Taxonomy of hybridly polarized Stokes vortex beams.

Structured beams carrying topological defects, namely phase and Stokes singularities, have gained extensive interest in numerous areas of optics. The non-separable spin and orbital angular momentum states of hybridly polarized Stokes singular beams provide additional freedom for manipulating optical fields. However, the characterization of hybridly polarized Stokes vortex beams remains challenging owing to the degeneracy associated with the complex polarization structures of these beams. In addition, experimental noise factors such as relative phase, amplitude, and polarization difference together with beam fluctuations add to the perplexity in the identification process. Here, we present a generalized diffraction-based Stokes polarimetry approach assisted with deep learning for efficient identification of Stokes singular beams. A total of 15 classes of beams are considered based on the type of Stokes singularity and their associated mode indices. The resultant total and polarization component intensities of Stokes singular beams after diffraction through a triangular aperture are exploited by the deep neural network to recognize these beams. Our approach presents a classification accuracy of 98.67% for 15 types of Stokes singular beams that comprise several degenerate cases. The present study illustrates the potential of diffraction of the Stokes singular beam with polarization transformation, modeling of experimental noise factors, and a deep learning framework for characterizing hybridly polarized beams.

Health Research Requires Efficient Platforms for Data Collection from Personal Devices.

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment.

Collecting health-related research data using consumer-based wireless smart scales.

BACKGROUND: Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population's health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin. OBJECTIVE: The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research. METHOD: We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer's official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers. RESULT: We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021. CONCLUSION: The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning.

The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this article, we demonstrate the use of a machine learning-aided segmentation and analysis pipeline for the quantification of mitochondrial morphology in fluorescence microscopy images of fixed cells. The deep learning-based segmentation tool is trained on simulated images and eliminates the requirement for ground truth annotations for supervised deep learning. We demonstrate the utility of this tool on fluorescence microscopy images of fixed cardiomyoblasts with a stable expression of fluorescent mitochondria markers and employ specific cell culture conditions to induce changes in the mitochondrial morphology.

Consistent pattern between physical activity measures and chronic pain levels: the Tromsø Study 2015 to 2016.

ABSTRACT: Epidemiological literature on the relationship between physical activity and chronic pain is scarce and inconsistent. Hence, our aim was to assess the relationship applying comprehensive methodology, including self-reported and accelerometer measures of physical activity and different severity levels of chronic pain. We used data from the Tromsø Study (2015-2016). All residents in the municipality, aged 40 years and older were invited to participate (n = 32,591, 51% women). A total of 21,083 (53%) women reported on questionnaires. Additionally, 6778 participants (54% women) were invited to wear accelerometers (6125 with complete measurements). Our exposure measures were self-reported leisure time physical activity, exercise frequency, duration, and intensity and 2 accelerometer measures (steps per day and minutes of moderate to vigorous physical activity per day). Outcome measurements were chronic pain and moderate-to-severe chronic pain. We used Poisson regression to estimate chronic pain prevalence and prevalence ratios for each physical activity measure, with adjustments for sex, age, education level, smoking history, and occupational physical activity. Our main analyses showed an inverse dose-response relationship between all physical activity measures and both severity measures of chronic pain, except that the dose-response relationship with exercise duration was only found for moderate-to-severe pain. All findings were stronger for the moderate-to-severe pain outcomes than for chronic pain. Robustness analyses gave similar results as the main analyses. We conclude that an inverse dose-response association between physical activity and chronic pain is consistent across measures. To summarize, higher levels of physical activity is associated with less chronic pain and moderate-to-severe chronic pain.

Greedy knot selection algorithm for restricted cubic spline regression.

Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one such method, for example, highly relevant to Cox proportional hazard regression model analysis. RCS regression uses third-order polynomials joined at knot points to model non-linear relationships. The standard approach is to place knots by a regular sequence of quantiles between the outer boundaries. A regression curve can easily be fitted to the sample using a relatively high number of knots. The problem is then overfitting, where a regression model has a good fit to the given sample but does not generalize well to other samples. A low knot count is thus preferred. However, the standard knot selection process can lead to underperformance in the sparser regions of the predictor variable, especially when using a low number of knots. It can also lead to overfitting in the denser regions. We present a simple greedy search algorithm using a backward method for knot selection that shows reduced prediction error and Bayesian information criterion scores compared to the standard knot selection process in simulation experiments. We have implemented the algorithm as part of an open-source R-package, knutar.

Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning.

Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope's point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria.

Criterion validity of the Saltin-Grimby Physical Activity Level Scale in adolescents. The Fit Futures Study.

BACKGROUND: The Saltin-Grimby Physical Activity Level Scale (SGPALS) is commonly used to measure physical activity (PA) in population studies, but its validity in adolescents is unknown. This study aimed to assess the criterion validity of the SGPALS against accelerometry in a large sample of adolescents. A secondary aim was to examine the validity across strata of sex, body mass index (BMI), parental educational level, study program and self-reported health. METHODS: The study is based on data from 572 adolescents aged 15-17 years who participated in the Fit Futures Study 2010-11 in Northern Norway. The participants were invited to wear an accelerometer (GT3X) attached to their hip for seven consecutive days. We used Spearman's rho and linear regression models to assess the validity of the SGPALS against the following accelerometry estimates of PA; mean counts/minute (CPM), steps/day, and minutes/day of moderate-to-vigorous physical activity (MVPA). RESULTS: The SGPALS correlated with mean CPM (ρ = 0.40, p<0.01), steps/day (ρ = 0.35, p<0.01) and MVPA min/day (ρ = 0.35, p<0.01). We observed no differences between correlations within demographic strata (all p>0.001). Higher scores on SGPALS were associated with a higher CPM, higher number of steps per day and more minutes of MVPA per day, with the following mean differences in PA measurements between the SGPALS ranks: CPM increased by 53 counts (95% CI: 44 to 62), steps/day increased by 925 steps (95% CI: 731 to 1118), and MVPA by 8.4 min/day (95% CI: 6.7 to 10.0). Mean difference between the highest and lowest SGPALS category was 2947 steps/day (6509 vs. 9456 steps/day) and 26.4 min/day MVPA (35.2 minutes vs 61.6 minutes). CONCLUSION: We found satisfactory ranking validity of SGPALS measured against accelerometry in adolescents, which was fairly stable across strata of sex, BMI, and education. However, the validity of SGPALS in providing information on absolute physical activity levels seem limited.

Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions.

This study describes a method for extracting the stride parameter ground contact time (GCT) from inertial sensor signals in sprinting. Five elite athletes were equipped with inertial measurement units (IMU) on their ankles and performed 34 maximum 50 and 100-m sprints. The GCT of each step was estimated based on features of the recorded IMU signals. Additionally, a photo-electric measurement system covered a 50-m corridor of the track to generate ground truth data. This corridor was placed interchangeably at the first and the last 50-ms of the track. In total, 863 of 889 steps (97.08%) were detected correctly. On average, ground truth data were underestimated by 3.55 ms. The root mean square error of GCT was 7.97 ms. Error analyses showed that GCT at the beginning and the end of the sprint was classified with smaller errors. For single runs the visualization of step-by-step GCT was demonstrated as a new diagnostic instrument for sprint running. The results show the high potential of IMUs to provide the temporal parameter GCT for elite-level athletes.

A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks.

To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.

First Report of Two Cases of Acute Gastric Ischemia after Robot-Assisted Radical Cystectomy.

Gastrointestinal ischemia is rare after small pelvis surgery. Minimal invasive robotic surgery requires adaptation of the surgical approach for cystectomy and derivation construction such as the use of pneumoperitoneum and Trendelenburg positioning of the patient. Two cases with gastric ischemic complications after robot-assisted radical cystectomy are described. The first case was a 68-year-old female who had prolonged gastroparalysis and blood in a replaced gastric tube at day 10 after robotic cystectomy and Bricker urinary derivation. Gastroscopy revealed ischemia of gastric and proximal duodenal mucosa while computed tomography showed multiple calcifications and thrombi in the coeliac trunk branches and splenic infarcts. The stenosis of the origin of the mesenteric superior artery was stented via an endovascular procedure, and the patient recovered with normal gastroscopy 1 month postoperatively. The second case was a 73-year-old male who developed abdominal pain and fever 5 days after robotic cystectomy and Bricker. On abdominal computed tomography imaging, subcutaneous emphysema, intra-abdominal air, and calcification at the origin of the coeliac trunk were found. At laparotomy 5 days after the cystectomy, a 3 cm hole in the fundus of the stomach was found which was removed with the major stomach curvature. Gastroscopy 5 days after hemigastrectomy revealed no remnant ischemia. The prolonged pneumoperitoneum during robotic cystectomy, the deep Trendelenburg position, and the preoperatively impaired vascular system can be the reasons of our first two cases of gastric ischemia. This rare complication should be kept in mind in patients with symptoms of gastric ischemia since it can result in gastric perforation.

Physical activity and cold pain tolerance in the general population.

BACKGROUND: The relationship between habitual physical activity (PA) and experimental pain tolerance has been investigated in small samples of young, healthy and/or single-sex volunteers. We used a large, population-based sample to assess this relationship in men and women with and without chronic pain. METHODS: We used data from the sixth and seventh Tromsø Study surveys (2007-2008; 2015-2016), with assessed pain tolerance of participants with the cold pressor test (CPT: dominant hand in circulating cold water at 3°C, maximum test time 106 s), and self-reported total amount of habitual PA in leisure time (n = 19,087), exercise frequency (n = 19,388), exercise intensity (n = 18,393) and exercise duration (n = 18,343). A sub-sample had PA measured by accelerometers (n = 4,922). We used Cox regression to compare CPT tolerance times between self-reported PA levels. For accelerometer-measured PA, we estimated hazard ratios for average daily activity counts, and for average daily minutes of moderate-to-vigorous PA done in bouts lasting 10 min or more. Models were tested for PA-sex, and PA-chronic pain and PA-moderate-to-severe chronic pain interactions. RESULTS: Leisure-time PA, exercise intensity and exercise duration were positively associated with CPT tolerance (p < .001; p = .011; p < .001). More PA was associated with higher CPT tolerance. At high levels of leisure-time PA and exercise intensity, men had a significantly higher CPT tolerance than women. Accelerometer-measured PA was not associated with CPT tolerance. CONCLUSIONS: This study is one of the first to show that higher self-reported habitual PA was connected to higher experimental pain tolerance in a population-based sample, especially for men. This was not found for accelerometer-measured PA. SIGNIFICANCE: This study finds that higher level of self-reported leisure-time physical activity is associated with increased cold pressor pain tolerance in a large population-based sample. Though present in both sexes, the association is strongest among men. Despite the robust dose-response relationship between pain tolerance and self-reported activity level, no such relationship was found for accelerometer-measured activity, reflecting a possible discrepancy in the aspect of physical activity measured. Though the study design does not permit causal conclusions, the findings suggest that increasing physical activity may increase pain tolerance in the general population.

Association between objectively measured physical activity and longitudinal changes in body composition in adolescents: the Tromsø study fit futures cohort.

OBJECTIVES: Physical activity may be important in deterring the obesity epidemic. This study aimed to determine whether objectively measured physical activity in first year of upper secondary high school predicted changes in body composition over 2 years of follow-up in a cohort of Norwegian adolescents (n=431). DESIGN: A longitudinal study of adolescents (mean age of 16 (SD 0.4) at baseline, 60.3% girls) participating in the Fit Futures studies 1 (2010-2011) and 2 (2012-2013). SETTING: All eight upper secondary high schools in two municipalities in Northern Norway. PARTICIPANTS: Students participating in both studies and under the age of 18 at baseline and with valid measurement of physical activity at baseline and body composition in both surveys. PRIMARY AND SECONDARY OUTCOMES: Change in objectively measured body mass index and waist circumference and change in dual-energy X-ray absorptiometry measured fat mass index, lean mass index (LMI) and appendicular LMI (aLMI) between baseline and follow-up. RESULTS: At baseline, boys had significantly higher physical activity volume (p=0.01) and spent on average of 6.4 (95% CI 2.1 to 10.6) more minutes in moderate-to-vigorous physical activity (MVPA) than girls (p<0.01). In girls, multivariate regression analyses showed that more sedentary time was negatively associated with changes in LMI (p<0.01) and aLMI (p<0.05), whereas more light activity had opposite effects on these measures (p<0.01 and p<0.05, respectively). No significant associations between measures of baseline physical activity and changes in body composition parameters were observed in boys. CONCLUSIONS: In this cohort of Norwegian adolescents, sedentary and light physical activity was associated with changes in LMI and aLMI in girls, but not boys. Minutes spent in MVPA in first year of upper secondary high school was not associated with changes in measures of body composition in neither sex after 2 years.

Xanthogranulomatous pyelonephritis caused by fusobacterium nucleatum. Case report and review of literature.

Xanthogranulomatous pyelonephritisis (XGP) is a rare and aggressive form of chronic urinary tract infection. The most common microorganism is Proteus but other microorganisms have been described in the literature. In this article, we describe a sixty-year-old male who presented with urosepsis and sever thrombocytopenia secondary to Fusobacterium nucleatum urinary tract infection which led to poorly functioning XGP. The rarity of the microorganism and the devastating clinical course encourages us to report it as anaerobes don't usually cause UTI but unusual microorganism should be suspected if the clinical course is atypical or if urinary tract abnormalities are present.

Prevalence of accelerometer-measured physical activity in adolescents in Fit Futures - part of the Tromsø Study.

BACKGROUND: Previous studies show large variations in physical activity (PA) levels among adolescents. However, the number of studies is limited and even fewer studies have assessed PA in adolescents by accelerometer devices. This study aimed to describe accelerometer-measured PA levels in adolescents in a population-based cohort in Northern Norway. METHODS: In 611 students aged 16-17 years attending the Fit Futures Study, PA was measured by Actigraph GT3X for seven consecutive days. PA was expressed as total PA volume (counts per minute, CPM), time spent in intensity zones, steps per day, and fulfilment of WHO recommendation (i.e. accumulation of 60 min or more of at least moderate intensity PA per day). Potential correlates of PA such as sex, socioeconomic status, study program, self-perceived health, and PA variations by weekday versus weekend were also examined. RESULTS: 16% of the girls and 25% of the boys fulfilled current WHO-recommendations. Total PA volume (CPM) was higher in boys than in girls (353 (SD 130) versus 326 (SD 114) CPM, p < 0.05). PA levels differed with study program and increased with better self-perceived health, but were not associated with socioeconomic status. Both boys and girls were more active on weekdays than weekends (altogether; 350 (SD 124) versus 299 (SD 178) CPM, p < 0.05). CONCLUSIONS: In this cohort of adolescents, less than 25% of 16-17-year-old boys and girls fulfilled the WHO recommendations. The levels of physical activity in 16-17-year-old adolescents are similar to previous data reported in adults.

Evaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordings.

Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms' hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based algorithms. The sensitivity analysis and hyperparameter tuning revealed that all algorithms were able to achieve increased classification performance by employing larger intervals and windows, while more stringently defining artificial movement. These classification gains were associated with the ability to lower the false positives (type I error) and do not necessarily indicate a more accurate detection of the total non-wear time. Moreover, our results indicate that with tuned hyperparameters, epoch-based non-wear algorithms are able to perform just as well as raw-based non-wear algorithms with respect to their ability to correctly detect true wear and non-wear episodes.

Criterion validity of two physical activity and one sedentary time questionnaire against accelerometry in a large cohort of adults and older adults.

OBJECTIVES: We compared the ability of physical activity and sitting time questionnaires (PAQ) for ranking individuals versus continuous volume calculations (physical activity level (PAL), metabolic equivalents of task (MET), sitting hours) against accelerometry measured physical activity as our criterion. METHODS: Participants in a cohort from the Tromsø Study completed three questionnaires; (1) The Saltin-Grimby Physical Activity Level Scale (SGPALS) (n=4040); (2) The Physical Activity Frequency, Intensity and Duration (PAFID) questionnaire (n=5902)) calculated as MET-hours·week-1 and (3) The International Physical Activity questionnaire (IPAQ) short-form sitting question (n=4896). We validated the questionnaires against the following accelerometry (Actigraph wGT3X-BT) estimates: vector magnitude counts per minute, steps∙day-1, time (minutes·day-1) in sedentary behaviour, light physical activity, moderate and vigorous physical activity (MVPA) non-bouted and ≥10 min bouted MVPA. RESULTS: Ranking of physical activity according to the SGPALS and quartiles (Q) of MET-hours∙week-1 from the PAFID were both positively associated with accelerometry estimates of physical activity (p<0.001) but correlations with accelerometry estimates were weak (SGPALS (PAL): r=0.11 to 0.26, p<0.001) and weak-to-moderate (PAFID: r=0.39 to 0.44, p<0.01). There was 1 hour of accelerometry measured sedentary time from Q1 to Q4 in the IPAQ sitting question (p<0.001) and also weak correlations (r=0.22, p<0.01). CONCLUSION: Ranking of physical activity levels measured with PAQs appears to have higher validity than energy expenditure calculations. Self-reported sedentary time poorly reflects accelerometry measured sedentary time. These two PAQs can be used for ranking individuals into different physical activity categories supporting previous studies using these instruments when assessing associations with health outcomes.

Physical activity levels in adults and elderly from triaxial and uniaxial accelerometry. The Tromsø Study.

INTRODUCTION: Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives to promote physical activity. Physical activity assessed by accelerometry is challenged by varying data processing procedures, which influences the outcome. We aimed to describe the levels and prevalence estimates of physical activity, and to examine how triaxial and uniaxial accelerometry data influences these estimates, in a large population-based cohort of Norwegian adults. METHODS: This cross-sectional study included 5918 women and men aged 40-84 years who participated in the seventh wave of the Tromsø Study (2015-16). The participants wore an ActiGraph wGT3X-BT accelerometer attached to the hip for 24 hours per day over seven consecutive days. Accelerometry variables were expressed as volume (counts·minute-1 and steps·day-1) and as minutes per day in sedentary, light physical activity and moderate and vigorous physical activity (MVPA). RESULTS: From triaxial accelerometry data, 22% (95% confidence interval (CI): 21-23%) of the participants fulfilled the current global recommendations for physical activity (≥150 minutes of MVPA per week in ≥10-minute bouts), while 70% (95% CI: 69-71%) accumulated ≥150 minutes of non-bouted MVPA per week. When analysing uniaxial data, 18% fulfilled the current recommendations (i.e. 20% difference compared with triaxial data), and 55% (95% CI: 53-56%) accumulated ≥150 minutes of non-bouted MVPA per week. We observed approximately 100 less minutes of sedentary time and 90 minutes more of light physical activity from triaxial data compared with uniaxial data (p<0.001). CONCLUSION: The prevalence estimates of sufficiently active adults and elderly are more than three times higher (22% vs. 70%) when comparing triaxial bouted and non-bouted MVPA. Physical activity estimates are highly dependent on accelerometry data processing criteria and on different definitions of physical activity recommendations, which may influence prevalence estimates and tracking of physical activity patterns over time.

Validity of the Polar M430 Activity Monitor in Free-Living Conditions: Validation Study.

BACKGROUND: Accelerometers, often in conjunction with heart rate sensors, are extensively used to track physical activity (PA) in research. Research-grade instruments are often expensive and have limited battery capacity, limited storage, and high participant burden. Consumer-based activity trackers are equipped with similar technology and designed for long-term wear, and can therefore potentially be used in research. OBJECTIVE: We aimed to assess the criterion validity of the Polar M430 sport watch, compared with 2 research-grade instruments (ActiGraph and Actiheart), worn on 4 different locations using 1- and 3-axis accelerometers. METHODS: A total of 50 participants wore 2 ActiGraphs (wrist and hip), 2 Actihearts (upper and lower chest position), and 1 Polar M430 sport watch for 1 full day. We compared reported time (minutes) spent in sedentary behavior and in light, moderate, vigorous, and moderate to vigorous PA, step counts, activity energy expenditure, and total energy expenditure between devices. We used Pearson correlations, intraclass correlations, mean absolute percentage errors (MAPEs), and Bland-Altman plots to assess criterion validity. RESULTS: Pearson correlations between the Polar M430 and all research-grade instruments were moderate or stronger for vigorous PA (r range .59-.76), moderate to vigorous PA (r range .51-.75), steps (r range .85-.87), total energy expenditure (r range .88-.94), and activity energy expenditure (r range .74-.79). Bland-Altman plots showed higher agreement for higher intensities of PA. MAPE was high for most outcomes. Only total energy expenditure measured by the hip-worn ActiGraph and both Actiheart positions had acceptable or close to acceptable errors with MAPEs of 6.94% (ActiGraph, 3 axes), 8.26% (ActiGraph, 1 axis), 14.54% (Actiheart, upper position), and 14.37% (Actiheart, lower position). The wrist-worn ActiGraph had a MAPE of 15.94% for measuring steps. All other outcomes had a MAPE of 22% or higher. For most outcomes, the Polar M430 was most strongly correlated with the hip-worn triaxial ActiGraph, with a moderate or strong Pearson correlation for sedentary behavior (r=.52) and for light (r=.7), moderate (r=.57), vigorous (r=.76), and moderate to vigorous (r=.75) PA. In addition, correlations were strong or very strong for activity energy expenditure (r=.75), steps (r=.85), and total energy expenditure (r=.91). CONCLUSIONS: The Polar M430 can potentially be used as an addition to established research-grade instruments to collect some PA variables over a prolonged period. However, due to the high MAPE of most outcomes, only total energy expenditure can be trusted to provide close to valid results. Depending on the variable, the Polar M430 over- or underreported most metrics, and may therefore be better suited to report changes in PA over time for some outcomes, rather than as an accurate instrument for PA status in a population.

Change in physical activity is not associated with change in mental distress among adolescents: the Tromsø study: Fit Futures.

BACKGROUND: Previous research shows that physical activity has a protective effect on mental distress in adults, but the relationship is less researched and seems more ambiguous for adolescents. Studies in this field have typically been cross-sectional by design and based on self-reported physical activity measures, which are known to be vulnerable to response bias. The aim of this study was to investigate the relationship between change in objectively assessed physical activity as measured by accelerometer and change in mental distress among adolescents using longitudinal data from The Tromsø Study: Fit Futures. METHOD: This study was based on data from 676 upper-secondary school students (mean age 16.23 years at baseline, 45.26% boys) from The Tromsø Study: Fit Futures. Physical activity, mental distress and covariates were measured at baseline (T1) and follow-up (T2) 2 years later. Physical activity was objectively measured with an ActiGraph GT3X accelerometer over 7 days. Mental distress was measured with the Hopkins Symptom Checklist-10 (HSCL-10). Change score variables were computed as the difference between T1 and T2 in number of steps, number of minutes of moderate to vigorous physical activity (MVPA) and mental distress between T1 and T2, and analyzed using linear regression analysis. RESULTS: Changes in steps per day were not associated with changes in mental distress in neither the crude, partially, nor fully adjusted model. Neither was changes in minutes of MVPA per day. Interaction effects between change in both steps per day and minutes of MVPA and gender were also not statistically significant, nor was the interaction effects between baseline levels of mental distress and physical activity. CONCLUSION: The results of our study indicate that for adolescents in the sample, change in physical activity is unrelated to change in mental distress over a two-year period.