Considerations when using the activPAL monitor in field-based research with adult populations

Charlotte L.Edwardson*,Elisabeth A.H.Winkler,Danielle H.BodioatTom Yates

Melanie J.Daviesa,b,David W.Dunstanc,d,e,f,g,h,Genevieve N.Healyc,d,i

aDiabetes Research Centre,Leicester General Hospital,University of Leicester,Leicester LE5 4PW,UK

bNIHR Leicester-Loughborough Diet,Lifestyle,and Physical Activity Biomedical Research Unit,Leicester LE5 4PW,UK

cSchool of Population Health,The University of Queensland,Brisbane,QLD 4006,Australia

dBaker IDI Heart and Diabetes Institute,Melbourne,VIC 3004,Australia

eSchool of Exercise and Nutrition Sciences,Deakin University,Burwood,VIC 3125,Australia

fDepartment of Epidemiology and Preventive Medicine,Monash University,Melbourne,VIC 3800,Australia

gSchool of Sport Science,Exercise and Health,The University of Western Australia,Perth,WA 6009,Australia

hCentral Clinical School,Monash University,Melbourne,VIC 3800,Australia

iSchool of Physiotherapy and Exercise Science,Curtin University,Perth,WA 6102,Australia

Considerations when using the activPAL monitor in field-based research with adult populations

Charlotte L.Edwardsona,b,*,Elisabeth A.H.Winklerc,Danielle H.Bodicoata,b,Tom Yatesa,b,

Melanie J.Daviesa,b,David W.Dunstanc,d,e,f,g,h,Genevieve N.Healyc,d,i

aDiabetes Research Centre,Leicester General Hospital,University of Leicester,Leicester LE5 4PW,UK

bNIHR Leicester-Loughborough Diet,Lifestyle,and Physical Activity Biomedical Research Unit,Leicester LE5 4PW,UK

cSchool of Population Health,The University of Queensland,Brisbane,QLD 4006,Australia

dBaker IDI Heart and Diabetes Institute,Melbourne,VIC 3004,Australia

eSchool of Exercise and Nutrition Sciences,Deakin University,Burwood,VIC 3125,Australia

fDepartment of Epidemiology and Preventive Medicine,Monash University,Melbourne,VIC 3800,Australia

gSchool of Sport Science,Exercise and Health,The University of Western Australia,Perth,WA 6009,Australia

hCentral Clinical School,Monash University,Melbourne,VIC 3800,Australia

iSchool of Physiotherapy and Exercise Science,Curtin University,Perth,WA 6102,Australia

Research indicatesthathigh levelsofsedentary behavior(sitting orlying with low energy expenditure)are adversely associated with health.Akey factor in improving our understanding of the impact of sedentary behavior(and patterns of sedentary time accumulation)on health is the use of objective measurement tools that collect date and time-stamped activity information.One such tool is the activPAL monitor.This thigh-worn device uses accelerometer-derived information aboutthigh position to determine the startand end ofeach period spentsitting/lying,standing,and stepping,aswellas stepping speed,step counts,and postural transitions.The activPAL is increasingly being used within field-based research for its ability to measure sitting/lying via posture.We summarise key issuesto considerwhen using the activPALin physicalactivity and sedentary behavior field-based research with adult populations.It is intended that the findings and discussion points be informative for researchers who are currently using activPAL monitors or are intending to use them.Pre-data collection decisions,monitor preparation and distribution,data collection considerations,and manual and automated data processing possibilities are presented using examples from current literature and experiences from 2 research groups from the UK and Australia. ©2017 Production and hosting by Elsevier B.V.on behalf of Shanghai University of Sport.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Inclinometer;Measurement;Physical activity;Posture;Sedentary behavior;Sitting

1.Introduction

Over the past decade,there has been substantial,and growing,scienti fic interest in sedentary behavior.1–3In 2012,an expert consensus de fined sedentary behavior as“any waking activity characterised by an energy expenditure≤1.5 metabolic equivalents and a sitting or reclining posture”.4It is now recognised that sedentary behavior is common(on average adults spend 46%–73%of waking hours sedentary),5–10and that too much time spent sedentary may be detrimental to health both in the short term11–14and long term.3,15–17

The availability of objective measurement tools with date and time-stamped information about activity is a key factor in improving our understanding of the impact of sedentary behavior and patterns of sedentary time accumulation on health.Most of the evidence on the associations of objectively assessed sedentary time and health has been derived from tools that infer sedentary time from a lack of movement.8,10,18–20However,this can lead to misclassi fication of low-intensity non-sedentary behaviors as sedentary behaviors.21A key example of this is standing.Like sitting or lying,standing involves minimal movement and low energy expenditure.22However,unlike sitting or lying,this behavior is characterised by its upright posture which elicits higher muscle contractile activity23with associated bene ficial impacts on physiological processes such as glucose metabolism.11,13–24

Notably,while the 2012 de finition of sedentary behavior includes both energy expenditure and postural elements,4no field-based tool as yet directly and accurately captures both of these elements.The thigh-mounted activPAL monitor (activPALTM,activPAL3TM,and activPAL3TMmicro;PAL Technologies Ltd.,Glasgow,UK)is 1 measurement device that directly measures the postural aspect of sedentary behavior.The activPAL device(referring to all models)is a small and slim thigh-worn monitor.Via proprietary algorithms(Intelligent Activity Classi fication),accelerometer-derived information about thigh position and acceleration are used to determine body posture(i.e.,sitting/lying and upright)and transition between these postures,stepping,and stepping speed (cadence),from which energy expenditure is inferred indirectly. The activPAL has almost perfect correlation and excellent agreement with direct observation for sitting/lying time,upright time,sitting/lying to upright transitions and for detecting reductions in sitting.21,25–27Additionally,it accurately distinguishes standing from stepping26and identifying stepping speed(cadence);28however,accuracy for detecting stepping is compromised at very slow(i.e.,＜0.5 m/s)walking speeds.29As such,the use of the activPAL device in physical activity and sedentary behavior research has increased rapidly in recent years(460%increase from 2008 to 2014 on the Scopus citation database).

With the increasing use of activPAL monitors to address important questions in sedentary behavior research,it is timely to consider some of the methodological and practical considerations speci fic to these monitors.Existing best practice recommendations for objective activity monitoring,such as those outlined by Matthews et al.,30provide an excellent starting point.However,these are either general,or focused on other monitors that have key differences to activPAL devices,from the method and location of attachment,to the properties of the resultant data.Accordingly,some existing recommendations may not be applicable,and the unique opportunities and challenges speci fic to activPAL monitors warrant consideration and further elucidation.

This report provides an overview of the key data collection and processing issues to consider when using the activPAL activity monitor in physical activity and sedentary behavior field-based research with adult populations.The considerations discussed are categorised under:pre-data collection,monitor preparation and distribution,data collection,data processing, and data reporting.The considerations are mainly based on the procedures and protocols reported in the current literature (free-living adult studies identi fied from the PAL Technologies’bibliography(September 2014)and by searching the term“activPAL”in PubMed(October 2015)).Only those accessible in full-text form were included(Table 1).However,given the paucity of detail in the published literature,we also based considerations on the experiences from 2 international research groups(Table 2).These experiences span across both epidemiological and intervention study designs,various adult population groups and settings.It is intended that these findings and discussion points be informative for researchers who are both currently using activPAL monitors or are intending to use such devices.It is not intended that the practices employed to date should be taken as best practice for the field.

2.Pre-data collection considerations

2.1.Wear period:number of days of monitoring

The number of days of monitoring ideally depends on the study design and purpose.The majority ofstudies(71%)thatwe considered in the literature(Table 1)and those in Table 2 have asked participants to wear the activPAL for 7 full days.To our knowledge only 1 study has reported how many days of monitoring are required to provide adequate reliability for several activPAL outputs(sitting,standing,stepping,and transitions)in adults.31Applying the Spearman–Brown Prediction Formula,32Reid et al.31showed to achieve intra-class correlations(ICCs)of 0.8 and 0.9,respectively,5 days and 11 days respectively were needed for sitting,5 days and 10 days respectively for standing, and 7 days and 15 days respectively for stepping in a population of older adults living in residential care.However,this approach has limitations,as each day is treated as randomly sampled(but they consecutive)and no distinction is made between particular daysofthe week.In reality,mean activity levelsand correlations are likely to vary by day of the week.33

More recently,generalisability theory has been applied to investigate the reliability of activPAL measured sitting time and moderate-to-vigorous physical activity(MVPA).34Generalisability theory gives a better indication of repeatability than the ICCs,particularly when more sources of variation, including seasonality,are considered.35Barreira et al.34showed that in women,to achieve G-coef ficients(interpreted identically to an intra-ICC value)of 0.8 and 0.9,respectively,4 and 9 wear days were needed for sitting and 7 and 21 wear days were needed for MVPA.Achieving an acceptable degree of repeatability,whether by ICC or G-coef ficients,indicates that within-individual variation is low in proportional to other sources of variation.The number of days required to achieve a particular ICC or G-coef ficient relates to both properties of the measure and the population,and therefore should be reported for a wider range of outputs and populations using up-to-date methods.

From a practical perspective,researchers are also limited by the 16 MB(32 MB for activPAL3 micro)memory capacity of the activPAL3 monitor,which with a sampling frequency of 20 Hz(80 Hz available in research mode)allows up to 14 days of monitoring.The activPAL3 micro has a larger memory capacity,yet still only allows for up to 14 days of monitoring. Pending better recommendations,for a single assessment, studies should use a protocol that is at least 7 days and ideally up to the 14 days limitation of the monitor.This recommendation takes into consideration that the number of days requirements are largely unknown,butattimes exceed 7 days.Multiple assessments,including covering multiple seasons,have been shown to improve reliability and better estimate long-term activity over single-season assessments.35

2.2.Wear period:time of wear

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Table 2 Summary of the studies used to inform the report—all of which used activPAL3.

In studies where the wear protocol was clear(Table 1,38/55 studies),32%asked participants to wear the monitor during waking hours only(e.g.,Ref.36),working hours only(e.g., Refs.37,38)or during periods based on researcher convenience (i.e.,staff working hours;e.g.,Ref.39).In these studies,the activPAL was usually attached with PAL stickies(PAL Technologies Ltd.)or an unspeci fied adhesive.A slightly smaller percentage of studies(26%)employed a 24 h protocol while requested that participants remove the monitor for showering/ bathing(e.g.,Refs.40,41).Again these studies tended to use PAL stickies for attachment.A higher percentage(42%)of the studies we examined requested participants to wear the activPAL monitor 24 h per day during waking and sleeping hours and water-based activities(e.g.,Refs.42,43);therefore, it appears that researchers are favoring this type of wear protocol for this monitor.This is in contrast to the ActiGraph device where researchers have mostly employed a waking wear protocol.30A continuous wear protocol can easily be achieved with the activPAL monitor by waterproo fing the device with a small flexible sleeve to cover the monitor,wrapping it in 1 piece of waterproof medical grade adhesive dressing(e.g.,Tegaderm, Hypa fix,or Opsite)and then attaching to the leg using adhesive dressing.A continuous wear protocol like this may increase wear time compliance.44

3.Monitor preparation and distribution

3.1.Initialisation

The activPAL software allows researchers to select an immediate start time for recording(from the time the device is unplugged from the docking station until the battery runs out, the memory is full,or the device is plugged into the docking station again),or a future date and time,along with the stop date and time.Unfortunately,there is only a 4-day limit on initialisation.For example,to begin recording on a Monday morning,the earliest that the device could be initialised would be on the previous Thursday morning.

The default sampling frequency in the software is 20 Hz for the activPAL3,but 80 Hz can be selected in research mode,or 10 Hz for the activPAL(uniaxial version).The software also gives researchers the option of changing the minimum sitting/ upright time period to de fine a new posture from 1 s to 100 s. The default of 10 s(i.e.,≥10 s of sitting/lying or upright data is needed to register as a new event)is recommended by the manufacturer.In the studies in Table 2,and in other published studies(e.g.,Ref.45),the default of 10 s has been selected; however,research suggests that this may not be appropriate for all populations.46In a sample of very young children(mean age of 4.5 years),Alghaeed et al.46found that a 2 s,in comparison to 1 s,5 s,and 10 s,minimum sitting/upright time period had the smallest error in detecting the number of breaks performed against direct observation.This suggests that young children transition rapidly between postures and the default of 10 s to register a new event may not be appropriate for this age group.Therefore,it is important that researchers apply the appropriate settings for their population,and report the settings in publications.However,to our knowledge,similar studies have yet to be conducted with other populations so the extent to which this setting impacts on the number of breaks in adult populations,for example,remains unclear.

An important,and often overlooked,consideration when initialising the monitors is to be aware of the system used to record the date/time-stamped data,especially when intending to match the monitor data with date–time stamped data from other monitors and diaries,orwhen collecting data in a time-zone that involves discontinuities(such as“losing”or“gaining”1 h with daylight savings).Furthermore,when dealing with activPAL dataaspartofa multi-sensordatacollection thatrequiresmatching outputon fine time scales,clock driftshould be considered.47

3.2.Attachment location and method

The algorithm used by the activPAL software uses information from static acceleration(due to gravity)and angle of the thigh to classify posture(lying/sittingvs.upright)and dynamic acceleration(due to body movement)to determine stepping.48Accordingly,it is important that the activPAL device is worn on the midline anterior aspect of the upper thigh as recommended by the manufacturer.An example of correct placement for the activPAL is shown in the example instruction sheet(the example instruction sheet can be found as Supplementary File 1).Of the studies in Table 1 that did report information on the exact activPAL location(i.e.,where on the leg and which leg),they all reported attaching the activPAL to the right thigh apart from studies conducted in stroke populations where it was attached to the unaffected leg(e.g.,Refs.39,49).While it can be useful to standardise which leg the activPAL is attached to, participants involved in the studies in Table 2 occasionally experienced slight irritation from the adhesive dressing.In this situation they were advised to attach the activPAL to the opposite leg.This enabled the participant to attach the monitor in the same optimal location on the opposite leg in preference to using a sub-optimal location on the same leg(e.g.,too far away from the midline)or ceasing to wear the monitor completely. Two studies42,50in Table 1 mentioned collecting the times and reasons from participants of every occasion when the monitor was removed from the indicated position.

The orientation in which the activPAL monitor is worn is critical to how the software classi fies posture.The correct orientation is indicated by the figure on the front of the activPAL monitor(Fig.1A),but this can be obscured when using waterproof coverings.In this case,we have redrawn the orientation figure on the waterproof covering(Fig.1B).If,however,participants wear the device upside down,the activPAL software (from Version 7.2.32)allows researchers to reprocess the data as though it were worn in the correct orientation.There is no automatic function within the software to determine when the device has been worn in the incorrect orientation so it may be useful to ask participants to record this in a wear log.

As stated in Section 2.2 the activPAL monitor is attached using various different adhesives(i.e.,PAL stickies,medical dressing,e.g.,Hypa fix);the type of adhesive being largely dependent on the wear protocol.Some of the adhesives are designed to be short lasting(i.e.,1 day)and some longer term (i.e.,5–7 days).Although manufacturers state that a piece of the hypoallergenic waterproof dressing can stay attached for 5–7 days,participants involved in the studies outlined in Table 2 preferred to change theirdressing every 2–3 days.Therefore,for a 7-day monitoring period,participants were provided with 4 dressings for re-attachment and 4 alcohol wipes to assist in the attachment and removal of the dressing.This has cost implications,but may enhance wear time compliance.The provision of additionaldressing wasalso reported in 3 studies41,51,52in Table 1.

Fig.1.(A)The figure on the front of the activPAL indicating the correct orientation of the device.(B)An activPAL with waterproof coverings and a stick man to indicate correct orientation.

3.3.Instructions to participants

Only 3 studies in Table 1 reported providing participants with written placement instructions to take away with them in addition to the standard verbal instructions.36,42,50To our knowledge,no evidence exists on various instructional techniques and their impact on correct monitor attachment/ reattachment or compliance.Example written instructions as used in the studies in Table 2 are provided in Supplementary Files 1 and 2.Media could also be used for participants to access at home,such as these instruction videos posted on YouTube(http://youtu.be/BuaRHz_BOA4 and https://www. youtube.com/watch?v=CHCCX2GW3DM).

4.Data collection

4.1.activPAL wear,sleep,and key periods of interest log/diary

Over half of the studies(55%)in Table 1 reported collecting some form of log/diary data(e.g.,working hours,sleep, and wake times)to assist them with isolating key periods of interest such as worn waking hours during data processing(see Section 5).Requesting that participants complete a wear diary/ log may act as a reminder to participants to keep wearing the monitor and to re-attach the monitor each day in a waking hours only protocol.Recording removal reasons and“other comments”from participants(e.g.,reactions to dressing,device not flashing,worn upside down)via a diary/log can also be informative,particularly for a new population group and/or pilot data collection.General measurement principles suggest that instructions should be aimed at shifting the diary/logreporting towards“recording”rather than“recalling”and“estimating”for better accuracy,and that pilot testing should be used to ensure usability of the diary/log for your target population.

The studies in Table 1 reported limited detail about the contentoftheirlogs/diaries.In the studiesin Table 2,diaries/logs were paper-based and ranged from a single page to detailed page-per-day booklets with accompanying instructions(the example instructions sheet can be found as Supplementary Files 2 and 3).All were tailored to the interests of each study (e.g.,work-based studies included work details).The paperbased diaries(both short and long)have been well received by our participants but have typically entailed much missing data and high amounts of time entering data.Recently we have developed and trialled electronic diaries in a sample of of fice workers with the aim of reducing turn-around time,data entry costs,and missing data(by setting some fields as mandatory). Depending on the electronic approach used,the data can be available to the researcher on a daily basis,enabling the researcher to query issues of missing or questionable data and request clari fication from participants in a timely manner.This may be more importantforinformation,such as work times,that are essentialforanalysisand cannotbe inferred from the monitor data alone.The electronic diary method showspromise,butmay not be universally suitable.What works best is likely to depend on the study population,study size and requirements,and the resources and skills available in the research team.

4.2.Compliance

In accelerometer studies generally,compliance is typically considered in terms of the amount of time the monitor is worn during the period of interest(e.g.,waking hours)and the number of valid wear days provided(i.e.,the number of days on which wear time was adequate to consider that monitoring has suf ficiently captured most of the period of interest).Table 3 presents the compliance figures for 4 of the studies outlined in Table 2.These data may be helpful in indicating the degree to which the sample size should be in flated to allow for missing data due to non-compliance.For comparison,we also present compliance figures for the hip-worn ActiGraph accelerometer (ActiGraph,Pensacola,FL,USA)worn in the same studies but with a waking hours only and removal during water-based activities protocol.Both provided a similar number of“valid”days,but activPAL showed longer waking wear days than the ActiGraph,possibly due to different wear protocols and attachment methods.

Table 3 Number of valid wear days and wear/wake time(on valid days)for the activPAL and ActiGraph monitors in some of the research studies outlined in Table 2.

5.Data processing

5.1.activPAL software and files available

The activPAL software(PAL Technologies)provides a quick and easy-to-use system for initialisation,downloading and exporting data(in raw form and pre-classi fied by proprietary algorithms),and enabling researchers to visualise pre-classi fied data(i.e.,sitting/lying,standing,stepping)in various formats (e.g.,hour by hour,by day,or by week)from individual files. However,it is not a comprehensive,interactive data processing or analytic tool,and therefore even fairly routine requirements, such as isolating sitting/lying during waking hours,excluding periods of non-wear,and linkage to other time-stamped data (e.g.,logs),requires processing the data outside of the activPAL software in other software packages(e.g.,SAS,STATA, Excel,R).To assist researchers,in the following sections we provide:an overview of the data available from the activPAL; describe ways that our research groups and others have isolated key periods of interest in the activPAL data,identi fied non-wear and invalid data;and applied external limiters to the data(e.g., analysed only work hours).

When data from the activPAL device are downloaded,they can be saved as csv files in numerous formats.A 15 s epoch summary file shows the number of seconds spent in various activities,number of steps and sit-to-upright transitions occurring during that 15 s time window.These files do not indicate the order in which multiple activities occurred within the 15 s window.Such precise information is available from the event based summaries(“Events” files).These are a chronological list of all bouts of sitting/lying,standing,and each step,with the time each bout begins and bout duration.The“Events X Y Z” files also include triaxial acceleration data.These event-based summaries offer more precise data for the amount of each activity(less rounding error)and are ideally suited for extracting information on bout frequency and duration.The raw data can also be output;these were not reportedly used in the studies we examined(Table 1).Raw data files are currently being used in the context of methods development,including exploring the recognition of activity types not currently available through the activPAL software,such as distinguishing lying from sitting53and identifying wake time and number of awakenings during sleep54and cycling.55

5.2.De fining a day

Researchers often de fine days as calendar days(i.e.,midnight to midnight).However,all time-based de finitions are problematic when examining“bouts”of behavior or events because a single bout of behavior may begin in one day and end in the next.This poses problems in examining issues such as sitting accumulation because segmenting the bout at the day boundary means systematically underestimating bout duration. A simple modi fication could be to use calendar days but assign entire bouts to the day or date the bout begins.

Fig.2.Heat map of waking hours(“sleep”has been removed)activity(green, stepping;yellow,standing;red,sitting;white,sleep or not worn)from a participant showing bouts of behavior after midnight on 4–10 December,2011.

The person-oriented day approach,from one wake time to next day wake time(Table 1),56offers a behaviorally relevant approach that also avoids sub-dividing activity bouts across multiple days and further may increase the number of valid days or amount of waking wear.It reduces the likelihood of pockets of valid data being removed and considered“invalid”just because they occur after an arbitrary time(e.g.,midnight) and the following day does not include enough wear to be labelled as valid.For example,in Fig.2(which displays only valid waking hours data),a participant was still moving around from late on the 10th of December until the early hours the next morning(11th of December),which was not a valid wear day.A calendar day would discard all data from the 11th of December because it occurs after midnight but the person-oriented day approach would not.Person-oriented day durations are not always 24 h;the practical impact is often non-existent as waking day durations are variable under most de finitions.

5.3.Isolating key periods of interest in the data

activPAL monitors provide continuous streams of data, regardless of whether the monitor is being worn or placed on a bedside table,for example.Isolating only the periods re flective of actual participant behavior during the period of interest(e.g., waking hours or working hours)is a necessity for high quality data.The process for differentiating these periods requires researchers to determine from the data,or from other sources of information(e.g.,diary),when the monitor was put on in the morning and taken off in the evening(waking hours protocol), what time the participant woke up(or arose out of bed)and went to sleep(or went to bed),or the start and end of work.At present,there is an absence of validated,accurate methods to isolate these periods of interest and studies to date(Table 1) seldom describe in detail the methods they have employed. Methods that have been reported,as well as ones we are currently employing in the studies outlined in Table 2,are described in the following sections.

5.3.1.Time intensive methods

Most(75%)of the studies we examined(Table 1)that described how they isolated data for their period of interest(such as waking wear,time at work)reported using external information collected,typically self-reported logs/diaries.However, this is time intensive to implement.The exact procedure used to match up the diary with the activPAL data is rarely described,but 2 main approaches are apparent from those studies that have included detail.Sometimes,the literal log/diaryreported period is analysed,especially when using 15 s epoch files(for which this approach is easy)(e.g.,Ref.57).Other times,log/diary-reported periods are matched with“bouts”of activity(it is not unreasonable to assume that a change in period,location,or context would coincide with a change in activity),especially when using the events files,and a rule is implemented as to whether to include or exclude an entire bout of activity from the period of interest(e.g.,Refs.58–60).This method has the advantage of not segmenting bouts when examining bout durations or accumulation.One rule used involved choosing whether including a bout would include too much time outside of the period of interest.For example, sedentary periods crossing the start/end of each workday were included if most of the sedentary period was within the stated work hours and no more than 10 min was outside.59An alternative rule employed in several studies involved including bouts which crossed the period of interest(e.g.,working hours)if≥50%of that bout was within the period of interest.61Internal validation work showed excellent agreement(95%of observations within±5 min)between our events-modi fied start and finish work times and participants’originally reported times.62

A common occurrence with self-reported logs/diaries is they are often not fully completed.Participants may still wear the activPAL monitor but how these missing data are dealt with is again not described.An example of how this is dealt with is provided by the Stand Up Victoria and AusDiab studies(Table 2). When participants failed to report a time of waking or sleeping for a particular day,these were visually identi fied from the events files by research staff.The method the staff used was to examine the events files,starting with times around late evening or early morning,for an extremely long bout of sitting/lying or standing.Then depending on this bout duration,the duration and degree of movement observed in the surrounding bouts, either just this long bout or also some of the surrounding bouts,were selected as the likely unreported sleep or wake time.

5.3.2.Low burden methods

Low burden approaches may be preferable for studies that are large,require quick turn-around of data in bulk,and/or have other staf fing and resource issues.Low burden approaches have had limited use,especially for continuous wear protocols,until recently(Table 1).As yet validation of such approaches is yet to appear in the peer-reviewed literature but is likely to be forthcoming based on conference abstracts.63–65

For waking wear protocols,examples of low burden approaches reported in the literature(Table 1)for isolating waking hours data from non-wear data include the classi fication of very long bouts(＞8 h)of sitting/lying as non-wear(e.g., Ref.51),the application of the Choi method66(commonly applied to ActiGraph accelerometer data),i.e.,60 min of no movement with an interruption allowance of 2 min or less67and＞3 h of continual sitting/lying with 0 or 1 count in the accelerometer channel.34

For the 24 h protocols,methods also need to be employed to distinguish between sleep and waking hours.A very simple method that has been used is to limit data to time periods when it is assumed participants are awake(e.g.,between 07:00 and 23:59,e.g.,Ref.68).The simplicity of this method is appealing but may result in low data quality.Our diary data show that adults’sleep and wake times are highly variable and not well approximated by any single time period.Chastin et al.69took a similar approach but allowed for individuals to have varying waking periods by classifying the first standing event after≥2 h of sitting/lying between midnight and 9:00 as the beginning of wake,and the last standing event before＞3 h of sitting/lying after 22:30 as the beginning of sleep.This latter method is less restrictive about when sleep occurs but still presupposes a nocturnal sleep pattern,which may not always be the case,such as in shift workers.

5.3.3.Future possibilities

Viewing activPAL data via heat maps from studies outlined in Table 2 suggests that for adults,an automated approach to isolating waking wear from sleep ideally ought to avoid placing assumptions on when sleep occurs and consider that any period identi fied as sleep may be part of a larger fragmented sleep pattern,i.e.,that periods immediately before and after may also be part of what should be isolated from the main waking wear day.For example,sleep rarely occurs as 1 large uninterrupted bout of sitting/lying(Fig.3A).Many participants have some small amounts of movement(stepping and/or standing)registered throughout sleep(Fig.3B);these can be genuine waking movements involving arising from bed but in the case of standing can simply re flect leg positioning in bed while asleep,for example if someone has their leg hanging out of the bed.This fragmentation occurs to a lesser extent(but may still occur) when participants have removed their monitors overnight.We are in the process of trialling an approach that considers these factors.63,65

It has been contended that an integrated approach considering all movements over a whole day,combining issues relevant for the sedentary,physical activity,and sleep research fields, while acknowledging the inter-dependent nature of these movements may yield relevant insights and opportunities to improve health.70Continuously worn activity monitors that can also measure parameters of interest to sleep researchers have potential as a cost-effective means of achieving this.It would be useful to develop more sophisticated methods that involve distinction within this broad time period that we have loosely termed sleep(i.e.,time in bed asleep,time in bed awake,brief periods out of bed)and can indicate sleep quality.Preliminary attempts in this direction have shown promise.54

5.4.What constitutes a valid day?

Fig.3.Heat map of activity for the total monitoring period(green, stepping;yellow,standing;red,sitting/lying;white,pre-and post-study). (A)Uninterrupted sleep and(B)interrupted sleep.

The criteria used for the minimum number of hours constituting a valid day or valid time period of interest are irregularly reported(Table 1,49%reported valid criteria).Those researchers who have reported criteria have used either a minimum of 10 h ofwearorwaking hours(e.g.,Refs.71,72),required thefull 24 h period(e.g.,Refs.58,73),considered a day as valid if wear time comprised≥80%of waking hours,31or if the monitor had been removed for＜2 h in the 24 h time period(e.g.,Ref.31). These criteria have been applied when the full day is of interest. When working hours have been the time period of interest, researchers have applied either a minimum of 4 h or 6 h of valid dataperworking day(e.g.,Refs.38,59),orthe monitorhad to be worn≥80%or≥90%of work time(e.g.,Ref.74).For variable duration periods in particular(such as work hours),the percentage approach avoids systematically excluding observations that were short(but were suf ficiently complete and therefore valid).

Fig.4.Heat map of activity for the total monitoring period(green,stepping; yellow,standing;red,sitting/lying;white,pre-and post-study)showing an“invalid”day due to the majority of the day standing(indicated by yellow color) on 7th July.

While automated identi fication of speci fic non-wear periods is dif ficult,the identi fication of entire non-wear days is much simpler.Accordingly,one study75excluded days with＜1000 steps.Similarly in the STAND76and Walking away studies (Table 2),days with＜500 steps were excluded.The optimal cut-offs are likely to depend on the study population(e.g.,1000 steps may be too high for older adults).Additional rules may also be bene ficial for activPAL data speci fically.Prolonged bouts of standing can occur during the daytime if the activPAL is removed and propped up against something(Fig.4).It is important that this type of activity is not included in the valid data.We are trialling the identi fication of a day as“invalid/nonwear”if the vast majority(e.g.,＞95%)of the day is spent in any one posture,e.g.,standing or sitting/lying.

6.Data reporting

Broader reporting issues and guidance when using physical activity monitors have been previously covered by Matthews et al.30These should be considered for studies using the activPAL.Due to space constraints,researchers typically cannot provide the full level of detail required to replicate studies and judge comparability in study results in their manuscript.They could instead report their methodology as Supplementary material.We present an example table template in Appendix 1.

7.Summary,key recommendations,and future research

The activPAL monitor has demonstrated excellent reliability and validity for use in both physical activity and sedentary behavior research and the device offers exciting possibilities to advance sedentary behavior research speci fically.This report provided an overview of the issues to consider,before,during, and after data collection,when using the activPAL monitor in physical activity and sedentary behavior field-based research with adult populations.

Based on the experiences of our research groups with adult populations in the UK and Australia,and the practices observed across a diverse array of epidemiologic studies in free-living adults,we suggest the following key recommendations when using the activPAL monitor in field-based research:

•Employ a 24 h wearing protocol if possible.

•Deploy for at least 7 days.

•Provide verbal,visual,and written instructions to participants on how to wear the device correctly and change dressings;and,if possible,have a study researcher attach the monitor(or demonstrate and check self-attachment).

•Provide a diary(paper or electronic)to collect information on wake and sleep time,time in and out of bed,any removal times,and other contexts of interest(e.g.,work times).

•Use events files for data processing,especially if reporting measures relating to bout durations.

•No waking wear identi fication method is universally accurate and accepted.Use quality controls(e.g.,visual examination heat maps)to check classi fications,ideally against an external source of data,such as a diary.

•Be transparent when reporting activPAL collection and processing methods.

Future research should focuson the following areasin orderto bridge the gaps in our understanding related to the use of the activPAL formeasuring physicalactivity and sedentary behavior:

•Limited research exists on the intra-and inter-individual variability of physical activity and sedentary behavior as measured by the activPAL.This would increase understanding around which populations and measures may require longer observation periods.

•Current literature has employed a variety of wear protocols including waking hours with removals for water activities, waking and sleep hours with removals for water activities and continuous 24 h monitoring.It has been suggested in previous research with ActiGraph accelerometers that wear time compliance increases with continuous wear protocols compared to waking wear protocols.This has yet to be con firmed with the activPAL monitor.

•The accuracy of the minimum sitting/upright period default setting of 10 s(i.e.,≥10 s of sitting/lying or upright data is needed to register as a new event)for detecting the number of breaks in sitting needs to be tested in adult populations.

•Studies have used a variety of attachment methods and it is not clear whether the type of attachment(e.g.,PAL stickiesvs.piece of Hypa fix)may in fluence wear compliance.

•Most studies reported giving participants verbal instructions on how to wear the activPAL with a small number also providing written instructions for participants to refer to during the wear period.Instruction techniques such as online videos are emerging.However,it is not known to what extent these various methods of instruction impact on correct monitor attachment,re-attachment and wear compliance.

•As noted by Matthews et al.,30lack of compliance is a source of lost data and can increase cost in research studies.The percentage of participants providing at least 4 days of valid data was reasonable(Table 3)but could still be improved.Research(e.g.,qualitative)exploring how to maximise compliance in different populations is needed.

•Studies have employed a variety of methods to isolate key periods of interest from the continuous data that are collected by the activPAL and to de fine a valid day.The extent to which this impacts on data outputs and therefore comparability between studies is unclear.

•There is currently an absence of validated,accurate methods to isolate key periods of interest(e.g.,waking hours)from the continuous data that are collected by the activPAL.The development and validation of automated processes that consider the fragmented sleep patterns that are frequently observed in activPAL data collected continuously is a priority.

•The development of accurate identi fication methods for additional activity types beyond the current classi fications is an important emerging area,both in terms of waking activities(e.g.,differentiating lying from sitting,identifying cycling)and sleep(e.g.,awakenings during sleep).

Acknowledgments

The research was supported by the National Institute for Health Researsch(NIHR)Diet,Lifestyle&Physical Activity Biomedical Research Unit based at University Hospitals of Leicester and Loughborough University,the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care—East Midlands(NIHR CLAHRC—EM)and the Leicester ClinicalTrials Unit.The views expressed are those of the authors and not necessarily those of the National Health Service(NHS),the NIHR,or the Department of Health.

Elisabeth Winkler was supported by a National Health and Medical Research Council(NHMRC)Centre for Research Excellence Grant on Sitting Time and Chronic Disease Prevention—Measurement,Mechanisms and Interventions (APP1057608).Genevieve Healy was supported by a Heart Foundation Postdoctoral(PH 12B 7054)and NHMRC Career Development(#1086029)Fellowship.David Dunstan was supported by an Australian Research Council Future Fellowship (FTE 100100918).

Authors’contributions

CLE conceived of the idea for the review,reviewed existing research,and wrote the first draft of the manuscript;GNH conceived of the idea for the review along with CLE and reviewed/edited of the manuscript;EAHW reviewed existing research and wrote the first draft of the manuscript with CLE; DHB helped draft some sections of the manuscript and reviewed/edited the full manuscript;TY,MJD,and DWD reviewed/edited the manuscript.All authors have read and approved the final version of the manuscript,and agree with the order of presentation of the authors.

Competing interests

The authors declare that they have no competing interests.

Appendix 1

Template table(which expands on the reporting recommendations made by Matthews et al.30)for reporting use of activPAL monitors completed for an example study(STAND76).

Appendix:Supplementary material

Supplementary data to this article can be found online at doi:10.1016/j.jshs.2016.02.002.

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5 June 2015;revised 26 October 2015;accepted 4 December 2015

Available online 3 February 2016

Peer review under responsibility of Shanghai University of Sport.

*Corresponding author.

E-mail address:ce95@le.ac.uk(C.L Edwardson)

http://dx.doi.org/10.1016/j.jshs.2016.02.002

2095-2546/©2017 Production and hosting by Elsevier B.V.on behalf of Shanghai University of Sport.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).