials: State-of-the-Art Review.

BACKGROUND: Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders. METHODS AND RESULTS: In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided. CONCLUSION: Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices.

Validating the Fitbit Charge 4© wearable activity monitor for use in physical activity interventions.

OBJECTIVES: Commercially available wearable activity monitors can promote physical activity behaviour. Clinical trials typically quantify physical activity with research grade activity monitors prior to testing interventions utilising commercially available wearable activity monitors aimed at increasing step count. Therefore, it is important to test the agreement of these two types of activity monitors. OBJECTIVES: Observational. METHODS: Thirty adults (20-65 years, n = 19 females) were provided a Fitbit Charge 4©. To determine reliability using an intraclass correlation coefficient, two, one-minute bouts of treadmill walking were performed at a self-selected pace. Subsequently, participants wore both an ActiGraph wGT3X-BT and the Fitbit for seven days. To determine agreement, statistical equivalence and the mean absolute percentage error were calculated and represented graphically with a Bland-Altman plot. Ordinary least products regression was performed to identify fixed or proportional bias. RESULTS: The Fitbit showed 'good' step count reliability on the treadmill (intraclass correlation coefficient = 0.75, 95 % CI = 0.53-0.87, p < 0.001). In free-living however, it overestimated step count when compared to the ActiGraph wGT3X-BT (mean absolute percentage error = 26.02 % ±â€¯14.63). Measurements did not fall within the ± 10 % equivalence region and proportional bias was apparent (slope 95 % CI = 1.09-1.35). CONCLUSIONS: The Fitbit Charge 4© is reliable when measuring step count on a treadmill. However, there is an overestimation of daily steps in free-living environments which may falsely indicate compliance with physical activity recommendations.

Mediating effect of fat mass, lean mass, blood pressure and insulin resistance on the associations of accelerometer-based sedentary time and physical activity with arterial stiffness, carotid IMT and carotid elasticity in 1574 adolescents.

This study examined the mediating effect of total body fat mass, lean mass, blood pressure (BP) and insulin resistance on the associations of sedentary time (ST), light physical activity (LPA) and moderate-to-vigorous PA (MVPA) with carotid-femoral pulse wave velocity (cfPWV), carotid intima-media thickness (cIMT) and carotid elasticity in 1574 adolescents from the Avon Longitudinal Study of Parents and Children birth cohort, UK. ST, LPA and MVPA were assessed with ActiGraph accelerometer. ST and LPA were sex-categorised in tertiles as low (reference), moderate and high, while MVPA was categorised as <40 min/day (reference), 40-<60 min/day and ≥60 min/day. cfPWV, cIMT and carotid elasticity were measured with Vicorder and ultrasound. Fat mass and lean mass were assessed with dual-energy X-ray absorptiometry and homeostatic model assessment of insulin resistance (HOMA-IR) was computed. Mediation analyses structural equation models and linear mixed-effect models adjusted for cardiometabolic and lifestyle factors were conducted. Among 1574 adolescents [56.2% female; mean (SD) age 15.4 (0.24) years], 41% males and 17% females accumulated ≥60 min/day of MVPA. Higher ST was associated with lower cIMT partly mediated by lean mass. Higher LPA (standardized ß = -0.057; [95% CI -0.101 to -0.013; p = 0.014]) and the highest LPA tertile were associated with lower cfPWV. BP had no significant mediating effect movement behaviour relations with vascular indices. Lean mass partially mediated associations of higher MVPA with higher cIMT (0.012; [0.007-0.002; p = 0.001], 25.5% mediation) and higher carotid elasticity (0.025; [0.014-0.039; p = 0.001], 28.1% mediation). HOMA-IR mediated the associations of higher MVPA with higher carotid elasticity (7.7% mediation). Engaging in ≥60 min/day of MVPA was associated with higher carotid elasticity. In conclusion, higher LPA was associated with lower arterial stiffness, but higher MVPA was associated with thicker carotid wall explained by higher lean mass.

Measuring movement in health and disease.

Evaluating and quantifying the many aspects of movement - from open-field locomotion and stepping patterns in rodent models to stride trajectory and postural sway in human patients - are key to understanding brain function. Various experimental approaches have been used in applying these lines of research to investigate the brain mechanisms underlying neurodegenerative disease. Although valuable, data on movement are often limited by the shortcomings inherent in the data collection process itself. Steve Fowler and his research group have been instrumental in pioneering a technology that both minimizes these pitfalls in studies of rodent behavior and has applications to research on human patients. At the center of this technology is the force-plate actometer, developed by the Fowler group to assess multiple aspects of movement in rodent models. Our review highlights how use of the actometer and related behavioral measurements provides valuable insight into Huntington's disease (HD), an autosomal dominant condition of progressively deteriorating behavioral control. HD typically emerges in mid-life and has been replicated in multiple genetically engineered mouse models. The actometer also can be a valuable addition to cutting-edge neuronal and synaptic technologies that are now increasingly applied to studies of behaving animals. In short, the impact of the Fowler contribution to the neuroscience of movement is both meaningful and ongoing.

An Earlier First Meal Timing Associates with Weight Loss Effectiveness in A 12-Week Weight Loss Support Program.

Recent studies have reported that meal timing may play an important role in weight regulation, however it is unknown whether the timing of meals is related to the amount of weight loss. This study aimed to examine the relationship between indices of meal timing and weight loss during weight loss intervention in adults. A 12-week weight loss support program was conducted for 97 adults (age: 47.6 ± 8.3 years, BMI: 25.4 ± 3.7 kg/m2). After the program, body weight decreased by -3.0 ± 2.7%. Only the start of the eating window was positively correlated with the weight change rate in both sexes (men: r = 0.321, p = 0.022; women: r = 0.360, p = 0.014). The participants were divided into two groups based on the start of the eating window as follows: the early group (6:48 ± 0:21 AM) and the late group (8:11 ± 1:05 AM). The weight loss rate in the early group was significantly higher (-3.8 ± 2.7%) than that in the late group (-2.2 ± 2.5%). The present results showed that the start of the early eating window was associated with weight loss and suggested paying attention to meal timing when doing weight loss.

Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system.

Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

Time Trends in Physical Activity Using Wearable Devices: A Systematic Review and Meta-analysis of Studies from 1995 to 2017.

INTRODUCTION: Conflicting evidence exists on whether physical activity (PA) levels of humans have changed over the last quarter-century. The main objective of this study was to determine if there is evidence of time trends in PA, from cross-sectional studies that assessed PA at different time points using wearable devices (e.g., pedometers and accelerometers). A secondary objective was to quantify the rate of change in PA. METHODS: A systematic literature review was conducted of English-language studies indexed in PubMed, SPORTDiscus, and Web of Science (1960-2020) using search terms (time OR temporal OR secular) AND trends AND (steps per day OR pedometer OR accelerometer OR MVPA). Subsequently, a meta-analytic approach was used to aggregate data from multiple studies and to examine specific factors (i.e., sex, age-group, sex and age-group, and PA metric). RESULTS: Based on 16 peer-reviewed scientific studies conducted between 1995 and 2017, levels of ambulatory PA are trending downward in developed countries. Significant declines were seen in both males and females (P < 0.001) as well as in children (P = 0.020), adolescents (P < 0.001), and adults (P = 0.004). The average study duration was 9.4 yr (accelerometer studies, 5.3 yr; pedometer studies, 10.8 yr). For studies that assessed steps, the average change in PA was -1118 steps per day over the course of the study (P < 0.001), and adolescents had the greatest change in PA at -2278 steps per day (P < 0.001). Adolescents also had the steepest rate of change over time, expressed in steps per day per decade. CONCLUSIONS: Evidence from studies conducted in eight developed nations over a 22-yr period indicates that PA levels have declined overall, especially in adolescents. This study emphasizes the need for continued research tracking time trends in PA using wearable devices.

Effect of Paralympic powerlifting training on sleep and its relationship with training load

Abstract Aim: This study aims to compare the sleep parameters in Paralympic powerlifting athletes during days with and without training, and to analyze the relationship between the training load and sleep on the same day and the relationship between the previous night's sleep and the training load of the following day. Methods: Actigraphy was used to analyze the sleep parameters of 11 Paralympic powerlifting athletes for 14 days (7 days without and with training), whereas Ratings of Perceived Exertion (RPE) analysis was used to assess training load. In addition, the Horne and östberg chronotype questionnaire and the Epworth Sleepiness Scale were applied. Results: Athletes show morning and indifferent chronotype and low daytime sleepiness. We found that on training days, sleep onset latency (SOL) was lower (average 5.3 min faster), whereas total sleep time (TST) and sleep efficiency (SE) were higher (TST averaged 169 min and SE 7% higher) compared to non-training days. In addition, the TST of the night before the training days correlated positively with the RPE of the following day, and the training volume correlated negatively with the SE of the same day. Conclusion: Our findings show that Paralympic powerlifting training had positive effects in increasing TST and SE and decreasing SOL on training days. These results show the positive effects of this type of training in improving sleep in athletes with physical disabilities. In addition, a good night's sleep the day before training can make it possible to put more effort into the next day's training. Therefore, guiding athletes to sleep more before training with more intense loads is recommended.

Comparison between ActiGraph GT3X and ActivPAL to assess sedentary behavior during the school period

Abstract Aims: This study aimed to compare the sedentary time measured using the ActiGraph GT3X accelerometer with the measurement of sitting and standing time obtained by ActivPAL inclinometers. Methods: This was a cross-sectional study conducted with a sample of 60 schoolchildren (34 males) of one elementary public school in Brazil. The students used both an ActiGraph GT3X accelerometer and an ActivPAL inclinometer, concurrently, positioned at the beginning and removed at the end of the school shift, for four days. For analysis, paired Student's t-tests, Pearson's correlation coefficients, intraclass correlation coefficients, and Bland-Altman plots were used. Results: When comparing sedentary time with sitting time, although correlated (r = 0.53; p < 0.001), the mean minutes were different (134.2 min/day in ActiGraph GT3X vs 120.3 min/day in ActivPAL; p < 0.001), with a bias of 13.9 min/day. When comparing the measurement of sedentary time with the sum of the sitting time plus standing time, different mean minutes were also observed (134.2 min/day in ActiGraph GT3X vs 177.0 min/day in ActivPAL; p < 0.001), and although the correlation was stronger (r = 0.75; p < 0.001), the bias was higher (−42.8 min/day). Conclusion: Sedentary time derived from the ActiGraph GT3X device should be used with caution to evaluate sedentary behavior in a school setting and may be interpreted only as non-moving activities (stationary behavior).

Detailed analysis and comparison of different activity metrics.

Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. Although the measured raw acceleration signal can be diversely processed, and then the activity values can be calculated by different activity calculation methods, the documentations of them are generally incomplete or vary by manufacturer. These numerous activity metrics may require different types of preprocessing of the acceleration signal. For example, digital filtering of the acceleration signals can have various parameters; moreover, both the filter and the activity metrics can also be applied per axis or on the magnitudes of the acceleration vector. Level crossing-based activity metrics also depend on threshold level values, yet the determination of their exact values is unclear as well. Due to the serious inconsistency of determining activity values, we created a detailed and comprehensive comparison of the different available activity calculation procedures because, up to the present, it was lacking in the literature. We assessed the different methods by analysing the triaxial acceleration signals measured during a 10-day movement of 42 subjects. We calculated 148 different activity signals for each subject's movement using the combinations of various types of preprocessing and 7 different activity metrics applied on both axial and magnitude data. We determined the strength of the linear relationship between the metrics by correlation analysis, while we also examined the effects of the preprocessing steps. Moreover, we established that the standard deviation of the data series can be used as an appropriate, adaptive and generalized threshold level for the level intersection-based metrics. On the basis of these results, our work also serves as a general guide on how to proceed if one wants to determine activity from the raw acceleration data. All of the analysed raw acceleration signals are also publicly available.

Effect of self-regulatory behaviour change techniques and predictors of physical activity maintenance in cancer survivors: a 12-month follow-up of the Phys-Can RCT.

BACKGROUND: Current knowledge about the promotion of long-term physical activity (PA) maintenance in cancer survivors is limited. The aims of this study were to 1) determine the effect of self-regulatory BCTs on long-term PA maintenance, and 2) identify predictors of long-term PA maintenance in cancer survivors 12 months after participating in a six-month exercise intervention during cancer treatment. METHODS: In a multicentre study with a 2 × 2 factorial design, the Phys-Can RCT, 577 participants with curable breast, colorectal or prostate cancer and starting their cancer treatment, were randomized to high intensity exercise with or without self-regulatory behaviour change techniques (BCTs; e.g. goal-setting and self-monitoring) or low-to-moderate intensity exercise with or without self-regulatory BCTs. Participants' level of PA was assessed at the end of the exercise intervention and 12 months later (i.e. 12-month follow-up), using a PA monitor and a PA diary. Participants were categorized as either maintainers (change in minutes/week of aerobic PA ≥ 0 and/or change in number of sessions/week of resistance training ≥0) or non-maintainers. Data on potential predictors were collected at baseline and at the end of the exercise intervention. Multiple logistic regression analyses were performed to answer both research questions. RESULTS: A total of 301 participants (52%) completed the data assessments. A main effect of BCTs on PA maintenance was found (OR = 1.80, 95%CI [1.05-3.08]) at 12-month follow-up. Participants reporting higher health-related quality-of-life (HRQoL) (OR = 1.03, 95%CI [1.00-1.06] and higher exercise motivation (OR = 1.02, 95%CI [1.00-1.04]) at baseline were more likely to maintain PA levels at 12-month follow-up. Participants with higher exercise expectations (OR = 0.88, 95%CI [0.78-0.99]) and a history of tobacco use at baseline (OR = 0.43, 95%CI [0.21-0.86]) were less likely to maintain PA levels at 12-month follow-up. Finally, participants with greater BMI increases over the course of the exercise intervention (OR = 0.63, 95%CI [0.44-0.90]) were less likely to maintain their PA levels at 12-month follow-up. CONCLUSIONS: Self-regulatory BCTs improved PA maintenance at 12-month follow-up and can be recommended to cancer survivors for long-term PA maintenance. Such support should be considered especially for patients with low HRQoL, low exercise motivation, high exercise expectations or with a history of tobacco use at the start of their cancer treatment, as well as for those gaining weight during their treatment. However, more experimental studies are needed to investigate the efficacy of individual or combinations of BCTs in broader clinical populations. TRIAL REGISTRATION: NCT02473003 (10/10/2014).

Cross-sectional assessment of sleep and fatigue in middle-aged Japanese women with primary Sjogren syndrome or rheumatoid arthritis using self-reports and wrist actigraphy.

ABSTRACT: To investigate fatigue, health-related quality of life (HR-QOL), and sleep quality in women with primary Sjogren syndrome (pSS) or rheumatoid arthritis (RA) as compared with healthy controls using self-reports and wrist actigraphy.In this cross-sectional observational study, we evaluated a total of 25 patients (aged 40-75 years) with pSS, 10 with RA, and 17 healthy control subjects living in Japan. The HR-QOL was assessed using the Short Form-36. Fatigue was evaluated using the Short Form-36 vitality score, visual analog scale (VAS) for fatigue, and 2 questionnaire items using scores based on a 4-point Likert scale. Sleep quality was measured using the Japanese version of the Pittsburgh Sleep Quality Index, VAS for sleep quality, and wrist actigraphy for 14 days.Patients with pSS reported severer fatigue and lower HR-QOL than healthy controls, especially in mental health. Based on the Pittsburgh Sleep Quality Index score, 56% of the patients with pSS were poor sleepers, which was higher than healthy controls (29.4%). Furthermore, the patients with pSS scored significantly lower on the VAS for sleep quality than healthy controls (40.5 vs 63.7, P = .001). Although subjective assessments revealed slight sleep disturbances in patients with pSS, wrist actigraphy revealed no differences when compared with healthy controls for total sleep time (421.8 minutes vs 426.5 minutes), sleep efficiency (95.2% vs 96.4%), number of awakenings (1.4 vs 0.9), and wake after sleep onset (22.4 minutes vs 16.1 minutes). Poor subjective sleep quality was associated with enhanced fatigue. However, sleep efficiency, as determined by actigraphy, was not associated with fatigue. Notably, the patients with RA and healthy controls did not differ significantly in terms of fatigue or sleep quality, although patients with RA experienced more nocturnal awakenings than healthy controls (1.7 vs 0.9, P = .04).Patients with pSS experience severe fatigue, poor HR-QOL, and sleep disturbances, which are associated with fatigue. However, wrist actigraphy did not reveal differences in sleep quality, suggesting that it may not be an appropriate measure of sleep in patients with pSS.

Effectiveness of the mHealth intervention 'MyDayPlan' to increase physical activity: an aggregated single case approach.

BACKGROUND: e- and mHealth interventions using self-regulation techniques like action and coping planning have the potential to tackle the worldwide problem of physical inactivity. However, they often use one-week self-regulation cycles, providing support toward an active lifestyle on a weekly basis. This may be too long to anticipate on certain contextual factors that may fluctuate from day to day and may influence physical activity. Consequently, the formulated action and coping plans often lack specificity and instrumentality, which may decrease effectiveness of the intervention. The aim of this study was to evaluate effectiveness of a self-regulation, app-based intervention called 'MyDayPlan'. "MyDayPlan' provides an innovative daily cycle in which users are guided towards more physical activity via self-regulation techniques such as goal setting, action planning, coping planning and self-monitoring of behaviour. METHODS: An ABAB single-case design was conducted in 35 inactive adults between 18 and 58 years (M = 40 years). The A phases (A1 and A2) were the control phases in which the 'MyDayPlan' intervention was not provided. The B phases (B1 and B2) were the intervention phases in which 'MyDayPlan' was used on a daily basis. The length of the four phases varied within and between the participants. Each phase lasted a minimum of 5 days and the total study lasted 32 days for each participant. Participants wore a Fitbit activity tracker during waking hours to assess number of daily steps as an outcome. Single cases were aggregated and data were analysed using multilevel models to test intervention effects and possible carry-over effects. RESULTS: Results showed an average intervention effect with a significant increase in number of daily steps from the control to intervention phases for each AB combination. From A1 to B1, an increase of 1424 steps (95% CI [775.42, 2072.32], t (1082) = 4.31,p < .001), and from A2 to B2, an increase of 1181 steps (95% CI [392.98, 1968.16], t (1082) = 2.94, p = .003) were found. Furthermore, the number of daily steps decreased significantly (1134 steps) when going from the first intervention phase (B1) to the second control phase (A2) (95% CI [- 1755.60, - 512.38], t (1082) = - 3.58, p < .001). We found no evidence for a difference in trend between the two control (95% CI [- 114.59, 197.99], t (1078) = .52, p = .60) and intervention phases (95% CI [- 128.79,284.22], t (1078) = .74, p = .46). This reveals, in contrast to what was hypothesized, no evidence for a carry-over effect after removing the 'MyDayPlan' app after the first intervention phase (B1). CONCLUSION: This study adds evidence that the self-regulation mHealth intervention, 'MyDayPlan' has the capacity to positively influence physical activity levels in an inactive adult population. Furthermore, this study provides evidence for the potential of interventions adopting a daily self-regulation cycle in general.