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. Author manuscript; available in PMC: 2022 Aug 29.
Published in final edited form as: J Aging Phys Act. 2021 Oct 9;30(4):635–645. doi: 10.1123/japa.2021-0159

Accelerometer-Measured Daily Steps, Physical Function, and Subsequent Fall Risk in Older Women: The Objective Physical Activity and Cardiovascular Disease in Older Women Study

Benjamin T Schumacher a, John Bellettiere a, Michael J LaMonte b, Kelly R Evenson c, Chongzhi Di d, I-Min Lee e, David A Sleet f, Charles B Eaton g, Cora E Lewis h, Karen L Margolis i, Lesley F Tinker j, Andrea Z LaCroix a
PMCID: PMC9386784  NIHMSID: NIHMS1821977  PMID: 34627127

Abstract

Steps per day (steps/d) were measured by accelerometer for 7 days among 5,545 women aged 63 to 97 years between 2012 – 2014. Incident falls were ascertained from daily fall calendars for 13 months. Median steps/d were 3,216. There were 5,473 falls recorded over 61,564 fall calendar months. The adjusted incidence rate ratio comparing women in the highest vs. lowest step quartiles was 0.71 (95% CI, 0.54 – 0.95; P-trend across quartiles = 0.01). After further adjustment for physical function using the Short Physical Performance Battery, the incidence rate ratio was 0.86 (0.64 – 1.16; Ptrend = 0.27). Mediation analysis estimated that 63.7% of the association may be mediated by physical function (p = 0.03). In conclusion, higher steps/d were related to lower incident falls primarily through their beneficial association with physical functioning. Interventions that improve physical function, including those that involve stepping, could reduce falls in older adults.

Keywords: Epidemiology, Incident falls, Older adults

INTRODUCTION

In 2018, falls and injuries from falls among adults aged ≥ 65 years were the cause of nearly 3 million emergency room visits, 950,000 hospitalizations, and 32,000 deaths in the United States (Centers for Disease Control and Prevention WISQARS, 2020). Overall, approximately 28% of older adults experience at least one fall every year (Bergen et al., 2016; Florence et al., 2018). Given the aging U.S. population (Centers for Disease Control and Prevention, 2013), the number of falls is projected to quadruple by 2030 if current trends are not addressed (Houry et al., 2016). This increase will disproportionately affect women, who experience a higher falls rate than men, and for whom a 201% increase in falls has already been observed since 1970 (Bergen et al., 2016; Burns et al., 2016; Kannus et al., 2007).

The collective understanding of fall risk in older adults relating to physical activity is that active older adults are at a decreased fall risk relative to inactive older adults (2018 Physical Activity Guidelines Advisory Committee, 2018). This understanding, derived from a randomized controlled trial (Medical Advisory Secretariat, 2008), prospective cohort studies (Buchner et al., 2017; Cauley et al., 2013; Heesch et al., 2008; Iinattiniemi et al., 2008), a case-control study (Peel et al., 2006), and meta-analyses (El-Khoury et al., 2013; Gillespie et al., 2012; Zhao et al., 2017) demonstrates that engaging in physical activity reduces falls risk. This reduction in falls risk occurs through muscle strengthening, enhanced neuromuscular coordination and balance, cardiorespiratory health, and improvement in lower extremity physical functioning (Ganz & Latham, 2020; Sherrington et al., 2017). Walking is the most common aerobic activity among adults (2018 Physical Activity Guidelines Advisory Committee, 2018; Centers for Disease Control and Prevention, 2012), and steps per day (steps/d) is a metric that has been shown to be associated with overall health (2018 Physical Activity Guidelines Advisory Committee, 2018; Richardson et al., 2008; Tudor-Locke et al., 2011), cardiovascular disease morbidity and mortality (Hall et al., 2020), and all-cause mortality (Lee et al., 2019), although the minimum number of daily steps required for health benefits remains unclear. Further, increasing ownership of wearables that measure personal steps/d (pedometers, smart watches, activity trackers) could give rise to clear, concise, and actionable public health messaging and targets (2018 Physical Activity Guidelines Advisory Committee, 2018). This led the Physical Activity Guidelines Advisory Committee to call for further research on steps/d as a potential risk factor for falls (2018 Physical Activity Guidelines Advisory Committee, 2018).

It is well known that a major risk factor for falls in older adults is poor lower extremity physical functioning (hereafter referred to as physical function). Low physical function may limit one’s walking ability and thus the number of steps taken (Tudor-Locke et al., 2011), increase fear of falling, and, in turn, this fear of falling may further reduce ambulatory movement and physical activity (Wert et al., 2010). Therefore, it is imperative to understand the association between steps/d and subsequent fall risk in the context of physical functioning. The objectives of this study were to investigate the association between accelerometer-measured steps/d and subsequent fall risk and to assess the role of physical functioning, objectively measured using the Short Physical Performance Battery, as a confounder, modifier, or mediator of this association in a community-based cohort of ambulatory, older women enrolled in the Objective Physical Activity and Cardiovascular Disease Health in Older Women (OPACH) Study.

METHODS

Study Participants

The analytic sample for the present study was derived from OPACH, an ancillary study of the Women’s Health Initiative’s (WHI) Long Life Study (LLS). Details about the design, recruitment, and measures have been previously published for OPACH (LaCroix et al., 2017) and WHI (Anderson et al., 1998; Anderson et al., 2003). All ambulatory women from the Long Life Study (2012–2013; n = 7,875) were invited to concurrently enroll in OPACH (LaCroix et al., 2017). The sampling frame for the LLS included all surviving WHI participants from the WHI Hormone Therapy Trials and Black and Hispanic/Latina women from all other WHI components. Briefly (detailed description in Supplemental Figure 2), of the 7,048 women, 6,118 completed a Long Life Study home visit, were given falls calendars, and returned their triaxial accelerometer (Buchner et al., 2017). Women were excluded if they consented to OPACH but did not receive falls calendars during their LLS visit or had missing falls calendar data (n = 333), missing daily step counts (n = 43), wore their accelerometer for less than 4 adherent days (where a day ≥ 10 hours of device wear while awake was considered adherent (LaCroix et al., 2017), n = 193), or had extreme values for falls or steps (> 325 falls, n =1; ≥ 60,000 steps/d, n =3), leaving 5,545 women in the analytic sample (Supplemental Figure 2). All women provided informed consent either in writing or by telephone. The institutional review board at the Fred Hutchinson Cancer Research Center approved the study protocols for the Long Life Study and OPACH (LaCroix et al., 2017) and University of California, San Diego’s IRB has approved subsequent OPACH data analysis.

Measures

Exposure Assessment

During the LLS visit, participants in OPACH were provided with and asked to wear a GT3X+ triaxial accelerometer (Acti-Graph, Pensacola, FL) on their right hip, above the iliac crest, using a belt for 7 days (LaCroix et al., 2017). If a device was not available during the LLS visit, one was mailed to the participant soon thereafter, and the first day of device wear was considered to be that particpant’s OPACH baseline. The GT3X+ device then counted all steps accrued throughout the day, including purposeful exercise and activities of daily living. The accelerometer was to be removed only during water activities (e.g. bathing, swimming) (LaCroix et al., 2017). Data collected by the accelerometer while a participant was in bed was identified using self-reported sleep logs completed every night of accelerometer wear (Rillamas-Sun et al., 2015). Missing in-bed and out-of-bed times were imputed based on person-specific averages when available or population averages when no sleep logs were available (Bellettiere et al., 2019).

The triaxial accelerometers measured acceleration at 30 Hz. Data were converted to 15-second epochs using the normal filter supplied with ActiLife version 6. The Choi algorithm (Choi et al., 2011) was used to remove periods of accelerometer non-wear using vector magnitude acceleration counts with a 90-minute window, 30-minute stream frame, and 2-minute tolerance (Choi et al., 2011). The average number of steps/d was computed by summing daily steps across all adherent days and dividing by the number of adherent days. Variation in steps/d due to differences in accelerometer wear time was accounted for using the residualized method (Willett & Stampfer, 1986).

Outcome Ascertainment

During the LLS visit, along with an accelerometer, all women also received a 13-month falls calendar (Supplemental Figure 1). To ensure a full year of incident falls surveillance, participants were provided with 13 months of falls calendar pages and were instructed to record daily on the calendars if they had fallen. A fall was defined as “lost balance and fell to the ground or a lower level or if they had to use a wall, rail, or other object to prevent themselves from falling to the ground” (Buchner et al., 2017). At the end of each month, participants sent that month’s calendar to OPACH study staff. When a fall calendar was not received, a reminder postcard was sent via mail (LaCroix et al., 2017). The ascertainment of falls with daily recording on a fall calendar compares favorably to recall-based surveillance methods and has been accepted as a gold standard (Hale et al., 1993; Hannan et al., 2010). Incidence density rates of falls were computed as the number of reported falls divided by the number of person-months (i.e. number of calendar pages returned).

Physical Function

Lower extremity physical function was measured during the LLS visit using the Short Physical Performance Battery (SPPB) (Guralnik et al., 1994, 2000). The SPPB is a series of objective physical performance tests that assess balance, gait, strength, and endurance. These are evaluated by “examining ability to stand with the feet together in the side-by-side, semi-tandem, and tandem positions”, walking speed at usual pace over a standardized distance, and “time to rise from a chair and return to the seated position 5 times” (Guralnik et al., 1994).

Covariates

Self-reported demographic covariates included a woman’s age at OPACH enrollment, race/ethnicity (non-Hispanic white/non-Hispanic Black/or Hispanic), and educational attainment (high school/GED or less/some college/college graduate or more). Questionnaire items most recent to the OPACH baseline (within the previous 12 months) were used to ascertain self-reported falls risk factors including: vision (excellent/very good/good/fair/poor), bodily pain (none/very mild/mild/moderate/severe), alcohol use (never/1 drink per week/1–2 per week/3–4 per week/5–6 per week/everyday), and sleep aid use (no use in past 4 weeks/less than once a week/1–2 times a week/3–4 times a week/5 or more times a week). A measure of multiple morbidity was derived as the sum of up to 11 self-reported chronic conditions (categorized as 0/1/2/≥3) from WHI self-reported and adjudicated outcomes surveillance (Rillamas-Sun et al., 2016). During the LLS visit, a participant’s height and weight were measured, and body mass index (BMI) was computed as weight (kg) divided by height (m) squared.

Statistical Analyses

Women were categorized into quartiles of accelerometer-measured steps/d. Comparison of baseline covariates by quartile of steps/d were performed with chi-square tests for categorical variables and analysis of variance (ANOVA) tests for continuous variables.

To assess the association between steps/d and subsequent fall risk, both parametric and nonparametric regression models were specified. Due to the extreme right skew of the incident fall rate distribution, we used parametric negative binomial regression models. These models were progressively adjusted as follows: Model 1 included age (continuous), race/ethnicity, and education; Model 2 introduced potential confounders in addition to the Model 1 demographics—vision, body pain, alcohol use, sleep aid use, BMI (continuous), and number of chronic conditions (0 – 11, continuous). Physical function, measured by SPPB, could be conceptualized as a confounder, mediator, or an effect modifier of the steps/d and fall risk association. To assess SPPB as a confounder of the steps/d-falls association, Model 3 adjusted for all Model 2 covariates and SPPB score (0 – 12, continuous). Effect measure modification was assessed by including a SPPB-by-steps/d product term to Model 2. P-values for trend (Ptrend) across quartiles were computed by including quartiles of steps as an ordinal term in the respective regression model. We present results for Models 1 – 3 in the full analytic sample, and, to further assess effect modification, SPPB-stratified samples using previously specified categories in OPACH (Buchner et al., 2017): 0–8 for low SPPB and 9–12 for high SPPB. SPPB was adjusted for within the two SPPB-strata to: (1) prevent any residual confounding and (2) to assess the adjusted incidence rate ratios using the same modeling strategy as the unstratified analyses. These analyses employed multivariable imputation through the MICE package in R using chained equations with 100 multiple imputations to handled missing covariate data (Buuren & Groothuis-Oudshoorn, 2011). The final parametric analysis assessed SPPB as a mediator of the steps/d and fall risk association. Complete case mediation analysis was performed using the mediate_zi function of the maczic package in R (Cheng et al., 2021) to estimate the proportion of the steps/d and fall risk association that was mediated through SPPB. Mediation confidence intervals were estimated using quasi-Bayesian approximation and treatment and control values were specified at the 25th and 75th percentile cutpoints of steps/d, respectively.

Complete case, nonparametric negative binomial generalized additive models (GAM) using restricted cubic splines were specified with covariates from Model 3 to consider potential nonlinear associations between incident fall rates and continuous steps/d. Predicted probabilities from these GAMs were used to create graphs that display the predicted risk of falling across the continuum of steps/d. Results were insensitive to the number and placement of the knots, so three knots were placed at the stratum-specific steps/d quartile cutpoints (Durrleman & Simon, 1989). Product terms were included in the GAMs to further explore effect measure modification of steps/d on incident falls risk by levels of SPPB, age tertiles, and past-year fall history (all stratification variables specified a priori) over the full range of steps/d.

A sensitivity analysis using the multiply imputed data was conducted to determine if the intensity of the steps/d were associated with subsequent falls risk. Vector magnitude (VM) counts per 15 second epoch from the accelerometer were used to classify steps as “light intensity” or “moderate-to-vigorous intensity” using the cutpoints defined in the OPACH calibration study (Evenson et al., 2015). The average number of steps/d that were accrued at VM counts of < 519 and ≥ 519 were considered light and moderate-to-vigorous steps, respectively. Negative binomial regression models were fit to assess the association between quartiles of light and MV steps and incident falls risk using the aforementioned modeling approach.

All analyses were conducted in R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Study Population and Baseline Characteristics

Among the 5,545 women in the analytic sample, 5,473 falls (average 1.0 ± 2.3 falls, range 0–60) were recorded over 61,564 person-months (average 11.1 ± 3.4 person-months, range 1–13). Median steps/d were 3,216 (25th and 75th percentiles: 2,184 and 4,597, respectively). Mean accelerometer wear time while awake was 14.9 ± 1.3 hours (range 10.4 – 19.3). Women were aged 78.8 ± 6.7 years on average (range 63–97); 50.4% were non-Hispanic white, 32.8% non-Hispanic Black, and 16.7% Hispanic. At baseline, older age, non-Hispanic white race, lower educational attainment, higher number of chronic conditions, having a history of falls in the year prior to OPACH baseline, higher BMI, lower SPPB score, lower alcohol use, worse vision, and worse bodily pain were associated with fewer steps/d (Table 1).

Table 1.

Participant Characteristics by Accelerometer Measured Average Daily Step Quartiles in the Selected OPACH Sample, 2012–2014 (n = 5,545)

Characteristic† Quartile 1 < 2184 Steps (n = 1,386) Quartile 2 >= 2184 & <= 3216 (n = 1,387) Quartile 3 > 3216 & <= 4597 (n = 1,386) Quartile 4 > 4597 (n = 1,386) P-value*
Steps/d, Mean (SD) 1489.1 (538.5) 2707 (293.1) 3829.9 (383.5) 6435.4 (1787.6) <0.001
Age, Mean (SD) 82.3 (6.1) 79.8 (6.2) 77.8 (6.3) 75.3 (6.0) <0.001
SPPB, Mean (SD) 6.7 (2.6) 8.0 (2.4) 8.8 (2.2) 9.5 (2.0) <0.001
Number of Chronic Conditions, Mean (SD) 2.6 (7.5) 2.3 (7.9) 1.7 (4.7) 1.5 (6.0) <0.001
BMI, Mean (SD) 29.0 (6.3) 29.1 (6.1) 28.0 (5.2) 26.2 (4.6) <0.001
Race/Ethnicity, n (%) <0.001
 white, N.H. 859 (62.0) 731 (52.7) 629 (45.4) 578 (41.7)
 Black, N.H. 426 (30.7) 480 (34.6) 484 (34.9) 430 (31.0)
 Hispanic 101 (7.3) 176 (12.7) 273 (19.7) 378 (27.3)
Educational Attainment, n (%) <0.001
 High School/GED or less 305 (22.0) 287 (20.7) 279 (20.1) 254 (18.3)
 Some College 573 (41.3) 543 (39.1) 533 (38.5) 486 (35.1)
 College Graduate or more 500 (36.1) 550 (39.7) 560 (40.4) 640 (46.2)
Fall History, n (%) <0.001
 No falls 861 (62.1) 959 (69.1) 965 (69.6) 993 (71.6)
 1 fall in past year 306 (22.1) 277 (20.0) 281 (20.3) 275 (19.8)
 2 or more falls in a past year 219 (15.8) 150 (10.8) 140 (10.1) 118 (8.5)
Injurious fall in past 12 months, n (%) ** 243 (17.5) 197 (14.2) 178 (12.8) 177 (12.8) 0.52
Alcohol use past 3 months, n (%) <0.001
 Never 581 (41.9) 520 (37.5) 447 (32.3) 360 (26.0)
 Less than 1 per week 426 (30.7) 424 (30.6) 463 (33.4) 427 (30.8)
 1 or more drinks per week 239 (17.2) 330 (23.8) 376 (27.1) 519 (37.4)
Use of sleep aid G.E. 1 time/week, n (%) 217 (15.7) 223 (16.1) 221 (15.9) 209 (15.1) 0.68
Excellent or very good vision, n (%) 594 (42.9) 627 (45.2) 683 (49.3) 762 (55.0) <0.001
Mild to severe body pain, n (%) 842 (60.8) 787 (56.7) 721 (52.0) 551 (39.8) <0.001
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SPPB = Short Physical Performance Battery.

†

Percentages may not sum to 100% due to missing data.

*

P-value for continuous variables from the One-way ANOVA and Chi-Sq. goodness of fit test for categorical variables across daily step quartiles.

**

Of women with at least one fall in past 12 months.

Bold indicates significance at the P < 0.05 level.

Accelerometer-measured Steps/d Quartiles and Subsequent Falls

In the total sample, crude incidence rates of falls decreased with each successively higher quartile of steps/d (Table 2). Incidence rate ratios (IRRs) and respective 95% confidence intervals adjusted for age, race/ethnicity, and education (Model 1) showed a higher rate of falls for women in the lowest quartile of steps/d as compared to those in the remaining quartiles. After adjustment for demographic characteristics and potential confounders (Model 2), associations were attenuated but remained statistically significant (aIRRs 0.77 (0.61–0.97), 0.72 (0.56–0.93), 0.71 (0.54–0.95) for Q2, Q3, and Q4, respectively; Ptrend = 0.01). Of the six additional covariates adjusted for in Model 2, bodily pain accounted for the largest attenuation between Model 1 and Model 2 aIRRs. There was no statistically significant interaction between quartiles of steps/d and continuous SPPB (Pinteraction = 0.14). After further adjustment for SPPB (Model 3), all associations were further attenuated, 95% confidence intervals included the null value, and the test for trend was no longer statistically significant (aIRRs 0.85 (0.67–1.07), 0.84 (0.64–1.09), 0.86 (0.64–1.16) for Q2, Q3, and Q4 relative to Q1, respectively; Ptrend = 0.27).

Table 2.

Adjusted Incidence Rate Ratios (IRR) of Subsequent Falls by Accelerometer Measured Average Daily Step Quartiles in the Selected OPACH Sample, 2012–2014 (n = 5,545)

Sample Quartile 1 < 2184 Steps Quartile 2 >= 2184 & <= 3216 Quartile 3 > 3216 & <= 4597 Quartile 4 > 4597 P-value for Trend*
All Participants †
 Number of falls 1814 1298 1200 1161
 Person-months 14562 15108 15656 16238
 Crude Fall Rate per 1,000 P-M 124.57 85.91 76.65 71.5
 Model 1, IRR (95% CI)a 1.00 (ref.) 0.72 (0.58–0.91) 0.65 (0.50–0.83) 0.61 (0.46–0.79) <0.001
 Model 2, IRR (95% CI)b 1.00 (ref.) 0.77 (0.61–0.97) 0.72 (0.56–0.93) 0.71 (0.54–0.95) 0.01
 Model 3, IRR (95% CI)c 1.00 (ref.) 0.85 (0.67–1.07) 0.84 (0.64–1.09) 0.86 (0.64–1.16) 0.27
Low Physical Functioning (0–8 SPPB)
 Number of falls 1250 720 565 353
 Person-months 9250 7727 6212 4043
 Crude Fall Rate per 1,000 P-M 135.14 93.18 90.95 87.31
 Model 1, IRR (95% CI)a 1.00 (ref.) 0.75 (0.57–0.99) 0.71 (0.52–0.98) 0.63 (0.42–0.94) 0.01
 Model 2, IRR (95% CI)b 1.00 (ref.) 0.78 (0.59–1.03) 0.76 (0.55–1.06) 0.74 (0.48–1.13) 0.06
 Model 3, IRR (95% CI)c 1.00 (ref.) 0.85 (0.64–1.13) 0.85 (0.61–1.19) 0.83 (0.54–1.27) 0.27
High Physical Functioning (9–12 SPPB)
 Number of falls 314 427 550 715
 Person-months 3573 6000 8433 10893
 Crude Fall Rate per 1,000 P-M 87.88 71.17 65.22 65.64
 Model 1, IRR (95% CI)a 1.00 (ref.) 0.82 (0.52–1.31) 0.75 (0.48–1.19) 0.79 (0.50–1.25) 0.36
 Model 2, IRR (95% CI)b 1.00 (ref.) 0.86 (0.54–1.38) 0.81 (0.51–1.29) 0.88 (0.54–1.41) 0.63
 Model 3, IRR (95% CI)c 1.00 (ref.) 0.87 (0.54–1.40) 0.83 (0.52–1.33) 0.90 (0.56–1.47) 0.74
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SPPB = Short Physical Performance Battery; P-M = person/falls calendar-months; CI = confidence interval

†

Including those with missing SPPB.

a

Adjusted for age, race/ethnicity, and education.

b

Adjusted for age, race/ethnicity, education, vision, body pain, alcohol use, sleep aid use, body mass index, and number of chronic conditions.

c

Adjusted for age, race/ethnicity, education, vision, body pain, alcohol use, sleep aid use, body mass index, number of chronic conditions, and SPPB.

Education, vision, body pain, alcohol use, sleep aid use, body mass index, and SPPB multiply imputed.

Continuous: Age, BMI, number of chronic conditions, and SPPB.

Bold indicates significance at the P < 0.05 level.

In the low physical functioning stratum (SPPB 0–8), crude fall rates followed the same pattern of decreasing risk as steps/d increased, but the rates were higher than those of the total sample (135.1, 93.2, 91.0, 87.3 falls per 1,000 person-months in Q1, Q2, Q3, and Q4, respectively; Table 2). All Model 1 aIRRs in the low physical functioning stratum were weaker relative to the aIRRs observed in the total sample (Q2, Q3, and Q4 aIRRs vs. Q1: 0.75 (0.57–0.99), 0.71 (0.52–0.98), 0.63 (0.42–0.94); Ptrend = 0.01). There were no statistically significant results in Models 2 and 3, but the point estimates suggested inverse associations that generally followed a similar pattern of attenuation that was observed in the overall cohort.

In the high physical functioning stratum, all aIRRs were less than 1 and slightly weaker relative to those in the low physical functioning stratum (Table 2), but all confidence intervals included the null value and no significant linear trends were observed.

Mediation Analysis

We estimated that 63.7% of the association between steps/d and falls risk was mediated by physical function, as scored by the SPPB (95% CI 28.1% - 272%; p = 0.03).

Nonparametric Modeling of Steps/d

Figure 1 shows the association of steps/d and predicted fall risk within categories of SPPB. The higher SPPB categories (7 – 9 and 10 – 12) were associated with reduced predicted fall risk, as visualized in Figure 1, though there was no statistical evidence of interaction between steps/d and these SPPB categories on falls risk (Pinteraction = 0.57). However, the association between steps/d on a continuous scale and subsequent fall risk appeared null within each of these higher SPPB categories after adjusting for Model 2 covariates. For example, those with SPPB scores between 10 and 12 had a predicted fall risk of approximately 81 falls per 1,000 person-months and this risk did not vary across the step count distribution. Within the SPPB category of lower scores (0 – 6), we observed that those taking the fewest steps/d had the highest predicted risk of falls and that those who are taking more steps/d had a lower predicted falls risk.

Figure 1.

Figure 1.

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Dose-Response Trajectories of Incident Fall Risk by Level of the Short Physical Performance Battery

SPPB = Short Physical Performance Battery. Associations are adjusted for age, race/ethnicity, education, vision, body pain, alcohol use, sleep aid use, body mass index, and the number of chronic conditions. SPPB scores range from 0 (lowest functioning) – 12 (highest functioning). Dots are at the 25th, 50th, and 75th percentile of the SPPB-specific step distribution. The highlighted portion represents the 5th to the 95th percentile of each stratum’s step distribution. Interaction P-value from the corresponding parametric model.

Stratification by age tertiles (Figure 2B), after adjusting for all Model 3 covariates, showed that increasing steps/d does not affect the risk of falls; Pinteraction = 0.46). Stratification by fall history prior to OPACH baseline showed an inverse association between steps/d and fall risk in those with ≥ 2 falls in the past year (using Model 3; Figure 2A). Those with a history of one fall in the past year had a lower risk of falling and this risk was unaffected by steps/d. Those with no history of falling had the lowest risk of falling and this risk was unaffected by steps/d. There was no statistically significant interaction between steps/d and falls history (Pinteraction = 0.48).

Figure 2.

Figure 2.

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Dose-Response Trajectories of Incident Fall Risk by Fall History (A) and Age Tertiles (B) Adjusting for Levels of the Short Physical Performance Battery

Associations are adjusted for age, race/ethnicity, education, vision, body pain, alcohol use, sleep aid use, body mass index, number of chronic conditions, and the Short Physical Performance Battery. Dots are at the 25th, 50th, and 75th percentile of the strata-specific step distribution. The highlighted portion represents the 5th to the 95th percentile of each stratum’s step distribution. Interaction P-values from the corresponding parametric model.

Sensitivity Analysis

Results from the sensitivity analysis indicated that the intensity of daily steps accrued does not alter the association with incident falls risk. The crude, partially adjusted, and fully adjusted rates of falls in quartiles of light and moderate-to-vigorous steps/d were comparable with one another in the overall cohort and when stratified on SPPB physical function score (see Appendix Tables 1 and 2).

DISCUSSION

To our knowledge, this is the first study that prospectively examines accelerometer-measured steps/d and subsequent fall risk in the context of objectively measured physical functioning in a community-dwelling cohort of older ambulatory women. The confounder-adjusted risk (Model 2) of falls was reduced by 23% and 28% in the middle 2 quartiles and by 29% in the highest quartile of steps compared to the lowest. After further adjustment for SPPB (Model 3), the adjusted risk of falls were 15%, 16%, and 14% reduced for Q2 – 4, respectively. SPPB mediated about 63.7% (95% CI 28.1% - 272%; p = 0.03) of the association between steps/d and falls. Nonparametric splines indicated that, except for the lowest SPPB category, variation in steps/d was not associated with fall risk, however, there was a strong, graded association of increased fall risk with lower SPPB scores.

Our findings contribute to the growing body of evidence (2018 Physical Activity Guidelines Advisory Committee, 2018) that higher levels of physical activity, in this case quantified by step counts, are not associated with increased fall risk, and may decrease fall risk through their relationship with better lower extremity physical function, as supported by our mediation anlysis. In a previous OPACH report examining accelerometer-measured physical activity intensity, the highest risk of falls was observed in those with the least amount of time spent doing moderate-to-vigorous physical activity (Buchner et al., 2017). Additionally, a cohort study of German men and women with a similar average age (75.6 years) and fall ascertainment method found that those who walked less than 1 hour per day sustained more falls per hour walked compared to those who walked for a greater amount of time, although this finding was not adjusted for physical functioning (Klenk et al., 2015). Other studies have reported that higher amounts of physical activity increased fall risk under certain circumstances (Chan et al., 2007; Jefferis et al., 2015; Lawton et al., 2008). For example, in older British men with no mobility limitations, only step counts (≥ 9,000 steps/d) were associated with an increased risk of experiencing a fall, while there was no observed association at lower step counts (Jefferis et al., 2015). In men with mobility limitations, lower counts of steps/d were associated with an increased fall risk. No study, to our knowledge, has investigated fall risk across the full range of steps/d as a continuous variable, but instead have examined steps/d in broad or binary categories.

Decreased physical functioning is a direct risk factor for falling in older populations (2018 Physical Activity Guidelines Advisory Committee, 2018; Veronese et al., 2014). In the present study, adjustment for SPPB resulted in the largest attenuation between steps/d and fall risk. The temporal, and perhaps bi-directional association, of steps/d and physical functioning has not been well defined—does decreased physical functioning cause a person to step less because of their lower function, or does a lack of steps/d cause a decrease in physical functioning? We postulate that a feedback loop may exist between physical functioning and steps/d such that reducing steps/d could lead to lower physical functioning which in turn could lead to even fewer steps/d in the future. Thus, physical functioning may be acting as a true intermediate, a true confounder, or a simultaneous cause and effect of steps/d when adjusted in these models. Thus, in our study, analyses were designed to consider SPPB in each role. Lower physical functioning could accelerate leg muscle atrophy and functional deterioration, which may lead to fewer steps/d, which may exacerbate the deterioration in leg muscle function and its contribution to balance, and may increase subsequent fall risk. Future studies should investigate how steps/d influences changes in SPPB over time to improve our understanding of temporality and possible bi-directionality.

About two-thirds of the steps/d and fall risk association (63.7%) was shown to be mediated by physical function. This large percentage mediation should be interpreted cautiously given the wide confidence interval. Moreover, in the OPACH study, SPPB was measured concurrently with accelerometer-measured steps/d, so whether SPPB was affected by past steps/d cannot be assessed. Given the strong association of SPPB levels with fall rates irrespective of step counts, this study suggests that interventions targeting SPPB improvement, many of which include stepping, are paramount to reducing the risk of subsequent falls in older women (Giné-Garriga et al., 2014; Sherrington et al., 2017).

Strengths and Limitations

While we were unable to study repeated measures of step counts to confirm that women did not change their steps/d within the observation period, previous studies have shown that a 7-day accelerometer wear yields a stable, reliable estimate of a participant’s step/d habits (Keadle et al., 2017; Saint-Maurice et al., 2020). As noted above, while the data collected in OPACH is robust and longitudinal, these data were not best suited for a mediation analysis which would optimally include repeated measures of steps/d and SPPB over time. Finally, step counts measured by an accelerometer have been shown in some small studies to underestimate the true number of steps/d taken, particularly at slower gait speeds. However, since there was no association between steps/d and subsequent falls across a range of SPPB levels including low levels with slow gait speeds, our findings are likely to be unaffected by this source of measurement error, if it does exist. Generalizability of these finding to men and younger populations will need to be assessed in future studies.

One of the biggest strengths of this study is the prospective follow-up with daily ascertainment of falls using monthly falls calendars. This has been shown to be the most valid method for ascertaining falls and avoids recall bias from asking questions about falls retrospectively (Hale et al., 1993; Hannan et al., 2010). OPACH enrolled a large WHI sub-cohort of racially and ethnically diverse older women, objectively measured steps/d using an accelerometer with movement intensity calibrated to the types of activity relevant to older women’s lives, and collected data that enabled adjustment for a variety of risk factors. Lastly, the conclusions drawn from these data are robust across our statistical approaches—parametric, non-parametric, and spline figures. The varied methods considering SPPB as a confounder, modifier and mediator ultimately contribute to a consistent and unified narrative.

CONCLUSION

Number of steps taken per day was not significantly associated with fall risk in older women after adjustment for physical functioning. Higher step counts may decrease the risk of falls through their relationship with improved lower extremity physical function, as supported by our mediation anlysis. These data also support the safety of taking more steps per day, which other studies have shown to have a myriad of health benefits, up to and including reduced risk of mortality (Araiza et al., 2006; Lee et al., 2019; Murtagh et al., 2010), and should be recommended by physicians if not contraindicated. Interventions designed to strengthen lower extremity function, several of which involve improving daily step counts (Giné-Garriga et al., 2014; Sherrington et al., 2017), would likely reduce the daunting burden of falls risk in older women.

Supplementary Material

Appendix Table 1. Adjusted Incidence Rate Ratios (IRR) of Subsequent Falls by Accelerometer Measured Average Moderate-to-Vigorous Intensity Daily Step Quartiles in the Selected OPACH Sample, 2012-2014 (n = 5,545)
Appendix Table 2. Adjusted Incidence Rate Ratios (IRR) of Subsequent Falls by Accelerometer Measured Average Light Intensity Daily Step Quartiles in the Selected OPACH Sample, 2012-2014 (n = 5,545)
Supplemental Figure 1. Falls Calendar Sheet Sent to OPACH Participants
Supplemental Figure 2. Derivation of the Accelerometer-Measured Daily Steps and Subsequent Falls Analytic Sample from the OPACH Study, 2012-2014

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix Table 1. Adjusted Incidence Rate Ratios (IRR) of Subsequent Falls by Accelerometer Measured Average Moderate-to-Vigorous Intensity Daily Step Quartiles in the Selected OPACH Sample, 2012-2014 (n = 5,545)
NIHMS1821977-supplement-Appendix_Table_1__Adjusted_Incidence_Rate_Ratios__IRR__of_Subsequent_Falls_by_Accelerometer_Measured_Average_Moderate-to-Vigorous_Intensity_Daily_Step_Quartiles_in_the_Selected_OPACH_Sample__2012-2014__n___5_545_.docx (21.2KB, docx)
Appendix Table 2. Adjusted Incidence Rate Ratios (IRR) of Subsequent Falls by Accelerometer Measured Average Light Intensity Daily Step Quartiles in the Selected OPACH Sample, 2012-2014 (n = 5,545)
NIHMS1821977-supplement-Appendix_Table_2__Adjusted_Incidence_Rate_Ratios__IRR__of_Subsequent_Falls_by_Accelerometer_Measured_Average_Light_Intensity_Daily_Step_Quartiles_in_the_Selected_OPACH_Sample__2012-2014__n___5_545_.docx (21.2KB, docx)
Supplemental Figure 1. Falls Calendar Sheet Sent to OPACH Participants
NIHMS1821977-supplement-Supplemental_Figure_1__Falls_Calendar_Sheet_Sent_to_OPACH_Participants.docx (973.6KB, docx)
Supplemental Figure 2. Derivation of the Accelerometer-Measured Daily Steps and Subsequent Falls Analytic Sample from the OPACH Study, 2012-2014
NIHMS1821977-supplement-Supplemental_Figure_2__Derivation_of_the_Accelerometer-Measured_Daily_Steps_and_Subsequent_Falls_Analytic_Sample_from_the_OPACH_Study__2012-2014.docx (37.2KB, docx)

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