Accelerometer Adherence and Performance in a Cohort Study... : Medicine & Science in Sports & Exercise (original) (raw)
An extensive literature review of studies on physical activity and health supported the 2008 US physical activity guidelines for adults (37). Recommendations included muscle strengthening activities and ≥150 min·wk−1 of moderate aerobic activity, ≥75 min·wk−1 of vigorous aerobic activity, or an equivalent combination of the two in episodes of at least 10 min. On the basis of these recommendations, national goals for physical activity targeted increasing population levels of moderate to vigorous physical activity (http://www.healthypeople.gov). One way to assess progress toward these goals is to use accelerometers to measure physical activity, as was done in the US National Health and Nutrition Examination Survey (NHANES) starting in 2003–2006. Accelerometers measure movement through a battery-powered, wearable, electronic device. More often, surveillance and epidemiologic studies are incorporating accelerometry, with advances in technology and reduced costs. Accelerometry offers benefits in terms of eliminating bias reporting; however, it relies on both the participant to wear the monitor (adherence) and the device to accurately record information.
Adherence is defined in this study as wearing the accelerometer as directed by the study staff according to the research protocol. As accelerometer adherence increases, the amount of missing data declines. Methods exist to attempt to increase adherence with accelerometer wearing (1,28,34). Identifying characteristics associated with adherence provide researchers information to develop strategies to adjust for missing data from nonparticipation and nonadherence to obtain more accurate population estimates, improvement in modeling of relationships, and assisting future studies to target efforts toward improving participation and wear of the accelerometer. Currently, the most commonly used accelerometers in surveillance and epidemiologic studies are the ActiGraph (Pensacola, FL) and Actical (Philips Respironics, Bend, OR). Both devices use a proprietary algorithm to convert accelerations to a count metric providing counts per unit of time. However, the counts between the two devices are not directly comparable because they have different accelerometer sensors and different ways to derive and filter accelerations (17,21).
Study protocols typically specify that the accelerometer is worn during waking hours and only removed during water activities and for sleeping. Some studies use participant-recorded logbooks to determine when the accelerometer was put on and taken off to complement the accelerometer readings (25,32). However, logbooks place additional burden on participants and may be incomplete. A number of studies have used a period of consecutive zero counts of varying durations to define nonwear using an automated algorithm, with some protocols allowing for a few minutes of movement during the prolonged period of zeros, for both the ActiGraph (3,4,10,22,23,25,27,31,39) and the Actical (16). To date, a consensus standard for defining nonwear has not been reached for either accelerometer.
We located only one study that explored nonwear time algorithms using the Actical accelerometer among adults (16). Applying an accurate wear time algorithm is important to derive precise measures of frequency and duration of physical activity at various intensity levels. Thus, the first aim of this article was to describe the participation and adherence of wearing an accelerometer to identify those less likely to comply. The second aim of this article was to document the performance of the Actical accelerometer. Both aims were accomplished in a large population-based cohort of Hispanic/Latino adults.
METHODS
The study aims were examined using the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The population-based cohort was designed to examine diabetes, pulmonary, and cardiovascular disease risk factors, morbidity, and mortality (18). From March 2008 to June 2011, 16,415 self-identified Hispanic/Latino men and women 18–74 yr were recruited and enrolled from randomly selected households in four US communities (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA). The study was approved by Institutional Review Boards at each site, and informed consent was obtained from all participants.
Objective physical activity measurement
During the HCHS/SOL baseline clinic visit, participants were asked to wear an Actical accelerometer (version B-1, model 198-0200-03) for 1 wk. This Actical is an omnidirectional accelerometer, measuring 1.14″ × 1.45″ × 0.43″, weighing 16 g, and powered by a CR2025 lithium battery. The device had 32 MB of nonvolatile flash memory, a sampling rate of 32 Hz, motion sensitivity from 0.05_g_ to 2.0_g_, and a bandwidth of 0.035–3.5 Hz. A microprocessor converted accelerations to a unit called counts over a given epoch or time period. Prior studies indicate that Actical has acceptable technical reliability for counts (9,38). More detailed technical specifications are available elsewhere (17).
Participants were fitted with a belt and left the clinic visit wearing the accelerometer. They were instructed to continue to wear it above the iliac crest on the right side, the location most sensitive to vertical movements consistent with ambulation. Participants were told to undertake their usual activities for the following week while wearing the accelerometer and to remove it only for swimming, showering, and sleeping. They were provided written instructions and a phone number to call if any questions arose. Study staff called the participants a few days later to answer questions, to ensure the instructions were clear, and to remind them to wear the accelerometer. Participants returned the accelerometer using a padded prepaid envelope. Upon receipt, staff downloaded the data and initialized the accelerometer for reuse. Participation was defined as returning the Actical and having any recorded wear time.
The Actical was programmed to capture accelerations in counts and steps in 1-min epochs. The four study sites programmed the monitor to start at varying times between 5:00 a.m. of the clinic visit day and 5:00 a.m. of the following day. For standardization, we included time for all sites beginning at 5:00 a.m. the morning after the clinic visit and truncated data at midnight on day 6 of the wear period, providing a consistent maximum 6-d wear period across all study participants. We then performed a systematic review of count patterns to identify and exclude days that indicated spurious recording. Nonwear was defined as consecutive zero counts for at least 90 min (window 1), allowing for short time intervals with nonzero counts lasting up to 2 min if no counts were detected during both the 30-min (window 2) upstream and downstream from that interval; any nonzero counts except the allowed short intervals were considered as wear time (3). Adherence was defined as ≥10 h·d−1 of wear time for at least three of six possible days of wear. The ≥10 h·d−1 criteria are often used in other studies (36), and the 3 of 6 d were chosen to represent at least 50% of the maximum days of wear.
The intensity levels were defined as follows (5,7,40): vigorous, ≥3962 counts per minute; moderate, 1535–3961 counts per minute; light, 100–1534 counts per minute; and sedentary, <100 counts per minute. Using the accelerometer data, we operationalized meeting the 2008 US physical activity guidelines using their terminology as follows (37):
- High: moderate physical activity ≥300 min·wk−1, vigorous physical activity ≥150 min·wk−1, or a combination of the two (multiplying vigorous physical activity by 2 and summing to obtain ≥300 min·wk−1) in ≥10-min bouts
- Medium activity: moderate physical activity 150 to <300 min·wk−1, vigorous physical activity 75 to <150 min·wk−1, or a combination of the two (multiplying vigorous physical activity by 2 and summing to obtain 150 to <300 min·wk−1) in ≥10-min bouts
- Not meeting physical activity recommendations
Because participants contributed between 3 and 6 d of adherent accelerometer data, the physical activity guidelines were prorated for the proportion of a week with available data. This assumed that the remainder of days within the week had the same average level of physical activity as the adherent days.
Other descriptive measures
Self-reported physical activity in a typical week was assessed using the modified Global Physical Activity Questionnaire (GPAQ). The GPAQ was originally developed as a result of an international collaboration with the World Health Organization (http://www.who.int/chp/steps/GPAQ/en/index.html), with evidence of test–retest reliability (2) and concurrent validity (2,15). The HCHS/SOL GPAQ questionnaire (available at the study website: http://www.cscc.unc.edu/hchs/) included six questions on work activity, three questions on transport, six questions on recreation, and one sitting question. We derived time spent on recreation, work, transportation, and sitting in minutes per day. The recreational, work, and transportation variables were used to derive the total time spent separately in moderate (small increases in breathing and heart rate) and vigorous (large increases in breathing and heart rate) physical activity.
Weight was measured to the nearest 0.1 kg and height to the nearest centimeter. Body mass index (BMI) was calculated as weight in kilograms divided by height in squared meters and grouped into underweight (<18.5 kg·m−2), normal weight (18.5 to <25 kg·m−2), overweight (25 to <30 kg·m−2), and obese (≥30 kg·m−2). Annual household income, education, marital status, employment, country of birth, language preference, immigrant generational status, general health, and health limitations were obtained by interview during the clinic visit. Participants self-identified into the following groups: Central American, Cuban, Dominican, Mexican, Puerto Rican, South American, or other. Health limitations were ascertained by self-reported health limiting them in moderate activities or in climbing several flights of stairs (response options: a lot, a little, not at all). Both questions came from the SF-12 (version 2) Health Survey (QualityMetrics, 2002).
Statistical analysis
The sample design and cohort selection have been previously described (18). Briefly, a stratified two-stage area probability sample of household addresses was selected in each of the four sites. The first sampling stage randomly selected census block groups on the basis of Hispanic/Latino concentration and proportion of high/low socioeconomic status. The second sampling stage randomly selected households from US Postal Service registries that covered the randomly selected census block groups. Households were screened for eligibility, and Hispanic/Latino persons age 18 to 74 yr were selected in each household that agreed to participate. Oversampling occurred at each stage, with block groups in areas of Hispanic/Latino concentration, households associated with a Hispanic/Latino surname, and those age 45 to 74 yr selected at higher rates than their counterparts. The household response rate was 33.5%. Of 39,384 individuals who were screened, selected, and met the eligibility criteria, 41.7% were enrolled, representing 16,415 participants from 9872 households.
Because oversampling occurred at both stages of sample selection to increase the likelihood that a selected address yielded an eligible household, participants in HCHS/SOL were selected with unequal probabilities of selection. Hence, participants had a sampling weight that was the product of their base weight (defined as the reciprocal of the probability of selection) and three adjustments (nonresponse relative to the sampling frame, trimmed to handle extreme values, and calibrated to the 2010 US Census according to age, gender, and Hispanic/Latino background). The HCHS/SOL target population was defined as all noninstitutionalized Hispanic/Latino adults age 18–74 yr residing in the defined geographical areas (census block groups) across the four participating sites. All analyses were performed using SAS 9.3 software (SAS Institute, Cary, NC), and SUDAAN software release 11 (RTI International, Research Triangle Park, NC) was used to account for the complex survey design and sampling weights.
Participation and adherence were determined overall by sociodemographic characteristics (site, Hispanic/Latino background, site by background, age, gender, household income, education, marital status, employment, US born, immigrant generation, and language preference), by health-related characteristics (BMI, general health, and health limitations), and for self-reported physical activity. Differences across groups were assessed using the Cochran–Mantel–Haenszel (CMH) chi-square general test of association with the Wald chi-square statistic for nominal variables, the test for trend for ordinal variables, and the _t_-test for continuous variables. P values are presented for descriptive purposes.
Descriptive statistics were calculated to evaluate the performance of the accelerometers in a variety of ways, focusing on the number of different counts per minute to understand how the accelerometer performed. Heat plots were generated to display all counts per minute among adherent participants. Descriptive statistics were calculated for the number of different values by gender, age, BMI, meeting 2008 physical activity guidelines, consecutive wear day, weekday/weekend, and number of adherent days. Because physical activity is dynamic, sustained measurements of the same values of counts per minute with the Actical may be a sign of nonwear (for zero counts per minute), device rounding due to precision limits, or device malfunction. Therefore, nonzero sustained counts per minute were also explored and identified when the same count value was repeated for more than 10 min.
RESULTS
Participation
Overall, 92.3% participants returned the accelerometer with at least some wear time. Characteristics of participants (n = 15,153) were compared with nonparticipants (n = 1262), regardless of the amount of time the accelerometer was worn (Tables 1 and 2). Accelerometer participation was higher (P ≤ 0.05) among those who were married or partnered, reported a higher household income, were first-generation immigrants, were not health limited with stairs, and reported lower sitting time. The weighted percent of participation differed by site (ranging from 86.9% in the Bronx to 96.1% in San Diego), background (ranging from 82.5% for mixed, other, or missing groups to 94.8% for Mexicans), and site–background (ranging from 85.3% by South Americans in the Bronx to 96.2% by Mexicans in San Diego; data not shown; mixed, other, or missing group not included). There were no notable differences in accelerometer participation by gender, age, education, employment status, US born, language preference, general health, BMI, moderate activity health limitations, physical health score, or self-reported physical activity (moderate, vigorous, recreational, work, and transportation in minutes per day).
Comparison of participation in the accelerometer portion of the study and adherence to the protocol overall and stratified by categorical variables, HCHS/SOL 2008–2011.
Comparison of participation in the accelerometer portion of the study and adherence to the protocol stratified by continuous variables, HCHS/SOL 2008–2011.
Adherence
Before assessing wear time adherence, we excluded 232 participants whose clinic date and Actical start date differed by more than 2 d to eliminate cases where the accelerometer may have been initiated on the wrong day. A systematic review of counts per minute for potential spurious recording identified several patterns. Five participants with no recorded sedentary time on all six monitoring days were excluded. We identified 124 participants with at least one instance of any nonzero counts per minute sustained for 10 or more consecutive minutes. All occurrences happened below 200 counts per minute and most below 100 counts per minute. Upon detailed review, we excluded three participants for whom most of their data had the same repeated nonzero values (specifically 12 counts per minute for one participant and 13 counts per minute for two participants). After exclusions, this left a sample size of 14,913 to assess adherence. Overall, 85.5% of this sample (12,750/14,913) met the adherence definition of ≥3 d of wear for at least 10 h·d−1, with 46.5% providing 6 d of adherent data, 19.5% providing 5 d, 11.5% providing 4 d, and 8.1% providing 3 d (Table 3).
Percentage of participants by number of adherent days wearing the accelerometer and by age group and gender, HCHS/SOL 2008–2011.
Adherent participants (n = 12,750) were more likely (P ≤ 0.05) to be male, older, married or partnered, or employed or retired, reported higher household income, were first-generation immigrants, preferred Spanish over English, have lower BMI when explored continuously, or reported higher work activity, lower recreational activity, or lower sitting time compared with those who wore the accelerometer but did not provide adherent data (Tables 1 and 2). Adherence was lower (P ≤ 0.05) among participants who were not employed and those who were US born. There were also differences by site (ranging from 76.4% Miami to 86.4% Bronx), background (ranging from 76.0% for mixed, other, or missing group to 86.3% for Dominicans), and site–background (ranging from 75.3% by Central Americans in Miami to 94.0% by South Americans in the Bronx; data not shown; mixed, other, or missing group not included). Adherence did not differ by education, general health, health limitations (stairs or moderate physical activities), physical health score, moderate physical activity, vigorous physical activity, or transportation physical activity.
Performance
Among the 12,750 adherent participants, the maximum count per minute was 12,000. Within the range of 0 to 12,000 values, there were 5846 different values of counts per minute (48.7%) recorded at least once and, therefore, 6154 values that never occurred among the adherent participants on adherent days (Fig. 1). In particular, there were four values less than 200 that never occurred across all adherent days of wear (1, 2, 3, and 6 counts per minute) and some values that were much more likely to occur than others. For example, 0 counts per minute occurred 33,132,407 times (50.7% of wear) and 13 counts per minute occurred more than 100,000 times. However, 7 counts per minute occurred less than 20,000 times. For all recorded counts per minute less than 200, the mean number of different counts per minute across the monitoring period (three to six adherent days) was 17.4 (SD, 9.3; median, 16; interquartile range, 16–17; range, 13–132).
Each box in the figure represents all possible counts per minute ranging from 1 to 12,000 among participants with at least three adherent days of accelerometer data (n = 12,750), HCHS/SOL 2008–2011. The three blue colors classify the frequency (minutes*day*participants) of counts per minute into categories. The white indicates the count per minute was never recorded for any participant. The _x_-axis unit is in hundreds, whereas the _y_-axis unit is in thousands. Zero counts per minute is not shown on the figure.
Among the 12,750 adherent participants, the mean number of different counts per minute across the full range of data during the monitoring period was 112.5 (SD, 64.3; median, 106; interquartile range, 90–122; range, 14–1606) (Table 4). The different number of counts per minute was higher (P ≤ 0.05) among men, younger ages, normal weight, participants from the San Diego site, those categorized at higher levels of physical activity, and those who were adherent in all 6 d.
Different number of counts across adherent days by demographic and health characteristics, HCHS/SOL 2008–2011.
DISCUSSION
Adherence
This study described participation and adherence of accelerometer wear to identify adults less likely to complete the accelerometer protocol as intended. Overall, 92.3% of the HCHS/SOL cohort returned the accelerometer with at least some wear time and 77.7% of the HCHS/SOL cohort met the adherence definition of wearing it at least 3 of 6 d for ≥10 h·d−1. Participation was higher for the HCHS/SOL participants compared with the 2003–2004 NHANES sample age 6 yr and older (74.4% or 7176/9643) (33). In the HCHS/SOL, both accelerometer participation and adherence were higher among those who were married or partnered, reported a higher household income, were first-generation immigrants, or reported lower sitting times. Notably, other factors were associated with either participation or adherence, but not both. Accelerometer participation, but not adherence, was higher among those with no stair limitations. Adherence, but not participation, was higher among those who were male, older, employed or retired, or not US born, those who preferred Spanish over English, those who reported higher work activity or lower recreational activity, and those with a lower BMI.
A few other studies of adults have explored factors associated with adherence of accelerometer wear, although definitions of adherence varied (7,11,19,20,33). In a nationally representative sample of Canadians, meeting the adherence definition for wearing the Actical was higher among 60- to 79-yr olds compared with 20- to 39-yr olds (7). NHANES data supported this pattern, finding that the ActiGraph accelerometer adherence was higher among those 60 yr and older compared with other age groups (33). The investigators also found 7 of 7 d of adherence were higher among men compared with women within the same age categories (20–39, 40–59, and ≥60 yr). Other adult studies have found higher participation or adherence to the accelerometer protocol among older adults (19,26), nonsmokers (19,20,26), and those who were married (20), had higher education (11,19,20), had higher income (11), worked or retired (19,26), had higher self-reported health (19,20), had higher cognitive function (11), had higher physical function (11), or reported more vigorous physical activity (20). The variety of correlates associated with either accelerometer participation or adherence can be used to develop strategies to adjust for missing data and help future studies target efforts toward improving participation.
Performance
Calibration studies among adults indicate cut point thresholds for intensity level when the Actical is positioned at the hip for sedentary (8,24), light (5,13), moderate (5,12–14,35,38), and vigorous activity (5,12). What has not been documented for the Actical is its performance in a large sample. For example, are the counts continuous across all intensity levels? How much variability do the counts provide? Our data were able to address these questions.
In this study, we found that the Actical counts per minute ranged from 0 to 12,000. This upper range is much lower than the plausible values up to 20,000 counts per minute described by Colley et al. (6). Across this range of counts, over half (50.7%) of the values (in counts per minute) were never recorded. This might be expected at higher values, where fewer participants engage in vigorous physical activity, but we also found instances of this at lower ranges. For example, the values of one, two, three, and six counts per minute never occurred among those with adherent data. According to the manufacturer, because of the nature of the Actical processing, counts below 100 are not as precise and often recorded using only a few values that appear repetitively rather than being truly continuous. This phenomenon can lead to sustained repetitions of the same count that are not spurious. We also found that the mean number of different counts per minute for each participant was 112.5, which is seemingly low given that this was assessed over three to six adherent days of monitoring. As expected, the number of different values was higher among those that were more physically active. Even so, the findings illustrate that because of the filtering, the Actical data are not truly continuous.
Understanding the performance of the Actical accelerometer can help researchers decide on nonwear time algorithms or identify the rare cases of spurious recording. The process of identifying missing and nonadherent accelerometer data is not standardized. Some studies use logbooks to help make the determination (e.g., [39]). Research to determine when the accelerometer is worn by participants, in the absence of keeping a logbook to determine on and off times, has been conducted primarily using the ActiGraph accelerometer. One study of adults recommended using a longer period of zero counts (i.e., 60 min) instead of a shorter period of zero counts (i.e., 20 min) to define accelerometer nonwear (10). However, this study lacked a referent standard. Another study improved on this by comparing three wear time algorithms with self-reported wear time (39). They found that allowing for very limited interruptions during the extended period of zeros optimized accuracy. The algorithm used did not meaningfully change the prevalence of moderate to vigorous physical activity, but it did affect the prevalence of sedentary behavior. True nonwear periods shorter than 60 min, which commonly occur when the accelerometer was removed in the evening (particularly after 23:00), were being misclassified as wear time. The authors proposed that this bias would also affect studies of correlates or those exploring within-person changes in physical activity. Choi et al. (3) developed an improved algorithm to discriminate between wearing states based on actual wearing time while participants were observed in a whole-room indirect calorimeter.
On the basis of the Actical performance, we found that consecutive counts can occur over long periods of time. Thus, we may be excluding zero counts per minute that were sedentary rather than nonwear. Increasing the number of consecutive minutes of zero counts that define nonwear will keep more data and thus increase adherence. It will also increase the time spent in sedentary behavior. The key is determining what criteria to use for maximum accuracy. One study of adults 56 yr and older contrasted wear from logbooks to 60, 90, 120, 150, and 180 consecutive minutes of zeros from the Actical to define nonwear (16). They found the highest sensitivity and specificity using 90 and 120 consecutive counts per minute of zeros to define nonwear when compared with logbooks. Moreover, the Actical filter could be altered by the company to allow for better sensitivity at the lower end of the range of counts. A small study reported that the ActiGraph GT3X was more sensitive than the Actical to movements in nonvertical planes and at thresholds of <8000 counts per minute, but that the Actical was more sensitive above this cut point (30).
Limitations
Several limitations of our work should be noted. First, there may be unmeasured characteristics associated with participation, adherence, or performance of the accelerometer that we did not assess. Second, the manufacturer states that the different versions of the Actical use similar data acquisition methodology and show equivalency across counts; however, the newer versions add features and upgrades. However, we are not aware of any published studies that explore equivalency across Actical versions. Thus, it is not known how the different versions might affect Actical performance. Third, our data collection protocol specified a 1-min epoch; it is not known how a shorter epoch may affect the Actical performance. Fourth, the cleaning program we used to determine nonwear for this study was developed on the ActiGraph and it is not known whether it performs as well for the Actical (3). A next useful study would be to explore accurate (gold standard) assessment of wear and nonwear of the Actical accelerometer against other cleaning algorithms.
CONCLUSIONS
Among this large cohort study of Hispanic/Latino adults, we found differences in some correlates of accelerometer participation and adherence. Studies should assess characteristics potentially associated with accelerometer participation and adherence to address a high percentage of missing accelerometer outcomes. For example, these characteristics could be used to create inverse probability weights which allow correction for the bias of the estimates obtained by a complete-case analysis. Because accelerometers become lighter and less intrusive, participation and adherence should improve. The performance of the Actical accelerometer provides insight into creating a more accurate nonwear algorithm. Further work is needed to develop and determine the most accurate algorithms against a criterion measure to define wear time for the Actical. It is likely that the algorithm of choice may differ by the type of accelerometer because the performance of counts varies across accelerometers (17,21).
The authors thank Stephen Campbell and James Locklear for their contributions to the analysis and the anonymous reviewers for their suggestions. The authors thank the staff and participants of HCHS/SOL for their important contributions. A complete list of staff and investigators has been provided by Sorlie et al. (29) and is also available on the study website (http://www.cscc.unc.edu/hchs/).
The authors declare no conflicts of interest.
The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) was carried out as a collaborative study supported by contracts from the National Institutes of Health (NIH) and National Heart, Lung, and Blood Institute to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following institutes/centers/offices contribute to the HCHS/SOL through a transfer of funds to the National Heart, Lung, and Blood Institute: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, and NIH Institution-Office of Dietary Supplements.
The results of the present study do not constitute endorsement by the American College of Sports Medicine or the NIH.
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Keywords:
ACTICAL; MISSINGNESS; NONWEAR; PHYSICAL ACTIVITY; SAMPLE WEIGHTS; SURVEILLANCE
© 2015 American College of Sports Medicine