Skip to main content
Download PDF
Download ePub

Associations of steps per day and step intensity with the risk of diabetes: the Hispanic Community Health Study / Study of Latinos (HCHS/SOL)

International Journal of Behavioral Nutrition and Physical Activity volume 19, Article number: 46 (2022) Cite this article

Abstract

Background

Higher levels of moderate-to-vigorous physical activity have been associated with a lower risk of diabetes, but less is known about how daily step counts (steps/day) are associated with diabetes risk. Therefore, we examined the association of steps/day and step intensity with incident diabetes.

Methods

We included 6634 adults from the population-based prospective cohort Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (2008–2017). Cox proportional hazard models that accounted for complex survey design and sampling weights were used to estimate the association of baseline accelerometer-assessed steps/day and step intensity with 6-year risk of incident diabetes as hazard ratios (HR) and 95% confidence intervals (CI). We further examined whether the percent of intense steps at a given accumulation of steps/day was associated with diabetes risk, and if associations were modified by specific cohort characteristics.

Results

The average age of cohort members was 39 years and 52% were female. Adults had an average of 8164 steps/day and spent 12 min/day in brisk ambulation (> 100 steps/min). Over 6 years of follow-up, there were 1115 cases of diabetes. There was a suggestive lower risk of diabetes with more steps/day– adults had a 2% lower risk per 1000 steps/day (HR = 0.98 (95% CI 0.95, 1.00)). Inverse associations between average steps/day and diabetes incidence were observed across many cohort characteristics, but most importantly among adults at high risk for diabetes – those who were older, or had obesity or prediabetes. Adults who accumulated 17 min/day in brisk ambulation compared to < 2 min/day had a 31% lower risk of diabetes (HR = 0.69 (95% CI 0.53, 0.89)). A greater percent of intense steps for a given accumulation of steps/day was associated with further risk reduction.

Conclusion

Adults who accumulate more daily steps may have a lower risk of diabetes. Accumulating more steps/day and greater step intensity appear to be important targets for preventing diabetes.

Background

In the United States (US), over 26 million adults (9.8%) have a diagnosis of type 2 diabetes and 91.8 million (37.6%) have prediabetes [1]. Diabetes is associated with significant morbidity and mortality [2], and will remain a large public health burden, as the prevalence of diabetes is projected to double by 2030 [3]. One preventive strategy advocated by the American Diabetes Association is engagement in moderate-to-vigorous physical activity (MVPA) [4]. Higher levels of MVPA have been associated with a lower risk of diabetes [5,6,7,8]. While most evidence relies on self-reported and accelerometer-measured MVPA, less is known about how daily step counts are related to diabetes. Daily step counts are an easily interpretable, trackable, and simple measure of physical activity volume and have become more familiar and accessible to the public with the increase in wearable devices [9]. In 2018, the US Physical Activity Guidelines Advisory Committee reviewed studies on step counts with health outcomes and determined there was insufficient evidence on the association of step counts with mortality, cardiovascular disease, and diabetes [9, 10]. The Committee called for more longitudinal research on the association of step counts and stepping cadence with health outcomes [9]. Since 2018, new studies have been published that suggest greater steps per day are associated with a lower risk of all-cause mortality [11,12,13,14]. However, there remains limited data on how daily steps counts are associated with diabetes risk.

Total step volume (steps/day) includes steps accumulated at a light, moderate, and vigorous intensity [9]. The rate of stepping, or cadence (steps/min), is considered a proxy for walking intensity and a directly-observed cadence of > 100 steps/min is suggested to be a moderate intensity activity or greater (≥ 3 metabolic equivalents) [15]. Four longitudinal studies have examined the association of steps/day with diabetes or incident dysglycemia (impaired fasting glucose or impaired glucose tolerance) [16,17,18,19]. In the NAVIGATOR Trial, participants had a 4% lower risk of diabetes per 2000 steps/day [16]. In three other cohorts, the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), Healthy Aging Initiative cohort in Northern Sweden, and the Women’s Health Initiative Objective Physical Activity and Cardiovascular Health (OPACH) Study, a lower risk of incident diabetes or dysglycemia was also observed with accumulating more steps/day [17, 18]. These studies focused on steps/day and only one [19] examined how step cadence was associated with diabetes risk. Additionally, it is unclear how well findings from these studies generalize to more diverse populations, especially among population groups such as Hispanic/Latinos who have high rates of diabetes [1]. Furthermore, only the OPACH study [19] of women examined if associations varied by any risk factors for diabetes such as age, obesity, prediabetes, and insulin resistance [1, 2, 20, 21]. Additionally, in populations such as Hispanic/Latinos, occupational physical activity is highly prevalent [22, 23] and findings have been mixed on if occupational physical activity is associated with a lower risk of diabetes [6, 24,25,26].

Given the limited information on how stepping is associated with diabetes, we examined how steps/day were associated with the risk of diabetes in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Furthermore, we examined if specific cohort characteristics (age, sex, occupational physical activity, obesity, insulin resistance, prediabetes, and Hispanic/Latino heritage) modified the association between steps/day and diabetes. Because steps/day is a combination of light, moderate, and vigorous intensity steps, we further examined if step cadence and stepping pattern (bouted stepping) were associated with the risk of diabetes. We further explored the impact of step intensity by examining if achieving the same steps/day at a greater percent of intense steps compared to a lower percent of intense steps was associated with a lower risk of diabetes. Chen et al. [7] observed with the HCHS/SOL cohort a lower risk of diabetes with more minutes spent in MVPA, but the steps/day relationship with diabetes has yet to be examined within the cohort [7].

Methods

Study population

The HCHS/SOL is the largest population-based cohort of Hispanic/Latino adults from four US metropolitan areas (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA) [27, 28]. A total of 16,415 self-identified Hispanic/Latino adults (18–74 years) were recruited and enrolled from randomly selected households (2008–2011) through a multistage area probability design. Visits were conducted in 2008–2011 and 2014–2017 and at both visits, participants had a physical examination and completed questionnaires. Participants are also contacted annually over the phone. The Institutional Review Boards approved the study at each site and all participants gave written informed consent.

Exposure

At baseline, participants were asked to wear an Actical accelerometer (version B-1, model 198–0200-03) during waking hours for 1 week. The Actical was attached to a belt on the right hip and captured accelerations in 1-min epochs. Non-wear was defined using the Choi algorithm [29]. Participants left the examination visit wearing the accelerometer and were told to undertake their usual activities for the week and to remove the accelerometer only during sleeping, showering, and swimming. The data from 5 AM the day after the examination visit until midnight on day 6 were included in the present analyses to provide a consistent maximum 6-day wear period for all participants. Accelerometer adherence was defined as at least 3 days with 10 h of wear. The 10 h criterion is often used in other studies [30] and 3 of 6 days was selected to represent at least half of the maximum days of wear [31].

Steps were determined by the manufacturer’s step algorithm. The Actical step function has performed well in validation studies, although it may undercount steps at slow walking speeds [32,33,34]. We averaged steps/day over adherent days and calculated several cadence-based indicators of step intensity, including minutes spent at > 40 steps/min (purposeful steps and faster movement), > 70 steps/min (slow walking and faster movement), and > 100 steps/min (brisk walk and faster movement) [15]. We derived peak 30-min cadence, representing the average cadence of the 30 highest (but not necessarily consecutive) minutes in a day. We examined bouts of consecutive minutes spent at different intensity levels (> 40 steps/min, > 70 steps/min, and > 100 steps/min). A bout was defined as > 10 consecutive minutes above the specified cadence while allowing for interruptions of up to 20% of time below the cadence threshold and less than 5 consecutive minutes below the threshold [35]. The bout also had to start and stop above the cadence threshold [36]. Interruptions allowed for real-life events such as stopping at a traffic light or taking a water break during exercise [35]. We calculated the percent of steps that were at a cadence of > 100 steps/min, termed ‘intense steps’, to examine the contribution of intensity when holding steps/day constant.

Outcome

We examined two definitions of incident diabetes in order to enhance comparisons of our results with other studies that have used various diabetes definitions. The first definition was based on three criteria: 1) self-reported diagnosis of diabetes, 2) self-reported use of diabetic medication, or 3) laboratory-tested fasting plasma glucose > 126 mg/dl, non-fasting plasma glucose of > 200 mg/dl, 2-h postload oral glucose tolerance test (OGTT) > 200 mg/dl, or glycosylated hemoglobin (HbA1c) > 6.5% [37]. The second diabetes definition was based on self-reported diabetic medication and laboratory values. Self-reported use of diabetic medication and blood samples were collected at Visits 1 and 2. Self-reported diagnosis of diabetes, in addition to collection at Visits 1 and 2, was also collected over eight annual follow-up telephone interviews that occurred during the time between Visits 1 and 2. Prevalent diabetes at baseline was defined using the three criteria definition. One participant had data only for self-reported diagnosis and not for diabetic medications and lab values; therefore, the sample size for diabetes based on two criteria was 6633 rather than 6634. Further details are provided in the Supplemental Methods.

Covariates

We included the following covariates assessed at baseline in our analysis: age, sex, Hispanic/Latino heritage, HCHS/SOL field center, education, marital status, employment, years lived in the US, self-rated general health, mobility limitations, cigarette packyears, alcoholic drinks per week, energy intake, the 2010 Alternative Healthy Eating Index (AHEI-2010), body mass index (BMI), insulin resistance as measured by homeostasis model assessment of insulin resistance (HOMA-IR), prediabetes, and report of occupational physical activity, with description of the measures in the Supplemental Methods.

Exclusions

Of the 16,415 enrolled participants, 11,623 attended Visit 2. Of the Visit 2 participants, we excluded those who had diabetes at baseline (n = 2541), did not wear the accelerometer at Visit 1 (n = 732), were not adherent to accelerometry (adherence was > 3 days with > 10 h of wear; n = 1085), experienced an accelerometer malfunction (n = 62), and were missing any covariates used in analysis (n = 515). We trimmed the top and bottom 1% of the steps/day distribution in order to remove extreme outliers (n = 54). After exclusions, the analytic sample consisted of 6634 adults.

Statistical analysis

The HCHS/SOL study used a stratified two-stage area probability sample design and oversampling occurred at each stage (Supplemental Methods) [28]. All results were adjusted for oversampling of specific population subgroups and for nonresponse at Visit 2 by applying sampling weights (Supplemental Methods). We further adjusted for missing accelerometer data using inverse probability weights (IPW) [38]. The IPWs predicted Actical adherence based on associated variables, as described elsewhere (Supplemental Methods) [31]. The final weight was a product of the sampling weight and IPW. The weights were trimmed and calibrated to the 2010 US Census according to age, sex, and Hispanic/Latino background in the Census block groups of the four HCHS/SOL field centers. Results represent the characteristics of underlying population rather than the cohort participants.

Cox proportional hazard models that accounted for the complex survey design and survey weights were used to estimate the association of step metrics with incident diabetes as hazard ratios (HR) and 95% confidence intervals (CI). Follow-up time was calculated as time from baseline to occurrence of incident diabetes, end of follow-up, or death, whichever occurred first. The time scale for all models was time since baseline. We tested the proportional hazards assumption by examining interactions between each step metric and follow-up time and the assumption was met for all models. We further estimated incidence rates (IR) per 10,000 person-years with Poisson models that accounted for the complex survey design and survey weights. All analyses were conducted with SAS version 9.4 and Stata version 15.0.

We examined continuous and categorical measures of all step metrics. First, we examined the dose-response association of steps/day, peak 30-min cadence, and minutes spent at different cadence with incident diabetes. For each step metric, we tested for non-linearity by specifying models with restricted cubic splines with knots at the 25th, 50th, and 75th percentile of the step metric distribution. For all step metrics, the spline terms were not significant suggesting the relationship between each step metric and diabetes was not curvilinear [39]. In models with step metrics specified as a continuous variable, the 10th percentile of the distribution was used as the referent group. Next, we categorized steps/day based on the graduated step index [40] (< 5000 (sedentary), 5000–7499 (low active), 7500–9999 (somewhat active), 10,000–12,499 (active), and > 12,500 steps/day (highly active)). We classified peak 30-min cadence into four categories [15] (< 60 steps/min (incident, sporadic, and purposeful movement), 60 - < 80 steps/min (slow walking), 80 - < 100 step/min (medium walking), > 100 steps/min (brisk walking and faster ambulation)). We separately examined minutes spent above > 40, > 70, and > 100 steps/min and specified categories of minutes spent at different cadence as quartiles. For minutes spent in bouts, we separately examined bouted minutes above thresholds of > 40, > 70, and > 100 steps/min. We included a category of no bouts and specified remaining categories based on tertiles. For all step metrics we estimated a p-value for trend by specifying each step metric as an ordinal variable in models. All models were adjusted for age, a quadratic term for age, sex, Hispanic/Latino background by HCHS/SOL field center specified as a 17-level categorical variable, education, married/partner status, employment status, years in the US, self-rated general health, mobility limitations, cigarette pack years, alcoholic drinks per week, energy intake, AHEI-2010, and accelerometer wear time. We conducted sensitivity analyses by further adjusting for BMI, which we expected to attenuate the association as a mediator.

We further explored the role of intensity by examining if having a greater percent of intense steps at a given steps/day was associated with a lower risk of diabetes than having a lower percent of intense steps at the same steps/day (e.g., 7000 steps/day with 30% versus 10% of these steps at ≥100 steps/min). All comparisons were made to the referent of the 10th percentile of steps/day and percent of intense steps (3400 steps/day and 1%). Last, we examined if the relationship between continuous steps/day and diabetes varied by age (< 50, > 50 years), sex (male, female), occupational physical activity (any, none), obesity (not obese, obese), insulin resistance as measured by HOMA IR (normal, high), prediabetes status (normal, prediabetes), and Hispanic/Latino background (Dominican, Central American, Cuban, Mexican, Puerto Rican, South American, Multi/Other). A likelihood ratio test compared a model with an interaction term between steps/day and the modifier to one without the interaction term and a p-value < 0.10 suggested the model with the interaction term was a better fit [41]. Examination of the stratum specific estimates were used to determine if the relationship between steps/day and diabetes varied within each modifier [42, 43]. The modification analysis with occupational physical activity was only among adults who reported working full or part-time (n = 3799).

Results

The mean age of the cohort was 39 years, half were female, 76% were born outside of the 50 US states, and 41% had prediabetes at baseline (Table 1). More than half of the cohort reported being employed and among employed adults, 53% reported any occupational physical activity. The average accelerometer wear time was 15.9 h/day (95% CI 15.7, 16.0). Adults had an average of 8164 steps/day (standard error = 92, median = 7317). On average, adults spent 66, 27, and 12 min/day at a cadence of > 40, > 70, and > 100 steps/min, respectively. Additionally, adults spent 24, 10, and 5 min in bouts at cadences of > 40, > 70, and > 100 steps/min, respectively. The average peak 30-min cadence was 77 steps/min.

Table 1 Descriptive characteristics at baseline for HCHS/SOL cohort (2008–2017)
Full size table

Step volume with diabetes

Over a median of 5.9 years (range 1 to 9.6 years) of follow-up, there were 1115 (12.5%) and 740 (8.2%) incident events of diabetes based on the two diabetes definitions. Generally, incidence rates and HRs were lower with greater steps/day; however, in the majority of analyses, the confidence intervals included the null value (Table 2). For diabetes based on three criteria, the risk of diabetes was 2% lower (HR = 0.98 (95% CI 0.95, 1.00)) per 1000 steps/day. The predicted risk of diabetes at 7000 and 10,000 steps/day was 8% (HR = 0.92 (95% CI 0.85, 1.01)) and 13% (HR = 0.87 (95% CI 0.74, 1.02)) lower compared to 3400 steps/day (Supplemental Table 1). Analyses with steps/day based on the graduated step index suggested an 18% lower risk of diabetes at 10,000–12,500 steps/day (HR = 0.82 (95% CI 0.57, 1.18) compared to < 5000 steps/day. All results were similar for diabetes based on two criteria. For both diabetes definitions, the addition of BMI to models attenuated all associations of steps/day with the risk of diabetes (Supplemental Table 2).

Table 2 The association of average daily steps and the graduated step index with incident diabetes, HCHS/SOL cohort (2008–2017)
Full size table

Interactions of steps/day and modifiers with diabetes

For diabetes classified by three criteria, we observed modification by obesity (Fig. 1a, Supplemental Tables 3–4). Adults with obesity had higher incidence rates of diabetes than adults without obesity but adults with obesity who accumulated 10,000 steps/day had 0.81 (95% CI 0.65,1.01) times the risk of diabetes compared to adults with obesity who accumulated 3400 steps/day, while there was no association among adults without obesity (HR = 1.01 (95% CI 0.81, 1.27)).

Fig. 1
figure 1

Hazard ratios and 95% CI of the association of steps/day with incident diabetes by modifying factors, HCHS/SOL cohort (2008–2017). a Diabetes based on self-reported diagnosis, medications, labs (3 criteria definition, n = 6634). b Diabetes based on medications and labs (2 criteria definition) n = 6633. Predicted estimates at the 2nd percentile (2000 steps/day), 25th percentile (5000 steps/day), 50th percentile (7000 steps/day), 75th percentile (10,000 steps/day), and 90th percentile (14,000 steps/day). Estimates compared to the 10th percentile (3400 steps/day) of steps/day. Abbreviations: CI = confidence interval, HOMA IR = homeostasis model assessment of insulin resistance, LRT = likelihood ratio test, PA = physical activity. All models adjusted for age (continuous), quadratic term for age, sex (male, female), Latino background by HCHS/SOL field center (17 level categorical variable), education (< high school/no GED, high school/GED, > high school), married/partner status (yes, no), employment (yes, no), years in the US (born in US, < 10 years, > 10 years), self-rated general health (excellent/very good, good, fair/poor), mobility limitations (yes, no), cigarette pack years (continuous), alcoholic drinks per week (continuous), energy intake (continuous), AHEI-2010 (continuous) and accelerometer wear . The model with occupational physical activity as a modifier is only among those who reported part- or full-time employment (n = 3799) and does not include a covariate for employment, but otherwise is adjusted for the same covariates as other models

Full size image

For diabetes classified by two criteria, we observed modification by age, sex, and prediabetes (Fig. 1b, Supplemental Tables 5–6). Among adults 50 and older, those who accumulated 10,000 steps/day had 0.69(95% CI 0.52,0.90) times the risk of diabetes compared to those who accumulated 3400 steps/day, while for younger adults the inverse association was weaker (HR = 0.87 (95% CI 0.66, 1.14)). Among women, the HR at 10,000 steps/day was 0.67 (95% CI 0.49,0.92) compared to women who accumulated 3400 steps/day while for men there was a weaker inverse association (HR = 0.88 (95% CI 0.68, 1.15)). Adults with prediabetes had higher incidence rates of diabetes than adults without prediabetes, but adults with prediabetes who accumulated 10,000 steps/day had a 26% (HR = 0.74(95% CI 0.58,0.95)) lower risk of diabetes than adults with prediabetes who accumulated 3400 steps/day, while there was no association among adults without prediabetes (HR = 1.06 (95% CI 0.74, 1.52)). No modification was observed by occupational physical, HOMA IR, and Hispanic/Latino heritage.

Step cadence with diabetes

For both diabetes definitions, spending more time at > 70 and > 100 steps/min and having a faster peak 30-min cadence were associated with a lower risk of diabetes (Table 3). For example, accumulating at least 17 min/day at > 100 steps/min was associated with a 31% (HR = 0.69 (95% CI 0.53,0.89)) lower risk of diabetes (three criteria definition) compared to those who accumulated less than 2 min/day. For both diabetes definitions, the HR estimates were generally lower with more time spent in bouts at each cadence but the confidence intervals included the null value for all analyses (Supplemental Table 7).

Table 3 The association of step cadence with incident diabetes, HCHS/SOL cohort (2008–2017)
Full size table

For both diabetes definitions, the addition of BMI to models attenuated all associations of cadence-based metrics with the risk of diabetes (Supplemental Table 2).

Percentage of intense steps at a given step volume

Lower risk of diabetes was observed with greater steps/day and a greater percent of intense steps at a given steps/day level (Fig. 2). Compared to adults who accumulated 3400 steps/day and had 1% of intense steps, adults who accumulated 7000 steps/day and had 10% of intense steps had a 10% (HR = 0.90 (95% CI 0.82, 0.99)) lower risk, whereas adults who accumulated the same steps/day but had 30% of intense steps had a 18% (HR = 0.82 (95% CI 0.66, 1.02) lower risk of diabetes (three criteria definition) (Supplemental Table 8). Findings were similar for diabetes based on two criteria.

Fig. 2
figure 2

The association of steps/day and percent of intense steps (> 100 steps/min) with incident diabetes, HCHS/SOL cohort (2008–2017). Compared to adults who accumulated 3400 steps/day and had 1% of intense steps (referent), a lower risk of diabetes was observed with greater steps/day, and at a given steps/day, a lower risk was found with greater percent of intense steps. a Diabetes based on self-reported diagnosis, medications, labs (3 criteria definition, n = 6634). b Diabetes based on medications and labs (2 criteria definition) n = 6633. Abbreviations: CI = confidence interval. Steps/day predicted estimates at the 2nd percentile (2000 steps/day), 25th percentile (5000 steps/day), 50th percentile (7000 steps/day), 75th percentile (10,000 steps/day), and 90th percentile (14,000 steps/day). Percent of intense steps (> 100 steps/min) predicted estimates at 50th percentile (10%), 75th percentile (20%), and 90th percentile (30%). All comparisons made to the referent of the 10th percentile of steps/day and percent of intense steps (3400 steps/day and 1%). Models adjusted for age (continuous), quadratic term for age, sex (male, female), Latino background by HCHS/SOL field center (17 level categorical variable), education (< high school/no GED, high school/GED, > high school), married/partner status (yes, no), employment (yes, no), years in the US (born in US, < 10 years, > 10 years), self-rated general health (excellent/very good, good, fair/poor), mobility limitations (yes, no), cigarette pack years (continuous), alcoholic drinks per week (continuous), energy intake (continuous), AHEI-2010 (continuous) and accelerometer wear time (continuous, hours per day)

Full size image

Discussion

In this cohort of Hispanic/Latino adults, we observed that taking more steps/day and spending more time at a faster cadence were associated with a lower risk of developing diabetes. Our results suggest that adults had about a 2 to 3% lower risk of diabetes per 1000 steps/day over 6 years. Our dose-response analysis suggested a gradual decline in risk with more steps/day, such that any amount of stepping was associated with a lower risk of diabetes but greater risk reduction was achieved by taking more steps/day. A faster peak 30-min cadence and more time at faster cadences were also associated with a lower diabetes risk. Further, our results suggested that accumulating the same steps/day at a greater percent of intense steps provided greater risk reduction than reaching the same steps/day level with a lower percent of intense steps. Thus, both volume and intensity may be important for lowering the risk of diabetes. We found stronger associations between steps/day and reduced diabetes incidence among adults at higher risk for diabetes, including older adults and those affected by obesity and prediabetes.

Our inverse association between steps/day and diabetes is similar to findings from the NAVIGATOR Trial where diabetes was defined by fasting glucose and 2-h OGTT. In the NAVIGATOR study, 9306 participants with impaired glucose tolerance, had a 4% (HR = 0.96 (95% CI 0.94, 0.99)) lower risk of diabetes per 2000 steps/day (2% per 1000 steps/day) [16]. With diabetes defined by medications and labs, we estimated a 3% lower risk per 1000 steps (HR = 0.97 (95% CI 0.94,1.00)). The OPACH study estimated a HR of 0.88 (95% CI 0.78, 1.00) between steps/day and diabetes defined by self-report of physician diagnosed diabetes requiring the need of insulin or hypoglycemic medication [19]. The AusDiab study also examined steps/day and diabetes risk and observed a 13% lower risk per 1000 steps/day of incident dysglycemia over 5 years, but only two participants developed diabetes [17]. In a recent analysis with 3055 Swedish adults of the Healthy Aging Initiative study, the authors observed a 59% (HR = 0.41 (95% CI 0.25, 0.66) lower risk of diabetes, defined by International Classification of Disease codes, for accumulating > 4500 steps/day compared to < 4500 steps/day [18]. Although our HR estimates suggest a lower diabetes risk with greater steps/day, the confidence intervals for many estimates did include the null value.

In addition to these studies on steps/day, there has been consistent evidence on the inverse association of self-reported physical activity with diabetes risk, but more evidence is needed on the dose response relationship [5, 6]. A recent meta-analysis on self-reported physical activity and diabetes risk observed that total physical activity, as well as many subtypes of physical activity (leisure-time activity, low, moderate, and vigorous intensity activity, walking, occupational activity, and resistance exercise) were associated with a lower risk of diabetes [6]. In the meta-analysis the dose-response relationship of leisure-time activity with diabetes was curvilinear, but there were too few studies to examine the total physical activity dose response relationship with diabetes [6]. In our analysis with HCHS/SOL, total physical activity volume measured as steps/day suggested a linear relationship with diabetes. A similar linear relationship was observed with the OPACH study between steps/day and risk of diabetes [19]. With the HCHS/SOL study, Chen et al. [7] examined accelerometer measured minutes in MPVA and observed a curvilinear relationship with diabetes risk. We further investigated this finding by examining the correlation between steps/day and minutes in MVPA and found a correlation of 0.66 (Supplemental Table 9). It is possible that the large amount of lower intensity activity that makes up the step/day distribution may account for the different dose response relationship between MVPA and steps/day with diabetes risk. However, further studies should examine the shape of the relationship between objectively measured total physical activity volume, steps, and MVPA with incidence of diabetes.

It is hypothesized that physical activity may lower the risk of diabetes through several biological mechanisms. Greater amounts of physical activity may reduce adiposity, a risk factor for diabetes [6]. Additionally, both acute and long-term physical activity increase glucose uptake in skeletal muscle cells [44]. Muscle contractions, independent of insulin, increase glucose transport from blood into skeletal muscle by translocation of the glucose transporters, especially glucose transporter 4 (GLUT4), from the intercellular location to the plasma membrane [45]. Long-term physical activity is associated with adaptations to skeletal muscle, including an increase in GLUT4 protein levels and mitochondrial enzyme content, and alteration of muscle fiber types that promote glucose transport [44]. These mechanisms lend support to the evidence that greater amounts of physical activity are associated with a lower risk of diabetes.

Diabetes risk is higher among older age groups and those with prediabetes or obesity [2, 20]. We found a stronger inverse association between steps/day and diabetes risk for adults 50 years and older, and those affected by obesity or prediabetes. In a previous HCHS/SOL analysis, a lower risk of diabetes was also observed with more accelerometer measured minutes spent in MVPA among adults older than 50 years supporting this finding [7]. Exercise interventions among adults with obesity and those with impaired fasting glucose have consistently documented a lower diabetes risk with participation in physical activity and diet interventions [46,47,48] and many of these trials have noted stronger risk reduction among older than younger adults [48, 49]. Our results suggest that adults at higher risk for diabetes, such as older adults and those with prediabetes and obesity, should be encouraged to engage in more steps/day to lower their risk. A stronger inverse association between steps/day and the risk of diabetes was observed for women than men. It is possible that other risk factors, such as hypercholesteremia that are more prevalent among men than women in HCHS/SOL [50], may have may elevated diabetes risk more among men.

Our results regarding cadence, a proxy for step intensity, suggest that a higher peak 30-min cadence and more time at a faster cadence were associated with a lower risk of diabetes. Peak 30-min cadence has been described as a summary of an individual’s best natural effort [15]. Adults with an average peak 30-min cadence of ≥100 steps/min, which is a cadence described as a brisk walk or faster ambulation [15], had about a 40% lower risk of diabetes compared to adults who had an average peak 30-min cadence of less than 60 steps/min. Additionally, adults who accumulated > 39 min and > 17 min at > 70 and > 100 steps/min respectively, cadences described as slow and brisk walking [15], were associated with about a 30% lower risk of diabetes compared to adults who accumulated less time at each of these cadences. The OPACH study examined peak 30-min cadence and percent of time at > 40 steps/min and did not find strong associations between these cadence measures and the risk of diabetes [19]. The OPACH study also classified steps of light and moderate-to-vigorous intensity using vector magnitude counts and observed a HR of 0.86 (95% CI 0.74, 1.00) between moderate-to-vigorous steps (per 2000 step increment) and diabetes risk, but no association between light steps and diabetes [19]. Two cross-sectional studies [51, 52] with the 2005–2006 National Health and Nutrition Examination Survey data reported that greater peak 30-min cadence was associated with improved cardiometabolic risk factors. We observed that more time spent in stepping bouts was generally associated with a lower risk of diabetes, however the confidence intervals included the null value in all analyses. The OPACH study found that steps that were accumulated in bouts of 5 min or more were not associated with diabetes risk [19]. Few studies reported on bouted stepping and diabetes risk, but others have examined accelerometer-assessed MVPA bouts with various health outcomes and found that total volume of physical activity was more important than the pattern of activity [53, 54].

In addition to observing a potential lower risk of diabetes with more steps/day, our results suggest that the risk of diabetes was even lower when these steps were accumulated at a higher intensity. Adults who accumulated 7000 steps/day with 10% versus 30% being intense steps (≥100 steps/min) had a 10% versus 18% lower risk of diabetes, respectively, compared to those accumulating 3400 steps/day with 1% being intense steps. At 7000 steps/day, 10 and 30% of intense steps are equivalent to accumulating 700 and 2100 steps at a brisk walk or faster pace which can be reached by taking a 7 min and 21 min brisk walk. In support of our finding, Strain et al. [55], in the UK Biobank study, observed a lower risk of mortality with greater physical activity volume and that accumulating the same volume with more intense activity rather than lower intensity activity had even greater benefits. Our study suggests that greater step intensity at a given step volume is associated with a further risk reduction of diabetes.

A strength of our study is that we focused on steps/day, an easy-to-understand metric [9] that has become more accessible due to increased activity tracker and smartphone usage [56], and can motivate adults to increase their physical activity [57]. Use of pedometers and activity trackers to monitor steps/day have been an effective strategy to increase daily steps/day [57, 58]. The common 10,000 steps/day goal present on many activity trackers was not based on scientific evidence but developed from the name of a Japanese pedometer [59]. Currently, there are no guidelines that provide recommendations on the number of steps/day needed to achieve optimal health outcomes [9]. Some research suggests that 7000–8000 steps/day are necessary to meet the aerobic physical activity guidelines based on minutes spent in MVPA [60]. Research on steps/day and health outcomes is quickly growing [11, 61] and our findings can contribute to future efforts to develop evidence-based steps/day guidelines.

Our analysis is unique, as it was conducted with the largest longitudinal Hispanic/Latino cohort. The Hispanic/Latino population accounts for 18% (60 million) of the US population [62] and is projected to grow to 28% by 2060 [63]. A strength of our study is that we used the Actical accelerometer to measure steps and step cadence, rather than relying on self-report of steps from pedometers. Because of the Actical accelerometer, which provided time-stamped data on step accumulation, we were able to capture indicators of step intensity and bouts, which has enabled further understanding of how stepping patterns are associated with the risk of diabetes. Additionally, we used two definitions of diabetes and generally observed similar results for both definitions.

There are limitations to our analysis to be considered. Reverse causality cannot be ruled out as we are unable to determine exactly when diabetes developed and adults who developed diabetes close to baseline may have lower steps/day. We used one measure of steps/day and cadence and acknowledge that activity patterns may change over time. However, data from the Women’s Health Study examined steps/day over 3 years and found that a 7-day accelerometer collection of steps/day was a reasonable estimate of longer-term physical activity [64]. Steps/day only captures ambulatory movement and not non-ambulatory movement, such as any upper body movement while standing still, or activities such as cycling or swimming. It is possible that non-ambulatory movement may be beneficial for preventing diabetes in addition to ambulatory movement. We examined if participants who were included in our analysis were healthier than those excluded and observed few differences in demographic and health characteristics between the two groups. Additionally, the sampling weights accounted for attrition and differences in accelerometer wear and adherence.

Conclusion

In conclusion, engaging in more steps/day and taking more steps at a brisk walk or faster pace is potentially associated with a lower risk of diabetes among US Hispanic/Latino adults. Our results suggest that adults had a 2 to 3% lower 6-year risk of diabetes per 1000 steps/day -- any amount of stepping was associated with a lower risk of diabetes but greater risk reduction was achieved by taking more steps/day. The inverse association between steps/day and diabetes risk was observed across many cohort characteristics, most prominently among adults at high risk for diabetes – those who were older, or had obesity or prediabetes. Accumulating the same steps/day at a greater percent of intense steps was associated with further risk reduction. Our results suggest adults can lower their risk of diabetes by taking more steps/day at any pace, but should engage in brisk walking for part of their day to gain the greatest benefit.

Availability of data and materials

HCHS/SOL study data can be accessed by contacting the Collaborative Studies Coordinating Center at the University of North Carolina at Chapel Hill at the following email address: HCHSAdminstration@unc.edu.

Abbreviations

AHEI:

Alternative Healthy Eating Index

BMI:

Body Mass Index

CI:

Confidence Interval

HbA1c:

Glycosylated hemoglobin

HCHS/SOL:

Hispanic Community Health Study/ Study of Latinos

HOMA-IR:

Homeostasis model assessment of insulin resistance

HR:

Hazard Ratio

IPW:

Inverse Probability Weights

MVPA:

Moderate-to-vigorous physical activity

OGTT:

Oral glucose tolerance test

References

  1. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics-2021 update: a report from the American Heart Association. Circulation. 2021;143(8):e254–743.

    Article  PubMed  Google Scholar 

  2. Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, et al. Impaired fasting glucose and impaired glucose tolerance: implications for care. Diabetes Care. 2007;30(3):753–9.

    Article  CAS  PubMed  Google Scholar 

  3. Rowley WR, Bezold C, Arikan Y, Byrne E, Krohe S. Diabetes 2030: insights from yesterday, today, and future trends. Popul Health Manag. 2017;20(1):6–12.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2016;39(11):2065–79.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Smith AD, Crippa A, Woodcock J, Brage S. Physical activity and incident type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of prospective cohort studies. Diabetologia. 2016;59(12):2527–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Epidemiol. 2015;30(7):529–42.

    Article  PubMed  Google Scholar 

  7. Chen GC, Qi Q, Hua S, Moon JY, Spartano NL, Vasan RS, et al. Accelerometer-assessed physical activity and incident diabetes in a population covering the adult life span: the Hispanic Community Health Study/Study of Latinos. Am J Clin Nutr. 2020;112(5):1318–27.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Sternfeld B, Gabriel KP, Jiang SF, Whitaker KM, Jacobs DR Jr, Quesenberry CP Jr, et al. Risk estimates for diabetes and hypertension with different physical activity methods. Med Sci Sports Exerc. 2019;51(12):2498–505.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kraus WE, Janz KF, Powell KE, Campbell WW, Jakicic JM, Troiano RP, et al. Daily step counts for measuring physical activity exposure and its relation to health. Med Sci Sports Exerc. 2019;51(6):1206–12.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 2018 Physical Activity Guidelines Advisory Committee scientific report. 2018 physical activity guidelines advisory committee; 2018.Available from: https://health.gov/paguidelines/second-edition/report.aspx.

    Google Scholar 

  11. Paluch AE, Bajpai S, Bassett DR, Carnethon MR, Ekelund U, Evenson KR, et al. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health. 2022;7(3):e219–e28.

    Article  PubMed  Google Scholar 

  12. Paluch AE, Gabriel KP, Fulton JE, Lewis CE, Schreiner PJ, Sternfeld B, et al. Steps per day and all-cause mortality in middle-aged adults in the coronary artery risk development in young adults study. JAMA Netw Open. 2021;4(9):e2124516.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lee IM, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern Med. 2019;179(8):1105–12.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Saint-Maurice PF, Troiano RP, Bassett DR Jr, Graubard BI, Carlson SA, Shiroma EJ, et al. Association of daily step count and step intensity with mortality among US adults. JAMA. 2020;323(12):1151–60.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tudor-Locke C, Han H, Aguiar EJ, Barreira TV, Schuna JM Jr, Kang M, et al. How fast is fast enough? Walking cadence (steps/min) as a practical estimate of intensity in adults: a narrative review. Br J Sports Med. 2018;52(12):776–88.

    Article  PubMed  Google Scholar 

  16. Kraus WE, Yates T, Tuomilehto J, Sun JL, Thomas L, McMurray JJV, et al. Relationship between baseline physical activity assessed by pedometer count and new-onset diabetes in the NAVIGATOR trial. BMJ Open Diabetes Res Care. 2018;6(1):e000523.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ponsonby AL, Sun C, Ukoumunne OC, Pezic A, Venn A, Shaw JE, et al. Objectively measured physical activity and the subsequent risk of incident dysglycemia: the Australian diabetes, obesity and lifestyle study (AusDiab). Diabetes Care. 2011;34(7):1497–502.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ballin M, Nordström P, Niklasson J, Alamäki A, Condell J, Tedesco S, et al. Daily step count and incident diabetes in community-dwelling 70-year-olds: a prospective cohort study. BMC Public Health. 2020;20(1):1830.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Garduno AC, LaCroix AZ, LaMonte MJ, Dunstan DW, Evenson KR, Wang G, et al. Associations of daily steps and step intensity with incident diabetes in a prospective cohort study of older women: the OPACH study. Diabetes Care. 2022;45(2):339–47.

    Article  PubMed  Google Scholar 

  20. Kirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, et al. Diabetes in older adults. Diabetes Care. 2012;35(12):2650–64.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Schneiderman N, Llabre M, Cowie CC, Barnhart J, Carnethon M, Gallo LC, et al. Prevalence of diabetes among Hispanics/Latinos from diverse backgrounds: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes Care. 2014;37(8):2233–9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Arredondo EM, Sotres-Alvarez D, Stoutenberg M, Davis SM, Crespo NC, Carnethon MR, et al. Physical activity levels in U.S. Latino/Hispanic adults: results from the Hispanic Community Health Study/Study of Latinos. Am J Prev Med. 2016;50(4):500–8.

    Article  PubMed  Google Scholar 

  23. Echeverría SE, Divney A, Rodriguez F, Sterling M, Vasquez E, Murillo R, et al. Nativity and occupational determinants of physical activity participation among Latinos. Am J Prev Med. 2019;56(1):84–92.

    Article  PubMed  Google Scholar 

  24. Hu G, Qiao Q, Silventoinen K, Eriksson JG, Jousilahti P, Lindström J, et al. Occupational, commuting, and leisure-time physical activity in relation to risk for type 2 diabetes in middle-aged Finnish men and women. Diabetologia. 2003;46(3):322–9.

    Article  CAS  PubMed  Google Scholar 

  25. Honda T, Kuwahara K, Nakagawa T, Yamamoto S, Hayashi T, Mizoue T. Leisure-time, occupational, and commuting physical activity and risk of type 2 diabetes in Japanese workers: a cohort study. BMC Public Health. 2015;15:1004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Medina C, Janssen I, Barquera S, Bautista-Arredondo S, González ME, González C. Occupational and leisure time physical inactivity and the risk of type II diabetes and hypertension among Mexican adults: a prospective cohort study. Sci Rep. 2018;8(1):5399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sorlie PD, Aviles-Santa LM, Wassertheil-Smoller S, Kaplan RC, Daviglus ML, Giachello AL, et al. Design and implementation of the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol. 2010;20(8):629–41.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lavange LM, Kalsbeek WD, Sorlie PD, Aviles-Santa LM, Kaplan RC, Barnhart J, et al. Sample design and cohort selection in the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol. 2010;20(8):642–9.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tudor-Locke C, Camhi SM, Troiano RP. A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and Nutrition Examination survey, 2003-2006. Prev Chronic Dis. 2012;9:E113.

    PubMed  PubMed Central  Google Scholar 

  31. Evenson KR, Sotres-Alvarez D, Deng YU, Marshall SJ, Isasi CR, Esliger DW, et al. Accelerometer adherence and performance in a cohort study of US Hispanic adults. Med Sci Sports Exerc. 2015;47(4):725–34.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Esliger DW, Probert A, Connor Gorber S, Bryan S, Laviolette M, Tremblay MS. Validity of the Actical accelerometer step-count function. Med Sci Sports Exerc. 2007;39(7):1200–4.

    Article  PubMed  Google Scholar 

  33. Johnson M, Meltz K, Hart K, Schmudlach M, Clarkson L, Borman K. Validity of the Actical activity monitor for assessing steps and energy expenditure during walking. J Sports Sci. 2015;33(8):769–76.

    Article  PubMed  Google Scholar 

  34. Feito Y, Bassett DR, Thompson DL. Evaluation of activity monitors in controlled and free-living environments. Med Sci Sports Exerc. 2012;44(4):733–41.

    Article  PubMed  Google Scholar 

  35. Mâsse LC, Fuemmeler BF, Anderson CB, Matthews CE, Trost SG, Catellier DJ, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc. 2005;37(11 Suppl):S544–54.

    Article  PubMed  Google Scholar 

  36. Evenson KR, Wen F, Herring AH. Associations of accelerometry-assessed and self-reported physical activity and sedentary behavior with all-cause and cardiovascular mortality among US adults. Am J Epidemiol. 2016;184(9):621–32.

    Article  PubMed  PubMed Central  Google Scholar 

  37. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(Suppl 1):S67–74.

  38. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013;22(3):278–95.

    Article  PubMed  Google Scholar 

  39. Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med. 2010;29(9):1037–57.

    PubMed  Google Scholar 

  40. Tudor-Locke C, Craig CL, Thyfault JP, Spence JC. A step-defined sedentary lifestyle index: <5000 steps/day. Appl Physiol Nutr Metab. 2013;38(2):100–14.

    Article  PubMed  Google Scholar 

  41. Vittinghoff EGD, Shiboski SC, McCulloch CE. Regression methods in biostastics: linear, logistic, survival, and repeated measures models. New York: Springer; 2005.

  42. Corraini P, Olsen M, Pedersen L, Dekkers OM, Vandenbroucke JP. Effect modification, interaction and mediation: An overview of theoretical insights for clinical investigators. Clin Epidemiol. 2017;9:331–8.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Rothman KJGS, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.

    Google Scholar 

  44. Röckl KS, Witczak CA, Goodyear LJ. Signaling mechanisms in skeletal muscle: acute responses and chronic adaptations to exercise. IUBMB Life. 2008;60(3):145–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Holloszy JO. Exercise-induced increase in muscle insulin sensitivity. J Appl Physiol (1985). 2005;99(1):338–43.

    Article  CAS  Google Scholar 

  46. Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20(4):537–44.

    Article  CAS  PubMed  Google Scholar 

  47. Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–50.

    Article  CAS  PubMed  Google Scholar 

  48. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.

    Article  CAS  PubMed  Google Scholar 

  49. Lindström J, Peltonen M, Eriksson JG, Aunola S, Hämäläinen H, Ilanne-Parikka P, et al. Determinants for the effectiveness of lifestyle intervention in the Finnish diabetes prevention study. Diabetes Care. 2008;31(5):857–62.

    Article  CAS  PubMed  Google Scholar 

  50. Daviglus ML, Talavera GA, Aviles-Santa ML, Allison M, Cai J, Criqui MH, et al. Prevalence of major cardiovascular risk factors and cardiovascular diseases among Hispanic/Latino individuals of diverse backgrounds in the United States. JAMA. 2012;308(17):1775–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tudor-Locke C, Schuna JM Jr, Han HO, Aguiar EJ, Green MA, Busa MA, et al. Step-based physical activity metrics and cardiometabolic risk: NHANES 2005-2006. Med Sci Sports Exerc. 2017;49(2):283–91.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Adams B, Fidler K, Demoes N, Aguiar EJ, Ducharme SW, McCullough AK, et al. Cardiometabolic thresholds for peak 30-min cadence and steps/day. PLoS One. 2019;14(8):e0219933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Jefferis BJ, Parsons TJ, Sartini C, Ash S, Lennon LT, Papacosta O, et al. Objectively measured physical activity, sedentary behaviour and all-cause mortality in older men: does volume of activity matter more than pattern of accumulation? Br J Sports Med. 2018;53(16):1013–20.

    Article  PubMed  Google Scholar 

  54. Jefferis BJ, Parsons TJ, Sartini C, Ash S, Lennon LT, Papacosta O, et al. Does total volume of physical activity matter more than pattern for onset of CVD? A prospective cohort study of older British men. Int J Cardiol. 2019;278:267–72.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Strain T, Wijndaele K, Dempsey PC, Sharp SJ, Pearce M, Jeon J, et al. Wearable-device-measured physical activity and future health risk. Nat Med. 2020;26(9):1385–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. About one-in-five Americans use a smart watch or fitness tracker: Pew Research; 2020. Available from: https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/. Accessed 24 June 2020.

  57. King AC, Whitt-Glover MC, Marquez DX, Buman MP, Napolitano MA, Jakicic J, et al. Physical activity promotion: highlights from the 2018 physical activity guidelines advisory committee systematic review. Med Sci Sports Exerc. 2019;51(6):1340–53.

    Article  PubMed  Google Scholar 

  58. Chaudhry UAR, Wahlich C, Fortescue R, Cook DG, Knightly R, Harris T. The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults. Int J Behav Nutr Phys Act. 2020;17(1):129.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Park J. Why is 10,000 steps a day the goal? Fitbit’s CEO has some answers. Fortune. 2020. https://fortune.com/2020/06/30/why-10000-steps-per-day-goal-fitbit-ceo/#:~:text=The%2010%2C000%2Dstep%20goal%20satisfies,to%20start%20for%20most%20peopleAccessed 10 Mar 2022.

    Google Scholar 

  60. Tudor-Locke C, Craig CL, Brown WJ, Clemes SA, De Cocker K, Giles-Corti B, et al. How many steps/day are enough? For adults. Int J Behav Nutr Phys Act. 2011;8:79.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Hall KS, Hyde ET, Bassett DR, Carlson SA, Carnethon MR, Ekelund U, et al. Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. Int J Behav Nutr Phys Act. 2020;17(1):78.

    Article  PubMed  PubMed Central  Google Scholar 

  62. ACS demographic and housing estimates: Census Bureau; 2018 .Available from: https://data.census.gov/cedsci/all?q=Hispanic%20or%20Latino&hidePreview=false&t=Hispanic%20or%20Latino&tid=ACSDP1Y2018.DP05. Accessed 24 June 2020.

  63. Hispanic population to reach 111 million by 2060: Census Bureau; 2018. Available from: https://www.census.gov/library/visualizations/2018/comm/hispanic-projected-pop.html. Accessed 24 June 2020.

  64. Keadle SK, Shiroma EJ, Kamada M, Matthews CE, Harris TB, Lee IM. Reproducibility of accelerometer-assessed physical activity and sedentary time. Am J Prev Med. 2017;52(4):541–8.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the staff and participants of HCHS/SOL for their important contributions. A complete list of staff and investigators has been provided by Sorlie et al. [27] and is also available on the study website (http://www.cscc.unc.edu/hchs/).

Funding

Kelly Evenson, Daniela Sotres-Alvarez, and Yasmin Mossavar-Rahmani were supported by grant #R01HL136266 from the National Heart, Lung, and Blood Institute (NHLBI).

Cuthbertson and Moore were supported by a National Heart, Lung, and Blood Institute National Research Service Award (T32-HL007055). The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The Hispanic Community Health Study / Study of Latinos (HCHS/SOL) was carried out as a collaborative study supported by contracts from the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/ Centers/ Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, and NIH Institution-Office of Dietary Supplements. Additional support was provided by the New York Regional Center for Diabetes Translation Research (P30 DK111022) through funds from the National Institute of Diabetes and Digestive and Kidney Diseases. The results of the present study do not constitute endorsement by the NIH.

Author information

Authors and Affiliations

  1. Department of Epidemiology, University of North Carolina at Chapel Hill, 123 W. Franklin St. Suite 410, Chapel Hill, NC, 27516, USA

    Carmen C. Cuthbertson, Christopher C. Moore, Gerardo Heiss & Kelly R. Evenson

  2. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Daniela Sotres-Alvarez

  3. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    Carmen R. Isasi & Yasmin Mossavar-Rahmani

  4. Children’s Mercy Kansas City and University of Missouri Kansas City, Kansas City, MO, USA

    Jordan A. Carlson

  5. Department of Psychology, San Diego State University, San Diego, CA, USA

    Linda C. Gallo

  6. Psychology Department, University of Miami, Miami, FL, USA

    Maria M. Llabre

  7. Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA

    Olga L. Garcia-Bedoya

  8. Boston Fusion Corp, Lexington, MA, USA

    David Goldsztajn Farelo

Authors
  1. Carmen C. Cuthbertson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher C. Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniela Sotres-Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gerardo Heiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carmen R. Isasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yasmin Mossavar-Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jordan A. Carlson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Linda C. Gallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maria M. Llabre
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Olga L. Garcia-Bedoya
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. David Goldsztajn Farelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kelly R. Evenson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CCC designed and conducted the analyses and drafted the manuscript, CCM and KRE revised and advised on early versions of the manuscript. All authors critically reviewed the final manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Carmen C. Cuthbertson.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board at the coordinating center (University of North Carolina Office of Human Research Ethics, 07-1003) and each community site (Albert Einstein College of Medicine (2007-432, 2013-2749, 2019-10870), University of Illinois at Chicago (2013-1261), San Diego State University (3677, 1586091), and University of Miami (20070461, 20131007)) approved study protocols. All participants gave informed consent. Additionally, this analysis was considered exempt by the UNC Office of Human Research Ethics (19-2807).

Consent for publication

Not applicable.

Competing interests

The authors declare they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cuthbertson, C.C., Moore, C.C., Sotres-Alvarez, D. et al. Associations of steps per day and step intensity with the risk of diabetes: the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Int J Behav Nutr Phys Act 19, 46 (2022). https://0-doi-org.brum.beds.ac.uk/10.1186/s12966-022-01284-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12966-022-01284-2

Keywords

International Journal of Behavioral Nutrition and Physical Activity

ISSN: 1479-5868

Contact us