Lower Step Count Research Articles

The impact of daily step count with mobile health apps on clinical outcomes among general population

Abstract Background/Introduction Numerous studies have investigated the correlation between daily step count and the reduction of all-cause mortality and cardiovascular events. However, the optimal daily step count for the general population remains controversial. Purpose This study aims to assess cardiovascular risk based on individual step counts using a large cohort that includes step count data from mHealth Apps (Kencom), health checkups databases, and Japanese insurance claims databases. Methods We analyzed data from 85,204 users registered with Kencom between January 2016 and September 2021. The mean step count during one year before and after baseline data collection was used to define each individual's step count. We divided participants into low (N=45,539) and high (N=39,665) step count groups based on their mean step counts. Additionally, we categorized users into five groups based on their step counts (<4000; 4000-5999; 6000-7999; 8000-9999; and ≥10000) to evaluate cardiovascular events. The primary outcome was the occurrence of cardiovascular events, including myocardial infarction, stroke, angina pectoris, heart failure, and atrial fibrillation, as classified by the International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). The cumulative incidence was estimated over 5 years. Results The high step count group was generally younger and predominantly male. No significant differences in cardiovascular risk factors such as hypertension, dyslipidemia, diabetes mellitus, and current smoking status were found between the two groups. Stratifying baseline characteristics by step count revealed significant differences among the five groups, with the exception of sex. The cumulative incidence of cardiovascular events was significantly lower in the high step count group compared to the low step count group (6.5% vs. 7.5%, log-rank P=0.005, Figure 1). After adjusting for confounding factors, the risk reduction of the high step count group remained significant (HR: 0.92, 95% CI: 0.87-0.98, P=0.005). Among the five step count groups, the incidence of primary outcome significantly differed, with only Group 5 (≥10000 steps) showing a significant reduction in the risk of cardiovascular events compared to Group 1 (<4000 steps) after adjustment (HR: 0.87, 95% CI: 0.77-0.99, P=0.03). Subgroup analyses based on sex indicated that the risk reduction was significant in males in Group 5 but not in females. Conclusion The risk of cardiovascular events was higher in the low step count group compared to the high step count group. After adjusting for confounders, only the highest step count group (≥10,000 steps/day) showed a significant reduction in cardiovascular event risk compared to the lowest step count group.

Physical activity and sedentary behavior among ambulatory children with cerebral palsy using accelerometer: a cross-sectional study.

Physical activity (PA) is paramount for childhood development and growth. However, children diagnosed with Cerebral Palsy (CP) were often considered sedentary, and their physical inactivity was associated with adverse health conditions and complications. Therefore, this study aimed to objectively describe and compare the PA levels and SB levels of children with and without CP of the same age group. It also studied the factors correlating with PA, SB, and step count per day in children with CP. A cross-sectional study using a wrist-worn accelerometer was conducted. PA and SB were measured over seven consecutive days. Eighty-five children aged 6-12 years, consisting of 41 children with CP and 44 TD children, participated in this study with a mean age of 9.18 ± 1.95 and 8.45 ± 1.78 years, respectively. According to the gross functional measures, 53.6% of children with CP were classified as first classification. A significant amount of time was spent in SB and Light PA (LPA) by children with CP compared to TD children, and no significant differences were observed in moderate PA (MPA) or step count. Gender mainly affected MPA as girls spent more time in MPA than boys. The age, height, and weight of children with CP correlate significantly with SB. As children's age, height, and weight increase, SB increases. Additionally, children with higher weights have lower step counts per day. This study showed that children with CP spend more time in LPA and SB than typically developed children. Therefore, concerted efforts are needed to encourage physical activity and reduce the sedentary lifestyle, to take into account the gender and anthropometric measures of children to enhance the quality of life among children with CP, and to consider gender and anthropometric measures of the children.

Patient reported outcome measures correlate with step-count in total hip arthroplasty.

Passive smartphone-based apps are becoming more common for measuring patient progress after total hip arthroplasty (THA). Optimum activity levels during early THA recovery have not been well documented. Correlations between step-count and patient reported outcome measures (PROMs) during early recovery were explored. This study also investigated how demographics impact step-count during early post-operative recovery. Smartphone captured step-count data from 666 THA patients was retrospectively reviewed. Mean age was 64 ± 11 years. 55% were female. Mean BMI was 29 ± 8kg/m2. Mean daily step-count was calculated for each patient over four time-windows: 60 days prior to surgery (preop), 42-49 days postop (6 weeks), 91-98 days postop (3 months), and 183-197 days postop (6 months). Spearman correlation coefficients and linear regression were used to assess the association between PROMs (HOOS-12, HOOS-Jr, and UCLA) and step-count. Patients were separated into three step-count levels: low (< 2500 steps/day), medium (2500-5500 steps/day), and high (> 5500 steps/day). Age > 65 years, BMI > 35 kg/m2, and sex were used for demographic comparisons. Post hoc analyses were performed using Welch's unequal variances t-tests, or Wilcoxon rank-sum tests, both with Bonferroni corrections, where appropriate when comparing between groups. Chi-squared analyses were also used when comparing categorical variables. UCLA correlated with step-count at all time-windows (p< 0.001). HOOS12-Function correlated with step-count preoperatively, at 6 weeks, and at 3 months (p< 0.001). High step-count individuals had improved UCLA scores compared to low step-count individuals preoperatively (Δ1.5, p< 0.001), at 6 weeks (Δ0.9, p< 0.001), at 3 months (Δ1.4, p< 0.001), and at 6 months (Δ1.4, p< 0.001). High step-count individuals had improved HOOS12-Function scores compared to low step-count individuals preoperatively (Δ9.6, p< 0.001), at 6 weeks (Δ5.3, p< 0.001), and at 3 months (Δ6.1, p< 0.001). Males had greater step-count at all time points (p< 0.001). Younger patients and low BMI patients had greater step-count across all time points (p< 0.001). High step-count improved PROMs scores compared to low step-count. Early post-operative step-count was significantly impacted by age, sex, and BMI. Generic recovery profiles may not be appropriate across diverse populations.

Effect of Perioperative Physical Activity on Skeletal Muscle Loss 6 Months After Esophageal Cancer Surgery.

Sarcopenia contributes to a poor prognosis in patients with esophageal cancer; thus, any clinical support that prevents loss of skeletal muscle mass preoperatively and postoperatively should be actively investigated. This study aimed to evaluate physical activity during the perioperative period and its impact on postoperative skeletal muscle mass. Sixty-two patients who underwent esophagostomy at the Hamamatsu University School of Medicine between 2019 and 2023 were evaluated. The physical activity (measured by the step count) of patients scheduled for esophagectomy was assessed preoperatively using a fitness tracker. The percentage change in skeletal muscle mass index (SMI) was calculated preoperatively and 6 months postoperatively. Factors associated with decreased SMI 6 months after esophagectomy were analyzed using multivariate analysis. The median decrease in SMI was -6.2%. Multivariate analysis revealed that factors associated with the reduction of SMI were age >69 years [odds ratio (OR)=7.21, 95% confidence interval (CI)=1.36-38.19, p=0.020], preoperative step count <7,800 steps/day (OR=5.17, 95% CI=1.38-19.33, p=0.015), and postoperative step count <2,400 steps/day (OR=3.55, 95% CI=1.01-12.45, p=0.048). A low perioperative step count and older age were significant risk factors for skeletal muscle loss in patients with esophageal cancer undergoing surgery. For patients with a low number of steps in the perioperative period or for older patients, interventions to increase the number of steps may prevent skeletal muscle loss.

A translationally informed approach to vital signs for psychiatry: a preliminary proof of concept

The nature of data obtainable from the commercial smartphone – bolstered by a translational model emphasizing the impact of social and physical zeitgebers on circadian rhythms and mood – offers the possibility of scalable and objective vital signs for major depression. Our objective was to explore associations between passively sensed behavioral smartphone data and repeatedly measured depressive symptoms to suggest which features could eventually lead towards vital signs for depression. We collected continuous behavioral data and bi-weekly depressive symptoms (PHQ-8) from 131 psychiatric outpatients with a lifetime DSM-5 diagnosis of depression and/or anxiety over a 16-week period. Using linear mixed-effects models, we related depressive symptoms to concurrent passively sensed behavioral summary features (mean and variability of sleep, activity, and social engagement metrics), considering both between- and within-person associations. Individuals with more variable wake-up times across the study reported higher depressive symptoms relative to individuals with less variable wake-up times (B [95% CI] = 1.53 [0.13, 2.93]). On a given week, having a lower step count (−0.16 [−0.32, −0.01]), slower walking rate (−1.46 [−2.60, −0.32]), lower normalized location entropy (−3.01 [−5.51, −0.52]), more time at home (0.05 [0.00, 0.10]), and lower distances traveled (−0.97 [−1.72, −0.22]), relative to one’s own typical levels, were each associated with higher depressive symptoms. With replication in larger samples and a clear understanding of how these components are best combined, a behavioral composite measure of depression could potentially offer the kinds of vital signs for psychiatric medicine that have proven invaluable to assessment and decision-making in physical medicine. Clinical Trials Registration: The data that form the basis of this report were collected as part of clinical trial number NCT03152864.

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wearing Fitbit Charge HR 2 devices in the Adolescent Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.

Low Daily Step Count Associated with Small Erector Spine Muscle Area and Sarcopenia in Idiopathic Pulmonary Fibrosis.

Objective The daily step count is associated with mortality in idiopathic pulmonary fibrosis (IPF). However, the factors associated with this phenomenon are not yet fully understood. We therefore clarified its association with clinical parameters. Methods Fifty-nine patients with IPF with available data for daily step counts; 6-minute walk distance (6MWD); chest, abdominal, and pelvic computed tomography (CT); pulmonary function; psychological evaluations; and sarcopenia assessments were prospectively enrolled. The daily step count was measured continuously for seven consecutive days. The cross-sectional areas of the erector spinae muscles at the level of the 12th vertebra (ESMCSA) and psoas major muscle volume (PMV) obtained by CT were assessed. Results The average age of the patients was 73.3±8.1 years old, and the percent predicted forced vital capacity was 81.6% ±15.8%. The average daily step count was 4,258 (2,155-6,991) steps. The average 6MWD, ESMCSA, and PMV were 413±97 m, 25.5±6.7 cm2, and 270±75.6 cm3, respectively. A linear regression analysis for daily step count showed that the ESMCSA and 6MWD were independent factors for the daily step count, whereas the PMV and skeletal muscle index were not. The daily step count, ESMCSA, and 6MWD were lower in patients with sarcopenia than in those without sarcopenia. Conclusions A lower daily step count was associated with a smaller erector spinae muscle area and sarcopenia in patients with IPF. Further studies are warranted to confirm the importance of physical therapy for muscle strengthening in patients with IPF.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Combined effects of sleep and objectively-measured daily physical activity on arterial stiffness in middle-aged and older adults

Although sleep quality and physical activity (PA) may influence on arterial stiffness, the combined effects of these two factors on arterial stiffness remain unknown. A total of 103 healthy middle-aged and older men and women (aged 50–83 years) with no history of cardiovascular disease and depression were included in this study. Arterial stiffness was measured using carotid-femoral pulse wave velocity (cfPWV), brachial-ankle PWV (baPWV), and femoral-ankle PWV (faPWV). Poor sleepers were defined as those with a Pittsburgh Sleep Quality Index score of >5.5. Using an accelerometer for seven consecutive days, low levels of PA were defined as low moderate-to-vigorous-intensity PA (MVPA) <19.0 min/day and low step counts <7100 steps/day, respectively. Poor sleepers with low PA levels, as determined by MVPA and daily steps, showed higher cfPWV, but not faPWV or baPWV, in middle-aged and older adults. Furthermore, in the analysis of covariance (ANCOVA) analyses adjusted for age, obesity, dyslipidemia, and sedentary behavior, the cfPWV result remained significant. Our study revealed that the coexistence of poor sleep quality and decreased PA (low MVPA or daily steps) might increase central arterial stiffness in middle-aged and older adults. Therefore, adequate sleep (good and sufficient sleep quality) and regular PA, especially at appropriate levels of MVPA (i.e., at least of 7100 steps/day), should be encouraged to decrease central arterial stiffness in middle-aged and older adults.

Association between doubly labelled water-calibrated energy intake and objectively measured physical activity with mortality risk in older adults

BackgroundPhysical activity or biomarker-calibrated energy intake (EI) alone is associated with mortality in older adults; the interaction relationship between the combined use of both factors and mortality has not been examined. We evaluated the relationship between mortality and calibrated EI and step counts in older adults.MethodsThis prospective study included 4,159 adults aged ≥65 years who participated in the Kyoto-Kameoka study in Japan and wore a triaxial accelerometer between 1 April and 15 November 2013. The calibrated EI was calculated based on a previously developed equation using EI biomarkers. The step count was obtained from the accelerometer ≥ 4 days. Participants were classified into the following four groups: low EI (LEI)/low step counts (LSC) group (EI: <2,400 kcal/day in men and <1,900 kcal/day in women; steps: <5,000 /day), n = 1,352; high EI (HEI)/LSC group (EI: ≥2,400 kcal/day in men and ≥1,900 kcal/day in women; steps: <5,000 /day), n = 1,586; LEI/high step counts (HSC) group (EI: <2,400 kcal/day in men and < 1,900 kcal/day in women; steps: ≥5,000 /day), n = 471; and HEI/HSC group (EI: ≥2,400 kcal/day in men and ≥1,900 kcal/day in women; steps: ≥5,000 /day), n = 750. Mortality-related data were collected until 30 November 2016. We performed a multivariable Cox proportional hazard analysis.ResultsThe median follow-up period was 3.38 years (14,046 person-years), and 111 mortalities were recorded. After adjusting for confounders, the HEI/HSC group had the lowest all-cause mortality rate compared to other groups (LEI/LSC: reference; HEI/LSC: hazard ratio [HR]: 0.71, 95% confidence interval [CI]: 0.41–1.23; LEI/HSC: HR: 0.59, 95% CI: 0.29–1.19; and HEI/HSC: HR: 0.10, 95% CI: 0.01–0.76). No significant interaction was observed between the calibrated EI and steps with mortality. The spline model showed that 35–42 kcal/100 steps/day of EI/100 steps was associated with the lowest mortality risk.ConclusionsHR mortality risk was lowest at 35–42 kcal/100 steps/day, suggesting that very high (≥56 kcal) or low (<28 kcal) EI/100 steps are not inversely associated with mortality. Adherence to optimal EI and adequate physical activity may provide sufficient energy balance to explain the inverse association with mortality among older Japanese adults.

GAIT AUTOMATICITY AND COMMUNITY MOBILITY OF OLDER ADULTS

Abstract Gait automaticity may decline with age-related impairments in the brain. As subcortical structures supporting gait automaticity decline, the prefrontal cortex (PFC) may provide compensatory function, allowing for maintenance of mobility but with loss of efficiency. Loss of efficiency may reduce ability to navigate complex community environments and restrict community mobility. We assessed the relation between PFC activation during walking in the laboratory with self-reported and objectively measured community mobility in participants aged 65+ (n=42, mean age=76, 60% female) from a randomized trial of a physical activity intervention to improve walking speed. PFC activation was measured by functional near-infrared spectroscopy as change from quiet standing to usual pace walking. Community mobility was measured objectively by 7-day global positioning system recordings of spatial (standard deviation ellipse area (SDEa), maximum distance from home) and temporal (percent time out of home (pTOH)) characteristics. Additionally, step count was recorded from 7-day actigraphy and the Life-Space Assessment (LSA) assessed self-reported mobility. Among participants with complete data at baseline (n=27), higher PFC activation during walking was associated with smaller SDEa (beta=-0.93 (-1.72, -0.15)), less pTOH (beta=-0.45 (-0.78, -0.12)), and lower step count (beta=-346 (-575, -118)) which persisted after adjusting for age, gender, education, or gait speed. PFC activation was not associated with maximum distance or LSA. There were no significant associations at the post-intervention visits (n=39). Greater PFC activation, likely indicating reduced gait automaticity, is related to lower spatial extent, duration, and intensity of community mobility. This association may be mitigated by participation in physical activity interventions.

Use of a wearable patch sensor for continuous activity and posture tracking to assess risk of early infection after transcatheter aortic valve replacement

Abstract Background Early infections after transcatheter aortic valve replacement (TAVR) represent one of the most common risk factors for non-cardiac 30-day mortality. Several wearable devices have been developed to measure vital signs and physical activity in real time while allowing full mobilization of the patient. However, their use has not been evaluated in a clinical setting with patients undergoing TAVR yet. Purpose We aimed to evaluate the feasibility of wearable patch sensors in assessing vital signs and daily activity before and after TAVR to predict an increased risk of early infections following TAVR. Methods Patients at high risk for infection undergoing transfemoral TAVR under local anesthesia were enrolled between September 2022 and January 2023. Postprocedural early infections were assessed, defined as C-reactive protein rise within 5 days after the procedure inducing antibiotic treatment. Vital signs (heart rate, respiratory rate, single-lead ECG) and activity (steps, posture) were monitored by a wearable patch sensor two days before (pre-TAVR) and two days after TAVR (post-TAVR). Results 54 patients completed 5 days of in-hospital monitoring with wearable patch sensors. The mean age was 80.3+5.2 years, 31 patients (57.4%) were male. Early infections occurred in 8 of 54 (14.8%) high-risk patients, of whom one was a urinary tract, one a pulmonary, and two access site-related etiology. Four infections remained with unknown focus. Respiratory rate was generally higher in patients developing an early infection after TAVR in both time periods, before (early infection 22±3 vs. no infection 18±2 bpm; p=0.001) and after TAVR (early infection 24±3 vs. no infection 19±3 bpm; p&amp;lt;0.001). Early infections were more prevalent in patients who showed less activity in the two days before TAVR regarding lower step count (early infection 242±360 vs. no infection 515±566; p=0.048) and high percentage of lying position during the whole day (early infection 89±12% vs. no infection 64±23%; p=0.071). Two days after TAVR, there were no significant differences in activity levels between both cohorts. Physical activity was reduced in both groups compared to the two days before TAVR, resulting in a reduced number of steps (pre-TAVR 473±546 vs. post-TAVR 267±434; p=0.039) and more daytime spent in lying position (pre-TAVR 57±19% vs. post-TAVR 73±24%; p&amp;lt;0.0001). Conclusion Wearable patch sensors are feasible to track vital signs and physical activity during hospital stay with promising opportunity for improving clinical management in TAVR patients. Early infections after TAVR are associated with decreased preprocedural mobility in high-risk patients. Therefore, these patients may be prematurely detected using wireless respiratory rate monitoring and infections prevented by intensified physiotherapy.

FeMFit: Feasibility of tracking the menstrual cycle using digital questionnaires and wearable devices

Abstract Introduction Despite its importance to female reproductive health and its impact on women's daily lives, the menstrual cycle and its effects on health remain poorly studied. Conventional clinical trials rely on infrequent in-person assessments. The aim of this study was to explore the technical feasibility of collecting longitudinal wearable and digital questionnaire data and its potential for gaining biological insights on the menstrual cycle. Methods This prospective observational cohort study was conducted online over twelve weeks using a digital study app and Fitbit inspire 2 devices. 42 individuals were recruited by their gynecologist in a practice in Berlin, Germany. Statistical analysis included descriptive statistics of user behavior and retention and comparative analyses of symptoms and sensor metrics at different phases of the menstrual cycle. Results We collected data of 114 ovulatory cycles from 33 participants, with nine out of 42 individuals dropping out within two weeks of study start. Participants reported a total of 2,468 symptoms during the luteal phase and menses. Despite difficulties with data completeness due to retention problems and drop-outs, the combined questionnaire and sensor data collection was technically feasible and produced interesting biological insights. Preliminary analysis of questionnaire and wearable data showed an increased heart rate in the mid and end luteal phase compared to menses and lower step count (mean 10,283 ±6,277) with strong symptoms of premenstrual syndrome (PMS, no/low PMS 11,694 ±6,458). Conclusions We demonstrate the feasibility of using an app-based approach for collecting combined wearable and questionnaire data on menstrual cycles. Dropouts showed that engagement efforts would need to be improved for larger studies. Despite the challenges faced in collecting wearable data on consecutive days, the collected data provided valuable biological insights that should motivate further research at large scale. Key messages • Using an app-based approach for collecting combined wearable and questionnaire data on menstrual cycles was technically feasible and delivered interesting biological insights. • User engagement efforts need to be improved to reduce drop-outs and ensure consecutive data collection.

Digital Tracking of Patients Undergoing Radical Cystectomy for Bladder Cancer: Daily Step Counts Before and After Surgery Within the iROC Randomised Controlled Trial

BackgroundEfforts to improve recovery after radical cystectomy (RC) are needed. ObjectiveTo investigate wrist-worn wearable activity trackers in RC participants. Design, setting, and participantsAn observational cohort study was conducted within the iROC randomised trial. InterventionPatients undergoing RC at nine cancer centres wore wrist-based trackers for 7 days (d) at intervals before and after surgery. Outcome measurements and statistical analysisStep counts were compared with participant and operative features, and recovery outcomes. Results and limitationsOf 308 participants, 284 (92.2%) returned digital activity data at baseline (median 17 d [interquartile range: 8–32] before RC), and postoperatively (5 [5–6] d) and at weeks 5 (43 [38–43] d), 12 (94 [87–106] d), and 26 (192 [181–205] d) after RC. Compliance was affected by the time from surgery and a coronavirus disease 2019 pandemic lockdown (return rates fell to 0–7%, chi-square p < 0.001). Step counts dropped after surgery (mean of 28% of baseline), before recovering at 5 weeks (wk) (71% of baseline) and 12 wk (95% of baseline; all analysis of variance [ANOVA] p < 0.001). Baseline step counts were not associated with postoperative recovery or death. Patients with extended hospital stays had reduced postoperative step counts, with a difference of 2.2 d (95% confidence interval: 0.856–3.482 d) between the lowest third and highest two-third tertiles (linear regression analysis; p < 0.001). Additionally, they spent less time out of the hospital within 90 d of RC (80.3 vs 74.3 d, p = 0.013). Lower step counts at 5, 12, and 26 wk were seen in those seeking medical help and needing readmission (ANOVA p ≤ 0.002). ConclusionsBaseline step counts were not associated with recovery. Lower postoperative step counts were associated with longer length of stay at the hospital and postdischarge readmissions. Studies are required to determine whether low step counts can identify patients at a risk of developing complications. Patient summaryPostoperative step counts appear to be a promising tool to identify patients in the community needing medical help or readmission. More work is needed to understand which measures are most useful and how best to collect these.

A Comparison of Objectively Measured Free-Living Physical Behaviour in Adults with and without Lower Limb Amputation.

57 adults with LLA wore an activPAL3™ for 8 days. A comparison data set (n = 57) matched on gender, age and employment status was used. Variables included: time sitting; standing; stepping; sit-to-stand transitions; step count and cadence. Comparisons were made between adults with and without LLA and between gender, level and cause of amputation. Participants with LLA due to trauma versus circulatory causes were less sedentary and more active; however, no difference in physical behaviour was recorded across gender or level of amputation. Participants with LLA spent more time sitting (p < 0.001), less time standing and stepping (p < 0.001) and had a lower step count (p < 0.001). Participants with LLA took more steps in cadence bands less than 100 steps·min-1 and fewer steps in cadence bands greater than 100 steps·min-1 compared to participants without LLA. People with LLA were less active and more sedentary than people without LLA and participated in less activity at a moderate or higher intensity when matched on age, gender and employment. Interventions are needed to promote active lifestyles in this population.

Effect of diabetes mellitus on physical activity in patients with knee osteoarthritis: A cross-sectional study.

Knee osteoarthritis (OA) may be comorbid with diabetes mellitus (DM), and physical activity is a recommended lifestyle strategy for both diseases. The present study investigated the physical activity differences by intensity between knee OA patients with or without DM, and evaluated if physical activity was associated with the presence of DM in knee OA patients. A total of 183 patients (mean age 74.9 ± 6.4years) with moderate-to-severe knee OA underwent evaluation of knee function (i.e., knee flexion/extension range-of-motion, knee-extension muscle strength, and knee pain), the timed up-and-go (TUG) test, and physical activity measurement using an accelerometer. Physical activity by intensity was compared between knee OA patients with and without DM. The association between physical activity, including knee function and the TUG test time, and DM was assessed. The 2 groups (with or without DM) did not differ significantly in knee OA severity or age. Compared to knee OA patients without DM, knee OA patients with DM had a significantly lower average daily step count (p < 0.001), and significantly shorter times spent performing light-intensity physical activity (LPA; p < 0.001) and moderate-to-vigorous-intensity physical activity (MVPA; p = 0.006). After adjusting for age, sex, and body mass index, we found that a lower average daily step count and shorter LPA time significantly correlated with DM (β = -0.200, p = 0.006; β = -0.216, p = 0.004, respectively) and a longer TUG test time (β = -0.196, p = 0.014; β = -0.208, p = 0.011, respectively). A shorter MVPA time significantly correlated with lower contralateral knee-extension muscle strength (β = 0.187, p = 0.032). Knee OA patients with DM had significantly lower physical activity levels than those without DM. Furthermore, the presence of DM correlated with a lower step count and a shorter LPA time in knee OA patients.

COVID-19 Containment Measures—a Step Back for Walking Mobility? A 2-Year, 60-Country Analysis of the Apple Mobility Data

COVID-19 containment measures curb viral spread but may hamper walking mobility. As a low daily step count is associated with increased noncommunicable diseases and mortality, assessing the relationship between pandemic responses and walking mobility can help trade-off public health measures. We investigated the association between containment stringency and walking mobility across 60 countries in the period between January 21, 2020 and January 21, 2022 and modeled how this could impact mortality hazard. Walking mobility was measured through the Apple Mobility Trends, containment measures stringency index through the Oxford COVID-19 response tracker (which considers local policies on closures, healthcare, and economy), and meteorological data by National Oceanic and Atmospheric Administration weather stations. Walking mobility was regressed over stringency in a mixed-effect model with weather variables as covariates. The impact of stringency on all-cause mortality due to reduced mobility was modeled based on regression results, prepandemic walking mobility, and the association between step count and all-cause mortality hazard. Across the 60 countries, the average stringency was 55 (9) (mean [SD]) out of 100. Stringency was negatively associated with walking mobility; a log-linear model fitted data better than a linear one, with a regression coefficient for stringency on ln (walking mobility) (95% confidence interval) of -1.201 × 10-2 (-1.221 × 10-2 to -1.183 × 10-2). Increasing stringency, thus decreasing walking mobility, nonlinearly incremented the modeled all-cause mortality hazard by up to ∼40%. In this study, walking mobility was negatively associated with containment measures stringency; the relationship between stringency, mobility, and the subsequent impact on health outcomes may be nonlinear. These findings can help in balancing pandemic containment policies.

Outdoor Kindergartens: A Structural Way to Improve Early Physical Activity Behaviour?

Studies have shown that outdoor play in nature is associated with a higher physical activity level than indoor play. We aimed to examine the effect of outdoor versus conventional kindergartens on objectively measured physical activity. Using a pre-test-post-test design, we collected data in four kindergartens that provided a rotating outdoor and conventional kindergarten setting. Step counts were measured during one week in the outdoor setting and one week in the conventional setting. Differences in step counts between the outdoor and conventional setting were analysed using a paired t-test. In total, 74 children were included. There was no statistically significant difference in total daily step counts between children in the two settings. When we looked at step counts during kindergarten hours, we saw that children were more physically active in the outdoor setting compared to the conventional setting (mean difference: 1089, p < 0.0001). When we looked at activity during time outside the kindergarten, we discovered that children had a lower step count in the outdoor setting as compared to the conventional setting (mean difference -652, p = 0.01). This study indicates that children are more physically active during the time they spend in outdoor kindergartens compared to conventional kindergartens, but may compensate with more inactivity outside kindergarten hours.

Association of Physical Activity and Screen Time With Body Mass Index Among US Adolescents

The Physical Activity Guidelines Advisory Committee Scientific Report identified important research gaps to inform future guidance for adolescents, including limited evidence on the importance of sedentary behaviors (screen time) and their interactions with physical activity for adolescent health outcomes, including overweight and obesity. To identify the independent associations of physical activity and screen time categories, and the interactions between physical activity and screen time categories, with body mass index (BMI) and overweight and obesity in adolescents. This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) Study collected from September 10, 2018, to September 29, 2020. Data were analyzed from July 8 to December 20, 2022. A total of 5797 adolescents aged 10 to 14 years from 21 racially and ethnically diverse study sites across the US were included in the analysis. Categories of total step count per day (with 1000 to 6000 steps per day indicating low, >6000 to 12 000 steps per day indicating medium, and >12 000 steps per day indicating high), as measured by a wearable digital device (Fitbit), and categories of self-reported screen time hours per day (with 0 to 4 hours per day indicating low, >4 to 8 hours per day indicating medium, and >8 hours per day indicating high). Participant BMI was calculated as weight in kilograms divided by height in meters squared and converted into sex- and age-specific percentiles in accordance with the Centers for Disease Control and Prevention growth curves and definitions. Individuals were classified as having overweight or obesity if their BMI was in the 85th percentile or higher for sex and age. Among 5797 adolescents included in the analytic sample, 50.4% were male, 61.0% were White, 35.0% had overweight or obesity, and the mean (SD) age was 12.0 (0.6) years. Mean (SD) reported screen time use was 6.5 (5.4) hours per day, and mean (SD) overall step count was 9246.6 (3111.3) steps per day. In models including both screen time and step count, medium (risk ratio [RR], 1.24; 95% CI, 1.12-1.37) and high (RR, 1.29; 95% CI, 1.16-1.44) screen time categories were associated with higher overweight or obesity risk compared with the low screen time category. Medium (RR, 1.19; 95% CI, 1.06-1.35) and low (RR, 1.30; 95% CI, 1.11-1.51) step count categories were associated with higher overweight or obesity risk compared with the high step count category. Evidence of effect modification between screen time and step count was observed for BMI percentile. For instance, among adolescents with low screen use, medium step count was associated with a 1.55 higher BMI percentile, and low step count was associated with a 7.48 higher BMI percentile. However, among those with high screen use, step count categories did not significantly change the association with higher BMI percentile (low step count: 8.79 higher BMI percentile; medium step count: 8.76 higher BMI percentile; high step count: 8.26 higher BMI percentile). In this cross-sectional study, a combination of low screen time and high step count was associated with lower BMI percentile in adolescents. These results suggest that high step count may not offset higher overweight or obesity risk for adolescents with high screen time, and low screen time may not offset higher overweight or obesity risk for adolescents with low step count. These findings addressed several research gaps identified by the Physical Activity Guidelines Advisory Committee Scientific Report and may be used to inform future screen time and physical activity guidance for adolescents.

SMARTPHONE-BASED STEP-COUNT MEASURES CORRELATE WITH HOOS-12 FUNCTION AND UCLA ACTIVITY PROMS DURING EARLY THA RECOVERY

Smartphone-based apps that measure step-count and patient reported outcomes (PROMs) are being increasingly used to quantify recovery in total hip arthroplasty (THA). However, optimum patient-specific activity level before and during THA early-recovery is not well characterised. This study investigated 1) correlations between step-count and PROMs and 2) how patient demographics impact step-count preoperatively and during early postoperative recovery.Smartphone step-count and PROM data from 554 THA patients was retrospectively reviewed. Mean age was 64±10yr, BMI was 29±13kg/m2, 56% were female. Mean daily step count was calculated over three time-windows: 60 days prior to surgery (preop), 5-6 weeks postop (6wk), and 11-12 weeks postop (12wk).Linear correlations between step-count and HOOS12 Function and UCLA activity scores were performed. Patients were separated into three step-count levels: low (&lt;2500steps/day), medium (2500-5500steps/day), and high (&gt;5500steps/day). Age &gt;65years, BMI &gt;30, and sex were used for demographic comparisons.Student's t-tests determined significant differences in mean step-counts between demographic groups and in mean PROMs between step-count groups.UCLA correlated with step-count at all time-windows (p&lt;0.01). HOOS12 Function correlated with step-count preoperatively and at 6wk (p&lt;0.01). High vs low step count individuals had improved UCLA scores preoperatively (∆1.8,p&lt;0.001), at 6wk (∆1.1,p&lt;0.05), and 12wk (∆1.6,p&lt;0.01), and improved HOOS12 Function scores preoperatively (∆8.4,p&lt;0.05) and at 6wk (∆8.8,p&lt;0.001).Younger patients had greater step-count preoperatively (4.1±3.0k vs 3.0±2.5k, p&lt;0.01) and at 12wk (5.1±3.3k vs 3.6±2.9k, p&lt;0.01). Males had greater step-count preoperatively (4.1±3.0k vs. 3.0±2.7k, p&lt;0.001), at 6wk (4.5±3.2k vs 2.6±2.5k, p&lt;0.001), and at 12wk (5.2±3.6k vs. 3.4±2.5k, p&lt;0.001). Low BMI patients had greater step-count at 6wk (4.3±3.3k vs. 2.6±2.7k, p&lt;0.01) and 12wk (5.0±3.6k vs. 3.6±2.6k, p&lt;0.05).Daily step-count is significantly impacted by patient demographics and correlates with PROMs, where patients with high step count exhibit improved PROMs. Generic recovery profiles may therefore not be appropriate for benchmarking across diverse populations.