Associations of Daily Pedometer Steps and Self-Reported Physical Activity With Health-Related Quality of Life

Abstract

Objective: The aim of this article is to examine associations of self-reported physical activity (PA) and pedometer steps with health-related quality of life (HRQoL) among a population-based sample of older adults. Method: Adults ≥55 years (N = 1,296) were recruited through random-digit dialing and responded to a questionnaire via computer-assisted telephone interviewing methods. Questionnaires assessed demographic variables and validated measures of PA and HRQoL. Participants received a step pedometer and waist circumference tape measure via post. Results: Compared with participants in the low-step group (0-6,999 steps/day), participants in the high-step group (>10,000 steps/day) had significantly higher scores on mental health (M_diff = 3.1, p < .001, confidence intervals [CI] = [1.8, 4.3]), physical health (M_diff = 3.5, p < .001, CI = [2.2, 4.7]), and global health (M_diff = 3.5, p < .001, CI = [2.3, 4.7]). Waist circumference and body mass index did not moderate any associations of pedometer steps and PA with HRQoL. Conclusion: Older adults exceeding established step thresholds reported significantly higher HRQoL indices compared with those achieving lower thresholds.

Keywords

physical activity pedometer health-related quality of life older adults

Introduction

By the year 2050, it is estimated there will be approximately two billion people aged 60 and older (World Health Organization [WHO], 2015). Aging is associated with declining physical and mental health, and an increased risk of developing chronic disease. Health-related quality of life (HRQoL) is one important indicator of an individual’s physical, mental, and overall health (U.S. Centers for Disease Control and Prevention, 2000). Achieving and maintaining a positive overall HRQoL among older adults is particularly important given that HRQoL begins to decline at age 55 years (Fryback et al., 2007). Given this decline, identifying and understanding factors related to HRQoL among older adults is important. Physical activity is one key behavior that may support an overall positive HRQoL as evidence continues to emerge supporting the positive association between physical activity and HRQoL among community-dwelling older adults (Bertheussen et al., 2011; Salguero, Martinez-Garcia, Molinero, & Marquez, 2011; Vallance, Eurich, Lavallee, & Johnson, 2012, 2013).

Despite consistent evidence suggesting a positive association between physical activity and HRQoL, limited research has examined different types and volumes of physical activity and their associations with HRQoL. The majority of studies examining the association between HRQoL and physical activity have utilized self-report measures of physical activity. Few studies have examined specific activities, such as walking, and associations with HRQoL. A focus on walking among older adults is relevant given walking is the most common form of daily physical activity among older adults (Eyler, Brownson, Bacak, & Houseman, 2003), and as mobility disability and ambulation (i.e., walking) become increasingly important as an individual ages (Satariano et al., 2012). Hence, the use of step pedometers is a valid method in which to monitor walking behavior. Although not without limitations (e.g., not able to discriminate walking intensity), one study with older adults has suggested pedometer-derived step counts to be a more valid measurement of overall physical activity when compared with self-report measures (Ewald, McEvoy, & Attia, 2010). Normative data has suggested the range for healthy older adults is 7,000 and 10,000 steps/day, which is estimated to be equivalent to the recommended 150 min of moderate-to-vigorous physical activity (MVPA) per week (Tudor-Locke, Craig, et al., 2011). Although few studies have examined associations of daily pedometer steps and self-reported MVPA with HRQoL among older adults (Vallance et al., 2012, 2013), there is a need to understand factors that may influence these associations (e.g., body mass index [BMI], waist circumference). A more comprehensive understanding of such factors may lead to the development of more appropriate physical activity and walking interventions designed to improve HRQoL among older adults.

We recently completed the Alberta Older Adult Health Behavior (ALERT) study which was a population-based study designed to examine various health behaviors and associated health outcomes (both objective and self-report) among older adults (Vallance et al., 2014). The purpose of this article was to examine associations of pedometer steps and self-reported MVPA with HRQoL among a population-based sample of older adults. The secondary objective was to examine the potential moderating roles of waist circumference and BMI on associations of daily pedometer steps and MVPA with HRQoL.

Method

A detailed description of the study procedures is presented elsewhere (Vallance et al., 2014). In summary, the ALERT study was conducted using Random Digit Dialing (RDD) and Computer Assisted Telephone Interviewing. RDD ensured that households in each of the five provincial health zones had an equal chance to be contacted whether or not their household was listed in a telephone directory. All participant recruitment and data collection were conducted through a centralized research unit—the Population Research Laboratory (PRL) at the University of Alberta (Edmonton, Alberta, Canada). Older adults across the province of Alberta were eligible to participate if they were at least 55 years of age, were able to walk unassisted (without the aid of a wheelchair or walking aid), community dwelling (not living in an aged care setting), and were able to complete a computer-assisted telephone survey in English. The ALERT study was approved by the University of Alberta’s Health Research Ethics Board as well as the Athabasca University Research Ethics Board.

Measures

Demographic, behavioral, and medical information was gathered via self-report and included age, sex, marital status, education, income, employment status, racial background, smoking, comorbidities, and Internet access.

Pedometer steps were assessed using the StepsCount SC-01 pedometer (Deep River, Ontario, Canada) over 3 consecutive days and averaged (Tudor-Locke, Bassett, Shipe, & McClain, 2011). During the 3-day monitoring period, participants were asked to record their daily step counts at the end of each day and to reset the pedometer to 0 each morning.

Self-reported MVPA was assessed using the Leisure Score Index (LSI) of the Godin Leisure-Time Exercise Questionnaire (GLTEQ; Godin, Jobin, & Bouillon, 1986; Godin & Shephard, 1985) which contains four questions assessing the average frequency and duration of moderate (MVPA) during leisure time over a typical week during the past month. The validity of the GLTEQ is well established (Jacobs, Ainsworth, Hartman, & Leon, 1993).

HRQoL was assessed using the RAND-12 Health Status Inventory (Ware, Kosinski, & Keller, 1996). The RAND-12 gives two scores; a mental health component (MHC) scale and a physical health component (PHC) scale each comprised of six items. A global health score (GHC) was also generated by combining the MHC and PHC scores. Scores for each component range from 0 to 100 where lower scores indicate greater disability (>50 = no disability; 40-50 = mild disability; 30-40 = moderate disability). A PHC score <42 suggests that perceived physical health problems are impeding life functioning while an MHC score <38 likely indicates that an individual is experiencing psychological symptoms that might be impeding life functioning (Ware et al., 1996).

BMI was calculated kilograms/metre² and based on self-reported weight (kg) and height (m). From this, participants were classified as normal weight (<25.0 kg/m²), overweight (25.0-29.9 kg/m²), and obese (≥30 kg/m²).

Waist circumference was measured (by each participant) using a waist circumference tape (Almedic, Stevens, Ontario, Canada). Participants were asked to stretch out the tape measure and wrap it around their (exposed) waist at the level of the navel. Participants were instructed to not hold their breath, rather, they were asked to breathe out and then measure (to the nearest 0.1 inch). Participants were asked to complete and record the measure 3 times and perform all three measures at 1 time.

Analysis

Demographic and health-related characteristics of participants were compared using ANOVA and chi-square test where appropriate. Average pedometer steps/day were categorized as (a) low-step range (0-6,999), (b) healthy-step range (7,000-10,000), or (c) high-step range (>10,000; Tudor-Locke, Craig, et al., 2011). MVPA was categorized according to the U.S. Department of Health and Human Services (USDHHS) guidelines for physical activity (U.S. Government, 2008). Participants were either (a) not meeting MVPA guidelines (<150 min of MVPA per week), (b) meeting MVPA guidelines (150-299.9 min of MVPA per week), or (c) exceeding guidelines for additional health benefits (≥300 min of MVPA per week). For Study Objective 1, our primary models assessed the association between MVPA and average pedometer steps/day as the independent variables of interest and our dependent variables of interest (physical, mental, and global health component scores of the RAND-12). Models were compared using the generalized Hausman specification test to determine whether the groups had the same (spatial) distribution.

For Study Objective 2, we examined whether associations of MVPA and steps with HRQoL varied by BMI (normal weight vs. overweight/obese) and/or waist circumference, >94cm (men), >80cm (women; cutpoints based on metabolic risk; WHO, 2011). Waist circumference above 94cm (for men) and 80cm (for women) are suggestive of an increased risk of metabolic complications. All models were adjusted for age, sex, BMI/waist circumference, education, marital status, and comorbidities (i.e., history of heart disease, stroke, diabetes, cancer). All analyses were conducted with Stata SE 10.1 (StatCorp, College Station, Texas).

Results

Details of study flow have been presented elsewhere (Vallance et al., 2014). In summary, the PRL made 102,977 dialings on 36,000 unique telephone numbers to obtain 1,296 completed interviews for the study (yield 3.6% per unique telephone number). The most common final call disposition was ineligibility due to age (i.e., no residents 55 years of age or older in the household; n = 8,193; 23%). All participants (N = 1,296) completed the self-report measures (e.g., MVPA, HRQoL) while 1,081 participants (81%) completed the pedometer steps and waist circumference assessments.

Demographic, behavioral, and health characteristics of the entire sample are presented elsewhere (Vallance et al., 2014). Table 1 presents this information for participants who completed the objective measures (n = 1,081) and those who did not (n = 215). Participants who completed the objective measures (i.e., pedometer steps, waist circumference) had a significantly lower BMI (p = .002) and were more likely to have had Internet access (p = .002) compared with participants not completing the objective measures. For women, 77.3% had a waist circumference above 80cm (i.e., increased risk for metabolic complications). For men, 58% had a waist circumference above 94cm (i.e., increased risk for metabolic complications). Overall, 44.5% of participants achieved USDHHS guidelines for physical activity (i.e., at least 150 min of MVPA per week) (U.S. Government, 2008). Of these participants, 46% exceeded 300 min of MVPA per week. Participants accrued an average of 5,183 (SD = 3,559) steps/day (<7,000 steps: n = 793; 7,000 to <10,000 steps: n = 185; ≥10,000 steps: n = 103).

Table 1.

Demographic and Health Characteristics of Study Participants.

	PA (n = 1,081)	No PA (n = 215)	p value
Age	66.3 ± 8.1	66.8 ± 8.5	.43
Sex
Male	474 (44)	81 (38)	.095
Marital status
Single	62 (6)	11 (5)	.037
Married	725 (67)	121 (57)
Common law	34 (3)	7 (4)
Divorced	93 (8)	30 (14)
Separated	25 (2)	5 (2)
Widowed	139 (13)	38 (18)
Education
Some high school	107 (10)	36 (16)	.014
High school	238 (22)	51 (24)
Some college	189 (18)	42 (20)
College/university degree	487 (45)	78 (36)
Trade	59 (5)	8 (4)
Income
<20k	60 (7)	19 (11)	.170
20,000-39,999	185 (21)	41 (24)
40,000-59,999	148 (17)	25 (15)
60,000-79,999	131 (15)	30 (17)
80,000-99,999	108 (12)	14 (8)
>100K	250 (28)	43 (25)
Employment status
Full-time	298 (28)	63 (29)	.557
Part-time	116 (11)	24 (11)
Unemployed, looking	21 (2)	3 (1)
Not working, not looking	11 (1)	2 (1)
Retired	572 (53)	106 (50)
Homemaker	21 (2)	8 (4)
Disability	17 (2)	5 (2)
Retired but working	10 (1)	0 (0)
Seasonal work	1 (0)	1 (0)
Self-employed	12 (1)	2 (1)
Racial background
Caucasian	1,027 (95)	194 (91)	.200
Aboriginal	12 (1)	4 (2)
Other	41 (4)	15 (7)
MVPA (min/week)	183.0 (244)	172.0 (311)	.567
Pedometer steps	5,183 (3,559)
Comorbidities
None	237 (22)	41 (19)	.644
1	285 (26)	58 (27)
2 or more	559 (52)	116 (54)
Diabetes	125 (12)	31 (14)	.240
Cancer	207 (19)	32 (14)	.141
Fibromyalgia	37 (3)	10 (5)	.379
Osteoarthritis	293 (27)	52 (24)	.377
Rheumatoid arthritis	83 (8)	31 (14)	.001
Emphysema	15 (1)	5 (2)	.308
Asthma	81 (7)	20 (9)	.366
COPD	56 (5)	10 (5)	.747
Heart disease	121 (11)	25 (12)	.854
Stroke	21 (2)	12 (6)	.002
Bowel disorder	77 (12)	18 (8)	.521
Thyroid	162 (15)	24 (16)	.757
Kidney	30 (3)	8 (4)	.453
Mood disorder	93 (9)	21 (10)	.582
Smoking	117 (11)	43 (20)	<.001
Waist circumference	36.8 (5.4)	28.2 (5.4)	.002
Men	97.6 (12.1)
Women	90.3 (14)
Body mass index	27.0 (5.0)
Internet access	947 (88)	171 (80)	.002
HRQoL
PHC	51.6 (8.8)	49.1 (9.8)	<.001
MHC	51.5 (8.6)	49.7 (9.5)	.006
GHC	51.5 (8.3)	49.2 (9.1)	.001

Note. Numbers may not equal 1,296 due to missing data. Data are presented as the Mean (SD) for continuous variables (e.g., age, pedometer steps waist circumference, body mass index) and frequency (%) for categorical variables. Demographic and clinical characteristics of participants were compared using ANOVA and chi-square tests where appropriate. PA = physical activity; MVPA = moderate-to-vigorous physical activity; COPD = chronic obstructive pulmonary disease; HRQoL = health-related quality of life; PHC = physical health component; MHC = mental health component; GHC = global health score.

Pedometer Steps and HRQoL

After multivariate adjustment, significant differences were observed between the low-, healthy-, and high-range step groups across MHC (p < .001), PHC (p = .001), and GHC (p < .001; see Figure 1). Compared with participants in the low-step group (0-6,999 steps/day: n = 793), participants in the healthy-step (7,000 to <10,000 steps/day: n = 185) group had significantly higher scores on MHC (M_diff = 1.3, p < .001, CI = [0.1, 2.4]), PHC (M_diff = 2.1, p < .001, CI = [1.0, 3.3]), and GHC (M_diff = 1.8, p = .001). Compared with participants in the low-step group, participants in the high-step group (≥10,000 steps/day: n = 103) had significantly higher scores on MHC (M_diff = 3.1, p < .001, CI = [1.8, 4.3]), PHC (M_diff = 3.5, p < .001, CI = [2.2, 4.7]), and GHC (3.5, p < .001, CI = [2.3, 4.7]).

Figure 1.

Differences in HRQoL between pedometer step categories.

MVPA and HRQoL

Significant differences were observed between the inactive, meeting base MVPA guidelines and exceeding base MVPA guidelines groups across MHC (p < .001), PHC (p < .001), and GHC (p < .001; see Figure 2). Compared with the inactive (not meeting guidelines) group, participants meeting the base MVPA guidelines had significantly higher scores on MHC (M_diff = 1.4, p = .014, CI = [0.3, 2.5]), PHC (M_diff = 2.4, p < .001, CI = [1.3, 3.5]), and GHC (M_diff = 1.9, p = .001, CI = [0.8, 3.0]). Compared with the inactive group, participants exceeding the base MVPA guidelines had significantly higher scores on MHC (M_diff = 3.0, p < .001, CI = [1.8, 4.2]), PHC (M_diff = 3.3, p < .001, CI = [2.1, 4.5]), and GHC (M_diff = 3.6, p < .001, CI = [2.4, 4.8]). There were no differences between the MVPA and pedometer step models on PHC (p = .073), MHC (p = .41), or GHC (p = .28) indices.

Figure 2.

Differences in HRQoL between physical activity categories.

Higher pedometer step and MVPA categories were significantly associated with lower waist circumference (all ps < .001), but not lower BMI. Waist circumference and BMI was not associated with HRQoL (all ps > .05). There were no interactions of MVPA with either waist circumference or BMI across the HRQoL indices (all ps > .31). There were also no interactions of steps with either waist or BMI across the HRQoL indices (all ps > .11).

Discussion

The purpose of this article was to examine associations of both self-reported MVPA and pedometer steps with HRQoL among a population-based sample of older adults. Both self-reported MVPA and objectively assessed walking behavior were significantly associated with HRQoL (i.e., physical health, mental health, and global health). Waist circumference and BMI did not moderate the associations of MVPA or walking with HRQoL domains.

Older adults in this sample averaged almost 5,200 steps per day which is slightly higher than a recent population-based sample of older adults in the United States (4,200 steps; Tudor-Locke et al., 2013). Among our sample, participants engaging in 7,000 to 10,000 steps/day (equivalent to achieving 30 min of MVPA per day; Tudor-Locke, Craig, et al., 2011; Tudor-Locke, Leonardi, Johnson, Katzmarzyk, & Church, 2011) reported significantly higher HRQoL scores compared with participants engaging in fewer than 7,000 steps per day. Furthermore, those exceeding 10,000 steps/day demonstrated significantly better HRQoL. Few studies have examined objective indices of walking and association with HRQoL indices. The current population-based study of older adults is the first to examine associations with HRQoL across recently established health-outcome-referenced values of steps/day specifically for older adults (Tudor-Locke, Craig, et al., 2011). Given higher HRQoL indices observed in the high-step group (i.e., >10,000 steps/day), our data reinforce a dose-dependent nature for walking and HRQoL.

Participants achieving public health physical activity guidelines reported significantly higher HRQoL indices compared with those not achieving these guidelines. Furthermore, participants exceeding the base MVPA guidelines for added health benefits (>300 min of MVPA per week) reported even better scores when compared with participants not achieving guidelines. Associations between self-reported physical activity and physical and mental health outcomes from HRQoL measures among population-based cohorts have been well established (Bertheussen et al., 2011; Brown et al., 2003). However, data from population-based studies of community-dwelling older adults are limited. For example, Balboa-Castillo, Leon-Munoz, Graciani, Rodriguez-Artalejo, and Guallar-Castillon (2011) reported older adults in the upper quartile of leisure-time physical activity had more optimal physical functioning, but not mental health, compared with participants who reported no physical activity. Our current results corroborate our previously published work with men exploring MVPA and HRQoL indices according to USDHHS guidelines (Vallance et al., 2012).

Overall, our data indicate both self-reported MVPA and objectively determined walking behavior (i.e., steps) appear to be the significant independent correlates of HRQoL among older adults. Feeny and colleagues (2013) recently suggested there are complex relationships between body size, physical activity, and HRQoL in the aging population that are yet to be understood. Of the few studies published, all suggest that an individual’s weight status and body size has little to no association with HRQoL as MVPA consistently emerges as the more important factor (i.e., Feeny et al., 2013; Herman, Hopman, Vandenkerkhof, & Rosenberg, 2012; Kruger, Bowles, Jones, Ainsworth, & Kohl, 2002). These studies did not specifically target older adults. Data from our sample of older adults corroborate this body of evidence by suggesting MVPA, and objectively assessed pedometer steps are significantly associated with more optimal HRQoL indices irrespective of waist circumference (no increased risk vs. increased risk of metabolic complications) and body mass (normal weight vs. overweight/obese) among a population-based sample of older adults. These findings underscore the value of physical activity and walking and suggest older adults can benefit from physical activity and walking regardless of their body size and waist circumference.

Strengths of our study include the large sample size, population-based design and features (e.g., RDD procedures, the use of trained interviewers, geographically representative sample of older adults), and the use of objective measures of walking behavior (pedometers) and waist circumference. Despite the strengths of the current study, it is likely that our sample of older adults do not represent all older adults as only those individuals who were interested in health may have participated in the study. Furthermore, daily steps may not be an accurate representation of individuals’ normal walking patterns. For example, reactivity (when individuals change their behavior due to awareness they are being monitored; Tudor-Locke, Bassett, Shipe, & McClain, 2011) to wearing a pedometer has been shown to be as high as 15% although some studies have found no evidence of pedometer reactivity (e.g., Matevey, Rogers, Dawson, & Tudor-Locke, 2006). Finally, the cross-sectional nature of this study limits the inferences that can be made regarding the results of this study.

Older adults achieving USDHHS physical activity guidelines reported significantly higher HRQoL indices compared with older adults not achieving guidelines. Furthermore, older adults accumulating daily steps equivalent to at least 30 min of MVPA per day (i.e., at least 7,000 steps/day; Tudor-Locke, Craig, et al., 2011) also reported significantly higher HRQoL indices compared with older adults in the referent step category (i.e., <7,000 steps/day). Waist circumference and BMI did not influence these associations. Given declining physical activity and HRQoL with increasing age (Buchman et al., 2014), developing population-based strategies to encourage MVPA and walking among older adults (irrespective of body size) could facilitate the maintenance (and perhaps improvement) of HRQoL indices among this population. Future research should consider sedentary behavior profiles among older adults and associations with BMI, waist circumference, and HRQoL.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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