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Introduction: There is conflicting data in the literature regarding the clinical utility of wearable devices. This study examined the association between patient reported outcome measures (PROMs) and step and stair flight counts obtained from wearable devices in postoperative total hip arthroplasty (THA) patients. Methods: Data was collected from a multicenter prospective longitudinal cohort study from October 2018 to February 2022. A smartphone-based platform with smartwatch was utilized for collection of daily step and stair flight counts. Subjects (N = 1644) completed the Hip disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR) and numerical rating scale (NRS) pain scores preoperatively and at 1 and 3 months postoperatively. Patients who reported living in a multi-level home (N = 931) were included in analysis of stair flight counts. Pearson correlation coefficients were calculated to determine correlations between step and stair flight counts with NRS pain and HOOS JR scores. Results: Step counts demonstrated a weak negative correlation to NRS pain scores at preoperative (r = − 0.15, p < 0.0001), 1-month (r = − 0.15, p < 0.0001), and 3-months follow-up (r = − 0.06, p = 0.05). Step counts demonstrated a weak positive correlation with HOOS JR scores at preoperative (r = 0.16, p < 0.0001), 1-month (r = 0.15, p < 0.0001), and 3-months (r = 0.13, p < 0.0001). Stair flight counts demonstrated a weak negative correlation with NRS pain preoperatively (r = − 0.19, p < 0.0001) and at 1-month (r = − 0.11, p = 0.003). Stair flight counts positively correlated with HOOS JR scores at preoperative (r = 0.24, p < 0.0001), 1-month (r = 0.15, p < 0.0001), and 3-months (r = 0.09, p = 0.02). Conclusion: The utilization of wearable technology can enhance the evaluation of patient outcomes after THA, primarily due to the observed correlation between data collected from wearables and PROMs. Our study highlights the importance of the use of objective data, in addition to subjective patient reported data, when analyzing postoperative patient progress, which propels forward the field of postoperative THA patient care. [ABSTRACT FROM AUTHOR]

Objective: We aimed to assess seasonal and age-related variations in sleep quality using Fitbit data and offer lifestyle recommendations for enhancing winter sleep quality. Methods: Fitbit sleep and activity data of 51 participants randomly recruited from members of the Association for Research in Supporting System of Chronic Disease, a nonprofit organization in the Yamanashi Prefecture of Japan, were collected and retrospectively analyzed from July to December 2022. Sleep stage targets were set at 10% to 25% for deep sleep, 50% to 60% for shallow sleep, and 20% to 25% for REM sleep. Participants were categorized into improved, unchanged, and worsened groups based on sleep stage unit changes between August and December. Results: The median (interquartile range) age was 71 (68–74) years old. There were eight participants in the improved group, 23 in the unchanged group, and nine in the worsened group. The improved group showed significantly more steps (990 ± 1,102 steps/day, p =.039) in December than in August, while the worsened group showed fewer steps (−507 ± 1,638 steps/day, p =.38). Conclusion: Increasing step count in winter may improve sleep quality, as assessed by sleep stage. Further research considering potential confounders and factors affecting winter sleep is needed to support and extend these findings. [ABSTRACT FROM AUTHOR]

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]

It was aimed to explore the connections among sleep quality, heart rate, sleep duration, quality of life, and step count in young adults. The study involved 116 young adults, comprising 61 women and 55 men. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), and sleep duration, heart rate, step count, and energy expenditure were monitored with the Fitbit® Inspire 2. Quality of life was evaluated using the Nottingham Health Profile (NHP) Questionnaire. Correlation analysis was performed using either Pearson or Spearman correlation analysis. The study found no significant relationships between sleep quality, sleep duration, heart rate, and step count (p< 0.05). A very strong positive correlation was found between sleep score and both time asleep (r = 0.730) and REM sleep (r = 0.743), as well as a strong correlation with deep sleep (r = 0.454). Time asleep had very strong positive correlations with REM sleep (r = 0.611) and light sleep (r = 0.782), and strong correlations with awake time (r = 0.438) and deep sleep (r = 0.466). Awake time and light sleep also showed a strong correlation (r = 0.585). Additionally, step count was strongly correlated with energy expenditure (r = 0.519), and a very strong correlation was observed between the section 1 score of the NHP and the PSQI (r = 0.649). These outcomes underscore the complex interplay between sleep and physical activity and emphasize the importance of considering multiple factors when assessing health and quality of life. [ABSTRACT FROM AUTHOR]

Step counts can be estimated from wrist-worn accelerometers through the Verisense Step Count Algorithm. No study has assessed agreement between stepping metrics from ActiGraph accelerometers during free-living. Thirty-four participants (age: 22.9 ± 3.4 years) provided 24 h accelerometer data (non-dominant wrist) and waist. Agreement of two Verisense Algorithms (Verisense 1 & 2) for estimating daily steps, moderate-to-vigorous physical activity (MVPA), peak 1-min and 30-min accumulated steps, against the waist and ActiLife step-count Algorithm was assessed. Mean bias ± 95% limits of agreement (LoA) for daily steps was +1255 ± 3780 steps/day (mean absolute percent error (MAPE): 21%) (Verisense 1) and +1357 ± 3434 steps/day (MAPE: 20%) (Verisense 2). For peak 1-min accumulated steps, mean bias and 95% LoA was −17 ± 23 steps/min (MAPE: 17%) (Verisense 1) and −6 ± 5 steps/min (MAPE: 9%) with Verisense 2. For peak 30-min accumulated steps, mean bias and 95% LoA was −12 ± 45 steps/min (MAPE: 25%) (Verisense 1) and −2 ± 38 steps/min (MAPE: 13%) (Verisense 2). For MVPA steps/day, mean bias and 95% LoA was −1450 ± 3194 steps/day (MAPE: 420%) (Verisense 1) and −844 ± 2571 steps/day (MAPE: 211%) (Verisense 2). For MVPA min/day, mean bias and 95% LoA was −13 ± 27 min/day (MAPE: 368%) (Verisense 1) and −8 ± 24 min/day (MAPE: 209%) (Verisense 2). The Verisense 2 algorithm enhanced agreement for stepping intensity metrics but further refinement is needed to enhance agreement for MVPA against the waist. [ABSTRACT FROM AUTHOR]

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]

Individuals with chronic pain due to knee osteoarthritis (OA) are insufficiently physically active, and alterations of facilitatory and inhibitory nociceptive signaling are common in this population. Our objective was to examine the association of these alterations in nociceptive signaling with objective accelerometer-based measures of physical activity in a large observational cohort. We used data from the Multicenter Osteoarthritis Study. Measures of peripheral and central pain sensitivity included pressure pain threshold at the knee and mechanical temporal summation at the wrist, respectively. The presence of descending pain inhibition was assessed by conditioned pain modulation (CPM). Physical activity was quantitatively assessed over 7 days using a lower back-worn activity monitor. Summary metrics included steps/day, activity intensity, and sedentary time. Linear regression analyses were used to evaluate the association of pain sensitivity and the presence of descending pain inhibition with physical activity measures. Data from 1873 participants was analyzed (55.9% female, age = 62.8 ± 10.0 years). People having greater peripheral and central sensitivity showed lower step counts. CPM was not significantly related to any of the physical activity measures, and none of the exposures were significantly related to sedentary time. In this cohort, greater peripheral and central sensitivity were associated with reduced levels of objectively-assessed daily step counts. Further research may investigate ways to modify or treat heightened pain sensitivity as a means to increase physical activity in older adults with knee OA. [ABSTRACT FROM AUTHOR]

Introduction: It is unknown what the benefits of cardio-oncologic rehabilitation programmes on cardiorespiratory fitness and cancer fatigue during anthracycline-based chemotherapies (AC) are with supervised exercise training (ET), compared to PA advice and tracking only. Methods: Patients with breast cancer or lymphoma were recruited from four cancer centres and randomly assigned to three months supervised ET during (EXduringAC) or after (EXpostAC) AC. All patients were counselled on physical activity (PA) and PA was objectively measured with an activity tracker. Primary endpoints were peak VO2, fatigue and quality of life (QoL) after AC (AC-end) and at follow-up (3 months after completion of AC). Secondary endpoints were daily PA and daily steps during AC and follow-up phase, which were compared between days with and without centre-based training sessions. All analyses were performed by linear mixed models. Results: Fifty-seven patients (median [1st and 3rd quartiles] age 47 years [38, 57 years]; 95 ​% women) consented to participate, of whom data from 51 patients were available. Despite the fact that PA on days with centre-based training sessions was 28 (95 ​% confidence interval 24–32) min higher with 4382 (3995–4768) more steps, neither PA nor steps differed between groups in neither AC nor follow-up phase, nor were there between group differences in peak VO2, QoL or fatigue at any time point. Conclusion: In physically active patients with cancer, PA advice and using an activity tracker was equally effective on changes in peak VO2, fatigue, or QoL as enrolling in centre-based ET performed during or after AC. Trial registration: NCT03850171, February 21, 2019.

Background: This paper examined whether the criterion validity of step count (SC), energy expenditure (EE), and heart rate (HR) varied across studies depending on the average age, body mass index (BMI), and predominant gender of participants. Methods: Data from 1536 studies examining the validity of various wearable devices were used. Separate multilevel regression models examined the associations among age, gender, and BMI with device criterion validity assessed using mean absolute percent error (MAPE) at the study level. Results: MAPE values were reported in 970 studies for SC, 328 for EE, and 238 for HR, respectively. There were several significant differences in MAPE between age, gender, and BMI categories for SC, EE, and HR. SC MAPE was significantly different for older adults compared with adults. Compared with studies among normal-weight populations, MAPE was greater among studies with overweight samples for SC, HR, and EE. Comparing studies with more women than men, MAPE was significantly greater for EE and HR. Conclusions: There are important differences in the criterion validity of commercial wearable devices across studies of varying ages, BMIs, and genders. Few studies have examined differences in error between different age groups, particularly for EE and HR. [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.

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]

Objective: This study aimed to quantify the association between step count and multiple health outcomes in a healthy population. Methods: PubMed, Embase, Web of Science, and The Cochrane Library were systematically searched for systematic reviews and meta‐analyses from inception to April 1, 2022. Literature screening, data extraction, and data analysis were performed in this umbrella review. The intervention factor was daily step counts measured based on devices. Multiple health outcomes included metabolic diseases, cardiovascular diseases, all‐cause mortality, and other outcomes in the healthy population. Results: Twenty studies with 94 outcomes were identified in this umbrella review. The increase in daily step count contributed to a range of human health outcomes. Furthermore, the special population, different age groups, countries, and cohorts should be carefully considered. Negative correlation between step counts and the following outcomes: metabolic outcomes, cardiovascular diseases, all‐cause mortality, postural balance, cognitive function, and mental health. However, there was no association between participation in the outdoor walking group and the improvement of systolic blood pressure and diastolic blood pressure. Analysis of the dose–response association between increasing daily step count and the risk of cardiovascular disease events and all‐cause mortality showed a substantially linear relationship. Conclusion: A wide range of health outcomes can benefit from the right number of steps. [ABSTRACT FROM AUTHOR]

With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30–60, 20–90, and 30–110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials. [ABSTRACT FROM AUTHOR]

Patients with end-stage osteoarthritis are recommended to engage in physical activity (PA) to reduce pain and improve function but may avoid PA due to joint pain. Our goal was to investigate patient-reported outcomes and objective mobility metrics (step counts) in total hip arthroplasty (THA) patients as a function of pre-operative PA levels. In total, 1647 patients enrolled in a multicenter prospective cohort study investigating a smartphone-based care management platform for self-directed rehabilitation that underwent THA and were included in analysis. The entire cohort's step count was divided into quartiles to categorize patients with low, moderate, and high baseline PA. Outcomes including pain, EQ-5D-5L, HOOS JR, and step counts were compared according to activity group by ANOVA. Pre-operative pain scores were lowest, with smallest improvements, in the high-baseline PA group. Low-PA patients demonstrated the greatest improvements in EQ-5D-5L, while changes in EQ-VAS, HOOS JR, and satisfaction were similar between groups. Low- and moderate-PA patients increased physical activity by six weeks, reaching 180% and 114% of pre-operative steps; high-PA patients did not return to full step counts until one-year post-operation. Patients who perform high levels of PA undergoing THA report lower levels of pain and higher function pre-operatively but may appreciate less improvement in PA up to one year post-operatively. These results may be helpful in appropriate counseling of patient expectations prior to surgery. [ABSTRACT FROM AUTHOR]

Background: eHealth applications can support early mobilization and physical activity (PA) after surgery. This systematic review provides an overview of eHealth services to enhance or record PA after visceral surgery interventions. Methods: Two electronic databases (MEDLINE PubMed and Web of Science) were systematically searched (November 2023). Articles were considered eligible if they were controlled trials and described digital devices used to promote PA after visceral surgery. The Cochrane risk of bias (RoB-2) tool was used to determine the methodological quality of studies. Results: A total of nine randomized controlled studies (RCT) were included in this systematic review. The studies differed with respect to the interventions, surgical indications and evaluation variables. The risk of bias of the individual studies was moderate. The six studies using activity trackers (AT) predominantly showed insignificant improvements in the postoperative step count. The more complex fitness applications could partially reveal significant advantages compared to the control groups and the home-based online training also showed a significant increase in functional capacity. Conclusion: Activity tracking alone has so far failed to show clinically relevant effects. In contrast, the more complex eHealth applications revealed advantages compared to usual postoperative care. More high-quality studies are needed for evidence-based recommendations for eHealth services in conjunction with visceral surgery. [ABSTRACT FROM AUTHOR]

While physical activity (PA) is understood to promote vascular health, little is known about whether the daily and weekly patterns of PA accumulation associate with vascular health. Accelerometer‐derived (activPAL3) 6‐ or 7‐day stepping was analyzed for 6430 participants in The Maastricht Study (50.4% women; 22.4% Type 2 diabetes mellitus (T2DM)). Multivariable regression models examined associations between stepping metrics (average step count, and time spent slower and faster paced stepping) with arterial stiffness (measured as carotid–femoral pulse wave velocity (cfPWV)), and several indices of microvascular health (heat‐induced skin hyperemia, retinal vessel reactivity and diameter), adjusting for confounders and moderators. PA pattern metrics were added to the regression models to identify associations with vascular health beyond that of stepping metrics. Analyses were stratified by T2DM status if an interaction effect was present. Average step count and time spent faster paced stepping was associated with better vascular health, and the association was stronger in those with compared to those without T2DM. In fully adjusted models a higher step count inter‐daily stability was associated with a higher (worse) cfPWV in those without T2DM (std β = 0.04, p = 0.007) and retinal venular diameter in the whole cohort (std β = 0.07, p = 0.002). A higher within‐day variability in faster paced stepping was associated with a lower (worse) heat‐induced skin hyperemia in those with T2DM (std β = −0.31, p = 0.008). Above and beyond PA volume, the daily and weekly patterns in which PA was accumulated were additionally associated with improved macro‐ and microvascular health, which may have implications for the prevention of vascular disease. [ABSTRACT FROM AUTHOR]

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

Patients with end-stage osteoarthritis are recommended to engage in physical activity (PA) to reduce pain and improve function but may avoid PA due to joint pain. Our goal was to investigate patient-reported outcomes and objective mobility metrics (step counts) in total hip arthroplasty (THA) patients as a function of pre-operative PA levels. In total, 1647 patients enrolled in a multicenter prospective cohort study investigating a smartphone-based care management platform for self-directed rehabilitation that underwent THA and were included in analysis. The entire cohort’s step count was divided into quartiles to categorize patients with low, moderate, and high baseline PA. Outcomes including pain, EQ-5D-5L, HOOS JR, and step counts were compared according to activity group by ANOVA. Pre-operative pain scores were lowest, with smallest improvements, in the high-baseline PA group. Low-PA patients demonstrated the greatest improvements in EQ-5D-5L, while changes in EQ-VAS, HOOS JR, and satisfaction were similar between groups. Low- and moderate-PA patients increased physical activity by six weeks, reaching 180% and 114% of pre-operative steps; high-PA patients did not return to full step counts until one-year post-operation. Patients who perform high levels of PA undergoing THA report lower levels of pain and higher function pre-operatively but may appreciate less improvement in PA up to one year post-operatively. These results may be helpful in appropriate counseling of patient expectations prior to surgery.

Purpose: Little is known about the changes and the factors in physical activity as following total hip arthroplasty (THA). There are potential discrepancies between subjective and objective measurements of physical activity. Thus, our porpose is to compare objective and subjective measurements of activity levels in patients undergoing THA preoperatively, three months and one year following surgery, and investigated the factors that predicts the objective activity level after THA. Methods: This prospective observational study included 42 patients with unilateral symptomatic hip osteoarthritis who underwent THA. The objective activity level (step counts, sit-to-stands, and upright time) by using a tri-axial accelerometer, PRO (Oxford hip score; OHS and University of California, Los Angeles activity score; UCLA score), and muscle volume around the hip joint by using preoperative computed tomography were measured. Results: The number of steps, OHS, and UCLA score before, at three months, and one year after THA averaged 5092, 6532, and 6545 steps, 30.3, 43.0, and 44.2 points, and 3.4, 4.8, and 4.6 points, respectively, with significant postoperative increases (P < 0.05). No significant difference was found between three months and one year postoperatively. In multivariate analysis, younger age and higher preoperative contralateral gluteal medius volume were the predictors of higher postoperative step counts (P < 0.05). Conclusions: Physical activity, including step counts, OHS, and UCLA score increased significantly until three months after unilateral THA. Early surgical intervention before contralateral muscle declines and preoperative rehabilitation including contralateral side may additionally improve postoperative activity levels. [ABSTRACT FROM AUTHOR]

Background and Objectives: Mild cognitive impairment (MCI) is an early stage of dementia in which everyday tasks can be maintained; however, notable challenges may occur in memory, focus, and problem-solving skills. Therefore, motor-cognitive dual-task training is warranted to prevent cognitive decline and improve cognition in aging populations. This study aimed to determine the influence of such dual-task activities during straight and curved walking on the activities of the prefrontal cortex and associated gait variables in older adults with MCI. Materials and Methods: Twenty-seven older adults aged ≥65 years and identified as having MCI based on their scores (18–23) on the Korean Mini-Mental State Examination were enrolled. The participants performed four task scenarios in random order: walking straight, walking straight with a cognitive task, walking curved, and walking curved with a cognitive task. The activation of the prefrontal cortex, which is manifested by a change in the level of oxyhemoglobin, was measured using functional near-infrared spectroscopy. The gait speed and step count were recorded during the task performance. Results: Significant differences were observed in prefrontal cortex activation and gait variables (p < 0.05). Specifically, a substantial increase was observed in prefrontal cortex activation during a dual task compared with that during a resting-state (p < 0.013). Additionally, significant variations were noted in the gait speed and step count (p < 0.05). Conclusions: This study directly demonstrates the impact of motor-cognitive dual-task training on prefrontal cortex activation in older adults with MCI, suggesting the importance of including such interventions in enhancing cognitive function. [ABSTRACT FROM AUTHOR]

Abstract Background Established health-related quality of life scores do not consider both subjective and objective indices of health. We propose the subjective and objective quality of life score (SOQOL) for the comprehensive assessment of health-related quality of life and aim to provide normative population data. The SOQOL is compatible with smartphone applications, allowing widespread use on a global scale. Methods Normative SOQOL population data was sourced from pre-existing datasets on the EQ-5D-5L, daily step count, and walking speed. Normative values were calculated using weighted grand means. We trialled the SOQOL in a group of five patients presenting to a spinal neurosurgery clinic. Results SOQOL scores decreased with age, and women had lower scores in every age group. In our case series, the spine patients with the biggest SOQOL deficit compared to age- and sex-matched population averages were found to be surgical while the rest were non-surgical. Conclusions The SOQOL shows promise as a simple and effective scoring tool that is compatible with smartphones, potentially useful for screening in primary and specialized care settings, and for assessment following healthcare interventions. This study, however, is preliminary, and the findings are primarily suggestive. They underline the necessity for future, more comprehensive studies to validate and expand upon these initial observations. The conclusion of both this abstract and the full paper will clearly state these limitations and the preliminary nature of the study.

Abstract Background Exercise has various health benefits for people with Parkinson’s disease (PD). However, implementing exercise into daily life and long-term adherence remain challenging. To increase a sustainable engagement with physical activity of people with PD, interventions that are motivating, accessible, and scalable are needed. We primarily aim to investigate whether a smartphone app (STEPWISE app) can increase physical activity (i.e., step count) in people with PD over one year. Our second aim is to investigate the potential effects of the intervention on physical fitness, and motor- and non-motor function. Our third aim is to explore whether there is a dose-response relationship between volume of physical activity and our secondary endpoints. Methods STEPWISE is a double-blind, randomized controlled trial. We aim to include 452 Dutch people with PD who can walk independently (Hoehn & Yahr stages 1–3) and who do not take more than 7,000 steps per day prior to inclusion. Physical activity levels are measured as step counts on the participant’s own smartphone and scaled as percentage of each participant’s baseline. Participants are randomly assigned to an active control group with an increase of 5–20% (active controls) or any of the three intervention arms with increases of 25–100% (intermediate dose), 50–200% (large dose), or 100–400% (very large dose). The primary endpoint is change in step count as measured by the STEPWISE smartphone app from baseline to 52 weeks. For our primary aim, we will evaluate the between-group difference in average daily step count change from baseline to 52 weeks. For our second aim, measures of physical fitness, and motor- and non-motor function are included. For our third aim, we will associate 52-week changes in step count with 52-week changes in secondary outcomes. Discussion This trial evaluates the potential of a smartphone-based intervention to increase activity levels in people with PD. We envision that motivational apps will increase adherence to physical activity recommendations and could permit conduct of remote clinical trials of exercise for people with PD or those at risk of PD. Trial registration ClinicalTrials.gov; NCT04848077; 19/04/2021. Clinicaltrials.gov/ct2/show/NCT04848077.

Physical activity (PA) is suggested to reduce osteoarthritis pain; however, it may be avoided by patients requiring arthroplasty. Our goal was to investigate objective and patient-reported outcomes as a function of pre-operative PA levels in patients undergoing total knee arthroplasty (TKA). A total of 1941 patients enrolled in a multicenter prospective cohort study investigating a smartphone-based care management platform for self-directed rehabilitation underwent TKA and were included in the analysis. Activity was categorized based on the cohort's step count quartiles into low, moderate, and high pre-operative PA. Pre-operative and post-operative pain, EQ5D5L, KOOS JR, and step counts were compared by ANOVA according to activity group. Pre-operative pain scores increased with the decreasing activity level (all, p < 0.05) and were most improved post-operatively in the low PA group. High PA patients demonstrated the smallest improvements in EQ-5D-5L and KOOS JR. Low and moderate PA patients increased physical activity by three months, reaching 176% and 104% of pre-operative steps; high PA patients did not return to full step counts by one year post-operatively. Patients undergoing TKA who present with higher levels of physical activity report lower levels of pain and higher function pre-operatively but appreciate less improvement up to one year post-operatively. These results may be helpful in appropriate counseling of patient expectations before TKA. [ABSTRACT FROM AUTHOR]

Children should engage in at least 60 min of physical activity daily to develop or maintain healthy habits (Centers for Disease Control and Prevention, 2021a). Previous research suggests that contingent attention is a powerful tool for increasing moderate to vigorous physical activity. In this study, the researchers examined the effects of a group game on steps per minute at recess in an inclusive classroom with preschoolers. The researcher taught the game called the T‐Rex game in which the game rules (chasing students who were moving) involved contingencies for natural attention to follow moderate to vigorous physical activity. The results showed a functional relation between step count and the group game in which students engaged in higher step counts when the group game was in place than during baseline. These findings extend other work by demonstrating that contingent attention embedded in group games can increase step count. [ABSTRACT FROM AUTHOR]

Maladaptive exercise (i.e., exercise that compensates for binge eating or is used to avoid negative consequences of not exercising-like weight gain) is a common eating disorder (ED) behavior, yet, some individuals with EDs only engage in adaptive exercise. CBT for EDs targets reducing maladaptive exercise but does not address adaptive exercise. Thus, research is limited on how adaptive and maladaptive exercise are impacted during CBT for EDs. The current study examined how assessor-rated adaptive and maladaptive exercise and objectively measured physical activity changed over a 12-week CBT treatment among adults with transdiagnostic binge eating and restrictive eating that did and did not engage in maladaptive exercise at the start of treatment (n = 13 non-maladaptive exercise group, n = 17 maladaptive exercise group). The overall amount of adaptive and maladaptive exercise was measured via the Eating Disorder Examination Interview and objectively measured physical activity (e.g., step count, minutes of moderate-to-vigorous physical activity [MVPA]) was measured via a wrist-worn fitness tracker. Throughout treatment, the overall amount of adaptive exercise did not significantly change for both groups, but the overall amount of maladaptive exercise significantly decreased in the maladaptive exercise group. Step count did not significantly change for both groups, but minutes of MVPA significantly increased over treatment for the non-maladaptive exercise group. Increases in step count and minutes of MVPA were not associated with ED symptom changes in either group. These results enhance an understanding of exercise changes during transdiagnostic CBT-based ED treatment for individuals with varying baseline exercise profiles. Level of evidence: Level 1, randomized controlled trial [ABSTRACT FROM AUTHOR]

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]

• The significant association between daily steps and FOF disappeared after adjusting for step intensity, suggesting that daily steps may not independently influence FOF in older women. • Higher step intensity (including peak-1, peak-30, purposeful walking steps, and purposeful walking time) was significantly associated with FOF, even after adjusting for steps. • The effects of step intensity and duration should be considered in future studies of FOF reduction by walking. This study aimed to investigate the relationship between objectively measured step counts, step intensity and FOF in community-dwelling older Chinese women. Cross-sectional analyses were conducted on pooled data (n = 1101) from the baseline survey of the Physical Activity and Health Study (PAHIOWS). Step counts and step intensity were measured using wGT3X-BT accelerometers, and FOF was categorized into two levels (low and high) for logistic regression analysis. Higher step counts were significantly and negatively associated with FOF; however, after adjusting for step intensity, the association was no longer statistically significant. On the contrary, higher step intensity was negatively associated with FOF independent of step counts. This finding may provide new insights into the physical activity management of FOF in older women. [ABSTRACT FROM AUTHOR]

Background: Frailty is a syndrome related to declining functioning attributed to aging. As a reversible, often improved condition, preventive measures for frailty can be effective. Objective: This study aimed to evaluate a frailty prevention program for older men comprising face‐to‐face meetings and an original social mobile application. We also examined the effect of COVID‐19‐associated social isolation on the implementation of this program. Methods: We conducted a mixed‐methods pilot study featuring a single group before and after comparison of 11 man participants, ≥65 years, living in Japanese cities with a spouse or relatives. Program outcome measures included frailty, loneliness, grip strength, step count and mental health status. Process evaluation considered the experience of participants through a semi‐structured interview. Results: The intervention period included an interval of self‐isolation owing to the spread of COVID‐19. Among the outcomes, grip strength decreased after intervention. No significant negative changes were observed in other items. Themes emerging from participant experiences with the program included 'Cooperation with researchers and contributing to research', 'Greater awareness of health', 'Interest in and experience of "newness"', 'Hesitation over active engagement', 'Casual ties with members' and 'Relationships that do not breach personal boundaries'. Conclusions: Although the COVID‐19 pandemic overlapped with the intervention, participants maintained their mental health. The program was a novel, stimulating experience for older men and demonstrated the usefulness of information and communication technology in supporting their social activities. Implications for practice: As a community health nursing initiative for older people, we propose activities that combine group and online support. Frailty prevention programs for older men must contain content that stimulates their interests, such as 'health' and 'newness'. [ABSTRACT FROM AUTHOR]

With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30–60, 20–90, and 30–110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials.

[Display omitted] [ABSTRACT FROM AUTHOR]

In order to combat rampant health and wellness deterioration over the summer months, residential summer camps have been presented as a possible solution to thwart such declines in overall health and wellbeing. The purpose of this study was to determine the differential effects of prompting, and prompting combined with reinforcement on female campers' step counts at a residential summer camp. The participants in this study were 104 female campers (Mage = 13.71), all attending the same residential summer camp. An alternating treatment design was implemented to evaluate campers' daily physical activity across three conditions (baseline, prompting, and prompting combined with contingent reinforcement) throughout the duration of the 51 days camp experience. In accordance with standard Applied Behavior Analysis research, data were plotted graphically in order to employ visual analysis to determine functional relationships between the intervention (prompting only, prompting plus group contingency, and target behavior [step count]). Results of this study indicate that prompting with group contingencies (29%) and without (13%) successfully led to an increased amount of daily physical activity. Further, small variance within the group contingency group illustrates the breadth of success for this technique. This is the first comprehensive intervention focused primarily on influencing female campers' physical activity levels in the residential camp context and demonstrates a cost-effective strategy for improving activity among female campers. [ABSTRACT FROM AUTHOR]

Background: Exercise has various health benefits for people with Parkinson's disease (PD). However, implementing exercise into daily life and long-term adherence remain challenging. To increase a sustainable engagement with physical activity of people with PD, interventions that are motivating, accessible, and scalable are needed. We primarily aim to investigate whether a smartphone app (STEPWISE app) can increase physical activity (i.e., step count) in people with PD over one year. Our second aim is to investigate the potential effects of the intervention on physical fitness, and motor- and non-motor function. Our third aim is to explore whether there is a dose-response relationship between volume of physical activity and our secondary endpoints. Methods: STEPWISE is a double-blind, randomized controlled trial. We aim to include 452 Dutch people with PD who can walk independently (Hoehn & Yahr stages 1–3) and who do not take more than 7,000 steps per day prior to inclusion. Physical activity levels are measured as step counts on the participant's own smartphone and scaled as percentage of each participant's baseline. Participants are randomly assigned to an active control group with an increase of 5–20% (active controls) or any of the three intervention arms with increases of 25–100% (intermediate dose), 50–200% (large dose), or 100–400% (very large dose). The primary endpoint is change in step count as measured by the STEPWISE smartphone app from baseline to 52 weeks. For our primary aim, we will evaluate the between-group difference in average daily step count change from baseline to 52 weeks. For our second aim, measures of physical fitness, and motor- and non-motor function are included. For our third aim, we will associate 52-week changes in step count with 52-week changes in secondary outcomes. Discussion: This trial evaluates the potential of a smartphone-based intervention to increase activity levels in people with PD. We envision that motivational apps will increase adherence to physical activity recommendations and could permit conduct of remote clinical trials of exercise for people with PD or those at risk of PD. Trial registration: ClinicalTrials.gov; NCT04848077; 19/04/2021. Clinicaltrials.gov/ct2/show/NCT04848077. [ABSTRACT FROM AUTHOR]

Introduction: While there is evidence on agreement, it is unknown whether commercial wearables can be used as surrogates for research-grade devices when investigating links with markers of cardiometabolic risk. Therefore, the aim of this study was to investigate whether data from a commercial wearable device could be used to assess associations between behavior and cardiometabolic risk markers, compared with physical activity from a research-grade monitor. Methods: Forty-five adults concurrently wore a wrist-worn Fitbit Charge 2 and a waist-worn ActiGraph wGT3X-BT during waking hours over 7 consecutive days. Log-linear regression models were fitted, and predictive fit via a one-out cross-validation was performed for each device between behavioral (steps, and light and moderate-to-vigorous physical activity) and cardiometabolic variables (body mass index, weight, body fat percentage, systolic and diastolic blood pressure, glycated haemoglobin, grip strength, estimated maximal oxygen uptake, and waist circumference). Results: Overall, step count was the most consistent predictor of cardiometabolic risk factors, with negative associations across both Fitbit and ActiGraph devices for body mass index (−0.017 vs. −0.020, p <.01), weight (−0.014 vs. −0.017, p <.05), body fat percentage (−0.021 vs. −0.022, p <.01), and waist circumference (−0.013 vs. −0.015, p <.01). Neither device was found to provide a consistently better prediction across all included cardiometabolic risk markers. Conclusions: Step count data from a commercial-grade wearable device showed similar associations and predictive relationships with cardiometabolic risk markers compared with a research-grade wearable device, providing preliminary support for their use in health research. [ABSTRACT FROM AUTHOR]

Abstract Background The number of steps by an individual, has traditionally been assessed with a pedometer, but increasingly with an accelerometer. The ActiLife software (AL) is the most common way to process accelerometer data to steps, but it is not open source which could aid understanding of measurement errors. The aim of this study was to compare assessment of steps from the open-source algorithm part of the GGIR package and two closed algorithms, AL normal (n) and low frequency extension (lfe) algorithms to Yamax pedometer, as reference. Free-living in healthy adults with a wide range of activity level was studied. Results A total 46 participants divided by activity level into a low-medium active group and a high active group, wore both an accelerometer and a pedometer for 14 days. In total 614 complete days were analyzed. A significant correlation between Yamax and all three algorithms was shown but all comparisons were significantly different with paired t-tests except for ALn vs Yamax. The mean bias shows that ALn slightly overestimated steps in the low-medium active group and slightly underestimated steps in high active group. The mean percentage error (MAPE) was 17% and 9% respectively. The ALlfe overestimated steps by approximately 6700/day in both groups and the MAPE was 88% in the low-medium active group and 43% in the high active group. The open-source algorithm underestimated steps with a systematic error related to activity level. The MAPE was 28% in the low-medium active group and 48% in the high active group. Conclusion The open-source algorithm captures steps fairly well in low-medium active individuals when comparing with Yamax pedometer, but did not show satisfactory results in more active individuals, indicating that it must be modified before implemented in population research. The AL algorithm without the low frequency extension measures similar number of steps as Yamax in free-living and is a useful alternative before a valid open-source algorithm is available.

Abstract Background Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. Methods Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. Results Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p

Although walking has proven efficacy for glycemic control, patients struggle to meet daily step goals. This secondary analysis investigated the effect of step count measurement rate on glycemic control. Patients with type 2 diabetes from eight hospitals in Japan participated in a 12-month randomized controlled trial. The intervention group received DialBetesPlus, a self-management support system that allowed patients to monitor step count using a pedometer. We divided the intervention group into two groups based on whether daily step count measurement rate (the percentage of days with pedometer use) increased or decreased during the last three months of the intervention (month 10-12), relative to the first three months of the intervention (month 1-3). Patients with a reduced measurement rate experienced a worsening in glycemic control, with between-group difference of 0.516% in the amount of change in HbA1c (p=0.012). We conclude that step count measurement may lead to a better glycemic profile. [ABSTRACT FROM AUTHOR]

While a higher level of physical activity (PA) is inversely associated with a higher breast cancer (BC) risk, the health benefits of daily steps on obesity-related BC biomarkers remain unclear. We aimed to understand the associations of changes in step counts with levels of five obesity-related BC biomarkers during a two-year follow-up. In total, 144 non-cancer women (47.96 ± 5.72) were observed on both 2019 and 2021. A structured questionnaire, daily steps and fasting blood samples were collected before (t0, 2019) and after (t1, 2021). Levels of biomarkers (IGF-binding proteins 3, adiponectin, soluble leptin receptor, C-reactive protein, and resistin) were assayed by ELISA. Participants were divided into persistent low steps, decreasing steps, increasing steps, and persistent high steps. Associations of categories on proposed biomarkers were estimated using linear regression models, with persistent low steps as reference. Associations between time-varying step counts with biomarkers were quantified using mixed linear models. Compared with persistent low steps, increasing steps is associated with a reduction in C-reactive protein level (β=−0.74, 95%CI=−1.23-−0.26, P-value = 2.98 × 10−3). An inverse association between time-varying step counts with C-reactive protein level was identified, consistent across different obesity types and baseline step level categories. No association with daily step counts was observed for other proteins. [ABSTRACT FROM AUTHOR]

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83–0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53–0.68 and 0.55, 95% CI 0.46–0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32–0.74). We have validated a digital susceptibility/risk biomarker—pfSTEP—that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm. [ABSTRACT FROM AUTHOR]

• A randomised pilot trial amongst adults with Cystic Fibrosis • Intervention consisted of wearable technology, goal setting and text message feedback • Active comparator group wore wearable technology only • Physical activity increased for both groups • Text messages based on collaborative goal setting showed significantly greater improvements Regular participation in physical activity (PA) is encouraged for people with Cystic Fibrosis (CF). This study aimed to assess the effectiveness of an intervention using wearable technology, goal setting and text message feedback on PA and health outcomes in people with CF. This was a pilot randomised trial conducted at University Hospital Limerick. Participants were randomly assigned to the intervention (INT) or active comparator (AC). The 12-week intervention consisted of wearable technology (Fitbit Charge 2) which was remotely monitored, and participants set step count goals. Participants were sent a one-way text message once a week over 12 weeks to positively reinforce and encourage PA participation. The AC group received the wearable technology alone. Follow up was assessed at 24 weeks. Outcomes assessed were PA, aerobic capacity, lung function, sleep, quality of life and wellbeing. Step count increased significantly for the INT group over 12 weeks when compared to the AC group (p=0.019). The INT group had a 28% week-to-week percentage change (Weeks 1-12), while the AC group reduced by 1%, p=0.023. Within group changes demonstrated that VO2 peak (ml/kg/min) significantly increased for the INT group at 12 weeks (24.4 ±7.65 to 26.13 ±7.79, p=0.003) but not at 24 weeks (24.45 ±7.05, p=0.776). There were no significant differences observed for VO2 peak (ml/kg/min) for the AC group. There was no significant effect on lung function, sleep, well-being, or quality of life for either group. A personalised PA intervention using wearable technology, goal setting and text message feedback increased PA and aerobic capacity in people with CF. Integration of this intervention into usual care may encourage regular PA participation for people with CF. [ABSTRACT FROM AUTHOR]

IntroductionMajor depressive disorder (MDD) is a common debilitating psychiatric condition and a major cause of productivity loss in workers. Using intermittent, subjective indicators, previous studies have shown that physical activity can predict lower levels of depressive symptoms. However, there is an unmet need for continuous and objective measures to identify MDD development before it results in productivity loss. The aim of this study was to elucidate the association between continuously measured walking activity and the development of MDD.MethodsThis retrospective, observational, longitudinal cohort study used health insurance claims data. Individuals aged 20–74 years were included if they had a record of MDD diagnosis and daily step count data for the 60 days before and after the first recorded MDD-related visit, which was defined as the index date. Multivariate analysis was conducted to compare 7-day moving averages of step counts on each day of the analysis period with the mean step count on the index date. Joinpoint regression analysis was used to determine when the trajectory of the moving step count average changed (inflection point).ResultsIn total, 2,143 patients with a mean age of 41.2 (standard deviation [SD]: 10.6) years were included. The majority of patients were men (69.5%) and employed full-time (94.1%). Antidepressants were prescribed for 59.2% of patients. The 7-day moving average step count decreased from 6,310 (SD: 3758) at day −60 to 5,879 (SD: 3183) at the index date (first recorded MDD-related visit), and then increased to 6,062 (SD: 4029) at day +60. Compared with the index date, the 7-day moving average of step counts was significantly higher at days −60 to −1, +23 to +33, and + 42 to +60, and significantly lower at days +2 and + 3. Joinpoint regression analysis of 7-day moving average step counts from day −60 to day 0 identified an inflection point at day −13.ConclusionIn working-age Japanese people, a formal diagnosis of MDD was preceded by a notable decline in daily step counts by approximately 2 weeks. MDD diagnosis and (presumed) treatment were followed by a gradual increase in daily step counts.

The COVID-19 outbreak resulted in the shutdown of athletic training facilities. Although the effects of these restrictions on daily activity and sleep patterns have been widely analyzed, the employed tools often lacked accuracy, and were based on subjective measures. This study assessed the effects of home confinement on objective physical activity (PA), physiological and sleep parameters in active individuals. Sixteen male elite fitness coaches (age: 29±3 years; height: 183±6 cm; body mass: 82±5 kg, body mass index: 24.7±1.8 kg/m2) participated in this retrospective study. One-way analysis of variance was conducted to analyze selected PA, physiological and sleep parameters collected by smartwatch (Garmin Fenix 6 pro, USA) data during four consecutive months [i.e., pre-confinement, 1st and 2nd months of confinement, and post-confinement, year 2020]. Ramadan intermittent fasting (RIF) month occurred during the 2nd month of confinement. Compared to pre-confinement, significant changes were registered for almost all parameters during the 1st and/or the 2nd month of confinements (p

Objective: To identify clusters of patients according to changes in their physical behaviour during and after cardiac rehabilitation, and to predict cluster membership. Methods: The study included 533 patients (mean age 57.9 years; 18.2% females) with a recent acute coronary syndrome who participated in a 12-week multi-disciplinary cardiac rehabilitation programme, within a cohort study design. Physical behaviour (light physical activity, moderate-to vigorous physical activity, step count, and sedentary behaviour) was measured using accelerometry at 4 timepoints. To identify clusters of patients according to changes in physical behaviour during and after cardiac rehabilitation, latent class trajectory modelling was applied. Baseline factors to predict cluster membership were assessed using multinomial logistic regression. Results: During and after cardiac rehabilitation, 3 separate clusters were identified for all 4 physical behaviour outcomes: patients with steady levels (comprising 68–83% of the patients), and improving (6–21%) or deteriorating (4–23%) levels. Main predictor for membership to a specific cluster was baseline physical behaviour. Patients with higher starting physical behaviour were more likely to be a member of clusters with deteriorating levels. Conclusion: Separate clusters of physical behaviour changes during and after cardiac rehabilitation could be identified. Clusters were mainly distinguished by baseline physical behaviour level. LAY ABSTRACT Physical behaviour is a construct including both physical activity and sedentary behaviour. Healthy levels of physical behaviour are important for cardiac patients. Cardiac rehabilitation programs are designed to promote a heart-healthy lifestyle for this group. Nevertheless, not all patients perform sufficient physical activity after cardiac rehabilitation. It is important to identify patients at risk for disappointing physical behaviour outcomes at an early stage to provide additional care. Outcomes of the current study show that cardiac patients can be clustered according to their change in physical behavior during and after cardiac rehabilitation. The majority showed steady levels and no improvements, but we could also identify groups of patients with improving and deteriorating levels. Patients with higher starting physical activity levels or low sedentary behaviour levels were more likely to be a member of clusters with deteriorating levels. These patients could benefit of additional interventions.

Objectives: Daily step counts are an easy-to-understand indicator of physical activity; however, there is limited evidence regarding the optimal daily step count to prevent sarcopenia. This study examined the dose-response relationship between daily step count and the prevalence of sarcopenia and explored the optimal dose. Design: Cross-sectional study. Setting and participants: The study included 7949 community-dwelling middle-aged and older adults (aged 45–74 years) from Japan. Measurements: Skeletal muscle mass (SMM) was assessed using bioelectrical impedance spectroscopy, and muscle strength was quantified through handgrip strength (HGS) measurement. Participants who exhibited both low HGS (men:

Maintaining physical activity is an important clinical goal for people with Parkinson's disease (PwPD). We investigated the validity of two commercial activity trackers (ATs) to measure daily step counts. We compared a wrist- and a hip-worn commercial AT against the research-grade Dynaport Movemonitor (DAM) during 14 days of daily use. Criterion validity was assessed in 28 PwPD and 30 healthy controls (HCs) by a 2 × 3 ANOVA and intraclass correlation coefficients (ICC2,1). The ability to measure daily step fluctuations compared to the DAM was studied by a 2 × 3 ANOVA and Kendall correlations. We also explored compliance and user-friendliness. Both the ATs and the DAM measured significantly fewer steps/day in PwPD compared to HCs (p < 0.01). Step counts derived from the ATs showed good to excellent agreement with the DAM in both groups (ICC2,1 > 0.83). Daily fluctuations were detected adequately by the ATs, showing moderate associations with DAM-rankings. While compliance was high overall, 22% of PwPD were disinclined to use the ATs after the study. Overall, we conclude that the ATs had sufficient agreement with the DAM for the purpose of promoting physical activity in mildly affected PwPD. However, further validation is needed before clinical use can be widely recommended. [ABSTRACT FROM AUTHOR]

Background: The number of steps by an individual, has traditionally been assessed with a pedometer, but increasingly with an accelerometer. The ActiLife software (AL) is the most common way to process accelerometer data to steps, but it is not open source which could aid understanding of measurement errors. The aim of this study was to compare assessment of steps from the open-source algorithm part of the GGIR package and two closed algorithms, AL normal (n) and low frequency extension (lfe) algorithms to Yamax pedometer, as reference. Free-living in healthy adults with a wide range of activity level was studied. Results: A total 46 participants divided by activity level into a low-medium active group and a high active group, wore both an accelerometer and a pedometer for 14 days. In total 614 complete days were analyzed. A significant correlation between Yamax and all three algorithms was shown but all comparisons were significantly different with paired t-tests except for ALn vs Yamax. The mean bias shows that ALn slightly overestimated steps in the low-medium active group and slightly underestimated steps in high active group. The mean percentage error (MAPE) was 17% and 9% respectively. The ALlfe overestimated steps by approximately 6700/day in both groups and the MAPE was 88% in the low-medium active group and 43% in the high active group. The open-source algorithm underestimated steps with a systematic error related to activity level. The MAPE was 28% in the low-medium active group and 48% in the high active group. Conclusion: The open-source algorithm captures steps fairly well in low-medium active individuals when comparing with Yamax pedometer, but did not show satisfactory results in more active individuals, indicating that it must be modified before implemented in population research. The AL algorithm without the low frequency extension measures similar number of steps as Yamax in free-living and is a useful alternative before a valid open-source algorithm is available. [ABSTRACT FROM AUTHOR]

Background: Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. Methods: Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. Results: Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (− 0.12 ± 0.28 MET), standing (− 0.27 ± 0.33 MET), fast walk (− 0.82 ± 1.92 MET) and very fast run (− 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: − 0.34-0.26; TEE: 362–494 kcal; AEE: − 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits. Conclusion: This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count. [ABSTRACT FROM AUTHOR]