Chiropractic & Manual Therapies 2013 (Mar 6); 21: 10 ~ FULL TEXT
Iben Axén and Lennart Bodin
Intervention & Implementation Research,
Institute of Environmental Medicine,
Karolinska Institutet, Nobels väg 13,
Stockholm 171 77, Sweden
Background Low back pain (LBP) is a prevalent condition and has been found to be recurrent and persistent in a majority of cases. Chiropractors have a preventive strategy, maintenance care (MC), aimed towards minimizing recurrence and progression of such conditions. The indications for recommending MC have been identified in the Nordic countries from hypothetical cases. This study aims to investigate whether these indications are indeed used in the clinical encounter.
Methods Data were collected in a multi-center observational study in which patients consulted a chiropractor for their non-specific LBP. Patient baseline information was a) previous duration of the LBP, b) the presence of previous episodes of LBP and c) early improvement with treatment. The chiropractors were asked if they deemed each individual patient an MC candidate. Logistic regression analyses (uni-- and multi-level) were used to investigate the association of the patient variables with the chiropractor's decision.
Results The results showed that “previous episodes” with LBP was the strongest predictor for recommending MC, and that the presence of all predictors strengthens the frequency of this recommendation. However, there was considerable heterogeneity among the participating chiropractors concerning the recommendation of MC.
Conclusions The study largely confirms the clinical use of the previously identified indications for recommending MC for recurrent and persistent LBP. Previous episodes of LBP was the strongest indicator.
From the Full-Text Article:
In the past few decades, the prevalence of low back pain, LBP, has been found to be
extremely high  and the resulting costs of the condition are substantial  . Upon further
scrutiny, the condition has been found to be recurrent in most cases and persistent in some [3-
5] . These facts invite preventive approaches, both from a personal and societal perspective.
Secondary prevention, to minimize the recurrences or the impact of episodic LBP, and
tertiary prevention, to minimize the effects of persistent LBP, seem warranted.
In the chiropractic profession, there is a traditional preventive approach named Maintenance
Care, MC. It has been defined as: “…treatment, either scheduled or elective, which occurred
after optimum recorded benefit was reached”  and “a regimen designed to provide for the
patient’s continued well-being or for maintaining the optimum state of health while
minimizing recurrences of the clinical status”  . However, a review concluded that there is
no evidence-based definition, no identified indications for use nor evidence of effect of MC
 . During the past decade, efforts have been made in the Nordic countries to describe the
intent [9,10] , content [9,10] and frequency [10,11] of this approach. In the US, efforts have
been made to develop consensus definitions regarding this practice  .
The indications for MC have also been studied in a series of studies through a process of
triangulation. In short, the indications were identified in qualitative focus group discussions
 , and then tested in questionnaires across the Nordic countries [11,13,14] . As a third step,
case management strategies were explored to investigate chiropractors’ decisions using
hypothetical but clinically relevant cases in a questionnaire  as well as in an interview
study  . During the process, clinicians argued that it was difficult for them to identify the
most important indicator, as several factors will always be considered in the clinical
encounter. However, when asked to grade the suggested factors, the chiropractors in Sweden,
Finland and Denmark agreed that secondary prevention would be recommended to a patient
who reported previous episodes of the condition, and that the indication for tertiary care was
improvement with treatment  . Further, the practice of using preventive strategies seemed
similar in the Nordic countries, albeit there seemed to be a group of clinicians who seemed to
use MC to a larger extent than most  .
As these indications have been identified through hypothetical cases they are, in that sense,
theoretical constructs. Whether they represent clinical reality is still unknown. This study
aimed to test if these theoretically defined indications are really in use in a clinical
setting/situation. To test the efficacy of MC in future studies in the clinical setting, it is
important to know what indications are actually used. This will ensure that the relevant
subgroups of patients are included, i.e. the subgroups that chiropractors usually recommend
MC to. It will then be possible to study if the outcome of the MC treatment is associated with
The data stems from a multicenter observational study in which 262 patients consulting for
LBP were followed for six months. Thirty-three chiropractors were involved in the data
collection, which took place between May 2007 and September 2008. The study procedures
are described elsewhere  . In this study, only data collected at baseline and at the 4th visit
At baseline, variables concerning previous duration and previous episodes were collected. At
the 4th consultation, information regarding self-rated improvement (5 graded scale:
Definitely worse, probably worse, unchanged, probably better and definitely better) and the
chiropractors’ opinion regarding MC (was this a patient to whom MC would be
recommended?) were collected. The baseline and 4th visit variables were dichotomised as
follows: long or short previous duration (more than and less than 30 pain days, respectively),
few or many previous episodes (less than 4 and 4 episodes or more the previous 2 years,
respectively), improved and not improved (definitely better vs. all the other categories) and
MC candidate (yes or no).
Predictive models were analyzed in which the independent variables duration, episodes and
improvement (alone or in different combinations) were tested against the dependent variable
“MC candidate”. The hypothesis was that a patient with a) long previous duration, b) many
previous episodes and c) definite improvement at the 4th visit would be an MC candidate.
Consequently, a patient with short previous duration, few previous episodes and not reporting
improvement by the 4th visit would not be an MC candidate. Our analyses aimed to apply the
independent variables in univariate models as well as in different combinations in
multivariate models. Thus we hypothesized that a dose–response relationship would be
present (a combination of two predictors would lead to a MC recommendation more often
than only one). The participating clinicians were blind to the study hypothesis as they were
told that we wanted to observe what really goes on in clinical practice. To investigate the
“chiropractor effect” on the MC decision, we applied additional analytic models where a
random variable for chiropractors was added, thus incorporating the hypothesized random
variation between chiropractors in their recommendations for MC.
The data were initially described in a cross-table showing the presence of MC against the
predictor variables and then analyzed using two different logistic regression models. In the
first specification of these models only the patient dimension of the data was used. Analyses
were done with each predictor variable separately and then in a multiple analysis with all
three variables included. We refer to this analysis as an ordinary logistic regression model or
one-level model. In our second specification the chiropractor variable was used as a random
variable and the data thus followed a two-level structure, the patient level with 252 subjects
and the clinician level with 33 chiropractors. We refer to this analysis as a multilevel model
with two levels. For both specifications of the logistic models we also analyzed if interactions
between the predictor variables were of statistical significance. In addition, the models were
applied with stratification for gender. The outcome parameter was the odds ratio (OR), shown
with 95% confidence interval (CI). Model fit was summarized in Akaike’s index  where
smaller values show a better fit to the data. The AIC index may be used to compare
competing models  , as the criterion was developed to find an optimal and at the same
time parsimonious model, not entering unnecessary variables  . It is stated that a
difference in AIC of more than two units indicates a marked preference for the model with a
smaller criterion measure  .
As an additional measure of model fit and ability to predict an outcome the ROC curves 
were drawn. Statistical significance was regarded as present if p < 0.05. All analyses were
performed using SPSS v 20  and STATA v 12  .
The study was approved by the local ethics committee at the Karolinska Institutet:
2007/1458-31/4. All the participating clinicians and patients signed informed consent forms.
The sample is extensively described elsewhere  . Complete data on 252 subjects were
available. In short, the participants were on average 44 years old with a fairly even gender
distribution (52% male). The LBP was rated on a numeric rating scale, NRS  at 4.4/10 at
baseline. Table 1 lists the major features of the sample.
Table 1: Description of the study sample (N = 252)
Gender, male 52%
Age, mean 44 (SD 11.6)
Pain intensity, NRS Mean 4.4 (SD 2.2)
Duration =30 days previous year 57%
Episodes =4 previous 2 years 47%
Definitely better by the fourth visit 71%
MC indicated 80%
The cross tabulation of predictors and the MC recommendation revealed that patients with
three predictors (long previous duration, many previous episodes and definite improvement
by the fourth visit) were regarded as MC candidates in 93.4% of cases. This group amounted
to 24% of the total sample. On the other end of the scale, patients with no predictors were
regarded as MC candidates in 73% of the cases. This group, however, amounts to only 6% of
the total sample.In cases where two predictors were present, patients were regarded as MC
candidates more often than when only one predictor was present. The different combinations
of predictors and the MC decisions are listed in Table 2.
Table 2: Cross tabulations of predictive indicators and the outcome “Maintenance Care (MC) recommendation”
Predictors MC –yes (N) Total % (N) # Predictors MC-yes (N)
Long duration +
Many episodes + 93.4 % (57) 24 (61) 3 93.4% (57)
Outcome of the predictors in bold are à priori expected to be associated with
recommendations for MC.
Long duration +
Many episodes + 86.8 % (33) 15 (38)
SHORT duration +
Many episodes + 84.6 % (11) 5 (13)
Definitely better 2 83.3% (70)
Long duration +
FEW episodes + 78.8 % (26) 13 (33)
SHORT duration +
Many episodes + 87.5 % (7) 3 (8)
Long duration +
FEW episodes + 61.5 % (8) 5 (13) 1 68.8% (64)
SHORT duration +
FEW episodes + 68.1 % (49) 28 (72)
SHORT duration +
FEW episodes + 73.3 % (11) 6 (15) 0 73.3% (11)
Estimates from the two regression models are shown in Table 3. The predictor “many
previous episodes” shows statistical significance in both the regression models. It is evident
that the multilevel specification has a considerably better fit to data, that is, the chiropractors
show a non-ignorable heterogeneity in their recommendation of MC. Adding interaction
factors for the predictive variables did not reach statistical significance in any of the models.
Table 3: Logistic regression models for prediction of the Maintenance Care (MC) - recommendation
Ordinary (one level1) Multi-level (two levels1)
regression regression1 The levels are patients (n = 252) for the one-level model and patients (n = 252) and chiropractors (n = 33) for the two-level model.
Factors OR 95% CI p-value OR 95% CI p-value
Many previous episodes 3.5 1.6–7.7 0.002 4.1 1.5–10.7 0.005
Long duration 1.4 0.7–2.8 0.39 1.5 0.6–3.6 0.35
Definitely better by 1.4 0.7–3.0 0.33 1.2 0.5–3.0 0.62
the 4th visit
Akaike’s Index (AIC)2 244.69 225.41
2 A smaller value for AIC indicates better fit to data.
Our subsequent analyses are summarized in Table 4 where the model fit in multilevel models
is described by Akaike’s index. In the table the three univariate regressions for each one of
the predictor variables are shown first, followed by a model where the independent variable is
the sum of ‘positive’ predictor variables. Finally the multiple regression model with all three
predictive variables simultaneously included is shown. The best fit is by this index reported
by one of the simplest models, where only the variable “many previous episodes” is included
(AIC = 222.5). Properties of the three predictor variables with respect to their sensitivity and
specificity in relation to the MC recommendation are shown in Figure 1. The variable “many
previous episodes” has the highest value for the area under the ROC curve, although it has a
somewhat lower sensitivity than the other two variables, but it is considerably better with
respect to specificity. The stratification according to gender of the patients did not reveal any
significant differences (results not shown).
Table 4: Model fit for multi-level logistic regressions for the separate predictive factors, for the number of predictive factors and for all three predictive indicators simultaneously analysed.
Predictive factor Model fit by Akaike’s index AIC1
Many previous episodes 222.5
Long duration 230.1
Definitely better by the 4th visit 236.6
Number of predictive factors (0–3) 226.6
Long duration, Many previous episodes, 225.4
Definitely better by the 4th visit.1 A smaller value for AIC indicates better fit to data.
Figure 1: Three ROC curves for the investigated predictors for the MC-recommendation together with a reference line for no discrimination (a random predictive capability).
Areas under the ROC curves are 0.65 for Many Episodes, 0.60 for Long Duration and 0.50
for Definitely Better, the latter almost coinciding with the reference line.
Please refer to the FULL-TEXT article for Figure 1.
In this study, the decisions of chiropractic clinicians to recommend secondary and tertiary
preventive care, MC, for recurrent and persistent LBP were tested using theoretically defined
indications. We tested models in which three, two, one and none of the indications were used.
We propose that, in clinical reality, this information is weighed together consciously or
subconsciously to form a clinical decision.
The results largely confirm the findings of the previous studies in the area [10,13,14] . That is,
the theoretical construct previously identified was found to reflect reality. The clinical
encounter is always tailored to the individual patient, but clinicians are clearly using some
overarching principles when recommending MC. The clinicians in this study weigh these
factors together when deciding on MC. The presence of many previous episodes was found to
be the main indicator for such care in the clinical encounter. This might suggest that
clinicians are viewing MC mainly as secondary prevention aimed at preventing future
episodes, and is in line with the MC intent described in previous studies [10,13,14] .
The accuracy of the predictive models was examined using ROC curves. The area under the
ROC curve for the factor “many previous episodes”, suggests that the predictive accuracy of
this model is better than “long duration” and far better than “definite improvement”. It is
interesting that the sensitivity of the best model is less accurate than the specificity. Thus, the
absence of many previous episodes more accurately predicts a decision not to recommend
MC (specificity 0.72), than the presence of many previous episodes predicts a MC
recommendation (sensitivity 0.53). From previous studies, it is known that clinicians consider
a number of factors before recommending MC, factors such as psychosocial situation, work
demands, patient motivation and so on  . We did not record these variables in this study,
nor can we know if clinicians use some other, maybe tacit, knowledge in their decision
By adding the clinicians as a factor in the multi-level regression model, the model fit was
improved. We conclude that the heterogeneity among chiropractors in regards to
recommending MC is substantial. This is also in line with the findings of a previous study
Further, the initial hypothesis was not confirmed in full, as not all patients with three
predictors were regarded as MC candidates and a majority of patients with no predictors also
were given this recommendation (keeping in mind that the latter is a small group). Again, it is
possible that some other unknown or unrecorded variables were considered in these cases,
making the decisions go either way depending on the type and presence or absence of that
information. This could possibly explain the fact that even the patients with no predictors to a
large extent (73%) were regarded as MC candidates. A previous study explained the
clinician’s intent of continuing treatment despite the lack of progress in terms of taking on the
role as a health coach  . Further, a recent consensus process among the chiropractic
profession described “wellness care” with a primary preventive intent: to promote general health including counseling on behavior related to diet, exercise and tobacco  . We did not
investigate these aspects of the MC decision, and this subgroup (with no predictors) was very
small, rendering conclusions subject to caution.
It is important to note that we do not know if the patients involved in this study that were
deemed “MC candidates” were actually given the MC recommendation, if they accepted it
and what the outcome of that preventive strategy was.
The major limitation of this study is the scarcity of variables, which is a result of time
restrictions in the clinic. The objective was to test a theoretical construct, which was possible
using the available data which are part of the normal clinical encounter. However, it would
have been possible to add an explorative element to the study with data concerning
psychosocial factors, motivation, work demands etc. which would possibly add an
explanatory value to the results. Still, as the main hypothesis was largely confirmed, the
theoretical construct was found to be reflective of reality to some extent. The results are also
restricted by the detail of the available data. Had more categories been added to previous
duration and previous episodes, detailed associations regarding subgroups may have been
explored. Both variables are self-reported and may be subject to memory bias. For previous
duration, the cut point of 30 days the previous year has been used in several studies [26-28]
and found to be useful in separating patients with good and poor long term prognosis. For
episodes, no evidence-based definition exists  , and the decision to ask the patients to
remember whether they had many (=4) or fewer (<4) was based upon discussions with
The previously identified indications for recommending MC are indeed being used in the
clinicians’ decision-making. When the patient is presenting with a history of back pain (>30
days the previous year), many (=4) previous episodes and definite improvement by the fourth
chiropractic visit, the overwhelming majority of clinicians (93%) would consider
recommending MC. The strongest indicator for this recommendation was the presence of
many previous episodes. However, the model also indicated heterogeneity among clinicians
in making this decision.
The results of this study may be used in future studies designed to test the efficacy of MC, in
order to include the clinically relevant subgroup of patients.
The authors declare that they have no competing interests.
IA was responsible for the study from which the data were extracted. She was responsible for
the interpretation of the results and for writing the first manuscript draft. LB was responsible
for the statistical analyses and their interpretation. Both authors read and approved the final
We would like to thank Professor Charlotte Leboeuf-Yde for valuable input into the design of
this study. Further, our gratitude extends to the chiropractors and patients who generously
have donated their time in answering questionnaires and text messages. The study was funded
in part by the Swedish Chiropractors’ Association and in part by the European Chiropractors’
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