Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Jan 1.
Published in final edited form as: Arch Gerontol Geriatr. 2015 Sep 16;62:68–74. doi: 10.1016/j.archger.2015.09.004

Relating Wrist Accelerometry Measures to Disability in Older Adults

Megan J Huisingh-Scheetz 1, Masha Kocherginsky 2, Elizabeth Magett 1, Patricia Rush 1, William Dale 1, Linda Waite 3
PMCID: PMC4663184  NIHMSID: NIHMS728883  PMID: 26452423

Abstract

Objective

This analysis assessed the extent to which: 1) wrist accelerometer measures were associated with difficulty performing specific activities of daily living and instrumental activities of daily living and 2) these measures contributed important information about disability beyond a typical self-reported vigorous activity frequency question.

Methods

We used data from the National Social Life, Health and Aging Project (NSHAP) accelerometry sub-study (n=738). Activity was assessed using two wrist-accelerometer measures assessed over 3 days (routine activity expressed as mean count/15 second epoch during wake time, and immobile time expressed as the proportion of wake time spent immobile), and self-reported average vigorous activity frequency. The association between routine activity, immobile time and difficulty performing fourteen activities of daily living (ADLs) and instrumental activities of daily living (IADLs) plus two summary measures (any ADL or IADL difficulty), was assessed using logistic regression models, with and without controlling for self-reported vigorous activity.

Results

Self-reported activity was mildly correlated with routine activity (r = 0.27) and immobile time (r = −0.21). Routine activity, immobile time, and self-reported vigorous activity were significantly associated with twelve, ten, and fourteen disability measures, respectively. After controlling for self-reported activity, significant associations remained between routine activity and eight disabilities, and immobile time and six disabilities.

Conclusion

Wrist accelerometry measures were associated with many ADL and IADL disabilities among older adults. Wrist acclerometry in older adults may be useful to help assess disability risks and set individualized physical activity targets.

Keywords: Accelerometry, Disability, Motor Activity, Frail Older Adult, Self Report

Physical activity (PA), whether done through formal exercise or routine every day activities, and limited sedentary time are important health indicators in older adults.1, 2 However, older adults face challenges to vigorous exercise participation due to higher rates of mobility limitations, vision loss, muscle weakness, social isolation, and endurance-limiting diseases.3 These limitations often result in changing patterns of activity with aging: reducing vigorous exercise, and spending more time doing low to moderate intensity exercise like walking or gardening, or routine activity like light housework or shopping, or being sedentary. Only half of adults over 65 report spending the recommended 150 minutes per week in moderate to vigorous activity.4 Increasing activity and reducing sedentary time are both feasible interventions among seniors if programs are individualized.5

Accurately assessing older adult physical activity patterns is challenging. Questionnaires designed to measure activity are limited to the specific activities evaluated, can become lengthy, and are subject to recall bias.6, 7 In older adults, cognitive impairment and fear of losing independence can also complicate the accuracy of self-report and may not reflect true daily activity patterns.8, 9 Providers' time constraints further limit adequate activity assessment.10 National guidelines currently do not recommend a standard physical activity screen.11 Tools that measure vigorous activity, routine activity, and sedentary behavior simultaneously in older adults are limited.6, 12, 13 Therefore, alternative mechanisms for assessing activity in older adults must be explored.

Accelerometry may be a useful objective assessment of activity in disabled adults. Prior work using hip accelerometers worn for 7 days showed that adults with mobility impairments spend less time in activity and more time sedentary than those without mobility impairments.14 Duration of activity bouts using hip accelerometry was also associated with self-reported disability in older adults.15 Accelerometry has been used to detect activity recovery following stroke,16, 17 to measure change in activity following pharmacotherapy for rheumatoid arthritis,18 and to assess activity adherence in multiple sclerosis patients.19 Although hip accelerometry has a substantial body of evidence supporting its use in activity assessment, wrist accelerometers now firmly dominate the commercial markets. The relationship between wrist accelerometry measures and disability in older adults is not known despite a growing availability of these devices.

The objective of this analysis was to determine whether wrist accelerometer measures of activity and immobility are associated with difficulty performing activities of daily living (ADL) and instrumental activities of daily living (IADL). We also considered whether wrist accelerometry adds important information about disability beyond the traditional question on frequency of self-reported vigorous activity that might be used in a clinical encounter. Understanding whether and how wrist accelerometry measures are related to disability will help determine their clinical functionality and use in this population.

METHODS

Study Design

The National Social Life, Health and Aging project (NSHAP) is a longitudinal U.S. population-based survey that collected extensive information on physical, cognitive, and social health. A nationally-representative sample of 3,005 community-dwelling older adults (ages 57- 86) was recruited for Wave 1 (2005-2006).20 These participants and their partners (n=3,377) were re-interviewed in W2 (2010-2011). The NSHAP survey was conducted in the home by trained, non-medical interviewers and included a computer-assisted personal interview (CAPI), a biomeasure assessment, and a leave-behind questionnaire. The detailed sampling design and study methods have been reported elsewhere.20 The data collection was approved by the local Institutional Review Board.

Study Participants

A subset of 738 age-eligible (ages 62-91) W2 participants was included in a wrist accelerometer sub-study.

Wrist Accelerometry Sub-Study

Respondents in the accelerometry sub-study wore the Actiwatch® Spectrum on their non-dominant wrist for 72 consecutive hours (not removed during water activities like bathing.)21-24 It is an uniaxial, omnidirectional, piezoelectric, waterproof accelerometer used to measure sleep and (in)activity patterns.21, 22 A detailed description of the device and its use in NSHAP has been reported elsewhere.25 It continuously collects acceleration/deceleration data, which are averaged over 15-second intervals called “epochs” and recorded as an activity “count.” If no activity occurs during the epoch, such as during sleep or rest, “0” is recorded for that activity count. Data were pre-processed using the Actiware® software available from the manufacturer.21 Non-wear time was automatically excluded using a built-in galvanic sensor that identified when the device was worn. Rest and wake intervals were determined using manufacturer-suggested guidelines, cues from respondent recordings, data on ambient light in each epoch, and were manually curated by study investigators as described in detail elsewhere.26 Actiwatch® accelerometer counts have been shown to be moderately and significantly correlated with indirect calorimetry-measured energy expenditure during routine activity in older adults with chronic disease and in middle-aged but sedentary adults.23, 24, 27

Routine Activity and Sedentary Behavior

We calculated two summary measures from the accelerometer output to estimate “routine activity” and “immobile time.” Routine activity was estimated by summing the activity counts per 15-second epoch for the wake intervals and dividing by the total number of epochs during wake time. Immobile time was estimated by the proportion of “0” activity counts among all activity counts during wake time, multiplied by 100%. Summary measures were used because equations predicting kilocalorie expenditure or METs from accelerometer activity count data have had inconsistent accuracy among older adults engaging in routine activity outside of the laboratory setting.28 Continuous summary measures were also chosen because wrist accelerometer count cut-offs that distinguish sedentary, mild, moderate, and vigorous metabolic equivalents were established in children rather than older adults for the Actiwatch®.29 Because we could not estimate sedentary time, we estimated time spent completely immobile. Immobile time underestimates actual sedentary time (e.g. a sedentary activity like watching television or reading will not be categorized as immobile due to low-level wrist movements).30 Using continuous accelerometry measures provides the highest resolution and most power for detecting significant pair-wise correlations.

Disability

Participants’ self-reported degree of difficulty (4-point scale) completing seven ADLs and seven IADLs. ADL and IADL limitation was defined by the presence of any difficulty with each task (yes = 1/no = 0). The ADLs included: walking one block, dressing, walking across a room, transferring in/out of bed, toileting, bathing, and eating. The IADLs included: driving at night, driving during the day, light housework, shopping, meal preparation, managing money, and taking medications. Separate indicator variables were also created identifying participants with at least one ADL disability or at least one IADL disability.

Self-Reported Physical Activity

Self-reported vigorous PA was assessed using a self-report question that represents what might be asked in a typical physician-patient encounter: “On average over the last 12 months, how often have you participated in vigorous PA or exercise? By vigorous PA, we mean 30 MINUTES OR MORE of things like sports, exercise classes, heavy housework, or a job that involves physical labor.” Answer choices included: a) 5 or more times per week, b) 3 or 4 times per week, c) 1-2 times per week, d) 1-3 times per month, e) Less than 1 time per month, or f) Never. This question and response options were found to be significantly associated with future functional decline and 10-year mortality.31, 32

Covariates

Age at the survey date, gender, race (white/Caucasian, black/African American, Other), Hispanic ethnicity, employment status (currently working, not working), education (< high school, high school, some college, bachelors or more), and household assets (<$10,000; $10-49,000; $50-99,000; $100-499,000; $500,000+) were self-reported. Body mass index (BMI) was calculated using objectively measured height and weight: [703*(weight, pounds)/(height, inches)2].33 BMI was then categorized as underweight/normal (BMI 16 to <25), overweight (BMI 25 to < 30), and obese (BMI ≥ 30). Only 2 respondents had a BMI < 18. Cognitive function was assessed using the survey-adapted Montreal Cognitive Assessment (MoCA-SA) (range 0-20, higher scores indicate better function).34 Timed walk (seconds) was measured using the fastest of two timed 3-meter walks performed at the participant's “usual pace.”35 We also calculated and controlled for total wake wear time (hours).

Statistical Analysis

Sample design and survey weights were used in all analyses to account for the complex survey design and to obtain point estimates and standard errors that reflect the U.S. national population in 2010, with the exception of Spearman rank correlation estimates. Sample characteristics were described using means and frequencies. The relationship between self-reported PA and the wrist actigraphy measures were assessed using Spearman rank correlation.

Separate logistic regression models were used to assess whether routine activity (Model 1), immobile time (Model 2), and self-reported PA (Model 3) were associated with ADL/IADL difficulty, controlling for covariates (age, gender, education, race, ethnicity, household assets, BMI categories, timed gait, cognitive function, employment status, wear time). Accelerometer measures were divided by 10 to show the effect per an increase of 10 activity counts (routine activity) or 10 percent (percent of wake time spent immobile), and reflect differences between two individuals who are 10 points apart on a given activity measure. We then expanded Models 1 and 2 by adding the self-reported PA variable as a predictor to determine if the magnitude and significance of each accelerometry predictor changed (Models 4 and 5). Although we expect a decrease in significance resulting from the additional covariate36 to a logistic regression model, no change in effect size would suggest that accelerometry and self-reported measures are independently associated with the outcome. Analyses were conducted using all available wear time, and, as a sensitivity analysis, were repeated using only days with ≥ 10 hours of wear time (defined as “valid” days in some other studies, for example NHANES).37 Results were similar, so reported analyses are based on all days. Statistical analyses were conducted with Stata 14.0 and NSHAP data release 2.2 (version d34186f4ce5f).38

RESULTS

Descriptive statistics for the accelerometer sub-study sample are provided in Table 1. Among the 738 age-eligible sub-study participants, 623 had complete data for accelerometry, self-reported physical activity, and covariates. The mean age was 72 (95% CI: 71.4-72.6), 52.6% (95% CI: 48.1-57.1) were female, 83.8% were White/Caucasian (95% CI: 79.0-87.7), 26.3% (95% CI: 21.9-31.2) were still working, and 5.4% had less than $10,000 of household assets (95% CI: 3.6-8.0%). The mean MoCA-SA score for the group was 14.4 (95% CI: 14.0-14.9), the mean timed 3 meter walk was 5.7 seconds (95% CI: 4.4-6.9). The most prevalent ADL disability was difficulty walking one block (24.0%, 95% CI: 20.5-27.9%), and the most common IADL disability was difficulty driving at night (35.0%, 95% CI: 30.2-40.1%). Approximately 45% of the sample reported participating in vigorous activities three or more times per week over the last year: 5+ times/week = 25.6% (95% CI: 20.3-31.8) and 3-4 times/week = 19.2% (95% CI: 16.2-22.5). The mean wake time activity count was 54.0 (95% CI: 51.9-56.2) and the average percent of the wake time spent immobile was 27.1% (95% CI: 26.1-28.2). The average total wake wear time was 42.1 hours (95% CI: 41.2-43.0).

Table 1.

Study Sample Characteristics (n = 623*)

Estimate 95% CI
Age (years, mean) 72.0 71.4-72.6
Female 52.6% 48.1.-57.1%
Education
    < High School 11.3% 8.0-15.6%
    High School Graduate 25.3% 20.6-30.5%
    Some College 38.9% 34.4-43.6%
    College Graduate 24.5% 19.4-30.5%
Race
    White/Caucasian 83.8% 79.0-87.7%
    Black/African American 6.1% 4.1-9.1%
    Hispanic, non-Black 6.5% 3.9-10.7%
    Other 3.5% 1.9-6.4%
Household assets
    <$10,000 5.4% 3.6-8.0%
    $10-49,000 11.3% 8.5-14.9%
    $50-99,000 11.3% 8.1-15.7%
    $100-499,000 42.9% 37.3-48.4%
    $500,000 29.1% 23.5-35.4%
Employment Status
    Working 26.3% 21.9-31.2%
Survey-Adapted Montreal Cognitive Assessment (mean) 14.4 14.0-14.9
Weight Status
    Underweight or Normal Weight 25.8% 21.3-30.9%
    Overweight 35.7% 31.2-40.4%
    Obese 38.5% 34.1-43.1%
Timed 3 meter Walk (seconds, mean) 5.7 4.4-6.9
Activities of Daily Living Disability
        Walking One Block 24.0% 20.5-27.9%
        Dressing 11.3% 8.8-14.5%
        Walking Across a Room 10.1% 8.0-12.7%
        Transferring In/Out of Bed 9.2% 6.5-12.8%
        Toileting 8.0% 6.0-10.5%
        Bathing 5.8% 4.1-8.1%
        Eating 2.2% 1.3-3.8%
        Difficulty with at least one ADL 31.1% 26.8-35.8%
Independent Activities of Daily Living
        Driving at Night 35.0% 30.2-40.1%
        Light Housework 13.3% 10.4-16.7%
        Shopping 9.9% 7.3-13.4%
        Meal Preparation 8.6% 6.1-12.0%
        Managing Money 6.7% 5.0-9.0%
        Driving During the Day 4.8% 3.6-6.3%
        Taking Medications 3.9% 2.6-5.9%
        Difficulty with at least one IADL 44.8% 38.7-51.0%
Self Reported Vigorous Physical Activity Level in the last 12 Months**
    5+ times/week 25.6% 20.3-31.8%
    3-4 times/week 19.2% 16.2-22.5%
    1-2 times/week 15.2% 11.9-19.2%
    1-3 times/month 8.4% 6.3-11.0%
    < 1 time/month 11.3% 9.0-14.1%
    Never 20.4% 16.2-25.3%
Wrist Accelerometry Measures
    Routine Activity (counts/15 second epoch, mean) 54.0 51.9-56.2
    Immobile Time (mean) 27.1% 26.1%-28.2%
    Wake Time Worn (hours, mean) 42.1 41.2-43.0
Open in a new tab
*

Participants without missing data for covariates, self-reported physical activity, and accelerometry variables

**

Sports, exercise, heavy housework, physical labor

Spearman correlation coefficients between self-reported PA and the accelerometer measures revealed that self-reported PA was only mildly, though significantly, correlated with routine activity count (r = 0.27, p-value <0.001) and immobile time (r = −0.21, p-value <0.001). Routine activity and immobile time were highly, negatively correlated (r = −0.81, p-value <0.001). These results suggest that self-reported PA behavior over the last year reflects only a small portion of the day-to-day information captured by the accelerometry measures.

Multivariate logistic regression models (Table 2) revealed that routine activity was significantly associated with difficulty performing all ADLs (p-value range 0.02 to <0.001) except transferring in and out of bed (p-value=0.07). Routine activity was significantly associated with difficulty performing five IADLs (range 0.004 to <0.001) including light housework, shopping, meal preparation, driving during the day, as well as with “any IADL difficulty”. All estimated odds ratios (ORs) were <1, indicating that an increase of 10 points in routine activity corresponds to decreased risk of reporting disability (e.g. every 10 point increase in mean activity count is associated with a 15% decreased risk of reporting difficulty walking one block). Immobile time was significantly associated with difficulty performing five ADLs (p-value range 0.03 to 0.003) including dressing, walking across a room, toileting, bathing, and eating, and five IADLs (p-value range 0.03 to <0.001) including light housework, shopping, meal preparation, managing money, and driving during the day. All estimated ORs were >1, indicating that a 10% increase in awake time spent immobile is associated with an increased risk of reporting a disability (e.g. a 55% increased risk of reporting difficulty walking across a room). Self-reported vigorous PA was significantly associated with difficulty performing all ADLs (p-value range 0.048 to <0.001) and six IADLs (range 0.04 to <0.001) except driving at night and taking medications.

Table 2.

Logistic Regression Models: Association between Accelerometry-Measured Routine Activity and Immobile Time and Self-Reported Vigorous Activity Frequency with Disability

Model 1 Model 2 Model 3 Model 4 Model 5
Routine Activity Immobile Time Self-Reported Vigorous Physical Activity Routine Activity Self-Reported Vigorous Physical Activity Immobile Time Self-Reported Vigorous Physical Activity
Logistic Regression Outcomes n OR (p-value) OR (p-value) OR (p-value) OR (p-value) OR (p-value) OR (p-value) OR (p-value)
Activities of Daily Living (ADL)
    Walking One Block 622 0.85 (0.02) 1.12 (0.33) 0.64 (<0.001) 0.93 (0.26) 0.64 (<0.001) 0.98 (0.85) 0.64 (<0.001)
    Dressing 622 0.83 (0.02) 1.30 (0.03) 0.75 (0.001) 0.88 (0.09) 0.76 (0.002) 1.19 (0.14) 0.76 (0.001)
    Walking Across a Room 623 0.66 (<0.001) 1.55 (0.007) 0.76 (0.01) 069 (<0.001) 0.80 (0.05) 1.46 (0.02) 0.79 (0.05**)
    Transferring In/Out of Bed 623 0.82 (0.07) 1.21 (0.25) 0.78 (0.05*) 0.86 (0.14) 0.80 (0.08) 1.14 (0.44) 0.79 (0.75)
    Toileting 623 0.77 (0.02) 1.48 (0.007) 0.73 (0.003) 0.82 (0.05**) 0.76 (0.007) 1.38 (0.03) 0.76 (0.009)
    Bathing 623 0.74 (0.01) 1.48 (0.008) 0.66 (0.001) 0.79 (0.03) 0.68 (0.002) 1.36 (0.07) 0.67 (0.002)
    Eating 468 0.43 (0.003) 2.26 (0.003) 0.65 (0.01) 0.42 (0.01) 0.65 (0.04) 2.29 (0.02) 0.63 (0.03)
    Any ADL Difficulty 622 0.87 (0.04) 1.1 (0.46) 0.65 (<0.001) 0.94 (0.32) 0.32 (<0.001) 0.95 (0.64) 0.65 (<0.001)
Independent Activities of Daily Living (IADL)
    Driving at Night 619 0.94 (0.32) 1.07 (0.55) 0.89 (0.06) 0.96 (0.48) 0.90 (0.08) 1.03 (0.78) 0.89 (0.78)
    Light Housework 623 0.75 (0.002) 1.47 (0.002) 0.71 (<0.001) 0.80 (0.01) 0.73 (0.001) 1.36 (0.02) 0.72 (0.001)
    Shopping 623 0.60 (<0.001) 1.60 <0.001) 0.68 (0.001) 0.64 (<0.001) 0.72 (0.007) 1.48 (0.002) 0.71 (0.004)
    Meal Preparation 621 0.77 (0.004) 1.30 (0.03) 0.81 (0.005) 0.80 (0.02) 0.84 (0.02) 1.22 (0.10) 0.83 (0.02)
    Managing Money 622 0.82 (0.05) 1.42 (0.02) 0.75 (0.04) 0.87 (0.14) 0.78 (0.05) 1.31 (0.06) 0.78 (0.06)
    Driving During the Day 620 0.59 (0.002) 1.69 (0.02) 0.68 (<0.001) 0.60 (0.004) 0.70 (0.004) 1.57 (0.05***) 0.70 (0.003)
    Taking Medications 623 0.95 (0.68) 0.91 (0.63) 0.98 (0.88) 0.95 (0.70) 1.0 (0.97) 0.90 (0.50) 0.97 (0.81)
    Any IADL Difficulty 553 0.88 0.02 1.16 (0.16) 0.80 (<0.001) 0.91 (0.06) 0.80 (<0.001) 1.08 (0.49) 0.80 (<0.001)
Open in a new tab
*

0.048

**

0.046

***

0.048

Adding the self-reported vigorous PA behavior to models with routine activity or immobile time changed the estimated effects (ORs) very little, but reduced the significance for some outcomes (Table 2). In Model 4, adjusted for the self-reported vigorous PA behavior, accelerometer-measured routine activity was no longer significantly associated with walking one block, dressing, “any ADL difficulty”, and “any IADL difficulty,” whereas self-reported vigorous PA was no longer significantly associated with walking across a room, transferring in/out of bed, or managing money, but became a significant predictor of “any ADL difficulty.” Routine activity remained significantly associated with walking across a room, toileting, bathing, eating, light housework, shopping, meal preparation, and driving during the day. Routine activity and self-reported vigorous activity were both significant predictors of seven of the sixteen outcomes suggesting they have independent and additive effects in these models.

In Model 5, when adjusted for self-reported vigorous PA, immobile time was no longer significantly associated with difficulty dressing, bathing, meal preparation and managing money, whereas self-reported vigorous PA was no longer significantly associated with difficulty transferring in/out of bed and managing money. Immobile time remained significantly associated with walking across a room, toileting, eating, light housework, shopping, and driving during the day. Immobile time and self-reported vigorous activity were both significant predictors of six of the sixteen outcomes suggesting they have independent and additive effects in these models.

DISCUSSION

Difficulty performing ADLs and IADLs and factors associated with these disabilities are important to understanding the functional status of older adults. In a nationally-representative sample of older adults, we demonstrated summary measures of routine activity and immobile time measured by wrist accelerometry were strongly and consistently associated with difficulty performing certain ADLs and IADLs. The direction and magnitude of the associations changed very little when self-reported vigorous PA behavior was also considered, although the significance level changed for a few. Of the two accelerometry measures that we considered, routine activity was associated with the most disabilities. Hip, rather than wrist, accelerometers have traditionally been preferred for measuring day-today activity in research.24, 30, 39 However, wrist accelerometry data are far more widely available and easily obtainable from commercial devices. Our results provide some preliminary evidence that wrist accelerometry measures have significant relationships with older adult function as well.

Routine activity remained a significant predictor in half and immobile activity remained a significant predictor in six of the sixteen ADL/IADL models even after controlling for self-reported vigorous activity. Self-reported vigorous activity is a well-documented predictor of disability, and our models confirmed these relationships.40 Of particular interest was that the accelerometer variables and the self-reported vigorous activity variable were both significant predictors in the same model for ~40% of the outcomes. These findings suggest that the day-to-day wrist accelerometry measures have an ADDITIONAL effect on these outcomes above and beyond self-reported vigorous activity frequency in older adults. Simply asking older adults the average number of vigorous activities performed without objectively assessing their day-to-day patterns may be inadequate to assess their activity risk factors. Assessment of day-to-day activity and inactivity patterns using wrist accelerometry may improve the disability risk assessment and provide new, targetable behavior measures such as reducing the amount of time spent immobile by 10%, or focusing on increasing the time spent in low-to-moderate activity if vigorous exercise is not feasible.

There are several challenges to implementing accelerometry in a clinical setting. The medical or research grade devices can be costly and cumbersome to implement in the ambulatory setting, although less expensive, lighter commercial models are increasingly available.41 To date, no study has evaluated the cost-effectiveness of providing a personal accelerometer with individualized activity goals to older patients, and such a study would be necessary before their routine clinical application. The cut-offs used to distinguish exercise intensity and sedentary behavior are also not well established in older adults for wrist accelerometry, though they have been for some hip accelerometers.42 Establishing such cut-offs for older adults would help translate wrist accelerometer counts to more broadly understood clinical terminology such as “light activity” or “moderate activity.” Exploration of new ways to use the wrist accelerometer output to inform the assessment of disability, such as studying hourly patterns of movement over the course of a day, may also prove to be valuable to the clinician.39 The use of accelerometers in clinical practice has just begun to be explored,43 and future research should address the utility of these devices in assessing clinically meaningful activity patterns and targets in older adults.

While the benefits of vigorous and moderate-intensity activity have been known for some time,11, 44 sedentary behavior has also been recently recognized as a discrete health risk beyond a measure of activity.45-48 There are currently no screening or treatment guidelines for sedentary behavior in the adult population.49 With growing evidence suggesting reduced sedentary time will have a positive health effect in older adults, the case for implementing these measures into the standard geriatric assessment is becoming stronger.50-53 Since older adults spend more time doing routine activities and being sedentary than young adults, screening for and treating deficits in these areas would likely have a great impact on their functional health.51, 54 Accelerometers may help in measuring sedentary behavior. Although we don't measure sedentary behavior directly, our measure of immobile time is a conservative estimate of the sedentary behavior, and its relationship with health outcomes supports this theory. Hip accelerometers or accelerometers with an inclinometer feature are capable of measuring sedentary behavior with acceptable accuracy.6, 47 Newer algorithms with wrist accelerometers are allowing similar measurements.55 Guidelines for screening and treatment for routine activity and sedentary behavior should be established specifically for older adults.56, 57

Study Limitations

This study has several limitations. While the sample reflects the U.S. population of community-dwelling older adults in 2010-11, our findings do not capture those in nursing homes or other group facilities, those with dementia or the very ill living at home. We conducted a cross-sectional study comparing the accelerometry measures to self-reported disability. The findings indicate a relationship exists between these measures, but the study design is not able to detect causal relationships. We used 72 hours of continuous wrist accelerometry to assess routine activity and immobile time, thought to be adequate in this mostly retired sample.58 Sampling longer periods of five to seven days may better capture true lifestyle averages and would likely strengthen the accelerometry findings. We also chose a conservative activity count cut-off of zero to detect immobile time, representing just a small portion of total sedentary time. Using a better measure of sedentary time, such as with a hip accelerometer, may also have shown stronger disability relationships. We measured average self-reported vigorous activity adherence through a single question though other, longer and validated tools exist for measuring vigorous PA.12, 13 Wide spread use of these tools in the clinical setting has not been documented, and our suspicion was that a shorter question was more representative of the typical physician-patient encounter.

Conclusions

In a nationally-representative sample, we found only a weak correlation between the wrist accelerometry measures of routine activity and immobile time versus self-reported PA in older adults. We also found that these three measures of (in)activity are uniquely important to the disability assessment. Measuring only self-reported vigorous PA frequency may not adequately identify all activity risk factors, leading to missed opportunities to intervene. Missing these health risks/benefits is of particular public health importance for seniors who spend more of their time in routine and sedentary activity, and are most likely to realize benefits from increasing light activity and reducing sedentary activity.59 Future studies should investigate whether wrist accelerometry measures predict incident disability and disability outcomes and should study the potential clinical applications of accelerometry.

Highlights.

  • Wrist acclerometry was related to difficulty performing activities of daily living.

  • Average routine activity and immobile time were calculated for the wake periods over three days of wear.

  • Higher mean activity level was associated with fewer reported difficulties.

  • Less time spent immobile was associated with fewer reported difficulties.

  • Associations persisted after controlling for self-reported moderate-to-vigorous activity over the prior year.

Acknowledgements

The National Social Life, Health and Aging Project is supported by the National Institute on Aging and the National Institutes of Health (R37AG030481; R01AG033903). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Huisingh-Scheetz receives funding through the John A. Hartford Foundation Center of Excellence in Geriatric Medicine and Training National Program Award to support her career development.

Funding

This work was supported by the John A. Hartford Foundation and the National Institutes of Health including the National Institute on Aging, the Division of Behavioral and Social Sciences Research for the National Health, Social Life, and Aging Project (NSHAP) (grant numbers R01 AG021487, R37 AG030481), and the NSHAP Wave 2 Partner Project (R01 AG033903), and by NORC, which was responsible for the data collection.

Abbreviations

PA

Physical Activity

NSHAP

National Social Life, Health and Aging Project

W2

Wave 2

NORC

National Opinion Research Center

CAPI

Computer-Assisted Personal Interview

ADL

Activities of daily living

IADL

Instrumental activities of daily living

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Author Contributions

Huisingh-Scheetz M - planned the study, performed statistical analysis, wrote the paper

Kocherginsky M – performed statistical analysis, revised paper

Magett E – planned the study, wrote portions of paper, revised the paper

Rush P – planned the study, revised the paper

Dale W – planned the study, revised the paper

Waite L – planned the study, revised the paper

REFERENCES

  • 1.Koeneman MA, Chinapaw MJ, Verheijden MW, van Tilburg TG, Visser M, Deeg DJ, et al. Do major life events influence physical activity among older adults: the Longitudinal Aging Study Amsterdam. Int J Behav Nutr Phys Act. 2012;9:147. doi: 10.1186/1479-5868-9-147. (doi):10.1186/479-5868-9-147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Pavey TG, Peeters GG, Brown WJ. Sitting-time and 9-year all-cause mortality in older women. Br J Sports Med. 2012 doi: 10.1136/bjsports-2012-091676. [DOI] [PubMed] [Google Scholar]
  • 3.Rimmer JH, Riley B, Wang E, Rauworth A, Jurkowski J. Physical activity participation among persons with disabilities: barriers and facilitators. Am J Prev Med. 2004;26(5):419–25. doi: 10.1016/j.amepre.2004.02.002. [DOI] [PubMed] [Google Scholar]
  • 4.Adult participation in aerobic and muscle-strengthening physical activities--United States, 2011. MMWR Morb Mortal Wkly Rep. 2013;62(17):326–30. [PMC free article] [PubMed] [Google Scholar]
  • 5.Rejeski WJ, Axtell R, Fielding R, Katula J, King AC, Manini TM, et al. Promoting physical activity for elders with compromised function: the lifestyle Interventions and Independence for elders (LIFE) study physical activity intervention. Clin Interv Aging. 2013;8:1119–31. doi: 10.2147/CIA.S49737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Atkin AJ, Gorely T, Clemes SA, Yates T, Edwardson C, Brage S, et al. Methods of Measurement in epidemiology: sedentary Behaviour. Int J Epidemiol. 2012;41(5):1460–71. doi: 10.1093/ije/dys118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Kowalski K, Rhodes R, Naylor PJ, Tuokko H, Macdonald S. Direct and indirect measurement of physical activity in older adults: a systematic review of the literature. Int J Behav Nutr Phys Act. 2012;9:148. doi: 10.1186/1479-5868-9-148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Sager MA, Dunham NC, Schwantes A, Mecum L, Halverson K, Harlowe D. Measurement of activities of daily living in hospitalized elderly: a comparison of self-report and performance-based methods. J Am Geriatr Soc. 1992;40(5):457–62. doi: 10.1111/j.1532-5415.1992.tb02011.x. [DOI] [PubMed] [Google Scholar]
  • 9.Seymour DG, Ball AE, Russell EM, Primrose WR, Garratt AM, Crawford JR. Problems in using health survey questionnaires in older patients with physical disabilities. The reliability and validity of the SF-36 and the effect of cognitive impairment. J Eval Clin Pract. 2001;7(4):411–8. doi: 10.1046/j.1365-2753.2001.00296.x. [DOI] [PubMed] [Google Scholar]
  • 10.Barnes PM, Schoenborn CA. Trends in adults receiving a recommendation for exercise or other physical activity from a physician or other health professional. NCHS Data Brief. 2012;(86):1–8. [PubMed] [Google Scholar]
  • 11.CenterforDiseaseControlandPrevention [2011 September 16];Physical Activity for Everyone. 2011 Available from: URL: http://www.cdc.gov/physicalactivity/everyone/guidelines/index.html.
  • 12.Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46(2):153–62. doi: 10.1016/0895-4356(93)90053-4. [DOI] [PubMed] [Google Scholar]
  • 13.Forsen L, Loland NW, Vuillemin A, Chinapaw MJ, van Poppel MN, Mokkink LB, et al. Self-administered physical activity questionnaires for the elderly: a systematic review of measurement properties. Sports Med. 2010;40(7):601–23. doi: 10.2165/11531350-000000000-00000. [DOI] [PubMed] [Google Scholar]
  • 14.Loprinzi PD, Sheffield J, Tyo BM, Fittipaldi-Wert J. Accelerometer-determined physical activity, mobility disability, and health. Disabil Health J. 2014;7(4):419–25. doi: 10.1016/j.dhjo.2014.05.005. [DOI] [PubMed] [Google Scholar]
  • 15.Ortlieb S, Gorzelniak L, Nowak D, Strobl R, Grill E, Thorand B, et al. Associations between multiple accelerometry-assessed physical activity parameters and selected health outcomes in elderly people--results from the KORA-age study. PLoS One. 2014;9(11):e111206. doi: 10.1371/journal.pone.0111206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Vanroy C, Vissers D, Cras P, Beyne S, Feys H, Vanlandewijck Y, et al. Physical activity monitoring in stroke: SenseWear Pro2 activity accelerometer versus Yamax Digi-Walker SW-200 pedometer. Disabil Rehabil. 2014;36(20):1695–703. doi: 10.3109/09638288.2013.859307. [DOI] [PubMed] [Google Scholar]
  • 17.Gebruers N, Vanroy C, Truijen S, Engelborghs S, De Deyn PP. Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Arch Phys Med Rehabil. 2010;91(2):288–97. doi: 10.1016/j.apmr.2009.10.025. [DOI] [PubMed] [Google Scholar]
  • 18.Prioreschi A, Hodkinson B, Tikly M, McVeigh JA. Changes in physical activity measured by accelerometry following initiation of DMARD therapy in rheumatoid arthritis. Rheumatology (Oxford) 2014;53(5):923–6. doi: 10.1093/rheumatology/ket457. [DOI] [PubMed] [Google Scholar]
  • 19.Klaren RE, Motl RW, Dlugonski D, Sandroff BM, Pilutti LA. Objectively quantified physical activity in persons with multiple sclerosis. Arch Phys Med Rehabil. 2013;94(12):2342–8. doi: 10.1016/j.apmr.2013.07.011. [DOI] [PubMed] [Google Scholar]
  • 20.O'Muircheartaigh C, Eckman S, Smith S. Statistical design and estimation for the national social life, health, and aging project. J Gerontol B Psychol Sci Soc Sci. 2009;64(Suppl 1):i12–9. doi: 10.1093/geronb/gbp045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.PhilipsRespironics. Actiwatch. 2013 2/19/2013. Available from: URL: http://www.healthcare.philips.com/main/homehealth/sleep/actiwatch/default.wpd#&&/wEXAQUOY3VycmVudFRhYlBhdGgFCUVkdWNhdGlvbrs7D4d8dwFrxbRmM0TsUP60b3xr.
  • 22.PhilipsRespironics Equivalence of Activity Recordings and Derived Sleep Statistics: Actiwatch-64, Actiwatch-2, and Actiwatch Spectrum. 2008 Available from: URL: http://actigraphy.respironics.com/downloadsAWDataEquivalenceReport_0309_final.pdf.
  • 23.Van Remoortel H, Raste Y, Louvaris Z, Giavedoni S, Burtin C, Langer D, et al. Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS One. 2012;7(6):e39198. doi: 10.1371/journal.pone.0039198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Chen KY, Acra SA, Majchrzak K, Donahue CL, Baker L, Clemens L, et al. Predicting energy expenditure of physical activity using hip- and wrist-worn accelerometers. Diabetes Technol Ther. 2003;5(6):1023–33. doi: 10.1089/152091503322641088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Huisingh-Scheetz M, Kocherginsky M, Schumm PL, Engelman M, McClintock MK, Dale W, et al. Geriatric Syndromes and Functional Status in NSHAP: Rationale, Measurement, and Preliminary Findings. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S177–90. doi: 10.1093/geronb/gbu091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Lauderdale DS, Philip Schumm L, Kurina LM, McClintock M, Thisted RA, Chen JH, et al. Assessment of sleep in the national social life, health, and aging project. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S125–33. doi: 10.1093/geronb/gbu092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Rabinovich RA, Louvaris Z, Raste Y, Langer D, Van Remoortel H, Giavedoni S, et al. Validity of physical activity monitors during daily life in patients with COPD. Eur Respir J. 2013;42(5):1205–15. doi: 10.1183/09031936.00134312. [DOI] [PubMed] [Google Scholar]
  • 28.Crouter SE, Churilla JR, Bassett DR., Jr Estimating energy expenditure using accelerometers. Eur J Appl Physiol. 2006;98(6):601–12. doi: 10.1007/s00421-006-0307-5. [DOI] [PubMed] [Google Scholar]
  • 29.Ekblom O, Nyberg G, Bak EE, Ekelund U, Marcus C. Validity and comparability of a wrist-worn accelerometer in children. J Phys Act Health. 2012;9(3):389–93. [PubMed] [Google Scholar]
  • 30.Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013;45(5):964–75. doi: 10.1249/MSS.0b013e31827f0d9c. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Feinglass J, Lin S, Thompson J, Sudano J, Dunlop D, Song J, et al. Baseline health, socioeconomic status, and 10-year mortality among older middle-aged Americans: findings from the Health and Retirement Study, 1992 2002. J Gerontol B Psychol Sci Soc Sci. 2007;62(4):S209–17. doi: 10.1093/geronb/62.4.s209. [DOI] [PubMed] [Google Scholar]
  • 32.He XZ, Baker DW. Body mass index, physical activity, and the risk of decline in overall health and physical functioning in late middle age. Am J Public Health. 2004;94(9):1567–73. doi: 10.2105/ajph.94.9.1567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.WorldHealthOrganization WHO: Global Database on Body Mass Index. 2011 Jul 28; Available from: URL: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html.
  • 34.Kotwal AA, Schumm P, Kern DW, McClintock MK, Waite LJ, Shega JW, et al. Evaluation of a Brief Survey Instrument for Assessing Subtle Differences in Cognitive Function Among Older Adults. Alzheimer Dis Assoc Disord. 2014 doi: 10.1097/WAD.0000000000000068. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85–94. doi: 10.1093/geronj/49.2.m85. [DOI] [PubMed] [Google Scholar]
  • 36.Robinson LD, JNSSRaCAiLRMISR Some Surprising Results About Covariate Adjustment in Logistic Regression Models. International Statistical Review. 19911991:227–40. [Google Scholar]
  • 37.Luke A, Dugas LR, Durazo-Arvizu RA, Cao G, Cooper RS. Assessing physical activity and its relationship to cardiovascular risk factors: NHANES 2003-2006. BMC Public Health. 2011;11:387. doi: 10.1186/1471-2458-11-387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Stata Statistical Software: Release 12. StataCorp LP; College Station, TX: 2011. [Google Scholar]
  • 39.Lambiase MJ, Gabriel KP, Chang YF, Kuller LH, Matthews KA. Utility of actiwatch sleep monitor to assess waking movement behavior in older women. Med Sci Sports Exerc. 2014;46(12):2301–7. doi: 10.1249/MSS.0000000000000361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Pinto RZ, Ferreira PH, Kongsted A, Ferreira ML, Maher CG, Kent P. Self-reported moderate-to-vigorous leisure time physical activity predicts less pain and disability over 12 months in chronic and persistent low back pain. Eur J Pain. 2014;18(8):1190–8. doi: 10.1002/j.1532-2149.2014.00468.x. [DOI] [PubMed] [Google Scholar]
  • 41.Evenson KR, Buchner DM, Morland KB. Objective measurement of physical activity and sedentary behavior among US adults aged 60 years or older. Prev Chronic Dis. 2012;9:E26. [PMC free article] [PubMed] [Google Scholar]
  • 42.Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. The association between sedentary behaviour, moderate-vigorous physical activity and frailty in NHANES cohorts. Maturitas. 2015;80(2):187–91. doi: 10.1016/j.maturitas.2014.11.010. [DOI] [PubMed] [Google Scholar]
  • 43.Howell J, Strong BM, Weisenberg J, Kakade A, Gao Q, Cuddihy P, et al. Maximum daily 6 minutes of activity: an index of functional capacity derived from actigraphy and its application to older adults with heart failure. J Am Geriatr Soc. 2010;58(5):931–6. doi: 10.1111/j.1532-5415.2010.02805.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.U.S.PreventiveServicesTaskForce Behavioral Counseling Interventions to Promote a Healthful Diet and Physical Activity or Cardiovascular Disease Prevention in Adults Clinical Summary of U.S. Preventive Services Task Force Recommendation. 2012 doi: 10.7326/0003-4819-157-5-201209040-00486. [DOI] [PubMed] [Google Scholar]
  • 45.Bankoski A, Harris TB, McClain JJ, Brychta RJ, Caserotti P, Chen KY, et al. Sedentary activity associated with metabolic syndrome independent of physical activity. Diabetes Care. 2011;34(2):497–503. doi: 10.2337/dc10-0987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Davis MG, Fox KR, Stathi A, Trayers T, Thompson J, Cooper AR. Objectively Measured Sedentary Time and Lower Extremity Function in Older Adults. J Aging Phys Act. 2013 doi: 10.1123/japa.2013-0042. [DOI] [PubMed] [Google Scholar]
  • 47.Dunlop D, Song J, Arnston E, Semanik P, Lee J, Chang R, et al. Sedentary Time in U.S. Older Adults Associated With Disability in Activities of Daily Living Independent of Physical Activity. J Phys Act Health. 2014 doi: 10.1123/jpah.2013-0311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Biswas A, Oh PI, Faulkner GE, Bajaj RR, Silver MA, Mitchell MS, et al. Sedentary Time and Its Association With Risk for Disease Incidence, Mortality, and Hospitalization in Adults: A Systematic Review and Meta-analysis. Ann Intern Med. 2015;162(2):123–32. doi: 10.7326/M14-1651. [DOI] [PubMed] [Google Scholar]
  • 49.Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen N. Too Little Exercise and Too Much Sitting: Inactivity Physiology and the Need for New Recommendations on Sedentary Behavior. Curr Cardiovasc Risk Rep. 2008;2(4):292–8. doi: 10.1007/s12170-008-0054-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Binder EF, Schechtman KB, Ehsani AA, Steger-May K, Brown M, Sinacore DR, et al. Effects of exercise training on frailty in community-dwelling older adults: results of a randomized, controlled trial. J Am Geriatr Soc. 2002;50(12):1921–8. doi: 10.1046/j.1532-5415.2002.50601.x. [DOI] [PubMed] [Google Scholar]
  • 51.Gardiner PA, Eakin EG, Healy GN, Owen N. Feasibility of reducing older adults' sedentary time. Am J Prev Med. 2011;41(2):174–7. doi: 10.1016/j.amepre.2011.03.020. [DOI] [PubMed] [Google Scholar]
  • 52.Pahor M, Blair SN, Espeland M, Fielding R, Gill TM, Guralnik JM, et al. Effects of a physical activity intervention on measures of physical performance: Results of the lifestyle interventions and independence for Elders Pilot (LIFE-P) study. J Gerontol A Biol Sci Med Sci. 2006;61(11):1157–65. doi: 10.1093/gerona/61.11.1157. [DOI] [PubMed] [Google Scholar]
  • 53.Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care. 2008;31(4):661–6. doi: 10.2337/dc07-2046. [DOI] [PubMed] [Google Scholar]
  • 54.Chastin SF, Fitzpatrick N, Andrews M, Dicroce N. Determinants of sedentary behavior, motivation, barriers and strategies to reduce sitting time in older women: a qualitative investigation. Int J Environ Res Public Health. 2014;11(1):773–91. doi: 10.3390/ijerph110100773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Staudenmayer J, He S, Hickey A, Sasaki J, Freedson P. Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. J Appl Physiol (1985) 2015;119(4):396–403. doi: 10.1152/japplphysiol.00026.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Johannsen DL, DeLany JP, Frisard MI, Welsch MA, Rowley CK, Fang X, et al. Physical activity in aging: comparison among young, aged, and nonagenarian individuals. J Appl Physiol. 2008;105(2):495–501. doi: 10.1152/japplphysiol.90450.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Eckel SP, Bandeen-Roche K, Chaves PH, Fried LP, Louis TA. Surrogate screening models for the low physical activity criterion of frailty. Aging Clin Exp Res. 2011;23(3):209–16. doi: 10.1007/bf03324962. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Hart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011;8:62. doi: 10.1186/1479-5868-8-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Sparling PB, Howard BJ, Dunstan DW, Owen N. Recommendations for physical activity in older adults. BMJ. 2015;350:h100. doi: 10.1136/bmj.h100. [DOI] [PubMed] [Google Scholar]

RESOURCES