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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Objective

Although patients with fibromyalgia often report that specific weather conditions aggravate their symptoms, empirical studies have not conclusively demonstrated such a relationship. Our aim was to examine the association between weather conditions and daily symptoms of pain and fatigue in fibromyalgia, and to identify patient characteristics explaining individual differences in weather sensitivity.

Methods

Female patients with fibromyalgia (n = 333, mean age 47.0 years, mean time since diagnosis 3.5 years) completed questions on pain and fatigue on 28 consecutive days. Daily weather conditions, including air temperature, sunshine duration, precipitation, atmospheric pressure, and relative humidity, were obtained from the Royal Netherlands Meteorological Institute. Multilevel regression analysis was applied.

Results

In 5 (10%) of 50 analyses, weather variables showed a significant but small effect on either pain or fatigue. In 10 analyses (20%), significant, small differences between patients were observed in the random effects of the weather variables, suggesting that symptoms of patients were, to a small extent, differentially affected by some weather conditions, for example, high pain with either low or high atmospheric pressure. These individual differences were explained neither by demographic, functional, or mental patient characteristics, nor by season or weather variation during the assessment period.

Conclusion

There is more evidence against than in support of a uniform influence of weather on daily pain and fatigue in female patients with fibromyalgia. Although individuals appear to be differentially sensitive to certain weather conditions, there is no indication that specific patient characteristics play a role in weather sensitivity.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Fibromyalgia is characterized by chronic widespread pain and other symptoms such as fatigue, waking unrefreshed, and cognitive symptoms ([1]). It has a prevalence of approximately 2% in the general population with a female preponderance ([2]). The cause of fibromyalgia and its underlying physiologic mechanisms are unknown. Several studies have demonstrated an increased sensitivity to a variety of stimuli in patients with fibromyalgia ([3]).

Many patients (up to 92%) report that specific weather conditions aggravate their symptoms ([4-12]). Modulation of symptoms by weather factors was once even a minor criterion for the diagnosis of fibromyalgia ([4]). However, most empirical studies have not found an association between symptoms of fibromyalgia and weather conditions on either the same day ([8, 9, 13]) or an adjacent day, with neither the weather predicting symptoms nor symptoms predicting the weather ([8, 14]). This indicates that there is no unidirectional influence of weather conditions on symptoms of fibromyalgia ([9, 13, 14]). Although fatigue is a primary symptom of fibromyalgia ([1]), only one study has examined the impact of weather on fatigue ([8]).

This demonstration of a lack of uniform effects of weather on symptoms does not, however, refute the notion that some patients may be more sensitive to the weather or changes in the weather than other patients. In addition, one aspect of the weather (e.g., high air temperature) may aggravate symptoms in some patients, while in other patients symptoms may be aggravated by the opposite aspect (i.e., low air temperature). This could explain why empirical cross-sectional studies do not confirm the influence of weather on symptoms reported by patients with fibromyalgia. To determine individual-specific weather influences, longitudinal studies are required. As yet, only one such longitudinal study has been performed ([14]). This study of a relatively small sample of 55 patients with fibromyalgia did not find an effect of weather (a composite weather variable and several single weather parameters) on pain. However, the data suggested that patients who had developed the disease within the previous 10 years experienced greater weather sensitivity to pain than those who had the disease for a longer period of time. No influence of age, education, personality characteristics, or mood on the weather–pain link was observed, although the sample size was too small to refute a possible role of moderator variables. Therefore, replication of the longitudinal study with a larger sample of patients, in particular those with a relatively short disease duration, is required.

The aim of our study was to examine the association between weather conditions and daily symptoms of pain and fatigue (levels and changes) in a large sample of female patients with fibromyalgia while taking individual differences into account, and to identify patient characteristics that could explain individual differences in this association.

Box 1. Significance & Innovations

  • This is the first study to examine the association between weather conditions and levels of, and changes in, daily pain and fatigue in a large sample of patients with fibromyalgia, while taking individual differences into account.
  • Only 5 (10%) of 50 analyses indicated at best a small influence of weather conditions on daily pain and fatigue.
  • Ten (20%) of 50 analyses suggested that patients are, to a small extent, differentially affected by weather conditions, e.g., high pain with either low or high atmospheric pressure.
  • There is no indication that demographic, functional, or mental patient characteristics play a role in weather sensitivity.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Patients

The study population included 333 of 403 adult women who participated in a questionnaire study followed by a diary study, and who completed at least 14 diaries during a period of 28 consecutive days. Details of both studies have been described previously ([15, 16]). Briefly, patients were recruited from 3 hospitals in Utrecht and Almere, The Netherlands, and classified as having fibromyalgia by their rheumatologists according to the American College of Rheumatology criteria ([6]). No other eligibility criterion was applied to enhance the generalizability of findings. In the information letter, the study of associations between weather and symptoms was introduced as 1 of 4 research questions.

The study was conducted according to the principles of the Declaration of Helsinki (6th revision, Seoul, 2008) and in accordance with the Dutch Medical Research Involving Human Subjects Act. The study was approved by the research and ethics committee of the University Medical Center Utrecht, The Netherlands. All patients provided written informed consent.

Assessments

Patients were sent a questionnaire booklet and 4 paper journals, each containing 7 daily diaries ([15, 16]). They were asked to complete the diaries around the same time each evening, starting on the following Monday. The questionnaire booklet included questions on date and time of completing the booklet, demographics (e.g., age, marital status, education level), and disease (e.g., time since first symptoms, time since diagnosis). The diaries contained questions on immediate pain, fatigue, and depressive mood at the end of the day (“How much … do you feel right now?”), as well as on physical activity during the day (“How much physical activity have you had today?”) and the quality of the previous night's sleep (“How well did you sleep last night?”). Answers to these questions were given on a scale from 1–5, where 1 = “not at all” and 5 = “very much.” To increase daily compliance, patients were asked to write down day-specific weather information (air temperature and atmospheric pressure) at the time of reporting, obtained from a specified text page available on television. The completed questionnaire booklet was returned before the start of the diary study. The paper journals were returned at the end of each week.

The daily weather conditions were obtained from the weather station of the Royal Netherlands Meteorological Institute in De Bilt, a town near the hospitals where the patients were recruited. The weather conditions included air temperature (daily mean in degrees Celsius), sunshine duration (in hours per day), precipitation (<0.05 mm or ≥0.05 mm per day), atmospheric pressure (daily mean in hPa), and relative humidity (daily mean in percentage). Precipitation was dichotomized.

Statistical analyses

SPSS, version 16.0, was used to calculate means and to check the score distributions of continuous predictor variables. Score distributions were normal (skewness between −1 and 1), except for time since first symptoms and time since diagnosis (skewness of 1.9 and 4.2, respectively). Time since first symptoms was divided into 3 categories: <10 years (n = 165), 10–19 years (n = 101), and ≥20 years (n = 57). Time since diagnosis was divided into 2 categories: <5 years (n = 257) and ≥5 years (n = 72).

Multilevel regression modeling, as implemented in MLwiN, version 2.21, was used to examine the associations between the 5 weather variables and 2 fibromyalgia symptoms, and to examine which of several patient characteristics could explain individual differences in these associations. In this analysis, patients with missing data can remain in the analysis, thereby increasing the precision of the estimates and the power of the statistical tests ([17]). The 28 repeated measures of pain and fatigue and the 5 weather variables at the within-subject level were nested within the 333 patients at the between-subject level.

A number of associations were tested: 1) whether the level of the weather variable was associated with the level of the fibromyalgia symptom on the same day; 2) whether the day-to-day change in the weather variable was associated with the day-to-day change in the fibromyalgia symptom across the same day; 3) whether the level of the weather variable on the previous day was associated with the level of the fibromyalgia symptom on the day in question; 4) whether the level of the weather variable on the previous day was associated with the subsequent day-to-day change in the fibromyalgia symptom; and 5) whether the day-to-day change in the weather variable was associated with the subsequent level of the fibromyalgia symptom. The day-to-day change in the fibromyalgia symptom was examined by including the measurement of this symptom on the previous day as a covariate.

All of these associations were examined in models constructed step by step. The dependent variable in each of the models was either pain or fatigue. The first model tested the effects of the covariates: week of the study (weeks 1–4) and day of the week (Monday to Sunday). These variables were dummy coded and only included in the subsequent analyses when their overall effects were significant. The second model tested the effects of the weather variables, each in a separate analysis. The third model tested whether the effects of the weather variables differed between patients by testing the random effect of each weather variable. If this random effect was significant, a fourth model examined possible moderator variables that could explain individual differences in weather–symptom associations. This was done by testing the interaction effects between each of the possible moderator variables and each of the weather variables with a significant random effect, after first adding the moderator variable itself as a main effects variable in the model. The moderator variables examined were: 1) the patient characteristics age, education level, marital status, time since first symptoms, time since diagnosis, depressive mood, physical activity, and sleep quality; 2) the season in which patients completed (most of) their diaries; and 3) the degree of weather variation during the assessment period (i.e., the difference between the maximum and minimum levels of the weather variable for each patient). The significance of the variables in the models was determined by the decrease in the −2 log likelihood of the model including the variable and examining the significance of the resulting value with a chi-square test ([17]). The P value was set to 0.05 in the first model and to 0.01 in the other models to adjust for chance findings (Type I errors) caused by the inclusion of the 5 weather variables. Further adjustment of the P value would increase the probability of Type II errors, and therefore was not made.

To demonstrate the magnitude of the differences between patients in the individualized associations between the weather variables and the fibromyalgia symptoms, partial correlation coefficients (adjusted for significant covariates) were calculated separately for each patient in SPSS. Correlation coefficients above the absolute values of 0.10, 0.30, and 0.50 were considered to be small, moderate, and large, respectively ([18]).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Patient characteristics

Table 1 shows the characteristics of the 333 patients who completed at least 14 diaries in the diary study. They answered the symptom items of 8,573 (92%) of the possible 9,324 diaries (333 × 28). Occasional missing scores left 8,572 pain scores and 8,568 fatigue scores.

Table 1. Demographic and disease-related characteristics of the 333 patients with fibromyalgia
 Value
  1. a

    Education levels: low = primary school or lower vocational secondary education; medium = intermediate general secondary education or intermediate vocational education; high = higher general secondary education, higher vocational education, preuniversity education, or university level.

Age, years 
Mean ± SD47.0 ± 12.1
Range18–85
Education level, no. (%)a 
Low15 (5)
Medium256 (77)
High62 (19)
Marital status, no. (%) 
Married or partnered, with or without cohabitation247 (74)
Single, with or without children37 (11)
Divorced37 (11)
Widowed12 (4)
Work status, no. (%) 
Employed full time43 (13)
Employed part time113 (34)
Disability pension93 (28)
Sick leave10 (3)
Time since first fibromyalgia symptoms, years 
Median9.0
Range0.5–50
Time since fibromyalgia diagnosis, years 
Median2.0
Range0–45
Comorbidity, no. (%) 
Rheumatic condition (besides fibromyalgia, e.g., rheumatoid arthritis)46 (14)
Lung disease (e.g., asthma)34 (10)
Cardiovascular condition (e.g., hypertension)17 (5)
Cancer11 (3)
Diabetes mellitus7 (2)
Psychological or psychiatric problems (e.g., depression, posttraumatic stress symptoms)57 (17)
Other comorbidity (e.g., endocrine, dermatologic, internal, chronic fatigue syndrome)139 (42)

More than half of the patients (n = 190 [57%]) answered the symptom items on all 28 days, 61 patients (18%) on 22–27 days, 61 patients (18%) on 21 days, and 21 patients (6%) on 14–20 days. Patients completed the diaries between July 25, 2005 and January 1, 2006, during the meteorological autumn (69%), summer (26%), and winter (5%).

Aggregation of the data across all patients and days showed that moderate to high overall levels were reported for pain (mean ± SD 3.35 ± 1.02) and fatigue (mean ± SD 3.77 ± 1.04). The overall correlation between pain and fatigue levels was 0.47. The correlation between a given symptom level on a certain day and that symptom level on an adjacent day was 0.53 for pain and 0.50 for fatigue.

Weather characteristics

There was precipitation on 47% of all assessment days. The lowest number of days with precipitation for a patient was 5 and the highest number of days was 20 (mean ± SD 12.5 ± 4.1). Mean ± SD levels of the other weather variables, across all patients and days, are described in Table 2. Mean ± SD levels of the weather variation within patients (i.e., the difference between the maximum and minimum levels of the weather variable for each patient) are also described in Table 2.

Table 2. Weather characteristics*
 Levels across all patients and assessment daysWeather variation within patientsa
Mean ± SDRangeMean ± SDRange
  1. The amount of precipitation was dichotomized.

  2. a

    Weather variation is the difference between the maximum and minimum levels of the weather variable for individual patients during the 28 days of the study.

Air temperature, degrees Celsius13.7 ± 4.4−2.0 to 22.58.8 ± 2.44.7–14.8
Sunshine duration, hours5.4 ± 3.70.0–13.010.7 ± 2.16.3–12.8
Atmospheric pressure, hPa1,017.0 ± 8.1979.0–1,042.027.8 ± 12.312.1–63.0
Relative humidity, %84.0 ± 6.567.0–98.023.1 ± 3.910.0–28.0

The correlation between the levels of the 5 weather variables was highest for sunshine duration and relative humidity (r = −0.70, P < 0.001), and was smallest for air temperature and atmospheric pressure (r = 0.02, P = 0.10).

The air temperature levels reported by the patients highly correlated with the air temperature levels obtained from the meteorological institute (r = 0.89, P < 0.001; n = 7,720). This provided a check on whether the diaries were completed on the correct day. Atmospheric pressure levels only correlated highly between these 2 sources (r = 0.89, P < 0.001; n = 7,338) after removing 36 outlier values (i.e., Z scores smaller than −3.29 or larger than 3.29) ([19]). Nineteen of these 36 outlier values were obtained from a single patient; the cause of these outlier values is unknown.

Model 1: effects of covariates

Pain levels varied as a function of both week of the study (decrease −2 log likelihood = 35.92, 3 df, P < 0.001), with lower scores in the first week (mean 3.27) than in the other 3 weeks (mean 3.40, 3.37, and 3.35, respectively), and day of the week (decrease −2 log likelihood = 35.24, 6 df, P < 0.001), with the lowest scores on Sundays (mean 3.27) and the highest scores on Thursdays and Fridays (mean 3.40 and 3.42, respectively). Therefore, both week of the study and day of the week were included as covariates in the subsequent analyses on pain.

Fatigue levels varied as a function of day of the week (decrease −2 log likelihood = 77.58, 6 df, P < 0.001), with the lowest scores on Sundays (mean 3.60) and the highest scores on Fridays (mean 3.84). Therefore, day of the week was included as a covariate in the subsequent analyses on fatigue.

Model 2: effects of weather variables

In 5 (10%) of the 50 analyses (2 symptoms, 5 weather variables, and 5 types of associations), the weather variable showed a significant, small association with either pain or fatigue.

Pain levels were negatively associated with a change in sunshine duration (P = 0.01, unstandardized regression coefficient B = −0.005, SE 0.002; i.e., each 1-hour increase in sunshine duration was associated with a 0.005-unit lower pain level). In addition, pain levels were positively associated with a change in relative humidity (P = 0.005, B = 0.004, SE 0.001; i.e., each 1% increase in relative humidity was associated with a 0.004-unit higher pain level). Furthermore, a change in pain was positively associated with a concurrent change in relative humidity (P = 0.007, B = 0.004, SE 0.001; i.e., each 1% increase in relative humidity was associated with a 0.004-unit increase in pain).

Fatigue levels were positively associated with the air temperature level on the previous day (P < 0.001, B = 0.01, SE 0.003; i.e., each 1–degree Celsius increase in air temperature was associated with a 0.01-unit higher fatigue level). In addition, a change in fatigue was negatively associated with the relative humidity level on the previous day (P = 0.009, B = −0.004, SE 0.002; i.e., each 1% increase in relative humidity level was associated with a 0.004-unit decrease in fatigue).

Model 3: random effects of weather variables

To examine whether the effect of the weather variable differed between patients, the random effect of the weather–symptom association was tested. In 10 analyses (20%), significant, small differences between patients were observed, suggesting that symptoms of patients were, to a small extent, differentially affected by some weather conditions.

For pain, significant, small random effects of air temperature levels on the same day and on the previous day (P < 0.001, B = 0.002, SE 0.0004 for both), of atmospheric pressure levels on the same day (P < 0.001, B = 0.000001, SE 0.0000004), of precipitation on the previous day (P < 0.001, B = 0.040, SE 0.011), and of relative humidity levels on the same day and on the previous day (P = 0.009, B = 0.0002, SE 0.00006 and P = 0.006, B = 0.0002, SE 0.00006, respectively) were found.

For fatigue, significant, small random effects of air temperature levels on the same day and on the previous day (P < 0.001, B = 0.003, SE 0.0005 and P < 0.001, B = 0.002, SE 0.0005, respectively) and relative humidity levels on the same day and on the previous day (P = 0.001, B = 0.0002, SE 0.00007 and P < 0.001, B = 0.0003, SE = 0.00008, respectively) were found.

Table 3 shows the differences between patients in the individualized associations between the weather variables and pain and fatigue on the same day. For each weather–symptom combination, there was no relationship between the weather variable and the symptom in approximately one-third of the patients, a positive relationship in approximately one-third of the patients, and a negative relationship in the remaining one-third of the patients.

Table 3. Percentages of patients with different levels of weather sensitivity, according to Pearson's partial correlation coefficients*
 PositiveVery smallNegative
LargeModerateSmallSmallModerateLarge
  1. Weather sensitivity is defined as the correlation between the fibromyalgia symptom and the weather condition on the same day for each patient. Correlations with pain are corrected for week of the study and day of the week; correlations with fatigue are corrected for day of the week. The correlation is considered large, moderate, or small when the absolute level is larger than 0.50, 0.30, or 0.10, respectively ([18]).

Pain       
Air temperature3.08.421.133.123.210.20.9
Sunshine duration0.65.724.131.928.08.11.5
Precipitation2.17.629.932.619.96.90.9
Atmospheric pressure1.58.416.634.925.610.82.1
Relative humidity0.69.929.827.723.27.80.9
Fatigue       
Air temperature2.112.823.930.922.06.41.8
Sunshine duration1.26.722.337.025.46.40.9
Precipitation0.36.125.732.729.15.20.9
Atmospheric pressure0.69.523.230.624.510.70.9
Relative humidity1.56.426.634.919.011.00.6

Model 4: interaction effects between weather variables and patient characteristics

To examine whether patient characteristics, season, and weather variation could explain differences between patients in the individualized associations between the weather variables and pain and fatigue, the interaction effects between these variables and each weather variable with a significant random effect on pain or fatigue were tested. Since there were 10 weather variables with a significant random effect and 8 patient characteristics, 80 interaction effects were tested: 48 for pain and 32 for fatigue. Only 2 (3%) of these interaction effects with patient characteristics were significant (data not shown). Season and weather variation did not show any significant interaction effect.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

The current study investigated the association between weather conditions and daily symptoms of pain and fatigue in a large sample of female patients with fibromyalgia. A few significant but small and inconsistent associations were found. Patients differed from each other in some of these associations, but these differences were small and no patient characteristic was found that explained these differences.

Patients with fibromyalgia often report that specific weather conditions aggravate their symptoms ([4-12]). However, with the exception of 2 studies with very small sample sizes ([5, 20]), this influence of the weather has not been confirmed in cross-sectional studies and one longitudinal study ([8, 9, 13, 14]). The current longitudinal study found few significant influences of air temperature, sunshine duration, and relative humidity on pain and fatigue. These occasional significant influences were small and inconsistent, i.e., depending on whether either the level of or a change in the symptom or the weather condition was measured, and whether these were measured on the same day or on adjacent days. In combination with the findings of previous studies, this leads us to conclude that there is more evidence against than in support of a uniform influence of specific weather conditions on daily symptoms of pain and fatigue in female patients with fibromyalgia.

However, these findings do not rule out the possibility that weather–symptom relationships may exist for individual patients. Some patients may be more sensitive to weather or weather changes than other patients, and some patients may also be affected positively and other patients affected negatively by specific weather conditions. This notion was confirmed in the current study, which showed that, to a small degree, patients differed significantly from each other in the individualized correlations between the weather conditions and pain and fatigue, with approximately equal numbers of positive and negative correlations. Such individual differences in weather–symptom associations might be due to patient characteristics, such as demographic, functional (e.g., physical activity), and mental (e.g., mood) characteristics. For instance, physically active and less depressed people may spend more time outdoors and consequently are more exposed to the weather ([21]). However, in agreement with the only previous study that included a rather small sample size ([14]), these differences between patients were not explained by patient characteristics. Our largely negative findings led to the conclusion that there is no support for, or indication of, patient characteristics explaining patient-specific influences of weather on daily symptoms of pain and fatigue in patients with fibromyalgia.

Why then do patients with fibromyalgia often report that certain weather conditions affect their symptoms? The pathologic mechanism of fibromyalgia is largely unknown. Influential cognitive psychologists have argued that people nevertheless look for explanations to understand their circumstances. Humans act like scientists in that they employ personal constructs to understand observations ([22]). Since there is always weather, and both the weather and symptoms change regularly, it is easy to couple the two and hard to disprove such a link between the two. This explanation is in line with those of attention bias or confirmation bias suggested by comparable studies in patients with rheumatoid arthritis ([23, 24]). Social factors may amplify such cognitive misattributions, for example, by incorporating such statements from each other and distributing them through the social media ([25]). As a result, patients may believe in an influence of specific weather conditions, irrespective of whether this is the reality.

The current study has several strengths. First, the influence of weather on pain as well as on fatigue was investigated while taking into account both between-subject and within-subject differences. Second, associations on the same day as well as on adjacent days were examined, as were levels of as well as changes in the weather conditions and the symptoms of pain and fatigue. Although these choices resulted in a large number of analyses with an increased probability of false-positive findings, only small and inconsistent significant effects of weather variables on symptoms were found.

The current study also has several limitations. First, patients were aware that we studied the association between weather and symptoms, and this may have affected the results. Second, with the paper and pencil method, patients may not have completed all diaries on the correct day as possibly indicated by the imperfect match between day-specific weather information registered by the patients and by the meteorological institute. Third, the weather conditions obtained from the meteorological institute may not fully reflect the conditions where the patient was at the time of the assessment. Moreover, a number of patients spent (a large part of) their days indoors, which may have reduced possible effects of weather on symptoms ([21]). Nevertheless, one can also be prone to effects of weather conditions when indoors, such as sunshine duration and precipitation when looking outside the window and, more notably, atmospheric pressure ([13]). Fourth, most patients participated in the autumn, whereas the remainder took part in the summer or winter. Equal weather conditions may feel different in another season. Fifth, findings may not generalize to other climates due to the unstable marine climate of The Netherlands ([8]). Future (international) research could use ecological momentary assessment ([26]) and global positioning systems to register fibromyalgia symptoms and patients' locations more precisely at varying times across the entire year. In addition, future research may focus on a wider range of patient characteristics to explain real or attributed individual differences in weather sensitivity, such as personality traits ([27, 28]), beliefs about the importance of specific weather conditions ([9]), and illness cognitions such as beliefs regarding the cause and changeability of the chronic pain condition ([29]).

In conclusion, although some significant associations were found, this study provides more evidence against than in support of a uniform influence of weather conditions on daily pain and fatigue in female patients with fibromyalgia. Individuals appear, to a small extent, differentially sensitive to certain weather conditions, but there is no indication that specific patient characteristics play a role in this weather sensitivity.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Geenen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Bossema, van Middendorp, Bijlsma, Geenen.

Acquisition of data. Van Middendorp, Geenen.

Analysis and interpretation of data. Bossema, Jacobs, Geenen.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
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