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Journal of Neuromuscular Diseases
J Neuromuscul Dis. 2017; 4(1): 25–52.
PMCID: PMC5345641

Measuring Habitual Physical Activity in Neuromuscular Disorders: A Systematic Review


Background: Free-living or habitual physical activity (HPA) refers to someone’s performance in his or her free-living environment. Neuromuscular disorders (NMD) manifest through HPA, and the observation of HPA can be used to identify clinical risks and to quantify outcomes in research. This review summarizes and analyses previous studies reporting the assessment of HPA in NMD, and may serve as the basis for evidence-based decision-making when considering assessing HPA in this population.

Methods: A systematic review was performed to identify all studies related to HPA in NMD, followed by a critical appraisal of the assessment methodology and a final review of the identified HPA tools.

Results: A total of 22 studies were selected, reporting on eight different direct tools (or activity monitors) and ten structured patient-reported outcomes. Overall, HPA patterns in NMD differ from healthy control populations. There was a noticeable lack of validation studies for these tools and outcome measures in NMD. Very little information regarding feasibility and barriers for the application of these tools in this population have been published.

Conclusions: The variety and heterogeneity of tools and methods in the published literature makes the comparison across different studies difficult, and methodological guidelines are warranted. We propose a checklist of considerations for the assessment and reporting of HPA in NMD.

Keywords: Physical activity, daily activity, exercise, activity monitor, neuromuscular disorders, muscular dystrophy


New technology has been developed over the last decade that is able to monitor and measure daily life activity behaviour, generating a large amount of data for analysis and mass-market applications. Increasingly, this technology has been implemented into research and many of these tools have been correlated to long-term health outcomes and motor capacity in diseases with impaired mobility such as Parkinson’s disease [1], stroke [2] and cerebral palsy [3].

Patients with neuromuscular disorders (NMD) frequently experience limitations caused by muscle weakness, pain, fatigue, reduced mobility and overall functionality, all of which result in a limited participation in activities of daily living and a sedentary lifestyle with its associated risks [4–8]. Consequently, the need for further research has been emphasized with the goal of analysing and improving physical activity (PA) safely and developing evidence-based exercise recommendations [5, 9, 10]. However, the rareness and heterogeneity of NMDs [11], have resulted in relatively few clinical trials utilizing this technology and there is still a lack of awareness of the potential of this as an outcome measure and it is still unclear which tool may be most appropriate in which setting [12, 13].

Assessing free-living or habitual physical activity

Free-living or habitual physical activity (HPA) encompasses any activity that people do in their daily life and natural environment. HPA qualifies as an outcome of performance rather than capacity, as it refers to someone’s involvement and real participation in daily-life activities (DLA) rather than their ability to execute an action (usually tested in clinic, i.e. under request and ideal circumstances) [14, 15]. Important outcomes associated with HPA patterns are survival and disease progression; however, a more immediate benefit of a healthy HPA behaviour would be quality of life [16].

Assessing someone’s HPA is essential to: [1] determine whether altered activity behaviours are present; [2] establish an appropriate dose of PA to impact on specific health parameters; [3] set measurable goals for PA-related interventions; and [4] establish a surrogate outcome measure for clinical trials [17–22].

In research, HPA can be measured either directly or indirectly. Indirect tools refer to patient-reported outcomes (PRO) such as questionnaires and activity diaries, which are often simple to apply, cost-efficient and a good option for large cohort studies. However, PRO are susceptible to subjectivity and bias related to self-reporting and/or interpretation usually requiring larger sample sizes when assessing changes over time or differences between groups [23, 24]. Nonetheless, regulatory agencies have acknowledged the importance of PRO in drug development and marketing approval as long as these come with enough evidence to validate their reliability [25]. To validate an indirect tool objectively, it should be compared to a direct tool.

Direct methods, on the other hand, assess actual physiological changes; these can be: physiological markers (e.g. heart rates), motion sensors (e.g. pedometers or accelerometers) or calorimeters (e.g. doubly labelled water method) that correlate with physical activity. Direct tools are considered more objective and accurate [26, 27]; however, these also present limitations [28, 29]. They are typically more expensive, time-consuming and tend to place a higher burden on the researcher and the participant [17, 28, 30, 31]. Additionally, the most common outputs of these devices rely on the manufacturers’ algorithmic estimations and cannot always be generalizable to the target population and the raw data’s full interpretation still depends upon the researcher’s experience [32]. Doubly labelled water is the gold standard for the other tools to validate Energy Expenditure (EE) estimations, currently. Observation is the standard method to validate type-of-activity detection (i.e. steps) [27].

There is yet no gold standard tool for assessing HPA in NMD and the adaptation of any of these tools into clinical practice and research is not straightforward. Future application in clinical practice requires further research to confirm the validity and reliability of tools in the particular population being studied [33–38]. A good start for this is to learn from previous experiences [1–3, 33–35].

Review aims

The aim of this review was to identify and analyse the tools and methodology previously used to assess HPA in NMD. To accomplish this, studies in NMD that reported any type of HPA assessment were selected and reviewed. Any HPA tools and methodologies used were identified and critically analysed. Finally, we conclude with an evidence-based checklist of interest to researchers and clinicians measuring HPA in NMD.

This review’s protocol was initially registered with the PROSPERO (International prospective register of systematic reviews) database [39].


Search methodology

A systematic literature search was carried out through the following databases: EMBASE; MEDLINE; and PsychINFO. The search strategy was to retrieve literature that investigated the use of free-living (habitual) activity assessment tools used in NMD. Hence the search used identified keywords from each database as appropriate. Terms included “free living activity”, “performance”, “physical activity”, and “daily life activity” in combination with “neuromuscular disease(s)”, “neuromuscular disorder(s)”, “muscle disease(s)” and/or “muscular dystrophy”.

Study selection

The search included publications from 1996 until the end date of search (March 2016).

Papers fulfilling the following criteria were included for analysis: [1] participants diagnosed with genetic neuromuscular disorders (NMD); [2] utilised measurement tools for habitual physical activity (HPA) assessing the subject’s performance for at least 24 successive hours; [3] patient-reported outcomes should include at least 60% items related to HPA. Publications were excluded when they: [1] were not published in English; [2] were single cases, on-going studies, non-research reports, conference abstracts or pre-clinical studies; [3] measured “capacity” or “life participation” instead of HPA “performance” or were of use only in controlled laboratory environments [14, 40].

The titles and abstracts of all retrieved references were screened by one reviewer (AJ) to exclude papers that did not instantly fulfil the inclusion criteria. Publications selected were properly revised by two independent reviewers (AJ, JN) to verify the selection criteria. Conflicting viewpoints and exclusion decisions were discussed with a third reviewer (SC) until a consensus was reached. Finally, the three main reviewers (AJ, JN, SC) proceeded with a deep analysing and summarising of the final selection of papers. After that, all HPA-tools identified were subsequently labelled as a patient’s (or next of kin) reported outcome (PRO) or as a direct tool (D).

1. Summary of direct HPA tools

The variables analysed are presented in Table 1. This review methodology follows criteria which adhere to the methods proposed by Matthews et al. of ‘Best Practices for Using Physical Activity Monitors in Population-Based Research’ [41]. When more than two variables were missing on the report, an attempt was made to contact the study authors by email.

Table 1
Direct HPA-tools report – methodological variables for analysis

2. Summary of indirect HPA tools

Patient-reported outcomes (e.g. questionnaires or PA diaries) were analysed by the identification of: [1] tool utilised; [2] data collection: respondent (i.e. patient, carer, etc.); location of completion (i.e. clinical settings, home, etc.); recollection method (i.e. face to face, phone, post, online, etc.); [3] data analysis criteria and methodology; [4] endpoints reported.


Study selection

The results of the literature and selection processes are displayed in Fig. 1. The initial search yielded 1,070 titles and abstracts from which 88 were selected using the predefined criteria, a good number of these refer to a physical activity or an exercise parameter different from HPA as defined previously. Of these, the second double-peered filter excluded 67 papers. Four conference abstracts and three on-going studies with published protocols were excluded at this stage [42–48]. One paper included only a minor number of patients with a muscle disease among a larger non-muscle disease population (2/75 muscular dystrophy) [49], this was excluded too. The remaining twenty-two papers were selected for analysis and are summarised in Table 2 [9, 36–38, 50–67].

Literature selection Flow Diagram.
Table 2
Summary of all identified papers assessing HPA in NMD

Study sample characteristics

Study-sample numbers ranged from five [56] to 321 participants [65] and included both paediatric and adult populations. Twelve studies (54%) reported on a specific NMD entity: Duchenne muscular dystrophy (DMD) [37, 54–56, 66]; spinal and bulbar muscular atrophy (SBMA) [52]; myotonic dystrophy type 1 (DM1) [38, 53]; mitochondrial myopathies [58]; Charcot-Marie-Tooth [60]; Pompe disease [61]; McArdle’s disease [51]; and facioscapulohumeral muscular dystrophy (FSHD) [63]. The remaining publication-samples were grouped as NMD, including three studies reporting a mixed-diseased sample involving non-neuromuscular patients [49, 64, 67]. Nine studies (40%) involved a healthy control group for comparison, referred as “healthy controls” or “able-bodied” [9, 36–38, 51, 54, 58, 59, 62].

Study design and methodology

Four studies were randomized controlled trials (RCT) assessing an intervention [52, 53, 63, 67].

Only three studies investigated the validation of an HPA tool [37, 56, 64]. Two studies reported assessing the feasibility of these tools as outcome measures for the primary study aim [49, 54]. The remaining studies did not specifically test the HPA tool but utilised it for a secondary aim e.g. Wiles et al. [38]. The primary aim of this study was to quantify falls and stumbles in DM1, with a secondary aim of investigating the correlation between these falls and the patients’ step count [38].

Eleven studies reported the use of more than one HPA tool, five of these as a control method (e.g. PA diaries for PA monitors) or to supplement the endpoints’ report (e.g. Energy Expenditure calculations) [9, 36, 37, 59, 63].

Direct HPA assessment tools and methodology

Of the selected studies, sixteen reported the use of a direct HPA tool [9, 36–38, 50, 52, 54, 56–59, 61–64, 67]. Seven of these studies compared against healthy controls and showed differences between the groups [9, 36–38, 59, 64, 69]. Characteristics of their methodology are presented in Table 3 and the eight different tools identified as direct are presented in Fig. 2. Full data collection protocol (data criteria for analysis, definition of non-wear episode and the processing of missing data) was only identifiable in three of the papers [54, 56, 61]. Four papers reported the use of a patient-reported outcome (i.e. activity diary) as a quality control method [9, 36, 37, 64].

Types of direct HPA tools reported. Two studies combined a heart rate (HR) monitor with an additional activity monitor: [1] Yamax Digi-Walker and [2] StepWatch Activity Monitor (SAM) [37].
Table 3
Direct HPA-tools report and methodology. This table presents all papers identified using direct HPA-tools and display the methodological variables reported. Y: information has been reported

Patient reported HPA outcomes used in NMD

Fifteen papers reported the use a patient-reported HPA tool (PRO) (Table 4). The PRO most commonly used in this cohort was a type of written diary of the patient’s activities during the day. The questionnaires used to measure HPA were: Physical Activity Scale for Elderly (PASE) [67]; Bouchard Three-Day Physical Activity Record [51, 55]; International Physical Activity Questionnaire (IPAQ) [58]; EPIC-Norfolk Physical Activity Questionnaire-2 [59]; Physical Activity Disability Survey-Revised (PADS-R) [60]; Sickness Impact Profile (SIP) – mobility and ambulation subscales [62]; Checklist Individual Strength (CIS) – decreased Physical Activity subscale [63]; Godin Leisure Time Exercise Questionnaire (GLTEQ) [65]; and a modified version of the Baecke Physical Activity Questionnaire [66].

Table 4
Indirect HPA-tools report and methodology. This table presents all papers identified using an indirect HPA-tool or patient-reported outcome (PRO) and display the methodological variables reported. Y: information has been reported

Two papers reported a third person as the respondent, either the patient’s parents or patient’s next of kin [53, 55], whereas two papers did not provide any details about their methodology [58, 66].

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Evidence-based background of HPA assessment tools previously used in NMD

A selection of ten systematic reviews appropriate to this topic, and published between 2011 and 2016, were reviewed by the authors in search of additional evidence to back up each tool identified [13, 22, 28, 29, 34, 35, 69–72]. Validation studies were most reported in Cerebral Palsy (CP) for paediatrics and Multiple Sclerosis (MS) for adults and only McDonald et al.’s StepWatch validation study in DMD [37] was identified as an NMD-population paper; reported by Oftedal et al. [35].

From the seven identified direct HPA tools, six have been mentioned in previous systematic reviews: ActiGraph; StepWatch; Actical; and Yamax; Actical and SenseWear™ Pro3 [13, 22, 28, 29, 34, 35, 69–73]. The most reported endpoints have been: energy expenditure (EE); physical activity levels (PAL); and step count [29]. Table 5 condenses the validity evidence behind the direct tools identified in this review and ten different PROs have been described in Table 6. The reviewers identified at least one study comparing each PRO to an objective tool; three used double-labelled water (DLW) [74–76], twelve against an accelerometer [77–89] and one against a HR-monitor [83].

Table 5
Direct tools description. Each of the direct tools identified in this review are presented in detail: tool description; available endpoints; validation background; strengths; and weaknessess
Table 6
Indirect tools description. Each of the indirect tools identified in this review are presented in detail: tool description; available endpoints; validation background; and previous reports comparing it to a direct tool


This systematic review examines previous studies measuring habitual physical activity (HPA) in people affected with neuromuscular disorders (NMD). This report highlights that HPA assessment in this population has been carried out in an increasing number of studies over recent years, but is still in its infancy as compared to populations affected by more common conditions such as diabetes, both in the number of studies and in the number of patients included.

The first point of note was the use of wide and overlapping terminology to describe activity. Differences between ‘physical activity’, ‘exercise’, ‘physical fitness’ [40], ‘performance’ and ‘capacity’ [14] were not always clear. This led to the exclusion of a large number of papers in the screening process. More accurate distinction of these terms would have allowed for a more precise identification of outcome measures and facilitated comparisons between publications. Caspersen et al. [40] explain the difference between physical activity and exercise, where exercise includes an element of structure and planning. Physical fitness refers to the attributes someone has to achieve for either one or both of them (i.e. strength, endurance and flexibility). The difference between capacity and performance has been introduced before and is based on the ICF terminology [14].

The rationale for measuring HPA (directly or indirectly) in the studies reviewed was rarely explained in detail. Some studies report assessing a behavioural pattern whilst others assessed a dose-response effect to an intervention. The materials and methodology used were also heterogeneous. This variability made comparisons between results and the drawing of specific conclusions difficult. Nonetheless, it is possible to conclude that PA levels and patterns of NMD subjects differ significantly from the matched healthy controls [9, 36–38, 51, 54, 58, 59, 64].

Despite reviewing a large number of publications only three papers allowed for comparisons between direct and indirect methods [58, 63, 69]. Kalkman et al. [69] reported a relationship between the Sickness Impact Profile (SIP) scores for mobility and ambulation and the actometer activity reports in a mixed NMD cohort. Both tools strongly correlated to the PA-dependent variables of fatigue and functional impairment. However when the Myotonic Dystrophy patients were analysed independently, the correlation with the fatigue score was lost. Apaphabi et al. [58] reported a moderate association between subjective PA reports obtained from the IPAQ with those obtained from the SenseWear Armband; however, the r-values provided are not strong (0.25 to 0.35). Finally, Voet et al. [63] provided a tangible example of a possible risk faced when only utilising one assessment method. In this study, the aerobic exercise group (AET) showed increased physical activity levels when measured directly (actometer), but no significant changes were reported on the PRO (CIS-reduced PA questionnaire), which, if used alone, would have missed these changes. On the other hand, the actometer, if considered in isolation, might have led to overestimated conclusions as the results might not reflect the patient’s perception of activity levels.

Of the 22 papers reviewed, five reported estimations of Energy Expenditure (EE) and Physical Activity Levels (PAL) following previously published methodologies [9, 36, 51, 55, 139]. However, the presumed validity and reliability of these methods do not always translate into a disease population in particular due to the physiological differences in the cardiovascular and respiratory systems. Only three studies presented clearer attempts to validate the device used in the particular population investigated beforehand or attached to their HPA investigation [37, 56, 64]. Three other studies combined tools to provide information from one tool that could support the other, like an activity diary combined with a heart rate monitor [9, 37, 59].

Only two studies commented on both the strengths and barriers of the tools presented, allowing the reader to get a better idea of what to expect from these and the possible limitations of the results [37, 56]. Tool limitations that can resume in a loss of data or a higher inter-rater variability should be considered in advance as these might impact on study logistics and sample estimations.

It is important to emphasize that the reported outcomes and devices presented and discussed in this paper represent only those identified through the literature search; however, this is by no means an exhaustive list and does not cover some of the other options currently available and that have been used in other diseases or epidemiology studies [13, 29, 69, 73]. Still, it certainly presents a wider variety that the one discussed in 2007 at the TREAT-NMD meeting on PA monitoring in NMD [12], which emphasizes the growing interest in experimenting with these types of tools. Currently, the authors are aware of ongoing clinical trials in NMDs using more modern tools such as: ActiGrapha and GeneActivb accelerometers in DM1; GeneActivc in mitochondrial diseases and in idiopathic inflammatory myopathies, the StepWatch Activity Monitord and ActiMyoe in DMD [140] and MoveMonitorf in myasthenia gravis. These well-controlled trials usually employ direct along with indirect measures and will contribute to a growing body of evidence for the use of these tools in clinical NMD research. Despite the use of HPA devices being at an early phase in NMDs, lessons learnt in larger clinical groups with movement impairments are likely to be transferrable to the measurement of HPA in NMDs [1].


Assessing HPA effectively has many advantages and may allow the identification of subject changes and day-to-day limitations possibly missed with standard clinical measures [12]. This was reported for a study in Parkinson’s disease, where no significant changes in clinical measures of gait and disease burden over time were seen, but a meaningful reduction in daily ambulatory activity [141]. Identifying the appropriate tool and methodology appears equally important in NMD, not only to generate high-quality data for research and regulatory purposes, but also to avoid loss of time and resources through the use of tools or methods not suitable or informative for this population.

A few concepts should be considered when measuring HPA: reliability, validity and responsiveness of the tool to use; feasibility of the research site; and acceptability of the study participants [18, 142, 143]. Ideally, the selected tool should have as much evidence as possible supporting these for the targeted population or similar cohorts as several studies have encountered barriers when assessing participants with altered movement patterns or physiology. The following examples have relevant learning points:

  • 1.
    McDonald et al.’s findings regarding heart rate (HR) adaptations to activity in DMD patients compared to controls demonstrate how established formulas to estimate EE or PAL based on HR cannot directly translate to a disease group [37].
  • 2.
    Indirect tools tend to overestimate physical activity levels when the respondents are subjects with flawed physiological responses to activity [23, 144, 145]. Even the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD) has shown overestimation when compared to activity monitors [146].
  • 3.
    Altered mobility has a big impact on the reliability of motion sensors. Bachasson et al. [147] and Barak et al.[148] have described altered gait patterns commonly present in NMD patients, such as slower gait speed, lower stride frequency, shorter stride length, impaired balance and low walking endurance. All these variables will impact on the outputs obtained by activity monitors where validations were previously performed with subjects without gait abnormalities [29, 148].
  • 4.
    Wheel-chair users should be considered independently as the circumstances of these subjects require specific estimations, both for direct [140, 149] or indirect tools [146]. Ideally, activity monitors will be located on either trunk or upper limb and PRO should include activities that can be performed in a wheelchair.
  • 5.
    Finally, cognitive impairment should also be considered when assuming compliance rates and reliability for both direct and indirect tools. Certain characteristics, such as a shorter attention span, memory deficits, and errors in comprehension and reporting [24, 150] may interfere with reporting reliability and compliance rates [151].

The authors are not recommending a complete homogenization of tools or methods utilized for HPA assessment. This decision will always be subject to site experience and resources, and most importantly to the study aims. However, having a checklist of considerations prior to HPA assessment may facilitate the efficiency of the protocol design and the quality of the study report (Appendix A). We want to encourage publications to include enough information to allow for repeatability and a clear understanding of the methodology used.


This review captures the emerging evidence supporting the use of measures that assess habitual physical activity in NMD and some of the possible settings where this seems feasible.

Because of the wide variety of options and methodology, clinicians and researchers in the field need to become familiar with the concept and the different constructs within HPA measurement. The evidence-based knowledge presented here will assist the clinician and/or researcher to select the most appropriate tool and methodology for their defined aim.

Careful consideration of the study aim should precede choosing a particular tool and method for assessing HPA. Understanding their strengths and weaknesses is essential when considering available resources at the study site. The authors provide a considerations-checklist for designing a protocol to assess HPA in NMD in Appendix A.


The authors have no conflict of interest relevant to this study.


AJM carried out the study design, study selection process, data extraction, data interpretation, and manuscript drafting. JN and SC participated in the process of study selection, data extraction, and paper revision equally. JYH and HL supervised the whole manuscript process. All authors read, commented on and approved the final manuscript.


The authors acknowledge current funding grants from: [1] the National Institute of Health Research IHR and Wyck (PHENO-DM1 trial); and [2] the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n° 305697 (OPTIMISTIC Trial) as partners Newcastle University, UK and Catt-Sci Ltd, UK.

A.C. Jimenez-Moreno PhD studentship is a combined funding from the Medical Research Council (MRC) Centre for Neuromuscular Diseases (reference G1002274, grant ID 98482), Consejo Nacional de Ciencia y Tecnologia (CONACYT), Mexico (ID 611819) and the Barbour Foundation, UK.


In this section the authors propose a checklist of considerations to have in mind prior to assessing and reporting habitual physical activity (HPA) in neuromuscular disorders (NMD). The following segment summarizes previously proposed methodological guidelines or strategies for assessing physical activity in population-based research [1, 41, 142, 152] and has been adapted based on this review’s findings and authors’ previous experience utilising these tools with this population. A standardized methodology such as the Delphi method should follow to develop and validate guidelines for HPA assessment in NMD [153].

HPA in NMD considerations checklist

 1. Establish the aim for assessing HPA as part of your research question (i.e. What question(s) are you trying to answer?).

Do you want to:

-Identify any altered activity behaviour present in the specific disease group;

-Identify a specific dose of activity that differentiates patients based on health parameters;

-Measure an intervention effect;

-Measure activity or intervention compliance;

-Establish a surrogate outcome measure for disease progression;

-Identify ‘real-life’ barriers for patients’ best performance;

-Patients’ own perception of their own daily performance or participation?

 2. Establish the variable(s) needed to fulfil your question.


-Time spent on certain activity like: walking, sedentary time;

-Overall activity levels (PAL) or energetic estimations (EE or METs);

-Characterization of a specific type of activity (e.g. gait speed during walking in daily life, transitions, sleep patterns);

-Factors associated with HPA (like fatigue, falls, season effect).

 3. Establish the level of priority the HPA outcome will have within your study. (i.e. primary or secondary outcome). This will define the time and resources to be invested.

 4. Define your sample criteria.

-Establish your sample characteristics and identify those that may impact on the HPA (i.e. ambulation status, employment characteristics, level of independence).

 5. Establish your sample number.

-It is expected that direct tools will allow a smaller sample but it will still vary depending on the variable to compare.

-Are there any known factors such as non-compliance or possible loss of data associated with your selected tool that could impact on your sample number?

 6. Identify all possible HPA tools that could fulfil your study needs and identify each one’s strengths and weaknesses for your study aims and resources.

-Have these been used before in a similar population?

-Would they require a population-specific validation?

-Is there any established standard practice associated with the tool? Is this feasible in your study?

-Are your resources enough to fulfil the tool requirements (i.e. equipment costs, analysis, data rights, human resources, ethical approvals)?

-Does the research team involved have any previous experience with that tool or similar ones? If not, consider an interdisciplinary collaboration.

-Would only one tool be enough to fulfil the study aims?

 7. Consider the participant’s potential acceptability. This can impact on the overall experience and final results.

-How many days of assessment (or questions) are really required (i.e. not too short, neither too long)? For this point, consider previous publications and special characteristics of your cohort (i.e. differences between paediatrics and adults, differences between employed and unemployed, weekends vs weekdays).

-Consider staff and patient’s acceptability when defining: [1] body location of the monitor (i.e. comfortable, easy to collocate, visually acceptable) or [2] the interview process for PRO.

 8. Establish all the logistics required to increase the efficiency in your study.

-Establish standard operation procedures applicable for the site or sites involved.

-Consider appointing someone specific or a team for the coordination of the HPA operations.

-Consider staff training prior trial starts.

 9. As soon as the HPA outcome to investigate and the HPA tool to use have been established, define the exact methodology to follow and to report as follows:

Direct (objective) HPA-toolsIndirect (reported outcomes) HPA-tools
1. Rationale behind the outcome.1. Rationale behind the outcome.
2. Validity and reliability available and background (references) supporting the tool and methodology selected.2. Validity and reliability available and background (references) supporting the tool selected.
3. Established methodology: i.e. body-placement location, number and type of days, number of hours per day.3. Established methodology: i.e. location (clinic or home), respondent (patient, carer, researcher), format (electronic, paper).
4. Definition of wear and non-wear periods.4. Specific management procedures for missing data.
5. Definition of variables presented and background (references) for their use.5. Definition of summary variables to report (and references if applicable).
6. Compliance criteria to include for analysis.
7. Specific management of missing or invalid data.

Footnotes Identifier: NCT02858908 Identifier: NCT02118779 Identifier: NCT02398201 Identifier: NCT00847379 Identifier: NCT02500381, NCT01826474 Identifier: NCT02066519


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