4

Department of Veterans Affairs

Health Services Research & Development Service Evidence-based Synthesis Program

The Impact of Wearable 

Motion Sensing Technologies 

on Physical Activity: A 

Systematic Review

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 Healthcare System

Durham, NC

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

Principal Investigators: 

Jennifer M. Gierisch, PhD, MPH

Adam P. Goode, DPT, PhD

Co-Investigators: 

Bryan C. Batch, MD

Kim N, Huffman, MD, PhD

Katherine S. Hall, PhD

S. Nicole Hastings, MD, MHS

Kelli D. Allen, PhD

Ryan J. Shaw, PhD, RN

Frances A. Kanach, PhD

Jennifer R. McDuffie, PhD

Andrzej S. Kosinski, PhD

John W. Williams, Jr., MD, MHSc

Research Associate:

Avishek Nagi, MS
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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 JM, Goode AP, Batch BC, Huffman KM, Hall KS, Hastings SN, 

Allen KD, Shaw RJ, Kanach FA, McDuffie JR, Kosinski AS, Nagi A, Williams JW Jr. The Impact of 

Wearable Motion Sensing Technologies on Physical Activity: A Systematic Review. 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. 
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STRUCTURED ABSTRACT

Background: Participation in regular physical activity is important for improving health, but 

sedentary behavior is difficult to change. One option is to provide feedback on physical activity

with wearable motion sensing technologies (activity devices). This review sets out to synthesize 

the literature on the effectiveness of these devices for physical activity, weight, and patient 

satisfaction outcomes, and to describe moderating factors that may impact effectiveness (ie, 

population characteristics, location where device is worn on body, or device role in overall 

intervention approach). 

Methods: We searched MEDLINE, Embase, CINAHL, SPORTDiscus, and Cochrane 

CENTRAL from January 1, 2000, to January 6, 2015, for peer-reviewed, English-language 

randomized controlled trials among adults (≥18 years of age). Article inclusion, data abstraction, 

and quality assessment were conducted through a duplicate process, with discussion to resolve 

discrepancies. Trial quality was evaluated as low, unclear, or high risk of bias (ROB). Strength 

of evidence (SOE) was summarized as high, moderate, or low. Random-effects models were 

used to produce standardized mean differences (SMDs) for physical activity outcomes and mean 

differences (MDs) for weight outcomes. Heterogeneity was measured with I

2

. Qualitative 

synthesis was conducted for outcomes with <3 studies. 

Results: We identified 4787 unique citations; 14 trials met eligibility criteria. Women comprised 

62.5% of the population. Median age was 49.7 years (range 28.7 to 79.8 years). Study sizes 

ranged from 20 to 544 participants (median 62), with the majority of studies (n=8) randomizing 

<70 participants. Although all of the interventions had multiple components, in the majority of 

studies (n=8), the wearable device was used in a major role (ie, central motivational 

enhancement). The device was an accelerometer in all 14 studies.

Twelve trials (2 at low ROB, 2 at unclear ROB, 8 at high ROB) examined accelerometer

interventions for increasing physical activity; the majority (n=9) used an inactive comparator. 

Overall, a small significant effect was found for increasing physical activity (SMD 0.26; 95% CI 

0.04 to 0.49) with high heterogeneity (I

2

=64.7%). Moderate SOE was found for small increases 

in physical activity when compared with an inactive comparator (SMD 0.29; 95% CI 0.03 to 

0.55) with high heterogeneity (I

2

=70.3%). Low SOE and no statistically significant effect (SMD 

0.17; 95% CI -1.09 to 1.43) were found when compared with an active comparator. 

Eleven trials (2 at low ROB, 3 at unclear ROB, 6 at high ROB) examined the effect of 

accelerometer interventions on weight loss or maintenance. The overall pooled estimate showed 

a small significant effect for weight loss (MD -1.65 kg; 95% CI -3.03 to -0.28) with high 

heterogeneity (I

2

=81%). Moderate SOE and no significant effect were found for accelerometers 

versus inactive comparators (MD -1.44 kg; 95% CI -3.08 to 0.19). A positive trend with low 

SOE for accelerometers was found in 2 trials on weight loss, but only one was statistically 

significant. 

No studies reported the outcome of patient satisfaction with healthcare. Also, no moderating 

factors were found to significantly impact effectiveness or explain heterogeneity. 
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Conclusions: The small positive effects produced by interventions that include accelerometers 

may not result in a clinically significant impact on physical activity or weight loss; however, the 

small sample sizes with moderate to high heterogeneity in the current studies limit the 

conclusions that may be drawn. Larger, well-designed randomized controlled trials are needed. 

Clinicians and policymakers should consider these findings and the existing gaps in the literature 

before widespread use of these technologies. 

ABBREVIATIONS TABLE

AHRQ Agency for Healthcare Research and Quality

CCRBT Cochrane Collaboration Risk of Bias Tool

CI Confidence interval

COPD Chronic obstructive pulmonary disease

ESP Evidence-based Synthesis Program

GPS Global positioning system

HSR&D Health Services Research & Development

KQ Key question

MD Mean difference

MeSH Medical Subject Heading

NCP National Center for Health Promotion and Disease Prevention

PA Physical activity

PICOTS Population, intervention, comparator, outcome, timing, and setting

QUERI Quality Enhancement Research Initiative

RCT Randomized controlled trial

ROB Risk of bias

SEM Standard error of the mean

SMD Standardized mean difference

SOE Strength of evidence

VA Veterans Affairs

VHA Veterans Health Administration

VISN Veterans Integrated Service Networks
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TABLE OF CONTENTS

INTRODUCTION......................................................................................................................... 1

METHODS .................................................................................................................................... 3

Topic Development ..................................................................................................................... 3

Search Strategy ........................................................................................................................... 3

Study Selection ........................................................................................................................... 4

Data Abstraction ......................................................................................................................... 5

Quality Assessment ..................................................................................................................... 5

Data Synthesis ............................................................................................................................. 6

Rating the Body of Evidence ...................................................................................................... 7

Peer Review ................................................................................................................................ 7

RESULTS ...................................................................................................................................... 8

Literature Flow............................................................................................................................ 8

Characteristics of Included Studies ............................................................................................. 9

KQ 1: Among adults, what is the effectiveness of wearable motion sensing technologies on: 

(a) physical activity levels; (b) weight loss or maintenance; (c) patient satisfaction with 

healthcare? ................................................................................................................................ 12

Key Points ............................................................................................................................. 12

Physical Activity (KQ 1a) ..................................................................................................... 12

Weight Loss or Maintenance (KQ 1b) .................................................................................. 14

Patient Satisfaction (KQ 1c) ................................................................................................. 16

Quality of Evidence for KQ 1 ............................................................................................... 16

KQ 2: Among adults, does the impact of wearable motion sensing technologies vary by: (a) 

characteristics of the population; (b) type of adjunctive interventions; (c) adherence to use of 

the devices; or (d) characteristics of the device? ...................................................................... 20

Key Points ............................................................................................................................. 20

Variation by Population Characteristics (KQ 2a) ................................................................. 20

Variation by Activity Device Role (Major vs Minor; KQ 2b).............................................. 25

Variation by Adherence to Use of the Activity Device (KQ 2c) .......................................... 28

Variation by Characteristics of the Activity Device (Body Location; KQ 2d) ..................... 31

Quality of Evidence for KQ 2 ............................................................................................... 35

SUMMARY AND DISCUSSION .............................................................................................. 36

Strength of Evidence ................................................................................................................. 36

Summary of Evidence by Key Question ................................................................................... 38

Clinical and Policy Implications ............................................................................................... 39

Limitations ................................................................................................................................ 40

Research Gaps/Future Research ............................................................................................... 41

Conclusions ............................................................................................................................... 42

REFERENCES ............................................................................................................................ 44
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TABLES

Table 1. Inclusion and Exclusion Criteria ....................................................................................... 4

Table 2. Approach to Formulating Summary Risk of Bias for Each Outcome Across Domains... 6

Table 3. Description of Interventions and Comparators ................................................................. 9

Table 4. Summary of Findings: Variation by Population Subgroups (KQ 2a) ............................. 25

Table 5. Summary of Findings: Variation by Device Role (KQ 2b) ............................................ 28

Table 6. Summary of Findings: Accelerometer Adherence Across Individual Studies of 

Physical Activity and Weight Loss (KQ 2c) .............................................................................. 30

Table 7. Summary of Findings: Variation by Characteristics of the Device (KQ 2d).................. 35

Table 8. Summary of Intervention Effects and Strength of Evidence Ratings ............................. 37

Table 9. Evidence Gaps and Future Research .............................................................................. 41

FIGURES

Figure 1. Literature Flow Chart ...................................................................................................... 8

Figure 2. Effect of Accelerometer Interventions on Physical Activity ......................................... 13

Figure 3. Effect of Accelerometer Interventions on Weight Loss or Maintenance ...................... 15

Figure 4. Risk of Bias Grapha ....................................................................................................... 16

Figure 5. Risk of Bias Summary: Review Authors’ Judgments About Risk of Bias Items 

for Each Included Studya

 .......................................................................................................... 19

Figure 6. Effect of Accelerometer Interventions on Physical Activity by Population 

Characteristics .......................................................................................................................... 22

Figure 7. Effect of Accelerometer Interventions on Weight (in Kilograms) by Population 

Characteristics .......................................................................................................................... 24

Figure 8. Effect of Accelerometer Interventions on Physical Activity by Device Role ............... 26

Figure 9. Effect of Accelerometer Interventions on Weight (in Kilograms) by Device Role ...... 27

Figure 10. Effect of Accelerometer Interventions on Physical Activity by Device Location ...... 32

Figure 11. Effect of Accelerometer Interventions on Weight (in Kilograms) by 

Device Location ....................................................................................................................... 34

APPENDICES

Appendix A. Search Strategies ......................................................................................................48 

Appendix B. Peer Review Comments Table .................................................................................52 

Appendix C. Characteristics of Included Studies ..........................................................................59 
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THE IMPACT OF WEARABLE MOTION SENSING 

TECHNOLOGIES ON PHYSICAL ACTIVITY: A 

SYSTEMATIC REVIEW

INTRODUCTION 

Participation in regular physical activity is associated with a wide range of mental and physical 

health benefits. Patients with diabetes, obesity, and musculoskeletal disease, in particular, derive 

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

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

well-being.1 Despite these known benefits, a large proportion of adults, including military 

Veterans, are insufficiently active. Less than 50% of Veterans report engaging in physical 

activity at the level to promote health.

2

 Comparisons also show disparities among VA users and 

non-users, with higher rates of physical inactivity (20%) observed among Veterans using VA 

services.

2 This is a significant concern for VHA clinicians, managers, and policymakers given 

published reports showing that low aerobic fitness, stemming from inactivity, is associated with 

increased healthcare costs.

3,4

 

Physical activity has previously been measured using self-reported measures (eg, questionnaires, 

interviews); however there are problems with self-report measures, as these measures do little to 

motivate or change physical activity behavior and can be subject to reporting bias.5-7 Wearable 

motion sensing technologies (activity monitors), such as pedometers and accelerometers, have 

become popular tools to overcome some of these barriers. Epidemiologic and observational 

studies have used activity monitors to characterize activity intensity and daily activity patterns 

across diverse samples.8-12 Pedometers were the first generation of activity monitors and 

continue to be widely used to monitor daily ambulation activity, as a tool for prescribing 

increased mobility (eg, daily step targets) and motivating individuals to increase their activity 

level.

13,14 However, the reduced measurement properties of pedometers in overweight/obese 

populations and among individuals with slower ambulation speeds continues to be a 

limitation.15,16 

Although pedometers are a cost-effective tool, they are increasingly being replaced by 

accelerometers. Accelerometers offer the advantage of assessing all accelerative movements, in 

all directions, in addition to the ambulation data collected by a pedometer, and can be calibrated 

to detect differences in intensity.

13 Data from accelerometers also allow for intervention content 

to be tailored.

8 Until recently, research-grade activity monitors have not provided real-time 

feedback to patients due to a lack of a digital display, or required downloading and interpreting 

of data using specialized, costly analytic software. However, as the consumer market for 

wearable activity monitors has steadily grown, parallel shifts in the research market have also 

occurred, providing more options for personalized, real-time feedback. Activity monitors used by 

consumers and researchers now have extensive feedback programs that provide real-time data to 

the wearer via computer programs and mobile applications.17,18 Some of these devices even 

provide an option to relay this information to a third party such as family, friends, or clinicians. 

This ability to transmit data to patients’ physicians and care teams makes these devices attractive 
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for clinical applications, although this capability is in the early stages of implementation and 

evaluation.

18

The Veterans Health Administration (VHA), the largest integrated healthcare system in the 

United States, recognizes the potential benefit of promoting the use of wearable activity monitors 

to increase physical activity among Veterans. Before care managers in the VHA embrace this 

technology and implement these tools in clinical care and wellness programs, the disease states 

affected, the types of devices studied, and the strategies most likely to affect physical activity 

outcomes need to be identified. The objective of this review is to synthesize the literature on 

newer wearable activity devices to determine effectiveness for physical activity outcomes and to 

describe factors that impact the effectiveness of wearable activity devices. 
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METHODS

TOPIC DEVELOPMENT

The topic was nominated after a process that included a preliminary review of published peerreviewed literature and consultation with investigators, Veterans Affairs (VA) and non-VA 

experts, and key stakeholders. The VA National Center for Health Promotion and Disease 

Prevention (NCP) nominated this project. The goal of this report, as defined by NCP, is to assess 

whether the VA should invest in accelerometers and other wearable activity devices as a tool to 

motivate Veterans to be more physically active. 

The final key questions (KQs), developed in consultation with stakeholders, were: 

KQ 1: Among adults, what is the effectiveness of wearable motion sensing technologies 

(eg, activity devices such as accelerometer-based fitness trackers, global positioning 

systems [GPS]) on: 

a. Physical activity levels? 

b. Weight loss or maintenance? 

c. Patient satisfaction with healthcare? 

KQ 2: Among adults, does the impact of wearable motion sensing technologies (eg, 

activity devices such as accelerometer-based fitness trackers, GPS) vary by: 

a. Characteristics of the population (overweight/obese/sedentary adults, older adults, 

healthy volunteers, and individuals with chronic medical illnesses)?

b. Type of adjunctive interventions (does the activity device play a major or minor 

role)? 

c. Adherence to use of the device? 

d. Characteristics of the device (body location—waist, arm, wrist, or multisite) 

We followed a standard protocol for this review, and each step was pilot tested to train and 

calibrate study investigators. The PROSPERO registration number is CRD42015017343. 

SEARCH STRATEGY 

In consultation with an expert librarian, we searched MEDLINE (via PubMed), Embase, 

CINAHL, SPORTDiscus, and Cochrane CENTRAL from January 1, 2000, to January 6, 2015, 

for peer-reviewed, English-language randomized controlled trials (RCTs). We used Medical 

Subject Heading (MeSH) terms and selected free-text terms for wearable motion sensing 

technologies and for activities/outcomes of interest (eg, movement, exercise therapy, physical 

fitness), along with validated search terms for RCTs.

19 The exact search strategies used are 

provided in Appendix A. We further reviewed the bibliographies of included trials and 

systematic reviews

20-26 for missed publications. To assess for possible publication bias, we 

searched ClinicalTrials.gov (www.clinicaltrials.gov) to identify completed but unpublished 

studies meeting our eligibility criteria. All citations were imported into 2 electronic databases 

(for referencing, EndNote® Version X5, Thomson Reuters, Philadelphia, PA; for data 

abstraction, DistillerSR; Evidence Partners Inc., Manotick, ON, Canada). 
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STUDY SELECTION

Using prespecified inclusion and exclusion criteria (Table 1), 2 trained investigators assessed 

titles and abstracts for relevance to the KQs. Full-text articles identified by either investigator as 

potentially relevant were retrieved for further review and examined by 2 investigators against the 

eligibility criteria. Disagreements on inclusion/exclusion were resolved by discussion or by a 

third investigator. In addition, trials with 3 or more arms were examined for appropriateness of 

all arms for inclusion. For example, data from any active arm that did not include a wearable 

activity device were not abstracted for inclusion in the analysis. 

Table 1. Inclusion and Exclusion Criteria

Study 

Characteristic Inclusion Criteria Exclusion Criteria

Population Adults 18 years of age and older Elite athletes

Children

Inpatient populations

Pregnant women

Interventions Studies will be included if at least 

one of the groups used wearable 

activity devices that provide 

objective feedback on physical 

activity to the wearer (eg, nonpedometer-based trackers such as 

accelerometer-based fitness 

trackers, smartphone applications, 

GPS-based trackers), alone or in 

combination with other interventions 

to enhance physical activity

Pedometer-based (only) studies

Non-wearable systems

Systems that do not objectively 

monitor activity

Systems that do not provide 

feedback to the wearer

Interventions that use wearable 

devices to measure the effects of 

another intervention (eg, drugs) on 

ability to perform physical activity 

Comparators Usual care/standard of care, waitlist 

control 

Pedometer-based interventions

Other active comparator focused on 

enhancing physical activity (eg,

educational or behavioral 

interventions 

Validation studies of head-to-head 

comparisons of different wearable 

physical activity devices used to 

assess validity of devices

Outcomes Change in physical activity behavior 

(eg, total steps, total activity, 

proportion of participants at activity 

goal) 

Change in weight, body mass index, 

change or proportion of participants 

at goal

Patient satisfaction with healthcare

Any outcomes not listed

Timing ≥3 months postrandomization <3 months postrandomization 
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Study 

Characteristic Inclusion Criteria Exclusion Criteria

Setting Outpatient general medical settings 

(geriatrics, family medicine, general 

internal medicine, integrative 

medicine) 

Specialty medical care clinics (eg, 

orthopedic, rheumatology, 

endocrine, cardiology) 

Community settings

Intervention delivered primarily in

hospital inpatient setting

Studies where monitoring of 

physical activity is confined to a 

supervised setting

Study design RCTs, n>20 Not a clinical study (eg, editorial, 

nonsystematic review, letter to the 

editor, case series)

Prospective and retrospective 

observational studies

Measurement or validation studies 

Publication type English-language only

Peer-reviewed articles

Published from 2000 forward 

Non-English articles 

Abstracts only

Abbreviations: GPS=global positioning system; RCTs=randomized controlled trials

DATA ABSTRACTION

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

and confirmed by a second investigator. Data elements included date of publication, sample size, 

location of study, intervention/exposure details, descriptors to assess applicability, quality 

elements, and outcomes. Key population and activity device characteristics abstracted were age, 

sex, chronic medical illness status, type of wearable activity monitor (eg, brand, location worn on 

body), type of adjunctive interventions (eg, behavioral weight management strategies, physical 

activity education) adherence to use of measurement device, and duration and frequency of 

intervention. Disagreements were resolved by consensus or by obtaining a third investigator’s 

opinion when consensus could not be reached.

QUALITY ASSESSMENT

We used the key quality criteria described in the Cochrane Collaboration Risk of Bias Tool 

(CCRBT). The CCRBT was designed to evaluate the risk of bias (ROB) in RCTs.27 It evaluates 6 

different domains: (1) adequacy of random sequence generation; (2) allocation concealment; (3) 

blinding of participants and study personnel; (4) incomplete outcome data; (5) reporting bias due 

to selective outcome reporting; and (6) other forms of bias such as differences in relation to 

baseline measures, reliable primary outcomes, or protection against contamination. The 

Cochrane Collaboration provides guidelines to score each item. Each domain is evaluated as low 

ROB, unclear ROB, or high ROB. To draw conclusions about the overall ROB within trials we 

summarized assessments across items in the CCRBT and used the approach outlined in Table 2. 
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Table 2. Approach to Formulating Summary Risk of Bias for Each Outcome Across Domains

Risk of Bias Interpretation Criteria

Low ROB Bias, if present, is unlikely to alter the 

results seriously

Adequacy of random sequence 

generation, allocation concealment, 

and blinding scored as low ROB, and 

no important concerns related to the 

other domains

Unclear ROB ROB that raises some doubts about the 

results

One or 2 domains are scored “not 

clear” or “not done” 

High ROB Bias may alter the results seriously More than 2 domains are scored as 

“not clear” or “not done”

Abbreviations: ROB=risk of bias

DATA SYNTHESIS

When meta-analysis was feasible, we computed summary estimates of effect. We aggregated 

outcomes when there were at least 3 studies with the same outcome. For KQ 1, analyses were 

conducted separately for wearable activity device interventions versus inactive controls (eg, 

waitlist, usual care) and wearable activity device interventions versus active comparators (eg, 

group weight loss, counseling). Three trials had more than one intervention arm.28-30 Two of 

these trials compared different adjunctive interventions to continuous monitoring via 

accelerometers.28,29 For these 2 studies, we selected the intervention conditions with the lessintensive adjuncts (eg, monthly counseling vs weekly counseling). The third trial tested the 

impact of continuous versus intermittent accelerometer feedback.30 For this study, we selected 

the comparisons between continuous accelerometer use and control, as this was the type of 

accelerometer use evaluated in all other studies.

Continuous outcomes (difference in change from baseline to follow-up between intervention and 

control) were analyzed using standardized mean differences (SMDs) for physical activity 

outcomes and mean differences (MDs) for weight outcomes in a random-effects model with the 

Knapp-Hartung correction to confidence intervals. The method we used to interpret the SMD as 

an effect size was as follows: small effect size, SMD=0.2; medium, SMD=0.5; and large, SMD 

≥0.80.31 We used R (R Foundation for Statistical Computing, Vienna, Austria) with the metafor 

package32 to calculate the summary estimates of treatment effect. We evaluated for statistical 

heterogeneity using visual inspection and the I

2 statistic. Publication bias was assessed using 

findings from the ClinicalTrials.gov search. 

We explored potential sources of heterogeneity including characteristics of the population (eg, 

chronic medical illness status), adherence to the use of the wearable activity device, and 

characteristics of the device defined as location worn on body. We aimed to assess the 

differential impact of type of adjunctive interventions (eg, behavioral weight management 

intervention, physical activity education, goal-setting) as a source of potential heterogeneity. 

Because type and quantity of adjunctive interventions varied greatly from study to study, we 

operationalized this moderator as the role of the wearable activity device (ie, major vs minor 

component of intervention). To be categorized as being a major component of the intervention, 

the wearable activity device needed to be the central motivational enhancement intervention 

intended to improve the primary outcome of the study. Other adjunctive interventions might be 

included but had to play a minor role in enhancing physical activity. To be categorized as being a 
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minor component of the intervention, the wearable activity device needed to be an integrated 

component of a suite of other motivation enhancement interventions, such as a structured 

exercise program, diet or chronic disease counseling/education/monitoring, self-management 

techniques, or monetary or nonmonetary incentives. Two independent investigators categorized 

the role of the device, and any discrepancies were reconciled by the co-principal investigators. 

If a quantitative synthesis was not feasible, we analyzed the data qualitatively. We gave more 

weight to the evidence from higher-quality studies with more precise estimates of effect. We 

focused on documenting and identifying patterns of the intervention across outcome categories. 

We analyzed potential reasons for inconsistency in treatment effects across studies by evaluating 

differences in the study population, intervention, comparator, and outcome definitions.

RATING THE BODY OF EVIDENCE

The strength of evidence (SOE) for each KQ was assessed using the approach described in the 

Agency for Healthcare Research and Quality (AHRQ)’s Methods Guide for Effectiveness and 

Comparative Effectiveness Reviews.

33 We focused on the 2 main outcomes of physical activity 

and weight. The AHRQ approach requires assessment of 4 domains: risk of bias, consistency, 

directness, and precision. Additional domains are used when appropriate: coherence, doseresponse association, impact of plausible residual confounders, strength of association 

(magnitude of effect), and publication bias. These domains were considered qualitatively, and a 

summary rating of high, moderate, or low SOE was assigned after discussion by 2 investigators.

In some cases, a rating of high, moderate, or low was impossible or imprudent to make. In these 

situations, a rating of insufficient was assigned. This 4-level rating scale consists of the following 

definitions:

High—High confidence that the evidence reflects the true effect. Further research is very 

unlikely to change our confidence in the estimate of effect.

Moderate—Moderate confidence that the evidence reflects the true effect. Further 

research may change our confidence in the estimate of effect and may change the 

estimate.

Low—Low confidence that the evidence reflects the true effect. Further research is likely 

to change the confidence in the estimate of effect and is likely to change the estimate.

Insufficient—Evidence either is unavailable or does not permit estimation of an effect.

PEER REVIEW

A draft of this report was reviewed by technical experts and clinical leadership. A transcript of 

their comments and our responses is provided in Appendix B.
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RESULTS

LITERATURE FLOW

The literature search (Figure 1), which was limited to RCTs published between January 1, 2000, 

and January 6, 2015, identified 4787 unique citations from a combined search of MEDLINE (via 

PubMed; n=3334), Embase (n=839), CINAHL (n=265), SPORTDiscus (n=245), and Cochrane 

CENTRAL (n=104). After applying inclusion/exclusion criteria at the title-and-abstract 

screening level, 176 full texts were retrieved for further review. Of these, papers describing 14 

unique RCTs were retained for data abstraction. 

Figure 1. Literature Flow Chart

Records identified through 

databases searched

(n=6196)

Records screened after 

duplicates removed

(n=4787)

Records excluded

(n=4611)

Full-text articles assessed

for eligibility

(n=176)

Full-text articles excluded

(n=162)

- No full publication: 23

- Not RCT: 34

- Not intervention: 74

- Not population: 10

- Not outcome of interest: 4

- Not comparator: 3

- Follow-up <3 months: 12

- Not setting of interest: 2

Studies abstracted

(n=14 unique)
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CHARACTERISTICS OF INCLUDED STUDIES

Within the 14 included trials, women comprised 62.5% of the populations, median age was 49.7 

years (range 28.7 to 79.8 years), and only 4 trials reported race. The majority of trials were 

conducted in the United States (n=8) and were sponsored either wholly or partially by 

government agencies (n=9). Study sizes ranged from 20 to 544 participants (median 62), with the 

majority of studies (n=8) randomizing fewer than 70 participants. All included studies were 

published in the last 10 years, indicating the relatively new use of these activity devices in 

studies aimed at promoting physical activity. While we searched for any wearable nonpedometer 

devices, all identified trials used some form of accelerometer-based motion sensing technology. 

Characteristics of the individual studies are described in Appendix C. A search of 

ClinicalTrials.gov identified one completed but unpublished trial that we believe would meet our 

inclusion criteria (NCT00544245), revealing a small degree of publication bias. 

Twelve studies reported on outcomes related to physical activity,

30,34-44 and 11 on outcomes 

related to weight.28-30,35-41,43 No studies assessed the outcome of satisfaction with healthcare. 

Four trials were conducted on older adults, 5 on overweight/obese/sedentary adults, 3 on 

participants with a chronic medical illness, and 2 on healthy volunteers. We did not identify any 

studies that were specific to Veterans. Comparators were active (eg, behavioral counseling) in 3 

trials and inactive (eg, usual care, waitlist) in 11 trials. The accelerometer, usually worn on the 

waist (n=8 trials), was a major component of the intervention in 9 trials and a minor component 

in 5 trials. Only 4 trials examined adherence. Most studies (n=8) were judged to be at high ROB, 

with 4 studies at unclear ROB, and only 2 at low ROB. 

Interventions varied widely. The duration of interventions ranged from 12 to 52 weeks, and the 

number of planned interactions with participants in the accelerometer conditions ranged from 

none to 52 weekly contacts. Trials used a wide variety of adjunctive interventions in conjunction 

with accelerometers, including intensive diet, weight, and physical activity behavioral 

counseling; tailored written feedback; and web-based supportive educational modules. Selected 

details of interventions and comparators for each study are provided in Table 3. 

Table 3. Description of Interventions and Comparators 

Study Intervention Comparator

Greene, 201339 6 months of access to online social 

network to post about weight and 

physical activity progress + 

continuous accelerometer use and 

feedback

Inactive: Printed lifestyle guidelines 

on diet and exercise

Koizumi, 200942 12 weeks of accelerometer with 

feedback + goal-setting

Inactive: 12 weeks of blinded 

accelerometer use 
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Study Intervention Comparator

Luley, 201428 3-arm study (2 interventional):

Intervention arm 1: 

1-time 2-hour instruction on diet and

physical activity + 12 months of 

accelerometer use + 52 weekly 

individual letters with feedback on 

weight, diet, and physical activity

Intervention arm 2: 

1-time 2-hour instruction on diet and

physical activity + 12 months of 

accelerometer use + 12 monthly 

behavioral counseling calls

Inactive: 1-time 1-hour session, 

consisting of diet education, diet 

regimen, and physical activity

education

Nicklas, 201435 5-month weight loss intervention that 

included: hypocaloric diet (2 

prepared meals a day) + 4 

days/week supervised exercise + 

self-regulatory intervention that 

involved wearing an accelerometer, 

documenting activity, and 6 weekly 

sessions of behavioral counseling

Active: 5-month weight loss 

intervention consisting of diet 

education and regimen 

(individualized hypocaloric intake), 

physical activity education, 

structured exercise (supervised 

treadmill), and in-person counseling

Paschali, 200544 12 weeks of continuous 

accelerometer use and feedback + 4 

monthly in-person exercise 

behavioral counseling sessions + 

workbook

Active: 12-week blinded 

accelerometer use with 4 monthly inperson counseling sessions to 

review exercise diary, a 24-page 

information book and home-based 

walking plan with physical activity

education, behavioral selfmanagement, weight goal-setting, 

and chronic disease monitoring

Polzien, 200730 3-arm study (2 interventional):

Intervention arm 1:

Continuous technology-based 

behavioral weight control program: 7 

in-person individualized counseling 

sessions over 12 weeks + 12 weeks 

of continuous accelerometer use and 

feedback

Intervention arm 2:

Intermittent technology-based 

behavioral weight control program: 7 

in-person individualized counseling 

sessions over 12 weeks + 3 weeks of 

accelerometer use and feedback 

over 12 weeks

Active: 7 in-person individualized 

counseling sessions consisting of 

diet education, diet regimen (1200 to 

1500 kcal/day; dietary fat <20% of 

total energy intake), physical activity

education, and weight goal-setting 

Reijonsaari, 201241 12 months of continuous 

accelerometer use and feedback + 

access to telephone counseling 

(frequency not defined)

Inactive: Explanation of results of 

physical exams and information on 

physical activity and occupational 

healthcare

Shrestha, 201340 1-time 1.5-hour lifestyle instruction + 

6 months of continuous 

accelerometer use and feedback

Inactive: Self-directed exercise 

and/or U.S. Army-mandated 

physical training
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Study Intervention Comparator

Shuger, 201129 4-arm study (3 interventional) :

Intervention arm 1:

Group-based behavioral weight loss:

14 group weight loss sessions + 6 

individual phone calls + workbook 

over 9 months 

Intervention arm 2:

Accelerometer alone: 9 months of 

continuous accelerometer use and 

feedback

Intervention arm 3:

Group-based behavioral weight loss 

+ accelerometer use: 9 months of 

continuous accelerometer use and 

feedback + 14 group weight loss 

sessions + 6 individual phone calls + 

workbook

Inactive: Self-directed weight loss 

manual with diet education, physical 

activity education, and weight goalsetting

Slootmaker, 200943 3 months of continuous 

accelerometer use + tailored physical 

activity feedback and motivational 

tips via web-based portal

Inactive: A single written brochure 

with brief physical activity 

recommendations

Tabak, 201434 Self-directed technology-supported 

care program that included a tailored

web-based exercise program, 

accelerometer-based activity senor 

and motivational messaging, COPD 

self-management module, and as 

needed web-portal teleconsultation 

conducted over 9 months

Inactive: Usual care 

Thompson, 201438 12 weeks of continuous 

accelerometer use and feedback + 

weekly brief counseling sessions on 

increasing activity + treadmill desk

Inactive: 12 weeks of blinded 

accelerometer use

Thompson, 201436 24 weeks of continuous 

accelerometer use and feedback + 

weekly brief telephone counseling 

sessions focused on accelerometer 

feedback + 6 in-person brief 

counseling sessions

Inactive: 24 weeks of blinded 

accelerometer use

Wijsman, 201337 12 weeks of continuous 

accelerometer use and feedback + 

personal website + personal e-coach 

who gives updates on activity status 

and advice via web portal

Inactive: 3-month waitlist control

Abbreviation: COPD=chronic obstructive pulmonary disease
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KQ 1: AMONG ADULTS, WHAT IS THE EFFECTIVENESS OF 

WEARABLE MOTION SENSING TECHNOLOGIES ON: (A) PHYSICAL 

ACTIVITY LEVELS; (B) WEIGHT LOSS OR MAINTENANCE; (C) 

PATIENT SATISFACTION WITH HEALTHCARE? 

Key Points

We identified 12 studies (2 judged to be at low ROB, 2 at unclear ROB, and 8 at high

ROB) that met eligibility criteria comparing an accelerometer intervention for increasing 

physical activity outcomes. The majority of the included studies (n=9) compared the 

accelerometer device against a weak inactive comparator consisting of usual care, waitlist 

controls, or one-time educational interventions. 

The overall summary estimate (SMD 0.26; 95% CI 0.04 to 0.49) demonstrated a small 

positive effect of accelerometer interventions on physical activity levels that was 

statistically significant. When stratified by comparator, a small statistically significant 

effect (SMD 0.29; 95% CI 0.03 to 0.55) was found for accelerometer interventions on the 

outcome of increasing physical activity when compared to an inactive control. An effect 

of smaller magnitude but same direction was found for the active comparator group, but 

the pooled estimate was not statistically significant (SMD 0.17; 95% CI -1.09 to 1.43). 

Eleven trials (2 judged to be at low ROB, 3 at unclear ROB, and 6 at high ROB) 

examined the impact of accelerometer interventions on weight loss or maintenance. Nine 

of these studies used a weak/inactive comparator. The overall pooled estimate 

demonstrated a small but statistically significant effect (MD -1.65 kg; 95% CI -3.03 to -

0.28) of accelerometers on weight loss. Stratified results by inactive comparator mirrored 

the overall results but were not statistically significant (MD -1.44 kg; 95% CI -3.08 to 

0.19). Only 2 studies used active comparators; both demonstrated a positive trend of 

weight loss, but only one study was statistically significant. 

The large number of trials at high or unclear ROB, small number of participants in the 

majority of the trials, and moderate to high heterogeneity across summary estimates 

limits the strength of evidence on the impact of accelerometers on physical activity levels 

and weight loss or maintenance. 

Physical Activity (KQ 1a)

Synthesis of Findings

Twelve studies, (2 judged to be at low ROB,

37,41 2 at unclear ROB,34,38 and 8 at high 

ROB30,35,36,39,40,42-44) examined the impact of accelerometers on the outcome of physical activity. 

There was substantial variability in the mode and metrics of scales used to measure physical 

activity. Therefore, all summary estimates were calculated as SMDs. We stratified results by 

comparator type (inactive vs active) and present stratified and overall pooled estimates. The 

inactive comparator group contained 9 studies (1279 participants; 2 studies judged to be at low 

ROB,

37,41 5 at high ROB,36,39,40,42,43 and 2 at unclear ROB34,38). The active comparator group 

contained only 3 small studies, all judged to be at high ROB (n=109 participants).30,35,44
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Figure 2 shows the forest plot of the meta-analysis examining the effect of accelerometers on 

physical activity with an overall pooled estimate and stratified pooled estimates by inactive and 

active comparator subgroups. The overall pooled estimate indicated a small, statistically 

significant effect for interventions using accelerometers to increase physical activity (SMD 0.26; 

95% CI 0.04 to 0.49) with a high amount of heterogeneity (I

2

=64.7%). A similar small effect was 

found for interventions using accelerometers to increase physical activity when compared to an 

inactive comparator (SMD 0.29; 95% CI 0.03 to 0.55). This summary estimate had high 

heterogeneity (I

2

=70.3%). The high heterogeneity in these pooled estimates may be driven by 

several intervention characteristics including the duration of the interventions and the intensity of 

adjunctive interventions (eg, accelerometer only vs brief device-driven feedback vs intensive 

device-driven behavioral counseling and feedback), which underscore the overall heterogeneity 

across these intervention approaches. 

There was also a very small positive overall effect (SMD 0.17; 95% CI -1.09 to 1.43) that was 

not statistically significant for accelerometer devices when compared to an active comparator. 

This summary estimate had moderate heterogeneity (I

2

=52.3%). All of the studies included in 

this analysis were judged to be at high ROB. 

Figure 2. Effect of Accelerometer Interventions on Physical Activity

Abbreviations: CI=confidence interval; SMD=standardized mean difference
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Summary of Findings

We identified 12 studies that assessed the impact of accelerometer interventions on physical 

activity. Nine of these used weak controls consisting of usual care, waitlists, or one-time 

educational instruction and were categorized as using inactive comparators. The other 3 studies 

used more robust comparators and were classified as having active comparators. Compared to 

inactive control, the impact of accelerometer interventions on physical activity was similar to 

that observed in the overall summary estimate, favoring a small effect of accelerometer 

interventions on physical activity. Both the stratified and overall summary estimates displayed 

high heterogeneity as assessed by I

2 values >50%. 

While the 3 trials with active comparators demonstrated a positive trend of increasing physical 

activity, the negligible pooled summary estimate (SMD 0.17; 95% CI -1.09 to 1.43) was not 

statistically significant. When compared to the inactive control summary estimate, these findings 

suggest that accelerometers may not have as substantial an impact on increasing physical activity

when compared to a more robust and active comparator. However, the small number of studies, 

small sample sizes within these studies, and moderate to high heterogeneity limit conclusions 

that may be drawn. 

Weight Loss or Maintenance (KQ 1b)

Synthesis of Findings

Eleven trials (2 judged to be at low ROB,

37,41 3 at unclear ROB,28,29,38 and 6 at high 

ROB30,35,36,39,40,43) examined the impact of accelerometers on weight loss or maintenance. All but 

one study reported weight changes in kilograms (kg). The other study39 reported weight changes 

in pounds, which we converted to kilograms. Thus, all summary estimates were calculated as 

changes in weight expressed in kilograms. We stratified results by comparator type (inactive vs 

active) and present stratified and overall pooled estimates. The inactive comparator group 

contained 9 studies (n=1505 participants; 4 studies judged to be at high ROB,

36,39,40,43 3 at

unclear ROB,

28,29,38 and 2 at low ROB37,41). The active comparator group contained only 2 small 

studies judged to be at high ROB (n=79 participants).30,35 

Figure 3 shows the forest plot of the meta-analysis examining the effect of the accelerometer 

interventions on weight loss or maintenance. Only the inactive comparator group had sufficient 

studies to be pooled. Compared to inactive controls, the impact of accelerometer interventions on 

weight loss was similar to that observed in the overall summary estimate (MD -1.44 vs -1.65 kg, 

respectively). Thus, both the overall and stratified estimate favored a small impact of 

accelerometer interventions on weight loss. However, the inactive pooled estimate was not 

statistically significant. Both the stratified and overall summary estimates displayed high 

heterogeneity as assessed by I

2 values >80%. High heterogeneity in these pooled estimates is

likely driven by several intervention characteristics including the duration of the interventions 

and intensity of adjunctive interventions (eg, accelerometer only vs brief device-driven feedback 

vs intensive device-driven behavioral counseling and feedback) and underscores the overall 

heterogeneity across these intervention approaches.

Two small trials judged to be at high ROB compared interventions that used accelerometers with 

active comparators.30,35 While both trials demonstrated a positive trend of weight loss (MD range 
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-3.60 to -2.10), only one study35 was statistically significant. Differences in results are likely 

attributable to differences in the intensity of other intervention strategies used in conjunction 

with accelerometer use and in the duration of the interventions. The study with statistically 

significant findings35 used structured and supervised exercise training, meal preparation twice 

daily, and behavior counseling delivered over 5 months. The study that did not produce 

statistically significant results was only 12 weeks long and used behavioral counseling only in 

conjunction with accelerometer use.30 

Figure 3. Effect of Accelerometer Interventions on Weight Loss or Maintenance

Abbreviations: CI=confidence interval; MD=mean difference

Summary of Findings

We identified 11 studies that assessed the impact of accelerometer interventions on weight loss 

or maintenance. Nine of these used weak control conditions consisting of usual care, waitlists, or 

one-time educational instruction, and were categorized as using inactive comparators. Two 

studies used more robust comparators and were classified as having active comparators. While 

both trials with active comparators demonstrated a positive trend of weight loss (MD range -3.60 

to -2.10), only one study was statistically significant. Differences in results are likely attributable 

to differences in the intensity of other intervention strategies used in conjunction with 

accelerometer use and in the duration of interventions. Compared to inactive controls, the impact 

of accelerometer interventions on weight loss was similar to that observed in the overall 

summary estimate, favoring a small impact of accelerometer interventions on weight loss. 

However, the inactive pooled estimate was not statistically significant. Both the stratified and 

overall summary estimates displayed high heterogeneity as assessed by I

2 values >80%. High 
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heterogeneity in these pooled estimates are likely driven by several intervention and population 

characteristics that underscore the overall variability across these studies. 

In the active comparator group, most studies demonstrated a small weight loss that ranged from 

-0.36 to -2.65 kg. Yet, one study demonstrated a large weight loss of -8 kg (95% CI -10.41 to 

-5.59).28 This trial evaluated the effects of telemonitoring for management of obesity over 12 

months in patients with established metabolic syndrome. Both the control and intervention 

groups received dietary instruction at the beginning of the trial. The intervention group also 

received accelerometers and feedback via monthly behavioral counseling calls. Study authors 

posited that the magnitude of weight loss seen was secondary to the design of the intervention. 

The active interventions included key strategies associated with successful weight loss such as

self-monitoring, counselor feedback and communication, social support, a structured program, 

and the use of an individually tailored program. Indeed, other studies included in the pooled 

analysis that included tailored feedback delivered by a coach or device were more likely to be 

associated with statistically significant weight loss.36,37

Patient Satisfaction (KQ 1c)

No studies reported on this outcome. 

Quality of Evidence for KQ 1 

Risk of Bias

Figure 4 presents a summary of the evaluation of the ROB, which shows an ROB graph with 

review authors’ judgments about each ROB item presented as percentages across all included 

studies. 

Figure 4. Risk of Bias Grapha

a For the overall score, low ROB required random sequencing, allocation concealment, and blinding in order to be 

scored low risk with no other important concerns; unclear ROB was assigned if 1 or 2 domains were scored not clear 

or not done; high ROB was assigned if >2 domains were scored not clear or not done.

Abbreviation: PA=physical activity
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Selection Bias

Random Sequence Generation 

Across all included studies, treatment allocation was described as random, and no studies were 

judged to be at high ROB in this domain. However, 7 of the 14 trials (50.0%) did not give details 

about the method for generating the random sequence, resulting in a rating of unclear ROB. 

Allocation Concealment 

In 5 of the 14 trials (35.7%), methods for allocation concealment were described in sufficient 

detail to determine whether the intervention allocation could have been foreseen in advance of or 

during enrollment, resulting in a judgment of low ROB. In the majority of trials (9 of 14 

[64.3%]), there was an unclear ROB due to inadequate detail about allocation concealment

provided by authors. 

Performance Bias

For the outcome of physical activity, the blinding of participants and personnel was highly 

variable, as there are challenges to blinding physical activity interventions. Of the 12 trials 

measuring physical activity as an outcome, only 2 were judged to be at low ROB as a result of 

adequate reporting of blinding. In 7 of 12 trials (58.3%), there was an unclear ROB due to 

inadequate information regarding blinding. One study was judged to be at high ROB due to lack 

of blinding. 

Similar to physical activity outcomes, for the weight outcome, the blinding of participants and 

personnel was highly variable, as there are challenges to blinding weight change interventions. 

Of the 11 trials measuring weight change as an outcome, 4 (36.4%) were judged to be at low 

ROB as a result of adequate reporting of blinding. In 6 of 11 trials (54.5%), there was an unclear 

ROB due to inadequate information regarding blinding. One study was judged to be at high ROB

due to lack of blinding.

Satisfaction with healthcare was not identified as an outcome in any of the included studies. 

Therefore, no ROB is reported for this outcome. 

Detection Bias

For the outcome of physical activity, in 5 of the 12 trials (41.7%) there was sufficient 

information provided by the authors regarding outcome blinding assessment to judge this domain 

as low ROB. In the remaining trials (7 of 12 [58.3%]), insufficient information regarding 

outcome blinding assessment was provided by authors, resulting in a judgment of unclear ROB. 

For the outcome of weight, 6 of the 11 trials (41.7%) gave sufficient information provided by the 

study authors about outcome blinding assessment to judge this domain to be at low ROB. In the 

remaining trials (4 of 11 [36.4%]), insufficient information regarding outcome blinding 

assessment was provided by authors, resulting in a score of unclear ROB. 

Satisfaction with healthcare was not identified as an outcome in any of the included studies. 

Therefore, no ROB is reported for this outcome. 
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Attrition Bias

All trials reported the numbers randomized to each group. The majority of trials (13 of 14 

[92.9%]) reported complete outcome data that included information on attrition and exclusions 

from analysis. One trial did not disclose the reason for attrition/exclusion in sufficient detail, 

resulting in a judgment of high ROB. 

Reporting Bias

The majority of trials (12 of 14 [85.7%]) reported details of the measured outcomes sufficient to 

be judged to be at low ROB. 

Other Bias

The majority of trials (11 of 14 [78.6%]) provided sufficient details not to raise concerns about 

bias of a nature not covered within the other domains mentioned. Two trials did not provide 

sufficient methodological detail to judge this domain and were given a judgment of unclear 

ROB, whereas one trial was judged to be at high ROB stemming from use of a questionable 

analytical approach during the interim analysis and stopping the trial due to insignificant results.

Overall Risk of Bias

Overall ROB was assessed for each included study (Figure 5). The majority of studies (8 of 14 

[57.1%]) were judged to be at high ROB, 4 (28.6%) were at unclear ROB, and only 2 studies 

(14.3%) were judged to be at low ROB. 
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Figure 5. Risk of Bias Summary: Review Authors’ Judgments About Risk of Bias Items for Each Included Studya

a For the overall score, low ROB required random sequencing, allocation concealment, and blinding in order to be scored low risk with no other important 

concerns; unclear ROB was assigned if 1 or 2 domains were scored not clear or not done; high ROB was assigned if >2 domains were scored not clear or not 

done.

Abbreviation: PA=physical activity
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KQ 2: AMONG ADULTS, DOES THE IMPACT OF WEARABLE MOTION 

SENSING TECHNOLOGIES VARY BY: (A) CHARACTERISTICS OF THE 

POPULATION; (B) TYPE OF ADJUNCTIVE INTERVENTIONS; (C) 

ADHERENCE TO USE OF THE DEVICES; OR (D) CHARACTERISTICS 

OF THE DEVICE?

Key Points

We explored the variable impact of wearable motion sensing technologies (activity devices) on 

physical activity and weight by multiple single factors that may contribute to heterogeneity (ie, 

recruited populations, role of device relative to other motivational enhancement strategies, 

location of device on body as a proxy for ease of use). None of these individual factors was a 

robust predictor of heterogeneity. 

Across 4 population subgroups (overweight/obese/sedentary, older adults, healthy 

volunteers, and chronic medical illnesses), there were no major subpopulation differences 

in the magnitude and direction of effect estimates on physical activity or weight. Only the 

older adult subgroup for physical activity and the overweight/obese/sedentary subgroups 

for weight displayed relatively lower heterogeneity.

The impact of accelerometer interventions was not well-explained by the role of the 

device, operationalized as major role versus minor role of the accelerometer in the overall 

intervention approach. Pooled estimates mirrored the results of the overall summary 

estimate. The direction and magnitude of the effects were similar for both major and 

minor role groups; subgroups displayed moderate to high heterogeneity. 

Only 4 of 14 studies reported on adherence to the activity device, and these reports 

largely focused on the process measures related to accelerometer adherence (hours the 

device was worn, uploads to websites). The role of accelerometer adherence as a 

moderating factor of outcome remains to be fully examined in the literature. 

The predominant location of accelerometers was on the waist, followed by the arm. The 

impact of accelerometer interventions was not well explained by activity device 

characteristics operationalized as location of the device on the body. Pooled results on 

physical activity and weight were similar to the overall summary estimate.

Variation by Population Characteristics (KQ 2a)

Synthesis of Findings

We classified studies and organized findings by physical activity and weight. While satisfaction 

with healthcare was also an outcome of interest, no study reported on this outcome. We planned 

to perform meta-analytic regression to assess the impact of population characteristics on the 

outcomes of interest, but we had insufficient studies to perform these analyses. Thus, we 

categorized studies into population subgroups as determined by study trial recruitment criteria 

(ie, overweight/obese/sedentary, older adults, healthy volunteers, and chronic medical illnesses). 

For each subgroup with 3 or more studies, we calculated summary estimates. Other results are 

synthesized qualitatively. 
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Physical Activity

To assess whether the effects of accelerometer interventions vary by population characteristics, 

we conducted exploratory subgroup analyses based on study eligibility criteria. For the

overweight/obese/sedentary population, there were 158 participants in 4 studies. All 4 studies 

were judged to be at high ROB.

30,38,40,43 For older adults, there were 393 participants in 4 studies, 

including one study judged to be at low ROB,

37 one at unclear ROB,36 and 2 at high ROB.35,42

For healthy volunteers, there were 783 participants in 2 studies, with one study each at low41 and 

high39 ROB. For chronic medical illnesses, there were 54 participants in 2 studies, with one study 

each at unclear34 and high44 ROB. 

Figure 6 shows the forest plot of the meta-analysis examining the effect of accelerometer 

interventions on physical activity by the population subgroups. We had sufficient studies to pool 

effects for 2 population subgroups—the overweight/obese/sedentary population and older adults. 

Both subgroups displayed a similar magnitude and direction of effect—namely, a small positive 

effect of accelerometer interventions on increased physical activity—but neither estimate was 

statistically significant. The pooled SMD was 0.35 (95% CI -0.52 to 1.22) for the 

overweight/obese/sedentary group and displayed moderate heterogeneity (I

2

=51.3%). For the 

older adult subgroup, the pooled SMD was 0.34 (95% CI -0.11 to 0.80), with low heterogeneity 

(I

2

=38.0%). 

Two additional subgroups—healthy volunteers39,41 and those with chronic medical 

illnesses34,44—were evaluated in only 2 studies each and therefore could not be pooled. Results 

were mixed in the 2 studies in healthy volunteers. The differences between the 2 studies were 

likely due to differences in the population baseline activity levels and in participant interactions 

with adjunctive interventions. In a study at low ROB,

41 healthy insurance company employees 

(n=521 total) receiving a combined accelerometer and counseling intervention did not increase 

physical activity when compared to a control group (SMD -0.11; 95% CI -0.28 to 0.06). At 

baseline, the majority of employees in both arms met physical activity guidelines. In a study 

judged to be at high ROB,

39 there was a small effect indicating increased physical activity (SMD 

0.30; 95% CI 0.05 to 0.54) in 262 healthy employees and family members of PeaceHealth 

Oregon who used an intervention containing an accelerometer plus an online social network 

versus an education-only control group. The level of social interaction was much greater in the 

intervention using an online social network compared with the intervention with online or 

telephone counseling. 

For the 2 studies in chronic medical illness populations, accelerometer interventions produced no 

significant effect on physical activity in one study judged to be at unclear ROB34 (SMD 0.00; 

95% CI -0.80 to 0.80), and a moderate statistically significant effect on physical activity in the 

other study, which was at high ROB44 (SMD 0.80; 95% CI 0.06 to 1.55). These differences were 

likely due to differences in populations (chronic obstructive pulmonary disease [COPD]

34 vs type 

2 diabetes44) and interventions, with one intervention more focused on physical activity.44 In the 

COPD study34 24 participants received either usual care or a telehealth program where an 

accelerometer plus a smartphone activity coach was one of 4 components. The intervention arm 

did not increase physical activity; however, adherence to the physical activity portion of the 

program was low compared with the portion focused on COPD self-management. In the type 2 
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diabetes study (n=30 participants),44 accelerometers plus counseling increased physical activity 

as compared to counseling alone.

Figure 6. Effect of Accelerometer Interventions on Physical Activity by Population Characteristics

Abbreviations: CI=confidence interval; SMD=standardized mean difference

Weight 

For the outcome of weight, we conducted subgroup analyses by the following populations based 

on study eligibility criteria: overweight/obese/sedentary populations (1170 participants, 5 

studies, with 4 at high ROB30,38,40,43 and one at unclear ROB29); older adults (315 participants, 3 

studies at low,37 high,35 and unclear36 ROB); healthy volunteers (870 participants, 2 studies at 

high39 and low41 ROB); and chronic medical illness populations (120 participants, 1 study at 

unclear ROB28). There were insufficient studies to pool results for healthy volunteers and chronic 

medical illness populations. 

Figure 7 shows the forest plot displaying the effects of accelerometer interventions on weight by 

the population subgroups. Among overweight/obese/sedentary persons, 5 studies assessed the 

impact of accelerometer interventions on change in weight in kilograms. Compared with inactive 

and active control conditions combined, accelerometer interventions resulted in an MD of -1.22 

kg (95% CI -2.46 to 0.02). This estimate displayed relatively low heterogeneity (I

2

=26.8%). In 

older adults, accelerometer interventions produced a decrease in weight of a similar magnitude. 

Again, the pooled effect estimate was not statistically significant (MD -1.08 kg; 95% CI -5.22 to 
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3.06) and displayed high heterogeneity (I

2

=74.4%). This heterogeneity likely reflected 

populations somewhat different in age and other study eligibility criteria (eg, also recruiting for 

other comorbidities such as overweight). 

We identified only 2 studies in healthy volunteers,39,41 and both showed a small positive effect of 

accelerometer interventions on weight. In one study at high ROB,

39 262 healthy employees and 

family members of PeaceHealth Oregon participated in an intervention with accelerometers 

where the major focus was on developing an online social network for sharing goal-setting and 

attainment. When compared to control, this intervention produced an MD of -1.63 kg (95% CI 

-2.48 to -0.78). In the other study, at low ROB,

41 when compared to a control intervention, a 

combined accelerometer and counseling intervention produced an MD of -0.50 kg (95% CI -1.00 

to 0.00) in health insurance company employees. As above, baseline physical activity was higher 

in the latter study, and the intensity of the social component of the intervention was greater in the 

former.

One study judged to be at unclear ROB assessed the effect of accelerometer interventions on 

weight among study participants with chronic medical illnesses.28 Persons with metabolic 

syndrome were randomized to a one-time education-only control or 12 months of accelerometer 

use plus weight, diet, and physical activity feedback via monthly telephone calls or weekly 

letters. More weight loss was observed in the accelerometer condition than in the control arm 

(MD -8.00 kg; 95% CI -10.41 to -5.59). 
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Figure 7. Effect of Accelerometer Interventions on Weight (in Kilograms) by Population 

Characteristics

Abbreviations: CI=confidence interval; MD=mean difference

Summary of Findings

Physical Activity

For subgroups where quantitative analyses were performed (overweight/obese/sedentary and 

older adults), pooled effect sizes were not statistically significant (SMD 0.35; 95% CI -0.52 to 

1.22 and SMD 0.34; 95% CI -0.11 to 0.80), but were of similar magnitude and direction when 

compared to one another and to the overall summary estimate. For subgroups where only 

qualitative analyses could be performed (healthy and chronic medical illness), there were 2 

studies each with mixed results. For both subpopulations, one study showed a small, statistically 

significant increase in physical activity, while the other showed little to no effect that was not 

statistically significant. Thus, taken together, all subgroups support a small positive effect, with 

no apparent differential effectiveness on physical activity.

Weight 

For the outcome of weight loss, there were no major differences in effect sizes across the 4 

population subgroups with the exception of the chronic medical illness subgroup, where a single

study at high ROB showed a larger effect size (MD -8.00 kg; 95% CI -10.41 to -5.59). This 

difference might be attributed to the metabolic syndrome population studied, in which 

counseling convention strongly promotes lifestyle change above pharmacologic interventions. 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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Additionally, this intervention was focused on weight loss and included counseling and 

monitoring of nutrition—as well as physical activity and weight—over a 12-month period 

compared to a weak control of a one-time educational intervention. 

Summary 

Table 4 provides a summary of findings for KQ 2a. 

Table 4. Summary of Findings: Variation by Population Subgroups (KQ 2a)

Outcome

Population

Overweight/Obese/ 

Sedentary Older Adults Healthy Volunteers Chronic Medical Illnesses

Physical 

activity 

4 studies:

SMD 0.35

(95% CI -0.52 to 

1.22)

4 studies:

SMD 0.34 

(95% CI -0.11 to 

0.80)

2 studies:

SMD range:

 -0.11 to 0.30

2 studies:

SMD range: 

0.00 to 0.80

Weight 5 studies:

MD -1.22 kg

(95% CI -2.48 to 

0.02)

3 studies:

MD -1.08 kg 

(95% CI -5.22 to 

3.06)

2 studies:

MD range: 

-1.63 to -0.50 kg

1 study:

MD -8.00 kg 

(95% CI -10.41 to 

-5.59)

Abbreviations: CI=confidence interval; MD=mean difference; SMD=standardized mean difference

Variation by Activity Device Role (Major vs Minor; KQ 2b)

Synthesis of Findings

We classified studies and organized findings by physical activity and weight. While satisfaction 

with healthcare was also an outcome of interest, no study reported on this outcome. We 

categorized studies into subgroups as determined by activity device role (major or minor). A 

device was determined to have a major role if the device (accelerometer) was the only or central 

motivational enhancement intervention intended to improve the primary outcome of the study. In 

this case, other intervention components were centered on the feedback provided by the 

accelerometer. Other adjunctive interventions may have been included but played a minor role in 

enhancing physical activity or could not have been conducted without the feedback of 

accelerometer data. An activity device was determined to have a minor role if the accelerometer 

was an integrated component of a suite of other motivation enhancement interventions which 

may have included, but were not limited to, a structured exercise program, diet 

counseling/education, chronic disease counseling/monitoring, and/or self-management 

techniques. 

Physical Activity

To assess whether the effects of the accelerometer interventions on physical activity vary by

activity device role, we conducted an exploratory subgroup analysis by major versus minor role

of the device. The major role subgroup contained 8 studies (n=1041 participants), 5 judged to be 

at high ROB,

30,40,42-44 one at unclear ROB,36 and 2 at low ROB.37,41 The minor role subgroup 

contained 4 studies (n=374 participants), 3 at high ROB35,38,39 and one at unclear ROB.

34 

Figure 8 shows the forest plot of the meta-analysis examining the effect of accelerometer 

interventions on physical activity by activity device role. Both subgroups displayed a similarly
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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small, positive effect size for accelerometer interventions on increased physical activity, but 

neither group was statistically significant. The pooled SMD was 0.26 (95% CI -0.02 to 0.54) for 

the major device role subgroup and displayed high heterogeneity (I

2

=71.6%). The subgroup 

including studies where the activity device played a minor role showed a pooled SMD of 0.28 

(95% CI -0.43 to 1.00), with moderate heterogeneity (I

2

=43.2%). 

Figure 8. Effect of Accelerometer Interventions on Physical Activity by Device Role 

Abbreviations: CI=confidence interval; SMD=standardized mean difference

Weight 

To assess whether the effects of the accelerometer interventions on weight vary by activity 

device role, we conducted an exploratory subgroup analysis by major role versus minor role of 

the device. In the major role subgroup, there were 8 studies (n=1174 participants), 3 judged to be 

at high ROB,

30,40,43 2 at low ROB,37,41 and 3 at unclear ROB.28,29,36 In the minor role subgroup, 

there were 3 studies (n=410 participants), all at high ROB.

35,38,39 

Figure 9 shows the forest plot of the meta-analysis examining the effect of the interventions on 

weight by activity device role. The direction and magnitude of the effects were similar for both 

groups, favoring a small impact of less than 2 kg of weight loss, but neither group was 

statistically significant. Compared with inactive and active control conditions combined, the 

pooled MD was -1.47 kg (95% CI -3.47 to 0.53) for the major role subgroup and displayed high 

heterogeneity (I

2

=83.1%). The minor role subgroup showed a pooled MD of -1.99 kg (95% CI 

-4.10 to 0.12) and displayed low heterogeneity (I

2

=24.2%). 
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Figure 9. Effect of Accelerometer Interventions on Weight (in Kilograms) by Device Role

Abbreviations: CI=confidence interval; MD=mean difference

Summary of Findings

The impact of accelerometer interventions on physical activity and weight were not wellexplained by the role of the activity device, operationalized as major role versus minor role of 

accelerometer in the overall intervention approach. These estimates mirrored the results of the 

overall summary estimate. For physical activity, 8 studies were categorized as using 

accelerometers as the major intervention strategy,

30,36,37,40-44 and 4 were categorized as using 

accelerometers as a minor component in the overall intervention approach.34,35,38,39 For weight 

loss, 8 studies were categorized as major28-30,36,37,40,41,43 versus 3 categorized as minor.35,38,39 The 

direction and magnitude of the effects were similar for both subgroups, favoring a small positive 

impact on increasing physical activity and decreasing weight that mirrored the overall summary 

estimate. Yet neither group was statistically significant. Across physical activity and weight loss, 

the major subgroups displayed high heterogeneity. High heterogeneity in the major role of device 

subgroup is likely driven by several intervention characteristics that underscore the overall 

variability in the intervention approaches included in this subgroup. For example, studies in this 

group ranged from accelerometer-alone strategies29 to interventions that included in-person 

behavioral counseling and accelerometer-driven feedback used to enhance the self-monitoring 

and motivational aspects of device feedback.

30 In general, interventions that included an 

accelerometer as a major component of the intervention and that capitalized on the selfmonitoring and tailored device-driven feedback capabilities of these devices were associated 

with a greater decrease in weight (see Figure 9).

28-30 The same trend was not seen for greater 
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increases in physical activity with better implementation of activity device feedback and selfmonitoring functionality. 

Table 5 provides a summary of findings for KQ 2b. 

Table 5. Summary of Findings: Variation by Device Role (KQ 2b)

Outcome Role of Accelerometer

Major Role Minor Role

Physical activity SMD 0.26 (95% CI -0.02 to 0.54) SMD 0.28 (95% CI -0.43 to 1.00)

Weight MD -1.47 kg (95% CI -3.47 to 0.53) MD -1.99 kg (95% CI -4.10 to 0.12)

Abbreviations: CI=confidence interval; MD=mean difference; SMD=standardized mean difference

Variation by Adherence to Use of the Activity Device (KQ 2c) 

Synthesis of Findings

The primary aim of KQ 2c was to examine accelerometer adherence as a moderator of the 

outcomes of physical activity, weight loss, and satisfaction with healthcare. Only 4 of 14 studies 

reported any measure of accelerometer adherence.

30,35,41,43 Across these 4 studies, adherence to 

the activity device was defined variably by total arm-band time use, categorical measure of time 

the device was worn (eg, often vs regularly), percentage of days device was worn at least 10 

hours per day, and change in web-based service use at 2 time points. In light of dropout rates 

(range of 12% to 31%), adherence data were reported only on study completers. While these 4 

studies assessed physical activity levels and body weight as outcome measures, only one of these 

studies examined accelerometer adherence relationship to these outcomes (weight only). 

Satisfaction with healthcare was not assessed as an outcome in any of these studies. Individual 

determinants of accelerometer adherence were not examined in any of these studies. The 

synthesis of findings presented here combines results for physical activity and weight loss 

outcomes. 

A large worksite physical activity promotion study judged to be at low ROB41 randomized 544 

employees to either 12 months of continuous accelerometer use and distance counseling (by 

phone or the web) or a control group that received baseline fitness results only. Participants in 

the intervention arm were instructed to enter their physical activity score from the accelerometer 

manually to the web-based service. Use of the web-based service was assessed as a proxy of 

accelerometer adherence in this study. Overall, use of the web-based service was low among 

participants in the intervention arm, averaging just 15 logins during the last 6 months of the trial 

(0.6 logins per week). Comparisons of the 0- to 6-month versus 6- to 12-month time periods of 

the study show that use of the web-based service also decreased over the trial for both 

participants and coaches. Participants entered fewer days of physical activity scores (14% of 

days vs 9% of days), coaches sent fewer messages to participants (7.2 vs 6.1 messages), and 

participants sent fewer messages to coaches (4.3 vs 1.7 messages). This study did not examine 

the impact of accelerometer adherence on physical activity or weight loss outcomes. 

A second, smaller worksite physical activity promotion study judged to be at high ROB43

randomized 102 healthy young employees to either 3 months of continuous accelerometer use 

and tailored web-based support or a control group that received a single brochure with brief 

activity recommendations at baseline. Participants in the intervention arm were encouraged to 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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enter their physical activity score from the accelerometer manually to the web-based service. 

Uploaded accelerometer scores were paired with automatic, tailored physical activity advice and 

motivational tips. Seventy-three percent of those in the intervention arm reported wearing the 

accelerometer “regularly” or “often” and accessed the web-based service on average nearly once 

per week to upload accelerometer data. Fifty-two percent of intervention arm participants set a 

personal goal using the web-based service. However, 41% of participants found the tailored 

advice on the website unappealing. Barriers to compliance included a limited menu of preferred 

personal activities on the web-based service, and physical activity advice not relevant to the 

participant’s preferred personal activity. This study did not examine the impact of accelerometer 

adherence on physical activity or weight loss outcomes. 

In a small 12-week behavioral weight-loss study judged to be at high ROB,

30 57 healthy adult 

participants were randomized to: (1) standard in-person weight control program; (2) standard 

program + continuous technology-based behavioral program (continuous armband-accelerometer 

wear and feedback); or (3) standard program + intermittent technology-based behavioral 

program (3 weeks of armband accelerometer wear and feedback). Average total accelerometer 

wear time was low in both the intermittent and continuous groups (63.8 minutes/week and 71.0 

minutes/week, respectively) and was highly variable (range 51 to 350 minutes/week). Adherence 

to the accelerometer, defined as total arm band time, declined significantly across all 4-week 

time periods in the continuous technology group. Self-monitoring of exercise also decreased 

across the intervention period in the standard and intermittent groups. This was the only study to 

examine the relationship between accelerometer adherence and weight loss. Significant 

correlations were reported between change in body weight and total accelerometer wear time in 

both the intermittent (r=-0.68, p<0.01) and continuous (r=-0.71, p<0.01) technology arms. 

In a pilot study (high ROB) of spontaneous physical activity and weight loss,35 48 older adults 

were randomized to a 5-month weight loss intervention that included a hypocaloric diet and 

aerobic exercise or the same weight loss intervention plus a self-regulatory intervention (which 

included an accelerometer) to promote spontaneous physical activity. Following post-weight loss 

assessments at 5 months, both groups transitioned to a self-directed diet and exercise program 

during a 5-month follow-up period. Adherence to the accelerometer was high (see Table 6). 

Eighty-five percent of participants (n=17) submitted accelerometer data for the entire 10 months 

of the study, of which 87% wore the accelerometer for the prescribed ≥10 hours per day. Eightyone percent of participants met the daily spontaneous physical activity goal recorded by the 

accelerometer. Reported barriers to accelerometer wear compliance included device 

malfunction/battery change (13%), illness/health (9%), forgot to wear the monitor (7%), and too 

busy/time conflict (7%). The transition from the 5-month, intensive intervention to the selfmonitored program also seemed to pose a barrier for some who stopped wearing the monitor 

(15%). This study did not examine the impact of accelerometer adherence on physical activity or 

weight loss outcomes.

Summary of Findings

Only 4 of 14 studies provided any data on adherence to the activity device. Although our primary 

aim was to examine accelerometer adherence as a moderator of physical activity, weight loss, 

and satisfaction outcomes, none of these studies reported such analyses. Instead, these studies 

focused largely on the process measures related to accelerometer adherence. Despite the major 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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role played by the accelerometer in 3 of these studies, a consistent pattern of decline in 

participants’ use of the device was evident over study duration, and generally low levels of 

adherence over time were reported. Two studies conducted in workplace settings showed 

significant participant disengagement expressed in low levels of self-motivated, self-initiated, 

stated goals to be achieved during the study time period. In summary, we are unable to determine 

from this literature whether accelerometer adherence has an effect on the outcomes of interest 

when accelerometers are used as part of behavioral interventions. 

Table 6 provides a summary of findings for KQ 2c. 

Table 6. Summary of Findings: Accelerometer Adherence Across Individual Studies of Physical 

Activity and Weight Loss (KQ 2c)

Study Role of 

Accelerometer Accelerometer Adherence Description

Nicklas, 201435 Minor 85% of participants submitted accelerometer data for 

entire 10 months

87% of days completers wore accelerometer at least 10 

hours/day

81% of days spontaneous physical activity goal met 

Barriers to compliance: device malfunction/battery change 

(13%), illness/health (9%), forgot to wear (7%), too 

busy/time conflict (7%)

Polzien, 200730 Major Accelerometer wear time was low and highly variable in 

both continuous and intermittent technology groups

Significant decline in total accelerometer wear time use by 

continuous technology group across all 4-week time 

periods

Standard and intermittent groups decreased selfmonitoring of exercise across the 12-week intervention 

period 

Longer total accelerometer wear time was significantly 

associated with greater weight loss in both the continuous 

and intermittent technology groups

Reijonsaari, 

201241

Major Use of web-based service decreased with time 

(comparing 0 to 6-month vs 6- to 12-month study periods) 

for both participants and coaches

Declines in web-based service across time included 

entries of participant physical activity scores (14% of days 

vs 9% of days); coach communication to participants (7.2 

vs 6.1 messages); participant communication to coaches 

(4.3 vs 1.7 messages)

Slootmaker, 

200943

Major 73% of participants wore accelerometer “often” or 

“regularly”

52% of participants set personal goal on web-based 

service

Barriers to compliance: Limited menu of preferred, 

personal activities on web-based service; physical activity

advice not relevant to participant-preferred, personal 

activity; 41% found advice on website unappealing
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Variation by Characteristics of the Activity Device (Body Location; KQ 2d)

Synthesis of Findings

We classified studies and organized findings by physical activity and weight. While satisfaction 

with healthcare was also an outcome of interest, no study reported on this outcome. We 

categorized studies into subgroups as determined by characteristics of the activity device, 

operationalized as location of the device on the body. Locations of interest were waist, arm, 

wrist, and multisite. One study that did not specify device location was not included. We had 

sufficient studies to pool effects only for one subgroup (waist); other results are synthesized 

qualitatively. 

Physical Activity

To assess whether the effects of the accelerometer interventions on physical activity vary by 

device characteristics, we conducted exploratory subgroup analysis by the following device 

location sites: waist (n=809 participants; 7 studies, 5 judged to be at high ROB,

35,38,42-44 one at 

low ROB,

41 and one at unclear ROB36); arm (n=38 participants; 2 studies, both at high ROB30,40); 

wrist (n=235 participants; one study at low ROB37); and multisite (n=24 participants; one study 

at unclear ROB34). 

Figure 10 shows the forest plot examining the effect of the interventions on physical activity by 

activity device location. The pooled estimate displayed a weakly positive association regarding 

effect of location of accelerometer at the waist on increasing physical activity (SMD 0.24; 95% 

CI. -0.15 to 0.63) and moderate heterogeneity (I

2

=62.3%). This effect was similar in direction 

and magnitude to the overall summary estimate. 

Three additional subgroups—arm, wrist, and multisite—contained fewer than 3 studies and 

could not be pooled. The 2 studies where the device was worn on the arm30,40 showed mixed 

results, neither of which was statistically significant. Of the 2 studies, one (at high ROB)

30

showed a negative impact on physical activity (SMD -0.15; 95% CI -0.78 to 0.49). The second 

(also at high ROB)

40 showed a small positive effect on physical activity (SMD 0.60; 95% 

CI -0.30 to 1.50). The differences in the 2 studies were likely due to differences in the duration 

of the intervention (12 vs 24 weeks), comparator arms (active vs weak or active vs active), and 

overall analytic strategy of each study. Of note, both studies had high dropout rates, but only one 

study specifically stated that they used an intention-to-treat analysis. In Shrestha et al40 an 

accelerometer paired with an interactive website versus usual care did not show a statistically 

significant mean change in total physical activity between groups. The lack of difference in 

effect on physical activity was attributed to low initial recruitment (28 participants total) and a 

high dropout rate (attrition 64% at 6 months). Polzien et al30 used a technology-based program 

added to an in-person behavioral weight loss program. Although each group showed a significant 

increase in leisure time physical activity, there was no statistically significant difference between 

the active intervention arms. 

For one study where the accelerometer was worn on the wrist (low ROB),37 the intervention 

produced a modestly statistically significant increase in physical activity (SMD 0.56; 95% CI 

0.30 to 0.82). In this study, 119 adults were randomized to receive a 12-week web-based 

physical activity program focused on increasing daily activity. The intervention was composed 
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of 3 elements: an accelerometer-based activity monitor, a personal website, and a personal ecoach who gave feedback about ways to increase physical activity. After 13 weeks, daily 

physical activity increased in the intervention versus control group (11% vs 5%, p <0.11).

Finally, a pilot study (at unclear ROB)

34 showed no effect on physical activity of an 

accelerometer that could be worn at multiple sites as compared to a usual care group (SMD 0.00; 

95% CI -0.80 to 0.80). The intervention arm included 4 modules: a web-based exercise program 

for home exercising, an activity coach with real-time feedback, self-management of COPD 

exacerbations via diary on web portal, and teleconsultation. The activity coach consisted of an 

accelerometer-based sensor and a smartphone that displayed cumulative activity and sent 

motivational cues based on activity already performed. Objective activity at baseline and 3 

months for the intervention group (n=15) was 536.3 (standard error of the mean [SEM] 42.6) and 

511.0 (SEM 44.1), respectively. Objective activity at baseline and 3 months for usual care (n=14) 

was 360.5 (SEM 44.7) and 335.2 (46.3), respectively. The notable baseline difference may be 

influenced by the poorer clinical characteristics including a higher prevalence of dyspnea among 

usual care (p=0.03) despite computerized randomization distributed in sealed envelopes. 

The lack of difference in physical activity based on multisite location is likely secondary to very 

low use of the exercise module by physiotherapists and low use and adherence to the exercise 

module and activity coach by participants. Of note, median adherence to prescribed exercise 

schemes was 21%.

Figure 10. Effect of Accelerometer Interventions on Physical Activity by Device Location

Abbreviations: CI=confidence interval; SMD=standardized mean difference
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Weight 

To assess whether the effects of the accelerometer interventions on weight vary by device 

characteristics, we conducted exploratory subgroup analysis by the following device location

sites: waist (n=852 participants; 6 studies, 3 judged to be at high ROB,

35,38,43 one at low ROB,41

and 2 at unclear ROB28,36); arm (n=157 participants; 3 studies, 2 at high30,40 and one at unclear29

ROB); and wrist (n=226 participants; one study at low ROB37). 

Figure 11 shows the forest plot of the meta-analysis examining the effect of the interventions on 

weight by device location. The overall pooled estimate demonstrated a small statistically 

significant effect on weight (MD -1.65 kg; 95% CI -3.03 to -0.28), with high heterogeneity 

(I

2

=81%). We had sufficient studies to pool effects for 2 subgroups (waist and arm). Both 

produced effects of the same direction and magnitude, which mirrored those of the overall 

summary estimate. Compared with control conditions, the pooled MD was -2.01kg (95% CI 

-4.99 to 0.97) for the waist subgroup and displayed high heterogeneity (I

2

=89.1%). High 

heterogeneity in the waist subgroup is likely driven by differences in intervention approach (selfmonitoring vs self-monitoring plus individualized counseling/feedback), intensity (weekly

counseling via phone calls or letters vs self-driven interaction with device/website), and length of 

follow up (range 3 to 12 months). The arm location subgroup showed a similar pooled MD of -

2.08 kg but was statistically significant (95% CI -4.13 to -0.02), with low heterogeneity 

(I

2

=0.00%). 

One additional subgroup, wrist, contained fewer than 3 studies and could not be pooled but 

demonstrated small positive impacts on weight loss. In the one study (low ROB) where the 

accelerometer was worn on the wrist,37 the intervention produced a small impact on weight (MD 

-0.67 kg; 95% CI -1.33 to -0.01). In this study, 119 adults were randomized to receive either a 

12-week web-based physical activity program focused on increasing daily activity or a 3-month 

waitlist control. The intervention was composed of 3 elements: an accelerometer-based activity 

monitor, a personal website, and a personal e-coach who gave feedback about ways to increase 

physical activity. 
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Figure 11. Effect of Accelerometer Interventions on Weight (in Kilograms) by Device Location 

Abbreviations: CI=confidence interval; MD=mean difference

Summary of Findings

The predominant location of accelerometers was the waist, followed by the arm. The impact of 

accelerometer interventions was not well explained by where the device was worn on the body; 

pooled estimates mirrored those of the overall summary estimate. For physical activity, activity 

devices worn on the waist (n=7 studies) produced effects that ranged from negative and not 

statically significant to modest positive effects that were statistically significant. Of note, only 

one small (n=20) study of a waist-worn device, judged to be at high ROB, produced positive and 

statistically significant effects on physical activity.38 Activity devices worn on the arm (n=2 

studies) also displayed mixed results. One study (at low ROB) including a wrist-worn device37

produced a positive and statistically significant impact on physical activity, while a study that 

allowed multisite use34 showed no impact on physical activity. 

For weight loss, activity devices worn on the waist (n=6 studies) produced effects that ranged 

from no effect to positive and statistically significant effects. High heterogeneity in the waist 

subgroup is likely driven by differences in intervention approach (self-monitoring vs selfmonitoring plus individualized counseling/feedback), intensity (weekly counseling via phone 

calls or letters vs self-driven interaction with device/website), and differences in length of follow 

up (range 3 to 12 months). Activity devices worn on the arm (n=3 studies) displayed modestly 

positive effects on weight that were statistically significant and displayed low heterogeneity. One 

study including a wrist-worn device produced a small positive and statistically significant effect
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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on weight. This was the only device location that produced positive and statistically significant 

effects on both physical activity and weight, but this group included only one study (low ROB).37

Table 7 provides a summary of findings for KQ 2d. 

Table 7. Summary of Findings: Variation by Characteristics of the Device (KQ 2d)

Outcome Location of Accelerometer

Waist Arm Wrist Multisite

Physical activity 7 studies:

SMD 0.24 

(95% CI -0.15 to 

0.63)

2 studies:

SMD range:

-0.15 to 0.60 

1 study: SMD 0.56 

(95% CI 0.30 to 

0.82)

1 study:

SMD 0.00 

(95% CI -0.80 to 

0.80)

Weight 6 studies:

MD -2.01 

(95% CI -4.99 to 

0.97)

3 studies:

MD -2.08 

(95% CI -4.13 to 

-0.02)

1 study

MD -0.67 

(95% CI -1.33 to 

-0.01)

No studies 

Abbreviations: CI=confidence interval; MD=mean difference; SMD=standardized mean difference

Quality of Evidence for KQ 2 

The same 14 studies were included for KQs 1 and 2. The quality of evidence is discussed above, 

under KQ 1. 
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SUMMARY AND DISCUSSION

Physical activity is universally endorsed as a cornerstone for maintaining optimal physical and 

cognitive health.45,46 Increased physical activity is also considered an important therapeutic 

modality for patients with many chronic conditions including diabetes,47 arthritis,48 various 

cancers,49 obesity,50 and depression.51 Despite the proven benefits and widespread public health 

and clinical calls to increase physical activity, sedentary behavior has proven difficult to change. 

Self-monitoring is a key behavioral strategy to increase physical activity, and objective selfmonitoring, as opposed to self-report, is considered the gold standard.52 Pedometers have been 

found to be associated with significant increases in physical activity and significant decreases in 

both body mass index and blood pressure.53 Although pedometers are a cost-effective tool, they

are increasingly being replaced by accelerometers. Accelerometers offer the advantage of 

assessing accelerative movements, in all directions. In addition, accelerometers can detect 

differences in intensity13 and can be tethered with other electronic devices to provide real-time,

tailored feedback. Therefore this review sought to identify and summarize the evidence on the 

use of accelerometers on 2 major outcomes associated with many chronic conditions: physical 

activity and weight loss or maintenance. In addition, we sought to examine the effects of 

accelerometer use on satisfaction with healthcare. 

We identified 14 unique RCTs that assessed the impact of accelerometers on physical activity 

and weight loss or maintenance. All included studies were published in the last 10 years, 

indicating the relatively new use of motion sensing devices in studies aimed at promoting 

physical activity or weight loss. Although we searched for wearable motion sensing technologies

in general, no studies included newer wearable motion sensing technologies (eg, GPS, hand 

gesture, eye gesture, hand swipe) beyond an accelerometer. Twelve studies assessed the impact 

of accelerometers on physical activity and 11 on weight. No identified studies reported on the 

impact of accelerometers on satisfaction with healthcare. Four trials explicitly recruited older 

adults, 5 recruited obese/sedentary adults, 3 recruited participants with a chronic medical illness 

(diabetes, metabolic syndrome, COPD), and 2 recruited healthy volunteers. No studies 

specifically recruited Veterans. 

STRENGTH OF EVIDENCE

Table 8 presents an overview of findings and strength of evidence (SOE) by major outcomes. We 

found moderate SOE for small increases in physical activity level (SMD 0.29; 95% CI 0.03 to 

0.55) and small decreases in weight (MD -1.44 kg; 95% CI -3.08 to 0.19) over 3- to 12-month 

periods (median 5.5 months) when interventions that integrated accelerometers were compared 

with weak or inactive controls. We found low SOE for very small increases in physical activity 

levels (SMD 0.17; 95% CI -1.09 to 1.43) and small decreases in weight (MD range -3.60 to -2.10

kg) for accelerometer interventions compared with active control conditions. 
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Table 8. Summary of Intervention Effects and Strength of Evidence Ratings

Outcome

and 

Comparison

Number of 

Studies

(Subjects)

Domains and Ratings Pertaining to SOE

SOE and Summary 

Effect Estimate 

(95% CI)

Physical 

Activity: 

Inactive 

Comparator

9

(1279)

Risk of Bias: All RCTs, but some limitations due to 

poor study quality. Majority of RCTs were judged 

to be at high (n=5) or unclear (n=2) ROB. This 

domain was not rated as high ROB because 2 of 

the larger studies were at low ROB, and a large 

proportion (59.1%) of the weight for the subgroup 

summary estimate was generated from these 2 

studies.

Moderate

SMD 0.29 

(0.03 to 0.55)

Consistency: Some inconsistency. The individual 

study estimates for the most part had the same 

direction of effect as the subgroup summary 

estimate. One study, which was the largest and 

judged to be at low ROB, had an individual effect 

size on the opposite side of the null value. The 

magnitude of effect sizes varied across individual 

studies (SMD range -0.11 to 1.22).

Directness: Direct

Precision: Some imprecision. Most of the 

included studies had small sample sizes. The 

subgroup summary estimate was precise and 

similar to the overall summary estimate. The 

impact of bias on heterogeneity was inconclusive, 

as there was no consistency in the relationship 

between studies of variable ROB and direction of 

effect, and the effect estimate from the largest

study, scored at low ROB, was on the opposite 

side of the null.

Physical 

Activity: 

Active 

Comparator

3

(109)

Risk of Bias: All RCTs, but all studies judged to 

be at high ROB.

Low

SMD 0.17

Consistency: Minor inconsistency. Two of the 3 (-1.09 to 1.43)

individual estimates had a similar effect size, but

the range of individual effect sizes varied 

substantially from -0.05 to 0.80.

Directness: Direct

Precision: Imprecise. All of the included studies 

had small sample sizes. The subgroup summary 

estimate and the overall summary estimate had 

wide confidence intervals. The impact of bias on 

heterogeneity could not be ruled out, as one of the 

individual estimates was on the opposite side of 

the null and was judged to be at high ROB. This 

may have influenced heterogeneity and 

subsequently the overall summary estimate 

confidence interval.
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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38

Outcome

and 

Comparison

Number of 

Studies

(Subjects)

Domains and Ratings Pertaining to SOE

SOE and Summary 

Effect Estimate 

(95% CI)

Weight: 

Inactive 

Comparator

9

(1505)

Risk of Bias: All RCTs, but some limitations due 

to poor study quality. Most studies were judged to 

be at high (n=4) or unclear (n=3) ROB. This 

domain was not rated as high ROB because 2 of 

the larger studies were scored at low ROB, and a 

large proportion (49.6%) of the weight for the 

subgroup summary estimate was generated from 

these 2 studies.

Moderate

MD -1.44 kg

(-3.08 to 0.19)

Consistency: Consistent in direction and 

magnitude. The individual estimates for the active 

comparator group were on the same side of the 

null value. Aside from one study, the MDs were 

consistent in magnitude when compared to the 

subgroup summary estimate.

Directness: Direct

Precision: Precise. A few of the included studies 

had small sample sizes, which contributed to 

imprecision. The subgroup summary estimate was 

precise and similar to the overall summary 

estimate. The impact of bias on heterogeneity is 

possible, as one study judged to be at unclear 

ROB was outside the MD range of other included 

studies in this subgroup.

Weight:

Active 

Comparator

2

(79)

Risk of Bias: Both studies RCTs, but both judged 

to be at high ROB.

Low

MD range:

-3.60 kg

(-5.82 to -1.38)

to

-2.10 kg

(-4.30 to 0.10)

Consistency: Minor inconsistency. The individual 

studies had similar MDs on the same side of the 

null value.

Directness: Direct

Precision: Some imprecision. Both included 

studies had small sample sizes.

Abbreviations: CI=confidence interval; MD=mean difference; SMD=standardized mean difference; 

RCTs=randomized controlled trials; ROB=risk of bias; SOE=strength of evidence

SUMMARY OF EVIDENCE BY KEY QUESTION

Overall, we found a small positive effect of accelerometers on increasing physical activity and 

decreasing weight. Twelve studies provided estimates for the effect of accelerometers on level of 

physical activity. Pooled estimates demonstrated a small and favorable impact on increasing 

physical activity levels (SMD 0.26; 95% CI 0.04 to 0.49) with moderate heterogeneity 

(I

2

=64.7%). In stratified analysis by type of comparator, the effects versus the inactive 

comparator were similar; however, no significant effect was found when accelerometers were 

compared with an active comparator. A similar pattern was observed for the 11 studies that 

provided estimates for the effect of accelerometers on weight loss. The overall pooled estimates 

demonstrated a small and favorable impact on weight (MD -1.65 kg; 95% CI -3.03 to -0.28) but 

exhibited high heterogeneity (I

2

=81%). In stratified analysis by type of comparator, effects were 

muted when accelerometer interventions were compared with more robust controls than with 

weaker controls. The moderate to high heterogeneity in these pooled estimates may be driven by 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

on Physical Activity

39

several intervention characteristics, including the duration of the interventions, intensity of 

adjunctive interventions (eg, accelerometer only vs brief device-driven feedback vs intensive 

device-driven behavioral counseling and feedback) and study design features that resulted in 

most studies being judged to be at high or unclear ROB. For physical activity outcomes, an 

additional source of heterogeneity may stem from the substantial variability in the mode (selfreport vs objectively measured) and metrics (step counts per day vs minutes per day active) used 

to measure this outcome.

We also sought to explore the variable impact of wearable activity devices on physical activity 

and weight by multiple single factors that may contribute to heterogeneity (recruited populations, 

role of device relative to other motivational enhancement strategies, location of device on body 

as a proxy for ease of use). None of these individual factors was a robust predictor of 

heterogeneity. Thus, the variability in pooled estimates is likely due to a combination of factors 

related to underlying differences in populations, comparators, interventions, and study design 

and quality issues. However, other quantitative patterns were observed. In general, interventions 

that capitalized on the self-monitoring and tailored activity device-driven feedback capabilities 

of these devices were associated with greater decreases in weight loss. Effects were even greater 

when these strategies were paired with behavioral counseling focused on device feedback. The 

same trend was not consistently seen for greater increases in physical activity with better 

implementation of device feedback and self-monitoring functionality. Lastly, while we sought to 

explore the role of adherence to the activity device as a moderator of effects, only 4 studies 

provided any data on adherence to the device, and none of these studies reported such analyses. 

Instead, these studies focused largely on the process measures related to accelerometer 

adherence. In general, these studies reported a consistent pattern of decline in participants’ use of 

activity devices over time.

CLINICAL AND POLICY IMPLICATIONS

Commercially available wearable activity devices have surged in popularity in recent years, 

including accelerometers marketed for their ability to assist individuals who want to increase 

their physical activity. Accelerometers have proven to be a valid and reliable means of tracking 

step counts and activity levels; however, their effectiveness as an agent of behavior change has 

been less clear. Also it is unclear whether accelerometers offer additional advantages over 

pedometers as a means of self-monitoring physical activity.

The results of this review provide important new information by demonstrating a small positive 

effect of wearable accelerometers on increasing physical activity and decreasing weight. These 

findings are important for both clinician and policymakers who may be considering programs to 

encourage more widespread use of these technologies as a public health strategy to improve 

health and well-being. Although we found small but significant effects related to activity device 

use, the heterogeneity of the included studies precludes firm conclusions about how they can be 

deployed most effectively in clinical practice as a tool to facilitate increased physical activity. 

Furthermore, we did not find any studies that sought to integrate physical activity data from 

wearable accelerometers into the patient’s medical records to facilitate ongoing primary care and 

chronic disease management. Such research could be of real value to clinicians and 

policymakers. 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

on Physical Activity

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No studies actively recruited Veterans and, germane to the VA healthcare system, we found 

surprisingly few trials (n=3) that focused on chronic disease populations. Use and effectiveness 

of wearable accelerometers may differ among participants who are motivated to use these 

devices to achieve different goals; for example, those who are trying to increase physical activity 

to reduce pain from osteoarthritis compared to participants whose goal is to lose weight. Thus, it

is unclear if these devices should be offered to all patients or a subset of patients who may derive 

the most benefits from increased physical activity. 

For accelerometers to function optimally, patients need to wear the devices and understand the 

information they receive from them. In this review, we found limited data on activity device 

adherence. Other research suggests that more than half of individuals who purchase a wearable

activity device stop using it; of these, one-third stop in the first months.

54 Future research should 

(1) measure how often participants wear accelerometers; (2) measure how participants interact 

with their generated data; and (3) explore facilitators and barriers to adoption of wearable 

technologies. This would be valuable for clinicians, policymakers, and organizations by helping

programs target individuals who are most likely to wear and therefore benefit from these devices. 

Last, accelerometers are more costly than traditional pedometers, which may impact decisions 

regarding purchase and usage. In this review, we were unable to assess whether accelerometers 

increase physical activity beyond pedometers.

LIMITATIONS

Our review has a number of strengths, including a protocol-driven design, a comprehensive 

search, and careful quality assessment. We conducted both quantitative and qualitative synthesis 

when possible. Our review—and the literature—have limitations: the number of studies is small; 

many had design limitations (8 of 14 were judged to be at high ROB); the range of interventions 

evaluated was diverse; and the number and reporting of studies precluded any analyses of 

variability in accelerometers by more than one variable at a time. Our review was limited to 

English-language publications, but the likelihood of identifying relevant data unavailable from 

English-language sources is low. 

The small sample sizes of most included trials and the populations recruited also limited our 

findings and may have resulted in a type II error as a result. No studies actively recruited 

Veterans, yet we did identify 3 studies among those with chronic medical illnesses, 5 studies 

among overweight/obese/sedentary populations, and 4 studies among older adults. It is likely that 

the results of these studies are highly applicable to the VA, because these conditions and 

populations are common among the patients seen in the VA healthcare system. However, having 

so few studies with large sample sizes leaves unanswered questions of feasibility of integrating 

accelerometers into clinics and healthcare systems with large, heterogeneous patient populations 

and multiple providers.

Many of our pooled analyses suggested moderate to high heterogeneity. While we conducted 

subgroup analysis to explore multiple single factors that may contribute to heterogeneity 

(recruited populations, role of activity devices relative to other motivational enhancement 

strategies, location of device on body as a proxy for ease of use), none of these individual factors 

was a robust predictor of heterogeneity. Thus, the observed heterogeneity is likely attributable to 

a combination of factors that relate to underlying differences in populations, comparators, 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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41

interventions, and study design and quality issues. The overall low number and small size of 

included trials per outcome precluded us from conducting multivariable analyses. 

RESEARCH GAPS/FUTURE RESEARCH

This comprehensive review of the literature identified several gaps in the current evidence that 

warrant future investigation. We used the framework recommended by Robinson et al55 to 

identify gaps in evidence and classify why these gaps exist (Table 9). This approach considers 

the PICOTS (population, intervention, comparator, outcome, timing, and setting) to identify gaps 

and classifies them as due to (1) low strength of evidence or imprecise information, (2) biased 

information, (3) inconsistency or unknown consistency, and (4) not the right information. VA 

and other healthcare systems should consider their clinical and policy needs when deciding 

whether to invest in research to address gaps in evidence. 

Table 9. Evidence Gaps and Future Research

Evidence Gap Reason Type of Studies to Consider

Population

Absence of trials that actively recruited 

Veterans

Limited studies (n=3) among those with 

chronic medical illnesses

Insufficient 

information

RCTs

Quasi-experimental studies

Prospective cohort studies

Interventions

What are the optimal adjunctive interventions 

that enhance functionally of accelerometers 

in motivating behavior change? 

How does intensity or dose of adjunctive 

interventions enhance functionally of 

accelerometers in motivating behavior 

change? 

How is adherence to use of the activity 

device associated with key outcomes? Are 

there strategies to enhance adherence? 

 How do aspects of the activity device 

(location on body, ease of use, ability to pair 

with other technologies) impact physical 

activity and weight? 

Is accelerometer-based feedback inferior, 

equivalent, or superior to pedometer-based 

feedback for increases in physical activity or 

decreases in weight?

Insufficient 

information

RCTs of head-to-head 

comparisons of different 

types of adjunctive 

intervention packages 

Stepped and adaptive trial 

designs

Comparators

Relatively few studies that used active or 

more robust comparators 

No studies that assessed accelerometers vs

pedometers 

Insufficient 

information

RCTs 

RCTs of head-to-head 

comparisons of 

accelerometers and 

pedometers

Outcomes

Uncertain effects on: 

Patient satisfaction with healthcare

Limited information on:

Insufficient 

information

RCTs

Prospective cohort studies

Non-randomized controlled 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

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Evidence Gap Reason Type of Studies to Consider

Physical activity

Weight loss or maintenance

Adherence to devices over time

before-and-after studies

Secondary analyses of 

existing trial data

None of these studies examined factors that 

impact accelerometer adherence. Future 

research aimed at identifying individual 

characteristics associated with 

accelerometer adherence will inform efforts 

to create a hierarchy of who is more likely to 

adhere to these devices.

Determine how participants interact with their 

generated data as feedback. 

Explore facilitators and barriers to adoption 

of wearable activity devices.

Insufficient 

information

RCTs

Prospective or retrospective 

cohort studies

Non-randomized controlled 

before-and-after studies

Qualitative studies 

Secondary analyses of 

existing trial data

Setting

No studies that recruited through clinical 

settings 

Insufficient 

information

RCTs

Prospective or retrospective 

cohort studies

Nonrandomized controlled 

before-and-after studies

Abbreviation: RCTs=randomized controlled trials 

CONCLUSIONS

Overall this review found low to moderate strength of evidence that the use of wearable 

accelerometers produces small positive effects on physical activity and weight. These changes, 

however, may not result in clinically significant changes to health-related outcomes. The small 

number of studies with small sample sizes and the moderate to high heterogeneity limit the 

conclusions that may be drawn. What appears evident is that accelerometers may be necessary— 

but not sufficient—to change behavior. Unfortunately, the data available for this review did not 

allow us to determine the most effective means of using wearable accelerometers to facilitate 

behavior change. Nevertheless, we observed qualitatively that studies with more robust effects 

used adjunctive interventions (eg, tailored feedback and behavioral counseling) to foster 

motivation, had longer intervention durations, and compared accelerometers to weaker controls. 

Focusing on specific goals, using evidence-based strategies, personalizing designs, and 

integrating social support are additional, critical components for achieving increased physical 

activity or reduced weight. Stepped and adaptive trial designs, as well as those that incorporate 

patient preferences, may be particularly well-suited to address questions about the effectiveness 

of accelerometers within the context of complex behavioral interventions to increase physical 

activity and other related health outcomes.56,57

The overall paucity of studies that used accelerometers and the generally low quality of the 

included trials may be due, in part, to the only recent availability of accelerometers with visible 

displays to allow for self-monitoring and the ability to interface with behavioral programs (ie, 

ability to collect and share data in real time). This review raises a number of important questions 

and illuminates key gaps in the evidence. This includes a need to understand whether

accelerometers outperform traditional pedometers, how activity device features may enhance 

adherence and outcomes, and the effects on long-term outcomes. The relatively low response 

rates and attrition trends over time from the included studies in this report underscore the 
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

on Physical Activity

43

importance of integrating these devices in tailored, patient-focused behavioral interventions that 

offer dynamic content, real-time and personalized behavioral counseling, and progressive goalsetting. 

These findings are important for both clinicians and policymakers who may be considering 

programs to encourage more widespread use of these technologies as a public health strategy to 

improve health and well-being. Although we found small significant effects related to activity 

device use, the heterogeneity of the included studies precludes firm conclusions about how they 

can be deployed most effectively in clinical practice as a tool to facilitate increased physical 

activity. Furthermore, we did not find any studies that sought to integrate physical activity data 

from wearable accelerometers into the patient’s medical records to facilitate ongoing primary 

care and chronic disease management.
The Impact of Wearable Motion Sensing Technologies Evidence-based Synthesis Program

on Physical Activity

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APPENDIX A. SEARCH STRATEGIES

Database: MEDLINE (via PubMed)

Search date: 01/06/15

Set # Search Terms Results

#1 "Accelerometry"[Mesh] OR "Magnetometry"[Mesh] OR "Motor 

Activity/instrumentation"[Mesh] OR fitness track*[tiab] OR activity track*[tiab] OR 

fitness monitor*[tiab] OR gps[tiab] OR “global positioning”[tiab] OR activity 

monitor*[tiab] OR motion sens*[tiab] OR accelerometer[tiab] OR 

accelerometers[tiab] OR accelerometry[tiab] OR gyroscope[tiab] OR 

gyroscopic[tiab] OR gyroscopes[tiab] OR actograph[tiab] OR actographic[tiab] 

OR actography[tiab] OR actographs[tiab] OR wearable system[tiab] OR 

wearable systems[tiab] OR wearable sensor[tiab] OR wearable sensors[tiab] OR 

((step[tiab] OR steps[tiab]) AND (counting[tiab] OR counted[tiab] OR 

counter[tiab] OR counters[tiab] OR count[tiab])) OR actigraph[tiab] OR 

(basis[tiab] AND peak[tiab]) OR “bowflex boost”[tiab] OR “fit link”[tiab] OR 

(misfit[tiab] AND shine[tiab]) OR (polar[tiab] AND loop[tiab]) OR bodybugg[tiab] 

OR bodymedia[tiab] OR fitbit[tiab] OR fitbug[tiab] OR fuelband[tiab] OR 

garmin[tiab] OR gowear[tiab] OR gruve[tiab] OR ibitz[tiab] OR iqua[tiab] OR 

lumo[tiab] OR motoactiv[tiab] OR runtastic[tiab] OR scosche[tiab] OR 

smartband[tiab] OR striiv[tiab] OR tomtom[tiab] OR vivofit[tiab] OR 

vivosmart[tiab] OR wahoo[tiab] OR wakemate[tiab] OR withings[tiab]

52,751

#2 "Movement"[Mesh] OR "Exercise Movement Techniques"[Mesh] OR "Exercise 

Therapy"[Mesh] OR "Physical Fitness"[Mesh] OR "Physical Endurance"[Mesh] 

OR "Physical Exertion"[Mesh] OR fitness[tiab] OR activity[tiab] OR active[tiab] 

OR walk*[tiab] OR run*[tiab] OR step[tiab] OR steps[tiab] OR exercise[tiab] OR 

move*[tiab]

3,555,057

#3 (randomized controlled trial[pt] OR controlled clinical trial[pt] OR 

randomized[tiab] OR randomised[tiab] OR randomization[tiab] OR 

randomisation[tiab] OR randomly[tiab] OR trial[tiab] OR groups[tiab]) NOT 

(Editorial[ptyp] OR Letter[ptyp] OR Case Reports[ptyp] OR Comment[ptyp])

2,079,904

#4 #1 AND #2 AND #3 4858

#5 #4 NOT ("Child"[Mesh] NOT "Adult"[Mesh]) 4355

#6 #5, English, 2000 - present 3506

Database: Embase

Search date: 01/06/15

Set # Search Terms Results

#1 'accelerometry'/exp OR 'magnetometry'/exp OR (fitness NEAR/2 track*):ab,ti OR 

(activity NEAR/2 track*):ab,ti OR (fitness NEAR/2 monitor*):ab,ti OR gps:ab,ti 

OR 'global positioning':ab,ti OR (activity NEAR/2 monitor):ab,ti OR (motion 

NEAR/2 sens*):ab,ti OR accelerometer:ab,ti OR accelerometers:ab,ti OR 

accelerometry:ab,ti OR gyroscope:ab,ti OR gyroscopic:ab,ti OR gyroscopes:ab,ti 

OR actograph:ab,ti OR actographic:ab,ti OR actography:ab,ti OR 

actographs:ab,ti OR 'wearable system':ab,ti OR 'wearable systems':ab,ti OR 

'wearable sensor':ab,ti OR 'wearable sensors':ab,ti OR ((step OR steps):ab,ti 

AND (counting OR counted OR counter OR counters OR count):ab,ti) OR 

actigraph:ab,ti OR (basis NEAR/3 peak):ab,ti,df OR 'bowflex boost':ab,ti,df OR 

'fit link':ab,ti,df OR (misfit NEAR/3 shine):ab,ti,df OR (polar NEAR/3 

loop):ab,ti,df OR bodybugg:ab,ti,df OR bodymedia:ab,ti,df OR fitbit:ab,ti,df OR 

fitbug:ab,ti,df OR fuelband:ab,ti,df OR garmin:ab,ti,df OR gowear:ab,ti,df OR 

45,316
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Set # Search Terms Results

gruve:ab,ti,df OR ibitz:ab,ti,df OR iqua:ab,ti,df OR lumo:ab,ti,df OR 

motoactiv:ab,ti,df OR runtastic:ab,ti,df OR scosche:ab,ti,df OR 

smartband:ab,ti,df OR striiv:ab,ti,df OR tomtom:ab,ti,df OR vivofit:ab,ti,df OR 

vivosmart:ab,ti,df OR wahoo:ab,ti,df OR wakemate:ab,ti,df OR withings:ab,ti,df 

#2 'movement (physiology)'/exp OR 'physical activity, capacity and 

performance'/exp OR 'kinesiotherapy'/exp OR 'fitness'/exp OR fitness:ab,ti OR 

activity:ab,ti OR active:ab,ti OR walk*:ab,ti OR run*:ab,ti OR step:ab,ti OR 

steps:ab,ti OR exercise:ab,ti OR move*:ab,ti

4,564,954

#3 ('randomized controlled trial'/exp OR 'crossover procedure'/exp OR 'double blind 

procedure'/exp OR 'single blind procedure'/exp OR random*:ab,ti OR 

factorial*:ab,ti OR crossover*:ab,ti OR (cross NEAR/1 over*):ab,ti OR (doubl* 

NEAR/1 blind*):ab,ti OR (singl* NEAR/1 blind*):ab,ti OR assign*:ab,ti OR 

allocat*:ab,ti OR volunteer*:ab,ti) NOT ('case report'/exp OR 'case study'/exp 

OR 'editorial'/exp OR 'letter'/exp OR 'note'/exp)

1,431,100

#4 #1 AND #2 AND #3 3250

#5 #4 NOT ('child'/exp NOT 'adult'/exp) 2888

#6 #5 AND [embase]/lim NOT [medline]/lim 1051

#7 #6, Limits: English, 2000- 988

Database: CINAHL

Search date: 01/06/15

Set # Search Terms Results

#1 (MH "Accelerometry") OR (MH "Magnetics+") OR TI ( "fitness track*" or "activity 

track*" or "fitness monitor*" or gps or "global positioning" or "activity monitor*" or 

"motion sens*" or accelerometer or accelerometers or accelerometry or 

gyroscope or gyroscopic or gyroscopes or actograph or actographic or 

actography or actographs or "wearable system" or "wearable systems" or 

"wearable sensor" or "wearable sensors" or ((step or steps) and (counting or 

counted or counter or counters or count)) or actigraph or (basis and peak) or 

"bowflex boost" or "fit link" or (misfit and shine) or (polar and loop) or bodybugg 

or bodymedia or fitbit or fitbug or fuelband or garmin or gowear or gruve or ibitz 

or iqua or lumo or motoactiv or runtastic or scosche or smartband or striiv or 

tomtom or vivofit or vivosmart or wahoo or wakemate or withings ) OR AB ( 

"fitness track*" or "activity track*" or "fitness monitor*" or gps or "global 

positioning" or "activity monitor*" or "motion sens*" or accelerometer or 

accelerometers or accelerometry or gyroscope or gyroscopic or gyroscopes or 

actograph or actographic or actography or actographs or "wearable system" or 

"wearable systems" or "wearable sensor" or "wearable sensors" or ((step or 

steps) and (counting or counted or counter or counters or count)) or actigraph or 

(basis and peak) or "bowflex boost" or "fit link" or (misfit and shine) or (polar and 

loop) or bodybugg or bodymedia or fitbit or fitbug or fuelband or garmin or 

gowear or gruve or ibitz or iqua or lumo or motoactiv or runtastic or scosche or 

smartband or striiv or tomtom or vivofit or vivosmart or wahoo or wakemate or 

withings )

14,089

#2 (MH "Movement+") OR (MH "Exercise+") OR (MH "Therapeutic Exercise+") OR 

(MH "Physical Activity") OR (MH "Physical Fitness+") OR (MH "Exertion+") OR 

TI ( OR fitness OR activity OR active OR walk* OR run* OR step OR steps OR 

exercise OR move* ) OR AB ( OR fitness OR activity OR active OR walk* OR 

run* OR step OR steps OR exercise OR move* )

361,653

#3 (MH "Treatment Outcomes+") OR randomized OR PT clinical trial 317,587

#4 #1 AND #2 AND #3 636

#5 #4, English, 2000- 602
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Database: SPORTDiscus

Search date: 01/06/15

Set # Search Terms Results

#1 DE "ACCELEROMETERS" OR TI ( "fitness track*" or "activity track*" or "fitness 

monitor*" or gps or "global positioning" or "activity monitor*" or "motion sens*" or 

accelerometer or accelerometers or accelerometry or gyroscope or gyroscopic or 

gyroscopes or actograph or actographic or actography or actographs or 

"wearable system" or "wearable systems" or "wearable sensor" or "wearable 

sensors" or ((step or steps) and (counting or counted or counter or counters or 

count)) or actigraph or (basis and peak) or "bowflex boost" or "fit link" or (misfit 

and shine) or (polar and loop) or bodybugg or bodymedia or fitbit or fitbug or 

fuelband or garmin or gowear or gruve or ibitz or iqua or lumo or motoactiv or 

runtastic or scosche or smartband or striiv or tomtom or vivofit or vivosmart or 

wahoo or wakemate or withings ) OR AB ( "fitness track*" or "activity track*" or 

"fitness monitor*" or gps or "global positioning" or "activity monitor*" or "motion 

sens*" or accelerometer or accelerometers or accelerometry or gyroscope or 

gyroscopic or gyroscopes or actograph or actographic or actography or 

actographs or "wearable system" or "wearable systems" or "wearable sensor" or 

"wearable sensors" or ((step or steps) and (counting or counted or counter or 

counters or count)) or actigraph or (basis and peak) or "bowflex boost" or "fit link" 

or (misfit and shine) or (polar and loop) or bodybugg or bodymedia or fitbit or 

fitbug or fuelband or garmin or gowear or gruve or ibitz or iqua or lumo or 

motoactiv or runtastic or scosche or smartband or striiv or tomtom or vivofit or 

vivosmart or wahoo or wakemate or withings )

6204

#2 (random* OR trial) 56299

#3 #1 AND #2 639

#4 #3, English, 2000-, Academic Journals 543

Database: Cochrane CENTRAL

Search date: 01/06/15

Set # Search Terms Results

#1 [mh Accelerometry] OR [mh Magnetometry] 341

#2 "fitness track*":ab,ti or "activity track*":ab,ti or "fitness monitor*":ab,ti or 

gps:ab,ti or "global positioning":ab,ti or "activity monitor*":ab,ti or "motion 

sens*":ab,ti or accelerometer:ab,ti or accelerometers:ab,ti or 

accelerometry:ab,ti or gyroscope:ab,ti or gyroscopic:ab,ti or gyroscopes:ab,ti 

or actograph:ab,ti or actographic:ab,ti or actography:ab,ti or actographs:ab,ti or 

"wearable system":ab,ti or "wearable systems":ab,ti or "wearable sensor":ab,ti 

or "wearable sensors":ab,ti or ((step:ab,ti or steps:ab,ti) and (counting:ab,ti or 

counted:ab,ti or counter:ab,ti or counters:ab,ti or count:ab,ti)) or actigraph:ab,ti 

or (basis:ab,ti and peak:ab,ti) or "bowflex boost":ab,ti or "fit link":ab,ti or 

(misfit:ab,ti and shine:ab,ti) or (polar:ab,ti and loop:ab,ti) or bodybugg:ab,ti or 

bodymedia:ab,ti or fitbit:ab,ti or fitbug:ab,ti or fuelband:ab,ti or garmin:ab,ti or 

gowear:ab,ti or gruve:ab,ti or ibitz:ab,ti or iqua:ab,ti or lumo:ab,ti or 

motoactiv:ab,ti or runtastic:ab,ti or scosche:ab,ti or smartband:ab,ti or 

striiv:ab,ti or tomtom:ab,ti or vivofit:ab,ti or vivosmart:ab,ti or wahoo:ab,ti or 

wakemate:ab,ti or withings:ab,ti

2945

#3 #1 OR #2 3204

#4 [mh "Movement"] OR [mh "Exercise Movement Techniques"] OR [mh 

"Exercise Therapy"] OR [mh "Physical Fitness"] OR [mh "Physical Endurance"] 

OR [mh "Physical Exertion"] 

29,268
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Set # Search Terms Results

#5 fitness:ab,ti OR activity:ab,ti OR active:ab,ti OR walk*:ab,ti OR run*:ab,ti OR 

step:ab,ti OR steps:ab,ti OR exercise:ab,ti OR move*:ab,ti

134,034

#6 #4 OR #5 140,071

#7 #3 AND #6 1630

#6 #5, 2000 – present, In Trials 1281
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APPENDIX B. PEER REVIEWER COMMENTS TABLE

Question 

Text

Reviewer 

Number Comment Response

Are the 

objectives, 

scope, and 

methods for 

this review 

clearly 

described?

3 Yes N/A

4 Yes N/A

5 Yes N/A

6 No - My primary concern with this document is the broad 

conceptualization of the difference between an 

accelerometer and a pedometer. 

A pedometer is something that counts steps. 

An accelerometer is something that counts accelerations 

possibly with some additional metric of intensity. 

In the old days, pedometers counted steps using a spring 

and magnet internal mechanism. Now, most pedometers 

are actually using internal accelerometers to measure 

step counts. So, is a pedometer that uses an internal 

mechanism of an accelerometer a pedometer or an 

accelerometer. These are not technically mutually 

exclusive categories. Examples of accelerometer based 

pedometers include Stepwatch, Omron HJ-720 ITC and 

Fitbits. Many interventions using classic accelerometers 

such as actigraphs actually only feed back step count 

data even if they could feed back the full accelerometer 

data so would these count in this synthesis?

It appears that you chose to exclude studies of 

interventions that use activity monitors with internal 

accelerometer mechanisms that report step counts back 

to the user rather than the full accelerometer data. The 

decision to exclude interventions that use accelerometer 

based activity trackers to give step count feedback over 

the internet seems arbitrary. Accelerometer based 

pedometers have all of the advantages of accelerometers 

except that they filter out non-step count accelerations 

before presenting the data (step count) to the wearer. 

If you want to do a synthesis of trials that feedback nonstep count physical activity metrics obtained from an 

accelerometer, then the intro and justification needs to be 

rewritten to reflect this focus. As it is written, it does not 

Thank you for these comments. This report and other 

products have been explicitly developed to meet the 

needs of our stakeholder partners. There have been 

several excellent reviews of the effects of pedometers on 

physical activity and other health outcomes (Bravata, 

2007, JAMA; Richardson, 2008, Annals of Family 

Medicine). As such, we developed a “white paper” that 

provides a review of recent good- and fair-quality 

systematic reviews on the effect of pedometer use on 

weight change, physical activity, and specific conditions of 

interest (eg, osteoarthritis, COPD, heart disease) to 

summarize the evidence for our primary stakeholders. 

Stakeholders were also interested in wearable 

technologies that measured activity beyond step counts 

(non-pedometer–based wearable motion sensing 

technologies), as those technologies tend to be more 

costly than traditional pedometers that only capture or 

report step counts. We agree that distinguishing between 

pedometers and accelerometers can be challenging. Our 

inclusion criteria consisted of the use of a wearable activity 

device that provided objective feedback to the wearer and 

was not defined as a pedometer-only device by study 

authors. To determine this, we used the author’s definition 

of the device. If the underlying mechanics of a device were 

unclear, we searched online resources to determine each 

device type and manufacturer to ascertain if the 

predominate technology used something beyond, or in 

addition to, pedometers for step counts. We have added 

this information to Appendix C. All of the included 

wearable devices were considered to be accelerometers 

by manufacturer’s definition. Our exclusion criteria did 

seek to exclude pedometer-based studies only if they 
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Question 

Text

Reviewer 

Number Comment Response

currently make sense. It is simply not true that a) 

pedometers are less accurate than accelerometers 

(because many pedometers these days actually are 

accelerometers), b) accelerometers can link to electronic 

networks and devices but pedometers cannot (since the 

early days of the sportbrain, even spring and magnet 

pedometers have been able to upload step count data to 

networks and devices, now fitbits etc are accelerometer 

based pedometers that link up) .

I think you need to rewrite the criteria and clearly state 

that your question is - does feedback related to 

accelerometer count and intensity change physical 

activity , weight and other health outcomes. It is the data 

that is fed back to the user that varies - either step counts 

vs accelerations with intensity ratings.

identified themselves as a pedometer and feedback was 

not part of the intervention. We did not exclude studies 

where the intervention gave step count feedback over the 

internet. We did not restrict the type of feedback that was 

provided to the participant, and several articles included 

studies that measured physical activity on different scales 

and with different measures including step counts.

7 Yes N/A

Is there any 

indication of 

bias in our 

synthesis of 

the 

evidence?

3 No N/A

4 No N/A

5 No N/A

6 No N/A

7 No N/A

Are there any 

published or 

unpublished

studies that 

we may have 

overlooked?

3 No N/A

4 No N/A

5 No N/A

6 Yes - If you decide to include accelerometer based 

interventions that limit feedback to step count data then 

you have missed many studies. If the focus and 

exclusion criteria are really intended to exclude 

accelerometer based step count feedback interventions 

and if these criteria are made more coherent, then I think 

you may have all of the relevant studies.

Thank you for this comment. Our eligibility criteria did not 

exclude studies that limit feedback to step count data. We 

included any objective feedback that was provided to the 

participant (wearer) and that was not defined as a 

pedometer-only study by study authors. 

7 No N/A

Additional 

suggestions 

or comments 

3 Overall thorough and appears to be well-done. Key 

questions are addressed as well as can be for the 

amount of data available.

Thank you.
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Question 

Text

Reviewer 

Number Comment Response

can be 

provided 

below. If 

applicable, 

please 

indicate the 

page and line 

numbers 

from the draft 

report.

1) In the abstract, you report the SMD for the 9 studies 

using an inactive comparator for physical activity, and in 

the conclusions, you choose to highlight the SMD for the 

overall. I recommend being consistent in which estimate 

you highlight to avoid confusion. 

2) In the abstract, you conclude that the devices produce 

small positive effects on physical activity and weight that 

are not likely to have a clinically significant impact on 

health outcomes. This statement strikes me as too 

definite of a conclusion. I think that your support for the 

small positive effects is well-substantiated in the 

methods, results, and conclusions. I think your support 

for the interpretation of the lack of clinically significant 

impact on health outcomes is lacking. If you wish to keep 

this statement in the conclusions of the abstract, you 

need to provide support in the Discussion for the fact that 

these small differences in physical activity and weight do 

not translate to differences in clinical outcomes (ok to 

make this judgment using data from other studies). If you 

don't provide adequate support for this statement, you 

may need to leave this more open-ended. Furthermore, 

the confidence intervals for effects are still relatively wide, 

and do not exclude a medium-sized effect.

3) On Forrest plots, it would be easier/faster to interpret if 

the subgroup "Summary" line was labeled (e.g. Inactive 

Comparator Summary, Active Comparator Summary).

4) It wasn't a pre-defined KQ2 item, but it is mentioned a 

couple of times - would it be worth it to do a formal 

analysis on differences by duration of intervention?

1) We agree about being consistent with the estimates 

we provide. We now provide inactive, active, and 

overall summary estimate for both outcomes.

2) We agree that our conclusion was originally written 

too definitively. We have made changes to soften the 

conclusion and highlight the limitations in this body of 

literature. Specifically, we have indicated that the 

small positive effects found for these devices on 

physical activity and weight may not result in clinically 

important outcomes. We have also highlighted in the 

Abstract’s conclusion that the small number of studies 

with small sample sizes and the moderate to high 

heterogeneity limit the conclusions that may be drawn.

3) We have added labels to all the stratified summary 

estimates.

4) We did abstract the information on duration of 

intervention from individual studies. We did not, 

however, include intervention duration in the a priori

analysis plan developed in collaboration with our 

primary stakeholders. As such, we report on those 

analyses that are a priori. We agree that formal 

analysis on differences by duration of intervention 

would be interesting, and we will explore including 
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Question 

Text

Reviewer 

Number Comment Response

I think the section on Evidence Gaps and Future 

Research will end up being very helpful!

these in other analyses beyond this report.

Thank you.

4 Overall, I commend the authors on an extremely well 

written synthesis of this literature. I was unable to locate 

any additional RCTs that would meet the search criteria. I 

believe this report to be unbiased and a fair assessment 

of or current knowledge of these technologies. I have 

made some minor comments below for clarification and 

consideration. 

(p5, Abstract) – The abstract briefly mentions KQ2 in the 

background (i.e. factors affecting use), but nowhere else. 

The authors may consider adding a line about the paucity 

of literature in this regard should text limit be available.

(pg5, Abstract) – In my opinion, the concluding 

statements could be revised somewhat to acknowledge 

the limitations of these data and relative immaturity of this 

field.

(pg9, Introduction) – As the targeted audience for this 

report is VA clinicians, managers and policymakers – the 

authors may consider adding in some stats that are 

specific to Veterans and physical activity. For example, I 

believe there is some evidence that VA users in 

comparison to non-users are less likely to meet PA 

recommendations (Littman et al. 2009, MSSE). In 

addition, Consider including some relevant studies from 

VHA describing health-care costs and exercise capacity 

in Veterans (Weiss et al. 2004 Chest; Myers 2008 Curr 

Sports Med Report, etc.) including data derived from the 

Veterans Exercise Testing Study from Jon Myers’ group 

in Palo Alto.

(p61, Appendix B) – I’m not sure a column for risk of bias 

Thank you.

We agree. We have added a sentence to the Results 

section that we did not identify any moderating factors that 

significantly impacted the effectiveness of the outcomes. 

This was our KQ 2 objective.

We agree. We have made changes to soften the 

conclusion and highlight the limitations in this body of 

literature. 

Thank you for the suggestion to more clearly highlight the 

relevance of this inactivity problem for our stakeholders 

and intended audience. We have expanded the 

Introduction to include specific statistics on Veterans and 

physical (in)activity as well as the healthcare cost 

implications of sedentary behavior/lifestyle, citing the 

compelling literature you provided. 

Thank you for this comment. We decided to add the risk of 
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Question 

Text

Reviewer 

Number Comment Response

is necessary here as this was thoroughly covered in a 

prior section.

bias to the Appendix C table (formerly Appendix B table) 

since it was a standalone table and we felt that many 

readers may want to view the risk of bias assessment 

alongside the individual studies and study characteristics.

5 This report is a very elegant piece of work that clearly 

represents a tremendous amount of work undertaken to 

synthesize all of the available data on this topic. My 

concern is that the major take-home message of this 

report, from my perspective, may be a bit misleading 

which may to lead to erroneous assumptions about the 

outcomes. "We found that use of the wearable devices 

produces small positive effects that are not likely to have 

a clinically significant impact on health outcomes. 

Clinicians and policymakers should consider these 

findings before widespread use of this technology etc". I 

believe some of the evidence gaps should be noted in 

the concluding statement. Policy makers might be 

inclined to conclude that there is no point in investing in 

this technology when in fact your data suggest that there 

is a paucity of well designed studies and that more 

research is needed that could better inform this decision. 

For example, on page 14 regarding KQa,the final 

sentence of the summary states that the "small number 

of studies, small sample sizes within these studies, and 

the moderate to high heterogeneity limits conclusions 

that may be drawn." This statement sounds like a more 

overall accurate conclusion than what is currently stated 

on the abstract main conclusion which is what most 

people will be most likely to read. I also fear that it is a 

big jump to make a conclusion about clinical impact on 

health outcomes when the focus of this report is on 

behaviors (activity and weight management). 

On page 28 - Summary of findings. The last sentence is 

not clear. It has a double negative which I am not sure 

makes sense 'The same trend was not seen for 

decreases in PA etc." It seems that it should be "The 

same trend was not seen for greater increases in PA' etc.

Thank you for your thorough and thoughtful review. We 

agree about the conclusions in the previous draft report. 

We have made changes to soften the conclusions and 

highlight the limitations in this body of literature. 

Specifically, we have indicated that the small positive 

effects found for these devices on physical activity and 

weight may not result in clinically important outcomes. We 

have also highlighted in the Abstract’s conclusion that the 

small number of studies with small sample sizes and the 

moderate to high heterogeneity limit the conclusions that 

may be drawn.

Thank you for identifying this error. We have changed to 

“greater increases in PA” as suggested.
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Question 

Text

Reviewer 

Number Comment Response

On page 39 in the bottom paragraph of Clinical and 

Policy implications. This paragraph seems to be 

"promoting" use of devices and has some cautionary 

verbiage about the limitations of the evidence which 

seems to be contradictory to the conclusion in the 

structured abstract. It would seem to me that you can't 

have it both ways. These seem like mixed messages. I 

believe the data support this second conclusion more 

than the one in the structured abstract.

The gaps and future research table is very useful. 

Hopefully this will guide future research. It was interesting 

to note the final row in which it was stated that no studies 

were recruited through clinical settings which should 

have a huge bearing on clinical policy makers in that we 

truly have no evidence in favor or against the use of 

wearable monitors in clinical settings.

We agree. We have changed the structured Abstract as 

mentioned above to highlight the limitations in the body of 

literature. 

Thank you.

6 2) There are several intervention characteristics that we 

know have an impact on behavior change. Goal-setting is 

one that may be the most critical given the focus on selfmonitoring. Others include strategies to overcome 

barriers, motivational interviewing etc. It would be helpful 

to understand if / how participants set or were prescribed 

activity count goals for each of the included studies. Did 

all the trials include some kind of coaching around goal 

setting. If not, does explicit attention to goal setting 

impact outcomes

3) The second question related to heterogeneity of 

treatment effect is so profoundly limited by small sample 

size that it probably should be reported as unable to 

judge due to risk of type 2 error rather than as not 

difference detected. 

4) Further limitations should be emphasized – there is 

insufficient evidence to determine if accelerometer based 

feedback is inferior, equivalent or superior to pedometer 

2) Thank you for posing this question. Due to the high 

heterogeneity in adjunctive interventions delivered 

alongside the activity devices, we were unable to explore 

these additions quantitatively. Table 3 outlines the types of 

adjunctive interventions used for each study. Thus, we 

chose to categorize the use of the activity device 

alongside the adjunct intervention as “major vs minor.” It 

would be interesting for future analyses to examine the 

impact on outcomes that goal-setting may have with these 

devices.

3) We agree. We have highlighted in the report the small 

sample sizes found in individual trials. We added to the 

Limitations section that the small sample sizes found 

within this literature may have resulted in a type II error.

4) Thank you for this suggestion. We have added this 

information to the Research Gaps/Future Research 

section of the report.
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Question 

Text

Reviewer 

Number Comment Response

based feedback for any of the outcomes.

7 These comments are submitted on behalf of the National 

Center for Health Promotion and Disease Prevention 

(NCP). The report was thorough and organized and the 

information was presented in a clear manner. There were 

two points of potential clarification that we wanted to 

bring to the attention of the authors: 1. Pg. 1(lines 22-24)-

physical activity diaries are included as a self-reported 

measure that "does little to motivate or change physical 

activity behavior..." While diaries and other real time 

records of self-monitored behavior are self-reported, they 

are not intended to be the same as questionnaires and 

interviews that gather self-reported data retrospectively. 

In addition, the act of attending to a target behavior using 

self-monitoring strategies has been shown to result in 

behavior change. Unless there is evidence to suggest 

that self-monitoring of behavior IS NOT associated with 

behavior change, or self-monitoring of physical activity is 

NOT associated with changes in physical activity, it may 

be helpful to reword this sentence. 

2. Pg. 16 (lines 33-54), Figure 4 - What does the absence 

of color/white colored bar represent? Also, it appears that 

the overall percentage of high risk of bias (as indicated 

by the proportion of the bar colored red in the "Overall" 

row) is much larger than one would expect given the 

relatively low risk of bias (as depicted by minimal red 

coloring in each of the individual risk of bias items). 

Additional description of the information in Figure 4 may 

provide clarity on these issues.

Thank you for the positive comments about the 

thoroughness and organization of the report. 

1) We agree with the review team that the way the 

sentence on page1, lines 22-24, is written is misleading. 

We initially intended to indicate that these self-report 

measures, such as diaries, may not be an optimal 

measurement strategy rather than imply them as 

intervention strategies. We have since revised the report 

to read, “Physical activity has previously been measured 

using self-reported measures (eg, questionnaires, 

interviews.); however, these do little to motivate or change 

physical activity behavior and can be subject to reporting 

bias.”

2) Thank you for noticing this error. We have revised the 

Risk of Bias figure and removed the “clear” space from the 

PA and weight questions. This occurred since overall we 

had 14 studies included; however, some studies did not 

include both outcomes of interest. There is high risk of 

bias in many of the studies even without individual 

questions being rated as high risk because of the way the 

overall evaluation is calculated. In the Methods section, 

we describe the scoring process for “high risk” which 

includes two or more domain questions that are “not 

clear.” As such, many of the studies had “not clear” 

domain questions, leading to an overall high risk of bias. 

We have added the scoring as a footnote to Figure 4.
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APPENDIX C. CHARACTERISTICS OF INCLUDED STUDIES 

Study 

Population 

N Randomized

Device Name

Intervention Comparator Outcomes Duration 

(Weeks)

Funding 

Source

Risk of 

Bias

Greene, 

20131

Healthy 

volunteers

513

NR

6 months access to online 

social network to post 

about weight and PA 

progress + continuous 

accelerometer use and 

feedback

Printed lifestyle guidelines 

on diet (sample daily meal 

plan with recommended 

serving sizes), 

recommendations on daily 

level of exercise, and 

articles on benefits of 

exercise and healthy 

eating

PA: Yes

Weight: Yes

Satisfaction: No

12 to 24 Industry High

Koizumi, 

20092

Older adults

68

Kenz Lifecorder 

accelerometer

12 weeks of 

accelerometer with 

feedback + goal-setting

12-week blinded 

accelerometer with 

instruction to continue 

normal daily activity

PA: Yes

Weight: No

Satisfaction: No

12 to 24 NR High

Luley, 20143 Chronic 

medical illness

184

Aipermotion 

440 

accelerometer

3-arm study (2 

interventional):

4s intervention:

1-time, 2-hour instruction 

on diet & PA + 12 months 

of accelerometer use + 52 

weekly individual letters 

with feedback on weight, 

diet, and PA

ABC intervention:

1-time, 2-hour instruction 

on diet & PA + 12 months 

of accelerometer use + 12 

monthly behavioral 

counseling calls

1-time, 2-hour session, 

consisting of diet 

education, diet regimen, 

and PA education

PA: No

Weight: Yes

Satisfaction: No

25+ Gov Unclear
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Study 

Population 

N Randomized

Device Name

Intervention Comparator Outcomes Duration 

(Weeks)

Funding 

Source

Risk of 

Bias

Nicklas, 

20144

Older adults

48

Lifecorder 

PlusVR tri-axial 

accelerometer

5-month weight loss 

intervention that included 

hypocaloric diet (2 

prepared meals a day) + 4 

days/week supervised 

exercise + self-regulatory 

intervention that involved 

wearing an 

accelerometer, 

documenting activity, and 

6 weekly sessions of 

behavioral counseling

5-month weight loss 

intervention consisting of 

diet education and 

regimen (individualized 

hypocaloric intake), PA 

education, structured 

exercise (supervised 

treadmill) and in-person 

counseling

PA: Yes

Weight: Yes

Satisfaction: No

25+ Gov High

Paschali, 

20055

Chronic 

medical illness

30

BioTrainer 

accelerometer

12 weeks of continuous 

accelerometer use and 

feedback + 4 monthly inperson exercise 

behavioral counseling 

sessions + workbook

12-week blinded 

accelerometer with 4 

monthly in-person 

counseling sessions to 

review exercise diary. A 

24-page information book 

and home based walking 

plan with PA education, 

behavioral selfmanagement, weight 

goal-setting, and chronic 

disease-monitoring.

PA: Yes

Weight: No

Satisfaction: No

12 to 24 Gov High
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Study 

Population 

N Randomized

Device Name

Intervention Comparator Outcomes Duration 

(Weeks)

Funding 

Source

Risk of 

Bias

Polzien, 

20076

Overweight/ 

obese and/or 

sedentary

57

SenseWear 

Pro Armband

3-arm study:

Standard in-person 

behavioral weight control 

program: 

7 in-person individualized 

counseling session over 

12 weeks

Continuous technologybased behavioral weight 

control program: 

7 in-person individualized 

counseling session over 

12 weeks + 12 weeks of 

continuous accelerometer 

use and feedback

Intermittent technologybased behavioral weight 

control program: 

7 in-person individualized 

counseling session over 

12 weeks + 3 weeks of 

accelerometer use and 

feedback over 12 weeks

7 in-person individualized 

counseling sessions 

consisting of diet 

education, diet regimen 

(1200 to 1500 kcal/day; 

dietary fat <20% of total 

energy intake), PA

education, and weight 

goal-setting

PA: Yes

Weight: Yes

Satisfaction: No

12 to 24 Industry High

Reijonsaari, 

20127 Healthy volunteers

544

Uni-axial 

accelerometer

12 months of continuous 

accelerometer use and 

feedback + access to 

telephone counseling 

(frequency NR)

Written and verbal 

explanation of results of 

physical exams, general 

PA, and health 

information and 

occupational healthcare

PA: Yes

Weight: Yes

Satisfaction: No

25+ Fdn/Indiv Low
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Study 

Population 

N Randomized

Device Name

Intervention Comparator Outcomes Duration 

(Weeks)

Funding 

Source

Risk of 

Bias

Shrestha, 

20138

Overweight/ 

obese and/or 

sedentary

28

Polar FA20 

accelerometer

1-time, 1.5 hour lifestyle 

instruction + 6 months of 

continuous accelerometer 

use and feedback

Self-directed exercise 

and/or US Army 

mandated physical 

training

PA: Yes

Weight: Yes

Satisfaction: No

12 to 24 Gov High

Shuger, 

20119

Overweight/ 

obese and/or 

sedentary

197

BodyMedia 

SenseWear 

Armband

4-arm study (3 

interventional arms) :

Group-based behavioral 

weight loss: 

14 group weight loss 

sessions + 6 individual 

phone calls + workbook 

over 9 months 

Accelerometer alone: 

9 months of continuous 

accelerometer use and 

feedback

Group-based behavioral 

weight loss + 

accelerometer use: 

9 months of continuous 

accelerometer use and 

feedback + 14 group 

weight loss sessions + 6 

individual phone calls + 

workbook

Self-directed weight loss 

manual with diet 

education, PA education, 

and weight goal-setting

PA: No

Weight: Yes

Satisfaction: No

25+ Industry Unclear
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Study 

Population 

N Randomized

Device Name

Intervention Comparator Outcomes Duration 

(Weeks)

Funding 

Source

Risk of 

Bias

Slootmaker, 

200910

Overweight/ 

obese and/or 

sedentary

102

PAM (model 

AM101), 

uniaxial activity 

monitor + 

smartphone

3 months of continuous 

accelerometer use + 

tailored PA feedback and 

motivational tips via webbased portal

A single written brochure 

with brief PA 

recommendations

PA: Yes

Weight: Yes

Satisfaction: No

12 to 24 Gov High

Tabak, 

201411

Chronic 

medical illness

29

Activity Coach 

(accelerometerbased activity 

sensor + 

smartphone)

Self-directed technology 

supported care program 

that included a tailored 

web-based exercise 

program, accelerometerbased activity senor and 

motivational messaging, 

COPD self-management 

module, and as needed 

web-portal 

teleconsultation 

conducted over 9 months

Usual care with regular 

physiotherapy, if 

prescribed

PA: Yes

Weight: No

Satisfaction: No

25+ Gov Unclear

Thompson, 

201412

Overweight/ 

obese and/or 

sedentary

20

GRUVE triaxial 

accelerometer

12 weeks of continuous 

accelerometer use and 

feedback + weekly brief 

counseling sessions on 

increasing activity + 

treadmill desk

12 weeks of monitoring 

without feedback, then 

crossed over to receive 

monitoring, plus feedback, 

counting, and treadmill 

desk for 12 weeks 

PA: Yes

Weight: Yes

Satisfaction: No

12 to 24 Fdn High
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Study 

Population 

N Randomized

Device Name

Intervention Comparator Outcomes Duration 

(Weeks)

Funding 

Source

Risk of 

Bias

Thompson, 

201413

Older adults

49

FitBit

24 weeks of continuous 

accelerometer use and 

feedback + weekly brief 

telephone counseling 

sessions focused on 

accelerometer feedback + 

6 in-person brief 

counseling sessions

24 weeks of 

accelerometer without 

feedback

PA: Yes

Weight: Yes

Satisfaction: No

25+ Gov/Fdn Unclear

Wijsman, 

201314

Older adults

235

DirectLife

12 weeks of continuous 

accelerometer use and 

feedback + personal 

website + personal ecoach who gives updates 

on activity status and 

advice via web portal

3-month waitlist control PA: Yes

Weight: Yes

Satisfaction: No

12 to 24 Gov/ 

Industry

Low

Abbreviations: COPD=chronic obstructive pulmonary disease; Fdn=foundation; Gov=government; Indiv=individual; N=number; NR=not reported; PA=physical 

activity
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