Reliability of daily step activity monitoring in adults with incomplete spinal cord injury

Ishikawa, Saori; Stevens, Sandy; Kang, Minsoo; Morgan, Don W.

doi:10.1682/jrrd.2010.09.0190

Cited by 18 publications

(21 citation statements)

References 32 publications

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“…This was replicated by Norris et al [23], who reported similar findings using an approach that examined reliability coefficients from random 2-day to 7-day averages. In addition, obtaining a stable measure of freeliving walking activity based on pedometer use may require 7 days in healthy individuals [33], while more impaired populations such as incomplete spinal cord injury patients may require only 2 days of monitoring to achieve acceptably stable estimates [34]. This finding has practical implications with respect to utilizing wearable generated activity data in clinical research.…”

Section: Discussionmentioning

confidence: 99%

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device

et al. 2018

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Introduction: Wearable devices have been used to characterize physical activity in multiple sclerosis (MS). The objectives of this study were to advance the literature on the utility of free-living physical activity tracking from secondary analyses of a pilot study in MS patients. Method: The original observational study was conducted in participants with MS at PatientsLikeMe (www.PatientsLikeMe.com), an online network of patients with chronic diseases. Participants completed a baseline self-assessment, and received a Fitbit One^TM wearable device with instructions to upload data. Eligible participants (1) self-reported MS, (2) logged on to the PatientsLikeMe website 90 days prior to enrollment, and (3) consented to participate electronically. Participants (1) < 18 years, (2) living outside the United States, and (3) requiring wheelchair assistance for most daily activities were excluded. The secondary analyses were limited to participants with complete data on MS type, disease duration, and Multiple Sclerosis Rating Scale (MSRS) and at least 7 days of wearable data. Step count was used as a measure of physical activity. Results: The analysis cohort of 114 participants uploaded a mean of 20.1 days of wearable data over the 23-day study (87% adherence); participants averaged 4,393 steps per day. The mean age of participants was 52 years, predominantly female (75%), relapsing-remitting type (79%), with mean disease duration of 16 years. Mean MSRS score within 30-day of baseline was 32; 72% reported mild-moderate walking disability. The reliability of step count measured by intraclass correlation was 0.55 for a single day, ≥0.7 for 2-day average, and ≥0.9 for 7-day average. After controlling for covariates, self-reported disease severity (MSRS quartile) was an independent predictor of step count (p < 0.001). Least square means (LS means) for participants that were least disabled (lowest quartile) was 5,937 steps, which was significantly higher than participants in the second, third, and fourth quartiles (4,570, 3,490, and 3,272, respectively). Similarly, LS means of participants with no ambulatory disability (measured by MSRS walk component) was 6,931 steps, significantly higher than participants with greater disability (4,743, 4,394, 2,727 steps for symptomatic, mild, and moderate disability, respectively, p < 0.001). Discussion: Using an interactive platform, this study captured free-living mobility data in MS patients. Important metrics such as the use of a minimum of 2-day estimates and self-reported disability were found to be robust indicators and correlates, respectively, of participant activity levels. Further triangulation of such metrics may reduce the burden on patients, clinicians, and researchers when monitoring clinical status.

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Section: Discussionmentioning

confidence: 99%

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device

et al. 2018

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“…The relative magnitude of error in step counts for each term was computed by dividing the estimate of the single variance component by the total variance and multiplying by 100. 10 …”

Section: Methodsmentioning

confidence: 99%

“…The generalizability theory (G theory) has been applied in different populations to determine the number of days that is sufficient to achieve an acceptable reliability coefficient. 10–12 The G theory is unique in that it allows the researchers to identify the relative contribution of different sources of variance to total variance in measurement. 13,14 The findings of the studies by Ishikawa et al 10 demonstrated that 2 days were necessary to reliably measure daily PA of adults with incomplete spinal cord injury.…”

mentioning

confidence: 99%

“…10–12 The G theory is unique in that it allows the researchers to identify the relative contribution of different sources of variance to total variance in measurement. 13,14 The findings of the studies by Ishikawa et al 10 demonstrated that 2 days were necessary to reliably measure daily PA of adults with incomplete spinal cord injury. In addition, Wickel and Welk 12 revealed that 7 to 8 days are the minimum number of days of step activity monitoring required to reach stable measure of PA in healthy able-bodied youth.…”

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confidence: 99%

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Reliably Measuring Ambulatory Activity Levels of Children and Adolescents With Cerebral Palsy

Ishikawa

Kang

Bjornson³

et al. 2013

Archives of Physical Medicine and Rehabilitation

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Objective To identify sources of variance in step counts and to examine the minimum number of days required to obtain a stable measure of habitual ambulatory activity in the cerebral palsy (CP) population. Design Cross-sectional. Setting Free-living environments. Participants Children and adolescents with CP (N = 209; mean age ± SD, 8y, 4mo ± 3y, 4mo; n = 118 boys; Gross Motor Function Classification System [GMFCS] levels I–III) were recruited through 3 regional pediatric specialty care hospitals. Interventions Daily walking activity was measured with a 2-dimensional accelerometer over 7 consecutive days. An individual information-centered approach was applied to days with <100 steps, and participants with ≥3 days of missing values were excluded from the study. Participants were categorized into 6 groups according to age and functional level. Generalizability theory was used to analyze the data. Main Outcome Measures Mean step counts, relative magnitude of variance components in total step activity, and generalizability coefficients (G coefficients) of various combinations of days of the week. Results Variance in step counts attributable to participants ranged from 33.6% to 65.4%. For youth ages 2 to 5 years, a minimum of 8, 6, and 2 days were required to reach acceptable G coefficient (reliability) of ≥.80 for GMFCS levels I, II, and III, respectively. For those ages 6 to 14 years, a minimum of 6, 5, and 4 days were required to reach stable measures of step activity for GMFCS levels I, II, and III, respectively. Conclusions The findings of the study suggest that an activity-monitoring period should be determined based on the GMFCS levels to reliably measure ambulatory activity levels in youth with CP.

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“…Levels of walking activity using pedometers require about 7 days of data collection to obtain a stable and representative average for healthy persons 49 to as little as 2 days for those with incomplete SCI. 50 For a clinical trial of a walking intervention of 3 months duration, a minimal data set might include 2 weeks of daily monitoring prior to starting the comparison treatments, then for one week monthly or at the time of scheduled outcome measures. For a drug trial, activity might be measured continuously for at least a month – two weeks prior and at least 2 weeks after initiation to detect fluctuations in response to medications (e.g., dyskinesias or freezing of gait in Parkinson's disease, leg spasms in SCI).…”

Section: Sensors For Clinical Trialsmentioning

confidence: 99%

Wearable motion sensors to continuously measure real-world physical activities

Dobkin

2013

Current Opinion in Neurology

133

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Purpose of review Rehabilitation for sensorimotor impairments aims to improve daily activities, walking, exercise, and motor skills. Monitoring of practice and measuring outcomes, however, is usually restricted to laboratory-based procedures and self-reports. Mobile health devices may reverse these confounders of daily care and research trials. Recent findings Wearable, wireless motion sensor data, analyzed by activity pattern-recognition algorithms, can describe the type, quantity, and quality of mobility-related activities in the community. Data transmission from sensors to the cell phone and Internet enable continuous monitoring. Remote access to laboratory-quality data about walking speed, duration and distance, gait asymmetry and smoothness of movements, as well as cycling, exercise, and skills practice, opens new opportunities to engage patients in progressive, personalized therapies with feedback about performance. Clinical trial designs will be able to include remote verification of the integrity of complex physical interventions and compliance with practice, as well as capture repeated, ecologically sound, ratio-scale outcome measures. Summary Given the progressively falling cost of miniaturized wearable gyroscopes, accelerometers, and other physiologic sensors, as well as inexpensive data transmission, sensing systems may become as ubiquitous as cell phones for health care. Neurorehabilitation can develop these mobile health platforms for daily care and clinical trials to improve exercise and fitness, skills learning, and physical functioning.

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Reliability of daily step activity monitoring in adults with incomplete spinal cord injury

Cited by 18 publications

References 32 publications

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device

Reliably Measuring Ambulatory Activity Levels of Children and Adolescents With Cerebral Palsy

Wearable motion sensors to continuously measure real-world physical activities

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