Assessing Physical Activity in Persons with Knee Osteoarthritis Using Accelerometers: Data in the Osteoarthritis Initiative

Abstract

Objective

Physical activity measured by accelerometers requires basic assumptions to translate the output into meaningful measures. We used accelerometer data from the Osteoarthritis Initiative to investigate in the context of knee osteoarthritis (OA) the following data processing assumptions derived from the general adult US population: non-wear (a period the monitor was removed) is based on zero activity exceeding 60 minutes; a valid day of monitoring is based on wear time evidence exceeding 10 hours.

Methods

We examined the influence of non-wear thresholds ranging from 20 to 300 minutes of zero activity on 1) mean daily activity minutes (counts>0), 2) mean daily activity counts, and 3) mean daily moderate to vigorous physical activity (MVPA) minutes. The effect of selecting minimums of 8, 10, or 12 wear hours to signify a valid day of monitoring on data retention was examined.

Results

Our sample of 3536 days’ accelerometer data from 519 persons with knee OA showed mean daily activity minutes increased with the non-wear threshold until stabilizing at 463 minutes per day, corresponding to the 90-minute non-wear threshold. Similar patterns were observed for mean daily activity counts. Varying the non-wear threshold had no effect on mean daily MVPA minutes. Choosing the 90-minute non-wear threshold and a minimum of 10 wear hours to constitute a valid day provided 94% data retention.

Conclusion

Data supported applying the 90-minute non-wear threshold to the knee OA population instead of the general population 60-minute threshold, while retaining the 10-hour valid day threshold.

The number of US adults with arthritis-related activity limitations is expected to escalate from 17 million in 2006 to nearly 25 million by the year 2030.¹ A leading cause of arthritis-related activity limitation is knee osteoarthritis (OA), which affects an estimated 12% of US adults age 60 or older.² Physical activity has been found to reduce pain and improve function in persons with knee OA, which fuels the growing interest in the measurement of physical activity in this population.³

Objective measurement is central to the accurate assessment of physical activity. Sole reliance on self-report is problematic because subjects are known to both underestimate their daily walking distance⁴ and overestimate the amount of energy expended during moderate intensity daily activity.^5–6 Accelerometer technology validated from expensive gold standard methods (e.g., doubly labeled water) can be used successfully in community populations.^6–10 Advantages of accelerometer monitoring to objectively measure physical activity are minimal participant burden and capture of salient behavior characteristics including the frequency, intensity, and duration of physical activity.

One day of accelerometer monitoring can produce 1440 minute-by-minute recordings of “accelerometer counts”. A major challenge in the use of accelerometers is translating the downloaded output into meaningful physical activity outcomes. Often this analytic step is a “black box”, requiring blind reliance on algorithms provided by the accelerometer manufacturer. Basic data decisions embedded in these analytic algorithms can substantially alter the calculated physical activity outcomes.¹¹ Reliance on a one size fits all “black box” may be inappropriate and could lead to erroneous conclusions.

The accelerometer algorithm released by the National Cancer Institute (NCI), which was developed for the landmark physical activity study from the National Health and Nutritional Examination Survey (NHANES) sample,^12–13 provides an important benchmark to translate accelerometer output into physical activity measures. The NCI algorithm made the translation process transparent.¹⁴ A major caveat for OA research is that parameter values in the NCI algorithm were derived from the general adult US population.¹³ Two foundational NCI algorithm parameters are the thresholds to identify 1) “non-wear” and 2) a "valid day” of accelerometer monitoring. The non-wear threshold (NCI value = 60 minutes of zero activity counts) is an attempt to objectively distinguish periods of inactivity (e.g., sitting) from periods when the monitor was removed (i.e., non-wear). Underestimating the non-wear threshold may potentially lead to the inappropriate discard of periods of inactivity and underestimate sedentary time. The valid day threshold (NCI value = 10 hours evidence of monitoring) forms the basis to determine if accelerometer monitoring occurred for sufficient time to represent a full day of physical activity. If the estimated monitoring time in a day is below the designated threshold, accelerometer data from that day are considered invalid. Thus, the valid day threshold directly affects data loss.

These accelerometer algorithm parameters require assessment for the knee OA population, whose physical activity patterns differ from the general population due to pain and stiffness. Because the knee OA population may be more sedentary^15–16, criteria based on the general adult population to distinguish non-wear from inactivity may overstate non-wear and understate inactivity that is a consequence of the symptoms and/or joint structural changes of OA. The purpose of this study was to empirically investigate if the NCI accelerometer algorithm threshold values for non-wear and valid day derived from the general population are applicable to apply to accelerometer data from persons with knee OA for evaluating physical activity outcomes and to explore the impact of applying alternate threshold values to that accelerometer data. The availability of an algorithm examined in the context of knee OA to translate accelerometer recordings into physical activity measures will improve the quality of objective physical activity measures and reduce the likelihood that important and useful data will be lost due to inappropriate threshold application.

Materials and Methods

Study Population and Sample

This study analyzed data from the Osteoarthritis Initiative (OAI), a prospective natural history study investigating the development and progression of knee OA in persons with or at higher risk to develop knee OA aged 45–79 years at enrollment. Annual OAI interviews began in 2004 at four clinical sites: Baltimore Maryland, Columbus Ohio, Pittsburgh Pennsylvania, and Pawtucket Rhode Island and are currently ongoing (see http://www.oai.ucsf.edu/datarelease/About.asp). The OAI excluded subjects with rheumatoid or inflammatory arthritis; severe joint space narrowing in both knees on the baseline knee radiograph, or unilateral total knee replacement and severe joint space narrowing in the other knee; bilateral total knee replacement or plans to have bilateral knee replacement in the next 3 years; inability to undergo a 3.0T magnetic resonance imaging (MRI) exam of the knee because of contraindications; positive pregnancy test; unable to provide a blood sample; use of ambulatory aides other than a single straight cane for more than 50% of the time in ambulation; comorbid conditions that might interfere with the ability to participate in a 4-year study; current participation in a double-blind randomized trial. All OAI participants underwent knee radiography; the baseline visit identified 2679 participants with radiographic knee OA (i.e., radiographic evidence based on Kellgren-Lawrence grade ≥ 2 calculated from separate scores for osteophytes and joint space narrowing in a knee) from the total OAI enrollment of 4796 persons. The protocol for baseline radiographic measures may be found at http://www.oai.ucsf.edu/datarelease/OperationsManuals.asp. A subsequent physical activity ancillary study to the OAI collected accelerometer data on participants returning for ongoing 48-month follow-up visit.

A total of 981 persons consented to participate in accelerometer monitoring at the OAI 48-month follow-up visit during an evaluation period from September 2008 through April 2009, representing 79% of eligible participants (1240) during this period. These evaluation analyses were restricted to 519 participants with baseline radiographic knee OA. Accelerometer data were merged with OAI public data (from baseline to the most recent 36-month visit) to determine evaluation sample characteristics. For analysis purposes, body mass index (BMI) missing at 36-month (21 participants, 4.1%) was imputed by using data from previous visits. The evaluation sample in comparison to the entire OAI radiographic knee OA cohort was almost identical in baseline age and BMI, and was slightly more likely to be female (62% vs. 58%).

Accelerometer Measures and Procedures

Physical activity was monitored at the OAI 48-month follow-up visit using a GT1M Actigraph accelerometer (Actigraph; Pensacola, FL). The accuracy (walking speed¹⁷) and test-retest reliability¹⁸ of Actigraph accelerometers under field conditions have been established in many populations including persons with OA¹⁹. The GT1M Actigraph is a small uniaxial accelerometer that measures vertical acceleration and deceleration ²⁰. Accelerometer output is an activity count, which is the weighted sum of the number of accelerations measured over a time period (e.g. in this case 1 minute), where the weights are proportional to the magnitude of measured acceleration.

Accelerometer recordings were translated on a minute by minute basis into physical activity intensity categories (light, moderate, vigorous) from activity counts. The influence of different thresholds to assess physical activity intensity has been previously examined^21–23. For the purposes of standardization we apply intensity thresholds used by the NCI ¹³: light (1–2019 counts), moderate (2020–5998 counts), vigorous (5999 counts or greater).

Uniform scripted instructions were given on the wear and positioning of the accelerometer. Participants were instructed to wear the accelerometer upon arising in the morning and continuously until going to bed at night for seven consecutive days. The unit was worn on a belt at the natural waistline on the right hip in line with the right axilla. Participants maintained a daily log to record the dates of accelerometer monitoring. Skipped days reported on the log were excluded from the analysis.

Accelerometer Algorithm Parameter Definitions

Non-wear

Non-wear refers to a sustained period of little or zero activity that may represent an interruption in accelerometer monitoring. The benchmark NCI non-wear threshold applied by Troiano and colleagues¹³ to US adult accelerometer data consisted of an interval of at least 60 minutes of zero activity counts that contained no more than 2 minutes of low (<100) activity counts. A rolling window algorithm (i.e., scan each minute and begin a potential non-wear period when a zero activity count is found) identified non-wear periods. Consistent with the NCI approach, a non-wear period ended with either a third minute of low activity counts or a one minute activity count greater than or equal to 100. The term “zero activity” is used to designate a period of continuous zero activity counts or near-zero activity that allows up to two minutes of <100 activity counts. Non-wear parameter thresholds of 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270, and 300 minutes were investigated. A period of zero activity longer than the threshold time designated non-wear. Wear hours are calculated on a daily basis as 24 hours minus the non-wear hours.

Valid day

A valid day refers to daily wear hours of estimated accelerometer monitoring that meet/exceed a threshold. Accelerometer data from non-valid days are considered unreliable to describe a full day of physical activity. Thus, the definition of a valid day directly impacts data loss. The NCI benchmark definition for a valid day from Troiano and colleagues for US adults was a day containing 10 or more hours of monitoring (also called wear time).¹³ Importantly, wear time did not need to be continuous to be considered valid. We tested valid day thresholds of 8, 10, and 12 hours.

Outcomes

Mean daily wear hours and three physical activity outcomes were assessed: 1) mean daily activity counts; 2) mean daily minutes of activity (i.e. minutes with accelerometer activity counts>0); 3) mean daily MVPA (moderate to vigorous physical activity) minutes. A mean daily outcome represents the outcome summed over all wear hours divided by the number of monitored days for each person.

Analysis

In order to assess whether the general population based non-wear and valid day thresholds can be applied in OA population, we investigated the minimum non-wear threshold that resulted in stable physical activity outcomes and the influence of different threshold values on data loss.

Descriptive analyses graphically depict the relationship between non-wear thresholds and outcomes described above. This process was used to identify candidate non-wear thresholds that were associated with stable results in regard to physical activity outcomes when applied to accelerometer data from participants with knee OA. Similar descriptive analyses were performed to examine stability among stratified BMI, age, and gender subgroups. Data retention as a function of candidate non-wear and valid day thresholds was also plotted.

Statistical analysis of non-wear threshold

We compared the physical activity outcomes derived from candidate non-wear thresholds (e.g. 90-min, 120-min) with outcomes derived from the NCI 60-minute non-wear US adult population benchmark. Statistical testing used studentized t-statistics to maintain overall testing at an alpha = .05 level of testing²⁴ were applied when evaluating within person paired differences in physical activity outcomes. Due to skewed distributions, non-parameteric quantile regression²⁵ was used to estimate the difference in medians of physical activity outcomes and the standard error of those differences. Results of statistical testing are reported as confidence intervals to reflect the precision of the data and a plausible range of findings; a 95% confidence interval that excludes zero indicates a statistically significant result.

Statistical analysis of valid day threshold

A linear weighted Kappa coefficient was used to describe the agreement in the estimated number of valid days resulting from the application of different threshold values²⁶.

We used Stata/SE 10.0²⁷ for quantile regression and SAS 9.2²⁸ for other analyses.

Results

A total of 519 OAI participants with definite knee OA collectively contributed 3536 days of accelerometer data for analysis. This evaluation sample was predominantly female (62%), college educated (60%) and Caucasian (86%) with an average age of 62 (SD=9). Accelerometer assessment employed up to 7 days of continuous monitoring. Over 96% participants had 6–7 days’ monitoring. These 519 participants compared to the remaining 2160 OAI participants with knee OA at baseline, were almost identical in the distribution of age and self-reported physical activity but included more females (62% versus 57%), Caucasians (86% versus 76%), had slightly lower average BMI (29 versus 30 kg/m²).

Figure 1 presents an example of a complete day (1440 minutes) of accelerometer output that contains prolonged periods of inactivity. Since participants were instructed to take off the accelerometer during the evening upon retiring, the graph indicates that the monitor was first worn around 6:00 AM and was last taken off around 10:20 PM. However, the strings of zero activity counts during waking hours could either be due to non-wear or very sedentary activities. In this study, participants were permitted to remove the units if they took a nap, bath, or shower during the day. On average, units were removed 1.4 times per day, which included taking the unit off at bedtime (calculated from on/off times reported by participants via logs).

Figure 1.

An example of minute-by-minute accelerometer counts graphed over a 24 hour period

Non-wear threshold results

Candidate non-wear thresholds ranging from 20 to 300 consecutive minutes zero activity were investigated to identify potential interruptions in wear time as part of the data reduction process. The relationship between candidate non-wear threshold values and wear hours and physical activity outcomes are displayed graphically in Figure 2 (A and B). As expected, mean daily wear hours (Figure 2A) increased with length of the non-wear threshold. The average daily activity counts increased with length of the non-wear threshold until it stabilized near the 90-minte to120-minute threshold. Similarly, the average daily minutes of (nonzero) activity increased with the non-wear threshold, but stabilized near the 90-minute to 120-minute threshold (Figure 2B). It was evident that the average of daily MVPA minutes was invariant to the non-wear thresholds. This invariance was expected because the non-wear period was terminated by definition when a minute of MVPA occurred. As a result, the same amount of MVPA minutes over a day was captured by all non-wear thresholds.

Figure 2.

Figure 2A Relationship of non-wear parameter values to basic outcomes: Mean daily wear hours and mean daily activity counts from 519 persons with knee osteoarthritis contributing 3536 days of accelerometer monitoring

Figure 2B Relationship of non-wear parameter values to physical activity intensity outcomes: activity minutes (nonzero counts) and moderate-vigorous physical activity (MVPA) minutes from 519 persons with knee osteoarthritis contributing 3536 days of accelerometer monitoring

Stratified analyses that investigated the non-wear threshold among BMI categories are presented in Figure 3. Participants with normal weight (BMI <25 kg/m²) had the highest level of physical activity in terms of mean daily activity minutes. Overweight (BMI 25–30 kg/m²) and obesity (BMI>=30 kg/m²) were associated with reduced physical activity – a mean daily loss of about 10 and 40 activity minutes respectively. Across all BMI groups, average daily activity minutes increased as the non-wear threshold increased and stabilized around the 90-minute to 120-minute threshold. This is consistent with the overall results shown in Figure 2B. Additional stratified analyses in age (49–54, 55–64, 65–74, 75 or older) and gender also revealed stabilization around the 90-minute to 120-minute non-wear threshold (not shown).

Figure 3.

BMI stratification: Relationship of non-wear parameter values to mean daily activity minutes (nonzero counts) from 519 persons with knee osteoarthritis contributing 3536 days of accelerometer monitoring

Statistical comparisons using studentized t-test of physical activity outcomes based on 60-minute (the general population benchmark), 90-minute, and 120-minute non-wear thresholds are shown in Table 1. The magnitude of the outcome differences associated with 90-minute versus 120-minute threshold was extremely small, as indicated by the narrow confidence intervals. For example, there was less than 0.1% difference between the averaged physical activity outcomes based on the higher 90-minute or 120-minute thresholds. Statistical testing was precluded for MVPA minutes, which were identical for all thresholds.

Table 1.

Physical activity outcomes by non-wear parameter values from 519 persons with osteoarthritis arthritis contributing 3536 days of accelerometer monitoring

	Non-wear Parameter Values: Minimum consecutive minutes to signify non-wear			Differences and 95% Confidence Interval from studentized t-test*
Physical Activity Outcome	A. 60 Minutes Non-wear Minimum	B. 90 Minutes Non-wear Minimum	C. 120 Minutes Non-wear Minimum	90 vs. 60	120 vs. 60	120 vs. 90
Mean daily activity counts	190173	190238	190263	45.71 (38.35, 53.08)	62.86 (53.01, 72.70)	8.43 (3.38, 13.48)
Mean daily (nonzero) activity minutes	460	463	464	2 (1.73, 2.27)	2.71 (2.37, 3.05)	0.43 (0.17, 0.68)
Mean daily MVPA minutes**	14	14	14	-	-	-

^*Since the distributions of the differences are skewed, medians and standard errors of medians from quantile regression are used in the studentized t-tests that maintain an alpha=.05 level of testing with multiple statistical tests. A 95% confidence interval that excludes zero indicates the median difference is statistically different from zero at an overall α=.05 level of testing.

^**Statistical test precluded by zero variance on differences due to identical results in groups A, B, and C.

Valid day threshold results

The influence of the valid day threshold on data retention was graphically examined. Figure 4 shows the proportion of data retained for valid day threshold set at 8, 10, and 12 hours of evidence of monitoring as a function of non-wear thresholds. These valid day thresholds approximately represent wearing a monitor 1/2, 2/3 and 3/4 of waking hours under the common situation of 8 hours sleep and 16 hours awake. For example, at the 60-minute non-wear benchmark threshold, the 10-hour valid day threshold resulted in 10% data loss (i.e., 90% data retention). Increasing the threshold to 12 hours more than doubled data loss to 26%. The curves from this knee OA sample related to the 8-hour and 10-hour valid day threshold stabilized and captured over 94% of monitored days near the 90-minute to 120-minute non-wear threshold, whereas the 12-hour threshold resulted in 85% data retention. These graphs suggest that the benchmark threshold of 10 wear hours or more used in the general US adult population to identify a valid day is applicable in the context of knee OA, even when non-wear thresholds of 90 or 120 minutes are used.

Figure 4.

Percentage of monitored days with daily wear hours exceeding 8, 10, and 12 hours by non-wear parameter values from 519 persons with osteoarthritis contributing 3536 days of accelerometer monitoring

Agreement in the distribution of valid days for this knee OA sample was evaluated using weighted Kappa coefficients for non-wear thresholds of 60, 90, and 120 minutes in Table 2. Consistent with Figure 4, the frequency of valid days was greatest for the 120-minute non-wear threshold, followed by the 90-minute and 60-minute thresholds. There was good agreement between the 60-minute non-wear threshold and the higher thresholds (weighted Kappa=0.67–0.76) and very good agreement in the highest 90-minute and 120-minute non-wear thresholds (weighted Kappa=0.91).

Table 2.

Distribution of valid days by non-wear parameter values from 519 persons with osteoarthritis with accelerometer monitoring over 3536 days

	Non-wear Parameter Values			Weighted Kappa (95% CI)
Number of Valid Days (with >10 hours Wear Time)	A. 60 Minutes Non-wear Minimum	B. 90 Minutes Non-wear Minimum	C. 120 Minutes Non-wear Minimum	60 Minutes vs. 90 Minutes	60 Minutes vs. 120 Minutes	90 Minutes vs. 120 Minutes
7 days	306 (59.0%)	364 (70.1%)	384 (74.0%)	0.76 (0.71, 0.80)	0.67 (0.61, 0.73)	0.91 (0.88, 0.94)
6 days	108 (20.8%)	90 (17.3%)	79 (15.25%)
5 days	46 (8.9%)	24 (4.6%)	24 (4.6%)
4 days	25 (4.8%)	20 (3.9%)	11 (2.1%)
1–3 days	31 (6.0%)	19 (3.7%)	19 (3.7%)
0 day	3 (0.6%)	2 (0.4%)	2 (0.4%)

Discussion

This study examined basic assumptions used to translate physical activity measured by accelerometers into meaningful physical activity outcomes for a knee OA population. The presented methodology facilitated decisions on non-wear and valid day thresholds that minimized data loss while capturing stable and meaningful physical activity outcomes. Our findings supported 1) applying a higher threshold of at least 90 minutes of zero/little activity to signify accelerometer non-wear rather than the general population benchmark of 60 minutes; 2) retaining the general population valid day threshold of 10 wear hours in this knee OA population.

Non-wear represents interruptions in accelerometer data collection. It is important to note that both non-wear and very sedentary activities register as “0” counts in accelerometer readouts. It is difficult to accurately attribute long continuous periods of “0” activity counts to non-wear or a prolonged inactive or “non-movement” time period such as reading, watching TV, or sedentary office work. The released NCI accelerometer algorithm provides an efficient approach, which is independent of participant report, to objectively capture a participant’s activity pattern by distinguishing wear time from non-wear periods.

Compared to the general adult population, people with osteoarthritis may be more likely to have reduced levels of daily physical activity due to symptoms of joint pain and stiffness and/or structural changes. In fact, the average daily activity of our arthritis cohort is less than 80% of that of the published activity counts among the NHANES general population of adults aged 40 or older.¹³ Prolonged periods of accelerometer zero activity counts in this knee OA cohort may be more likely to reflect extended periods of sitting rather than non-wear than in the general population. Our findings suggested that an alternate non-wear threshold of at least 90 minutes of zero activity may be more appropriate for persons with knee OA than the 60-minute non-wear threshold based on the general adult population. By increasing the minimum minutes of zero activity counts required to signify non-wear, more inactivity minutes were included within wear time and less data are discarded.

This adjustment will not only better capture the sedentary lifestyle of this population, but will improve accelerometer data retention. In fact, our data showed that the average daily inactive minutes during wear time (e.g., wear time minus activity minutes) increased from 339 minutes to 392 minutes when non-wear threshold increased from 60 to 90 minutes (Figure 2A and 2B). It is worth noting that although wear time increases as the non-wear threshold increases, physical activity outcomes investigated here achieved stability around a 90-minute non-wear threshold value. While our findings support a higher threshold in this OA population than the general population threshold, the stability of results suggests it is not necessary to increase the non-wear threshold beyond 90 minutes.

Our study also examined the influence of the minimum number of hours of accelerometer wear required each day to represent a complete day of activity, represented by the valid day threshold. The physical activity literature has favored a 10-hour level of evidence, which roughly represents monitoring over 2/3 of a person’s waking hours¹¹ and was the choice for the NHANES study of the general adult population.¹³ A valid day of monitoring standard based on a 10-hour rule appeared to be applicable to this OA population. Our study found that increasing the valid day threshold from 10 to 12 hours more than doubled data loss (e.g., from 6% to 15% when applying a 90-minute non-wear threshold), while reducing the threshold from 10 to 8 hours did not substantially improve data retention. Because the average wear time for the accelerometers in this knee OA sample was 14 hours, it is reasonable that the 10-hour rule would result in good data retention.

It is important to note that the stratified analyses by BMI, age, or gender groups suggested that the same 90-minute non-wear threshold applies across different strata. Findings from a separate study that examined accelerometer data from rheumatoid arthritis participants, also supported a 90-minute non-wear threshold while retaining the 10-hour valid day threshold.²⁹ The similarity of the non-wear and valid day thresholds for both inflammatory arthritis (rheumatoid arthritis) and non-inflammatory arthritis (OA) suggests that these thresholds may be appropriate for arthritis populations more broadly.

This study has several implications for future studies. First, our study showed that decisions on how to process accelerometer data influence physical activity outcomes in persons with knee OA, which is consistent with similar work related to other adult populations.^{11, 29} Accelerometer measurement is becoming a common approach to measure physical activity in osteoarthritis populations.^{19, 30–31} However, methodology from those studies largely depends on accelerometer validation studies done in the general population. It is important that publications disclose the parameters for deriving physical activity outcomes when reporting findings based on accelerometer monitoring. This disclosure is particularly important if the main interest is reporting patterns of sedentary activity, which are most strongly influenced by data processing decisions. Greater non-wear threshold values will capture more inactive minutes. Second, comparisons of findings across different studies must consider the data processing assumptions used to translate accelerometer read-outs into physical activity outcomes. Differences or lack of differences in physical activity outcomes across studies could be partially masked by the accelerometer translation process. Third, it is noteworthy that moderate or vigorous activity assessment was robust to non-wear thresholds. Finally, the practical effect of varying the valid day threshold was on data retention. The general population standard of 10 hours of wear time evidence yielded 94% data retention in this knee OA sample when combined with a 90-minute non-wear threshold.

There are several limitations to acknowledge in the present study. One limitation of the waist mounted uniaxial accelerometer used in this study is that water activities cannot be captured. Also, it may underestimate vertical acceleration/deceleration activities, such as cycling or weight lifting. Thus physical activity was underestimated in persons who regularly participated in such activities. However, walking is the most popular leisure time sports activity in US. Accelerometer monitoring also captures walking for working and transportation purposes. ^{19, 32} Another limitation is that the methods used by NHANES to obtain thresholds were unknown and if different, may produce different results. It is recognized the OAI is not a probability sample. However participants were recruited from multiple geographic sites using recruitment targets balanced for age and gender groups, and represented a broad spectrum of radiographic knee OA.

In summary, accelerometer data processing decisions influences both stability of the physical outcomes and data retention rates. We obtained stable physical activity values from a higher non-wear threshold of 90 minutes than the 60 minutes general population threshold with the added benefit of retaining more accelerometer recordings on a daily basis among persons with knee OA. These OA-based thresholds retain more valid days of physical activity data.