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AbstractPurposeMethodResultsConclusionFinding valid and accurate methods to count steps is important after a stroke. To determine the criterion validity and accuracy of the Actigraph and Xiaomi Smart Band to count steps in individuals after stroke. To explore the influence of walking speed on the accuracy of these activity monitors.Individuals after stroke performed the 2-min Walk Test while wearing the ActiGraph and Xiaomi Smart Band. The 2-min Walk Test was recorded, and the observed step count was compared with the estimated step count of both activity monitors.Fifty individuals after stroke were included. The ActiGraph had a low correlation (rho 0.41) between observed and estimated step count and a high percentage absolute error (39%). The Xiaomi Smart Band had a high correlation (rho 0.83) and a moderate percentage absolute error (12%). At high walking speeds, the Xiaomi Smart Band had a low percentage absolute error (3%) and at slow walking speeds had 18% a moderate percentage absolute error.The Xiaomi Smart Band has good criterion validity and moderate-to-high accuracy counting steps after stroke. It is a low-cost activity monitor that is a viable alternative to the more expensive activity monitors used in research.IMPLICATIONS FOR REHABILITATIONThe ActiGraph has low criterion validity and accuracy at counting steps after stroke.The Xiaomi Smart Band has good criterion validity and accuracy at counting steps after a stroke.The Xiaomi Smart Band is a low-cost activity monitor that is a viable alternative to the more expensive activity monitors used in research.The ActiGraph has low criterion validity and accuracy at counting steps after stroke.The Xiaomi Smart Band has good criterion validity and accuracy at counting steps after a stroke.The Xiaomi Smart Band is a low-cost activity monitor that is a viable alternative to the more expensive activity monitors used in research. [ABSTRACT FROM AUTHOR]

OBJECTIVE To compare a commercially available accelerometer, FitBark 2 (FitBark 2nd Generation; FitBark) with a previously validated accelerometer, Actical (Actical; Respironics Inc) during periods of activity and rest. We hypothesized that the FitBark 2 would correlate strongly with the Actical during periods of activity and rest. METHODS 20 dogs between the ages of 1 and 9 years of variable sex, breed, and body weight were enrolled from April through August 2022 in a 1-week pilot trial. Dogs were fitted with a neck collar with both devices mounted on it and wore it continuously for 1 week. Six time points were established to evaluate varying degrees of activity and periods of rest, which included the week, a 1-mile walk, the day of the walk, the hour the walk occurred in, 6 AM to 7 AM on the morning of the walk, and 11 PM to 12 AM on the evening of the walk. Actical and FitBark 2 were compared using linear regression and correlation analysis. RESULTS Correlation analysis revealed a very strong correlation between the Actical and FitBark 2 during the entire week of the study, with a moderate correlation at other time points. Linear regression revealed the strength of the relationship by the r² = 0.85, F1,13 = 76.08. CONCLUSIONS FitBark 2 can be used to evaluate the activity and rest of dogs with varying degrees of correlation when compared to the Actical. CLINICAL RELEVANCE The time period assessed impacts the correlation of the FitBark 2 and Actical. This should be considered when using the FitBark 2 for data collection. [ABSTRACT FROM AUTHOR]

Abstract Purpose The aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery. Methods Twenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques. Results Mean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from − 0.87 to 1.36 with limits of agreement (LOA) ranging between − 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between − 2.8 and 5.73 with LOA between − 13.51 and 24.97. Conclusions These devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

Determine the level of agreement of three activity monitors compared with the gold standard (video review) on the activity level of patients with stroke. A prospective, observational, agreement study was performed on 47 individuals with sub-acute stroke in an inpatient rehabilitation facility. Data was collected during one physical therapy session. Individuals wore three device types; Actigraph (AG), Activpal (AP), and stepwatch activity monitor (SAM). Variables assessed were step counts for each limb (hemiparetic and non-hemiparetic) and percent time standing and other. Results from the activity monitors were compared to the video review and assessed for agreement using the intraclass correlation coefficient (ICC) and accuracy of mean difference from video observation. The step counts with the SAM on the non-hemiparetic limb had the highest ICC for step counts (ICC = 0.98, p < 0.001) and were overestimated with 21% accuracy. The SAM on the hemiparetic limb had 9.7% accuracy (ICC = 0.92, p < 0.001). For percent standing time all devices overestimated with poor reliability. For percent other activity time, the AP had the best accuracy and underestimated for both the hemiparetic limb (9.9% accuracy; ICC = 0.90, p < 0.001) and non-hemiparetic limb (8.3% accuracy; ICC = 0.84, p < 0.001). The use of multiple devices may be warranted to capture an accurate understanding of activity levels in this population of individuals with sub-acute stroke. There are concerns with all monitors and clinicians and researchers should be aware of what measures they are wanting to understand about their population. The stepwatch activity monitor worn on the hemiparetic limb provided the best accuracy and excellent reliability for step counts in this population of subacute stroke. For percent standing time all devices overestimated with poor reliability. For percent other time, the AP had the best accuracy and good reliability on the non-hemiparetic limb. The use of multiple devices may be warranted to capture a more accurate understanding of activity level in this population of individuals with sub-acute stroke. Clinicians and researchers need to be aware of the biases of these devices in this population. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery. Methods: Twenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques. Results: Mean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from − 0.87 to 1.36 with limits of agreement (LOA) ranging between − 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between − 2.8 and 5.73 with LOA between − 13.51 and 24.97. Conclusions: These devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution. [ABSTRACT FROM AUTHOR]

We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants (n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m2) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1) SPA-adjusted TEF (adjTEF)—difference in TDEE without SPA between visits, 2) Wakeful TEF—difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3) 24-h TEF—increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: −0.3 ± 3.3%; Actigraph: −1.8 ± 8.0%) and wakeful TEF (activPAL: −0.9 ± 6.1%; Actigraph: −2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement. NEW & NOTEWORTHY: Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters. [ABSTRACT FROM AUTHOR]

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = −0.15; 95% LOA [−0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M. [ABSTRACT FROM AUTHOR]

Quantitative measurement of physical activity may complement neurological evaluation and provide valuable information on patients' daily life. We evaluated longitudinal changes of physical activity in patients with Friedreich ataxia (FRDA) using remote monitoring with wearable sensors. We performed an observational study in 26 adult patients with FRDA and 13 age-sex matched healthy controls (CTR). Participants were asked to wear two wearable sensors, at non-dominant wrist and at waist, for 7 days during waking hours. Evaluations were performed at baseline and at 1-year follow-up. We analysed the percentage of time spent in sedentary or physical activities, the Vector Magnitude on the 3 axes (VM3), and average number of steps/min. Study participants were also evaluated with ataxia clinical scales and functional tests for upper limbs dexterity and walking capability. Baseline data showed that patients had an overall reduced level of physical activity as compared to CTR. Accelerometer-based measures were highly correlated with clinical scales and disease duration in FRDA. Significantly changes from baseline to l-year follow-up were observed in patients for the following measures: (i) VM3; (ii) percentage of sedentary and light activity, and (iii) percentage of Moderate-Vigorous Physical Activity (MVPA). Reduction in physical activity corresponded to worsening in gait score of the Scale for Assessment and Rating of Ataxia. Real-life activity monitoring is feasible and well tolerated by patients. Accelerometer-based measures can quantify disease progression in FRDA over 1 year, providing objective information about patient's motor activities and supporting the usefulness of these data as complementary outcome measure in interventional trials. [ABSTRACT FROM AUTHOR]

Gestational diabetes (GDM) is associated with a long-term risk of diabetes. We aimed to determine whether a text-messaging-based lifestyle support program would improve diabetes risk factors following GDM. Women with GDM were randomised following delivery to receive four text messages per week supporting a healthy lifestyle and parenting for 6 months, with feedback from an activity monitor (intervention), or to receive the activity monitor only (control). The primary outcome was a composite of weight, physical activity and dietary goals. There were 177 women randomised, with 88 intervention and 89 control participants. All the participants experienced COVID-19 lockdowns during the study. Six-month primary outcome data were obtained for 57 intervention participants and 56 controls. There were 7/57 (12%) intervention and 6/56 (11%) control participants who met the primary outcome (relative risk, 1.08; 95%CI, 0.63–1.85; p = 0.79). Two intervention participants met the dietary goals compared to none of the control participants (p = NS). The intervention participants were more likely to record >1000 steps/day (on 102 ± 59 vs. 81 ± 59 days, p = 0.03). When analysed monthly, this was not initially different but became significant 3–6 months post-partum. Interviews and surveys indicated that with the Intervention, healthier choices were made, but these were negatively impacted by COVID-19 restrictions. Participants found the messages motivational (74%) and the activity monitor useful (71%). In conclusion, no improvement in the diabetes risk factors occurred among the women receiving the text messaging intervention when affected by COVID-19 restrictions. [ABSTRACT FROM AUTHOR]

Consumer and research activity monitors have become popular because of their ability to quantify energy expenditure (EE) in free-living conditions. However, the accuracy of activity trackers in determining EE in people with Huntington's Disease (HD) is unknown. Research question: Can the ActiGraph wGT3X-B or the Fitbit Charge 4 accurately measure energy expenditure during physical activity, in people with HD compared to Indirect Calorimetry (IC) (Medisoft Ergo Card)? We conducted a cross-sectional, observational study with fourteen participants with mild-moderate HD (mean age 55.7 ± 11.4 years). All participants wore an ActiGraph and Fitbit during an incremental test, running on a treadmill at 3.2 km/h and 5.2 km/h for three minutes at each speed. We analysed and compared the accuracy of EE estimates obtained by Fitbit and ActiGraph against the EE estimates obtained by a metabolic cart, using with Intra-class correlation (ICC), Bland-Altman analysis and correlation tests. A significant correlation and a moderate reliability was found between ActiGraph and IC for the incremental test (r = 0.667)(ICC=0.633). There was a significant correlation between Fitbit and IC during the incremental test (r = 0.701), but the reliability was poor at all tested speeds in the treadmill walk. Fitbit significantly overestimated EE, and ActiGraph underestimated EE compared to IC, but ActiGraph estimates were more accurate than Fitbit in all tests. Significance: Compared to IC, Fitbit Charge 4 and ActiGraph wGT3X-BT have reduced accuracy in estimating EE at slower walking speeds. These findings highlight the need for population-specific algorithms and validation of activity trackers. • First validation of devices to capture energy expenditure in Huntington´s disease • ActiGraph and Fitbit have reduced accuracy in estimating energy expenditure • Hip-worn wearables estimate energy expenditure better than wrist-worn devices [ABSTRACT FROM AUTHOR]

Abstract Aims Physical activity (PA) is an important target for improving clinical outcomes in heart failure (HF) patients. Nonetheless, assessing the daily PA profile in this population is a challenging task, traditionally performed using self‐report questionnaires such as the International PA Questionnaire Short Form (IPAQ‐SF). This study aimed to evaluate the concurrent validity of the IPAQ‐SF and accelerometer‐assessed PA using six published cut‐points in patients with HF and reduced or mildly reduced ejection fraction. Methods and results The concordance between the IPAQ‐SF and a hip‐worn accelerometer regarding daily time spent performing moderate to vigorous PA in bouts of at least 10 min was assessed in 53 participants for seven consecutive days using six different cut‐points (Barnett, Dibben, Mark, Sanders, Troiano, and Vaha‐Ypya). Spearman's correlation and Bland–Altman plots were used to evaluate concurrent validity between methods. Regressions were used to study the association between patient variables, wear protocol (waking hour or 24 h), and absolute bias. The kappa index was used to evaluate the concordance between IPAQ‐SF and accelerometry for classifying patients as active or non‐active. All analyses were re‐run using non‐bouted metrics to investigate the effect of bouted versus non‐bouted analysis. The IPAQ‐SF and accelerometry showed low to negligible correlation (ρ = 0.12 to 0.37), depending on the cut‐point used. The regression analysis showed that the absolute bias was higher in participants following the waking‐hour protocol at all cut‐points except Dibben's (P ≤ 0.007). The concordance between the two methods to classify patients as active and non‐active was low when using Mark (κ = 0.23) and Barnett (κ = 0.34) cut‐points and poor for the remaining cut‐points (κ = 0.03 to 0.18). The results of the sensitivity analysis showed negligible to low correlation using non‐bouted metrics (ρ = 0.27 to 0.33). Conclusions Moderate to vigorous PA measures using IPAQ‐SF and accelerometers are not equivalent, and we do not encourage researchers to use IPAQ‐SF alone when assessing PA in HF patients. Moreover, applying personalized collection and processing criteria is important when assessing PA in HF patients. We recommend following the 24 h protocol and selecting cut‐points calibrated in patients with cardiovascular diseases. Finally, it is necessary to develop a new tailored questionnaire that considers walking intensity and is adjusted to the current World Health Organisation recommendations, which use non‐bouted metrics.

Aims: Physical activity (PA) is an important target for improving clinical outcomes in heart failure (HF) patients. Nonetheless, assessing the daily PA profile in this population is a challenging task, traditionally performed using self‐report questionnaires such as the International PA Questionnaire Short Form (IPAQ‐SF). This study aimed to evaluate the concurrent validity of the IPAQ‐SF and accelerometer‐assessed PA using six published cut‐points in patients with HF and reduced or mildly reduced ejection fraction. Methods and results: The concordance between the IPAQ‐SF and a hip‐worn accelerometer regarding daily time spent performing moderate to vigorous PA in bouts of at least 10 min was assessed in 53 participants for seven consecutive days using six different cut‐points (Barnett, Dibben, Mark, Sanders, Troiano, and Vaha‐Ypya). Spearman's correlation and Bland–Altman plots were used to evaluate concurrent validity between methods. Regressions were used to study the association between patient variables, wear protocol (waking hour or 24 h), and absolute bias. The kappa index was used to evaluate the concordance between IPAQ‐SF and accelerometry for classifying patients as active or non‐active. All analyses were re‐run using non‐bouted metrics to investigate the effect of bouted versus non‐bouted analysis. The IPAQ‐SF and accelerometry showed low to negligible correlation (ρ = 0.12 to 0.37), depending on the cut‐point used. The regression analysis showed that the absolute bias was higher in participants following the waking‐hour protocol at all cut‐points except Dibben's (P ≤ 0.007). The concordance between the two methods to classify patients as active and non‐active was low when using Mark (κ = 0.23) and Barnett (κ = 0.34) cut‐points and poor for the remaining cut‐points (κ = 0.03 to 0.18). The results of the sensitivity analysis showed negligible to low correlation using non‐bouted metrics (ρ = 0.27 to 0.33). Conclusions: Moderate to vigorous PA measures using IPAQ‐SF and accelerometers are not equivalent, and we do not encourage researchers to use IPAQ‐SF alone when assessing PA in HF patients. Moreover, applying personalized collection and processing criteria is important when assessing PA in HF patients. We recommend following the 24 h protocol and selecting cut‐points calibrated in patients with cardiovascular diseases. Finally, it is necessary to develop a new tailored questionnaire that considers walking intensity and is adjusted to the current World Health Organisation recommendations, which use non‐bouted metrics. [ABSTRACT FROM AUTHOR]

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Quantitative measurement of physical activity may complement neurological evaluation and provide valuable information on patients’ daily life. We evaluated longitudinal changes of physical activity in patients with Friedreich ataxia (FRDA) using remote monitoring with wearable sensors. We performed an observational study in 26 adult patients with FRDA and 13 age-sex matched healthy controls (CTR). Participants were asked to wear two wearable sensors, at non-dominant wrist and at waist, for 7 days during waking hours. Evaluations were performed at baseline and at 1-year follow-up. We analysed the percentage of time spent in sedentary or physical activities, the Vector Magnitude on the 3 axes (VM3), and average number of steps/min. Study participants were also evaluated with ataxia clinical scales and functional tests for upper limbs dexterity and walking capability. Baseline data showed that patients had an overall reduced level of physical activity as compared to CTR. Accelerometer-based measures were highly correlated with clinical scales and disease duration in FRDA. Significantly changes from baseline to l-year follow-up were observed in patients for the following measures: (i) VM3; (ii) percentage of sedentary and light activity, and (iii) percentage of Moderate-Vigorous Physical Activity (MVPA). Reduction in physical activity corresponded to worsening in gait score of the Scale for Assessment and Rating of Ataxia. Real-life activity monitoring is feasible and well tolerated by patients. Accelerometer-based measures can quantify disease progression in FRDA over 1 year, providing objective information about patient’s motor activities and supporting the usefulness of these data as complementary outcome measure in interventional trials.

Daily steps could be a valuable indicator of real-world ambulation in Parkinson's disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland–Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2–5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann–Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84–0.90; SAM range: 7.8–9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD. [ABSTRACT FROM AUTHOR]

Purpose: This study sought to assess the validity of several heart rate (HR) monitors in wearable technology during mountain biking (MTB), compared to the Polar H7® HR monitor, used as the criterion device. Methods: A total of 20 participants completed two MTB trials while wearing six HR monitors (5 test devices, 1 criterion). HR was recorded on a second-by-second basis for all devices analyzed. After data processing, validity measures were calculated, including 1. error analysis: mean absolute percentage errors (MAPE), mean absolute error (MAE), and mean error (ME), and 2. Correlation analysis: Lin's concordance correlation coefficient (CCC) and Pearson's correlation coefficient (r). Thresholds for validity were set at MAPE < 10% and CCC > 0.7. Results: The only device that was found to be valid during mountain biking was the Suunto Spartan Sport watch with accompanying HR monitor, with a MAPE of 0.66% and a CCC of 0.99 for the overall, combined data. Conclusion: If a person would like to track their HR during mountain biking, for pacing, training, or other reasons, the devices best able to produce valid results are chest-based, wireless electrocardiogram (ECG) monitors, secured by elastic straps to minimize the movement of the device, such as the Suunto chest-based HR monitor. [ABSTRACT FROM AUTHOR]

Simple Summary: Automated activity monitors (AAM) represent a useful tool in estrus detection to improve reproductive performance in dairy herds. There are only a few studies, however, investigating the association between the characteristics of an estrus event (i.e., the onset, peak, and end of estrus) determined by an AAM and the timing of artificial insemination (AI) in heifers, depending on the semen type used. Therefore, this observational study was conducted to determine the association between the interval from different characteristics of estrus and the timing of AI on pregnancy per AI (P/AI). Holstein heifers were fitted with a neck-mounted AAM and inseminated either with frozen conventional or sexed semen. Heifers inseminated from 9 to 32 h after the onset of estrus had the greatest P/AI, irrespective of semen type. Investigations on the optimum timing of artificial insemination (AI) following automated activity monitoring (AAM) depending on different types of semen in heifers are limited and in part show controversial results. Therefore, the objective of this observational study was to determine the association between the timing of AI using different characteristics of estrus (i.e., the onset, peak, and end of estrus) and pregnancy per AI (P/AI) in Holstein heifers. Heifers were fitted with a neck-mounted AAM system and inseminated with frozen conventional and sexed semen. The pregnancy per AI (n = 4159) from 2858 heifers from six commercial dairy farms in Germany inseminated upon the alert of an AAM system was evaluated. Estrous intensity was classified based on peak activity into low (35 to 89 index value) and high (90 to 100 index value). We detected a quadratic association between the interval from the onset of estrus to AI and P/AI (p = 0.02). The greatest P/AI was observed for heifers inseminated from 9 to 32 h after the onset of estrus. The intervals from the peak of activity to AI and the end of estrus to AI were not associated with P/AI (p ≥ 0.05). Heifers inseminated with frozen conventional semen (50.1%) had a greater P/AI compared with heifers inseminated with frozen sexed semen (43.3%; p = 0.03). There were no interactions between the intervals from the onset, peak, or end of estrus to AI or the type of semen and the P/AI (p ≥ 0.05). The pregnancy per AI was not associated with estrous intensity (50.5% for low intensity vs. 53.0% for high intensity; p = 0.37). In conclusion, inseminating heifers between 9 and 32 h after the onset of estrus, as detected by the AAM, optimized the P/AI regardless of semen type. [ABSTRACT FROM AUTHOR]

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = −0.15; 95% LOA [−0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

Gestational diabetes (GDM) is associated with a long-term risk of diabetes. We aimed to determine whether a text-messaging-based lifestyle support program would improve diabetes risk factors following GDM. Women with GDM were randomised following delivery to receive four text messages per week supporting a healthy lifestyle and parenting for 6 months, with feedback from an activity monitor (intervention), or to receive the activity monitor only (control). The primary outcome was a composite of weight, physical activity and dietary goals. There were 177 women randomised, with 88 intervention and 89 control participants. All the participants experienced COVID-19 lockdowns during the study. Six-month primary outcome data were obtained for 57 intervention participants and 56 controls. There were 7/57 (12%) intervention and 6/56 (11%) control participants who met the primary outcome (relative risk, 1.08; 95%CI, 0.63–1.85; p = 0.79). Two intervention participants met the dietary goals compared to none of the control participants (p = NS). The intervention participants were more likely to record >1000 steps/day (on 102 ± 59 vs. 81 ± 59 days, p = 0.03). When analysed monthly, this was not initially different but became significant 3–6 months post-partum. Interviews and surveys indicated that with the Intervention, healthier choices were made, but these were negatively impacted by COVID-19 restrictions. Participants found the messages motivational (74%) and the activity monitor useful (71%). In conclusion, no improvement in the diabetes risk factors occurred among the women receiving the text messaging intervention when affected by COVID-19 restrictions.

Background: To investigate how changes in sedentary behavior relate to health outcomes, it is important to establish the test–retest reliability of activity monitors in measuring habitual sedentary behavior in people with type 2 diabetes (T2D) as a prerequisite for interpreting this information. Thus, the authors' objective was to examine the test–retest reliability of a common activity monitor (activPAL™) in measuring sedentary behavior and physical activity in people with T2D. Methods: Sedentary-time, standing-time, stepping-time, step-count, and sit-to-stand transitions were obtained from two 7-day assessment periods separated by at least 1 week. Test–retest reliability was determined with the intraclass correlation coefficient (ICC) to compare sedentary and activity measures between the 2 time points. Results: A total of 30 participants with self-reported T2D completed the study (age 65 [6] y, 63% women, body mass index 33.3 [5] kg/m2). High test–retest reliability was found for sedentary-time (ICC =.79; 95% confidence interval [CI],.61–.89) and standing-time (ICC =.74; 95% CI,.53–.87). Very high test–retest reliability was found for stepping-time (ICC =.90; 95% CI,.81–.95), step-count (ICC =.91; 95% CI,.83–.96), and sit-to-stand transitions (ICC =.90; 95% CI,.79–.95). Conclusion: The activPAL™ device showed high to very high test–retest reliability in measuring all tested activity categories in people with T2D. [ABSTRACT FROM AUTHOR]

Purpose: To assess the criterion and construct validity of the Fitbit Zip® to measure physical activity among adults living with HIV. Methods: Participants were video recorded completing 2 walk tests while wearing the Fitbit Zip® and completed 3 selfreported physical activity questionnaires 1 week later. The authors calculated intraclass correlation coefficients (ICCs) to determine agreement between the number of steps taken and distance walked (Fitbit Zip®) with the visual count of number of steps taken and actual distance walked (walk tests). The authors tested 15 a priori hypotheses about predicted associations between questionnaire scores and physical activity measured by the Fitbit Zip®. Results: Among the 34 participants, there was "excellent" agreement between the number of steps taken measured by the Fitbit Zip® and visually counted number of steps taken (ICC = .99) and number of steps taken at slow (ICC = .75), moderate (ICC = .85), and fast (ICC = .78) walking speeds. There was "poor" agreement between the Fitbit Zip® recorded distance and actual determined distance walked (ICC = .20). Three (20%) construct validity hypotheses were confirmed. Conclusions: The Fitbit Zip® demonstrated criterion validity for its ability to measure number of steps taken but not distance walked, and did not demonstrate construct validity for measuring physical activity among adults with HIV. [ABSTRACT FROM AUTHOR]

Currently, there is no way to assess mechanical loading variables such as peak ground reaction forces (pGRF) and peak loading rate (pLR) in clinical settings. The purpose of this study was to develop accelerometry-based equations to predict both pGRF and pLR during walking and running. One hundred and thirty one subjects (79 females; 76.9 ± 19.6 kg) walked and ran at different speeds (2–14 km·h−1) on a force plate–instrumented treadmill while wearing accelerometers at their ankle, lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland–Altman plots. Our pGRF prediction equation was compared with a reference equation previously published. Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration rate for pLR prediction. All pGRF equation coefficients of determination were above 0.96, and a good agreement between actual and predicted pGRF was observed, with a mean absolute percent error (MAPE) below 7.3%. Accuracy indices from our equations were better than previously developed equations. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF. Walking and running pGRF can be predicted with high accuracy by accelerometry-based equations, representing an easy way to determine mechanical loading in free-living conditions. The pLR prediction equations yielded a somewhat lower prediction accuracy compared with the pGRF equations. Peak ground reaction forces can be accurately predicted through raw accelerometry data. These predictions are valid for a broad range of body masses and for ankle, lower back and hip accelerometer placements. Peak loading rate prediction presented lower accuracy compared with peak ground reaction force prediction. These findings result in a simple method to predict mechanical loading in clinical practice, which is relevant in some areas of sports medicine such as bone health and injury prevention. [ABSTRACT FROM AUTHOR]

Background: Brief counseling and self-monitoring with a pedometer are common practice within primary care for physical activity promotion. It is unknown how high-tech electronic activity monitors compare to pedometers within this setting. This study aimed to investigate the outcomes, through effect size estimation, of an electronic activity monitor-based intervention to increase physical activity and decrease cardiovascular disease risk. Method: The pilot randomized controlled trial was pre-registered online at clinicaltrials.gov (NCT02554435). Forty overweight, sedentary participants 55-74 years of age were randomized to wear a pedometer or an electronic activity monitor for 12 weeks. Physical activity was measured objectively for 7 days at baseline and follow-up by a SenseWear monitor and cardiovascular disease risk was estimated by the Framingham risk calculator. Results: Effect sizes for behavioral and health outcomes ranged from small to medium. While these effect sizes were favorable to the intervention group for physical activity (PA) (d = 0.78) and general health (d = 0.39), they were not favorable for measures. Conclusion: The results of this pilot trial show promise for this low-intensity intervention strategy, but large-scale trials are needed to test its efficacy.

Abstract Background Like most living organisms, the fruit fly Drosophila melanogaster exhibits strong and diverse behavioural reactions to light. Drosophila is a diurnal animal that displays both short- and long-term responses to light, important for, instance, in avoidance and light wavelength preference, regulation of eclosion, courtship, and activity, and provides an important model organism for understanding the regulation of circadian rhythms both at molecular and circuit levels. However, the assessment and comparison of light-based behaviours is still a challenge, mainly due to the lack of a standardised platform to measure behaviour and different protocols created across studies. Here, we describe the Drosophila Interactive System for Controlled Optical manipulations (DISCO), a low-cost, automated, high-throughput device that records the flies’ activity using infrared beams while performing LED light manipulations. Results To demonstrate the effectiveness of this tool and validate its potential as a standard platform, we developed a number of distinct assays, including measuring the locomotor response of flies exposed to sudden darkness (lights-off) stimuli. Both white-eyed and red-eyed wild-type flies exhibit increased activity after the application of stimuli, while no changes can be observed in Fmr1 null allele flies, a model of fragile X syndrome. Next, to demonstrate the use of DISCO in long-term protocols, we monitored the circadian rhythm of the flies for 48 h while performing an alcohol preference test. We show that increased alcohol consumption happens intermittently throughout the day, especially in the dark phases. Finally, we developed a feedback-loop algorithm to implement a place preference test based on the flies’ innate aversion to blue light and preference for green light. We show that both white-eyed and red-eyed wild-type flies were able to learn to avoid the blue-illuminated zones. Conclusions Our results demonstrate the versatility of DISCO for a range of protocols, indicating that this platform can be used in a variety of ways to study light-dependent behaviours in flies.

A link between inappropriate physical behaviour patterns (low physical activity and high sedentary behaviour) and poor health outcomes has been observed. To provide evidence to quantify this link, it is important to have valid and reliable assessment tools. This study aimed to assess the validity and reliability of the activPAL4TM monitor for distinguishing postures and measuring stepping activity of 6–12-year-old children. Thirteen children (8.5 ± 1.8 years) engaged in pre-determined standardised (12 min) and non-standardised (6 min) activities. Agreement, specificity and positive predictive value were assessed between the activPAL4TM and direct observation (DO) (nearest 0.1 s). Between-activPAL4TM (inter-device) and between-observer (inter-rater) reliability were determined. Detection of sitting and stepping time and forward purposeful step count were all within 5% of DO. Standing time was slightly overestimated (+10%) and fast walking/jogging steps underestimated (−20%). For non-standardised activities, activPAL4TM step count matched most closely to combined backward and forward purposeful steps; however, agreement varied widely. The activPAL4TM demonstrated high levels of reliability (ICC(1, 1) > 0.976), which were higher in some instances than could be achieved through direct observation (ICC(2, 1) > 0.851 for non-standardised activities). Overall, the activPAL4TM recorded standardised activities well. However, further work is required to establish the exact nature of steps counted by the activPAL4TM. [ABSTRACT FROM AUTHOR]

Objectives: Limited research on using smart wearables such as Fitbit devices among people with dementia has shown favourable outcomes. The aim of this study was to explore the acceptability and feasibility of using a Fitbit Charge 3 among people with dementia, living in the community, who took part in the physical exercise component of the Comprehensive REsilience‐building psychoSocial intervenTion pilot study. Methods: A mixed methods study was conducted; Quantitative data relating to wear rates for the Fitbit were recorded and qualitative data were collected by group and individual interviews with the people with dementia and their caregiver about their experience of wearing/using the Fitbit in the study. Results: Nine people with dementia and their caregiver completed the intervention. Only one participant wore the Fitbit consistently. Supporting set‐up and use of the devices was time consuming and caregiver involvement was essential for day‐to‐day support: none of the people with dementia owned a smartphone. Few of them engaged with the Fitbit features, primarily only using it to check the time and only a minority wanted to keep the device beyond the intervention. Discussion: When designing a study using smart wearables such as a Fitbit among people with dementia, consideration should be given to the following: the possible burden on caregivers supporting the use of the device; a lack of familiarity with this technology in the target population; dealing with missing data, and the involvement of the researcher in setting up and supporting use of the device. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 lockdown introduced restrictions to free-living activities. Changes to these activities can be accurately quantified using combined measurement. Using activPAL3 and self-reports to collect activity data, the study aimed to quantify changes that occurred in physical activity and sedentary behavior between prelockdown and lockdown. The study also sought to determine changes in indoor and outdoor stepping. Methods: Using activPAL3, four participants recorded physical activity data prelockdown and during lockdown restrictions (February–June 2020). Single events (sitting, standing, stepping, lying) were recorded and analyzed by the CREA algorithm using an event-based approach. The analysis focused on step count, sedentary time, and lying (in bed) time; median and interquartile range were calculated. Daily steps classified as taking place indoors and outdoors were calculated separately. Results: 33 prelockdown and 92 in-lockdown days of valid data were captured. Median daily step count across all participants reduced by 14.8% (from 5,828 prelockdown to 4,963 in-lockdown), while sedentary and lying time increased by 4% and 8%, respectively (sedentary: 9.98–10.30 hr; lying: 9.33–10.05 hr). Individual variations were observed in hours spent sedentary (001: 8.44–8.66, 002: 7.41–8.66, 003: 11.97–10.59, 004: 6.29–7.94, and lying (001: 9.69–9.49, 002: 11.46–11.66, 003: 7.63–9.34, 004: 9.7–11.12) pre- and in-lockdown. Discrepancies in self-report versus algorithm classification of indoor/outdoor stepping were observed for three participants. Conclusion: The study quantitively showed lockdown restrictions negatively impacted physical activity and sedentary behavior; two variables closely linked to health outcomes. This has important implications for public health policies to help develop targeted interventions and mandates that encourage additional physical activity and lower sedentary behavior. [ABSTRACT FROM AUTHOR]

Sedentary behaviors and low physical activity among hospitalized patients have detrimental effects on health and recovery. Wearable activity monitors are a promising tool to promote mobilization and physical activity. However, existing devices have limitations in terms of their outcomes and validity. The Activ8 device was optimized for the hospital setting. This study assessed the concurrent validity of the modified Activ8. Hospital patients performed an activity protocol that included basic (e.g., walking) and functional activities (e.g., room activities), with video recordings serving as the criterion method. The assessed outcomes were time spent walking, standing, upright, sedentary, and newly added elements of steps and transfers. Absolute and relative time differences were calculated, and Wilcoxon and Bland–Altman analyses were conducted. Overall, the observed relative time differences were lower than 2.9% for the basic protocol and 9.6% for the functional protocol. Statistically significant differences were detected in specific categories, including basic standing (p < 0.05), upright time (p < 0.01), and sedentary time (p < 0.01), but they did not exceed the predetermined 10% acceptable threshold. The modified Activ8 device is a valid tool for assessing body postures, motions, steps, and transfer counts in hospitalized patients. This study highlights the potential of wearable activity monitors to accurately monitor and promote PA among hospital patients.

Abstract Background Reduced physical activity (PA) was the strongest predictor of all-cause mortality in patients with chronic obstructive pulmonary disease (COPD). This scoping review aimed to map the evidence on the current landscape of physical activity, barriers and facilitators, and assessment tools across COPD patients. Methods Arksey and O’Malley’s scoping review methodology framework guided the conduct of this review. An electronic search was conducted on five English databases (PubMed, Cochrane Library, PsycINFO, CINAHL and Web of Science) and three Chinese databases (CNKI, CQVIP and WAN-FANG) in January 2022. Two authors independently screened the literature, extracted the studies characteristics. Results The initial search yielded 4389 results, of which 1954 were duplicates. Of the remaining 135 articles, 42 studies met the inclusion criteria. Among the reviewed articles, there were 14 (33.3%) cross-sectional study, 9 (21.4%) cohort study, 4 (9.5%) longitudinal study, 3 qualitative study, 12 (28.7%) randomized control trials. The main barriers identified were older age, women, lung function, comorbidities, COPD symptoms (fear of breathlessness and injury, severe fatigue, anxiety and depression), GOLD stage, frequency of exacerbation, oxygen use, lack of motivation and environment-related (e.g., season and weather). Twelve studies have evaluated the effects of physical exercise (e.g., walking training, pulmonary rehabilitation (PR), pedometer, self-efficacy enhancing intervention and behavioral modification intervention) on PA and showed significant positive effects on the prognosis of patients. However, in real life it is difficult to maintain PA in people with COPD. Conclusions Changing PA behavior in patients with COPD requires multidisciplinary collaboration. Future studies need to identify the best instruments to measure physical activity in clinical practice. Future studies should focus on the effects of different types, time and intensity of PA in people with COPD and conduct randomized, adequately-powered, controlled trials to evaluate the long-term effectiveness of behavioral change interventions in PA.

Purpose of reviewMeasurements obtained during real-world activity by wearable motion sensors may contribute more naturalistic accounts of clinically meaningful changes in impairment, activity, and participation during neurologic rehabilitation, but obstacles persist. Here we review the basics of wearable sensors, the use of existing systems for neurological and rehabilitation applications and their limitations, and strategies for future use.Recent findingsCommercial activity-recognition software and wearable motion sensors for community monitoring primarily calculate steps and sedentary time. Accuracy declines as walking speed slows below 0.8 m/s, less so if worn on the foot or ankle. Upper-extremity sensing is mostly limited to simple inertial activity counts. Research software and activity-recognition algorithms are beginning to provide ground truth about gait cycle variables and reveal purposeful arm actions. Increasingly, clinicians can incorporate inertial and other motion signals to monitor exercise, activities of daily living, and the practice of specific skills, as well as provide tailored feedback to encourage self-management of rehabilitation. Efforts are growing to create a compatible collection of clinically relevant sensor applications that capture the type, quantity, and quality of everyday activity and practice in known contexts. Such data would offer more ecologically sound measurement tools, while enabling clinicians to monitor and support remote physical therapies and behavioral modification when combined with telemedicine outreach.

Purpose: This study estimated the long-term individual-level productivity costs of physical inactivity. Methods: The data were drawn from the Northern Finland Birth Cohort 1966, to which the productivity cost variables (sick leaves and disability pensions) from Finnish registries were linked. Individuals (N = 6261) were categorized into physical activity groups based on their level of physical activity, which was measured in three ways: 1) self-reported leisure-time moderate- to vigorous-intensity physical activity (MVPA) at 46 yr old, 2) longitudinal self-reported leisure-time MVPA at 31–46 yr old, and 3) accelerometer-measured overall MVPA at 46 yr old. The human capital approach was applied to calculate the observed costs (years 2012–2020) and the expected costs (years 2012–2031). Results: The results showed that the average individual-level productivity costs were higher among physically inactive compared with the costs among physically active. The results were consistent regardless of the measurement type of physical activity or the period used. On average, the observed long-term productivity costs among physically inactive individuals were €1900 higher based on self-reported MVPA, €1800 higher based on longitudinal MVPA, and €4300 higher based on accelerometer-measured MVPA compared with the corresponding productivity costs among physically active individuals. The corresponding difference in the expected costs was €2800, €1200, and €8700, respectively. Conclusions: The results provide evidence that productivity costs differ according to an individual's level of physical activity. Therefore, investments in physical activity may decrease not only the direct healthcare costs but also the indirect productivity costs paid by the employee, the employer, and the government. [ABSTRACT FROM AUTHOR]

A universal approach to characterizing sport-related physical activity (PA) types in sport settings does not yet exist. Young adults (n = 30), 19–33 years, engaged in a 15-min activity session, performing warm-ups, 3-on-3 soccer, and 3-on-3 basketball. Videos were recorded and manually coded as criterion PA types (walking, running, jumping, rapid lateral movements). Participants wore an accelerometer on their right hip. Multiple machine learning models were developed and compared for predicting PA type. Most models underestimated time spent completing the activities performed least commonly. Point estimates for percent agreement, sensitivity, specificity, F-scores, and kappa were similar across models, with Hidden Markov Models (HMMs) being best at classifying rare events. Models detected activity type during sport-related movements with modest accuracy (kappas ≤.40). Given the better performance of HMMs, incorporating the temporal nature of sport-related activities is important for improving sport-related PA classification. [ABSTRACT FROM AUTHOR]

Background: Like most living organisms, the fruit fly Drosophila melanogaster exhibits strong and diverse behavioural reactions to light. Drosophila is a diurnal animal that displays both short- and long-term responses to light, important for, instance, in avoidance and light wavelength preference, regulation of eclosion, courtship, and activity, and provides an important model organism for understanding the regulation of circadian rhythms both at molecular and circuit levels. However, the assessment and comparison of light-based behaviours is still a challenge, mainly due to the lack of a standardised platform to measure behaviour and different protocols created across studies. Here, we describe the Drosophila Interactive System for Controlled Optical manipulations (DISCO), a low-cost, automated, high-throughput device that records the flies' activity using infrared beams while performing LED light manipulations. Results: To demonstrate the effectiveness of this tool and validate its potential as a standard platform, we developed a number of distinct assays, including measuring the locomotor response of flies exposed to sudden darkness (lights-off) stimuli. Both white-eyed and red-eyed wild-type flies exhibit increased activity after the application of stimuli, while no changes can be observed in Fmr1 null allele flies, a model of fragile X syndrome. Next, to demonstrate the use of DISCO in long-term protocols, we monitored the circadian rhythm of the flies for 48 h while performing an alcohol preference test. We show that increased alcohol consumption happens intermittently throughout the day, especially in the dark phases. Finally, we developed a feedback-loop algorithm to implement a place preference test based on the flies' innate aversion to blue light and preference for green light. We show that both white-eyed and red-eyed wild-type flies were able to learn to avoid the blue-illuminated zones. Conclusions: Our results demonstrate the versatility of DISCO for a range of protocols, indicating that this platform can be used in a variety of ways to study light-dependent behaviours in flies. [ABSTRACT FROM AUTHOR]

Investigations on the optimum timing of artificial insemination (AI) following automated activity monitoring (AAM) depending on different types of semen in heifers are limited and in part show controversial results. Therefore, the objective of this observational study was to determine the association between the timing of AI using different characteristics of estrus (i.e., the onset, peak, and end of estrus) and pregnancy per AI (P/AI) in Holstein heifers. Heifers were fitted with a neck-mounted AAM system and inseminated with frozen conventional and sexed semen. The pregnancy per AI (n = 4159) from 2858 heifers from six commercial dairy farms in Germany inseminated upon the alert of an AAM system was evaluated. Estrous intensity was classified based on peak activity into low (35 to 89 index value) and high (90 to 100 index value). We detected a quadratic association between the interval from the onset of estrus to AI and P/AI (p = 0.02). The greatest P/AI was observed for heifers inseminated from 9 to 32 h after the onset of estrus. The intervals from the peak of activity to AI and the end of estrus to AI were not associated with P/AI (p ≥ 0.05). Heifers inseminated with frozen conventional semen (50.1%) had a greater P/AI compared with heifers inseminated with frozen sexed semen (43.3%; p = 0.03). There were no interactions between the intervals from the onset, peak, or end of estrus to AI or the type of semen and the P/AI (p ≥ 0.05). The pregnancy per AI was not associated with estrous intensity (50.5% for low intensity vs. 53.0% for high intensity; p = 0.37). In conclusion, inseminating heifers between 9 and 32 h after the onset of estrus, as detected by the AAM, optimized the P/AI regardless of semen type.

ABSTRACT: The objectives of this study were to determine the effects of GnRH at the time of artificial insemination (AI) on ovulation, progesterone 7 d post-AI, and pregnancy in cows detected in estrus using traditional methods (tail chalk removal and mount acceptance visualization) or an automated activity-monitoring (AAM) system. We hypothesized that administration of GnRH at the time of AI would increase ovulation rate, plasma progesterone post-AI, and pregnancy per AI (P/AI) in cows detected in estrus. In experiment 1, Holstein cows (n = 398) were blocked by parity and randomly assigned to receive an injection of GnRH at the time of estrus detection/AI (GnRH, n = 197) or to remain untreated (control, n = 201) on 4 farms. The GnRH was administered as 100 µg of gonadorelin acetate. Ovarian structures and plasma progesterone were assessed in a subset of cows (GnRH, n = 52; control, n = 55) in experiment 1 at the time of AI and 7 d later. In experiment 2, a group of 409 cows in an AAM farm were enrolled as described for experiment 1 (GnRH, n = 207; control, n = 202). Data were categorized for parity (primiparous vs. multiparous), season (cool vs. warm), number of services (first vs. > first), DIM (>150 DIM vs. ≤150 DIM), and for AAM cows in experiment 2 for activity level (high: 90–100 index vs. low: 35–89 index). Pregnancy diagnosis was performed between 32 and 45 d post-AI (P1) and 60 to 115 d post-AI (P2). In experiment 1, there was no difference in plasma progesterone at day of estrus detection (control = 0.09 ng/mL vs. GnRH = 0.16 ng/mL), 7 d later (control = 2.03 ng/mL vs. GnRH = 2.18 ng/mL), and ovulation rate (GnRH = 83.2% vs. control = 77.9%) between treatments. There were no effects of GnRH in experiment 1 for P/AI at P1 (control = 43.3% vs. GnRH = 38.6%), P2 (control = 38.4% vs. GnRH = 34.5%), and for pregnancy loss (control = 9.8% vs. GnRH = 8.2%). In experiment 2, there were no effects of GnRH for P/AI at P1 (control = 39.6% vs. GnRH = 40.1%), P2 (control = 35.0% vs. GnRH = 37.4%), and for pregnancy loss (control = 9.5% vs. GnRH = 6.2%). There was a tendency for a parity effect on P/AI for P1, but not P2 or for pregnancy loss. High-activity cows had greater P/AI in P1 (low activity = 27.9% vs. high activity = 44.1%), P2 (low activity = 21.8% vs. high activity = 41.2%), and lower pregnancy loss (low activity = 20.7% vs. high activity = 5.1%), but there were no interactions between treatment and activity level. The current study did not support the use of GnRH at estrus detection to improve ovulatory response, progesterone 1 wk post-AI, and P/AI. More research is needed to investigate the relationship between GnRH at the time of AI and activity level in herds using AAM systems.

Featured Application: Facilitating activity during the evening shift and reducing reliance on sitting as the major means of performing out-of-bed activity are possible avenues to increase overall physical activity among patients post-stroke in acute hospital settings. Early activity post-stroke reduces secondary complications and improves rehabilitation outcomes. This study aimed to describe the physical activities of stroke patients in an acute hospital setting, compare activity patterns between working shifts, and assess associations between activity and clinical status. Twenty-one patients (mean age 69.4 ± 33.4 years,13 men) admitted due to acute ischemic stroke wore activity monitors for two weeks or until discharge. During the morning and evening shifts, the activity monitor collected daily data on walking and body position. The study discovered that patients' overall activity levels were low and that activity was higher during morning shifts than evening shifts (sitting time: 185.31 ± 109.31 min and 91.8 ± 98.46 min, p = 0.002; number of steps: 58.3 ± 32.73 and 30.4 ± 17.6 steps, p < 0.001). Upright and sitting time increased in morning shifts (p = 0.002), while the number of steps increased in both morning and evening shifts (p = 0.002). In the evening shift, there was a fair (r = 0.28, p = 0.02) positive correlation between grip strength and the number of steps, such that patients with higher grip strength took more steps. In addition, there were poor (r = −0.2, p = 0.02) correlations between motor function (Trunk Control Test and Functional Ambulation Category) and time in an upright position, such that patients with lower functional ability sat longer. Clinical characteristics and level of activity did not show any other correlations. To conclude, the main out-of-bed activity of patients was sitting during morning shifts. The findings highlight the temporal differences in activity throughout the day, as well as the disconnect between clinical characteristics and activity levels. [ABSTRACT FROM AUTHOR]

BackgroundConducting 5 A's counseling in clinic and utilizing technology-based resources are recommended to promote physical activity but little is known about how to implement such an intervention. This investigation aimed to determine the feasibility and acceptability, using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, of a pragmatic, primary care-based intervention that incorporated 5 A's counseling and self-control through an activity monitor.MethodsPrimary care patients (n = 40) 55-74 years of age were recruited and randomized to receive a pedometer or an electronic activity monitor (EAM), Jawbone UP24, to monitor activity for 12 weeks. Participants were also invited to a focus group after completing the intervention. Stakeholders (n = 36) were recruited to provide feedback.ResultsThe intervention recruitment rate was 24.7%. The attrition rate was 20% with a significantly higher rate for the pedometer group (p = 0.02). The EAM group increased their minutes of physical activity by 11.1 min/day while the pedometer maintained their activity (0.2 min/day), with no significant group difference. EAM participants liked using their monitor and would continue wearing it while the pedometer group was neutral to these statements (p

Introduction: Older African Americans have low levels of physical activity, which increases the risk of aging-related health conditions. This study aimed to determine the effectiveness of, and satisfaction with, a community-engaged physical activity intervention targeting older African Americans. Methods: Fifty-six older African Americans were randomized to a successful aging group (SAG) or a physical activity group (PAG) for 12 wk. The PAG consisted of two weekly group exercise sessions and two to three home-based exercise sessions per week. The SAG consisted of weekly group educational sessions related to various aspects of healthy aging. Physical activity was measured by ActiGraph accelerometers and the Community Healthy Activities Model Program for Seniors Activities Questionnaire in both groups, and a Fitbit was continuously worn by PAG participants. Results: Group session attendance was 93% and 86% in the PAG and SAG, respectively. The increase in ActiGraph-derived steps per day (1085.3 ± 265.6 vs 34.7 ± 274.3; P = 0.008) and daily minutes of moderate-to-vigorous physical activity (MVPA; 6.2 ± 1.6 vs 0.3 ± 1.7; d = 0.68; P = 0.01), and self-reported Community Healthy Activities Model Program for Seniors Activities Questionnaire MVPA bouts (3.5 ± 0.77 vs 0.33 ± 0.79; P < 0.001) were significantly greater within the PAG than the SAG. ActiGraph-derived minutes of daily MVPA and steps as well as Fitbit-derived steps per day were significantly greater on days when PAG participants engaged in group sessions compared with days when they self-reported home-based exercise or no exercise (P < 0.016). Participants in both study arms reported high levels of satisfaction (>4- on 5-point Likert scale). Conclusions: The intervention increased physical activity, was well attended, and resulted in high satisfaction. Future studies should assess long-term sustainability in this population. [ABSTRACT FROM AUTHOR]

Purpose: Sleep deficits are a common nonmotor symptom of Parkinson disease (PD). People with mild PD also achieve less physical activity (PA) than healthy older adults (HOA), but the relationship between sleep and PA in PD is unclear. This study examined associations between sleep and PA in participants with PD and HOA. Materials and Methods: Secondary analysis of a prospective observational study. Participants wore a commercially available activity monitor for two weeks. Wilcoxon Rank-Sum tests compared nighttime sleep, wakenings after sleep onset, number of wakenings, naps, step count, and PA intensity between PD and HOA groups. Age-adjusted regression models calculated associations between nighttime sleep and PA. Results: Per day, participants with PD slept 75 fewer minutes (p < 0.01), took 5,792 fewer steps (p < 0.001), achieved less PA at all intensities, and had 32% more sedentary time (p < 0.001) compared to HOA. Thirty minutes more sleep was associated with 26 fewer sedentary minutes for HOA (p = 0.01) and 25 fewer sedentary minutes for the PD group (p < 0.001). Conclusions: Sleep and PA are reduced in mild PD compared to HOA. Both groups demonstrated similar associations between reduced sleep and increased sedentary behavior. Results may encourage providers to screen for sleep deficits when promoting PA. The use of a wrist-worn commercial activity and sleep monitor was well tolerated by both healthy older adults and people with mild Parkinson Disease in this study. People with mild Parkinson Disease slept less and were less active than a group of healthy older adults. Less sleep was associated with more sedentary behavior in both groups. The relationship between poor sleep and sedentary behavior in mild Parkinson Disease suggests that rehabilitation interventions may be optimized by targeting both physical activity and sleep deficits. [ABSTRACT FROM AUTHOR]

Abstract Background For valid accelerometer-assessed physical activity (PA) data, several methodological aspects should be considered. We aimed to 1) visualize the applicability of absolute accelerometer cut-offs to classify PA intensity, 2) verify recommendations to measure PA over 7 days by examining inter-day variability and reactivity, 3) examine seasonal differences in PA, and 4) recommend during which 10 h day period accelerometers should be worn to capture the most PA in patients with heart failure (HEART) and healthy individuals (HEALTH). Methods Fifty-six HEART (23% female; mean age 66 ± 13 years) and 299 HEALTH (51% female; mean age 54 ± 19 years) of the COmPLETE study wore accelerometers for 14 days. Aim 1 was analyzed descriptively. Key analyses were performed using linear mixed models. Results The results yielded poor applicability of absolute cut-offs. The day of the week significantly affected PA in both groups. PA-reactivity was not present in either group. A seasonal influence on PA was only found in HEALTH. Large inter-individual variability in PA timing was present. Conclusions Our data indicated that absolute cut-offs foster inaccuracies in both populations. In HEART, Sunday and four other days included in the analyses seem sufficient to estimate PA and the consideration of seasonal differences and reactivity seems not necessary. For healthy individuals, both weekend days plus four other days should be integrated into the analyses and seasonal differences should be considered. Due to substantial inter-individual variability in PA timing, accelerometers should be worn throughout waking time. These findings may improve future PA assessment. Trial registration The COmPLETE study was registered at clinicaltrials.gov ( NCT03986892 ).

Background: Reduced physical activity (PA) was the strongest predictor of all-cause mortality in patients with chronic obstructive pulmonary disease (COPD). This scoping review aimed to map the evidence on the current landscape of physical activity, barriers and facilitators, and assessment tools across COPD patients.Methods: Arksey and O'Malley's scoping review methodology framework guided the conduct of this review. An electronic search was conducted on five English databases (PubMed, Cochrane Library, PsycINFO, CINAHL and Web of Science) and three Chinese databases (CNKI, CQVIP and WAN-FANG) in January 2022. Two authors independently screened the literature, extracted the studies characteristics.Results: The initial search yielded 4389 results, of which 1954 were duplicates. Of the remaining 135 articles, 42 studies met the inclusion criteria. Among the reviewed articles, there were 14 (33.3%) cross-sectional study, 9 (21.4%) cohort study, 4 (9.5%) longitudinal study, 3 qualitative study, 12 (28.7%) randomized control trials. The main barriers identified were older age, women, lung function, comorbidities, COPD symptoms (fear of breathlessness and injury, severe fatigue, anxiety and depression), GOLD stage, frequency of exacerbation, oxygen use, lack of motivation and environment-related (e.g., season and weather). Twelve studies have evaluated the effects of physical exercise (e.g., walking training, pulmonary rehabilitation (PR), pedometer, self-efficacy enhancing intervention and behavioral modification intervention) on PA and showed significant positive effects on the prognosis of patients. However, in real life it is difficult to maintain PA in people with COPD.Conclusions: Changing PA behavior in patients with COPD requires multidisciplinary collaboration. Future studies need to identify the best instruments to measure physical activity in clinical practice. Future studies should focus on the effects of different types, time and intensity of PA in people with COPD and conduct randomized, adequately-powered, controlled trials to evaluate the long-term effectiveness of behavioral change interventions in PA. [ABSTRACT FROM AUTHOR]

Aims Physical activity (PA) is important in patients with heart failure (HF) to improve health outcomes. The adherence to PA is low, and therefore, novel approaches are necessary to increase PA. We aimed to determine the difference in PA in patients with HF who have access to exergaming compared to patients who received motivational support and to explored predictors of a clinically relevant change in non-sedentary time between baseline and 3 months. Methods and results In total, 64 patients (mean age 69 ± 9 years, 27% female) wore an accelerometer 1 week before and 1 week after the intervention. Data were analysed using logistic regression analysis. Patients spent 9 h and 43 min (±1 h 23 min) during waking hours sedentary. There were no significant differences in PA between patients who received an exergame intervention or motivational support. In total, 30 of 64 patients achieved a clinically relevant increase in non-sedentary time. Having grandchildren [odds ratio (OR) 7.43 P = 0.03], recent diagnosis of HF (OR 0.93 P = 0.02), and higher social motivation (OR 2.31 P = 0.03) were independent predictors of a clinically relevant increase of non-sedentary time. Conclusion Clinicians should encourage their patients to engage in alternative approaches to improve PA and reduce sedentary habits. Future exergaming interventions should target individuals with chronic HF who have low social motivation and a low level of light PA that may benefit most from exergaming. Also (non-familial), intergenerational interaction is important to enabling patients in supporting patients in becoming more active. [ABSTRACT FROM AUTHOR]

The aim of the study was to examine changes in objective and self-reported physical activity (PA) among women aged 60 years and older. The study included 200 women aged over 60 years, divided into three groups according to age (60–65 years, 66–70 years, > 70 years). The subjective assessment was provided with the International Physical Activity Questionnaire (IPAQ) – short version with self-reported PA assessment, and objective data was provided by an Actigraph GT3-BT worn for seven days. Significant differences in moderate and high intensity PA, moderate-to-vigorous PA and steps per day were found between the oldest and youngest groups; as well as in low, moderate, moderate-to-vigorous PA and steps per day between groups middle and oldest. In all three age groups, 1) significant differences were observed between subjective and objective measurements of physical activity and 2) no correlation was found between assessment measures. It was found that only direct PA measurement declined with age in women over 60 years old, and that sedentary behavior is underestimated, and moderate and vigorous PA overestimated, with the self-reported IPAQ. [ABSTRACT FROM AUTHOR]

Abstract Background Adverse reactions to food are a common dermatological condition in dogs, requiring nutritional intervention using novel or hydrolysate protein‐based foods. Objective To evaluate a therapeutic food containing egg and phytonutrients in dogs with food allergies using an activity monitor and core outcome set for canine atopic dermatitis (COSCAD'18) in a controlled double‐masked, multicenter, prospective clinical trial. Animals Adult dogs with a history of adverse food reaction as diagnosed by a food elimination trial were recruited from general practices. Methods After a 21‐day baseline period, dogs were randomized to test or positive control (hydrolyzed protein) food for 21 days. Owner (pruritus visual analog score [PVAS], coat quality, food acceptance, and satisfaction) and veterinarian (canine atopic dermatitis lesion index [CADLI], physical examination) assessments were completed on days 0, 21, and 42. Dogs wore a collar‐mounted activity monitor to record sleep, scratching, and shaking behavior throughout the study. Statistical analysis included within‐group comparison to baseline and between‐group comparison at study end using a significance threshold of alpha = 0.05. Results At the end of the treatment period, all results were similar between groups for CADLI, PVAS, owner satisfaction, activity, and questionnaire data. Scores for hair dullness, brittleness, amount of dandruff, feces quality, and food acceptance were positive and were not statistically different between groups. Conclusions and Clinical Importance The therapeutic test food was well‐accepted and efficacious in managing signs of adverse reactions to food compared to baseline as well as compared to the positive control food.

Abstract Background Adverse reactions to food are a common dermatological condition in dogs, requiring nutritional intervention using a novel or hydrolysate protein‐based food. Objective To evaluate a therapeutic food containing egg and phytonutrients in dogs with food allergies using an activity monitor and core outcome set for canine atopic dermatitis (COSCAD'18) guidelines and in a controlled double‐masked, multicenter, prospective clinical trial. Animals Adult dogs with a history of adverse food reaction as diagnosed by a food elimination trial were recruited from general practices. Methods After a 21‐day baseline period, dogs were randomized to test or positive control (hydrolyzed protein) food for 21 days. Owner (pruritus visual analog score [PVAS], coat quality, food acceptance, and satisfaction) and veterinarian (canine atopic dermatitis lesion index [CADLI], physical examination) assessments were completed on days 0, 21, and 42. Dogs wore a collar‐mounted activity monitor to record scratching and shaking behavior throughout the study. Statistical analysis included within‐group comparison to baseline and between‐group comparison at study end using a significance threshold of alpha = 0.05. Results At the end of the treatment period, all results were similar between groups for CADLI, PVAS, owner satisfaction, activity, and questionnaire data. Scores for hair dullness, brittleness, amount of dandruff, feces quality, and food acceptance were positive and not statistically different between groups. Conclusions and Clinical Importance The therapeutic test food was well‐accepted and efficacious in managing signs of adverse reactions to food compared to baseline as well as compared to the positive control food.

Abstract Background Wearable technologies are being used to provide personalised feedback across multiple physical activity dimensions in countries such as the UK, but their feasibility has not been tested in South Asia, where physical inactivity is increasing. This study assessed the understanding, acceptability, and relevance of personalised multidimensional physical activity feedback in urban dwellers in Colombo, Sri Lanka. Methods A qualitative feasibility study was conducted among 35 adults to assess a community-based approach to provide multidimensional physical activity feedback. Healthy adults, adults at risk of non-communicable diseases and community-based primary healthcare professionals wore a physical activity monitor for 7 days and were then guided through their personalised multidimensional physical activity feedback. One-to-one interviews were conducted, transcribed verbatim and analysed using framework analysis. Results Four themes were generated: understanding of personalised physical activity feedback, perceived novelty of the feedback, motivation, and consideration of the multidimensional nature of physical activity. A majority of participants required guidance initially to understand the feedback, following which most were quickly able to interpret the data shown, and were willing to use the feedback as a basis for identifying goals to improve physical activity. Participants perceived the feedback and its delivery as novel because it provided new knowledge about physical activity guidelines and awareness on their own behaviour through graphics. Comparisons of personal performance against recommended physical activity levels and information on sedentary time were the most commonly motivating aspects of the feedback, prompting talk about behaviour change. All three groups showed poor planning on goal achievement, with some noticeable differences between those with and without health risk of non-communicable diseases. Following the feedback, most participants understood that physical activity is composed of several dimensions, while around half could recognise more suitable options to change behaviour. Of the physical activity dimensions, calorie burn received more attention than others. Conclusions Multidimensional physical activity feedback was considered understandable and acceptable and has the potential to support behaviour change among urban Sri Lankans with or without identified health risk. These findings highlight the feasibility of this technology-enabled approach as a personalised intervention to improve knowledge and motivation for physical activity behaviour.

A link between inappropriate physical behaviour patterns (low physical activity and high sedentary behaviour) and poor health outcomes has been observed. To provide evidence to quantify this link, it is important to have valid and reliable assessment tools. This study aimed to assess the validity and reliability of the activPAL4TM monitor for distinguishing postures and measuring stepping activity of 6–12-year-old children. Thirteen children (8.5 ± 1.8 years) engaged in pre-determined standardised (12 min) and non-standardised (6 min) activities. Agreement, specificity and positive predictive value were assessed between the activPAL4TM and direct observation (DO) (nearest 0.1 s). Between-activPAL4TM (inter-device) and between-observer (inter-rater) reliability were determined. Detection of sitting and stepping time and forward purposeful step count were all within 5% of DO. Standing time was slightly overestimated (+10%) and fast walking/jogging steps underestimated (−20%). For non-standardised activities, activPAL4TM step count matched most closely to combined backward and forward purposeful steps; however, agreement varied widely. The activPAL4TM demonstrated high levels of reliability (ICC(1, 1) > 0.976), which were higher in some instances than could be achieved through direct observation (ICC(2, 1) > 0.851 for non-standardised activities). Overall, the activPAL4TM recorded standardised activities well. However, further work is required to establish the exact nature of steps counted by the activPAL4TM.

Background and Aims: Activity monitors, such as Fitbits®, are being used increasingly for research purposes and data have been validated in healthy community‐dwelling older adults. Given the lack of research in older adults with neurocognitive disorders, we investigated the consistency of sleep data recorded from a wrist‐worn activity monitor in this population. Methods: Fitbit® activity monitors were worn by hospitalized older adults as part of a parent study investigating sleep and step count in patients recovering from hip fracture surgery in a tertiary care academic hospital in Hamilton, Canada between March 2018 and June 2019. In this secondary analysis, we compared the proportion of missing sleep data between participants with and without a neurocognitive disorder and used a multivariable model to assess the association between neurocognitive disorder and missing sleep data. Results: Of 67 participants included in the analysis, 22 had a neurocognitive disorder (median age: 86.5 years). Sleep data were missing for 47% of the neurocognitive disorder group and 23% of the non‐neurocognitive disorder group. The presence of a neurocognitive disorder was associated with an increased likelihood of missing sleep data using the Fitbit® activity monitors (adjusted odds ratio: 3.41; 95% confidence interval: 1.06–11.73, p = 0.04). Conclusion: The inconsistent nature of sleep data tracking in hospitalized older adults with neurocognitive disorders highlights the challenges of using interventions in patient populations who are often excluded from validation studies. As opportunities expand for activity monitoring in persons with neurocognitive disorders, novel technologies not previously studied in this group should be used with caution. [ABSTRACT FROM AUTHOR]