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Chronic pain is a complex disease with high prevalence rates, and many individuals who are affected do not receive adequate treatment. As a complement to conventional therapies, eHealth interventions could provide many benefits to a multimodal treatment approach for patients with chronic pain, whereby future use is associated with the acceptance of these interventions.
This study aims to assess the acceptance of eHealth pain management interventions among patients with chronic pain and identify the influencing factors on acceptance. A further objective of the study is to evaluate the viability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and compare it with its extended version in terms of explained variance of acceptance.
We performed a cross-sectional web-based study. In total, 307 participants with chronic pain, as defined according to the International Association for the Study of Pain criteria, were recruited through flyers, posters, and web-based inquiries between December 2020 and July 2021. In addition to sociodemographic and medical data, the assessment included validated psychometric instruments and an extended version of the well-established UTAUT model. For statistical analyses, group comparisons and multiple hierarchical regression analyses were performed.
The acceptance of eHealth pain management interventions among patients with chronic pain was overall moderate to high (mean 3.67, SD 0.89). There was significant difference in acceptance among age groups (
Given the association between acceptance and future use, the knowledge of the influencing factors on acceptance should be used in the development and promotion of eHealth pain management interventions. Overall, the acceptance of eHealth pain management interventions was moderate to high. In total, 8 predictors proved to be significant predictors of acceptance. The UTAUT model is a valuable instrument for determining acceptance as well as the factors that influence acceptance of eHealth pain management interventions among patients with chronic pain. The extended UTAUT model provided the greatest predictive value for acceptance.
According to the International Association for the Study of Pain, pain is defined as “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage” [
Chronic pain negatively affects quality of life, is associated with sleep disorders and mental illness, and leads to increased mortality [
In clinical medicine, eHealth approaches can offer a variety of different treatment options. eHealth is a broad term that includes the use of electronic options such as mobile phones and computers to expand medical care [
Across different patient groups, research has shown that eHealth interventions have outcomes that are comparable to those of face-to-face therapy [
The initial results of such interventions have been promising [
Numerous offerings, including pain apps, are already available, and a review comparing pain apps between 2011 and 2016 shows an increase in the number of apps [
Considering that three-fourths of users discontinue using an app within 48 hours of downloading it [
Until now, knowledge on the acceptance of eHealth interventions has been inconsistent. Before the development of the Unified Theory of Acceptance and Use of Technology (UTAUT), there was no validated instrument to determine the influencing factors of acceptance [
The UTAUT was developed as a combination of different models to estimate the intention to use technology as well as predict actual use behavior. It is suitable for assessing the likelihood of eHealth use in different groups [
To further develop urgently needed eHealth approaches in pain care, the lack of knowledge about the acceptance of eHealth pain management interventions must be remedied. Therefore, this study aimed to assess the acceptance of eHealth pain management interventions among patients with chronic pain and identify the factors that influence their acceptance of such interventions. The acceptance is influenced by sociodemographic factors such as age [
Previous studies have indicated overall low [
We hypothesized a positive relationship between the UTAUT model’s three core predictors of acceptance (PE, EE, and SI) and acceptance, as demonstrated in previous research [
Age [
Furthermore, we anticipated a significantly higher level of explained variance using the extended UTAUT model compared with the original UTAUT [
A cross-sectional study was conducted to determine the predictors that influence the acceptance of pain management apps. Between December 2020 and July 2021, participants were recruited with flyers and posters distributed at hospitals and practices of physicians and physiotherapists, as well as through web-based inquiries in pain-related social media support groups. Patients who endorsed pain were regarded as eligible for the survey. To be included in the data analysis, fulfilling the diagnosis criteria of chronic pain (International Association for the Study of Pain and International Classification of Diseases, Eleventh Revision, criteria) [
The study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the medical faculty of the University of Duisburg-Essen (19-89-47-BO).
Patient-related data were collected using self-generated items on sociodemographic characteristics, including gender, age, marital status, educational qualifications, employment, and place of residence. Medical data included chronic pain diagnosis criteria, prior treatment, and more detailed description of the symptoms.
Depressive symptoms were screened with the Patient Health Questionnaire depression scale (PHQ-8) [
The UTAUT was used to determine the factors that influence the acceptance of eHealth pain management interventions in patients with chronic pain. The instrument consists of 12 items. Responses are provided using a 5-point Likert scale ranging from 1=totally disagree to 5=totally agree. The added items, including sociodemographic, psychometric, medical, and eHealth-related variables, operate as direct predictors of acceptance. The individual items can be assigned to the predictors PE, EE, and SI. BI, which was operationalized as acceptance, was measured with 3 additional items. In this study, the values for Cronbach α were .90 for PE, .77 for EE, .82 for SI, and .87 for BI (=acceptance), indicating adequate to high internal consistency. More factors were added, including other sociodemographic, medical, and eHealth-related data, as direct predictors of acceptance to the original UTAUT model. The questionnaire with the exact wording of the items is presented in
Data analysis was performed using SPSS software (version 26.0; IBM Corp) and R (version 4.0.3; The R Foundation for Statistical Computing). Sum scores for PHQ-8 and eHEALS and mean scores for self-generated items for internet anxiety and eHealth-related knowledge were computed. Furthermore, the UTAUT model with its four scales (PE, EE, SI, and BI) was calculated, and the acceptance (=BI) scores were divided into categories based on previous research [
The vast majority, that is, 92.5% (284/307), of the participants were women, 7.2% (22/307) were men, and 1 (0.3%) self-identified as nonbinary. The mean age of the participants was 45.96 (SD 10.66) years. The participants ranged in age from 18 to 69 years.
Most (280/307, 91.2%) of the patients had endorsed pain for at least 12 months; for 83.1% (255/307), the pain lasted for >2 years. Pain frequency was most frequently reported as permanent (143/307, 46.6%) and daily (102/307, 33.2%). In total, 17.3% (53/307) of the participants had tried >6 different pain treatments. More than half (183/307, 59.6%) of the participants had already received 3 to 6 different pain treatments, and 23.1% (71/307) had tried <3 different pain treatments. More than half (184/307, 59.9%) considered prior treatment efficient. The reported treatments included surgery, medication, psychotherapy, and alternative healing methods. In the PHQ-8 questionnaire, 73.3% (225/307) of the participants achieved a score above the cutoff of 10, indicating depressive symptoms. For further details, refer to
Of the 307 participants, 208 (67.8%) had no experience with eHealth pain management interventions. However, regarding digital media use, only 7.8% (24/307) of the participants reported feeling very insecure or a little uncertain. This represents a high level of confidence in the use of digital media in the sample, with 81.4% (250/307) of the participants feeling secure about using digital media (mean 4.17, SD 1.01). The mean level of internet anxiety in this sample was 1.78 (SD 0.83), whereas values above 5 indicate a very high level of internet anxiety. Thus, internet anxiety was low in the sample. On average, the participants showed a high level of eHealth literacy (mean 30.40, SD 5.34) according to eHEALS [
Sociodemographic, medical, and psychometric data (N=307).
Variable | Value | |
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Woman | 284 (92.5) |
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Man | 22 (7.2) |
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Nonbinary | 1 (0.3) |
Age (years), mean (SD) | 45.96 (10.66) | |
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University and qualification for university | 146 (47.6) |
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Lower qualification | 161 (52.4) |
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Retired | 41 (13.4) |
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Employed | 176 (57.3) |
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Unemployed | 51 (16.6) |
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Other | 39 (12.7) |
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Large city (>100,000) | 86 (28) |
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Medium-sized city (>20,000) | 91 (29.6) |
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Small town (>5000) | 60 (19.5) |
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Rural municipality (<5000) | 70 (22.8) |
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Single | 57 (18.6) |
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Married | 152 (49.5) |
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In a relationship | 65 (21.2) |
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Divorced or separated | 29 (9.4) |
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Widowed | 4 (1.3) |
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3 to 6 months | 12 (3.9) |
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6 to 12 months | 15 (4.9) |
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>12 months | 280 (91.2) |
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1 to 2 years | 25 (8.1) |
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>2 years | 255 (83.1) |
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Permanent | 143 (46.6) |
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Daily | 102 (33.2) |
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Several times a week | 53 (17.3) |
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Once a week | 9 (2.9) |
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<3 | 71 (23.1) |
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3 to 6 | 183 (59.6) |
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>6 | 53 (17.3) |
Considered prior treatment efficient, n (%) | 184 (62.4) | |
PHQ-8a (sum score), mean (SD) | 13.20 (5.29) | |
PHQ-8 score <10, n (%) | 82 (26.7) |
aPHQ-8: Patient Health Questionnaire depression scale.
eHealth-related data (N=307).
Variable | Value | |||
No eHealth experience, n (%) | 208 (67.8) | |||
Duration of daily private internet use (hours), mean (SD) | 2.9 (1.22) | |||
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0 to 1 | 39 (12.7) | ||
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1 to 2 | 83 (27) | ||
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2 to 3 | 99 (32.2) | ||
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3 to 4 | 34 (13.7) | ||
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>4 | 44 (14.3) | ||
Confidence in dealing with eHealth, mean (SD) | 4.17 (1.01) | |||
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Very little confident | 10 (3.3) | ||
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A little unconfident | 14 (4.6) | ||
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Neutral | 33 (10.7) | ||
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Rather confident | 106 (34.5) | ||
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Very confident | 144 (46.9) | ||
Internet anxietya, mean (SD) | 1.78 (0.83) | |||
eHealth literacyb, mean (SD) | 30.40 (5.34) | |||
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Behavioral intention | 3.67 (0.89) | ||
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Social influence | 3.46 (0.77) | ||
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Performance expectancy | 3.36 (0.84) | ||
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Effort expectancy | 3.47 (0.79) |
aValues above 5 indicate a very high level of internet anxiety (range 1-5).
bHigher scores indicate a higher level of eHealth literacy (range 8-40).
cUTAUT: Unified Theory of Acceptance and Use of Technology.
General acceptance was moderate to high, with a mean of 3.67 (SD 0.89). In total, 9.1% (28/307) of the participants showed low level of acceptance, 47.2% (145/307) showed moderate level of acceptance, and 43.6% (134/307) showed high level of acceptance.
Of the 307 participants, 154 (50.2%) were below the median age of 47 years, and the mean acceptance score in this group was 3.80 (SD 0.87), whereas 153 (49.8%) had a median age of ≥47 years and had a mean acceptance score of 3.55 (SD 0.89). The Wilcoxon rank-sum test revealed a significantly higher acceptance in the younger age group (
Multiple hierarchical regression analysis revealed that the sociodemographic predictors included in the first step explained 4.1% of the variance of acceptance (
Hierarchical regression model of acceptance (the extended Unified Theory of Acceptance and Use of Technology model; N=307).
Predictor | βa | βb | T |
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Δ |
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0.041 | 0.041 | —e |
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Age (years) | –.01 | –.11 | –2.75 | — | — | .006 |
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Employed | –.11 | –.13 | –1.10 | — | — | .27 |
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Unemployed | –.14 | –.15 | –1.15 | — | — | .25 |
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Other | .00 | .00 | 0.09 | — | — | .98 |
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Medium-sized city | –.26 | –.30 | –3.12 | — | — | .002 |
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Small town | –.28 | –.31 | –2.91 | — | — | .004 |
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Rural municipality | –.12 | –.13 | –1.35 | — | — | .18 |
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0.069 | 0.028 | .001 |
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PHQ-8g | .01 | .04 | 1.08 | — | — | .28 |
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6 to 12 | –.02 | –.03 | –0.11 | — | — | .92 |
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12 to 24 | –.37 | –.41 | –1.81 | — | — | .07 |
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>24 | –.18 | –.20 | –1.04 | — | — | .30 |
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>6 | .05 | .06 | 0.58 | — | — | .57 |
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<3 | –.13 | –.15 | –1.64 | — | — | .10 |
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0.130 | 0.061 | <.001 |
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1 to 2 | –.17 | –.19 | –1.51 | — | — | .13 |
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2 to 3 | –.25 | –.28 | –2.27 | — | — | .02 |
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3 to 4 | –.17 | –.19 | –1.33 | — | — | .19 |
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>4 | –.29 | –.33 | –2.27 | — | — | .02 |
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A little unconfident | –.23 | –.26 | –0.96 | — | — | .34 | ||||||||||||||||||||||||||||
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Neutral | .28 | .31 | 1.35 | — | — | .18 | ||||||||||||||||||||||||||||
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Rather confident | .20 | .23 | 1.05 | — | — | .29 | ||||||||||||||||||||||||||||
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Very confident | .08 | .09 | 0.42 | — | — | .68 | ||||||||||||||||||||||||||||
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Internet anxiety | –.03 | –.03 | –0.66 | — | — | .51 | ||||||||||||||||||||||||||||
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eHealth knowledge | .03 | .03 | 0.83 | — | — | .41 | ||||||||||||||||||||||||||||
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No eHealth experience | .07 | .08 | 0.92 | — | — | .36 | ||||||||||||||||||||||||||||
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eHEALSh | .00 | .02 | 0.39 | — | — | .70 | ||||||||||||||||||||||||||||
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0.664 | 0.534 | <.001 |
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Effort expectancy | .37 | .33 | 6.74 | — | — | <.001 |
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Performance expectancy | .33 | .31 | 6.30 | — | — | <.001 |
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Social influence | .34 | .29 | 6.46 | — | — | <.001 |
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aStandardized coefficient beta.
bUnstandardized coefficient beta.
cDetermination coefficient.
dChanges in
eNot available.
fIn steps 2, 3, and 4, only the newly included variables are presented.
gPHQ-8: Patient Health Questionnaire depression scale.
heHEALS: eHealth literacy scale.
iUTAUT: Unified Theory of Acceptance and Use of Technology.
In our study, the explained variance for the restricted UTAUT with the three core predictors PE, EE, and SI was 61.2% (
The main objective of the study was to determine the acceptance of eHealth pain management interventions among patients with chronic pain, as well as identify the factors that influence acceptance. Overall, the acceptance among patients with chronic pain in this study was moderate to high. In total, 43.6% (134/307) of the participants showed a high level of acceptance, 47.2% (145/307) showed a moderate level of acceptance, and only 9.1% (28/307) showed a low level of acceptance.
We were able to confirm the positive relationship between the three core predictors (PE, EE, and SI) of the UTAUT model and acceptance, as demonstrated previously; for example, in patients with diabetes or obesity [
Age was a significant predictor of acceptance in this study. Young age, defined here as age <47 years, the median age in our sample, was associated with greater acceptance. This is consistent with the results from previous studies [
In contrast to previous studies [
Although we did not find an influence on the acceptance of eHealth pain management interventions for several of the aforementioned factors, overall acceptance was higher than in previous studies [
Even with the extended UTAUT, only selected factors were tested for their influence on the acceptance of eHealth pain management interventions. It is likely that there are additional factors that need to be investigated to further understand acceptance and implementation of eHealth pain management interventions. Further research should target these influences.
The results of the study should be interpreted in the context of the limitations discussed herein. A proportion of participants were reached through inquiries in web-based support groups. Part of the reason for this may be the timing of data collection. Because of the COVID-19 pandemic, for example, time spent by patients waiting inside physicians’ offices was reduced, and hand-distributed flyers were accepted reluctantly, which made offline recruitment more difficult. It is quite possible that by recruiting through the internet, mainly those who are already more willing to use the internet were reached. Thus, a selection bias cannot be ruled out. In future surveys, more emphasis should be placed on recruiting through in-person channels. Because of the predominance of female participants, the influence of gender could not be investigated. The excessive proportion (284/307, 92.5%) of women in the sample is not representative of the gender composition in the overall population of patients with chronic pain, which limits the generalizability of the results because of sampling bias. A reason for this could be that recruitment widely took place in social media groups, which largely consist of female members, including groups for people with endometriosis and fibromyalgia, where the majority of those affected are women [
This study was able to demonstrate overall moderate to high acceptance of eHealth pain management interventions among patients with chronic pain. This high rate of acceptance suggests that eHealth interventions can offer a viable alternative for situations in which face-to-face treatment is not possible. The factors PE, EE, and SI were core predictors of acceptance. The extended UTAUT proved to be a useful tool for determining acceptance as well as the factors that influence the acceptance of eHealth pain management interventions among patients with chronic pain. Understanding the factors that influence acceptance is important to provide tailored eHealth pain management interventions and promote their actual use. When access to face-to-face treatment is limited, eHealth interventions offer a good alternative. With all that, we emphasize that the aim is not to replace face-to-face treatment but to complement it; for example, eHealth interventions can help bridge the gap until face-to-face therapy is received or complement existing therapies. Finally, this study highlights the importance of taking patients’ expectations, needs, and capabilities into account when developing new treatment approaches.
The used Unified Theory of Acceptance and Use of Technology.
behavioral intention
effort expectancy
eHealth literacy scale
facilitating condition
performance expectancy
Patient Health Questionnaire depression scale
social influence
Unified Theory of Acceptance and Use of Technology
This work was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation; 422744262–TRR 289).
AB, MT, and E-MS initiated and conceptualized the study. LJ and PS performed the statistical analyses and interpretation of the data, and PS wrote the first draft of the manuscript. Data acquisition was performed by LJ and PS. AB, CR, MT, and E-MS contributed to the design of the study. UK and DM gave important input regarding the specifics of the target cohort. All authors contributed to the further writing of the manuscript and approved its final version.
None declared.