4

Department of Veterans Affairs

Health Services Research & Development Service Evidence-based Synthesis Program

White Paper: 

The Impact of Pedometers 

on Chronic Conditions – A 

Review of Reviews

September 2015

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Quality Enhancement Research Initiative

Health Services Research & Development Service

Washington, DC 20420

Prepared by:

Evidence-based Synthesis Program (ESP) Center

Durham VA Medical Center

Durham, NC

John W. Williams, Jr. MD, MHSc, Director 

Investigators:

JG Gierisch

AP Goode

KD Allen

BC Batch

RJ Shaw
White Paper: The Impact of Pedometers on Evidence-based Synthesis Program

Chronic Conditions – A Review of Reviews

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PREFACE

The VA Evidence-based Synthesis Program (ESP) was established in 2007 to provide timely and 

accurate syntheses of targeted healthcare topics of particular importance to clinicians, managers, and 

policymakers as they work to improve the health and healthcare of Veterans. QUERI provides funding 

for four ESP Centers, and each Center has an active University affiliation. Center Directors are 

recognized leaders in the field of evidence synthesis with close ties to the AHRQ Evidence-based 

Practice Centers. The ESP is governed by a Steering Committee comprised of participants from VHA 

Policy, Program, and Operations Offices, VISN leadership, field-based investigators, and others as 

designated appropriate by QUERI/HSR&D.

The ESP Centers generate evidence syntheses on important clinical practice topics. These reports help: 

Develop clinical policies informed by evidence;

Implement effective services to improve patient outcomes and to support VA clinical practice

guidelines and performance measures; and

Set the direction for future research to address gaps in clinical knowledge.

The ESP disseminates these reports throughout VA and in the published literature; some evidence 

syntheses have informed the clinical guidelines of large professional organizations. 

The ESP Coordinating Center (ESP CC), located in Portland, Oregon, was created in 2009 to expand the 

capacity of QUERI/HSR&D and is charged with oversight of national ESP program operations, program 

development and evaluation, and dissemination efforts. The ESP CC establishes standard operating 

procedures for the production of evidence synthesis reports; facilitates a national topic nomination, 

prioritization, and selection process; manages the research portfolio of each Center; facilitates editorial 

review processes; ensures methodological consistency and quality of products; produces “rapid response 

evidence briefs” at the request of VHA senior leadership; collaborates with HSR&D Center for 

Information Dissemination and Education Resources (CIDER) to develop a national dissemination 

strategy for all ESP products; and interfaces with stakeholders to effectively engage the program. 

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP CC Program 

Manager, at Nicole.Floyd@va.gov. 

Recommended citation: Gierisch JG, Goode AP, Allen KD, Batch BC, Shaw RJ. White Paper: The 

Impact of Pedometers on Chronic Conditions – A Review of Reviews. VA ESP Project #09-010; 2015.

This report is based on research conducted by the Evidence-based Synthesis Program (ESP) Center located at 

the Durham VA Medical Center, Durham, NC, funded by the Department of Veterans Affairs, Veterans Health 

Administration, Office of Research and Development, Quality Enhancement Research Initiative. The findings and 

conclusions in this document are those of the author(s) who are responsible for its contents; the findings and 

conclusions do not necessarily represent the views of the Department of Veterans Affairs or the United States 

government. Therefore, no statement in this article should be construed as an official position of the Department 

of Veterans Affairs. No investigators have any affiliations or financial involvement (eg, employment, consultancies, 

honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties) that 

conflict with material presented in the report. 
White Paper: The Impact of Pedometers on Evidence-based Synthesis Program

Chronic Conditions – A Review of Reviews

1 

INTRODUCTION

Physical activity is associated with improvement in many health conditions: obesity; reducing 

risk of cardiovascular disease, type 2 diabetes, and metabolic syndrome; some cancers; and 

mental health disorders.1 Adequate physical activity also increases the chances of living longer.

1

Despite these known benefits and the well-documented evidence that physical activity is 

beneficial, a large proportion of adults are insufficiently active, and inactivity continues to be a 

significant public health concern.

2

Epidemiologic and observational studies have used activity monitors to characterize activity 

intensity and daily activity patterns across diverse samples.3-5 Pedometers have emerged as one 

such popular self-monitoring tool for motivating physical activity.

6,7 Pedometers are small, 

relatively inexpensive devices worn on the body that measure each time the wearer’s hips move 

while taking a step, using a mechanical sensor to count the number of steps walked per day. The 

premise is that pedometers give immediate visual feedback of cumulative step counts and 

increase people’s awareness of their activity and how their behaviors have an effect on physical 

activity.

8-10 Patients with diabetes, obesity, or musculoskeletal disease in particular derive 

significant benefits from regular physical activity, including favorable effects on blood sugar, 

weight control and body fat distribution, blood pressure, lipid profiles, joint swelling and pain, 

and psychological well-being.11

Many systematic reviews have been published describing the benefits of using pedometers for

these conditions. Thus the primary purpose of this paper was to provide a review of these 

reviews in order to evaluate the association between pedometer use, physical activity, and other 

key health outcomes among adults with chronic medical illnesses commonly seen in the Veteran 

population seeking healthcare through VA medical facilities. 
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Chronic Conditions – A Review of Reviews

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METHODS 

SEARCH STRATEGY

In consultation with an expert librarian, we searched PubMed, Embase, CINAHL, SPORTDiscus 

and the Cochrane Library for English-language systematic reviews published from January 1, 

2007, to the present. We used Medical Subject Heading (MeSH) terms and selected free-text 

terms for “pedometers” specifically and the conditions of interest. 

ELIGIBILITY

Eligibility criteria included systematic reviews evaluating the use of pedometers by adults with 

type 2 diabetes, arthritis, hypertension, heart disease, or weight loss. To be a systematic review, 

an article must have included a methods section that described all of the following: (1) search 

strategy, (2) eligibility criteria, and (3) an a priori approach to synthesizing the data. For studies 

including a broader sample of chronic medical conditions, we specified that ≥80% of participants 

must have one of the conditions of interest. The systematic reviews had to describe the use of 

pedometers to increase physical activity, but could use any active or inactive comparator. 

Outcomes of interest included were change in physical activity behaviors (eg, total steps, total 

activity); cardiorespiratory fitness; hemoglobin A1c; arthritis pain and physical function; blood 

pressure; weight change (kg, lb, body mass index); and cholesterol levels. We accepted the 

outcome timing specified in the reviews’ eligibility criteria. We excluded validation studies that 

reviewed the accuracy of a pedometer to capture physical activity. We also excluded reviews of 

worksite wellness interventions. 

STUDY SELECTION

Using the inclusion and exclusion criteria, a single trained investigator assessed titles and 

abstracts for relevance. Full-text articles identified by one investigator as potentially relevant 

were retrieved for further review and examined by another investigator against the eligibility

criteria.

DATA ABSTRACTION

Data from included articles were abstracted into a customized database by a trained investigator. 

Data elements included date of publication, sample size, study location, intervention/exposure 

details, descriptors to assess applicability, quality elements, and outcomes. Key population and 

device characteristics abstracted were age, sex, chronic medical illness status, type of adjunctive 

interventions (eg, behavioral weight management strategies, physical activity education) 

adherence to use of measurement device, and duration and frequency of intervention. 

STUDY QUALITY

One investigator assessed the quality of the review using criteria adapted from the AMSTAR 

measurement tool.

12 The key criteria included that the review assesses a focused clinical 

question, search methods are adequate for replication and are comprehensive, selection bias is 

avoided, data are abstracted reliably, characteristics of primary literature are reported and quality 

is assessed appropriately, results are synthesized using appropriate methods, publication bias is 
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assessed, conflict of interest is reported, and conclusions are supported by results. Based on these 

criteria, reviews were categorized as good, fair, or poor quality. Poor-quality reviews were 

excluded.

DATA SYNTHESIS

Quantitative analysis of the reviews was not possible due to the limited number and diversity of 

the included studies; instead we present qualitative descriptions. We grouped reviews by chronic 

medical illness and then summarized the key characteristics and findings. If findings or 

conclusions differed importantly across reviews, we analyzed potential reasons for discrepancies 

such as review inclusion/exclusion criteria, the primary studies included, differences in outcome 

definition, and analytic approach. When synthesizing results, we gave more qualitative weight to 

recent reviews of higher overall quality (eg, good vs fair) and to reviews that included higher 

quality study designs (eg, randomized controlled trials vs retrospective observational studies). 
White Paper: The Impact of Pedometers on Evidence-based Synthesis Program

Chronic Conditions – A Review of Reviews

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RESULTS

LITERATURE FLOW

The flow of articles through the literature search and screening process is illustrated in Figure 1. 

A combined search of PubMed (n=58), Embase (n=35), CINAHL (n=30), SPORTDiscus (n=16) 

and the Cochrane Library (n=0) yielded 139 citations. There were no MeSH terms for the 

intervention of interest, so we searched text words in the title and abstract for terms such as 

pedometer, step, and counter to locate all relevant citations. We limited the search to systematic 

reviews and meta-analyses published between January 1, 2007, and December 12, 2014. After 

applying inclusion and exclusion criteria at the title-and-abstract level, 39 full-text articles were 

retrieved for further evaluation. Of these, 33 were excluded at the full-text screening stage, 

leaving 6 articles describing 6 unique systematic reviews for data abstraction.

Figure 1. Literature Flow 

Full-text review = 39

Combined citations = 159 Duplicates removed = 20 

Databases = 159 Manual = 0

Excluded = 100

Intervention not of interest 

or not a systematic review

Excluded = 33

• Ineligible study design = 15

• Ineligible condition = 6

• Not English or not peerreviewed original research= 6

• Not intervention of interest = 3

• Not outcome of interest = 3

Citations screened = 139

Eligible for abstraction = 6

PEDOMETER-BASED INTERVENTIONS FOR WEIGHT LOSS

We identified one good-quality eligible systematic review13 that evaluated pedometer-based 

walking interventions for weight loss among a mixed population of obese, overweight, and 

sedentary participants. This review searched 5 computerized databases for eligible RCTs and 

cohort studies published through September 12, 2006. The review included 4 RCTs and 5 cohort 

studies. 
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The review authors used the Downs and Black quality-scoring system. The overall results for 

quality assessment were used as a covariate for meta-regression analysis. Publication bias was 

assessed with funnel plots and heterogeneity assessed with Cochran’s Q statistic. Mixed-effects 

meta-regression was used to examine changes in the effect size of weight change using the 

covariates of quality assessment and study duration. 

The eligibility criteria specified that participants were sedentary and overweight or obese (BMI 

>25 kg/m2) at baseline. The intervention required the use of a pedometer as a motivational tool 

to increase walking, including step-count goal-setting and continuous self-monitoring. The 

included studies did not include a concurrent dietary intervention. The outcomes of step count 

and weight were extracted for each included study. The authors had sufficient information to 

conduct summary estimates of treatment effects using a random-effects meta-analysis with Stata 

9.0 (Stata Corp., College Station, TX). Data on adverse effects were not reported. 

The review included 4 RCTs that randomized 92 sedentary, overweight, or obese adult 

participants who were either healthy (n=31) or had comorbid conditions (n=15 with 

hypertension, n=46 with type 2 diabetes). Also included were 5 cohort studies that enrolled 215 

sedentary and/or obese participants with a variety of baseline conditions (ie, breast cancer 

survivors, healthy, or type 2 diabetes). Studies were not excluded due to author-rated quality. 

The sample size ranged from 15 to 106 participants. The majority of included participants (73%) 

were women (Table 1). 

Table 1. Characteristics of Systematic Review Evaluating Pedometer Interventions for 

Weight Loss 

Characteristic Richardson, 200813

Systematic review quality Good

Population description Age: 30 to 60+ years

Gender: 73% women

Conditions: Type 2 diabetes (n=4), sedentary (n=4), breast cancer 

survivor (n=1)

Study designs included 4 RCTs

5 cohort studies

Sample size range by design Sample size: 15 to 106 for a total of 307 participants

RCT: n=92 

Cohort: n=215 

Analytic approach 9 studies included meta-analysis and meta-regression

Impact of pedometers on 

physical activity

Range of weight change for the 9 cohorts was +0.30 to -3.70 kg 

with an unadjusted mean weight change of -1.42 kg.

Search date January 1, 1995, through July 8, 2005; updated September 12,

2006 (without EMBASE)

Databases searched CINAHL, Embase, MEDLINE, PsycINFO, SPORTDiscus, Web of 

Science
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Table 2 presents the intervention duration and behavioral counseling methods used in these 

studies. The duration of the interventions ranged from 4 weeks to 1 year with a median duration 

of 16 weeks. Pedometers were not used as a standalone technique in most studies; nearly all of 

the studies used a variety of other motivational enhancements such as behavioral counseling. The 

use of a pedometer with step-count goals was the motivation method used in 3 studies. Coaching 

or counseling in groups or individual sessions was used in 4 studies. One study did not report the 

details of the behavioral counseling program. Goal-setting used a combination of methods. In 3 

studies the participants were allowed to choose individualized step-count goals. Three studies 

assigned step goals to participants, and 3 studies used 10,000 steps per day as the final goal. The 

majority of studies utilized a written or web-based activity log to report step counts. Only one 

study used a pedometer with a 7-day memory to record step counts. Dropout rates were >20% to 

40% in 3 studies, >0% to ≤10% in 3 studies, and 0% in one study. Two studies did not report 

dropout findings, and no details were provided about whether dropouts were differentially higher 

in the intervention arm for any included study. 

Table 2. Description of Studies Included in Review by Richardson et al.13

Characteristic Studies (n=9) 

Type of counseling 

Individual

Group

Pedometer and step-count instruction only 

Not reported

1 

4 

3 

1

Treatment duration

≤12 weeks

>12 to 24 weeks

>24 weeks

4 

3 

2

Method of goal-setting

 Individualized

 Assigned

 Goal of 10,000 steps

3 

3 

3

Method of logging and reporting step counts

 Written or web-based activity log

 Pedometer memory download

Not reported

7 

1 

1

Dropouts

 0%

 >0% to ≤10%

 >10% to ≥20%

Not reported

1 

3 

3 

2

The majority of included studies (n=8) reported a small decrease in weight. The range of weight 

change for all 9 studies was +0.30 to -3.70 kg with an unadjusted weight change across the 

studies of -1.42 kg. Weight changes were statistically significant in 5 of the studies. Step counts 

increased in all but one study, which did not report a preintervention step-count. Increases in step 

count ranged from 1,827 to 4,556 steps/day; the median increase was 3,750 steps/day. The 

pooled summary effect estimate for weight change was -1.27 kg (95% CI -1.85 kg to -0.70 kg) 

with significant heterogeneity. Funnel plot analysis suggested an absence of publication bias. In 

meta-regression analysis, a significant linear association (beta -0.05, p=0.03) was found between 
White Paper: The Impact of Pedometers on Evidence-based Synthesis Program

Chronic Conditions – A Review of Reviews

6 

longer duration interventions and greater weight change. This would represent a weight 

reduction of 0.05 kg per week. 

We rated this review13 as having good methodological quality with some limitations. Although 

random effects meta-analysis was used, due to significant statistical heterogeneity, the clinical 

differences in the populations enrolled and the study designs utilized challenges generalizability. 

Dropout rates were high in several studies; however, no subgroup analysis was conducted to 

determine if this might have had a significant influence on pooled study estimates. 

Summary of Findings for Weight Loss

We identified one recent, good-quality systematic review.

13 Overall this study suggests a 

potential benefit of pedometer-based walking interventions for weight loss. The amount of 

weight loss was related to the duration of the intervention program. However, the average 

amount of weight loss was small and may not be a strong motivating factor for participation in 

this type of intervention. A pedometer-based walking intervention appears to be a reasonable 

treatment option for weight loss; however, since these studies did not include a concurrent 

dietary intervention, pedometers alone may not be sufficient for significant or sustained 

clinically important weight reduction. The included RCTs were small, and the review authors did 

not provide details of individual study quality; therefore, larger, good-quality RCTs with longterm outcomes are needed. 

PEDOMETER-BASED INTERVENTIONS FOR TYPE 2 DIABETES

We identified 2 good-quality14,15 and one fair-quality16 eligible systematic reviews that evaluated 

pedometer use in patients with type 1 and type 2 diabetes (Table 3). The review and metaanalysis by Russell-Minda et al16 included RCTs only. The review by Funk et al15 included both 

RCTs and quasi-experimental designs. Two reviews14,15 focused on use of pedometers in patients 

with diabetes. The other review16 included studies that used pedometers and multiple other types 

of health technologies for monitoring and managing diabetes. Eligibility criteria limited the study 

population to participants with a diagnosis of type 2 diabetes, required at least 5 participants per 

arm, step-counter use lasting at least 8 weeks, and reported changes in steps per day or 

hemoglobin A1c, or both. 

Table 3. Characteristics of Systematic Reviews Evaluating Pedometer Interventions for 

Diabetes

Characteristic Qiu, 201414 Funk, 201315 Russell-Minda, 200916

Systematic review 

quality

Good Good Fair

Population 

description

Outpatients with type 2 

diabetes

Adults ≥18 years of age

with type 2 diabetes

Adults, adolescents 

≥14 years of age, and 

youth 7 to 13 years of 

age with diabetes, type 

1 or 2

Intervention and 

comparator

descriptions

Interventions used step 

counters as motivating 

and monitoring tools 

compared with controls

Interventions between 

6 and 24 weeks 

designed to increase 

physical activity

Interventions between 

6 weeks and 6 months 

using pedometer to 

increase walking and/or
White Paper: The Impact of Pedometers on Evidence-based Synthesis Program

Chronic Conditions – A Review of Reviews

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Characteristic Qiu, 201414 Funk, 201315 Russell-Minda, 200916

exposed to usual care 

or step counters used

only for counting steps. 

Step counter use had 

to be at least 8 weeks. 

(walking) using 

pedometers to motivate

and measure 

compared with various 

controls including 

coaching, 

nonpedometer groups, 

usual care, or waitlist.

health; compared 2 

goal-setting strategies 

(steps/day vs bouts of

steps).

Timing of outcome 

assessment

3 to 12 months 6 weeks to 24 weeks 6 weeks to 6 months

Study designs 

included 

11 RCTs 9 RCTs, 1 quasiexperimental study4 RCTs

Sample size range by 

design

RCTs (n=147 to 861) RCTs (15 to 60), quasiexperimental (26)RCTs (30 to 70)

Geographical location 3 Belgium, 2 Britain, 1 

Norway, 2 America, 1 

Canada, 2 Australia

2 Canada, 1 Australia, 

5 USA, 1 Norway, 1 

Belgium 

2 USA, 1 Norway, 1 

Canada

Analytic approach Meta-analysis Systematic review Systematic review

Impact of pedometers 

on physical activity 

In general, pedometers 

were associated with 

increased physical 

activity (+1,800 

steps/day). Combined 

with goal-setting, steps 

increased to >3,000 

steps /day. A few 

studies showed no 

effect of pedometers. 

Pedometer-based 

walking interventions 

led to increases in 

physical activity. 

There was conflicting 

evidence among the 4 

RCTs included 

regarding the 

pedometer’s role in 

increasing steps/day.

Search date January 1994 through 

June 2013

2004 through 2011 1985 through May 

2008

Databases searched PubMed, Web of 

Science, Cochrane 

Library

MEDLINE, CINAHL, 

SPORTDiscus, ERIC, 

Academic Search 

Premier

CINAHL, Cochrane 

Library, Embase, 

MEDLINE, PsycINFO, 

SPORTDiscus, Scirus, 

Abbreviation: RCTs=randomized controlled trials

Our discussion focuses on the good-quality review and meta-analysis by Qiu et al,

14 which

searched 3 databases for RCTs published between January 1994 and June 2013. RCTs (n=11) 

utilizing step counters as tools to monitor and motivate increased physical activity were included. 

Active interventions were compared with a usual care control arm or a control arm given step 

counters exclusively for counting steps. 

The review authors rated 4 RCTs as having low risk of bias, 6 RCTs as having high risk of bias, 

and 1 as unclear risk of bias. Three RCTs reported adherence of 75% to 80%. Four RCTs did not 

report on adherence. Dropout rates ranged from 2.3% to 24.9%. All studies had blinded 

assessment of outcomes. No significant publication bias was detected by Begg’s test (p=0.368) 

or Egger’s test (p=0.147). 
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Chronic Conditions – A Review of Reviews

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Trials used a combination of intervention strategies, including setting personal goals to increase 

physical activity, cognitive behavioral therapy, behavioral modification with telephone support, 

and intensive dietary intervention. No major adverse events secondary to step counter use were 

reported. A random-effects model was used to analyze summary estimates (Stata version 11.0, 

College Station, TX). Heterogeneity, inconsistency and publication bias were measured among 

studies. All studies reported use of cointerventions with pedometer use. Cointerventions 

consisted of diary use (n=9), self-set goals (n=3), and prescribed step goals (n=2). 

Eleven RCTs met criteria for inclusion in the meta-analysis; however, only 7 trials (861 

participants) reported data for steps per day (Table 4). All trials enrolled adults; median age 

ranged from 52.5 to 68.3 years. Three RCTs used usual care as comparator. Two RCTs used an 

enhanced usual care (usual care plus phone calls) as comparator. The control group received 

postcards in one RCT. In another, the control group received standard physical activity education 

materials.

Table 4. Description of Studies Included in Review by Qiu et al.14a

Characteristic Studies (n=7)

Type 2 diabetes 7

Population age

Adults

Older adults (≥65 years of age)

7 

6

Sample size 

25 to 50

>50 to 100

>100

2 

2 

3

Type of intervention 

 Pedometer only

Adjuncts to pedometer intervention 

 Goal-setting 

 Behavioral counseling

 Peer support 

 Physical activity education 

 Nutrition education 

 Cognitive behavioral therapy

0 

4 

5 

Not reported

1 

Not reported

2

Treatment duration

≤3 months

3 to 6 months

>6 months weeks

0 

5 

2

Timing of outcome assessmentb

≤3 months

4 to 6 months

7 to 11 months

≥12 months

3 

3 

0 

1 a Eleven studies are represented in the review; however, only 7 reported results on the outcome of interest (change in 

steps/day). b Some studies assessed outcomes at multiple time points. Here we report on the longest interval reported per study. 
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Chronic Conditions – A Review of Reviews

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The meta-analysis associated an increase in steps per day with step counter use versus control 

(mean difference [MD] 1,822; 95% CI 751 to 2,894 steps per day). However, there was 

significantly high heterogeneity in the result (I2 = 85.9%, p<0.001). A meta-regression analysis 

showed heterogeneity was partially explained by goal-setting. Sample size, intervention duration, 

diary use, and study quality did not explain the heterogeneity observed. Step counter use paired 

with a physical activity goal (4 studies) increased physical activity versus control (MD 3,200; 

95% CI 2,053 to 4,347 steps per day) with low to moderate heterogeneity (I2=40.3%, p=0.170). 

Step counter use did not change physical activity significantly for the intervention group 

compared with control in 3 studies where there was no defined physical activity goal (MD 598; 

95% CI -65 to 1,260 steps per day) with moderate heterogeneity (I2

 = 63.1%, p= 0.067). The 

authors concluded that these results indicate the use of a physical activity goal is an important 

predictor of increased physical activity. There was no significant difference in physical activity 

in studies comparing step counter use defined by a set goal of 10,000 steps per day versus an 

individualized physical activity goal (p=0.300). Last, increased physical activity was associated 

with the use of a step diary (MD 2,186; 95% CI 962 to 3,411 steps per day). 

The review by Russell-Minda et al16 differed from Qiu et al14 in 2 major ways: (1) children and 

adults with type 1 and type 2 diabetes were included and (2) all types of self-monitoring devices 

were included. Only one of the 4 studies overlaps with the 11 studies included in the review by 

Qiu et al. This is likely because the review by Russell-Minda was published in 2009 and 

therefore predated many of the studies included in Qiu et al. Despite differences in inclusion 

criteria, the results of the review are similar to the results of the other 2 reviews. Specifically, the 

review by Russell-Minda et al concluded that, “Although our research synthesis found limited 

levels of evidence supporting the effectiveness of pedometer-based interventions for improving 

overall fitness levels and metabolic control (based on our quality assessment methods), 

pedometers can still be an effective method of motivation for patients with diabetes to make 

these necessary lifestyle changes and increase their daily steps.” 

Summary of Findings for Type 2 Diabetes

Overall we conclude that the use of pedometers in lifestyle interventions for adults with type 2 

diabetes is associated with an increase in physical activity. Further, there is reasonable evidence 

that setting a physical activity goal or using a step diary in addition to a pedometer are associated 

with an additional increase in physical activity. There is limited evidence to suggest that use of 

pedometers is associated with sustained increased physical activity (greater than 6 to 12 months). 

Future research could assess sustainability of changes in physical activity associated with 

accelerometer use. Without additional studies, further systematic reviews are not needed.

PEDOMETER-BASED INTERVENTIONS FOR MIXED POPULATIONS 

WITH CHRONIC MEDICAL CONDITIONS

We identified 2 eligible systematic reviews17,18 that evaluated pedometer interventions among 

mixed populations of adults with chronic medical conditions or among older adults (Table 5).

Both reviews included RCTs and observational studies. One good-quality review focused on a 

broad range of populations mostly selected for a chronic medical illness (eg, diabetes, obesity) or 

physical inactivity.18 The other, a fair-quality review, focused on older adults and other special 

populations living with a disability or chronic illness that may limit physical endurance.17 
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Table 5. Characteristics of Systematic Reviews Evaluating Pedometer Interventions for 

Mixed Populations with Chronic Medical Conditions

Characteristic Bravata, 200718 Tudor-Locke, 201117

Systematic review quality Good Fair

Population description Obese/overweight (2 studies)

Overweight &diabetes (1 study) 

Diabetes (2 studies) 

Hypertension (1 study)

Arthritis (1 study)

Sedentary (7 studies)

Adults selected for other 

medical illnesses (5 studies) 

General adult population not 

selected for illness (7 studies)

Older adults (13 studies)

Special populations (33 studies:

10 cancer, 3 chronic obstructive 

pulmonary disorder, 2 coronary 

artery disease, 15 diabetes, 3 

muscle or joint disorders)

Intervention and comparator

descriptions 

Intervention: unblinded 

pedometer with various 

adjunctive interventions (eg, 

physical activity or diet 

counseling, step diaries, goalsetting for step count) 

Comparator: usually blinded 

pedometer or attention control 

condition without pedometer

Mean duration of intervention: 

18 weeks; range 3 to 104 weeks

Intervention: unblinded 

pedometer with various 

adjunctive interventions not 

well-described (eg, education 

or counseling, goal-setting for 

step count) 

Comparator: not described 

Duration of intervention

Older adults: range 2 to 11 

weeks

Special populations: range 4 

to 52 weeks

Timing of outcome 

assessment

Variable; immediately postintervention Variable; immediately postintervention

Study designs included 8 RCTs (n=277)

18 observational studies 

(n=2490) 

Older adults:

4 RCTs (n=324; pedometer 

arms only)

9 quasi-experimental (n=1091)

Special populations:

23 RCTs (n=991; pedometer 

arms only)

9 quasi-experimental (n=403)

Sample size range by design RCTs: range n=21 to 62

Observational studies: range 

n=12 to 927

Older adults:

RCTs: range n=34 to 190; 

pedometer arms only

Quasi-experimental: range n=12 

to 340

Special populations:

RCTs: range n=8 to377; 

pedometer arms only

Quasi-experimental: range n=9 

to 220

Geographical location 2 Australia, 2 Europe, 2 Japan,

20 United States/Canada

Older adults:

10 United States/Canada, 1 
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Characteristic Bravata, 200718 Tudor-Locke, 201117

Japan, 2 Europe

Special populations:

2 Australia/New Zealand, 

23 United States/Canada, 1 

Japan, 26 Europe

Analytic approach Meta-analysis Qualitative 

Impact of pedometers on 

physical activity 

Overall: pedometer users 

increased physical activity by 

27% over baseline 

RCTs: users increased 2,491 

steps/day (p<0.001) compared 

with control 

Observational studies: users 

increased 2,183 steps/day 

(p<0.0001) over baseline

Older adults: pedometers 

increased physical activity by 

775 steps/day 

Special populations:

pedometers increased physical 

activity 2,215 steps/day 

Search date May 2006

February 2007 (MEDLINE

updated)

January 2000

Databases searched MEDLINE, Embase, 

SPORTDiscus, PsycINFO, 

Cochrane Library, Thompson 

Scientific

CINAHL, ERIC, MEDLINE, 

PsycINFO, SocINDEX, 

SPORTDiscus

We focus our discussion on the most recent, good-quality review by Bravata et al.

18 This review 

searched 6 computerized databases for eligible RCTs and observational studies published 

through February 2007. The review included 8 RCTs and 18 observational studies that evaluated 

the association between pedometer use, physical activity, and other health outcomes among 

adults in outpatient settings. Eligibility criteria allowed for a range of populations from those 

with comorbidities including diabetes, hypertension, and arthritis to adults with no a priori

chronic illnesses. Studies were included if they assessed pedometer use, reported change in steps 

per day, and included studies with at least 5 participants. When there were sufficient studies, the 

review authors computed pooled estimates of both mean difference in steps per day and standard 

mean difference of treatment effects using a random-effects meta-analysis. Study authors 

conducted assessments of publication bias but found no evidence as observed by visual 

inspection of funnel plots. 

The majority of studies were conducted in the United States or Canada (n=20), and the mean age 

of participants was 49. Most participants were active at baseline (mean steps per day=7,473). 

Most studies used an unblinded pedometer with various adjunctive interventions (eg, physical 

activity or diet counseling, step diaries, step-count goal-setting) compared with a blinded 

pedometer. All intervention approaches included additional strategies to promote physical 

activity in addition to study-provided pedometers. Nearly all studies (88%) included a goalsetting component. The mean duration of the interventions was 18 weeks (range 3 to 104 weeks). 

Descriptions of included studies are in Table 6. 
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Eight RCTs (n=277 participants) were included in the random effects meta-analysis. Intervention 

participants randomized to wearing unblinded pedometers statistically significantly increased 

their physical activity (MD=2,491 steps/day; 95% CI 1,098 to 3,885; p<.001); however the 

pooled effect exhibited very high heterogeneity (I

2

=91%). Among the 18 observational studies 

(n=2490 participants), the pedometer-wearing group statistically significantly increased their 

baseline steps (MD=2,183 steps/day; 95% CI 1,571 to 2,796; p<.001) which translates to a 27% 

increase over baseline. Again, results exhibited very high heterogeneity (I

2

=89%). Review

authors concluded that high heterogeneity was likely due to differences in intervention 

approaches across the included studies. In addition to change in steps per day, this review also 

reported on change in the following outcomes: BMI, blood pressure, cholesterol, and fasting 

glucose. This review found that pedometer use was associated with a favorable, statistically 

significant change in these outcomes: BMI, systolic blood pressure, and diastolic blood pressure 

(Table 6). 

Review authors also conducted meta-regression to evaluate participant and intervention 

characteristics associated with increases in physical activity. Studies of younger pedometer 

wearers and those with lower baseline activity levels had the most increases in physical activity 

but these did not reach statistical significance. For intervention characteristics, interventions that 

included goal-setting statistically significantly increased steps per day (10,000 steps/day goal = 

2,998 increase in steps; other steps goals=2,363 increase in steps/day). Moreover, studies that did 

not require use of a step diary (n=3 studies) did not see increases over baseline physical activity 

levels, but studies that did reported a mean change over baseline steps of 2,649 steps per day 

(p<.001). 

Table 6. Description of Studies Included in Bravata et al18

Characteristic Studies (n=26)

Population age All adults 

(n=26 studies; 2,767 participants)

Mean age (SD): 49(9)

Sample size 

<50

50 to 100

>100

15

8 

3 

Type of intervention 

 Pedometer only

Adjuncts to pedometer intervention 

 Goal setting 

 Behavioral counseling

 Peer support 

 Physical activity and/or nutrition education 

0a 

23

7 

1 

19

Treatment durationb

≤12 weeks

12-24 weeks

>24 weeks

13

10

3
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Characteristic Studies (n=26)

Changes in other health outcomes (n; MD; 95% CI; p)

 BMI

 Blood pressure

 Systolic

 Diastolic

 Cholesterol

 Total

 HDL

 LDL

 Fasting glucose 

n 18; MD -.38; CI -0.05 to -0.72; p=0.03

n 12; MD -3.8; CI -1.7 to-5.9; p<0.001

n 12; MD -0.3; CI -0.02 to -0.46; p=0.001

n 7; MD -0.09, CI -0.32 to 0.15; p=0.50

n 7; MD 0.06, CI -0.012 to 0.14; p=0.10

n 7; MD -0.06, CI -0.25 to 0.13; p=0.50

n 7; MD -0.03, CI -0.11 to 0.11; p=0.70 a All studies included some form of adjunctive intervention along with the study-provided pedometer. Seven studies 

included just pedometers and goal setting. 

b Some studies assessed outcomes at multiple time points. Here we report on the longest interval reported per study. 

Abbreviations: BMI=body mass index; CI=confidence interval; HDL=high-density lipoprotein; LDL=low-density 

lipoprotein; MD=mean difference; SD=standard deviation

One other systematic review 17 summarized the pedometer intervention literature among older 

adults or those with physical disorders or illnesses. This review included RCTs and quasiexperimental designs. The study authors identified 13 studies among older adults, aged 55 and 

over, and 33 studies among special populations of adults with conditions that limit physical 

activity (eg, muscle and joint disorders, diabetes, cancers). Like Bravata (2007), there was great 

heterogeneity in intervention duration. Among older adults, interventions ranged in duration 

from 2 weeks to 11 months; for special populations, from 4 weeks to 12 months. Review authors 

computed pooled changes in step counts. Among older adults, the weighted mean difference in 

steps per day was 775 or an overall effect size of 0.26 which translates to a small effect. For 

special populations with physical limitations, change in steps per day ranged in size from 562 for 

those with COPD to 2840 step/day for those with coronary artery disease (CAD). Weighted 

effect sizes ranged from 0.21 (small effect) for the COPD group to 1.21 (large effect) for the 

CAD group. This review did not provide any additional information on intervention 

characteristics that may enhance effects of pedometer-based interventions. 

Summary of Findings for Mixed Populations with Chronic Medical Conditions

Overall, we conclude the evidence suggests a potential benefit of pedometer-based interventions 

for older adults and composite populations of those with several chronic physical conditions (eg, 

diabetes, arthritis, cancers) that may limit ability to be physically active. However, effects are 

generally lower for populations including older or sicker populations. We were not able to assess 

the independent contribution of providing pedometers alone; nearly all studies outlined in these 

systematic reviews provided additional supports. Yet results of the authors’ meta-analyses

support that goal-setting appears to be a key behavioral strategy to enhance the effectiveness of 

pedometer-based interventions. 
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DISCUSSION 

Regular physical activity helps improve overall health and fitness, and reduces the risk for many 

chronic diseases. Physical activity is particularly important for conditions such as obesity, type 2 

diabetes, and composite conditions such as musculoskeletal pain and arthritis. Physical activity 

plays a critical role in controlling weight, managing type 2 diabetes, and reducing risk of 

developing type 2 diabetes as well as in keeping bones, joints, and muscles healthy. Despite the 

proven benefits and widespread public health and clinical calls to increase physical activity, 

sedentary behavior has proven difficult to change. While sedentary behavior is difficult to 

change, self-monitoring has been shown to lead to increased physical activity in some 

populations. 

Self-monitoring is a key behavioral strategy to increase physical activity, and objective selfmonitoring—as opposed to self-report—is considered the gold standard. Pedometers are a costeffective and popular tool and are increasingly used for self-monitoring physical activity and as a 

motivational tool. This review thus sought to identify and summarize the evidence on the use of 

pedometers on weight loss, type 2 diabetes, and composite conditions. 

We conclude that the evidence supports association between the use of pedometers and increases 

in physical activity for adults seeking to lose weight, adults with type 2 diabetes, and those with 

composite chronic medical conditions. There is some evidence of weight loss associated with 

using pedometers, which can have a positive impact across all of these conditions. There is also 

reasonable evidence that setting a physical activity goal or using a step diary in addition to using 

a pedometer can further increase physical activity. However, the evidence is limited on the 

sustainability and duration of usefulness that pedometers have on physical activity beyond 6 

months. Pedometers may also have more limited applicability to older or sicker populations. 

Future research should consider good-quality RCTs, prospective cohort studies, stepped and 

adaptive trial designs, and the sustainability of changes in physical activity associated with 

pedometer use. Moreover, future trials will need to compare pedometers against accelerometers. 

Accelerometers are quickly becoming the self-tracking tool of choice. Accelerometers have 

improved functionality over pedometers; they measure vertical acceleration rather than simply 

responding to vertical acceleration. Accelerometers are more versatile in terms of placement and 

position, can be used in various places on the body, and therefore can count better than 

pedometers, particularly for users who are overweight and obese. Perhaps the greatest advantage 

of accelerometers is that they are increasingly embedded into cell phones, which people carry 

directly on their body and are a part of their everyday life. This increases the likelihood of use 

and allows for the collection of data automatically and longitudinally via cellular transmission.

LIMITATIONS

Our study, and the literature, have limitations: (1) the number of evaluated systematic reviews is 

small, (2) there were design limitations in the evaluated studies, (3) our literature search was 

limited to English-language publications, (4) we did not perform meta-analyses across all 

reviews, and (5) only a single reviewer screened citations, performed full-text reviews, and

extracted data from the reviews.
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CONCLUSIONS

Overall, this review of reviews found that pedometer use is associated with an increase in 

physical activity and decrease in weight across various chronic conditions. The extent to which 

increases in physical activity are sustainable over the long term is unknown. Future trials will 

need to compare pedometers against accelerometers, as they are rapidly becoming the selftracking tool of choice.
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