The acceptance of the clinical photographic posture assessment tool (CPPAT)

Carole Fortin
Paul Van Schaik
Jean-François Aubin-Fournier
Josette Bettany-Saltikov
Jean-Claude Bernard
Debbie Ehrmann Feldman
The acceptance of the clinical photographic posture assessment tool (CPPAT)
10.1186/s12891-018-2272-7R E S E A R C H A R T I C L E Open Access
Background: There is a lack of evidence-based quantitative clinical methods to adequately assess posture. Our team developed a clinical photographic posture assessment tool (CPPAT) and implemented this tool in clinical practice to standardize posture assessment. The objectives were to determine the level of acceptance of the CPPAT and to document predictors as well as facilitators of and barriers to the acceptance of this tool by clinicians doing posture re-education. Methods: This is a prospective study focussing on technology acceptance. Thirty-two clinician participants (physical therapists and sport therapists) received a 3-5 h training workshop explaining how to use the CPPAT. Over a three-month trial, they recorded time-on-task for a complete posture evaluation (photo -and photo-processing). Subsequently, participants rated their acceptance of the tool and commented on facilitators and barriers of the clinical method. Results: Twenty-three clinician participants completed the trial. They took 22 (mean) ± 10 min (SD) for photo acquisition and 36 min ± 19 min for photo-processing. Acceptance of the CPPAT was high. Perceived ease of use was an indirect predictor of intention to use, mediated by perceived usefulness. Analysis time was an indirect predictor, mediated by perceived usefulness, and a marginally significant direct predictor. Principal facilitators were objective measurements, visualization, utility, and ease of use. Barriers were time to do a complete analysis of posture, quality of human-computer interaction, non-automation of posture index calculation and photo transfer, and lack of versatility. Conclusion: The CPPAT is perceived as useful and easy to use by clinicians and may facilitate the quantitative analysis of posture. Adapting the user-interface and functionality to quantify posture may facilitate a wider adoption of the tool.

Background

Physiotherapists are often consulted to assess and correct posture for persons with various musculoskeletal conditions [1,2]. Presently, there is a lack of high-quality evidence regarding the effectiveness of physiotherapy interventions on posture [3][4][5]. This may be due to the lack of evidence-based quantitative clinical methods to adequately assess the outcomes of therapeutic interventions [3,6,7]. Currently, quantitative methods for posture assessment require elaborate 3D analysis systems such as Motion Analysis and surface topography [8,9]. However, these systems are not easily accessible for most clinicians since they are expensive and require specialized trained technicians. Physiotherapists and physicians commonly assess posture by descriptive visual inspections that lack scientific validation [1,10,11]. There is a growing field of interest in using clinical tools to quantitatively assess posture. A promising technique to assess posture clinically is a method that calculates body angles and distances on photographs reflecting posture in all planes [12][13][14][15]. In recent years, different non-invasive computer-based methods as well as mobile applications (APPs) have been proposed to assess posture in a clinical setting [15][16][17][18][19][20]. Boland et al. [16] reported good intra and inter-rater agreement (ICCs ≥0.75) for seven out of 13 posture indices in ten young healthy adults using a mobile APP. Posture deviations of the head, trunk and pelvis were also measured using an iPhone APP in a large group of healthy collegiate students but the reliability and validity of such measurements are not provided. Aroeira et al. [15] reported that most of the new computer-based methods proposed in the literature to assess posture in adolescents with idiopathic scoliosis (four on 2D photogrammetry and 11 on laser or structured light, ultrasound and moiré scanner projection) focussed only on the back view and that the methodology of these studies was of low quality. These authors pointed out the importance of measuring posture of the whole body in patients with idiopathic scoliosis because the posture alterations may be extended to the whole body. Our team has developed a software program for quantitative analysis of whole body posture from digital photographs in youth with idiopathic scoliosis [18,19]. Measures obtained using this software-based method showed excellent test-retest and inter-rater reliability for marker placement as well as good concurrent validity with spinal angles measured on radiographs and 3D trunk posture indices measured from a topography system in adolescents with idiopathic scoliosis [18,19]. According to Aroeira et al. [15], this innovative clinical photographic posture assessment tool (CPPAT) is the only validated clinical tool offering assessment of the full body posture. The CPPAT could be used to standardize posture assessment in persons with scoliosis or other musculoskeletal pathologies.

The acceptance of rehabilitation technology by clinicians [21,22] and patients [23] is essential for its successful uptake to both improve clinical practice as well as outcomes for patients. Previous research [22] established that drivers of the use of a low-cost portable system for postural assessment include training/skills, clinical use, quality of human-computer interaction, visualization and time-on-task; barriers to use include time-on-task, costs, quality of human-computer interaction, training/skills, clinical use, IT/equipment required and technical measurement issues.

Furthermore, it is essential to develop an understanding of how different factors influence technology acceptance. Highly influential has been Davis's [24] technology acceptance model (TAM; see Fig. 1) and its further development [25][26][27]. Our study uses TAM and focuses on three core model variables: intention to use the CPPAT, perceived usefulness and perceived ease of use of the CPPAT. According to TAM, the intention to use a product (system) is the major factor influencing the extent to which potential users will employ the product (actual system use). In turn, intention to use is influenced by perceived usefulness and perceived ease of use. Perceived ease of use also indirectly influences intention to use through its direct effect on perceived usefulness. Product characteristics (system design features) directly influence both perceived usefulness and perceived ease of use and thereby indirectly influence intention to use and actual system use.

Research has also examined the relationship between task performance and perceived ease of use. Specifically, Venkatesh and Bala [25] measured 'objective usability' as novice-to-expert ratio of time-on-task and showed that objective usability predicts both perceived ease of use and perceived usefulness. Moreover, Chiou et al. [28] established that time-on-task predicts perceived usefulness. As task performance predicts perceived usefulness and perceived usefulness predicts intention to use, perceived usefulness may be a mediator of the effect of task performance on intention.

In our study, we address the following research questions: (1) what is the level of acceptance of this CPPAT by clinicians doing posture re-education, (2) what are the predictors of acceptance and (3) what are the drivers of and the barriers to acceptance of CCPAT for the evidence-based measurement of posture?

Method

Design

In a prospective mixed design study using quantitative and qualitative methods, we measured perceived ease of use, perceived usefulness and intention to use the software-based CPPAT (see Material and apparatus) for posture measurement as well as time-on-task for photo acquisition and photo-processing with the CPPAT.

Participants

We recruited 32 clinicians (22 physical therapists and ten sports therapists) working in public (35%) or private institutions in Canada (Montreal [MTL] and Quebec city [QC]), France (Lyon) and United Kingdom (UK -London, Middlesbrough, Chesterfield). Therapists working in public centers and private clinics were invited by e-mail in order to allow the clinician participants to attend the training and the focus group discussion. Collaborators affiliated with our research teams were responsible to recruit a total of 30 therapists in the three countries. The inclusion criteria were clinicians assessing posture of persons with musculoskeletal disorders within their clinical practice and having access to a dedicated space for photo acquisitions. Eight participants did not complete the trial because they had changed their workplace and one for unknown reasons. Clinician-participants (18 women) had an average of 19.6 years (SD = 9.7) of experience in clinical practice and 12.6 years (SD = 7.7) of experience in posture assessment. In terms of computer use, ten participants had a low level, seven a moderate level, two a high level and four participants did not answer this question (see Additional file 1 for description of levels of computer use [29,30]). The project was approved by the Institutional Ethics Committee of Sainte-Justine university hospital centre (approval reference number: 2015-691, 3905) and all clinician-participants signed a consent form.

Materials and apparatus

Description of the clinical photographic posture assessment tool (CPPAT)

CPPAT is a software-based program with a graphical interface for the analysis of four to six photographs of a patient's posture (front, back, left and right) acquired in standing using a standard procedure (see Fortin et al., [18,19] for more details). We have shown excellent test-retest and inter-rater reliability for marker placement among a senior and novice physical therapists (reliability coefficients between 0.90 and 1.00 and standard error of measurement ranging from 0.5°to 3.0°and 3 to 6 mm) [18].

The software uses interactive click-on markers with the computer mouse. The user selects each specific marker from the graphical interface and places it directly on the corresponding anatomical landmark or anatomical reference points (e.g. eyes, upper end, lower end and center of the waist) on the person's photographs (see Fig. 2). The program allows zooming in on a marker for more accuracy. Different sets of markers are available according to each view (anterior, posterior or lateral). Following the selection of the markers associated with the calculation of an angle, its value can be displayed. All measurements can be exported in Excel-or Word formats. We thus choose to study the acceptance of the CPPAT because this tool has good demonstrated reliability and validity, allows posture assessment of the whole body, was designed to be user-friendly and follows a standardized procedure.

Procedure

The first part of the project involved the training of clinicians. The principal investigator (CF) and a research physical therapist (J-F A-F) trained clinician-participants. Participants received a tool kit including a detailed procedure for standardization of photo acquisition (following Fortin et al.'s study [18]) and of photo-processing with the software program and markers. The training consisted of a three-to five-hour workshop in each centre. The workshop was divided into three parts: 1) rationale and explanation of the software program, 2) equipment requirements (simple digital camera on a tripod) and demonstration of posture assessment with the placement of markers and photo acquisition and 3) instruction and practice using the software for photo-processing. As part of their training, clinician-participants had to use the software program to assess posture of three persons (patients or colleagues) before beginning the trial, to ensure they were familiar with the posture assessment procedure. Following the training period, participants were asked to collect the number of patients assessed with the tool and time spent for photo acquisition and for photo-processing with the software program on a data sheet for three months.

The second part consisted of a post-trial focus group discussion in small groups of up to six participants. Before beginning the discussion, participants submitted their data sheet with the number of patients analysed with the tool, together with the time for photo acquisition and photo-processing. Subsequently, van Schaik et al.'s [22] questionnaire was used to measure technology acceptance in terms of perceived ease of use, perceived usefulness and intention to use (see Additional file 2). The focus group discussion was conducted by the researchers in UK (J B-S) and in France (CF) and a physical therapist research assistant (J-F A-F) in Quebec (Canada). We used a semi-structured procedure with specific questions regarding general positive and negative aspects of the tool, its utility, utilization and patients' feedback. Participants were also asked to comment on the advantages/disadvantages they experienced of the clinical method, drivers/barriers of system use and other possible applications of the method.

Statistical analysis

Descriptive statistics (mean and SD), confidence intervals and t-tests were used to characterize the number of patients assessed with the method and time for photo acquisition and photo-processing with the CPPAT. The data were examined for normality; skew and kurtosis were not extreme (|z[skew]| < 1.8; z[kurtosis] < 1.1) and the distributions were not significantly different from the normal distribution(Komolgorov-Smirnov test: p > .05). t-tests were used to determine if scores obtained for each sub-scale differed from the neutral score (represented by a value of 4 on a seven-point Likert scale). We assessed reliability of each acceptance measure scale by calculating Cronbach's alpha. Correlation analysis examined the association between the three acceptance variables. A first mediation analysis was conducted to test perceived usefulness as a potential mediator of perceived ease of use to predict intention to use the system; a second analysis tested mediation of the predictor average photo-processing time (see mediation models in Figs. 3 and 4) 1 .

A qualitative analysis was achieved using van Schaik et al.'s [22] procedure to document drivers and barriers to the acceptance of the CPPAT. The research physical therapist (J-F A-F) read all comments and initially categorised each comment into themes. He reviewed the themes again and created more general (higher-order) categories on top of the initial categories. He and the senior researcher (CF) then discussed and agreed a higher-order list of themes/ categories. Both researchers independently coded all comments using the higher-order categories and recorded their codings. Finally, they compared their results, noted the number of disagreements out of the total number of codings, and discussed and resolved any disagreements.

Results

System use

During the course of the trial, participants assessed a mean of 7.7 patients (SD = 4.17, CI(95%) = [6.14; 9.29]). The most frequent medical diagnosis were respectively idiopathic scoliosis, back pain and hyper-kyphosis. At their first evaluation, clinician-participants took 36 min

Level of acceptance

Perceived usefulness, perceived ease of use and intention to use were measured reliably (Cronbach's alpha = 0.89, 0.92 and 0.87, respectively). 3 Descriptive statistics indicated that

Mediation model of technology acceptance

Correlations among the variables showed that perceived usefulness was strongly correlated with perceived ease of use (r = .64, p = .001) and intention to use (r = .51, p = .01). Therefore, as perceived usefulness and perceived ease of use increased, intention to use also increased. The correlation between perceived ease of use and intention to use was small (r = .24, p = .26). In addition, mean analysis time was strongly negatively correlated with perceived usefulness (r = −.47, p = .03) and moderately negatively correlated with perceived ease of use (r = −.39, p = .08). Therefore, as time increased, perceived usefulness and perceived ease of use decreased. The positive correlation between intention to use and analysis time was small (r = .11, p = .64).

Mediation analyses were conducted to test perceived usefulness as a mediator of the predictors (1) perceived ease of use and (2) analysis time for intention to use. In the first analysis, the mediation model was statistically significant, explaining 27% of variance in intention to use (see Fig. 3). Perceived ease of use was significant as an indirect positive predictor of intention to use, mediated by perceived usefulness. Therefore, the reason why intention to use was higher when the system was perceived to be easier to use was that it was perceived to be more useful. However, perceived ease of use was not significant as a direct predictor. According to Zhao et al.'s [31] decision tree, the pattern of results can be interpreted as indirect-only mediation: the mediator fully mediated 5 and explained the prediction of intention to use by perceived ease of use. Apart from its function as a mediator, its significant regression coefficient on intention to use (see Fig. 3) shows that perceived usefulness was also a predictor of intention to use, independent of perceived ease of use.

The mediation model in the second analysis was also significant, explaining 37% of variance in intention to use (see Fig. 4). Analysis time was significant as an indirect negative predictor of the intention to use, mediated by perceived usefulness. Therefore, the reason why intention to use was reduced when analysis time was longer was that the system was perceived to be less useful. Analysis time was significant as a direct predictor, so mediation was partial: the prediction of intention to use by analysis time was partially mediated by perceived usefulness. According to Zhao et al.'s [31] decision tree, the pattern of results showing partial mediation can be interpreted as indicative of an incomplete theoretical framework. In other words, although part of the prediction of intention to use by analysis time was explained by the mediator perceived usefulness, in future research one or more other further mediators that were not included here may explain the significant direct prediction that was found. Apart from its function as a mediator, its significant regression coefficient on intention to use (see Fig. 4) shows that perceived usefulness also was a predictor of intention to use, independent of analysis time.

Drivers and barriers

Our clinician-participants indicated four principal facilitators/advantages and four principal barriers/disadvantages. Frequencies of advantages and disadvantages are presented in Figs. 5 and 6.

Facilitators/advantages

Principal advantages were objective measures (17), visualization for both patients and therapists (17), utility (16), and ease of use (12). Within objective measures, accuracy of measurements and ability to document quantitative changes of posture were the most frequent answers. Regarding visualization, answers showed advantages in helping patients' adherence to treatment, as well as guiding the therapists in seeing posture compensation. For utility, most frequent answers were useful for clinical research, as an x-ray substitute, screening tool, for patient education, treatment justification, and discussion with physicians. In terms of ease of use, the advantages were stated to be as follows: manipulation of images in the graphical interface and image processing. Four clinician-participants considered time as an advantage since they were able to achieve a complete evaluation of posture within an hour.

Barriers/disadvantages

The principal barriers stated were time to do a complete analysis of posture (19), the quality of human-computer interaction (18), non-automation of posture index calculation and photo transfer (18) and lack of versatility (14). Within the time category, participants included the time to take the photo, to transfer the photo into the software program, as well as processing the photo. For human-computer interaction, participants indicated that it was hard to print or copy the processed photo, the software program was only functioning on Windows systems (not on tablet, iPhone or MAC computer), it was complex to export data and the technology was complex in general for older therapists. Regarding non-automation, the most frequent answers were manual processing of the photo, a few software bugs, manual importation of photo, and manual conversion from pixels to cm for linear posture indices. In terms of versatility, being limited to four photos, all in standing and the lack of some posture indices such as head protraction in cm or not being able to add other posture indices were the most frequent comments reported. Some clinician-participants stated the absence of normative data (5) as well as the patients' discomfort with removing clothing (7) or therapists' comfort in terms of positioning themselves while putting the markers on anatomical landmarks of the lower extremities (3) as further disadvantages.

Discussion

The aim of this study was to assess the acceptance of a new CPPAT among therapists who frequently assess posture as part of their clinical practice. We found strong and moderate acceptance of the CPPAT respectively in terms of usefulness and ease of use with a slightly positive intention to use the CPPAT. Our mediation analysis revealed that perceived usefulness and perceived ease of use as well as analysis time were indirect predictors of intention to use. This is in agreement with previous studies that showed the importance of these components in technology acceptance [22,26,27,32].

According to Rogers [33], a new technology is more easily adopted if it is compatible with current practice, is seen as more advantageous than current practice and is easy to use (low complexity). Posture assessment was an integral part of the current practice of our participants. However, their perception of performing better in their job was divided: some saw an advantage to use the tool while others did not.

Other factors such as attitudes towards the new innovation, measurement properties of a tool, perception of self-efficacy, being able to observe its use by others and having the possibility to try it out are important for innovation adoption [33][34][35]. In our study, participants agreed with the good measurement properties of the tool and with the usefulness of the tool for quantifying body posture but their intention to use the tool was only slightly positive. In his model, Davis [24] pointed out that ease of use is often associated with the notion of no effort. For some participants, learning to use the tool seemed to be a greater effort than for other participants and may have led to a perception of poor self-efficacy. Indeed, several participants (n = 10) mentioned having a low level of computer use. Human-computer interaction and time to do a complete analysis of posture (photo acquisition and photo-processing with the CPPAT) were the most important barriers to acceptance.

In our laboratory, our research physical therapist takes on average 15 to 20 min for photo acquisition and our trainedstudents (same training offered to our clinician-participants) take ten to 12 min for photo-processing with the CPPAT for one complete trial. At the end of the three-month trial, 15 out of 23 (65%) and 11 out of 23 (48%) participants achieved this performance, respectively, for photo acquisition and for photo-processing with the CPPAT. With the exception of two participants, the better performance for photo acquisition was found in those participants who were used to take photos as part of their routine posture assessment of their patients.

Furthermore, some participants worked more specifically with children. It was therefore expected that it would take them more time to conduct the photo acquisition because children have more difficulty in maintaining a quiet standing posture [36]. Regarding photo-processing time, all participants conducted at least three complete evaluations of posture following their practice trial. This suggests that they had the minimal requirements to develop new skills and to achieve a good performance with the CPPAT.

Factors such as clinician-participants' age or level of computer use may have affected task performance [34,37]. Kaya [37] reported a negative effect of age and of low computer experience on attitudes toward computers in healthcare practitioners. Our clinician-participants had a mean of 20 years of experience in clinical practice and eight out of the ten participants who had taken a longer time for photo-processing with the CPPAT had a low level of computer use, which may explain their difficulty in performing better as well as their low level of interest to use the tool. This may also explain why for half of our participants the graphical interface of the software program was seen as user-friendly while not for the others.

Other barriers for the CPPAT acceptance were the lack of automation of posture index calculation and photo transfer, and the lack of versatility of the tool in terms of positions of posture acquisition and computer operating system. Further development of the tool focusing on automation of photo transfer and posture index analysis would contribute to a substantially decreased time for photo-processing and may thus promote an increased adoption of the tool. Some participants mentioned that they could not take photos in other positions apart from standing or could not add new posture indices. In the present study, they were asked to take photos in a standing position but we showed in a previous study that it is also possible to take and analyse photos in a sitting position [17]. Moreover, new posture indices would be easy to implement in a new version of the tool.

Limitations

The main limitation of our study was the small number of participants that completed the trial. However, this sample size was large enough to demonstrate a high level of perceived ease of use and perceived usefulness and demonstrate statistical mediation, and identify the main barriers for the CPPAT acceptance. Moreover, the sample size in each country was too small to formally compare the results between countries. The two participants in France were familiar with sophisticated systems to measure static and dynamic posture. Hence, they both found the tool easy to use and user-friendly. Participants from UK and Quebec (Canada) were more heterogeneous and tend to show similar results in the acceptance of the tool.

We also acknowledge that some participants did not have easy access to a dedicated space for photo acquisitions even though this was an inclusion criterion. Moreover, a non-facilitating environment including the absence of local champions is an important barrier and may affect innovation adoption [33,35]. Although we had identified champions in several centres before the study began, for several reasons, these persons could not act as champion in their respective centre. A local champion might have helped in resolving problems such as the accessibility to a dedicated space for photo acquisitions or minor bugs with the software program. Attitudes towards the new technology and self-efficacy are also important factors for innovation adoption [32,34]. In this study, we did not directly measure these factors and we did not use a validated questionnaire to measure the level of computer use. This will need to be done in a future study. Selection bias may have occurred since some clinician-participants knew the researchers and the physician leading this project. However, the answers of the clinician-participants seem to objectively reflect their 'true' acceptance of the tool.

Clinical implications

This study highlights the usefulness of the CPPAT for quantifying posture in a clinical setting. The majority of our participants found this tool useful to document quantitative changes of posture, for a complete or partial evaluation of posture, as a screening tool, for patient education as well as for treatment justification and for discussion with physicians. According to our participants, photos allow visualization of posture, which is perceived as a good means to help patients' adherence to treatment and guiding therapists in seeing posture compensation. Participants used the tool among persons presenting with spinal deformities such as scoliosis, hyper-kyphosis or hyper-lordosis, with back pain and lower-limb impairments. Taking photos facilitated the measurement of several body angles at a time and is more accurate and rapid than measuring direct body angles on a person, especially in those with back pain [13]. Few participants mentioned the need to have the software program functioning on Windows systems as well as on tablet, iPhone or MAC computer. Other mobile APPs have been developed to measure posture and showed promising results, but posture indices measurement errors of these APPs and their validity still need to be documented [16,20]. Some participants also indicated that less than 30 min should be taken for a complete assessment of posture. Being able to integrate automation of photo transfer and of posture index calculation into the CPPAT should allow clinicians to have a more efficient tool and may promote adherence to this tool. To be more cost-and time-effective, clinicians may also select a set of relevant posture indices according to a patient's condition to document change in posture over time. However, clinicians should interpret changes in posture over time with caution since reliability and validity of posture indices measurements of the CPPAT have been reported only in adolescents with idiopathic scoliosis and sensitivity to change of these posture indices measurements is not yet determined.

Conclusion

Our results indicate that the CPPAT is perceived to be useful and easy to use by clinicians. The CPPAT tool contributes to clinical practice by facilitating the quantitative analysis of posture and by enhancing the education of patients presenting with different musculoskeletal impairments. The principal barriers for the acceptance of CPPAT were the time to conduct a complete postural analysis and difficulties in interacting with the system. Adapting the software-human interface and automation for posture index calculation may facilitate the wider adoption of the tool.

Endnotes

1 In a mediation model, the followings effects are analysed: (1) the effect of the mediator (e.g., perceived usefulness) on the outcome (e.g., intention to use), with the predictor (e.g., perceived ease of use) held constant, (2) the direct effect of the predictor on the outcome with the mediator held constant and (3) the indirect effect of the predictor on the outcome through the mediator. 2 Bias-corrected accelerated confidence intervals are presented, with N = 1000 bootstrap samples. 3 For ease of interpretation of the results, average scores on each scale were calculated, reverse-scored and then used in subsequent analysis. 4 Cohen's (1988) conventions for effect size were used for d (small: ±0.2; medium: ±0.5; large: ±0.8) and r (small: ±0.1; medium: ±0.3; large: ±0.5). 5 Full mediation occurs when the regression coefficient of the predictor (e.g. perceived ease of use) on the outcome variable (e.g. intention to use) becomes non-significant when the mediator (e.g., perceived usefulness) is introduced.

Fig. 1
1Technology acceptance model (afterDavis & Venkatesh, 1996 [38])

Fig. 2
2Graphical interface with photos of the clinical photographic posture assessment tool (CPPAT)

(mean, SD = 19, CI(95%) = [29; 43]) 2 for photo acquisition and 54 min (mean, SD = 29, CI(95%) = [43; 66]) for photo-processing with CPPAT. At their last evaluation, the time-on-task with the CPPAT decreased significantly, to 22 min (mean, SD = 10, CI(95%) = [18; 27], t (20) = 3.99, p = .001, d = 0.88) for photo acquisition and to 36 min (mean, SD = 19, CI(95%) = [29; 44], t (20) = 5.29, p < .001, d = 0.74) for photo-processing.

Fig. 3
3Mediation analysis (perceived ease of use) respondents believed the CPPAT was useful (mean = 5.09, SD = 1.05, CI(95%) = [4.66; 5.52]), easy to use (mean = 4.83, SD = 1.29, CI(95%) = [4.32; 5.30]) and had the intention to use the tool (mean = 4.42 (SD = 1.27, CI(95%) = [3.90; 4.98]). Perceived usefulness, perceived ease of use and intention to use were significantly higher than neutral, with a large effect size (d = 1.04), medium effect size (d = 0.64) and small effect size (d = 0.31), respectively. 4

Fig. 4
4Mediation analysis (average analysis time)

Fig. 5
5Frequencies of facilitators/advantagesFig. 6 Frequencies of barriers/disadvantages

Level of computer use: description of each level of computer use. (DOC 27 kb) Additional file 2: Questionnaire Technology acceptance: items for perceived ease of use, perceived usefulness and intention to use. (DOC 33 kb) Abbreviations CPPAT: Clinical Photographic Posture Assessment Tool; TAM: technology acceptance model
AcknowledgementsThe authors acknowledge Julie Deceuninck for her contribution in the training of clinician-participants in Lyon (France) and the clinician participants.Availability of data and materialsThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Authors' contribution CF, JB-S and DEF designed the study. CF, J-FA-F and J-CB were responsible for data collection. CF, PvS and J-FA-F were responsible for data analysis and interpretation. CF and PvS drafted the manuscript. JB-S and DEF critically revised the manuscript. All authors revised and approved the final version of the manuscript.Ethics approval and consent to participateThe project was approved by the Institutional Ethics Committee of Sainte-Justine university hospital centre (file number: 2015-691, 3905) and all clinician-participants signed a consent form.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interest.
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BMC Family Practice Conducting research in individual patients: lessons learnt from two series of N-of-1 trials

Anke Cm
Department of Pharmacology/Pharmacotherapy
VU University medical center
Van der Boechorststraat 7, The Netherlands1081 BTAmsterdam

Wegman
Daniëlle Awm Van Der Windt
Department of General Practice
EMGO Institute
VU University medical center
The NetherlandsAmsterdam

Primary Care Sciences Research Centre
Keele University
ST5 5BGKeeleStaffordshireUK

Wim Ab Stalman
Department of General Practice
EMGO Institute
VU University medical center
AmsterdamThe Netherlands

Theo Pgm De Vries
Department of Pharmacology/Pharmacotherapy
VU University medical center
AmsterdamThe Netherlands

BMC Family Practice Conducting research in individual patients: lessons learnt from two series of N-of-1 trials
10.1186/1471-2296-7-54BioMed Central Page 1 of 8 (page number not for citation purposes) Debate * Corresponding author
Background: Double-blind randomised N-of-1 trials (N-of-1 trials) may help with decisions concerning treatment when there is doubt regarding the effectiveness and suitability of medication for individual patients. The patient is his or her own control, and receives the experimental and the control treatment during several periods of time in random order. Reports of N-of-1 trials are still relatively scarce, and the research methodology is not as firmly established as that of RCTs. Recently, we have conducted two series of N-of-1 trials in general practice. Before, during, and after data-collection, difficulties regarding outcome assessment, analysis of the results, the withdrawal of patients, and the follow-up had to be dealt with. These difficulties are described and our solutions are discussed.Discussion:To prevent or anticipate difficulties in N-of-1 trials, we argue that that it is important to individualise the outcome measures, and to carefully consider the objective, type of randomisation and the analysis. It is recommended to use the same dosages and dosage forms that the patient used before the trial, to start the trial with a run-in period, to formulate both general and individualised decision rules regarding the efficacy of treatment, to adjust treatment policies immediately after the trial, and to provide adequate instructions and support if treatment is adjusted.Summary: Because of the specific characteristics of N-of-1 trials it is difficult to formulate general 'how to do it' guidelines for designing N-of-1 trials. However, when the design of each N-of-1 trial is tailored to the specific characteristics of each individual patient and the underlying medical problem, most difficulties in N-of-1 trials can be prevented or overcome. In this way, N-of-1 trials may be of help when deciding on drug treatment for individual patients.

Background

In medical care, deciding on the optimal treatment for individual patients is one of the main concerns of the physician. Scientific evidence, recommendations from guidelines, medical expertise, patient preferences, and personal experiences all contribute to this decision process. Randomised controlled trials (RCTs) are generally considered to provide the strongest evidence for the efficacy of treatment. However, RCTs are designed to estimate an average treatment effect in a specific population [1]. In daily practice the physician has to determine the extent to which this average effect will apply to an individual patient. For instance, a patient who consults a physician may not be of a similar age, may have additional co-morbidity and medication, or may be interested in a different outcome compared to the subjects studied in the related RCT.

Usually, when a physician doubts the applicability of treatment recommendations (derived from RCTs) to a specific patient, the trial and error method is used ('trial of treatment') [2]. This means that a particular drug will be prescribed, and, subsequently, continued if considered effective, or changed if considered not beneficial. This decision may be strongly influenced by expectations and preferences of both patient and physician. N-of-1 trials provide more objective evidence of individual benefit or harm, while increasing the patient's involvement in the management of his or her disease [3]. In contrast to RCTs, N-of-1 trials do not assess what is best on average for a whole population, but what is best for an individual patient. The patient is his or her own control, and receives the experimental and the control treatment during several periods of time in random order. If possible, the patient, the physician and the researcher are blinded for the sequence of treatments [4]. Therefore, it is reasonable to argue that N-of-1 trials may help physicians to provide better care. However, it is impossible and undesirable to tackle each treatment problem with an N-of-1 trial. Firstly, there should be considerable doubt about the treatment policy. Secondly, the disease or complaint has to be chronic or recurrent, or drugs need to be prescribed for a long period of time or for frequently repeated periods of time. Finally, treatment effects should have a rapid onset and stop acting soon after discontinuation [4].

Reports of N-of-1 trials in general practice are still relatively scarce, and the research methodology is not as firmly established as that of RCTs. Recently, we have conducted two series of N-of-1 trials (Table 1), one in patients with osteoarthritis of the hip or knee (series A), and one in long-term users of temazepam (series B) [5,6]. Before, during, and after data-collection, a number of difficulties had to be dealt with. The aim of this paper is to describe the difficulties we encountered during these series and to discuss our solutions. Successively, difficulties with regard to the outcome assessment, the analysis of the results, the withdrawal of patients, and follow-up will be discussed. We used our own experiences, and searched the literature (Medline 1966 until now, using the search term "N-of-1 trial") to incorporate suggestions made by other researchers regarding the methodology of N-of-1 trials.

Discussion

Outcome assessment

Problem: which outcome measures should be used? In a series of N-of-1 trials it seems logical to use the same outcome measures for all patients within one series (as in an RCT). However, since every patient is analysed separately in N-of-1 trials, the outcome measures do not have to be the same for all patients in a series. It is more important that the outcome measures are relevant for each individual patient [7]. On the other hand, in a series of N-of-1 trials, it is of interest to demonstrate variation (heterogeneity) in the results across patients. Therefore, the outcome measures for the different patients should be comparable.

Measures taken: outcome measures were individualised

Using the methods proposed by Guyatt et al. [4,8] and Beurskens et al. [9], all patients from series A were asked before the start of their trial to identify their most important problems regarding osteoarthritis. Every day each patient scored the severity of these problems (four to eight items) on a 7-point ordinal scale ranging between 0 = no problem at all and 6 = very severe problem. Examples of such problems were: pain in the knee when going up and down stairs, pain in the knee when lifting shopping bags, stiffness of the knee when getting out of bed. As all items were scored on a similar scale (7-point ordinal scale), differences in the effect of the treatment on the severity of the individual complaints could be compared across patients. Outcome measures were also individualised for series B, in which the patients could select their main problem among a set of questions regarding quantity or quality of sleep.

Results of measures

Because we used individualised outcome measures, we were able to formulate treatment recommendations based on the results of patient-specific outcomes. In addition, the patients found it easy to answer questions reflecting their main problems, and completion rates were high. For all patients who finished their trial the completion rate for the main problems was at least 99% in series A, and 82% to 100% in series B. Furthermore, using the same 7-point scale for each problem enabled us to study heterogeneity among patient outcomes. The results ranged from no difference to large differences in favour of non-steroidal antiinflammatory drugs (NSAIDs) compared to paracetamol in series A, and similarly in series B from no difference to large differences in favour of temazepam compared to placebo or a lower dosage of temazepam.

Recommendations

Identification of the main complaint in each patient, yet using the same scale to assess the outcomes for all patients in a series, enables the investigator to present each patient with individualised results and treatment recommendations and, simultaneously, to make comparisons of the results across patients. This approach has been used sucessfully in other N-of-1 trials, investigating the effectiveness of theophylline for irreversible chronic airflow limitation [10] or the efficacy of quinine in leg cramps [11]. Alternatively, patients participating in N-of-1 trials have been asked to give a global assessment of the effectiveness of the medication, and indicate a preference after each treatment pair [12]. Such an assessment is patient centred, and can also take into consideration both beneficial and adverse effects of treatment.

Analysis of results

Problem: how should the outcomes of the N-of-1 trials be analysed? In both our series, the N-of-1 trials consisted of five pairs of treatment periods. Each patient received during one period of each pair the active treatment (treatment X), and during the other period the control treatment or placebo (treatment Y). For each patient, the sequence of treatments (XY or YX) was randomised within each pair of treatment periods (Figure 1).

Since N-of-1 trials are meant to evaluate individual treatment effects (and not to estimate the average effect in a larger population), results are analysed for each patient separately. The possibilities of statistical testing for signif-icance, however, are limited, but in some studies the paired t-test has been used [8,13,14]. The disadvantage of this test is that it assumes a normal distribution of the data, and independence of data from treatment periods. The problem of autocorrelation (i.e. the data are not independent) can be solved by using the average of the data in each treatment period instead of all individual data [4], while non-parametric tests can be applied to tackle violations of the normality assumption. Furthermore, timeseries analysis can sometimes be used. In time-series analysis, data over time are compared for separate treatment periods. Time-series analysis is appropriate when there is serial dependency in the data (i.e. successively measured data are significantly correlated). One limitation is that time-series analysis requires a large number of observations in each treatment period. As N-of-1 trials are often designed with relatively short treatment periods, timeseries analysis is not really appropriate [15].

Also, non-parametric tests, such as the sign test or the randomisation test can be used. These tests do not require a normal distribution of the data. The disadvantage of the sign test is that it lacks power. At least five pairs of treatment periods are necessary before the significance level of 0.05 can be reached (1/2 5 ) [4], irrespective of the magnitude of difference between the treatments. This is the reason why we used five pairs of treatment periods in both our series. With the randomisation test, the power can be increased by using less restricted randomisation schedules [16,17]. For example, if 5 of the 10 periods in our N-of-1 series had been randomly assigned (without restrictions) to treatment X and the other five to treatment Y, the smallest possible p-value would have been 1/252 = 0.004 (=1/ number of randomisation possibilities, in this case 1/ Table 1

: Details of two series of N-of-1 trials conducted by our research group

Patients who had been taking NSAIDs regularly in the treatment of pain and disability related to osteoarthritis of the hip or knee.

Long-term users of temazepam (10 or 20 mg) for sleep disturbances. The patient, the investigator and the GP were blinded for the sequence of treatments.

The patient, the investigator and the GP were blinded for the sequence of treatments.

[10!/5!5!] = 1/252). In case of four periods of treatment X and four periods of treatment Y, this would still be 0.014
(1/[8!/4!4!])
. However, restrictions are often necessary to ensure, for example, that the active treatment periods are not all concentrated at the end of the trial, while it could be possible that the patient improves spontaneously [17].

In our series, however, we did not investigate whether one treatment was better than the other, but whether one treatment was equally effective as the other. In other words, the trials we conducted were equivalence trials, rather than superiority trials, which meant that conventional significance testing could not be used for analysis [18][19][20]. Since 'no difference' between two different treatments cannot be proven, an equivalence range is defined before the start of the trial, i.e. a range of differences between treatments that are considered to be of no clinical importance. If the confidence interval of the difference between treatments lies entirely within this equivalence range, equivalence may be concluded with only a small probability of error [18]. To test equivalence a large number of data are required: the smaller the equivalence range, the more data are needed [18,21,22]. As stated above, the number of observations are often very small in N-of-1 trials, and equivalence testing may not be feasible.

Measures taken: computation of median differences in outcomes, and definition of cut-off points

For both series, we decided to define (a priori) cut-off points for a minimal important difference on each primary outcome. In this way we could distinguish between equivalence of effects, small, or large differences in the effectiveness of medication. Given the non-normal distribution of our data, we calculated differences in median scores between the two treatments for each outcome measure. For example, for series B, a median difference in the time to fall asleep of at least 30 minutes in favour of temazepam was considered to be a large effect, 5 to 30 minutes a small effect, and <5 minutes no effect of temazepam.

Recommendations

When setting up an N-of-1 trial the investigator should carefully consider the objective of the trial. If the aim is to evaluate whether another type of medication is more effective than current treatment, the trial can be designed as a superiority trial, and conventional tests of significance may be applied during the analysis. If, however, the aim is to confirm the equivalence of two (or more) treatments (which is the case in all efforts to reduce or stop medication), the N-of-1 trial is an equivalence trial, and requires a different method of analysis, with a strong emphasis on the definition of a minimal important difference, and sufficient observations to enable equivalence testing.

Several other researchers have formulated cut-off points or defined responders and non-responders according to the number of pairs in which a clear difference between treatments was seen [23][24][25][26]. Patient preferences can be used to determine the outcome of an N-of-trial, with superiority of a drug established if -in a pre-determined proportion of treatment pairs -the patients favours one treatment over the other. In some studies researchers have used this method to allow for a variable number of treatment pairs among patients, with a trial being stopped early in case of strong preferences by either physican or patient [10].

More recently, hierarchical Bayesian methods have been proposed for the statistical analysis of series of N-of-1 trials [27][28][29]. The objective of this approach is to use individual patient assessments to obtain an overall population estimate of treatment effectiveness that takes into account both the magnitude of the effect, as well as

Example of randomisation schedule for one patient receiving 5 pairs of treatment, each consisting of one week of treatment X and one week of treatment Y Figure 1 Example of randomisation schedule for one patient receiving 5 pairs of treatment, each consisting of one week of treatment X and one week of treatment Y.  [28]. Bayesian methods have the advantage of allowing for the introduction of co-variates in the model, and for embodying prior information [27]. Therefore, information acquired from previous patients will aid the interpretation of subsequent N-of-1 trial results [27,28]. As the understanding of treatment responses and precision of estimates improve over time, N-of-1 trials may need less cross-overs (treatment pairs) to obtain the same amount of information, or even become unnecessary [28].

Withdrawal

Problem: withdrawal from the trials Six of the 13 patients (46%) in series A, and three of the 15 patients (20%) in series B, did not complete their trial period. The reasons for withdrawal (as given by the patients) are summarised in Table 2.

Perceived lack of efficacy

Four patients in series A did not finish their trial because of perceived lack of efficacy. Three of these four patients received dosages of NSAIDs during their trial that were lower than they had taken before the trial. The other patient had taken additional paracetamol/codeine before the start of her trial, but was asked not do so during the trial. These findings suggest that the withdrawal of these patients was due to subtherapeutic dosages of medication during their trials. In series B, two patients did not complete their trial because of perceived lack of efficacy. The participants were aware that they would receive placebo or a lower dosage of temazepam half of the time, and may have expected poor results. These expectations may have contributed to a perceived lack of efficacy.

Perceived side effects

In both series one patient withdrew because of perceived side effects. Before the start of the trial the patient who withdrew from series A was used to taking diclofenac tablets. To ensure blinding during the trial she received identical capsules containing either diclofenac and placebo, or paracetamol. As the participant reported abdominal complaints during both periods it is possible that these symptoms were caused by the gelatine capsules. Similarly, a patient in series B received medication in a different dosage form (tablets instead of temazepam capsules). This patient reported nausea, and withdrew after a few days.

The reports of nausea and abdominal complaints may also have been the result of pure coincidence, or perhaps, be related to concerns regarding the effects of taking medication in another form.

Duration of the trial

In series A the duration of the trial was 20 weeks, and in series B it was 10 weeks, which made it easier for the patients in series B to complete their trial. In series A, one patient withdrew because the study took too long and because of coexisting symptoms (back pain). None of the patients in series B withdrew because the trial took too long.

Recommendations

A number of measures can be taken to prevent withdrawal due to subtherapeutic dosages or (perceived) side effects. Firstly, the drug can be prescribed in the same dosage and form as the patient is used to. Secondly, the trial can be started with a run-in period, during which the patient receives the drug in the dosage and form in which it will be prescribed during the trial. In this way, the patient can withdraw from the trial at an early stage, or the medication can be adjusted before the data-collection actually starts. Finally, if blinding is difficult because of insurmountable differences in the form, size, colour, smell or taste of the medication, the double-dummy design can be used. In a double-dummy design the patient receives active treatment X and placebo treatment Y during one period, and placebo treatment X and active treatment Y during the other. However, this will increase the number of tablets that need to be taken, possibly leading to noncompliance or withdrawal [23].

An alternative method to prevent withdrawal due to expectations of poor results is to keep the patients completely unaware of the dosages. However, this may not receive ethical approval. Furthermore, to prevent withdrawal due to the length of the trial, treatment periods can be kept as short as possible. Such a decision will affect the power of the trial. In a commentary on an N-of-1 trial in a pregnant woman with nausea and morning sickness [30][31][32] Campbell discussed the length of treatment periods [33]. While the total trial period should not be too long [34] the separate treatment periods should be long enough to achieve and detect a clinically important treatment effect [31].

Withdrawal rate in series of N-of-1 trials conducted in primary care range between 12 and 40%, with similar reasons for discontinuing participation [2,[11][12][13][24][25][26]29]. Nikles et al. proposed that withdrawal rates can be thought of as a general measure of compliance (or adherence), which is often not as good as clinicians believe. Non-adherence rates of around 50% have been reported for several chronic conditions [2].

Follow-up

Problem: reverting to the same medication as before the N-of-1 trial After the completion of a trial we visited participants at home to discuss their results. Subsequently, patients were asked about their intentions regarding future medication intake for osteoarthritis (series A) or sleep disturbances (series B), and general practitioners (GPs) were informed about these intentions. However, three months after the completion of series A, four of the six patients who intended to switch to paracetamol were taking NSAIDs for osteoarthritis, and one patient was taking both paracetamol and NSAIDs. In series B, two of the nine patients who intended to reduce or stop their temazepam intake had reverted to their previous dosage. The reasons for reverting to the same medication are summarised in Table 3.

Patients were not sufficiently instructed, coached and/or monitored One patient from series A indicated that she had reverted to taking NSAIDs because the paracetamol was not sufficiently effective. However, instead of taking 1000 mg paracetamol three times a day, the patient had been taking 500 mg twice a day. After solving this misunderstanding, the patient agreed to start taking paracetamol again. Another patient considered it a waste to throw away the NSAIDs and wanted to finish her supply before starting the paracetamol treatment. In one patient from series B the dosage of temazepam had not (yet) been reduced.

Since the end of the trial, the patient had not seen the GP and neither the patient nor the GP had taken any action to adjust the treatment policy. Obviously, these three patients were not sufficiently instructed by the researcher or sufficiently coached and monitored by the GP.

Motivation to adjust treatment

In one patient from series B temazepam had little effect on the quality or quantity of sleep, but the patient saw no disadvantage in taking temazepam and decided to take it just as frequently as before the trial.

Recommendations

Only few reports on N-of-1 trials have discussed the longterm effects of participating in an N-of-1 trial [10][11][12].

Both Woodfied et al. [11] and Mahon et al. [10] commented on the fact that a considerable number of participants decide to resume or continue the use of active medication, despite results of the trial indicating a lack of effectiveness. Some of these decisions were explained by the fact that participants returned to their own non-study physician after completion of the trial. To prevent patients from reverting to previous treatment, a number of measures can be taken: (1) before the start of the trial the patients should be well informed about the objectives and, especially, the possible consequences of the trial, (2) the GP (together with the researcher) should discuss the results with the patient and adjust (if appropriate) the treatment policy immediately, (3) the patients should be well instructed and encouraged to follow treatment recommendations, and (4) monitoring may be needed for some time. Furthermore, before the start of a trial, the patient's reasons for participation should be investigated. Patients should be willing to question their current treatment, and adjust their treatment, if necessary [35]. We suggest to define a minimal important difference for each patient before the start of the trial. This will establish individually relevant decision rules with clear agreements regarding the interpretation of results and decisions regarding the future treatment policy.

Summary

Given the importance of adjusting the design of an N-of-1 trial to the specific characteristics of each individual patient, it is difficult to formulate general 'how to do it' guidelines for N-of-1 trials. However, when the design of each N-of-1 trial is tailored to the specific characteristics of each individual patient and the underlying medical problem, most difficulties in N-of-1 trials may be prevented or overcome. In our opinion, it is important to carefully consider the objective of the trial, and, with that, the randomisation schedule and the type of analysis. Furthermore, it is important to individualise outcome measures, so that they are relevant for each patient. We recommend that dosage and form of medication is the same as before the trial, and that the trial starts with a runin period. General decision rules regarding the efficacy of treatment may help to demonstrate variation across patients, but we also recommend to formulate individualised decision rules for the future use of medication before the start of a trial. Finally, patients need to be provided with adequate instructions and support after adjustments of treatment.

Usefulness and implementation of N-of-1 trials

The value of N-of-1 trials for making decisions in individual patients, through bridging the gap between research and practice, allowing for an individual approach, and incorporating patient values, has been well-accepted [36].

Results of randomised N-of-1 trials have even been placed in the top of the hierarchy of evidence [37]. However, few studies have evaluated their usefulness in clinical practice, or assessed potential barriers to more widespread implementation of this approach. Wide experience has been gained in Australia, where a national N-of-1 service has been implemented. The service has been promoted through clinical meetings, newsletters, websites, and the media [26]. Several large series of N-of-1 trials have been undertaken [26,29,38]. Qualitative research showed that patients were generally very satisfied with the N-of-1 trial process. Their participation led to increased awareness and understanding of their condition, and its management, resulting in a sense of empowerment and control [2]. The effort of setting up an N-of-1 trial in primary care, however, is substantial [3], especially outside the setting of an N-of-1 trial service. It includes the costs of paperwork and consent forms, arranging identical placebo's from a pharmacy, designing and printing diaries, statistical analysis, and time spent informing and instructing patients.

The ultimate test of the usefulness of N-of-1 trials in clinical practice is to compare the outcome of N-of-1 trials in terms of costs and patient benefits to usual care in a randomised design. Two such randomised trials have been undertaken. Mahon et al. [10] evaluated whether patients with irreversible chronic airflow limitation who were prescribed theophylline guided by N-of-1 trials had better outcomes than patients treated according to standard practice. The use of N-of-1 trials did not yield important advantages over standard practice. Pope et al. compared the efficacy and cost-effectiveness of placebo-controlled N-of-1 trials with diclofenac/misoprostol to standard care among patients with osteoarthritis who were uncertain that NSAIDs would be helpful [12]. The N-of-1 trials yielded slightly better improvements than standard care, but the difference was not statistically significant. Both trials showed that the costs of N-of-1 trials were considerably higher than those of standard care, due to the costs associated with the production of placebo's, and time spent by nurses, physicians, and patients. These were the first trials that formally evaluated the cost-effectiveness of implementing N-of-1 trials in clinical practice. The number of patients involved was rather small, and followup was limited to six or 12 months. Additional research among patients with different types of medical problems is needed to firmly establish the value and need of the Nof-1 approach in primary care.

Objective 1 )
1Is paracetamol as effective as NSAIDs? 2) Is medication-use influenced by presenting the personal results to each individual patient? 1) Is placebo as effective as temazepam (10 or 20 mg), or, in some patients, is 10 mg temazepam as effective as 20 mg temazepam? 2) Is medication-use influenced by presenting the personal results to each individual patient?

Table 2 :
2Reasons for withdrawal from the trialsReasons for withdrawal
No. of patients in series A
No. of patients in series B

Perceived lack of efficacy
4
2
Perceived side effects
1
1
Duration of trial period too long
1
-

Series A: NSAIDs or paracetamol for osteoarthritis
Series B: reduction of temazepam for sleep disturbances

Table 3 :
3Follow-up: Reasons for reverting to the same medication as before the N-of-1 trial Reasons for reverting in series A • Perceived lack of efficacy of paracetamol • Deterioration of osteoarthritis • Misunderstanding regarding paracetamol dosage • Preference of small tablets (diclofenac) over larger ones (paracetamol) • Patient found it a waste to throw away the NSAIDs she still had in her possession Reasons for reverting in series B • Dosage of temazepam had not been adjusted after the N-of-1 trials • Patient sees no disadvantages in taking temazepam Series A: NSAIDs or paracetamol for osteoarthritis Series B: reduction of temazepam for sleep disturbances
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AcknowledgementsThe work was performed at the EMGO Institute, Department of General Practice, VU University Medical Center, Amsterdam, The Netherlands. The study was supported unconditionally by Leo Pharma, The Netherlands.Competing interestsThe author(s) declare that they have no competing interests.Authors' contributionsAll four authors discussed with eachother the themes of this manuscript and made substantial contibutions to the design of the manuscript. AW and DvdW drafted the manuscript and the other authors revised it critically. All four authors have read and approved the final version of the paper.
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