Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior.

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18-59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images.

The impact of the COVID-19 pandemic on physical activity and sedentary behavior during pregnancy: a prospective study.

BACKGROUND: Prior studies evaluating the impact of the COVID-19 pandemic on pregnancy physical activity (PA) have largely been limited to internet-based surveys not validated for use in pregnancy. METHODS: This study used data from the Pregnancy PA Questionnaire Validation study conducted from 2019-2021. A prospective cohort of 50 pregnant women completed the Pregnancy PA Questionnaire (PPAQ), validated for use in pregnancy, in early, mid, and late pregnancy and wore an ActiGraph GT3X-BT for seven days. COVID-19 impact was defined using a fixed date of onset (March 13, 2020) and a self-reported date. Multivariable linear mixed effects regression models adjusted for age, early pregnancy BMI, gestational age, and parity. RESULTS: Higher sedentary behavior (14.2 MET-hrs/wk, 95% CI: 2.3, 26.0) and household/caregiving PA (34.4 MET-hrs/wk, 95% CI: 8.5, 60.3 and 25.9 MET-hrs/wk, 95% CI: 0.9, 50.9) and lower locomotion (-8.0 h/wk, 95% CI: -15.7, -0.3) and occupational PA (-34.5 MET-hrs/wk, 95% CI: -61.9, -7.0 and -30.6 MET-hrs/wk, 95% CI: -51.4, -9.8) was observed in middle and late pregnancy, respectively, after COVID-19 vs. before. There was no impact on steps/day or meeting American College of Obstetricians and Gynecologists guidelines. CONCLUSIONS: Proactive approaches for the promotion of pregnancy PA during pandemic-related restrictions are critically needed.

Self-management processes, sedentary behavior, physical activity and dietary self-management behaviors: impact on muscle outcomes in continuing care retirement community residents.

BACKGROUND: Despite the known benefits of non-sedentary behavior, physical activity, and protein and caloric intake to health and muscle mass, strength, and function, many older adults do not meet physical activity and dietary recommendations. A better understanding of the factors associated with sedentary behavior, physical activity and dietary self-management behaviors, and muscle outcomes (muscle mass, strength, and function) is needed, particularly among continuing care retirement community residents. The objective of this study was to examine the factors associated with sedentary behavior, physical activity and dietary self-management behaviors, and muscle outcomes among continuing care retirement community residents. It also aimed to determine whether sedentary behavior and physical activity and dietary self-management behaviors mediate the relationships between self-efficacy, goal congruence, aging expectations, social support, and muscle outcomes. METHODS: A sample of 105 continuing care retirement community residents (age > 70 years) participated in this correlational, cross-sectional study. Questionnaires on pain, self-efficacy, goal congruence, aging expectation, social support, and daily protein and caloric intake were administered. Physical activity and sedentary behavior (ActiGraph wGT3X-BT), muscle mass (ImpediMed SFB7), muscle strength (Jamar Smart Digital Hand Dynamometer), and muscle function (Short Physical Performance Battery) were measured. Multiple regression, logistic regression, and mediation analyses were performed. RESULTS: Low goal congruence predicted engagement in sedentary behavior and light physical activity. Higher levels of self-efficacy and social support were associated with increased likelihoods of achieving greater moderate physical activity and meeting daily recommendations for caloric intake, respectively. Self-efficacy and goal congruence predicted muscle function and strength. Moreover, sedentary behavior and achieving greater moderate physical activity were found to partially but significantly mediate the relationship between self-efficacy and muscle function. CONCLUSION: Future research should evaluate whether attempts to reduce sedentary behavior and promote physical activity and dietary self-management behaviors and muscle outcomes are more successful when modifications to the self-management process factors are also targeted.

Pacemaker detected active minutes are superior to pedometer-based step counts in measuring the response to physical activity counseling in sedentary older adults.

BACKGROUND: In patients with permanent pacemakers (PPM), physical activity (PA) can be monitored using embedded accelerometers to measure pacemaker detected active hours (PDAH), a strong predictor of mortality. We examined the impact of a PA Counseling (PAC) intervention on increasing activity as measured by PDAH and daily step counts. METHODS: Thirteen patients (average age 80 ± 6 years, 84.6% women) with implanted Medtronic PPMs with a ≤ 2 PDAH daily average were included in this study. Patients were randomized to Usual Care (UC, N = 6) or a Physical Activity Counseling Intervention (PACI, N = 7) groups. Step count and PDAH data were obtained at baseline, following a 12-week intervention, then 12 weeks after intervention completion. Data were analyzed using independent t-tests, Pearson's r, chi-square, and general linear models for repeated measures. RESULTS: PDAH significantly differed by time point for all subject combined (P = 0.01) but not by study group. Subjects with baseline gait speeds of > 0.8 m/sec were responsible for the increases in PDAH observed. Step counts did not differ over time in the entire cohort or by study group. Step count and PDAH significantly correlated at baseline (r = 0.60, P = 0.03). This correlation disappeared by week 12. CONCLUSION(S): PDAH can be used to monitor PA and PA interventions and may be superior to hip-worn pedometers in detecting activity. A significant increase in PA, regardless of treatment group, suggests that patient awareness of the ability to monitor PA through a PPM increases PA in these patients, particularly in patients with gait speeds of < 0.8 m/sec. TRIAL REGISTRATION: ClincalTrials.gov NCT03052829. Date of Registration: 2/14/2017.

Acceptance and Feasibility of Seated Elliptical Pedaling to Replace Sedentary Behavior in Older Adults.

The purpose of this study was to explore the feasibility and acceptability of a seated pedaling device to reduce sedentary behavior (SB) in the homes of older adults. METHODS: Each participant (N = 20) was outfitted with an activity monitor and seated pedaling device in the home for 7 days and randomly assigned to one of four light-intensity pedaling groups (15, 30, 45, and 60 min/day). RESULTS: There was 100% adherence in all groups and significant group differences in the minutes pedaled per day (p < .001), with no significant difference in the total pedaling days completed (p = .241). The 15-, 30-, 45-, and 60-min groups experienced a 4.0%, 5.4%, 10.6%, and 11.3% reduction in SB on the days pedaled, respectively. CONCLUSION: Clinically relevant reductions in SB time were achievable in this 1-week trial. Long-term adherence and the impact of replacing SB with seated light activities on geriatric-relevant health outcomes should be investigated.

A Framework to Evaluate Devices That Assess Physical Behavior.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

Wearables for Promoting Physical Activity.

BACKGROUND: The rapid expansion and popularity of consumer-wearable physical activity monitors (WPAMs) has enabled the integration of technology into physical activity (PA) intervention, deployment, and evaluation. This brief review reports on the accuracy of consumer-WPAMs, considers the intervention effects of using consumer-WPAMs, and offers future considerations as the proliferation of this area of product development and consumer use continues to escalate. CONTENT: The studies reviewed document the utility for consumer-WPAMs to objectively assess PA, with output metrics similar to research-grade activity monitors. Early intervention efficacy for the use of consumer-WPAMs to increase PA holds considerable promise. Substantial increases in moderate- to vigorous-intensity PA (MVPA) have been reported across different research study designs and populations in which consumer-WPAMs have been used in isolation or in conjunction with other behavioral change strategies. The utility of consumer-WPAMs is currently being investigated in clinical populations, notably showing increases in PA in individuals at risk for cancer or post cancer survivors, in those with chronic obstructive pulmonary disease, and in postsurgical patients. There has been a proliferation of registered trials at clinicaltrials.gov, and an increase of disseminated works regarding the use of consumer-WPAMs is expected. SUMMARY: There are many research studies documenting the validity and intervention effectiveness of consumer-WPAMs; evidence is emerging on the health benefits linked to use of such devices. Future work on the long-term effects of consumer-WPAMs on behavior and health is warranted, and prospects appear exciting as wearable technology advances and adoption increases.

Increasing the Energy Expenditure of Seated Activities in Older Adults with a Portable Elliptical Device.

BACKGROUND: The ill-health effects of sedentary behavior are becoming well-documented, yet older adults spend 70-80% of waking hours sedentary. PURPOSE: To determine if a portable elliptical device increases energy expenditure (EE) while performing popular seated activities. METHODS: Twenty older adults (68.1 ± 1.4 years) participated to compare the measured EE between seated rest and three randomized seated pedaling activities: computer use, reading, TV viewing. Each pedaling activity included 5-min of self-selected paced/no resistance (SSP) and externally paced/added resistance pedaling (Paced). RESULTS: A significant increase in EE existed during SSP (+1.44 ± 0.12 kcal/min) and Paced (+2.19 ± 0.09 kcal/min) pedaling relative to Seated Rest (p < .001). EE during the Paced activities was significantly greater than all SSP activities (p <.01). CONCLUSION: Extrapolating these results, pedaling at a SSP for an hour while performing seated activities is equivalent to the net EE of walking 1.6 miles. Future home-based effectiveness and feasibility should be explored.

Translating exercise interventions to an in-home setting for seniors: preliminary impact on physical activity and function.

AIMS: The purpose of this study is to investigate whether an in-home, individually tailored intervention is efficacious in promoting increases in physical activity (PA) and improvements in physical functioning (PF) in low-active older adults. METHODS: Participants were randomized to two groups for the 8-week intervention. The enhanced physical activity (EPA) group received individualized exercise programming, including personalized step goals and a resistance band training program, and the standard of care (SoC) group received a general activity goal. Pre- and post-intervention PF measures included choice step reaction time, knee extension/flexion strength, hand grip strength, and 8 ft up and go test completion time. RESULTS: Thirty-nine subjects completed this study (74.6 ± 6.4 years). Significant increases in steps/day were observed for both the EPA and SoC groups, although the improvements in the EPA group were significantly higher when including only those who adhered to weekly step goals. Both groups experienced significant PF improvements, albeit greater in the EPA group for the 8 ft up and go test and knee extension strength. CONCLUSION: A low cost, in-home intervention elicited improvements in both PA and PF. Future research is warranted to expand upon the size and scope of this study, exploring dose thresholds (and time frames) for PA to improve PF and strategies to further bolster adherence rates to maximize intervention benefits.

Accuracy of Direct Observation to Assess Physical Activity in Older Adults.

The purpose of the study was to evaluate the accuracy of direct observation (DO) to estimate MET level and intensity category during laboratory-based and free-living activity in older adults. Older adults engaged in unstructured laboratory and free-living activity. Participants wore a portable metabolic system to measure energy expenditure and were directly observed. DO recorded MET-level point estimates. 32,401 in-laboratory and 87,715 free-living data points (9 participants, 67% male, 71.0 ± 6.9 years, 27.1 ± 4.3 kg·m-2) were included in final analysis. Results revealed 45.4% of in-laboratory and 61.1% of free-living mean DO activities fell within 0.5 METs of the measured MET values. DO accurately classified intensity category 45.0% of the time in-laboratory and 50.9% of free-living observations. DO-estimated activity cost resulted in low point estimate accuracy however there was low variability between the mean measured and estimated METs. This suggests, dependent on the desired outcome, DO could provide a viable option for activity assessment, however, the low point estimate accuracy presents a need for further research to continue to refine the approach to increase accuracy.

Acceptability and Feasibility of Physical Activity Assessment Methods for an Appalachian Population.

Nowhere is improving understanding and accurate assessment of physical activity more important for disease prevention and health promotion than among health disparities populations such as those residing in rural and Appalachian regions. To enhance accurate assessment of physical activity and potentially improve intervention capacity, we conducted a mixed-methods study examining the acceptability and feasibility of self-report physical activity questionnaires, pedometers, and accelerometers among rural Appalachian children, adolescents, and adults. Most participants reported positive experiences with all three physical activity assessment tools. Several acceptability ratings differed by age group and by sex within each age group. With very few exceptions, no significant differences in acceptability were found by race, education, employment status, health status, BMI categories, income levels, or insurance status within age groups or overall. Several factors may impact the choice of the physical activity assessment method, including target population age, equipment cost, researcher burden, and potential influence on physical activity levels. Children and adolescents appear to have more constraints on when they can wear pedometers and accelerometers. While pedometers are inexpensive and convenient, they may influence physical activity levels, rather than simply measure them. Accelerometers, while less influential on behavior, consume extensive resources, including high purchase costs and researcher burden.

Dose-response walking activity and physical function in older adults.

The aim of this study was to examine the dose-response relationship between walking activity and physical function (PF) in community-dwelling older adults. Physical activity (PA, pedometry) and PF (self-report [SF-36] and 6-minute walk test [6MWT]) were assessed in 836 individuals. Accumulated PA was categorized into four groups (1 = ≤ 2,500; 2 = 2,501-5,000; 3 = 5,001-7,500; and 4 = ≥ 7,501 steps/day). Across individual groups 1-4, SF-36 scores increased from 66.9 ± 25.0% to 73.5 ± 23.2% to 78.8 ± 19.7% to 81.3 ± 20.6%, and 6MWT increased from 941.7 ± 265.4 ft to 1,154.1 ± 248.2 ft to 1,260.1 ± 226.3 ft to 1,294.0 ± 257.9 ft. Both SF-36 and 6MWT scores were statistically different across all groups, apart from groups 3 and 4. PA and ranks of groups were highly significant predictors (p < .0001) for both SF-36 and 6MWT. There was a positive dose-response relationship evident for both SF-36 and 6MWT with increasing levels of PA. Low levels of PA appear to be an important indicator of poor functionality in older adults.

Guide to the assessment of physical activity: Clinical and research applications: a scientific statement from the American Heart Association.

The deleterious health consequences of physical inactivity are vast, and they are of paramount clinical and research importance. Risk identification, benchmarks, efficacy, and evaluation of physical activity behavior change initiatives for clinicians and researchers all require a clear understanding of how to assess physical activity. In the present report, we have provided a clear rationale for the importance of assessing physical activity levels, and we have documented key concepts in understanding the different dimensions, domains, and terminology associated with physical activity measurement. The assessment methods presented allow for a greater understanding of the vast number of options available to clinicians and researchers when trying to assess physical activity levels in their patients or participants. The primary outcome desired is the main determining factor in the choice of physical activity assessment method. In combination with issues of feasibility/practicality, the availability of resources, and administration considerations, the desired outcome guides the choice of an appropriate assessment tool. The decision matrix, along with the accompanying tables, provides a mechanism for this selection that takes all of these factors into account. Clearly, the assessment method adopted and implemented will vary depending on circumstances, because there is no single best instrument appropriate for every situation. In summary, physical activity assessment should be considered a vital health measure that is tracked regularly over time. All other major modifiable cardiovascular risk factors (diabetes mellitus, hypertension, hypercholesterolemia, obesity, and smoking) are assessed routinely. Physical activity status should also be assessed regularly. Multiple physical activity assessment methods provide reasonably accurate outcome measures, with choices dependent on setting-specific resources and constraints. The present scientific statement provides a guide to allow professionals to make a goal-specific selection of a meaningful physical activity assessment method.

Responsiveness of motion sensors to detect change in sedentary and physical activity behaviour.

BACKGROUND: The purpose of this study was to determine the responsiveness of two motion sensors to detect change in sedentary behaviour (SB) and physical activity (PA) during an occupational intervention to reduce sitting time. METHODS: SB and PA were assessed using a hip-worn Actigraph GTX3 (AG) and a thigh-worn activPAL (AP) during three consecutive workdays throughout baseline and intervention periods. Mean scores at baseline and intervention were estimated by hierarchical linear models (HLM) with robust SEs, adjusting for random variance of average scores between participants. Change scores (mean baseline minus mean intervention) were calculated for each device. Response to change was assessed for each device using the standardised response mean. RESULTS: 67 adults (45 ± 11 years; 29.3 ± 7.7 kg/m(2)) wore the acceleration-based motion sensors for 8.3 (SD=1.2) and 8.3 (SD=1.1) h during the baseline and intervention periods, respectively. HLM showed that AP sitting/lying time (-16.5 min, -5%), AP stepping (+7.5 min, 19%), AP steps/day (+838 steps/day, +22%), AP sit-to-stand transitions (+3, +10%), AG SB (-14.6 min, -4%), AG lifestyle moderate-intensity PA (LMPA, +4 min, +15%) and AG MPA (+3 min, 23%) changed significantly between the baseline and the intervention period. Standardised response means for AP sitting/lying time, stepping, steps/day, sit-to-stand transitions and AG SB, LMPA and MPA were above 0.3, indicating a small but similar responsiveness to change. CONCLUSIONS: Responsiveness to change in SB and PA was similar and comparable for the AP and AG, indicating agreement across both measurement devices.

Prompts to disrupt sitting time and increase physical activity at work, 2011-2012.

INTRODUCTION: The objective of this study was to assess change in sitting and physical activity behavior in response to a workplace intervention to disrupt prolonged sitting time. METHODS: Sixty office workers were randomized to either a Stand group (n = 29), which received hourly prompts (computer-based and wrist-worn) to stand up, or a Step group (n = 31), which received the same hourly prompts and an additional prompt to walk 100 steps or more upon standing. An ActivPAL monitor was used to assess sitting and physical activity behavior on the same 3 consecutive workdays during baseline and intervention periods. Mixed-effect models with random intercepts and random slopes for time were performed to assess change between groups and across time. RESULTS: Both groups significantly reduced duration of average sitting bouts (Stand group, by 16%; Step group, by 19%) and the number of sitting bouts of 60 minutes or more (Step group, by 36%; Stand group, by 54%). The Stand group significantly reduced total sitting time (by 6.6%), duration of the longest sitting bout (by 29%), and number of sitting bouts of 30 minutes or more (by 13%) and increased the number of sit-to-stand transitions (by 15%) and standing time (by 23%). Stepping time significantly increased in the Stand (by 14%) and Step (by 29%) groups, but only the Step group significantly increased (by 35%) the number of steps per workday. Differences in changes from baseline to intervention between groups were not significant for any outcome. CONCLUSION: Interventions that focus on disrupting sitting time only in the workplace may result in less sitting. When sitting time disruptions are paired with a physical activity prompt, people may be more likely to increase their workday physical activity, but the effect on sitting time may be attenuated.

Physically active, low-income African American women: an exploration of activity maintenance in the context of sociodemographic factors associated with inactivity.

Increasing physical activity among low-income African American women is an important target for addressing racial and economic disparities in chronic conditions and related risk factors. While barriers to physical activity for women have been examined empirically, successful strategies for navigating those barriers among physically active, low-income women have not been thoroughly explored. Informed by grounded theory, we conducted in-depth individual interviews between 2007-2010 with 14 low-income African American women who were physically active at nationally recommended levels for one year or more. We analyzed the data using thematic analysis techniques. Key themes emerged in three main categories: motivation for maintaining active lifestyle, strategies for maintaining physical activity, and challenges to maintaining physical activity. Important motivations included getting or staying healthy, social connections, and gratification. Two planning strategies emerged: flexibility and freedom. Critical challenges included financial constraints, physical strain and history of sedentary relapse. The motivations, strategies and challenges reported by low-income African American women who successfully maintained an active lifestyle provided important information for developing effective health promotion strategies for their inactive and underactive counterparts. A qualitative, asset-based approach to physical activity research contributes rich data to bridge the gap between epidemiological knowledge and community health improvement.

Sit-to-Stand Power Is a Stronger Predictor of Gait Speed than Knee Extension Strength.

With a growing aging population, the routine assessment of physical function may become a critical component of clinical practice. The purpose of this cross-sectional study is to compare two common assessments of muscular function: (1) isometric knee extension strength (KES) and (2) sit-to-stand (STS) muscle power tests, in predicting objective physical function (i.e., gait speed) in aging adults. 84 adults (56% female, mean (SD) age = 66.6 (9.4) years) had their relative KES, STS power, usual gait speed (UGS), and fast gait speed (FGS) assessed. Multiple linear regression examined the associations between KES, STS power, and gait outcomes. When entered in separate models, KES and STS power were both independently associated with UGS and FGS (Std. ß = 0.35-0.44 and 0.42-0.55 for KES and STS power, respectively). When entered in the same model, STS power was associated with UGS and FGS (Std. ß = 0.37 [95%CI: 0.15, 0.58] and 0.51 [95%CI: 0.31, 0.70], respectively), while KES was only associated with FGS (Std. ß = 0.25 [95%CI: 0.02, 0.48]). STS power seems to be a valid indicator of function in aging adults. Its feasibility as a screening tool for "low" function in the primary care setting should be explored.

Daily physical activity predicts degree of insulin resistance: a cross-sectional observational study using the 2003-2004 National Health and Nutrition Examination Survey.

BACKGROUND: This study examined the independent association of objectively measured physical activity on insulin resistance while controlling for confounding variables including: cardiorespiratory fitness, adiposity, sex, age, and smoking status. METHODS: Data were obtained from National Health and Nutrition Examination Survey 2003-2004, a cross-sectional observational study conducted by the National Center for Health Statistics of the Centers for Disease Control that uses a stratified, multistage probability design to obtain a nationally representative sample of the U.S. population. The analysis included 402 healthy U.S. adults with valid accelerometer, cardiorespiratory fitness, and fasting plasma glucose and insulin concentrations. After controlling for relevant confounding variables we performed a multiple linear regression to predict homeostatic model of insulin resistance (HOMA-IR) based on average daily minutes of moderate-to-vigorous physical activity (MVPA). RESULTS: In our bivariate models, MVPA, cardiorespiratory fitness and body fat percentage were all significantly correlated with log HOMA-IR. In the complete model including MVPA and relevant confounding variables, there were strong and significant associations between MVPA and log HOMA-IR (ß= -0.1607, P=0.004). In contrast the association between cardiorespiratory fitness and log HOMA-IR was not significant. CONCLUSION: When using an objective measure of physical activity the amount of time engaged in daily physical activity was associated with lower insulin resistance, whereas higher cardiorespiratory fitness was not. These results suggest that the amount of time engaged in physical activity may be an important determinant for improving glucose metabolism.

Estimating relative intensity using individualized accelerometer cutpoints: the importance of fitness level.

BACKGROUND: Accelerometer cutpoints based on absolute intensity may under or overestimate levels of physical activity due to the lack of consideration for an individual's current fitness level. The purpose of this study was to illustrate the interindividual variability in accelerometer activity counts measured at relative intensities (40 and 60% heart rate reserve (HRR)) and demonstrate the differences between relative activity counts between low, moderate and high fitness groups. METHODS: Seventy-three subjects (38 men, 35 women) with a wide range of cardiorespiratory fitness (maximal oxygen consumption (VO2max): 27.9 to 58.5 ml · kg⁻¹ · min⁻¹), performed a submaximal exercise test with measures of heart rate (HR) and accelerometer activity counts. Linear regression equations were developed for each subject to determine accelerometer activity counts for moderate and vigorous intensity physical activity corresponding to 40% and 60% of HRR. Interindividual variability of activity counts between subjects at both 40% and 60% of HRR was demonstrated by plotting values using a box and whisker plot. To examine the difference between absolute and relative activity cutpoints, subjects were categorized into 3 fitness groups based on metabolic equivalents (MET) (<10 MET, 10-13 MET, >13 MET). RESULTS: At 40 and 60% of HRR, activity counts ranged from 1455-7520, and 3459-10066 counts · min-1, respectively. Activity counts at 40% HRR (3385 ± 850, 4048 ± 1090, and 5037 ± 1019 counts · min⁻¹) and 60% HRR (5159 ± 765, 5995 ± 1131 and 7367 ± 1374 counts · min-1) significantly increased across fitness groups (<10 MET, 10-13 MET, and >13 MET, respectively). CONCLUSION: This study revealed interindividual variability in activity counts at relative moderate (40% HRR) and vigorous (60% HRR) intensities, while fitness level was shown to have a significant influence on relative activity counts measured at these intensities. Individualizing activity count cutpoints may be more representative of an individual's PA level relative to their fitness capacity, compared to absolute activity count cutpoints.

Accuracy of uploadable pedometers in laboratory, overground, and free-living conditions in young and older adults.

PURPOSE: The purpose of this study was to examine the accuracy of uploadable pedometers to accurately count steps during treadmill (TM) and overground (OG) walking, and during a 24 hour monitoring period (24 hr) under free living conditions in young and older adults. METHODS: One hundred and two participants (n=53 aged 20-49 yrs; n=49 aged 50-80 yrs) completed a TM protocol (53.6, 67.0, 80.4, 93.8, and 107.2 m/min, five minutes for each speed) and an OG walking protocol (self-determined "< normal", "normal", and "> normal" walking speeds) while wearing two waist-mounted uploadable pedometers (Omron HJ-720ITC [OM] and Kenz Lifecorder EX [LC]). Actual steps were manually tallied by a researcher. During the 24 hr period, participants wore a New Lifestyles-1000 (NL) pedometer (standard of care) attached to a belt at waist level over the midline of the left thigh, in addition to the LC on the belt over the midline of the right thigh. The following day, the same procedure was conducted, replacing the LC with the OM. One-sample t-tests were performed to compare measured and manually tallied steps during the TM and OG protocols, and between steps quantified by the NL with that of the OM and LC during the 24 hr period. Mean error step scores (MES, criterion - device) and 95% Limits of Agreement (LoA) were calculated. RESULTS: There were no significant differences between the OM and tallied steps for any of the TM speeds for either the young or older adult groups. The LC significantly underestimated steps for the young adult group during the 53.6 m/min TM speed (MES 31.4 [14.5, 48.3]) and during the OG < normal walking speed (MES 12.0 [0.9, 23.1] (p<0.01 for both age groups). The LC also significantly underestimated steps for the older adult group during the TM speeds of 53.6 m/min (MES 64.5 [45.6, 83.4]), 67.0 m/min (MES 15.1 [6.1, 24.0]), and 80.4 m/min (MES 3.2 [0.6, 5.9]) (p<0.01 for all speeds), in addition to the OG < normal walking speed (MES 14.7 [-13.3, 42.6] (p<0.01). The OM reported significantly lower steps during the 24 hr period for the young adult group by 949.1 steps (t=6.111, p<0.025) and for the older adult group by 612.9 steps (t=2.397, p<0.025). CONCLUSION: Both the OM and LC pedometers were more accurate as TM and OG walking speed increased. The OM significantly underestimated steps during the 24 hr compared with a standard of care evaluation. Overall, both uploadable pedometers appear acceptable to use in young or old age groups to measure walking behavior.