Scholarly article on topic 'Validity of activity monitors in health and chronic disease: a systematic review'

Validity of activity monitors in health and chronic disease: a systematic review Academic research paper on "Clinical medicine"

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Academic research paper on topic "Validity of activity monitors in health and chronic disease: a systematic review"

INTERNATIONAL JOURNAL OF BEHAVIORAL NUTRITION AND PHYSICAL ACTIVITY

REVIEW Open Access

Validity of activity monitors in health and chronic disease: a systematic review

Hans Van Remoortel11, Santiago Giavedoni21, Yogini Raste3, Chris Burtin1, Zafeiris Louvaris4, Elena Gimeno-Santos5, Daniel Langer1, Alastair Glendenning6, Nicholas S Hopkinson3, loannis Vogiatzis4, Barry T Peterson7, Frederick Wilson7, Bridget Mann6, Roberto Rabinovich2, Milo A Puhan8,9 and Thierry Troosters1,11*, on behalf of PROactive consortium

Abstract

The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using metaregression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (-12.07 (95%CI; -18.28 to -5.85) %) compared to triaxial (-6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (-3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.

Keywords: Chronic diseases, Doubly labelled water, Indirect calorimetry, Activity monitoring, Physical activity, Validation study, Systematic review

* Correspondence: thierry.troosters@med.kuleuven.be +Equalcontributors

faculty of Kinesiology and Rehabilitation Sciences, Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium and Respiratory Division, UZ Gasthuisberg, Leuven, Belgium "Respiratory Rehabilitation and Respiratory Division, UZ Gasthuisberg, Herestraat 49 bus 706, Onderwijs & Navorsing I, Labo Pneumologie, B-3000, Leuven, Belgium

Fulllist of author information is available at the end of the article

O© 2012 Van Remoortel et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Central Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Systematic review

Introduction

There is evidence that regular physical activity is associated with a reduced risk of mortality and contributes to the primary and secondary prevention of several chronic diseases [1]. For example, a reduced risk of coronary heart disease, cardiovascular disease, stroke and colon cancer has been reported in more active individuals [2]. In patients with chronic obstructive pulmonary disease (COPD), regular physical activity leads to a lower risk of both COPD related hospital admissions and mortality [3]. Physical activity limitation is a major problem in patients with chronic diseases and needs to be accurately measured if therapies aimed at improving this are to be properly evaluated. A range of devices are available for this purpose but most have been validated in young, healthy subjects and their applicability to older or unwell populations, where movements tend to be slower, is not well established.

Physical activity is defined as any bodily movement, produced by skeletal muscles, requiring energy expenditure

[4]. Daily physical activity can be considered as "the totality of voluntary movement produced by skeletal muscles during everyday functioning" [5]. Estimates of daily physical activity can be obtained by different approaches; questionnaires, energy expenditure measurements and activity monitors. Questionnaires rely on the subject's recollection of activities and allow categorization of patients by physical activity (very active, active, sedentary and inactive) [6], but may lack the precision needed to detect changes in physical activity on a day to day basis.

Daily physical activity can be expressed as an overall measure of active energy expenditure, using indirect cal-orimetry techniques such as doubly labelled water or metabolic carts. Although doubly labelled water is regarded as a criterion method, this technique does not quantify the duration, frequency and intensity of physical activity performed. Metabolic cart systems which measure expired O2 and CO2 however cannot be used over extended periods of time.

Physical activity can also be monitored directly using physical activity monitors. In general, three classes of

Figure 1 Flow chart describing the identification and inclusion of relevant studies.

Table 1 Overview uniaxial activity monitors used in validation papers

Name Manufacturer Field(F)/Lab(L) + reference Size (and weight) Placement Cost Epoch length Data storage Outcomes (measured) Outcomes (calculated)

Actigraph Model 7164 (formerly CSA, MTI) Actigraph LLC Pensacola, FL F [14-24] + L [7,25-47] 5.1 x 4.1 x 1.5 cm (45.5 g) hip, ankle or wrist NA 5 s to 1 min. 22 days (1 min epochs) AC, steps EE, activity intensity level

Actigraph Model GT1M Actigraph LLC Pensacola, FL F [48-50] + L [29,49,51-57] 3.8 x 3.7x 1.8 cm (27 g) hip, ankle or wrist €239 (unit), €249 (software) 1 s to several minutes 378 days (1 min epochs) AC, steps EE, activity intensity level

Caltrac Muscle Dynamics Fitness Network, Torrance, USA F [58-62] + L [7,34,35,63-65] 7 x 7 x 2 cm waist €71 (unit) NA no data storage AC EE

Kenz Lifecorder EX Suzuken Co Ltd., Nagoya, Japan F [66,67] + L [51,68-70] 7.25 x 4.15 x 2.75 cm (40 g) waist €49 (unit)+ €250 (software) 5 s to 10 min 200 days Steps, activity level EE, activity intensity level

Calorie Counter Select II Suzuken Co Ltd., Nagoya, Japan L [7,71] 5x3x1 cm waist NA 1 day 7 days steps EE

ActivPAL PAL Technologies Ltd, Glasgow, UK L [57,72-74] 5 x 3,5 x 0,7 cm (15 g) midline on the anterior aspect of the thigh NA 1 s to 1 min 10 days Steps (cadence), different body positions, activity score

PALlite PAL Technologies Ltd, Glasgow, UK L [74] 5 x 3.5 x 0.7 cm (20 g) ankle €239 1 s to 1 min 10 days Steps

PAM model AM101B.V. Doorwerth, Netherlands L [37] 5.8 x 4.2 x 1.3 cm (28 g) waist NA 1 s to 1 min. 3 months PAM scores

Actiwatch Mini Mitter Co, Sunriver, OR, USA L [75] 4.4 x 2.3 x 1 cm (16.1 g) wrist €713 (unit), €213 (reader) 15 s to 1 min 30 days (1 min epochs) AC

Biotrainer IM Systems, Baltimore, MD, USA L [41,64,76] 7x 7 x 2 cm (51.1 g) hip NA NA 9 days AC EE

Nike and iPod sensor Apple Inc, Cupertino, CA, USA L [77] 2,4 x 3,5 x 0,8 cm (9 g) shoe €19 (sensor) NA 16 GB Ground contact time Distance, speed, EE

Polar Activity Watch 200 Polar Electro Oy, Kempele, Finland L [78] NA wrist €152 (watch +software) 1 min Up to 9 files Steps, HR EE, activity intensity level

Z t s r

Q 3 Q. P

Field study (F), lab study (L) or field + lab study (F + L). PAM; physical activity monitor, AC; activity counts, HR; heart rate, ECG; electrocardiogram, EE; energy expenditure, NA; not available, HR; heart rate.

activity monitors are being used increasingly in chronic disease populations (e.g. COPD): pedometers, acceler-ometers and integrated multisensor systems. Pedometers are devices which estimate the number of steps taken through mechanical or digital measurements in only the vertical plane. This is a limited measure of physical activity [7,8]. Accelerometers detect acceleration in one, two or three directions (uni-, bi- or triaxial acceler-ometers). These devices allow determination of the quantity and intensity of movements [9]. Integrated multisensor systems combine accelerometry with other sensors that capture body responses to exercise (e.g. heart rate or skin temperature) in an attempt to optimise physical activity assessments.

With the advancement of technology, the number of activity monitors available to measure physical activity is growing. However, despite these advances, it remains a challenge to assess physical activity in slowly moving patients (such as those with COPD, chronic heart failure and diabetes type II) [10-12]. In these patients small changes in physical activity are likely to be important effects of interventions aimed at enhancing physical activity. Therefore, in order for investigators to interpret the effect of interventions on physical activity, activity monitors that have been properly validated in these patient groups are needed.

In order to make evidence based statements on the validity of activity monitors, a systematic review was conducted to identify available activity monitors that have been validated in both healthy adults and chronic disease populations.

Methods Inclusion criteria

Studies meeting the following criteria were included: (1) Population: healthy adults and adults with a diagnosis of chronic disease in whom inactivity is a likely contributor to morbidity or a target for treatment, but whose locomotor function is relatively preserved (COPD, heart

failure, diabetes type II, frail elderly, primary pulmonary hypertension, chronic low back pain, fibromyalgia syndrome, obesity). (2) Measurement: any commercially available activity monitor for outdoor activity monitoring from uniaxial to triaxial accelerometers and multisensor devices to tools incorporating spatial information (e.g. GPS) or other information on motion. (3) Study design: studies that evaluated the validity of an activity monitor, i.e. testing an activity monitor against a criterion method, such as indirect calorimetry. Two types of validation studies were included; field validation studies (validation of an activity monitor against doubly labelled water) and laboratory validation studies (validation of an activity monitor using a metabolic cart or metabolic chamber and/or manual step-counting or video observation). (4) Clinical trials using activity monitoring as an outcome and which might contain a reference to a validation paper were included for hand-searching. (5) A search window between 1st of January 2000 until 1st of March 2012 was selected in order to capture sensors in contemporary use. This approach still allowed for the identification of older validation studies (published before 2000) of devices in current use in clinical trials. Main exclusion criteria were 1) studies in children (subjects younger than 18 years), 2) studies in subjects with abnormal biomechanical movement patterns (e.g. cerebral palsy, lower limb amputation), 3) studies only investigating the number of steps using pedometers because of the inaccuracy in measurement of total energy expenditure [7] and lack of ability to measure physical activity patterns [8].

No language restrictions were used; any non-English studies retrieved through the literature search were translated to determine their appropriateness for inclusion.

Search strategy and systematic review

Eligible studies were identified by searching the following databases: MEDLINE, EMBASE and CINAHL. A librarian was consulted prior to initiating the search in

Table 2 Overview biaxial activity monitors used in validation papers

Name Manufacturer Field(F)/Lab(L) + reference Size (lxwxh) Placement and (weight) Cost Epoch Data length storage Outcomes (measured) Outcomes (calculated)

Biotrainer Pro IM Systems, Baltimore, MD, USA L [32] 7.6 x 5 x 2.2 cm (51.1 g) hip €142 (unit), €142 (software), €70 (cable) 15 s to 22 days 5 min (1 min epochs) AC, steps, activity intensity level EE

Actitrac IM Systems, Baltimore, MD, USA L [76] 5.6 x 3.8 x 1.3 cm (34 g) wrist €570 (unit), €285 (software), €70 (cable) 2 s to 44 days 2 min (1 min epochs) AC

AMP-331 Activity Monitoring Pod, Dynastream Innovations Inc., Cochrane, AB, Canada L [26,38] 7,13 x 2,4 x 3,75 cm (50 g) right ankle (directly over the Achilles tendon) NA 1 min 28 hours steps, cadence, epochs (1 min epochs), walking speed, 3.5 days stride length, (3 min epochs) distance EE

Field study (F), lab study (L) or field + lab study (F + L). AC; activity counts, EE; energy expenditure, NA; not available.

Table 3 Overview triaxial activity monitors used in validation papers

Manufacturer Field(F)/Lab(L) + reference

Size (l x w x h) Placement Cost Epoch Data Outcomes Outcomes and (weight) length storage (measured) (calculated)

Actigraph GT3x

RT3- Research Tracker

Actigraph LLC Pensacola, FL

Stayhealthy Inc. Monrovia, CA

TriTrac R3D

Tracmor

Hemokinetics Inc, Madison, WI

Philips Research, Eindhoven, The Netherlands

L [79]

F [80,81] + L [32,38,54,55, 80,82,83]

F [14,16,80] + L [31-33,41,63, 75,80,82,84-86]

F [87-94] + L [95-97]

TracmorD (Philips Philips New DirectLife) Wellness Solutions

Dynaport activity monitor

Dynaport minimod

Biotel 3dNx

Actimarker, EW4800P

ActivTracer Actical

e-AR (earworn activity recognition sensor)

PASE (Physical Activity Sensing Earpiece)

McRoberts BV, The Hague, The Netherlands

McRoberts BV, The Hague, The Netherlands

Biotel Ltd, Bristol, UK

Panasonic Electric Works Co Ltd, Osaka, Japan

GMS, Tokyo, Japan

Mini Mitter Co, Sunriver, OR, USA

Sensixa Ltd, London, UK

F [99]

L [100,101]

MMA7260Q, Freescale Semiconductor, Austin, Texas

F [102] + L [29,103]

F [67]

L [104]

F [105] + L [26,38,44, 54,106-112]

L [113]

L [114]

Unilever Discovery, F [115] + Sharnbrook L [55]

Bedfordshire, UK

Activity Style Omron Healthcare, Pro HJA-350IT Kyoto, Japan

CAM (Continuous Maastricht Activity Monitor) Instruments B.V.

L [116]

4.6 x 3.3 x

1.5 cm (19 g)

7.1 x 5.6x 2.8 cm (65.2 g)

10.8 x 6.8 x 3.3 cm (170.4 g)

7.2 x 2.6x 0.8 cm (22 g)

3,2 x 3,2 x 0,5 cm (12,5 g)

12.5 x 9.5 x 3 cm (375 g)

8.5 x 5 x 1 cm (70 g)

12.5 x 5.8 x 0.8 cm

6 x 3.5 x 1.3 cm (24 g)

4.8 x 6.7 x 1.6 cm (57 g)

2.8 x 2.7 x .0 cm (17.5 g)

5,6 x 3,5 x 1,0 cm (7.4 g)

0,6 x 0,6 x 0,14 cm (40 g, including data logging system)

3,6 x 3,0 x 1,2 cm (16 g)

7.4 x 4.6 x 3.4 cm (60 g)

Hp, ankle €936 (device 1 s to

or wrist + software) 1 min

hip or waist €142 per 1 s to

unit, €214 1 min for docking station

waist $500 1 min

waist €142 per NA unit, €214 for docking station

Lower back €113 NA

waist + one €4900 1 s to

19 days VMU, steps EE, activity intensity level

21 days AC, VMU

14 days

21 days

22 weeks

2 days

AC, VMU

movement

leg sensor (+software) 1 min (continuously) intensity,

(thigh)

waist €1500 (unit) 1 s to

More days if different SD memory body card is used positions

7 days

hip or waist €800 5 s to 60s 700 days

waist €86 (device) 1 min

180 days

movement intensity, different

body positions, steps

VMU VMU

VMU EE, activity intensity level

hip, ankle €678 (incl. 15 s to 45 days AC, steps EE, activity

or wrist software)/ 1 min. (1 min €321 (unit) epochs)

Wrist, waist, ankle

L [117] 6.3 x 4.5 x 1.8 (102 g) leg

15 s to 1 min.

8 days

intensity level

Acceleration EE, activity units intensity level

VMU VMU

Activity intensity level

Activity intensity level, Different body positions

Field study (F), lab study (L) or field + lab study (F + L). AC; activity counts, VMU; vector magnitude units, EE; energy expenditure, NA; not available.

Table 4 Overview multisensor activity monitors used in validation papers

Name Manufacturer Field(F)/Lab(L) Size (lxwxh) Placement Cost Epoch Data Outcomes Outcomes

+ reference and (weight) length storage (measured) (calculated)

PAMS (Physical Activity Monitoring System)

Actireg

Vitaport (+ 4 uniaxial accelerometers (ADXL202))

ICSensors 3031-010, Druck, L [11

The Netherlands

Premed AS, Oslo, Norway

F [119,120] + L [46]

University of Cologne, Cologne, L [121] Germany (Vitaport)/Analog devices, Breda, The Netherlands (Uniaxialaccelerometers)

5,0 x 3,0 x 0,8 cm (Tracmor, 16 g)+ 4 tilt sensors (total weight = 1,3 kg)

8.5 x 4.5 x 1.5 cm (60 g)

1.5 x 1.5 x 1 cm (uniaxialaccelerometer, 8 g)/6 x 11 x 3 cm (Vitaport, data recorder, 500 g)

Sensewear Pro Armband Bodymedia, Pittsburgh, PA, USA F [50,122-126] + 8.8 x 5.6 x 2.1 cm (formerly Healthwear Armband) L [32,46,100, (82 g)

125-138]

SenseWear Mini Armband

Actiheart Ikcal

Multi-sensor board

Bodymedia, Pittsburgh, PA, USA

F [124]

Mini Mitter Sunriver, OR, USA L [29,108,139] 0.5 x 1.1 x 2.2 cm (clip) +

10 cm (wire) (10 g)

Teltronic AG, Biberist, Switzerland

Department of Epidemiology, University of Washington, USA

L [46] L [111]

NA 25 g

lower back (Tracmor) +

lateralaspect of the trunk and to the lateral aspect of the mid-thigh (sensors)

waist (storage unit) + chest and right thigh (sensors)

4 sensors: 2 on skin of the ventralside of each thigh, 2 on the skin of the sternum,)

Upper right arm at triceps (midhumerus point)

Upper left arm at triceps (midhumerus point)

3th intercostals space (clip)+ 2 ECG electrodes (chest)

Chest (elastic belt around the sternum)

€440 (device) +

€380 (software)

NA NA voltage units body position

(lying, sitting, standing)

s to 30 days body position min and movement

acceleration units

Steps, activity intensity level

€ 15.000 1 s to 3 days

€800 1 min 14 days (device) +

€1597 (software)

€722 1 s to 28 days Steps, activity

(device) + 1 min intensity level

€1597 (software)

€1330 15 s to 11 days Acceleration HR,

1 min (1 min HR variability, epochs) ECG amplitude

IDEEA (Intelligent Device for MiniSun, LLC, Fresno, CA, USA Energy Expenditure and Activity)

F [140]+ 7 x 5.4 x 1.7 cm (59 g) L [141] (recorder) + 1.8 x 1.5 x 0.3 cm (2 g) (sensor)

Waist (processing unit) + sole of both feet, both thighs and chest (sensors)

NA Acceleration, HR

1 s to NA Steps, activity 1 min intensity level,

different body positions

1 s to 7 days Activity code, 1 min speed, distance,

power output

Activity intensity level, EE

motility legs, motility trunk, motility body

Q 3 Q. P

Field study (F), lab study (L) or field + lab study (F+L). AC; activity counts, EE; energy expenditure, NA; not available.

order to identify appropriate search terms to describe the population (from healthy adults to patients with chronic disease), physical activity and activity monitoring. A combination of MeSH terms (MEDLINE), Emtree terms (Embase) and Cinahl headings (Cinahl) with free text words (all databases) were used (see Additional file 1 for detailed information). Refworks (www.refworks. com) was used to store and share all papers and to collect all the information of title and abstract screening, full text assessment and the hand-searching process. Each review team consisted of 3 reviewers who independently screened the titles and abstracts of the retrieved articles. Each abstract was labelled as A) excluded papers', 'B) order for full text assessment'or 'C) hand-search for references only, i.e. clinical trials which may have a reference to an older validation study. After independently reviewing the articles for inclusion, the reviewers compared their labels to ensure consensus. Once agreement had been reached, a full text copy of

each article that met the inclusion criteria was obtained (Label B). Thereafter, the same review teams looked at the full texts of the potential validation papers in detail and decided in consensus, whether the articles were indeed suitable validation papers for data extraction. Subsequently, hand-searching of the clinical trials using an activity monitor outcome which might contain a reference to a validation paper (Label C), was performed by three independent reviewers. After independently reviewing these full texts, validation papers were identified which met the inclusion criteria for full text assessment. Again, the reviewers compared their decisions to ensure consensus. Data of all included validation papers were extracted into predefined prepared Excel tables.

Data extraction

For the field studies, correlation coefficients between total and active energy expenditure from activity monitor (TEEAM and AEEAM respectively) and total

device

population

Fisher z (55% CI)Pooled r (96% CI)

Uniaxial

Leenders_1 2001 13 Actigraph Modal 7164 Healthy adults .64 16

Masse 2004 136 Cdtrac Healthy adults 18 19

Fuller 2008 59 Caltrac Healthy adults 59 18

Fogelholm 1998 20 Caltrac Chronic disease 33 11

Choquette 2009 17 Caltrac Healthy adults 37 11

Yamada_1a 2009 32 Kam Healthy adults 55 19

R a fermant anant5ca 2002 24 Kanz Healthy adults 83 14

Sutxota/ (l-squared ■ 76.9%. p =0.000)

Triaxi al

Carter 2008 23 3dNX

Dug as 2009 20 Actlcal

Yainada_1b 2009 32 Actl marker

Maddison 2009 36 RT3

Pias qui 2005 29 Tracmor

8onomi 2010 30 Tracmor0

Leenders 2 2001 13 TnTrac

Subtotal (l-squered m 51.3%. p -0.055)

Overall (l-squared ■ 79.8%. p = 0.000)

Healthy adults .59 21

Healthy adults 57 29

Healthy adults .76 19

Healthy adults .32 .22

Healthy adults 80 20

Healthy adults 46 15

Healthy adults 61 16

Mulllserisor

Mlgnault 2005 6 HealthWear Armband Chronic disease .96 .27

StOnge 2007 45 HealthWear Armband Healthy adults .86 18

LM_1 2011 21 IDEEA Healthy adults .73 13

Johannsen_1b 2010 30 SenseWear Mini Armband Healthy adults .84 21

Johannsen_1a 2010 30 Sense Wear Pro Armband Healthy adults 82 21

Koehler 2011 14 SenseWear Pro Armband Healthy adults .73 25

Mackey 2011 19 SenseWear Pro Armband Healthy adults .89 .23

SLrbiofat (l-squared = 0.0%. p = 0.543)

1111111

-.20,2,4.6.81

0.76 (0.14,1.38) 0.18(0.01, 0.35) 0 68(0 42.0 94) 0.34 (-0.13, 0 82) 0 39 (-0 14, 0 91) 0 62(0 26, 0 99) 1.19(0.76. 1 62)

0.58 (0.30. 0.66) 0,52(0.29.0.70)

0 68(0 24, 1 12) 0 65(0 17. 1 12) 1.00(0 63, 1 36) 0 33 (-0.01, 0.67) 1,10(0 71, 148) 0 50(0 12, 0 87) 0 71(0 09. 1 33)

0 71(0 48,0 93) 0,61 (0.45,0.73)

1.95(0.81.3.08) 1.29(0 99.1 60) 0 93(047, 1 39) 1.22(0 84. 1.60) 1.16(0 78, 1 53) 0 93(0 34. 1 52) 1,42(0.93, 1.91) 1,21 (1.05.1.37)

0.84 (0.78. 0.88)

082(063, 1 01) 0,68(0.56,0.77)

Figure 2 Study-specific correlation coefficients (r) and Fisher z-scores (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from doubly labelled water (TEEDLW). Each dot represents the z-score of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for TEEDLW.

and active energy expenditure measured with doubly labelled water (TEEdlw and AEEDLW respectively) were extracted. The percentage mean differences (ÀTEE and ÀAEE) with 95% confidence intervals were obtained from the reports to assess agreement between energy expenditure estimates from the activity monitor (TEEAM and AEEAM) versus energy expenditure measures from doubly labelled water (TEEDLW and AEEdlw). For the laboratory studies, correlation coefficients between activity monitor outcome and EE measured by metabolic cart/chamber were extracted. A sub-analysis included to compare correlation coefficients derived from walking based protocols to correlation coefficients derived from protocols based on activities of daily living. Agreement between energy expenditure outcomes from the activity monitor versus criterion method (indirect calorimetry) were extracted by the mean difference at different treadmill walking speeds; slow walk (<3.2 km/hr or 1 mph), intermediate speed walk (3.2-6.4 km/hr or 2-4 mph), fast walk (6.5-

8.05 km/hr or 4-5 mph) and running (8.06-11 km/hr or 5-7 mph). Accuracy of steps measured by activity monitoring was expressed as the percentage mean difference between steps measured by an activity monitor versus actual steps measured by the criterion method (video observation and/or manual step counting).

Statistical analysis

Descriptive statistics were used to report information about type of activity monitor, activity monitor outcomes and studied population. Papers were separated by type of validation, 'field validation papers' (validation of an activity monitor against indirect calorimetry, using the doubly labelled water technique) and 'lab validation papers' (validation of an activity monitor against indirect calorimetry, using a metabolic cart, metabolic chamber or direct observation).

We also analysed the results separately per type of device (uni-, bi-, triaxial and multisensor devices). We performed (DerSimonian and Laird) random-effects meta-

population

r-ifrar difference TEEflM- TEE0L*

_m%l95%Cn_

Uniaxial

Rothney 2010 22 Actigraph GT1M Healthy ad Jts 17

Leendera_1a, .freedson" 2Û06 13 Act! graph Model 7164 Healthy ad Jts 04

LeerKlars_1a_ .hendelmarr 2006 13 Actigraph Model 7164 Healthy adults 04

Johansson 2006 8 Actigraph Modet 7164 Healthy adJIs 35

Lof 2003 34 Actigraph Model 7164 Healthy ad Jts 12

FJIer 2008 59 Caltrac Healthy adJte .19

Fogelholm 1998 20 Callrac Chronic tisease 10

Choquette 2Û09 17 Caltrac Healthy sd Jts 12

Yamada_1a 2009 32 Kenz Healthy adJIs .19

Rafamantanantsoa 2002 24 Konz Healthy ad Jts 14

Subtotal (/-squired = 11.1%, p =0.1S9) Trla*lal

Yernadejb 2Û09 32

Goris 2001 30

Le0nders_1b_henclelmar 2006 13 Subtotal (l-squared = 0.0%. p -0.676)

Mult ¡sensor

Hustvsdt Anridsson Mgnault St Onge Lot_1a L0f_1b

Johannsen_1b Johannsen_1a Koetiler Mackey

Subtotal (l-squared = 0.0%. p - 0 987; Overall (l-squared = 0.5%, p = 0.454)

Acti marker

Tracmor

TriTrac

Healthy adults Healthy adults Healthy ad Jts

.19 .20 04

2004 18 Actireg Healthy ad Jts .20

2006 15 Actireg Chronic tisease 18

200B 6 Heallhwear Armband Chronic disease .26

2007 45 Heallhwear Armband Healthy ad Jte .17

2011 21 IDEEA Healthy ad Jts 13

2011 21 IDEEA Healthy ad Jts 13

2010 30 SenseWear Mini Armband Healthy ad Jts 21

2010 30 SensoWear Pro Armbend Healthy adJIs 21

2011 14 Sensewear Pro Armband Healthy ad Jts .25

2011 19 SensoWear Pre Armband Healthy ad Jts .23

-0 11 (-14.98. -21 85 (-36 52, -21 85 (-3« 52, -2 52 (-24.76. -3 47 (-21 36. -14.32 (-48 62. 0.78 (-13.86. -14 69 (-30 08 -13.50 (-33 42, -20 79 (-31 38, -12.07 |-18.28

14 75) :. -7.19) !. -7.19) 19.13) 14.42) !. 19.97) 15.52) I. 0.71) , 642) 1, -10.20) I, -5 65)

0.38 (-21.85.22.61) -4 42 (-28 75. 19.90) -11 48 (-27 19,4.23) -6.85 [-18.20. 4.49)

4 43 (-12 80. 21.66) -1.06 (-12 73. 10.62) -341 (-19 24. 12.43) -4 70 (-2941. 20.02) -8 83 (-20 35. 2 70) -8 01 (-23 57, 7 56) -0.78 (-28.28. 26.73) -4.05 (-20.96. 21.87) -1 78 (-20 63. 17.07) -1.41 (-26.68. 23.87) -3.64 (-8.97, 1.70)

-8.31 (-11.79.-4.831

-25 0 25

Figure 3 Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from doubly labelled water (TEEDLW). Each dot represents the mean difference of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for TEEDLW. *Leenders et al. 2006 (Actigraph Model7164); TEEAM estimated with most frequently used Freedson and Hendelman equation (walking outdoors), (not reported) data of % mean difference (±SD) between TEEAM - TEEDLW with other previously published equations can be found in the originalpaper [14].

analyses to pool the correlation coefficients and mean differences across studies and expressed heterogeneity by the I2 statistic, which estimates the percentage of total variation between studies that is due to heterogeneity rather than chance. I2 is calculated from basic results obtained from a typical meta-analysis as I2 = 100% x (Q-df)/Q, where Q is Cochran's heterogeneity statistic and df the degrees of freedom. Negative values of I2 are put equal to zero so that I2 lies between 0% en 100% with larger values showing larger heterogeneity. We used the Fisher r to z-transformation in order to pool normally distributed data (z scores) rather than the skewed distribution of Pearson correlation coefficients [13]. We back transformed the pooled z-scores to correlation coefficients for easier interpretation.

We used random-effects linear regression models (meta-regression analyses) with the studies' results as the dependent variable (and considering each studies' standard error) to compare the type of devices (covari-ate) and to assess the type of population (covariate) as a potential explanation for heterogeneity. For those few studies where no measures of variability were reported we imputed the median standard deviations of those

studies where the standard deviation was available. We did not perform meta-analyses for the laboratory studies where none of the studies provided standard deviations for ATEE and AAEE but presented the point estimates as graphs. Coefficient of variation for TEEDLW and AEEDLW was calculated per study population to investigate whether the degree of variation in TEEDLW and AEEDLW affected the correlation coefficients and/or mean differences, (i.e. higher correlations/mean differences in populations with larger variation in TEE and/or AEE).

Results

The systematic literature search resulted in a total of 2875 abstracts which were scrutinised by four review teams across Europe. Figure 1 represents the different processes used in the systematic review.

Forty monitors were tested in validation studies; 12 uniaxial, 3 biaxial, 16 triaxial accelerometers and 9 multisensor devices. Fifty-five percent of activity monitors (22/40) were used only in lab validation studies, 10% (4/ 40) only in field validation studies and 35% (14/40) in both a lab as well as a field validation study. An

year n device

population

Flshtr z (95% CI) Pooltdr (95%cl)

Uniaxial

Cdbert_1a

leenders

Gartfier

Ass ah

Choquette

2011 2001

2005 2004 1698 2009 2009

SuMoitfl =

56 Actigraph GT1M 13 Actigraph Model 71W 01 Actlgraph Model 7164 136 Acligraph Model 7164 22 Actl graph Model 7164 35 Actlgraph Model GTM1 17 Caltrac 77,$%, p = 0,0001

Healthy ad Jts Healthy adult s Healthy adults Healthy adull s Chronic disease Healthy ad Jts Healthy ad Jts

Triaxial

van Haes 2011 55 GENE A

JacotoMa 2007 13 RT3

Piasqui 2005 29 Tracrrtor

Vertount 2001 13 Tracmor

Pletllainen 2005 20 Tracmor

Bonoml 2010 30 Tracmor[>

JacobMb 2007 13 TnTrac

Subtotal (l-$quared = p =0.147)

Multisersor

Arvidsson L6Ma L0l_ It) Colbert_1b Mackoy

2005 2011 2011 2011 2011

21 21 56 19

Actirag IDEEA IDEEA

SenseWear Pro Anriband Healthy ad Jts SonsoWearPro Armband Healthy ad Jts

Subtotal (f-squar&d ^ 0.0%, p = 0,512) Overall (l-squared = 63.9%, p = 0.000|

.55 NP

42 0.37

10 0 37

21 0 46

.83 0.50

.37 0.55

-.30 0.42

Healthy ad Jts 47 0.34

Chronic disease .67 031

Heathy ad Jts .75 0 36

Healthy ad Jts .63 0.59

Healthy ad Jts .65 0.16

Healthy ad Jts 48 0 30

Chronic as ease .35 031

Chronic disease Healthy ad Jts Heathy ad Jts

0.53 0 18 0.16 NP 0.41

I II I

0.63(0.36,0.90)

0.45 [-0.17. 1.07)

0 31 [0 09. 0 53)

0 21 [0 04. 0 38)

1.20(0 75. 1.65)

0 39(0 04. 0 73)

-031 (-083,0 21)

041 (0 16.0.87) 0.39 (0,16, 0.681

0 51 [0 26. 0 76)

0 81 (0.19. 1 43)

1 07(069. 1 46) 0.74 (0.12, 1.36) 0.83(035. 1.30) 0 52(0.15. 0 90) 0.38 [-0.24, 1 00)

0 63(049.0 57) 0.59(0.45,0.70)

0 42 (-0 14, 0.39)

0 47 [0 01. 0 93) 054(0 07, 1 00) 0.55 [0 31. 0.55)

1 00(0 51. 1 49)

0.60(041.0.78) 0.54 (0-39, 0,65| 055(041,0 70) 0.60(0,39,0.601

-.2 0 2 .4 6 S 1

Figure 4 Study-specific correlation coefficients and Fisher z-scores (diamond) between active energy expenditure estimate from the activity monitor (AEEam) and active energy expenditure measure from doubly labelled water (AEEDLW). Each dot represents the z-score of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for AEEDLW.

overview of the different activity monitors is shown in Tables 1, 2, 3 and 4.

The most frequently available outcomes present in validated activity monitors are (total and/or active) energy expenditure (70%, 28/40), steps (38%, 15/40) and different levels of physical activity intensity (38%, 15/ 40). The majority of the validation studies (118/134, 88%) were performed in healthy adults. Few studies (16/ 134, 12%) were performed in chronic disease populations; obesity (n = 4), chronic obstructive pulmonary disease (n = 5), chronic heart failure (n = 1), chronic organ failure (n = 1), chronic low back pain (n = 1), fibromyalgia syndrome (n = 1), peripheral arterial disease (n = 1), diabetes mellitus type II (n = 1) and a general chronic disease population (cardiac, obese or knee arthritis, n = 1).

Field validation studies

Individual correlation coefficients, with converted Fisher z-scores, for total energy expenditure (TEE) between TEEAM and TEEDLW are presented in Figure 2.

Variability of study populations' TEEDLW was relatively small; coefficient of variation (CV) ranged from 0.11 to 0.29. Pooled r in uniaxial devices (r = 0.52 (95%CI, 0.29 to 0.70)) was significantly lower compared to multisensor devices (r = 0.84 (95%CI, 0.78 to 0.88), p<0.001) but not to triaxial devices (r = 0.61 (95%CI, 0.45 to 0.73, p = 0.37)). Because of the relatively large difference in accuracy between the uniaxial, the triaxial and multisensor devices 53% of the between-study heterogeneity was accounted for by type of device in meta-regression analyses.

ATEE (TEEam - TEEdlw) was less accurate in uniaxial compared to triaxial accelerometers and multisensor devices (-12.07 (95%CI, -18.28 to -5.85) % in uniaxial versus -6.85 (95%CI, -18.20 to 4.49) % in triaxial (p = 0.39 for comparison against uniaxial devices) and -3.64 (95%CI, -8.97 to 1.70) % in multisensor devices, p = 0.03 for comparison against uniaxial devices, Figure 3). ATEE were smaller in studies with chronic disease populations than in studies with healthy populations (-9% (95%CI -19 to 1)) but the difference did not reach statistical significance (p = 0.09).

population

m «an difference AEEah - ACC,., ^

In % (95% CI)

Uniaxial

Leenders 3001 13 Actigraph Model 7164 Healthy ad Jts .37

Assah_Hendetman_1± 3009 35 Actigraph Model 7164 Healthy ad Jts 55

Asssh_Freedson_1 " 2009 35 Actigraph Model 7164 Healthy ed Jts .55

Gardner 1998 23 CaItrac Chronic disease .50

Star1lng.J a 1999 35 Caltrac Healthy ed Jts (women) 28

Start ing_1b 1999 32 Cattroc Healthy ad Jts (men) .28

Subtotal (I-s qua red = Î0.SX.I1 = 0.000)

Tri axial

jacobija 2007 13 RT3

JacobMb 3007 13 TriTrac

Leercders 2001 13 TriTrac

Subtotal (l-squared = 11.0%, P * 0.130)

Multrsenscr

StOnge 2007 45

Johannsen_1b 2010 30

*tohannsen_1a 2010 30

Mackey 2011 19

Subtotal (l-squared = O-OX, p ■ 0.996)

Healthwear Aim band SenseWesrMini Armband Sensewear Pro Arm band SenseWear Prb Armband

Chronic tts ease Chronic disease Healthy ad Jts

Healthy ad Jts Healthy ad Jts Healthy ad Jts Healthy ad Jts

Overall (l-squared = 79.8%, p = 0.000)

.31 31 .37

.49 .49 41

-1—I—I-

-75 -50 -35

-58 68(-82.61.-34 75) 40 44 (-51 89. 132 78) -10.57 (-58 93,47 60) 31 01 (10 15. 51 87) -56 44 (-74.41, -38 47) -54.24 (-79.50, -28 98) -24.221-62.05, 13.61|

-9.83 (-26.24,6.58) -13 90 (-54.67. 26 87) -40.12 (-64.74. -15.50) -21.01 (-41.92, -0.11)

-25.63 (-68.35. 17.10) -21.17 (-67 64,25 30) -21.65 (-66.73. 23.42) -27.05 (-62.87. 8 78) -24.35 (-45.28. -3.42)

-24.63 (-43.11.-6.15|

~i—I—T"

0 25 50 75

Figure 5 Study-specific % mean difference (diamond) between active energy expenditure estimate from the activity monitor (AEEam) and total energy expenditure measure from doubly labelled water (AEEDLW). Each dot represents the mean difference of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for AEEDLW. *Assah et al. 2009 (Actigraph Model7164); AEEAM estimated with most frequently used Freedson and Hendelman equation, (not reported) data of % mean difference between AEEam - AEEDLW with other data derived and previously published equations can be found in the originalpaper [48].

Figure 6 (See legend on next page.)

(See figure on previous page.)

Figure 6 Study-specific correlation coefficients (r) and Fisher z-scores (diamond) between activity monitor outcomes and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols. Each dot represents the z-score of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis.

Correlations for active energy expenditure (AEE) between AEEAM and AEEDLW were higher in triaxial (0.59 (95%CI, 0.45 to 0.70)) and multisensor devices (0.54 (95%CI, 0.39 to 0.65)) compared to uniaxial (0.39 (95% CI, 0.16 to 0.58)) devices, p = 0.12 for triaxial and p = 0.32 for multisensor against uniaxial devices) (Figure 4) Types of devices accounted for only 12% of the between-study heterogeneity in the meta-regression analysis. All monitors underestimated AEE (AAEE (AEEAM - AEEDLW) -24.22 (95%CI, -62.05 to -13.61) % in uniaxial, -21.01 (95%CI, -41.92 to -0.11) % in triaxial and -24.35 (95%CI, -45.28 to -3.42) % in multisensor devices. No significant differences were found between devices (Figure 5). But AAEE were statistically significantly smaller in studies with chronic disease populations than in studies with healthy populations (-44%, 95%CI -73 to -13, p = 0.006).

Laboratory validation studies

For correlation analysis, TEE and AEE, as determined from indirect calorimetry, were used as criterion outcomes (in 89% and 11% of the studies, respectively) against different outcomes of the activity monitor (activity counts (37%), vector magnitude units (7%), total energy expenditure (48%), active energy expenditure (2%) or monitor-specific activity scores (6%).

Pooled correlation coefficients between indirect calor-imetry (TEEIC) and activity monitor outcome were lower in uniaxial (0.80 (95%CI, 0.75 to 0.84)) compared to multisensor devices (0.85 (95%CI, 0.72 to 0.92)) but the difference did not reach statistical significance (p = 0.43) No differences were found with biaxial (0.73 (95%CI, 0.33 to 0.91), p = 0.50) and triaxial (0.84 (95%CI, 0.78 to 0.89), p = 0.28) devices, either (Figure 6).

Correlation coefficients between TEEIC and activity monitor outcome were higher when tested using laboratory protocols based on walking activities (overall pooled r = 0.84 (95%CI, 0.79 to 0.87), no significant differences between types of devices, Figure 7) compared to protocols using activities of daily living involving the upper and lower limbs (overall pooled r = 0.75 (95%CI, 0.68 to 0.81, no significant differences between types of devices), Figure 8).

There was evidence of heterogeneity of results across all analyses (overall I2 ranged from 84.6% (Figure 7) to 85.9% (Figure 8)). Again, the results did not differ for

chronic disease and healthy populations in any of the analyses on laboratory validation studies.

Mean differences between TEEAM and TEEIC at different treadmill walking speeds are presented in Figures 9, 10, 11 and 12. TEE was underestimated during slow walking speed in 69% of studies (n = 16/23), whereas in only 37% of studies (n = 15/40) during intermediate walking speed, 30% of studies (n= 10/33) during fast walking speed and 37% of studies (n = 7/19) during running reported underestimation of TEE. Underestimations in the slow walking group were relatively larger.

All accelerometers underestimate steps during slow walking; from 0.94 to 60% underestimation. One uniaxial device (activPAL), mounted on the thigh, showed a high accuracy in measuring steps during slow walking with only 0.94% overestimation. More accurate estimates of steps were reported at higher speeds; from 13% under to 2% overestimation during intermediate walking speed (except one study with 35% underestimation using Sen-seWear Armband), and from 0.18 to 4.3% overesti-mation during fast walking (Figure 13).

Discussion

This systematic review of the literature identified forty activity monitors (12 uniaxial, 3 biaxial, 16 triaxial and 9 multisensor devices) that had been validated against indirect calorimetry (doubly labelled water, metabolic cart and/or metabolic chamber) in healthy adults (88% of studies) or adults with chronic disease (12% of studies).

Field and laboratory validation studies had highly heterogeneous results which could partly be explained by the type of activity monitor and the activity monitor outcome. These factors need consideration when a validation study is evaluated.

First, selecting the type of activity monitor is important. Pedometers are limited in their ability to detect certain physical activity patterns which might occur in chronic disease populations (for example, an unstable gait profile or lack of intensity of physical activity). Accelerometers can overcome this. Multi-axial acceler-ometers have the ability to measure accelerations in different orientations, which provides information about the total amount, intensity and duration of daily physical activity. Some multisensor devices, which combine

study year n device popul ation r

uniaxi al

Brooks 2005 72 Actigraph Model 7164 Healthy adults 72

Crouter 2006 15 Actigraph Model 7164 Healthy adults 85

Focht 2003 10 Actigraph Model 7164 Chronic Disease .72

Freedsor 1998 15 Actigraph Model 7164 Healthy adults 93

Fudge 1a 2007 13 Actigraph Model 7164 Healthy adults .7

Heil 2003 56 Actigraph Model 7164 Healthy adults .82

King_1a 2004 21 Actigraph Model 7164 Healthy adults .46

Leenders_1a 2003 23 Actigraph Model 7164 Healthy adults 86

Meiansonia 1934 23 Actigraph Model 7164 Healthy adults 82

Nichols 1999 20 Actigraph Model 7164 Healthy adults 94

Slootmaker_1a2009 32 Actigraph Model 7164 Healthy adults 85

Stone 1a 2007 87 Actigraph Model 7164 Healthy adults 82

Welk_1a 2000 52 Actigraph Model 7164 Healthy adults 66

Miller 1a 2010 33 Actigraph Model 7164 Healthy adults 94

Mller_1b 2010 30 Actigraph Model 7164 Healthy adults 89

Miller_1c 2010 30 Actigraph Model 7164 Healthy adults 78

AbeMa 2008 20 Actigraph Model GTM1 Healthy adults 9

FudgeJ b 2007 9 Actigraph Model GTM1 Healthy adults 9

Hageman 1b 2004 43 Biotrainer Healthy adults .43

Welk_1b ~ 2000 52 Biotrainer Healthy adults 59

Welk_2a 2003 49 Biotrainer Healthy adults .93

Hageman_1a 2004 43 Caltrac Healthy adults 67

Meiansonib 1994 23 Caltrac Healthy adults 89

Nichols 1992 56 Caltrac Healthy adults .57

Pambianco_1a 11990 20 Caltrac Healthy/Obese 68

Pambianco_1b 1990 20 Caltrac Healthy/Obese 79

AbeMb 2008 23 Kenz Healthy adults 86

Kumahara 2004 10 Kenz Healthy adults 96

Yokohama 2002 35 Kenz Healthy adults 97

SlootmakeMb2009 32 PAM model AM101B.V. Healthy adults 91

Harrington 2011 62 activPAL Healthy adults 76

Subtotal (l-squared = 79.3%, p = O.OOO)

Fisher z (96% CI) Pooled r (95% CI)

biaxial

Stone_1b 2007 47 AMP-331 King J b 2004 20 BioTrainer-Pro Subtotal (l-squared = 0.0%, p = 0.457;

triaxi al

Homer 2011

Stone_1d 2007

Ohkawara 2011

Fudge_1c 2007

Devoe_1b 2003

Jacobi 2007

Kingjd 2004

Stone_1c 2007

Levine 2001

Devoe_1a 2003

King_1c 2004 L Benders J b 2003 Subtotal (l-squared

multi sensor

Duncan 2011

Dwyer 2009

King J e 2004

Pal el 2007

11 8ioTel3dNX 47 Actical 44 ActivityStylePro 15 SioT el 3dNX

17 RT3

18 RT3 21 RT3 86 RT3

7 Tracmor 17 Tritrac R3D

21 Tritrac R3D 28 Tritrac R3D = 83.7%. p= 0.000)

Healthy adults 65 Healthy adults .51

Healthy adults 81

Healthy adults %

HJA-350ITHoalthy adults .98

Healthy adults 9

Healthy adults 57

Healthy/overweight ,4-7

Healthy adults 62

Healthy adults 93

Healthy adults 99

Healthy adults 58

Healthy adults 67

Healthy adults 9

53 Multi-sens or board

17 Sensewear Pro Armband

21 Sensewear Pro Armband

8 Sensewear Pro Armband

Healthy adults Healthy adults Healthy adults Chronic Disease

95 85 .77 93

0.78 0.56 0.72

Subtotal (/-squared = 70.3%, p = 0.016) Overall (l-squared = 84.6%, p = 0.00Q)

0.67 0.69 0.17 1 09 032 0.89 004 0.90 0.7S 1 26 089 0.94 051 1 36 1.04 067 1.00 067

0 15 0.40 1.37 050

1 03 0.38 035 060 082 1 21 1 75 1 16 074 1 04

1 14 1 82 1.65 222 141 1.43 096 1 69 1 55 221 1 62 1.37 1 07 212 1.80 1 42 1 95 227 077 096 1.95 1 12 1 81 0.92 1 30

1 55 1.77 269

2 44 1.89 1 25 1 33

0 48, 1 07 0 09. 1 04 0 47. 0 97

0 43. 1.82 1.65. 2 24 1.99, 2.60 0 91. 2 04

0 12. 1 17 0.00. 1 02 0.26. 1 19

1 44, 1 87 1.67. 3 63 0 14. 1 19 0 35. 1 27 1.08. 1.86 0 96. 1 68

1.55. 2 11 0 73. 1 78 0.56. 1.48 0 76. 2.53 0 99. 1 89

0.83 (0.78, 0.87)

0.62 (0.44, 0.75)

0.87 (0.74, 0.93)

) 0.89 (0.76, 0.96) 1.21 [1.08, 1 35) 0.84 (0.79. 0.87)

II I II I

0 24 6 81 2

Figure 7 Study-specific correlation coefficients and Fisher z-scores (diamond) between activity monitor outcomes and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on walking activities. Each dot represents the z-score of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis.

population

Fisher г (95% CI) Pooled r (95% Ct(

uniaxial

Bassett.ie

CrOLier_1a

Hendelman_1a

Patterson

Swarti

Chen_1a

Sassetl.lt)

Bassettjc

Kurmahara

2000 81

яме 20

2000 25

1993 15

2000 10

2003 60 2000 81 2000 81

2004 79 Subtotal (l-squared = 0.0%, p

Act igraph Model 7164 Actigraph Model 71W Actigraph Model 7164 Actigraph Model 7164 Actigrapti Model 7164 Actiwatch Caltrec Hene Кепи = 0.644)

biaxial

Croutor_1b 2006 20 AMP-331

weik_2a 2003 40 Actitrac

Subtotal (l-squared = 93.6%. p =0.000)

tri axial

Croiler_1c

Ohkawara

LangeMa

Van Hees

Souten

Souten

Campbell

Chen_1b

Hendelman Jaklcic Wotk_1a Subtotal (I-

Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults Healthy adults

Healthy adults Healthy adults

Г-squared - 86.8%. p =0.000)

multisensor

Koiey Duncan Cole Fnan Jaklcic

Subtotal (l-squared = 37.0%. p =0.160)

Overall (l-squared = 05-9%. p =0-000)

62 65 59 .6 55 73 58

2006 20 Actical Healthy adults 71

2011 44 Activity Style Pro HJA-350IT Healthy adults .95

2009 20 Oynaport Mini mod Healthy adults/COPD 7

2009 15 Г ум;:or* Minlrnod Healthy adults .84

1994 1С Tracmor Healthy adults 95

1997 13 Tracmor Healthy adults 89

2002 20 TritrecR3D Healthy adults 48

2003 60 Tritrac P3D Healthy adults 93

1997 Tritrac R3D Healthy adults .93

2000 25 Tritrac R3D Healthy adults .62

1999 20 Tritrac R3D Healthy adults 66

2000 52 Tritrac R3D Healthy adults 59

2010 277 Actigrapti GT1M Healthy adults .8

2011 53 Multi-sensor boarcJ Healthy adults .81

2004 24 Sensewear Pro Armband Chronic disease 78

2004 20 Sens evtear Pro Armband Healthy adults 56

2004 34 Sensewear Pro Armband Healthy adults 6

2009 20 Sensewear Pro Armband Healthy adults/COPD 75

0 73 (0 50, 0 95) 0 78(0 30, 1 25) 063(026, 1 10) 069(0 13. 1 26) 0 63 [-0.11. 1 37) 093(067. 1 19) 0 66 [0 44. 0 86) 0 62 (0 40, 0 84) 0 63 (0 41.0 86) 0 70(0 61. 0 80)

0 56(0 09. 1 04) 1.74(1.42, 2.06) 1.16 (001,232)

089(041, 1 36) 1.83(1 53,2 14) 087(039, 134) 1.22(0 66. 1 79) 1.83(1 09.257) 1.42 (0 80.2 04) 0 52(0 05, 1.00) 1.66(1 40,1 92) 1.66(1 48. 1 84) 0 73(0 31. 1 14) 079(032, 1.27) 0 68 (0 40. 0 96) 1.17(0 88, 1 47)

1.10(098, 1 22) 1.13(0 85, 1 40) 1.05(0 62, 1 47) 063(0.16, 1 11) 0 69(034, 1 05) 097(050, 1 45) 099(083, 1 15)

0.60 (0.64, 0.66)

0.82(0.01,0.98)

0.82 (0.71, 0.90)

0.76(0.68, 0.82)

0 98(0 82, 1.14 ) 0.75(0.68, 0.81)

0 2 4 6 8 1

Figure 8 Study-specific correlation coefficients and Fisher z-scores (diamond) between activity monitor outcomes and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on activities of daily living activities involving the upper and lower limbs. Each dot represents the z-score of the respective study together with a 95% confidence interval(CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis.

physiological parameters with accelerometry, are available to assess both body posture and body movement. An additional promising class of monitors integrate positioning systems (Global Positioning System (GPS) and Bluetooth® systems for outdoor and indoor activities respectively) with accelerometry and other sensors. However, to date, these have been used infrequently in patients with chronic disease [142,143]. Based on this systematic review, heterogeneity among studies was significantly explained by the types of devices, although no statistical significance was reached between different types of devices.

A second factor to take into consideration is the activity monitor outcome. When measuring TEE in field validation studies (doubly labelled water), high correlations with the TEE estimate of the activity monitor were found in most activity monitors. These correlations are,

however, to a large extent driven by patient characteristics (i.e. body weight, age, height) [87] which is an important predictor of TEE. Consequently, the comparison of TEE estimated from activity monitors, with TEE measured with indirect calorimetry or doubly labelled water is not necessarily a proof of validation. In a field setting it has been reported that only 19% of the TEE is accounted for by physical activity in both healthy subjects [87] and in patients with coronary heart disease [144].

Another factor that needs to be considered is the study population. Most of the study populations (88%) were healthy adults (from young healthy adults to healthy elderly). Only 12% of validation studies were performed in patients with chronic diseases (COPD, chronic heart failure, chronic organ failure, diabetes mellitus type II, obesity, peripheral arterial disease chronic low back pain

study year n device population % difference ТЕЕд„ -TEE1C (95% CI)

uniaxi al

Slootmaker 2009 32 Actigraph Model 7164 Healthy adults ■ -44.00

King 2004 21 Actigraph Model 7164 Healthy adults 9 -60.40

Miller_la 2010 30 Actigraph Model 7164 Healthy adults ■ -17.00

Millerjb 2010 30 Actigraph Model 7164 Healthy adults ■ -11.00

MilleMc 2010 30 Actigraph Model 7164 Healthy adults Я -20 00

Pambianco_1a 1990 10 Caltrac Healthy adults я 19 18

Pambianco_1b 1990 10 Caltrac Obese adults U -4 00

Yokoyama 2002 35 Kenz Healthy adults я 22.20

Hikihara_le 2011 33 Kenz Healthy adults л -12 70

Slootmaker 2009 32 PAM model AM1018.V Healthy adults ш -47.51

Harrington 2011 62 activPAL Healthy adults я -13 00

biaxial

King_1a 2004 20 BioTrainer-Pro Healthy adults я -14.50

triaxial

Hiklharajlb 2011 33 Actl marker Healthy adults и -9.00

Hiklharajlc 2011 33 ActivTracer Healthy adults ш -11.30

Ohkarwera 2011 22 Activity Style ProHJA-350IT Healthy adults я 3.20

King_1b 2004 21 Tritrac R3D Healthy adults я 1120

Leandars 2003 28 Tritrac R3D Healthy adults ш 9.09

Atallah 2011 25 e-AR Healthy adults I 0.00

multisensor

King 2004 21 Sensewear Pro ArmBand Healthy adults я 58 91

Furlanetto_la 2010 30 Sensewear Pro ArmBand Healthy adults я -15.00

Furtanetto_ib 2010 30 Sensewear Pro ArmBand Healthy adults • -2.00

Furlanetto_lc 2010 30 SenseWaar Pro ArmBand Chronic disease ■ -40.00

Furtanetto_1d 2010 30 SenseWear Pro ArmBand Chronic disease я -26 00

I 1 1 I I I

-75 -50 -25 0 25 50 75 Figure 9 Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on slow walking speed.

Each dot represents the % mean difference of the respective study.

and fibromyalgia syndrome). These patients walk more slowly than healthy subjects, which is reflected, for example, by a reduced six minute walking distance [145,146]. This review, as well as original research [147], suggests that most monitors are less accurate at lower walking speeds. These findings are consistent with a systematic review of pedometers which found evidence of reduced accuracy during slow walking [148]. Hence, there is a need to perform validation studies specifically in chronic disease populations.

When measuring TEE in lab validation studies by assessment of oxygen consumption, higher correlations were reported for walking activities compared to other daily life activities which implies that the walking component of physical activity is better detected than other activities of daily living.

Most activity monitors use prediction equations to calculate energy expenditure from the activity signals. This

is helpful to validate monitors against indirect calorim-etry, but, given the inherent inaccuracy of these estimates and fundamental differences between the different prediction equations (some of which are proprietary to particular device manufacturers), perhaps greater weight should be given to direct monitor outputs (steps, activity counts, VMU, etc.) and their relation to activity energy expenditure (AEE), rather than the ability of a monitor to estimate energy expenditure precisely [48,87-89]. It is very unlikely that an activity monitor will be able to capture accurately all the factors affecting energy expenditure (i.e. movement efficiency, resting metabolism, distribution of fat-free mass and fat mass). In patients with COPD, for example, Baarends et al. showed that non-resting energy expenditure (TEE-REE) was elevated in COPD compared to healthy controls [149]. Since it is generally accepted now that these patients are less active than healthy controls [150,151], it is clear that patients

study year n device

uniaxial

Bassatl_1a 2000 81 Actigraph Model 7164

Bas$etl~it> 2000 81 Actigraph Modal 7164

Crouter_ia 2006 20 Actio W1 Modal 7164

Freedson 1998 15 Actigraph Model 7164

king la 2004 21 Actigraph Model 7164

Slootmaker .la 2009 32 Actigraph Model 7164

Welk 1a 2000 52 Actigraph Model 7164

Miller la 2010 30 Actigraph Model 7164

Miller lb 2010 30 Actigraph Model 7164

Miller 1c 2010 30 Actigraph Model 7164

Wetkjb 2000 52 BioTrainer

Bassett 1c 2000 81 Caltrac

Bassattjd 2000 81 Caltrac

Pambianco. ia 1990 10 Caltrac

Pambiancol it> 1990 10 Caltrac

Hikihara_1a 2011 33 Kan;

Basseuje 2000 81 Kenz Li f«corder

Vbkoyema 2002 35 Kern Lilecorder

Slootmaker. .ib 2009 32 PAM model AM101B.V

Harrington 2011 62 actlvPAL

population

% differs nee TEEiM -TEEK(95% CI)

biaxial

CrouteMb King J b

2006 2004

AMP-331 BioTralner-Pro

Healthy adJts Healthy adults Healthy adJts Healthy adJts Healthy adults Healthy adults Healthy adults Healthy adults Healthy adJts Healthy adults Healthy adults Healthy adults Healthy adJts 10 Healthy adults 10 Obese aduts Healthy adJts Healthy adult; Healthy adJts Healthy adJts Healthy adults

Healthy adults Healthy adults

triaxlal

Crouter^lc 2006 20 Actical Healthy adJts

Splerer_1a 2011 27 Actical Healthy adults

Hikihara_1b 2011 33 Actirnarker Healthy adults

Hikihara^Jc 2011 33 ActivT racer Healthy adults

Ohkavara 2011 22 Activity Style Pro HJA-350IT I Healthy adults

King J d 2004 21 RT3 Healthy adults

King" 1c 2004 21 Tritrac R3D Healthy adJts

Leeriders _1 a 2003 28 Tntrac R3D Healthy adults

Leendars 1b 2003 28 Tritrac R3D Healthy adults

Welk ic" 2000 52 Tritrac R 3D Healthy adults

Alaiian 2011 25 a-AR Healthy adJts

multlsensor

Crouter_2a 2008 48 Actiheart Healthy adults

Splerer_1b 2011 27 Adiheart Healthy adults

Fruin 2004 20 Sens eWear Pro Arm Band Healthy adults

Jaklcic 2004 34 Sens eWear Pro Arm Band Healthy adJts

King_1e 2004 21 Sens eWear Pro Arm Band Healthy adults

Fur1anetto_1a 2010 30 Sens eWear Pro Arm Band Healthy adults

Furianetto lb 2010 30 Sans eWear Pro Arm Band Chronic disease

25 60 22 92 -4 44

-9.20 -25.60 -7 47 5.00 20 00 8 00 23.73 19.70 31.98

16 40 -1.82 -1050

17 39 1610 -30.74 5.00 6.64

-4.56 21.84

-15.00 -8 00 -7 30 7.40 47.09 31.31 21.93 18 87 33 60 1 00

-11 76 -25 00 20 00 30 95 45 39 -5 00 -IS 00 5.08

Figure 10 Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on intermediate walking speed. Each dot represents the % mean difference of the respective study.

expend more energy than controls to achieve the same movements. It would be unrealistic to expect an activity monitor to pick this up. Hence, the lack of accuracy against energy expenditure does not render activity monitors invalid tools to assess physical activity in patients over time (for which precision is more important) or to capture the physical activity level of a patient (for which validity, represented by the correlation with true energy expenditure is more important than absolute accuracy). The acceptable correlations between VO2 and activity monitor outputs in triaxial and multisensor devices are therefore encouraging for the use of monitors to assess physical activity in an adult population. With specific validation studies, these findings can possibly be extrapolated to elderly and patients with chronic diseases.

The current systematic review may also help researchers to decide on appropriate activity monitor outcomes. Combination of the three most frequently available outcomes (TEE/AEE, steps and different levels of physical activity intensity), which is likely to provide a comprehensive insight in overall physical activity of a patient, is available in 3 uniaxial (Actigraph 7164/GT1M, Kenz Lifecorder EX and Polar Activity Watch 200), 1 biaxial (Biotrainer Pro), 3 triaxial (Dynaport Minimod, Actical and Actigraph GT3X) and 2 multisensor activity monitors (SenseWear Armband and multisensor board). Some general considerations can also be taken into account when selecting an activity monitor in clinical trials such as the type of monitoring (e.g. daily physical activity), size and scope of the study, usability of the monitor and cost [152].

study year n device population % difference TEEiM-TEEK(95% CI)

uniaxial

CrouteMa 2006 20 Actigraph Model 7164 Healthy adults ■ 24.66

Crouter_3a 2006 15 Actigraph Model 7164 Healthy adults m 22.70

Crouter_3b 2006 15 Actigraph Model 7164 Healthy adults 6 80

Freedson 1S98 15 Actigraph Model 7164 Healthy adults » 8.06

King 1e 2004 21 Actigraph Model 7164 Healthy adults m 6.91

Klng_1f 2004 21 Actigraph Model 7164 Healthy adults * 5.43

Slootmaker_1a 2009 32 Actigraph Model 7164 Healthy adults m -37 90

Slootmaker 1b 2009 32 Actigraph Model 7164 Healthy adults u -45 90

Welk 1a 2000 52 Actigraph Model 7164 Healthy adults u -7.71

Miller" 1a 2010 30 Actigraph Model 7164 Healthy adults ■ 7 00

Miller lb 2010 30 Actigraph Model 7164 Healthy adults 20.00

Miller 1c 2010 30 Actigraph Model 7164 Healthy adults 8.00

Welk_lb 2000 52 BioTrainer Healthy adults ■ 22 75

Pambianco_1a 1990 10 Caltrac 10 Healthy adults № 5.30

Pambianco_1b 1990 10 Caltrac 10 Obese adults H 19.21

Hlklhara_la 2011 33 Kenz Healthy adults ■ -12 90

Yokoyama 2002 35 Kenz Lifeoorder Healthy adults * 1320

Slootmeker_1c 2009 32 PAM modal AM101B V. Healthy adults m -31.10

Hamngton 2011 62 activPAL Healthy adults ■ 31 25

biaxial

CrouteMb 2006 20 AMP-331 Healthy adults • 339

King_1a 2004 20 BioTrainer-Pro Healthy adults M 37.17

trl axial

CroUteMc 2006 20 Actical Healthy adults H 17.87

Splerer_la 2011 27 Actical Healthy adults m -5 00

Hiklharajlb 2011 33 Acti marker Healthy adults u -11.70

Hiklharajlc 2011 33 ActivTracer Healthy adults m -9.10

Ohkawara 2011 22 Activity Style Pro H JA-350IT Healthy adults * 2 60

King 1c 2004 21 RT3 Healthy adults H 36.84

Klngjb 2004 21 TntracR3D Healthy adults m 24.00

Leenders 2003 28 Tritrac R3D Healthy adults m 18.46

Welk_1c 2000 52 Tntrac R3D Healthy adults m 44 59

multl sensor

Crouter_2a 2008 48 Actiheart Healthy adults m -3.33

Splererjlb 2011 27 Actihaart Healthy adults ■ -23 00

Klng_1d 2004 21 SensaWaar Pro ArmBand Healthy adults m 21 22

I I l 1 1 1

•75 -50 -25 o 25 50 75 Figure 11 Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on fast walking speed. Each dot represents the % mean difference of the respective study.

Methodological issues

A point of difficulty in collecting, analysing and interpreting the data was the wide range of statistical approaches used in the original papers. Indeed, we had to compute the standard deviation of the mean difference (between EEAM and EEIC) because some field validation studies didn't report this.

Correlation analysis but also Bland and Altman analysis were the two main statistical approaches used in validation studies and were used for data extraction. A systematic review of the statistical methods used to validate physical activity questionnaires revealed similar findings, with the majority of the studies using correlation analysis compared to Bland and Altman analysis [153]. Correlation analyses are a common evaluation approach and allow statements on validity, whereas agreement between activity monitor and criterion method (indirect calorimetry) with Bland and Altman plots are

preferred when the aim is to identify systematic bias in measures [154]. Since not all activity monitors have the possibility to estimate total and/or active energy expenditure, this type of analysis is not uniformly applicable. Multiple regression analysis with TEE/AEE as the dependent variable is a correct technique to tackle this [87]. Consistent statistical guidelines for reporting the validity of an activity monitor would be helpful.

Conclusion

Validation studies of activity monitors are highly heterogeneous, and this is partly explained by the type of activity monitor and the activity monitor outcome. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.

Figure 12 Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on running speed. Each dot represents the % mean difference of the respective study.

Figure 13 Accuracy of steps at different walking speeds. The dots are reflecting walking speed: slow walking (<3.2 km/hr (□)), intermediate walking (3.2-6.4 km/hr (■)) and fast walking (6.5-8 km/hr (▲)).

Additional files

Additional file 1: Details of the search strategy terms in the different databases.

Abbreviations

AEE: Active energy expenditure; AEEam: Estimation of active energy expenditure by an activity monitor; AEEDLW: Measurement of active energy expenditure by doubly labelled water; AEEIC: Measurement of active energy expenditure by indirect calorimetry; AM: Activity monitor; COPD: Chronic obstructive pulmonary disease; DLW: Doubly labelled water; GPS: Global positioning system; IC: Indirect calorimetry; TEE: Totalenergy expenditure; TEEam: Estimation of totalenergy expenditure by an activity monitor; TEEdlw: Measurement of totalenergy expenditure by doubly labelled water; TEEIC: Measurement of totalenergy expenditure by indirect calorimetry.

Competing interests

The authors declare not having financial or non-financialcompeting interests. Authors' contributions

Allauthors contributed to the different processes in the systematic review (title and abstract screening, fulltext assessment and data extraction). HVR worked out an appropriate search term strategy in different databases. HVR and SG have been involved in analysing the data and drafting the manuscript which was revised by allthe other authors. MP performed the statisticalanalysis. Allauthors read and approved the finalmanuscript.

Authors' information

This systematic review is part of the PROactive project (www.proactivecopd. com) which aims to develop a Patient Reported Outcome (PRO) tool capturing physical activity in COPD in close harmony with a valid activity monitor.

Acknowledgements

The authors would like to thank Jens De Groot (librarian at Biomedical Library, Katholieke Universiteit Leuven, Belgium) for his assistance in the search strategy of the different databases. This work (as part of the PROactive project) is funded by the Innovative Medicines Initiative Joint Undertaking (IMI-JU) and EFPIA # 115011.

The authors would like to acknowledge the members of the PROactive consortium for the outstanding contribution to this work. PROactive consortium: Chiesi Farmaceutici S.A.: Caterina Brindicci, Tim Higenbottam; Katholieke Universiteit Leuven: Thierry Troosters, Fabienne Dobbels, Marc Decramer; Glaxo Smith Kline: Margaret X. Tabberer; University of Edinburgh, Old College South Bridge: Roberto A Rabinovich, William McNee; Thorax Research Foundation, Athens: Ioannis Vogiatzis; Royal Brompton and Harefield NHS Foundation Trust: MichaelPolkey, Nick Hopkinson; MunicipalInstitute of MedicalResearch, Barcelona: Judith Garcia-Aymerich; Universität Zürich, Zürich: Milo Puhan, Anja Frei; University Medical Center, Groningen: Thys van der Molen, Corina De Jong; Netherlands Asthma Foundation, Leusden: Pim de Boer; British Lung Foundation, UK: Ian Jarrod; Choice Healthcare Solution, UK: PaulMcBride; European Respiratory Society, Lausanne: Nadia Kamel; Pfizer: Katja Rudell, Frederick J. Wilson; Almirall: Nathalie Ivanoff; Novartis: Karoly Kulich, Alistair Glendenning; AstraZeneca AB: Niklas X. Karlsson, Solange Corriol-Rohou; UCB: Enkeleida Nikai; Boehringer Ingelheim: Damijen Erzen.

C.B. is a doctoralfellow of the Research Foundation Flanders. D.L. is a post doctoralfellow of the Research Foundation Flanders. N.S.H. and Y.R. are supported by the NIHR Respiratory BiomedicalResearch Unit at RoyalBrompton and Harefield NHS Foundation Trust and Imperial College, London, United Kingdom.

Author details

1Faculty of Kinesiology and Rehabilitation Sciences, Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium and Respiratory Division, UZ Gasthuisberg, Leuven, Belgium. 2ELEGIColt Laboratory, Centre for Inflammation Research, University of Edinburgh, Edinburgh, Scotland, United Kingdom. 3NIHR Respiratory Biomedical Research Unit at RoyalBrompton and Harefield NHS Foundation Trust and ImperialCollege, London, United Kingdom. 4Department of Physical

Education and Sports Sciences, Thorax Foundation, Research Centre of Intensive & Emergency Thoracic Medicine, Athens, Greece and National& Kapodistrian University of Athens, Athens, Greece. 5Centre for Research in EnvironmentalEpidemiology, Barcelona, Spain. 6GlobalHealth Economics and Outcomes Research, Novartis Horsham Research Centre, Horsham, United Kingdom. 7Precision Medicine, Pfizer Worldwide Research and Development, Sandwich, Kent, United Kingdom. 8Horten Centre for Patient-oriented Research, University Hospitalof Zürich, Zürich, Switzerland. 9Department of Epidemiology, Johns Hopkins Bloomberg Schoolof Public Health, Baltimore, MD, USA. 10PROactive consortium, Europe, Europe. "Respiratory Rehabilitation and Respiratory Division, UZ Gasthuisberg, Herestraat 49 bus 706, Onderwijs & Navorsing I, Labo Pneumologie, B-3000, Leuven, Belgium.

Received: 17 November 2011 Accepted: 13 June 2012 Published: 9 July 2012

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doi:10.1186/1479-5868-9-84

Cite this article as: Van Remoortel et al.: Validity of activity monitors in health and chronic disease: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 2012 9:84.

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