Reallocating Time to Physical Activity and Sleep: Associations with Body Mass Index in Cancer Survivors.

BACKGROUND: Body mass index (BMI) above ≥ 25 kg/m2 is associated with increased risk for cancer-related morbidity and mortality. Achieving recommended amounts of physical activity (PA), sedentary time (ST), and sleep can help cancer survivors (CS) attain a healthy BMI. This cross-sectional study examined the potential role of reallocating time between moderate and light PA, ST, and sleep on BMI in CS. METHOD: A sample of CS (N = 73, Mage = 53.7 ± 12.9) wore an activPAL and Actiwatch accelerometer for 7 days, 24 h per day to measure PA intensity and sleep, respectively. Self-reported height and weight or scale/stadiometer were used to calculate BMI. Isotemporal substitution models were used to reallocate time, averaged over the 7-day period, from one activity of interest to another and examine the associations with BMI. Statistical significance was set at p < .05. RESULTS: The following reallocations of 30 min were significantly associated with BMI: sleep to ST (+ 0.80 kg/m2, p = 0.02) and ST to light PA (- 0.53 kg/m2, p = 0.008). No significant associations with BMI were observed for reallocating time to or away from moderate-vigorous PA. CONCLUSION: The results of this study suggest that sleep and light PA may have important implications for achieving a healthy BMI in CS. Therefore, future research should include interventions which target light PA and sleep to determine if they can improve BMI in CS.

Sensors for Human Physical Behaviour Monitoring.

The understanding and measurement of physical behaviours that occur in everyday life are essential not only for determining their relationship with health, but also for interventions, physical activity monitoring/surveillance of the population and specific groups, drug development, and developing public health guidelines and messages [...].

Reallocating time to physical activity and sleep: associations with quality of life in cancer survivors.

PURPOSE: Quality of life (QOL) is an important psychosocial outcome in cancer survivors (CS). Physical activity (PA), reducing sedentary time (ST), and sleep can help CS improve QOL; however, these behaviors are commonly studied in isolation, despite their interdependence during the 24-h day (i.e., time in one activity cannot increase without time decreasing in another activity). This study examined the effects of reallocating time between moderate to vigorous PA (MVPA), light PA, ST, and sleep on QOL in a mixed sample of CS. METHODS: A cross-sectional sample of CS (N = 73) diagnosed with breast (29.7%), colorectal (33.8%), or other (36.5%) cancer. MVPA, light PA, and ST were measured using the activPAL™ accelerometer, and sleep duration using the Actiwatch™ accelerometer. Both were worn for 7 days, 24 h per day. QOL was self-reported using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire. Isotemporal substitution models were used to reallocate 30 min between activities. Statistical significance was set at p < .05. RESULTS: Participants accumulated, M = 24.0 ± 18.9 min/day of MVPA, 291.7 ± 100.4 min/day of light PA, 593.1 ± 108.3 min/day of ST, and 486.6 ± 57.6 min/night of sleep. Isotemporal substitution effects for reallocating time between MVPA, light PA, ST, and sleep were not significantly associated with QOL. CONCLUSIONS: Findings from this study suggest that among CS who are achieving adequate levels of PA and sleep, changes in 30 min of these activities may not impact QOL. Future studies should aim to recruit a larger, more representative sample and explore the role of bouted activity time.

Temporal patterns of physical activity in successful weight loss maintainers.

BACKGROUND/OBJECTIVES: Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management. METHODS: We compared patterns of PA in a cohort of weight loss maintainers (WLM, n = 28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m2), controls without obesity (NC, n = 30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m2), and controls with overweight/obesity (OC, n = 26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m2). PA was assessed during 7 consecutive days using the activPALTM activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models. RESULTS: Across the week, WLM engaged in ≥60 min of MVPA on 73% of days, significantly more than OC (36%, p < 0.001) and similar to NC (59%, p = 0.10). Across the day, WLM accumulated more MVPA in the morning (i.e., within 3 h of waking) compared to both NC and OC (p < 0.01). WLM engaged in significantly more MVPA accumulated in bouts ≥10 min compared to NC and OC (p < 0.05). Specifically, WLM engaged in more MVPA accumulated in bouts of ≥60 min compared to NC and OC (p < 0.05). CONCLUSIONS: WLM engage in high amounts of MVPA (≥60 min/d) on more days of the week, accumulate more MVPA in sustained bouts, and accumulate more MVPA in the morning compared to controls. Future research should investigate if these distinct patterns of PA help to promote weight loss maintenance.

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.

"What Is a Step?" Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research.

(1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. (2) Methods: Twenty participants (Age: 26.7 ± 4.9 years) performed treadmill walking between 0.89-to-1.79 m/s (2-4 mph) while wearing a hip-worn DigiwalkerSW200, OmronHJ720ITC, GT3X+ and GT9X, and a wrist-worn GT3X+ and GT9X. A DigiwalkerSW200 and OmronHJ720ITC underwent shaker testing to determine device-specific frequency and amplitude step-detection thresholds. Simulated signal testing was used to determine thresholds for the ActiGraph step algorithm. Steps during human testing were compared using bias and confidence intervals. (3) Results: The OmronHJ720ITC was most accurate during treadmill walking. Hip and wrist-worn ActiGraph outputs were significantly different from the criterion. The DigiwalkerSW200 records steps for movements with a total acceleration of ≥1.21 g. The OmronHJ720ITC detects a step when movement has an acceleration ≥0.10 g with a dominant frequency of ≥1 Hz. The step-threshold for the ActiLife algorithm is variable based on signal frequency. Acceleration signals at the hip and wrist have distinctive patterns during treadmill walking. (4) Conclusions: Three common research-grade physical activity monitors employ different step-detection strategies, which causes variability in step output.

Validation of a previous day recall for measuring the location and purpose of active and sedentary behaviors compared to direct observation.

PURPOSE: Gathering contextual information (i.e., location and purpose) about active and sedentary behaviors is an advantage of self-report tools such as previous day recalls (PDR). However, the validity of PDR's for measuring context has not been empirically tested. The purpose of this paper was to compare PDR estimates of location and purpose to direct observation (DO). METHODS: Fifteen adult (18-75 y) and 15 adolescent (12-17 y) participants were directly observed during at least one segment of the day (i.e., morning, afternoon or evening). Participants completed their normal daily routine while trained observers recorded the location (i.e., home, community, work/school), purpose (e.g., leisure, transportation) and whether the behavior was sedentary or active. The day following the observation, participants completed an unannounced PDR. Estimates of time in each context were compared between PDR and DO. Intra-class correlations (ICC), percent agreement and Kappa statistics were calculated. RESULTS: For adults, percent agreement was 85% or greater for each location and ICC values ranged from 0.71 to 0.96. The PDR-reported purpose of adults' behaviors were highly correlated with DO for household activities and work (ICCs of 0.84 and 0.88, respectively). Transportation was not significantly correlated with DO (ICC = -0.08). For adolescents, reported classification of activity location was 80.8% or greater. The ICCs for purpose of adolescents' behaviors ranged from 0.46 to 0.78. Participants were most accurate in classifying the location and purpose of the behaviors in which they spent the most time. CONCLUSIONS: This study suggests that adults and adolescents can accurately report where and why they spend time in behaviors using a PDR. This information on behavioral context is essential for translating the evidence for specific behavior-disease associations to health interventions and public policy.

Patient-centricity in digital measure development: co-evolution of best practice and regulatory guidance.

Digital health technologies (DHTs) have the potential to modernize drug development and clinical trial operations by remotely, passively, and continuously collecting ecologically valid evidence that is meaningful to patients' lived experiences. Such evidence holds potential for all drug development stakeholders, including regulatory agencies, as it will help create a stronger evidentiary link between approval of new therapeutics and the ultimate aim of improving patient lives. However, only a very small number of novel digital measures have matured from exploratory usage into regulatory qualification or efficacy endpoints. This shows that despite the clear potential, actually gaining regulatory agreement that a new measure is both fit-for-purpose and delivers value remains a serious challenge. One of the key stumbling blocks for developers has been the requirement to demonstrate that a digital measure is meaningful to patients. This viewpoint aims to examine the co-evolution of regulatory guidance in the United States (U.S.) and best practice for integration of DHTs into the development of clinical outcome assessments. Contextualizing guidance on meaningfulness within the larger shift towards a patient-centric drug development approach, this paper reviews the U.S. Food and Drug Administration (FDA) guidance and existing literature surrounding the development of meaningful digital measures and patient engagement, including the recent examples of rejections by the FDA that further emphasize patient-centricity in digital measures. Finally, this paper highlights remaining hurdles and provides insights into the established frameworks for development and adoption of digital measures in clinical research.

Measuring What Is Meaningful in Cancer Cachexia Clinical Trials: A Path Forward With Digital Measures of Real-World Physical Behavior.

PURPOSE: The burden of cancer cachexia on patients' health-related quality of life, specifically their physical functioning, is well documented, but clinical trials thus far have failed to show meaningful improvement in physical functioning. The purpose of this review is to summarize existing methods of assessing physical function in cancer cachexia, outline a path forward for measuring what is meaningful to patients using digital measures derived from digital health technologies (DHTs), and discuss the current landscape of digital measures from the clinical and regulatory standpoint. DESIGN: For this narrative review, peer-reviewed articles were searched on PubMed, clinical trials records were searched on clinicaltrials.gov, and records of digital measures submitted for regulatory qualification were searched on the US Food and Drug Administration's Drug Development Tool Qualification Program database. RESULTS: There are gaps in assessing aspects of physical function that matter to patients. Existing assessment methods such as patient-reported outcomes and objective performance outcomes have limitations, including their episodic nature and burden to patients. DHTs such as wearable sensors can capture real-world physical behavior continuously, passively, and remotely, and may provide a more comprehensive picture of patients' everyday functioning. Recent regulatory submissions showcase potential clinical implementation of digital measures in various therapeutic areas. CONCLUSION: Digital measures of real-world physical behavior present an opportunity to detect and demonstrate improvements in physical functioning in cancer cachexia, but evidence-based development is critical. For their use in clinical and regulatory decision making, studies demonstrating meaningfulness to patients as well as feasibility and validation are necessary.

Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development.

Alzheimer's disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD's impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.

An Evaluation of Participant Perspectives and Wear-Time Compliance for a Wrist-Worn Versus Thigh-Worn Accelerometer in Cancer Survivors.

BACKGROUND: Accelerometers are frequently used to measure free-living physical activity and sleep in cancer survivors. To obtain valid data, participants must adhere to wear-time guidelines; therefore, understanding survivor's preference may be critical when selecting an accelerometer. This study compared cancer survivors' reported discomfort and interference, and wear-time compliance between a wrist-worn accelerometer and a thigh-worn accelerometer. METHODS: This was a secondary data analysis. Cancer survivors (N = 52, mean age = 51.8 [13.0], 82.3% female) wore the Actiwatch-2 (wrist) and the activPAL (thigh) for 7 days, 24 hours per day. On day 7, participants completed a questionnaire to evaluate each accelerometer using a 1 to 5 Likert scale and open-ended questions. The Kolmogorov-Smirnov test evaluated differences in discomfort and interference. Paired samples t test evaluated differences in wear-time compliance. Open-ended responses were analyzed using thematic analysis methods. RESULTS: No differences were observed in discomfort, interference, or wear-time compliance (P = .08). Qualitative analysis resulted in 2 themes: discomfort and ease of use and interference and adverse reaction. Interferences were primarily reported with the Actiwatch-2, whereas discomfort and ease were primarily reported with the activPAL. CONCLUSION: No significant differences were observed regarding discomfort, interference, and compliance. Results of this study can prepare researchers for common issues regarding accelerometer compliance, allowing researchers to offer resources to alleviate discomforts or interferences that may affect wear-time compliance.

The Case for the Patient-Centric Development of Novel Digital Sleep Assessment Tools in Major Depressive Disorder.

Background: Depression imposes a major burden on public health as the leading cause of disability worldwide. Sleep disturbance is a core symptom of depression that affects the vast majority of patients. Nonetheless, it is frequently not resolved by depression treatment and may even be worsened through some pharmaceutical interventions. Disturbed sleep negatively impact patients' quality of life, and persistent sleep disturbance increases the risk of recurrence, relapse, and even suicide. However, the development of novel treatments that might improve sleep problems is hindered by the lack of reliable low-burden objective measures that can adequately assess disturbed sleep in this population. Summary: Developing improved digital measurement tools that are fit for use in clinical trials for major depressive disorder could promote the inclusion of sleep as a focus for treatment, clinical drug development, and research. This perspective piece explores the path toward the development of novel digital measures, reviews the existing evidence on the meaningfulness of sleep in depression, and summarizes existing methods of sleep assessments, including the use of digital health technologies. Key Messages: Our objective was to make a clear call to action and path forward for the qualification of new digital outcome measures which would enable assessment of sleep disturbance as an aspect of health that truly matters to patients, promoting sleep as an important outcome for clinical development, and ultimately ensure that disturbed sleep will not remain the forgotten symptom of depression.

Predicting hospitalization from real-world measures in patients with chronic kidney disease: A proof-of-principle study.

Objective: To investigate if in-clinic measures of physical function and real-world measures of physical behavior and mobility effort are associated with one another and to determine if they predict future hospitalization in participants with chronic kidney disease (CKD). Methods: In this secondary analysis, novel real-world measures of physical behavior and mobility effort, including the best 6-minute step count (B6SC), were derived from passively collected data from a thigh worn actigraphy sensor and compared to traditional in-clinic measures of physical function (e.g. 6-minute walk test (6MWT). Hospitalization status during 2 years of follow-up was determined from electronic health records. Correlation analyses were used to compare measures and Cox Regression analysis was used to compare measures with hospitalization. Results: One hundred and six participants were studied (69 ± 13 years, 43% women). Mean ± SD baseline measures for 6MWT was 386 ± 66 m and B6SC was 524 ± 125 steps. Forty-four hospitalization events over 224 years of total follow-up occurred. Good separation was achieved for tertiles of 6MWT, B6SC and steps/day for hospitalization events. This pattern persisted in models adjusted for demographics (6MWT: HR = 0.63 95% CI 0.43-0.93, B6SC: HR = 0.75, 95% CI 0.56-1.02 and steps/day: HR = 0.75, 95% CI 0.50-1.13) and further adjusted for morbidities (6MWT: HR = 0.54, 95% CI 0.35-0.84, B6SC: HR = 0.70, 95% CI 0.49-1.00 and steps/day: HR = 0.69, 95% CI 0.43-1.09). Conclusion: Digital health technologies can be deployed remotely, passively, and continuously to collect real-world measures of physical behavior and mobility effort that differentiate risk of hospitalization in patients with CKD.

An intervention to decrease sedentary behavior in older adults: A secondary analysis of a randomized controlled trial.

Background: Sedentary behaviors are associated with adverse health outcomes in older adults. The feasibility of behavioral interventions in this population is unclear. Methods: In the Sit Less, Interact, Move More (SLIMM) trial of 106 participants who had obesity, those randomized to the SLIMM intervention (N = 54) were instructed to replace sedentary activities with stepping. An accelerometer was used to measure physical activity. In this secondary analysis, mixed effect models were used to examine the effects of the SLIMM intervention on sedentary and stepping durations and steps/day by age (<70 and ≥ 70 years). Results: Mean ages in the <70 years (N = 47) and ≥70 years (N = 59) groups were 58 ± 11 and 78 ± 5. In the older subgroup, compared to standard-of-care (N = 29), the SLIMM intervention (N = 30) significantly increased stepping duration (13, 95%CI 1-24 min/d, p = 0.038) and steps per day (1330, 95% CI 322-2338, p = 0.01) and non-significantly decreased sedentary duration by (28,95% CI -61-5 min/d, p = 0.09). In the age <70 subgroup, there was no separation between the standard of care (N = 23) and SLIMM (N = 24) groups. Discussion: In older adults who had obesity, SLIMM intervention significantly increased stepping duration and steps per day. Interventions targeting sedentary behaviors by promoting low intensity physical activity may be feasible in this population.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Accuracy of accelerometer regression models in predicting energy expenditure and METs in children and youth.

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8-16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (± 95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.

A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

Physical Activity and Stool Metabolite Relationships Among Adults at High Risk for Colorectal Cancer.

BACKGROUND: Adenomatous polyps are associated with an increased risk of developing colorectal cancer. Physical activity (PA) and spending less time sedentary may reduce risk of polyp recurrence and cancer incidence. This study examined associations between PA, sedentary time, and stool metabolites in adults at high risk for developing colorectal cancer. METHODS: Participants were ≥18 years old with ≥1 adenomatous polyps removed in the previous 3 years. PA and sedentary time were assessed using an activPAL™ accelerometer. Stool samples were analyzed for short-chain fatty acids, and primary/secondary bile acid metabolites by mass spectrometry. Linear regression models examined associations between PA, sedentary time, and stool parameters, with dietary fiber as a covariate. RESULTS: Participants (N = 21) were 59 (9) years old and had a body mass index of 28.1 (3.35 kg/m2). Light-intensity PA was associated with butyrate (ß = 1.88; 95% confidence interval [CI], 0.477 to 3.291) and propionate (ß = 1.79; 95% CI, 0.862 to 2.724). Moderate to vigorous PA was associated with deoxycholic acid (ß = -6.13; 95% CI, -12.14 to -0.11) and ursodeoxycholic acid (ß = -0.45; 95% CI, -0.80 to -0.12) abundance. CONCLUSIONS: Both light and moderate to vigorous PA were associated with gut microbial metabolite production. These findings suggest the importance of examining PA intensity alongside stool metabolites for colorectal cancer prevention.

Targeting Sedentary Behavior in CKD: A Pilot and Feasibility Randomized Controlled Trial.

BACKGROUND AND OBJECTIVES: We tested the feasibility of reducing sedentary behavior common in CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We carried out a Sit Less, Interact, Move More intervention in a 24-week parallel-group, randomized controlled trial in patients with stages 2-5 CKD. In the intervention group (n=54), accelerometry performed at baseline and repeated every 4 weeks was used to develop and monitor adherence to individualized plans targeting sedentary and stepping durations. The control group (n=52) was provided national physical activity recommendations; accelerometry was performed at baseline and every 8 weeks. Between-groups changes from baseline to the average follow-up values at weeks 8, 16, and 24 of the sedentary and stepping durations were the coprimary end points. RESULTS: The mean age was 69±13 years. Fourteen percent were on dialysis or received a kidney transplant. Eight percent of the control group and 17% of the intervention group were lost to follow-up. Sedentary and stepping durations did not change in the control group. Within the intervention group, the maximum decrease in sedentary duration (-43; 95% confidence interval, -69 to -17 min/d) and increase in stepping duration (16; 95% confidence interval, 7 to 24 min/d) and the number of steps per day (1265; 95% confidence interval, 518 to 2012) were seen at week 20. These attenuated at week 24. In mixed effects models, overall treatment effects between groups on sedentary (-17; 95% confidence interval, -43 to 8 min/d) and stepping (6; 95% confidence interval, -3 to 15 min/d) durations and the number of steps per day, a secondary end point (652; 95% confidence interval, -146 to 1449), were not significantly different. The intervention significantly reduced secondary end points of body mass index (-1.1; 95% confidence interval, -1.9 to -0.3 kg/m2) and body fat percentage (-2.1%; 95% confidence interval, -4.4% to -0.2%). CONCLUSIONS: It is feasible to reduce sedentary duration and increase stepping duration in patients with CKD, but these were not sustained. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: National Health and Nutrition Examination Survey (NHANES), NCT02970123.