Pedometer Research Articles

Modification of SMWT for remote heart’s functional capacity determination from oxygen consumption rate

Cardiovascular disease (CVD) is series of diseases affects blood circulation and heart which are fatal to human being. CVD patients recorded that every each of them must have at least one among the following behavioural risks: smoking, diabetes, obesity, hypertension, high cholesterol, and sedentary lifestyle. The diagnosis for CVD is commonly through cardiac stress test. It is a test where electrocardiogram will be used to monitor heart activity under induced stress such as walking on treadmill. However, the facilities for the cardiac stress test are very limited and increasing number of patients with potential CVD caused a long queue for an appointment. This situation makes it hard for healthy people living with behavioral risk wanting for their heart to be checked. Therefore, in this paper, we proposed to determine heart functional capacity which can be done anywhere and sufficient to access heart condition. For this proposal, despite using treadmill-test, Six-Minutes Walking Test (SMWT) is utilized. SMWT induces stress while the heart’s endurance parameters such as heart rate is extracted. This study developed an integrated sensor device which comprises of a sensor to detect step count calculate distance, and a pulse sensor that detect heart rate. Then, oxygen consumption rate during the SMWT, is computed. Metabolic equivalent of task (MET) is equal to the oxygen consumption rate during activity over resting oxygen consumption rate. Heart functional capacity classification is about exercise intensity and the MET of SMWT can be compared to the classification which allows this paper to determine heart functional capacity remotely using SMWT. Based on the results, the heart’s functional capacity classification after performing SMWT using sensor device is Class 1, the same when using manual calculation for distance and heart rate. The device sensor somehow produce error at 20.63% when calculating oxygen uptake. Nevertheless, this paper is identified that the SMWT is able to classify the heart’s functional capacity.

Understanding wrist skin temperature changes to hormone variations across the menstrual cycle

Consumer devices are increasingly used to monitor peripheral body temperature (PBT) for menstrual cycle tracking, but the link between PBT and hormone variations remains underexplored. This study examines the relationship between these variables with a focus on nightly wrist skin temperature (WST). Fifty participants provided physiological and self-reported data, including WST, daily step counts, glucose levels, hormone levels (E3G, LH), and diary entries. Results show a negative correlation between WST and hormone levels when E3G and LH are below average, and this trend was robust to demographics and self-reported stress. Increased variance between mid-cycle hormonal peaks and WST fluctuations may stem from differences between basal body temperature (BBT) and WST. This research suggests that algorithms reliant on body temperature for tracking hormonal changes or other aspects of the menstrual cycle may need to account for increased variance in WST trends if they are meant to be deployed on wearable devices.

Validation of steps countering recorded with a smartwatch.

Activity trackers are becoming increasingly popular in everyday life, sport and research. Accurate measurement of step counts is essential for understanding an individual's daily activity levels and promoting healthier lifestyles. As the sensor technology has become very sophisticated, such a device can be worn on the body for several hours or days without restricting the subject's daily activities. The aim of this study was to evaluate the accuracy of a commercially available wrist-worn pedometer in accurately recording step counts. A total of twenty-one subjects participated in this comparative study by running at various speeds in a laboratory setting while wearing a Garmin Forerunner 245 Music watch (Garmin Ltd, Schaffhausen, Switzerland) on each wrist. Simultaneously, their steps were recorded by a camera system. The step counts recorded by the pedometer were compared with the counts obtained by the camera system. Descriptive statistical analyses such as Pearson correlation and Bland-Altman plots were used to assess the accuracy and reliability of the pedometer. For most settings the results on the treadmills showed a high correlation between the pedometer and the camera system. On the step machine the results showed a low correlation between the pedometer and the camera system, the pedometer showed a systematic bias by consistently overestimating the number of steps. Bland-Altman plots showed that the results were within the limits of agreement. Although pedometers provide a convenient tool for monitoring step count, this study highlights some limitations and potential inaccuracies of a commercially available pedometer. Our study indicates that the Garmin watch is sufficiently accurate to be used as a measurement tool in a treadmill running environment.

Accuracy of smartwatches for the remote assessment of exercise capacity

Exercise capacity is a strong independent predictor of cardiovascular and all-cause mortality. The utilization of well-established submaximal tests of exercise capacity such as the 6-min walk test (6MWT), 3-min step test (3MST) and 10-chair rise test (10CRT) in the community would improve patient care but requires remote monitoring technology. Consumer grade smartwatches provide such an opportunity, however, their accuracy in measuring physiological responses to these tests is unclear. The aim of this study was to determine the accuracy of consumer grade smartwatches in assessing exercise capacity to develop a framework for remote, unsupervised testing. 16 healthy adults (7 male (44%), age median 27 [interquartile range (IQR) 26,29] years) performed 6MWTs using two protocols: (1) standard—straight 30 m laps (6MWT-standard) and 2) continuous lap—circular 240 m laps around a park (6MWT-continuous lap), 3MSTs and 10CRTs. Each one of these four tests was performed three times across two clinic visits. Each participant was fitted with a Garmin Vivoactive4 and Fitbit Sense smartwatch to measure three parameters: distance, step counts and heart rate (HR) response. Reference measures were a meter-wheel, hand tally counter and ECG, respectively. Mean HR was measured at rest, peak exercise and recovery. Agreement was measured using Bland–Altman analysis for repeated measures and summarized as median absolute percentage errors (MAPE). Distance during 6MWT-continuous lap had better agreement than during 6MWT-standard for both Garmin (MAPE: 6.4% [3.0, 10.4%] versus 20.1% [13.9, 28.4%], p < 0.001) and Fitbit (8.0% [2.9, 10.1% versus 18.8% [15.2, 28.1%], p < 0.001). Garmin measured step count more accurately than Fitbit (MAPE: 1.8% [0.9, 2.9%] versus 8.0% [2.6, 12.3%], p < 0.001). Irrespective of test, both devices showed excellent accuracy in measuring HR at rest and recovery (≤ 3%), while accuracy decreased during peak exercise (Fitbit: ~ 12% and Garmin: ~ 7%). In young adults without mobility difficulties, exercise capacity can be measured remotely using standardized tests and consumer grade smartwatches.

Feasibility of a problem-solving exercise program based on short physical performance battery for older patients with chronic respiratory diseases: A multicenter, pilot clinical trial

BackgroundThis study aimed to assess the feasibility and safety of a problem-solving exercise program based on the items in the short physical performance battery (SPPB) for older patients with chronic respiratory diseases (CRDs) to inform future randomized controlled trials. MethodsThis was a multicenter, prospective, non-randomized feasibility study. Participants with CRD received an enhancement program based on the SPPB decline items (balance, walk, and/or chair stand) for 4 weeks. The feasibility, safety, and efficacy of the problem-solving exercise program in improving the SPPB score, physical function, and step count (measured using a pedometer) were assessed. ResultsOverall, 36 patients were enrolled in this study, and adherence to the exercise program was high (100%). No exercise program-related adverse events were observed. The implementation of the exercise program ranged from 70 to 100%. The mean daily step count increased from 2152 ± 1498 steps during the first week to 2899 ± 1865 steps in the last week (p<0.01). Additionally, the SPPB total score increased from 8.9 ± 1.8 points to 10.7 ± 1.3 points at the end of the program (p<0.001). ConclusionsThe problem-solving exercise program based on SPPB is feasible and safe for older patients with CRDs. However, the effectiveness of this exercise program should be validated in large-scale, randomized-controlled trials in the future. Trial registrationUniversity Hospital Medical Information Network Center (UMIN-CTR) UMIN: approval number: UMIN000048761.

Association Between Accelerometer-Assessed Physical Activity and Cognitive Function in Older Adults: A Cross-Sectional Study

BackgroundResearch suggests that physical activity (PA) improves cognitive function across various domains. However, the specific role of different PA measures, including step count, remains to be explored. Our aim was to assess the correlation between objectively measured PA and cognitive function. MethodWe included 663 adults, aged ≥66 years, from the Swedish SNAC-K study (2016–2019). Global cognition and three cognitive domains (processing speed, executive function, and episodic memory) were assessed with validated tests. PA was measured through ActivPAL3 accelerometers. We applied age-stratified (<70 vs. ≥80 years), multi-adjusted, quantile regression to examine the cross-sectional associations between cognitive function and PA, considering steps/day and time spent in moderate-to-vigorous PA (MVPA). ResultsEach 1000-step increment (β = 0.04; 95% CI: 0.01, 0.07) and each additional hour of MVPA per day (β = 0.28; 95% CI: 0.02, 0.54) were correlated with better processing speed in the youngest-old, but not in the oldest-old. When further stratifying by MVPA (<60 min vs. ≥60 min/week), each 1000-step increment was associated with better processing speed in the youngest-old, regardless of their MVPA levels. ConclusionOur study links accelerometer-assessed PA (steps and MVPA) with better processing speed in the youngest-old adults. Step count correlated with processing speed regardless of intensity. Further research is needed to determine the directionality of these associations.

Determinant factors of sedentary time in individuals with COPD

BackgroundThe factors influencing sedentary time (ST) of individuals with chronic obstructive pulmonary disease (COPD) have not been thoroughly explored. AimTo identify determinant factors of ST in individuals with stable COPD. MethodsIndividuals with COPD had their ST and variables of physical activity (PA) cross-sectionally assessed during seven days with an activity monitor. Main variables were ST/day (<1.5 METs), steps/day, time/day in light PA (light PA/day, 1.5–2.9 METs) and in moderate-to-vigorous PA (MVPA/day, ≥3 METs). Additional assessments included 6-min walking test (6MWT), Medical Research Council (MRC) scale, lung function and body composition. Multiple linear regression models were built with variables correlating significantly with ST/day. Results50 individuals were analyzed (44 % males; 66 ± 8 years; FEV1 50 ± 19%pred). ST/day was 488 ± 160 min (61 ± 15 % of the day). 6MWT, MRC scale, MVPA/day, steps/day and light PA/day correlated significantly with ST expressed as minutes/day or as % of the day. In the multiple regression analyses, variables explaining the variance of ST in minutes/day were MRC scale (1 %) and light PA/day (53 %) (model R2 = .541, p < 0.001) and of ST in % of the day were steps/day (53 %) and light PA/day (46 %) (model R2 = .994, p < 0.001). ConclusionIn individuals with COPD, the variance in sedentary time can be explained by dyspnea in daily life, step count and specially time/day in light PA, which reinforces the increase in light PA (rather than necessarily MVPA) as a strategy to reduce sedentary time.

Automated, Real-Time Integration of Biometric Data From Wearable Devices With Electronic Medical Records: A Feasibility Study.

A major barrier to the incorporation of biometric data into clinical practice is the lack of device integration with electronic medical records (EMRs). We developed infrastructure to transmit biometric data from an Apple Watch into the EMR for physician review. The study objective was to test feasibility of using this infrastructure for patients undergoing radiotherapy. The study included patients with breast or prostate cancer receiving ≥3 weeks of radiotherapy who reported owning an Apple Watch. Daily resting heart rate (HR), HR variability, step count, and exercise minutes were automatically transferred to our EMR using a custom app installed on each patient's iPhone. Biometric data were presented to the treating radiation oncologist for review on a weekly basis during creation of the on-treatment note. Feasibility was defined a priori as physician review of biometric data for at least 90% of patients. Time trends in biometric data were tested using the Jonckheere-Terpstra test. Patient satisfaction was assessed using the System Usability Scale (SUS), with scores above 80 considered above-average user experience. Of the 20 patients enrolled, biometric data were successfully transmitted to the EMR and reviewed by the radiation oncologist for 95% (n = 19) of patients, thus meeting the a priori feasibility threshold. For patients with radiation courses ≥4 weeks, exercise minutes decreased over time (P = .01) and daily mean HR variability increased over time (P = .02). The median SUS was 82.5 (IQR, 70-87.5). Our study demonstrates the feasibility of real-time integration of biometric data collected from an Apple Watch into the EMR with subsequent physician review. The high rates of physician review and patient satisfaction provide support for further development of large-scale collection of wearable device data.

Association of Accelerometer-Measured Physical Activity With College Student Success and Retention.

This study examined the relationship between physical activity (PA) and academic performance and retention among college students using accelerometer data while controlling for sex and socioeconomic background. Data were collected from 4643 first-year college students at a private university in the south-central United States who began their studies in the Fall semesters between 2015 and 2022. Daily step counts were collected using accelerometers worn as part of the institutions PA requirements. The expected family contribution was calculated based on information provided on the Free Application for Federal Student Aid. Grade point average (GPA) and retention data were collected directly from official university databases. Female students generally had lower median age and steps count and a higher median GPA than males. The retention rates from fall to spring and fall to fall were 95.9% and 83.8%, respectively, with no significant difference in retention rates between males and females. GPA was significantly positively correlated with steps, expected family contribution, and age, and negatively correlated with being male and having an expected family contribution of zero. The fall to spring retention rate was significantly positively correlated with steps and GPA. The study's findings suggest that increasing levels of PA, as measured by daily step counts, may be effective in promoting academic performance and retention, even when controlling for sex and socioeconomic background. The use of device-based measures of PA in this study contributes to the literature on this topic, and policymakers and educational institutions should consider interventions focused on academic performance and physical activity to help students persist.

Relationship Between Step Counts And Body Composition In Middle-aged Women

Relationship Between Step Counts And Body Composition In Middle-aged Women

Tracking Well-Being: A Comprehensive Analysis of Physical Activity and Mental Health in College Students Across COVID-19 Phases Using Ecological Momentary Assessment.

Physical activity (PA) is a critical factor in maintaining mental health, particularly among college students who are vulnerable to stress, anxiety, and mood disturbances. The COVID-19 pandemic introduced unprecedented disruptions to daily routines. The purpose of this cohort study was to examine the longitudinal PA behaviors before and during COVID-19 using device-based assessment in a sample of college students. The participants were a convenience sample of 1-year college students from the University of Vermont Wellness Environment study. A daily survey was distributed to the participants every night on a study app measured three mental health outcomes of mood, anxiety, and stress for 16 weeks. Participants wore Apple Watches to monitor PA. A total of 167 participants completed at least 50% of daily surveys and had 20 days of valid Apple Watch data, which resulted in 11 387 participant-days' of observations. Changes in average daily moderate-to-vigorous physical activity (MVPA) and step counts were examined week-over-week from an 8-week period before COVID to an 8-week period during COVID using cluster-robust piecewise regression (16-weeks total). Linear mixed models examined the association between PA and mental health outcomes, while also examining the moderating influence of COVID phase. Significantly lower MVPA was observed from the end of pre-COVID to start of COVID by -18.2 min/day (p < 0.001) and significantly fewer steps/day was observed from end of pre-COVID to start of COVID by -3277 steps/day (p < 0.001). An MVPA "catch-up" effect was observed as there were small but positive week-over-week improvements during COVID for MVPA (b = 1.32 min/day, p < 0.001). The influence of COVID-19 phases had a notable impact on the relationships between PA/exercise and mental health outcomes. A discernible trend emerged, indicating stronger connections during the COVID period for anxiety and stress compared to the pre-COVID era. Interestingly, the moderating effect of COVID was opposite for mood and exercise. The COVID-19 pandemic led to a dramatic decline in PA among college students, coinciding with a period of heightened stress and anxiety. Despite a slight recovery in PA levels during the pandemic, the strengthened association between exercise and anxiety/stress during this time underscores the vital role of PA in promoting mental health. These findings highlight the importance of implementing behavior change strategies to maintain and promote student wellbeing.

Long-term Trends In Step Counts Among Non-working Adults: National Health And Nutrition Survey Japan 2001-2019

Long-term Trends In Step Counts Among Non-working Adults: National Health And Nutrition Survey Japan 2001-2019

The Effect Of Loss And Gain Incentives On Wearable Tracker Adherence And Step Count In Teens With Obesity

The Effect Of Loss And Gain Incentives On Wearable Tracker Adherence And Step Count In Teens With Obesity

Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part A: Fundamentals

The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel’s accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations.

Passive sensing of smartphone use, physical activity and sedentary behavior among adolescents and young adults during the COVID-19 pandemic.

The research that links excessive screen time to adverse health outcomes is based on self-reported screen use. Few studies have documented how passively-sensed smartphone and app use relate to health behaviors like activity and sitting. Furthermore, they have not considered that daily fluctuations in smartphone/app use may have different relationships to these behaviors than a person's usual smartphone/app use. This study evaluated whether physical activity or sedentary (sitting) behavior are associated with either smartphone screen time or specific smartphone app use by adolescents and young adults during the COVID-19 pandemic. Adolescents and young adults aged 13-29 years wore activPAL4 micro activity monitors while their smartphones logged daily screen time and app use durations for nine days. Data were collected in 2020-2021 and analyzed in 2022-2023. Participants (N = 125) had a mean (SD) age of 19.7 (4.3) years. Participants' usual smartphone screen time was negatively associated with daily step counts. Daily deviations in smartphone screen time were negatively associated with daily step counts and moderate-vigorous physical activity durations. Time spent on Instagram, YouTube and, to a lesser extent, TikTok were linked with reduced activity levels. Daily sedentary behavior was not associated with usual or daily screen time. Interventions to promote physical activity during the transition into adulthood may benefit from limiting excessive smartphone screen time. Specific intervention targets could include limiting use of apps with infinite scrolling feeds algorithmically tuned to maintain user engagement, such as Instagram, YouTube and TikTok.

Validity Evidence Of Step Count Cut-points To Determine The Moderate-to-vigorous Physical Activity Levels

Validity Evidence Of Step Count Cut-points To Determine The Moderate-to-vigorous Physical Activity Levels

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BackgroundWrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.MethodsWe developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.ResultsIn OPTIMISE cohort, STEPHEN’s estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06–0.25] vs. 0.23 [0.13–0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06–0.26] vs. 0.42[0.32–0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03–0.25] vs. 0.36[0.15–0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).ConclusionsSTEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/. The package includes trained models, but users have the flexibility to train their own models.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

The Adaptive Physical Activity Study for Stroke: A Feasibility Sequential Multiple Assignment Randomized Trial

Abstract Background Between 1990 and 2017, stroke cases nearly doubled, ranking as the second leading cause of death and disability globally. Inadequate physical activity (PA) is a leading risk factor. Recent guidelines advise stroke survivors to follow population-based PA guidelines. Given stroke heterogeneity personalised PA interventions are recommended. Therefore, this study used a Sequential Multiple-Assignment Randomized Trial (SMART) design to develop an adaptive mHealth intervention to improve PA post-stroke. Methods This SMART is in progress, with recruitment completed and follow-up data ending November 2024. A 12-week intervention programme was developed incorporating public patient involvement (PPI), empirical evidence and behavioural change theory to increase participation in Structured Exercise (SE), Lifestyle Physical Activity (LPA), or both. The study enrolled 54 ambulatory, community-dwelling people who were randomly assigned to either SE or LPA treatment groups. Participants used Fitbit Inspire 2 devices to monitor step counts and set personalized weekly step goals. At six weeks post-randomisation, participants were evaluated and classified as responders or non-responders based on their step count target achievement. Non-responders were re-randomized to switch treatments or augment their current regime with an additional intervention. Responders maintained their original treatment protocol. Primary outcomes address recruitment, retention and adherence rates. Secondary outcomes encompass overall health. Results Preliminary results include 54 participants (56% male) aged 51.58 (± 15.12) with Ischaemic (44%), Haemorrhagic (28%) or unknown (28%) stroke type at baseline. The mean weekly step was 7046 (±3797) while the mean Fatigue Severity Scale score was 38.02 (± 14.60). Participants had a mean Hospital Anxiety and Depression Scale score of 12.33 (± 6.98). Additionally, the mean Stroke Specific Quality of Life was 155.04 (58.84). Conclusion This is the first trial to use a SMART to design an adaptive intervention post-stroke internationally. Findings will inform the development of a PA intervention and subsequently will be evaluated against treatment as usual in a definitive trial.

Calculation and Comparison of Step Guidelines for Preschooler Physical Activity While Indoors.

Preschooler physical activity (PA) is vital for growth and development. The World Health Organization PA guidelines state preschoolers should achieve ≥ 180 min/day of Total PA (TPA) of which ≥ 60 min is moderate-to-vigorous PA (MVPA). A step/day recommendation to match these guidelines may be a more practical metric for caregivers to promote PA. The purpose of our investigation is to improve upon existing step recommendations by creating one for indoor-based activities, acknowledging where preschoolers spend most of their time, and various cut-points for PA. In a laboratory study (Aim 1), a validated direct observation protocol was used to quantify preschooler PA intensity while performing indoor activities (n = 35). Actigraph GT3x accelerometers were placed on the waist, wrist, and ankle to measure step count. In a field study (Aim 2), habitual PA of 881 preschoolers (7113 valid days) via waist-worn accelerometers was used to assess the agreement between created guidelines (Aim 1) and other step recommendations with PA guidelines using Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). In the laboratory study, calculated indoor step ranges were 6960-7440 (waist), 7200-8640 (wrist), and 7680-9120 (ankle) to align with existing PA guidance. In the field, the higher step guidelines (> 12 000) achieved very limited agreement, due to few preschoolers achieving this metric. The 6000 step/day guidelines had the highest agreement (AUCs: TPA: 0.637; MVPA: 0.751; TPA + MVPA: 0.761) with PA guideline. When preschoolers are indoors, 6000 steps may be the minimum for adequate PA.