Why Do Pedometers Work?

Why Do Pedometers Work?A Reflection upon the Factors Related toSuccessfully Increasing Physical Activity

Catrine Tudor-Locke1 and Lesley Lutes2

1 Walking Behaviour Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA

2 Department of Psychology, East Carolina University, Greenville, North Carolina, USA

Abstract The results of two recent independent meta-analyses focused on pedometer-based programmes conclude that they work; that is, they are effective. Specif-ically, physical activity increases while blood pressure and weight decrease as aresult of participating in a pedometer-based intervention. An improved un-derstanding of the unique measurement and motivational properties of pedo-meters as behaviour-change tools will assist researchers and practitioners tomaximize benefits. In an effort to begin to outline why pedometers work, forwhom, and under what conditions, the purpose of this current opinion article isto explore the published literature (drawing heavily from those studies pre-viously identified in published meta-analyses and our own work in this area) toidentify factors related to using pedometers to increase physical activity. Inparticular it is important to: (i) gain a better understanding of the activity-promoting characteristics of pedometers; (ii) determine effective elements ofpedometer-based programming; and (iii) identify participants who engage in,and benefit most from, such programming. Pedometers are most sensitive towalking behaviours, which is consistent with public health and clinical ap-proaches to increasing physical activity. Specifically, they offer an affordableand accessible technology that is simplistic in output, low-literacy friendly, andimmediately understandable to end-users. Support materials are becomingreadily available for researchers and practitioners in terms of expected (nor-mative or benchmark) values, patterns of change, indices to aid screening andinterpretation, and measurement protocols. Pedometer-based programmetheory is now being articulated and tested, and the critical elements necessaryto shape a successful programme are becoming more clearly defined. Moreresearch is needed, however, to compare the effectiveness of self-selected in-dividualized goals with tailored goals (based on a specified baseline char-acteristic, for example), standardized goals (e.g. percentage-based increments)and pre-set uniformly administered goals (i.e. a volume total of 10 000 steps/day or an incremental total of 2000 extra steps/day for everyone). Since moststudies of pedometer-based programmes have been of relatively short duration,it is unknown to what extent observed changes are sustainable or whether it ispossible to continue to accrue benefits over long-term adherence. Peer deliveryof treatment has the potential for enabling wider and less costly dissemination,although this has not been directly evaluated. In addition, the majorityof pedometer-based programme participants to date have been women,

CURRENT OPINIONSports Med 2009; 39 (12): 981-9930112-1642/09/0012-0981/$49.95/0

ª 2009 Adis Data Information BV. All rights reserved.

suggesting thatmore research is needed onmen and how they react to this formof physical activity intervention. Increases in steps/day have been negativelycorrelated with baseline values, indicating that those with lower baselinesteps/day stand to make the greatest relative incremental increases in physicalactivity behaviour. A clearly articulated programme theory is lacking in mostinterventions. A clearer understanding is needed of what programme features,including the nature of goal-setting, are necessary for optimal participantsuccess. Additionally, we need a better profile of the participant who benefitsmost, and/or requires additional or alternative strategies to succeed in theirpersonal behaviour-change attempts. Continued efforts to refine answers re-garding what works well for whom under what conditions will foster evidence-based applications of pedometer-based programmes.

Physical activity assessment has benefited fromthe rapid expansion of body-worn technologies,including accelerometers and pedometers, whichhave greatly advanced researchers’, practitioners’and lay people’s interest in quantifying, and abilityto quantify, physical activity patterns and volumes.Of the two instruments, however, the pedometerhas been used more frequently as a motivationaltool imbedded in an intervention programme.

As testament to the growing interest in pedo-meter-based programming, two recent meta-analyses examined such programmes’ efficacy interms of increasing walking behaviours.[1,2] Speci-fically, Richardson et al.[1] summarized findingsfrom nine randomized and controlled pedometer-based programmes and found that participantsincreased their activity by 1800–4500 steps/day andlost a modest amount of weight (on the order ofapproximately 0.05 kg/week) over the course ofinterventions lasting from 4 weeks to 1 year (med-ian duration 16 weeks). Bravata et al.[2] consideredboth randomized, controlled studies and observa-tional studies, and reported similar changes: pedo-meter users increased their physical activity byapproximately 2100–2500 steps/day and decreasedtheir body mass index (BMI) by 0.38 kg/m2.

The pedometer-based interventions described inthe published meta-analyses[1,2] offer inspiration,but few alone can serve as useful programme tem-plates due to a lack of a clearly articulated detailedprogramme or intervention theory.[3] Theory-based health behaviour-change programmes arebelieved to bemore effective than those that do not

use theory; unfortunately, a recent review of theory-based programming indicated only 35.7% of healthbehaviour-change programmes published between2000 and 2005 evenmentioned theory.[4] A detailedprogramme theory is necessary to organize andexplain what happens in a programme and why. Itis initially informed by the existing programme-related literature and clinical experience.[3] There-fore, in an effort to begin to outline why pedo-meters work, for whom, and under what condi-tions, the purpose of this current opinion article isto reflect upon characteristics of pedometers, pedo-meter-based programming, and the participantswho engage in such programming. Such a dis-course is necessary to better understand theoreticalmechanisms in an effort to refine and replicateoptimal programming templates.

1. Characteristics of Pedometers

1.1 Measurement Mechanism

Pedometers are generally designed to be mostsensitive to detecting ambulatory activity, and thisis a ‘good thing’ in terms of measuring and moti-vating walking behaviours. Of all types of physicalactivity, walking is most commonly encouraged.[5]

It is the most commonly reported form of leisure-time physical activity,[6] and it is also a functionalcomponent of shopping, transportation andwalking the dog, to name but a few examples ofother forms of walking behaviours.[7] Pedometersoffer a simple estimate of physical activity volume

982 Tudor-Locke & Lutes

ª 2009 Adis Data Information BV. All rights reserved. Sports Med 2009; 39 (12)

in terms of steps taken. This uncomplicated andstraightforward output is a direct indicator ofmovement as a result of behavioural choices. Tradi-tional pedometers detect steps by using a hori-zontal, spring-suspended lever arm which movesup and down as a result of vertical accelerations ofthe hip. A step is recorded when a vertical accel-eration above the manufacturer-designed forcesensitivity threshold of the pedometer (e.g. 0.35gfor the Yamax� pedometer [Yamax Corp., Tokyo,Japan]) deflects the lever arm sufficiently to com-plete an electronic circuit. The electronic circuitrywithin a pedometer is designed to accumulate stepsand continually display this updated informationon a digital screen.

A force sensitivity threshold is an importantpedometer characteristic, regardless of its under-lyingmeasurementmechanism, since it is necessaryto censor out ‘non-steps’ (e.g. inevitable jostlingduring car driving[8]). The sensitivity/specificitytrade-off, however, results in a loss of recorded lowacceleration steps, typical of slower paces (e.g.steps taken while standing in line at the grocerystore). Since health promotion efforts have focusedon the benefits of brisk walking, or that of at leastmoderate intensity, this censoring feature shouldnot be problematic inmost populations, and in factcan be interpreted as an attribute as it convenientlypushes the participant to focus more on detectablewalking behaviours.

Unfortunately, specific pedometer force sensi-tivity thresholds for detecting steps can vary widelybetween available instruments[9] and are only asconsistent between instruments of the same brandas factory quality control efforts impose. This un-fortunate circumstance undermines the consistencyof operationally defining a step, and impairs ourability to compare step outputs across populationsand studies. At this time there is no conversionfactor available to ‘correct’ step outputs from dif-ferent instruments. However, a number of pedo-meter brand-to-brand comparisons have beenconducted,[10-12] which have identified research-quality pedometers. It is important to clearlydeclare here that the effectiveness of pedometer-based programming is prefaced on the use of validand reliable instruments, like the Yamax� brandpedometers, for example. The effectiveness of

lesser instruments is questionable and potentiallydetrimental to behaviour-change efforts.[13]

Since pedometers are typically designed to bemost sensitive to vertical accelerations at the hip, italmost goes without saying that pedometers (andwaist-worn accelerometers) do not detect non-ambulatory activities, including swimming, cyclingand weight training. The prevalence of participa-tion in these activities is quite low. Only 5.8% ofadults report swimming, 11% report cycling and8.6% report weight-training when asked about ac-tivities performed over the past 30 days.[14] For-tunately, these types of activities are salient andtherefore more easily recalled, so a combination ofpedometer and self-report should suffice to capturethem.[15] Adding ‘bonus steps’ to daily pedometer-determined steps for performance of these types ofnon-ambulatory activities (e.g. 200 extra steps forevery 10 minutes of active time) is a strategy thatmay work well on the individual level for inter-ventions, but appears not to be necessary forpopulation level analyses.[16]

Traditional pedometers are also not designed todetect intensity of activity. Furthermore, pedometerattachments that rotate the instrument off the ver-tical plane will impair measurement function. Forexample, in obese individuals it is possible thata pedometer will be tilted off the vertical axis inmanufacturer-recommended attachment sites (i.e.typically at the waist, centered over the right knee),resulting in compromised detection of lower forcesteps.[17,18] To solve this problem, there are instru-ments that offer improved precision in obeseindividuals[17] (see below), with the caveat thatthese might result in the sensitivity/specificitytrade-off mentioned above. However, some re-searchers have reported that pedometers can bemoved about on the waist band (e.g. placed on themid-axillary line or in line with the posterior thighwhere it is less likely to be tilted) without compro-mising measurement properties.[18,19] Specifically,we have had success with teaching participantshow to attach the pedometer so that it is not tilted,and to monitor the accuracy of their pedometerson a daily basis with a simple 20-step test, adjustingplacement as necessary.[19]

Emerging technologies include a piezoelectricaccelerometer mechanism that generates a sine

Why Do Pedometers Work? 983


wave corresponding to vertical accelerations at thehip during walking and running. A count of thesine waves is interpreted as steps taken. Some ofthese instruments can also provide outputs relatedto the intensity of these steps. An added feature isan on-boardmemory function that recalls previousdays’ data. The NL-1000� (New-Lifestyles Inc.,Lees Summit, MO, USA), NL-2000�(New-Life-styles Inc.), Kenz Lifecorder EX� (Suzenken Co.,Ltd, Nagoya, Japan) and the Omron HJ 720ITC�

(Omron Corp., Kyoto, Japan) are examples. Thelatter two instruments can also transfer data di-rectly to a computer by way of a USB cable, facil-itating data management requirements. Althoughthese added features are useful to researchers, theevaluation of their impact on individual behaviouris limited. For example, although the measurementmechanism of the piezoelectric pedometers doesprovide a more precise estimate of steps taken inobese individuals,[17] the published meta-analysisdemonstrated pedometer effectiveness (even inobese individuals) without necessarily using thistechnology.

1.2 Acceptability to End-Users

Although a wide variety of commercial pedo-meters are available, most are small, unobtrusive intheir attachment on the body (typically clippingdirectly to a waist band), and inexpensive (ap-proximately $US20–$US50; year of costing 2009).As such, pedometers offer an accessible technologythat is simplistic in output, low-literacy friendlyand immediately understandable to end-users.Their output is personalized, since each individualcan be equipped with their own pedometer. Fur-thermore, with consistent wear, pedometers areplausibly effective for longer term monitoring andbehaviour change, much like a wristwatch can beused for self-monitoring behaviours that are time-dependent throughout the day. Focus groupsconducted following completion of a pedometer-based programme revealed that pedometers arewell accepted and are considered to be highly use-ful goal-setting tools, capable of immediately in-creasing personal awareness of physical activitylevels, and providing sources of readily availablevisual feedback.[19-21] Although pedometer manu-

facturers offer an array of value-added features(delayed reset buttons, multiple day memories,‘talking’ pedometers, etc.) and outputs (estimatesof distance walked, energy expended, time in ac-tivity of at least moderate intensity, etc.), it hasbeen our observed experience of the participantswith whom we have worked that most are com-fortable with a simple output of steps taken and asingle reset button. Furthermore, mathematicalmanipulations (accomplished by an on-boardpedometer microprocessing feature or resultingfrom post-data processing) of the simple step out-put to extrapolate distance walked (based oninputted stride length or height) and/or energy ex-pended (based on sex, age, mass or a selection ofthese) result in diminishing accuracy.[22]

The cumulative and readily available visualfeedback is a pedometer characteristic that warrantsfurther discussion. The cumulative nature of thecounted steps provides a constant and changingbarometer reflecting personal behaviour choices asthey occur in real-time. Unlike instruments that mayrequire downloading and/or other types of proces-sing prior to personal access, traditional pedometersoffer instantaneous awareness to the end-user thatcan then be directly acted upon. That being said,emerging technologies are capable of providing bothimmediate feedback and more complex data analy-sis after download. Used for baseline behaviouralassessment, the immediate feedback provides theuser with a readily digestible estimate of personalphysical activity level, which can inform decisionsabout behaviour change. Used as part of a guidedand repetitive self-monitoring, feedback and goal-setting process, the pedometer provides up-to-the-minute information that can spur and honebehaviour choices.

1.3 Availability of Related ‘Software’Facilitating Use

We have previously likened pedometers tocomputer hardware (e.g. keyboards,monitors, disks,etc.).[23] However, without the accompanyingsoftware (e.g. expected values, standardized pro-tocols, indices to interpret change, data manage-ment rules, reporting procedures, etc.) their useis limited. We are now benefiting from the



independent and collective efforts of a growingnumber of researchers and practitioners who con-tinue to publish quality work related to the impactof pedometer-based programmes on activity. Casesin point are the two meta-analyses previously men-tioned[1,2] and a third,[24] all of which provide vitalexpected (normative or benchmark) values, neces-sary for interpreting change and comparisonpurposes, and variance estimates required for deter-mining sample size. Similar data are now availablefor young populations.[25] Patterns of change[26,27]

have also been published, which are useful for fa-cilitating local implementation and evaluation.Efforts have also been made to identify practicalindices reflective of public health guidelines,[28] toclassify hierarchical levels of physical activity,[29,30]

and to link threshold values with outcomes ofinterest including BMI[31,32] and body fat percen-tage.[33] Practical guidance for measurement pro-tocols and procedures has been published[34] andrecently expanded to young populations.[35] Theserich and growing publicly available resources areinvaluable to facilitate optimal use of pedometersin physical activity interventions.

2. Characteristics of Pedometer-BasedPhysical Activity Interventions

2.1 Nothing Quite Like a Good Theory

It is important to reiterate that pedometers aresimple tools and that successful interventions areultimately based on empirically validated treat-ments that are informed by good theory.[3] Thisincludes identifying and clearly articulating keycomponents or activities that must be present in aprogramme; this in turn is informed by existingbehavioural theories, models and accepted tech-niques. Behavioural and social scientists interestedin physical activity have employed a number oftheories and models originally developed for otherbehavioural applications (e.g. addiction andsmoking cessation), often in combination, to betterunderstand this unique behaviour and to designsuccessful interventions. These theories andmodelsgenerally include: (i) classical learning theory;(ii) the health belief model; (iii) the transtheore-tical model; (iv) relapse prevention; (v) social

cognitive theory; (vi) theory of reasoned action andplanned behaviour; (vii) social support; (viii) self-regulation theory; and (ix) ecological approaches.[36]

Table I catalogues the theoretically based models/techniques and intervention goal algorithms ofpedometer-based interventions identified in therecent meta-analyses.[1,2] At present only a handfulof these interventions[26,58,61,63] have explicitly ac-knowledged using a recognized health behaviour-change theory to guide programme design anddelivery. We know of only one that has presentedits programme theory in a logic model (i.e. anaccepted tool used to illustrate programme com-ponents, outcomes and linkages between them).[3]

For example, drawing primarily from socialcognitive theory and the transtheoretical model,we previously identified the critical inputs under-lying the First Step Program – a pedometer-baseddaily physical activity intervention originallydeveloped for individuals with type 2 diabetesmellitus.[3] These critical inputs included: (i) individ-ualized programming (e.g. self-selected incre-mental goals); (ii) flexibility in structure of regi-men; (iii) activity that is of moderate intensity andfocused specifically on walking behaviours; (iv) ac-ceptable self-monitoring and feedback tools; and(v) follow-up contact. In addition, a facilitatedprogramme was selected to guide participantsthrough decision balance techniques early in thebehaviour-change process, and self-contracts wereimbedded as a regular part of an incremental andindividualized goal-setting process. Relapse pre-vention and planning was incorporated during thebehaviour-change period.[64]

Social cognitive theory[65] dictates that theconstructs of self-efficacy and social support areimportant mediating variables in programmesdesigned to increase physical activity behaviours.Since the most influential source of self-efficacy isperformance accomplishment or mastery, it fol-lows that opportunities to directly experiencephysical activity (i.e. go for a walk) are integral togood programme design. Brief group walksincorporated into the First Step Program wereuseful for increasing personal awareness of stepstaken in a specific time frame (necessary forselecting informed incremental goals) and alsoprovided opportunity for socialization.[21] Social



Table I. Theoretical models/techniques and intervention goal algorithms of pedometer-based interventions identified in recent meta-analyses[1,2]

Source, year Target population Explicitly identified theoretical model

(in bold type) or technique

Intervention goal algorithm

Araiza et al.,[37] 2006 Adults with type II diabetes

mellitus

None identified Achieve 10000 steps/day

de Block et al.,[38] 2006 Adults with COPD Motivational interviewing To › lifestyle PA

Butler and Dwyer,[39] 2004 Sedentary adults None identified To › PA 30min (~3000 steps) daily during wk 1 and 2, › to

40min (~4000 steps) during wk 3 and 4

Chan et al.,[26] 2004 Sedentary adult employees Social cognitive theory and

transtheoretical modelTo › PA weekly

Croteau,[40] 2004 Adult employees Goal setting based on baseline step

countsIf <8000 steps/day at baseline, then › 10% every 2wk until

>10 000; if 8000–10 000, then increased 5% every 2wk until

>10 000; if >10 000 at baseline, then maintain

Eastep et al.,[41] 2004 Healthy adults Feedback Centre-based exercise class and recommendations to increase

daily PA through walking

Engel and Lindner,[42] 2006 Adults with type II diabetes Coaching (including problem-

solving, education, self-efficacy,

goal-setting, and social support

If healthy older adult step goals =6000–8500, if older adult withdisabilities and chronic illness, step goals = 3500–5500

Hultquist et al.,[43] 2005 Sedentary non-smoking

adult women

Self-monitoring Achieve 10000 steps/day

Izawa et al.,[44] 2005 Adults with a history of

myocardial infarction

Self-efficacy theory and

transtheoretical model

Centre-based cardiac rehabilitation programme – no goals

specified

Jensen et al.,[45] 2004 Obese older adult women Goal-setting and self-monitoring › daily steps by 5000 and additional goals if warranted

Kilmer et al.,[46] 2005 Adults with neuromuscular

diseases

Self-monitoring › daily steps by 25% compared with baseline

Koulouri et al.,[47] 2006 Healthy adults None identified › daily steps by 2000 compared with baseline

Lindberg,[48] 2000 Sedentary healthy adults Self-monitoring and motivational

messages

Achieve 10000 steps/day

Moreau et al.,[49] 2001 Adult postmenopausal

women

Self-monitoring and goal setting › distance by 1.4 km above their baseline wk 1, › 0.5 each time

until the desired walking of › to 3.0 km/day by wk 3

Ransdell et al.,[50] 2004 Multi-generational women

in families

Self-monitoring › duration and volume of activity by 10% every 2 weeks and to

› lifestyle-oriented PA

Schneider et al.,[51] 2006 Overweight and obese

adults

Self-monitoring Achieve 7000 steps/day for wk 1, 8000 steps/day for wk 2, 9000

steps/day for week 3, and 10 000 steps/day thereafter

Sidman et al.,[52] 2004 Adult sedentary women Goal-setting Achieve 10000 steps/day or to › daily steps by 1000–3000

Stovitz et al.,[53] 2005 Healthy adults Goal-setting › daily steps by 400 each week

Sugiura et al.,[54] 2002 Adult menopausal women Goal-setting and centre-based

exercise› daily steps by 2000–3000 in addition to the exercise class

Swartz et al.,[55] 2003 Overweight inactive women Self-monitoring Achieve 10000 steps/day

Continued next page

986Tu

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support can take many forms beyond profes-sional follow-up contact and includes personalsupport networks encompassing peers, familyand friends. Activating personal support net-works offers a prospect of establishing ongoingsupport that extends well beyond formal pro-gramme structures, and provides a natural oppor-tunity to foster sustainability of behaviourchange.[3,19]

2.2 The Importance and Nature of a Goal

Since pedometers are ‘personal wear’ items,which reflect individual behaviours, they fit verywell into a programme of self-monitoring, per-sonalized feedback, and self-selected incrementalgoal-setting. Programmes that have encouragedincreased physical activity in the absence of a goalhave shown no significant improvements insteps/day compared with those with increases of‡2000 steps/day in programmes that have pro-moted the use of the 10 000 steps/day goal orother goals (although few studies have evaluatedalternative goals, which limits conclusions speci-fically about the ultimate magnitude of the goaland its efficacy).[2]

Although it is tempting to adopt an ‘across theboard’ prescription of a specified total or incre-mental number of steps/day, individualized pro-gramming is more personally relevant, easilyadjusted as needed, and is likely to be wellendured by typically sedentary individuals (themost likely target of a pedometer-based physicalactivity intervention). Individualized program-ming that uniquely engages and responds to anindividual and encourages self-selection of a per-sonal goal (e.g. the First Step Program) is not quitethe same thing as a tailored intervention, whichmay include automatic responses and messagingcreated to address the needs of a group of in-dividuals defined, for example, on baseline steps/day. However, such an approach may be bothefficient and effective. For example, in three recentstudies,[66-68] participants uploaded or manuallyentered their daily step counts over the Internetand received pre-set tailored feedback regardinggoal-setting based on automatic algorithms. AllT

able

I.Contd

Source,year

Targetpopulation

Explicitly

identifiedtheoreticalmodel

(inbold

type)ortechnique

Interventiongoala

lgorithm

Talbotetal.,[5

6]2003

Olderadultswithknee

osteoarthritis

Goal-setting,self-m

onitoringand

feedback

›10%

comparedwithbaselineevery

4wksforanoverallgoalo

f

30%

abovebaselinesteps/day

ThomasandWilliams,[57]2006

Adultemployees

Goal-settingandself-m

onitoring

›daily

stepseachweekwithanultim

ate

goalo

f10000

steps/day

Tudor-Lockeetal.,[5

8]2004

AdultswithtypeIIdiabetes

Socialcognitivetheory

and

transtheoreticalmodel

›daily

stepsby3000comparedwithbaseline

VanWorm

eretal.,[59]2004

Oldermale

andfemale

adultswithcoronary

artery

disease

Noneidentified

Nonedescribed

Williamsetal.,[6

0]2005

Postm

enopausalA

frican

Americanwomen

Behaviouralcontractingandself-

monitoring

›daily

stepgoalscomparedwithbaselinebytakinginto

considerationthe10000steps/daygeneralrecommendationand

theparticipant’sself-selectedtargetedvalue

Wilsonetal.,[6

1]2005

AfricanAmericanbreast

cancersurvivors

Healthbeliefmodel

Progressivestepgoals(notdescribed

Wyattetal.,[62]2004

Adultsfrom

asingle

state

Self-m

onitoring

›daily

stepsby2000comparedwithbaseline

COPD=chronicobstructivepulm

onary

disease;PA=physicalactivity;›

indicatesincrease.



studies showed significant improvements in steps/day over the course of the intervention.

An alternative approach has been to promotepercentage-based goals.[46,50] The concern withstrictly implementing this approach is that the ab-solute incremental number of steps will be higherwith higher baselines, presenting greater andpossibly insurmountable challenges (and perhapsunder-challenging those with lower baselines).Since there is little research that has studied alter-native goals or goal-setting strategies, more is need-ed comparing the effectiveness of: (i) self-selectedgoals; (ii) goals tailored to individuals defined bya baseline characteristic; (iii) standardized goals(e.g. percentage-based increments); and (iv) pre-setuniformly administered goals (i.e. a volume totallike 10 000 steps/day or an incremental total like2000 steps/day for everyone). It may very well bethat setting and working towards any goal thatrepresents an increase over baseline values is ofmuch greater importance than the manner in whichit is set.

2.3 Self-Monitoring

As discussed above (sections 1.2 and 2.2), thepedometer is an acceptable self-monitoring andfeedback tool. However, its power is enhanced if itis coupled with some formal process of recordingdaily values (e.g. on a simple calendar) as a strategyto help reinforce activity behaviours.[19,21] Meta-analysis results[2] have indicated that participantsin pedometer-based programmes who recordedtheir daily step count increased their activity byapproximately 2000–3200 steps/day over baseline.This is significantly greater than those who werenot required to record their data (mean change832 steps/day). Participants have reported that theact of recording (i.e. writing down)[19,20,40] pedo-meter data over the course of days on a simplepaper calendar provides additional visual feedbackof progress, illuminates personal behaviour pat-terns of interest (e.g. weekend vs weekday steps),and produces a tangible record of personal success.Requiring participants to submit their data (e.g. inperson, by mail or by electronic means) also pro-vides a sense of accountability.[20] Follow-up con-tact is a form of social support and is considered

important to continuedmotivation. It has taken theform of telephone calls,[21] postcards,[58] emails[20,40]

and face-to-face interactions[26] – to name a few ex-amples. The future brings the promise of increasedavailability of commercially interactive websites andpurchasable software to be used in tandem withobjectively monitored physical activity enteredeither manually[69] or by instrument download.[66,67]

2.4 Strategies

Since pedometers capture a cumulative count ofsteps taken throughout the day, ultimate flexibilityin structure of a personalized regimen is assured;almost limitless options are available to accrue dailysteps. Croteau[70] catalogued strategies reportedby participants engaged in a pedometer-based pro-gramme: 64.7% walked to a meeting or work-related errand, 50%walked after work, 35.5%walkedbefore work, 47.1% walked at lunch, 32.4% walkedon the weekend, 32.4% walked while travelling,32.4% walked with the dog, and 29.4% walked to adestination (e.g. work/store). In addition, 50% ofparticipants reported parking further away, 23.5%preferentially used the stairs rather than an elevator,and 52.9% performed other cardiovascular activity.Participation in sports and exercise has been shownto produce consistently higher steps/day over 1continuous year of monitoring.[71] Programme par-ticipants have reported adopting personal strategiesof taking a ‘day off’ without guilt (secure in theknowledge that they could alter their behaviour onsubsequent days), and ‘banking steps’ in anticipa-tion of a sedentary day.[19]

2.5 Delivery Options

Little research has been conducted on the pos-sible role that interventionists’ characteristicsmight play in moderating participants’ attempts atbehaviour-change in pedometer-based program-mes. Interventionists’ characteristics include suchaspects as the programme deliverer’s similarityto programme recipients, personal attitudes andphysical activity behaviours, comfort with the in-tervention, and training.[3] We have recently de-monstrated that a pedometer-based programmedelivered either by a peer leader (e.g. an individualwith type 2 diabetes who had previously completed



the same programme) or a professional (e.g. anurse or dietician)[72] elicited similarly favourablechanges in steps/day, weight, waist girth, restingheart rate, and blood pressure. Peer delivery hasthe potential for enabling wider and less costlydissemination, although this has not been directlyevaluated.

Another delivery option that is rapidly gainingpopularity is by computer. Initial reviews of phy-sical activity interventions[73,74] delivered in thismanner have shown variable success of tailoredinterventions delivered by computer. However,more recent studies of computer-delivered, theory-based interventions that have also incorporated apedometer have shown clear and consistent success(i.e. an increase of 1300–2000 steps/day over base-line values).[66-68]

Since most studies of pedometer-based pro-grammes have been of relatively short duration, itis unknown to what extent observed changes aresustainable or whether it is possible to continue toaccrue benefits over long-term adherence. Clear-ly, the optimal length of an intervention is un-known. As stated above, Richardson et al.[1]

catalogued pedometer-based interventions last-ing from 4 weeks to 1 year. They reported astrong negative relationship between studyduration and resulting weight loss, indicatingthat benefits are potentiated with prolonged ad-herence. Chan and Tudor-Locke[75] reported thatparticipants who completed the First Step Pro-gram (i.e. wore the pedometer for at least 9 weeksand completed a survey at 12 weeks) reportedhigher steps/day at 12 weeks (approximately12 000 steps/day) and at the 1-year follow-up(approximately 11 000 steps/day) compared withbaseline values (approximately 7800 steps/day).The First Step Program documented an im-mediate increase in steps/day that peaked at theend of formal contact, and although it deterio-rated somewhat over time, still remained elevatedcompared with baseline values.[27,58] It is possiblethat by implementing ‘booster sessions’ (extendedbut infrequent contact), researchers and practi-tioners might be able to facilitate prolonged be-haviour change. However, we do not yet knowthe optimal pattern of contact necessary to sustainadherence.

3. Characteristics of Participants

The study of the characteristics of pedometer-based programme participants has been primarilylimited to a descriptive nature. The majority ofpedometer-based programme participants to datehave been women (73% of programme participantsfrom studies included in the Richardson meta-analysis were women[1]), who took between 4700 to7000 steps/day (i.e. sedentary or low active[29,30])and were overweight at baseline. However, thesecharacteristics may also reflect recruitment strate-gies to some extent. There is some evidence tosuggest that pedometers may be appealing onlyfor short-term behaviour monitoring in men.[76]

An exploratory analysis of factors related topedometer-based programme adherence and com-pletion revealed that those most likely to completethe programme were overweight or obese class I(i.e. with a BMI between 30 and 35kg/m2).[77] Theauthors speculated that the higher attrition innormal weight individuals potentially suggests apersonal sense of programme irrelevance. Forthose with greater levels of obesity (i.e. class II andclass III), higher attrition might have indicated amore overwhelming sense of challenge. The au-thors also observed that attrition was characterizedby lower initial incremental changes in steps/dayand subsequent regression towards baseline values.Increases in steps/day have been negatively corre-lated (r= -0.368) with baseline values, indicatingthat those with lower baseline steps/day stand tomake the greatest relative incremental increases inphysical activity behaviour.[26] However, there wasno noted correlation between increases in steps/dayand baseline BMI values. The meta-analysis con-ducted by Bravata et al.[2] of both randomizedcontrolled trials and observational studies ofpedometer-based programmes indicated that sex,BMI and race/ethnicity were not significant pre-dictors of increased activity.

In terms of anticipated changes in health out-comes as a result of increased steps/day, we re-cently reported that individual responses varywidely.[72] Specifically, we noted that althoughFirst Step Program participants’ waist girth de-creased by 1.5–1.7 cm on average, the range ofchange was widely variable (-16 cm to +10 cm).



More research is necessary to identify who bene-fits most from pedometer-based programming inorder to target such interventions more appro-priately.

4. Conclusions

The results of the two recent meta-analyses[1,2]

focused on pedometer-based programmes con-clude that they work. An improved understand-ing of the unique properties of pedometers asbehaviour-change tools will assist researchers andpractitioners to maximize these attributes. Aclearer understanding is also needed of what pro-gramme features, including the nature of goal-set-ting, are necessary for optimal participant success.Finally, we need a better profile of the participantwho benefits most, and/or requires additional oralternative strategies to succeed in their personalbehaviour-change attempts. It is premature to of-fer an optimal programme template; however, wehave compiled a summary of the factors related tosuccessfully increasing physical activity in ped-ometer-based programmes in table II. Continuedefforts to refine answers to what works well forwhom and under what conditions will foster

evidence-based applications of pedometer-basedprogrammes.

Acknowledgements

No sources of funding were used to assist in the prepara-tion of this article. Dr Tudor-Locke receives royalties from thesale of a self-help book focused on using pedometers to in-crease physical activity. The authors have no other conflicts ofinterest that are directly relevant to the content of this article.

References1. Richardson CR, Newton TL, Abraham JJ, et al. A meta-

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Table II. Factors related to successfully increasing physical activity in pedometer-based programmes

Characteristics of pedometers Characteristics of the programme Characteristics of participants

� Most sensitive to ambulatory activity

� Simple estimate of physical activity

volume expressed as steps/day� Displays accumulated steps taken

� Able to censor out ‘non-steps’

� Acceptable to end-users

� Affordable, valid and reliable

instruments available

� Small and unobtrusive, typically

attached to the waist band

� Accessible technology, low-literacy

friendly, immediately understandable

� Offers readily available and

personalized visual feedback

� Useful self-monitoring, goal-setting,

and feedback tools

� Immediately increases awareness of

physical activity levels

� Increasingly available resource

materials to support measurement and

motivation efforts

� Need to clearly articulate underlying

programme theory

� Minimally, a programme of self-

monitoring, incremental goal-setting,

and personalized feedback

� Fundamental importance of a goal,

possibly 10 000 steps/day, possibly self-

selected, but little alternative research

has been conducted

� Flexibility in structure of a

personalized regimen

� Need to record and submit daily

values

� Follow-up contact as one form of

social support

� Opportunities to build self-efficacy

� Activating personal support networks

� Peer delivery is effective and may

enable dissemination

� Optimal programme duration and

pattern of contact unknown to support

sustainability

� Majority of participants have been

women; may reflect recruitment

strategies to some extent

� May be only appealing for short-term

behaviour monitoring in men

� Those most likely to complete are

overweight and obese class I

� Attrition indicated by lower initial

incremental changes in steps/day,regression to baseline values

� Individual responses vary

� Little is known about who benefits

most



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Correspondence: Dr Catrine Tudor-Locke, Director, WalkingBehaviour Laboratory, Pennington Biomedical ResearchCenter, Baton Rouge, LA 70808, USA.E-mail: [email protected]



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Why Do Pedometers Work?