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Self-efficacy and health-related quality of life: a cross-sectional study of
primary care patients with multi-morbidity
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* Published: 14 February 2019

SELF-EFFICACY AND HEALTH-RELATED QUALITY OF LIFE: A CROSS-SECTIONAL STUDY OF
PRIMARY CARE PATIENTS WITH MULTI-MORBIDITY

* Michele Peters ORCID: orcid.org/0000-0002-0076-59811,
* Caroline M. Potter1,
* …
* Laura Kelly1 &
* Ray Fitzpatrick1

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Health and Quality of Life Outcomes volume 17, Article number: 37 (2019) Cite
this article

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ABSTRACT

BACKGROUND

Multi-morbidity in chronic long-term conditions is a major concern for health
services. Self-management in concert with clinical care forms part of the
effective management of multi-morbidity. Self-efficacy is a mechanism through
which self-management can be achieved. Quality of life is adversely impacted by
multi-morbidity but could be improved by effective self-management. This study
examines the relationship between self-efficacy and quality of life in primary
care patients with multi-morbidity.

METHODS

A cross-sectional survey was conducted with primary care patients in England.
Potential participants were mailed a questionnaire containing quality of life
measures (the EQ-5D-5L and the Long-Term Conditions Questionnaire (LTCQ)), the
Disease Burden Impact Scale (DBIS) and the Self-efficacy for Managing Chronic
Disease Scale. Descriptive statistics, analysis of variance and linear
regression analyses were conducted to examine the relationship between quality
of life (dependent variable), self-efficacy, and demographic and disease-related
variables.

RESULTS

The 848 participants living with multi-morbidity reported a mean of 6.46 (SD
3.49) chronic long-term conditions, with the mean number of physical conditions
5.99 (SD 3.34) and mental health conditions 0.47 (SD 0.66). The mean scores were
15.45 (SD 12.00) for disease burden, 0.69 (SD 0.28) for the EQ-5D-5L, 65.44 (SD
23.66) for the EQ-VAS, and 69.31 (SD 21.77) for the LTCQ. The mean self-efficacy
score was 6.69 (SD 2.53). The regression models were all significant at
p < 0.001 (adjusted R2 > 0.70). Significant factors in all models were
self-efficacy, disease burden and being permanently sick or disabled. Other
factors varied between models, with the most notable being the presence of a
mental health condition in the LTCQ model.

CONCLUSIONS

Multi-morbid primary care patients with lower self-efficacy and higher disease
burden have lower quality of life. Awareness of self-efficacy levels among
patients with multi-morbidity may help health professionals identify patients
who are in need of enhanced self-management support. Providing self-management
support for chronic disease has been hailed as a hallmark of good care. Higher
self-efficacy may lead to enhanced quality of life in multi-morbidity.

BACKGROUND

Multi-morbidity is a major concern for health services, health research and
health policy [1,2,3]. Strategies and guidelines to manage multi-morbidity are
set out by researchers, health policy and governmental bodies internationally
[2,3,4]. Defined as two or more chronic long-term chronic conditions [5],
multi-morbidity has been described as the most common chronic condition
experienced by adults [6]. Higher use of health services and polypharmacy are
more common in people with multi-morbidity than those without [1, 7], making its
management complex [8]. Furthermore, multi-morbidity adversely affects patient
outcomes such as quality of life and disease burden [7, 9,10,11,12]; and impacts
on carers, health services and the economy [3].

Due to its increasing prevalence, taking account of multi-morbidity is essential
in the design of health services [13] Difficulties with the management of
multi-morbidity arise as guidelines and health and care services are often
targeted at or organised around single conditions [14, 15]. However, health
systems, and professionals working within these, are expected to provide care
that is patient-centred and continuous [8], and to support patients to actively
self-manage their chronic conditions [4, 15]. Self-management support is highly
important in the management of chronic conditions and multi-morbidity
[14,15,16], and health professionals consider it a key vehicle for managing
multi-morbidity and reducing service use [14]. Self-management is based on the
central premise that individuals need to self-care in a range of health care
practices on a day to day basis between medical appointments [17, 18].

Self-efficacy, first defined by Bandura in 1977 [19], refers to the confidence a
person has about their capacity to undertake behaviour(s) that may lead to
desired outcomes. Self-efficacy is a mechanism through which effective
self-management can be achieved [17]. Measuring self-efficacy is a standardised
and convenient approach to assess patients’ self-management potential and has
been recommended as a component of chronic care management [20]. Marks et al.
[21] hypothesised that higher self-efficacy is associated with better outcomes,
and that better outcomes reduce health services burden. A meta-analysis
concluded that self-management support for a range of single conditions was
associated with small but significant improvements in health outcomes, but only
a minority of interventions reported reductions in the use of health services
[22]. Multi-morbid patients usually find self-management harder [14, 23], for
example, because treatments prescribed by different health care providers can
lead to conflicts in care across conditions [14]. Self-efficacy can be improved
through self-management support, and improvements of the chronic disease
outcomes are related to improvements in self-efficacy [24]. Furthermore, it has
been shown that higher self-efficacy leads to reduced health care utilization
[25].

The assessment of quality of life and self-efficacy have both been identified as
part of the core outcome set for multi-morbidity [26]. This paper examines the
relationship between self-efficacy and quality of life in primary care patients
with multi-morbidity.

METHODS

A cross-sectional postal survey was conducted in primary care in three diverse
regions in England (Oxfordshire, North West Coast, Yorkshire & Humber). The main
aim of the study was to validate a new measure for long-term conditions (these
findings are published elsewhere [27]). The study was reviewed by England’s
National Research Ethics Service Committee East Midlands – Derby (reference
15/EM/0414) and approvals were granted by the Health Research Authority of
England’s National Health Service (NHS), and local health care organisations
linked to participant recruitment sites.

RECRUITMENT

Potential participants were invited through 15 primary care practices, with the
target population being adults (i.e. 18 years of age or above) who had received
a diagnosis at least 12 months ago of one of 11 specified chronic conditions:
cancer within the last 5 years, chronic back pain, chronic obstructive pulmonary
disease (COPD), diabetes, depression, irritable bowel syndrome (IBS), ischaemic
heart disease (IHD), multiple sclerosis (MS), osteoarthritis (OA), severe mental
health (including psychoses, bipolar disorder and schizophrenia which are the
severe mental conditions included in the UK Quality and Outcomes Framework
[28]), and stroke. The conditions were selected in an earlier phase of the work
[29] to cover a broad range of conditions in terms of their onset, disease
burden, trajectory, physiology etc. For conditions with lifelong implications
(i.e. COPD, diabetes, IBS, IHD, MS, OA, stroke), participant eligibility was
defined as the presence of the condition. For conditions where prolonged
remission or cure is possible (i.e. cancer, chronic back pain, depression,
severe mental health), additional criteria in relation to duration of disease
and/or current treatment were specified, similar to the approach taken by
Barnett et al. [30]. Primary care practices were provided with study materials
(including participant information sheet, survey pack, pre-paid reply envelope).
They selected eligible patients from their practice database according to the
inclusion criteria, and mailed the questionnaire packs to 2983 potential
participants.

QUESTIONNAIRES

The survey included a self-efficacy scale [31, 32], the Long-Term Conditions
Questionnaire (LTCQ) [27], the EuroQol 5 Dimension 5 Level (EQ-5D-5L) [33, 34],
the Disease Burden Impact Scale (DBIS) [12, 35], and demographics questions. The
deprivation score for each participating general practitioner (GP) practice was
derived from https://tools.npeu.ox.ac.uk/imd/ (1st May 2018) and converted into
quintiles. This information was entered into SPSS for each participant.

Self-efficacy was assessed by the 6-item Self-efficacy for managing Chronic
Disease Scale [31, 32]. Each item is rated on a 1 ‘not confident at all’ to 10
‘totally confident’ scale. The score is the mean of the items, with the score
range 1–10. A higher score indicates higher self-efficacy or more confidence in
managing chronic disease(s).

Quality of life was assessed by the LTCQ and the EQ-5D-5L. The LTCQ is a 20-item
measure that addresses the concept of ‘living well with long-term conditions’.
It has been found valid and reliable in health and social care users [27]. The
LTCQ was specifically developed to assess outcomes in people with a range of
long-term conditions (including physical and mental health conditions), and
single and multiple morbidities. Items are scored on a 5 -point scale from
‘Never’ to ‘Always’. A single score is calculated from the 20 items, with scores
ranging from 0 to 100 and higher scores indicating ‘living well’.

The EQ-5D-5L [33, 34] is a generic measure of health status that includes five
questions covering mobility, self-care, usual activities, pain and
depression/anxiety, and a Visual Analogue Scale (EQ-VAS). Each question has five
response options where 1 is ‘having no problem’s and 5 is ‘being unable to do
the activity’ or ‘extreme pain or anxiety/depression’. The EQ-5D-5L score,
calculated from the five questions, has a theoretical range of − 0.285 (a state
worse than death) to 1 (best possible health state) [36]. The EQ-VAS measures
overall health on the day of completion of the questionnaire and the score
ranges from 0 (the worst health you can imagine) to 100 (the best health you can
imagine).

The DBIS [12, 35] assesses the personal disease burden of chronic long-term
conditions. Developed specifically for primary care, it asks participants to
self-report their chronic condition(s), and in a second step to give a rating of
the degree to which each condition interferes with daily activities. The
original questionnaire includes 21 conditions that are rated on a six point
scale where ‘0’ means that a participant does not have the condition, and 1
(none) to 5 (high) to indicate the degree of interference of a condition. The 21
conditions in the original DBIS were all physical health conditions and, as it
is permitted by the original developers to add further conditions [12], four
further groups were added: MS, depression or anxiety, bipolar disorder, and
psychosis or schizophrenia. Therefore, the DBIS in this study included 25
conditions. Space was also provided for additional conditions not already
listed, and participants added up to three further conditions. This means that
the disease burden score range for this study was 0 (indicating not having any
chronic conditions) to 140, with a higher score representing a higher disease
burden.

ANALYSIS

All data were entered into SPSS (version 22), a statistical software package.
The self-efficacy, LTCQ, EQ-5D-5L and disease burden (DBIS) scores were
calculated according to the developers’ instructions. For self-efficacy, the
score can be calculated if at least four out of the six items have been
completed (i.e. 837 (98.7%) / 848 participants). For the LTCQ score, 76 (8.9%)
cases had missing data and for the EQ-5D-5L, the number of cases with missing
data was 20 (2.4%) for the EQ-5D-5L score and 6 (0.7%) for EQ-VAS. No data
imputation was undertaken for the LTCQ score, EQ-5D-5L score or the EQ-VAS.

For the disease burden or the DBIS score, it was assumed that if there was no
response for a given condition that the participant did not have this condition
(ie coded as 0) according to the method by Ramon-Roquin et al. [37]. The
conditions added under ‘other’ (open text box) also required some recoding, for
example if the ‘other’ condition was one of the 25 conditions listed. If
conditions were listed twice through the use of the ‘other’ box, the worst
impact score was retained, or if the same impact score was reported, only one
score was retained. Based on the open text answers, two additional categories
were created ‘other mental health’ and ‘other neurological’ to cover mental
health or neurological conditions not in the list of 25 conditions, such as
eating disorders, obsessive compulsive disorder or neurological conditions other
than MS. After the calculation of the score, 19 participants had a score of 0,
indicating that they have none of the conditions listed and 50 reported only one
condition. These 69 participants were removed from the analysis as a minimum of
two conditions need to co-exist to meet the definition of multi-morbidity.

Descriptive statistics were used to report the sample characteristics, and
self-efficacy, LTCQ, EQ-5D-5L, EQ-VAS and DBIS scores. Analysis of Variance
(ANOVA) was used to examine the relationship between demographics, presence of a
mental health problem, GP practice deprivation score and hospital admission for
chronic disease in the last 12 months to the DBIS, LTCQ, EQ-5D-5L, EQ-VAS and
self-efficacy scores respectively. ANOVA was also used to examine the
relationships between DBIS and LTCQ, EQ-5D-5L scores and EQ-VAS, and the
relationship between the presence/absence of individual conditions on the
self-efficacy score. Multiple linear regression analyses were conducted to
examine the relationship between quality of life (dependent variables LTCQ,
EQ-5D-5L and EQ-VAS scores) and self-efficacy, burden of disease (DBIS score),
demographics, presence of mental health problem, deprivation score and hospital
admission. The level of significance was set at p < 0.05. Exact values for p are
reported for values ≥0.001, otherwise they are reported as p < 0.001.

RESULTS

PARTICIPANTS

The total sample size was 848 primary care patients with multi-morbidity, with a
slightly larger proportion of respondents being female. The mean age of
participants was 67.0 (SD 13.93). The majority were married (n = 505, 59.6%) and
of a white ethnic background (n = 813, 95.9%). One hundred and fourteen (13.6%)
of respondents had been admitted to hospital for a chronic condition in the year
preceding the study. Further demographic details and health related information
can be found in Table 1.

Table 1 Demographics and health information
Full size table

CHRONIC CONDITIONS AND DISEASE BURDEN

The mean number of LTCs reported was 6.46 (SD 3.49), the mean number of physical
LTCs was 5.99 (SD 3.34) and mental health conditions 0.47 (SD 0.66). All but 6
respondents reported at least one physical health condition, and 334 (39.4%)
reported at least one mental health condition. The most commonly reported
conditions were hypertension, problems with vision and being overweight
(Table 2). The mean disease burden (DBIS) score was 15.45 (SD 12.00). The
disease burden score was significantly different by employment (p < 0.001),
marital status (p = 0.029), presence of a mental health problem (p < 0.001),
deprivation score of the GP practice (p < 0.001), and hospital admission in the
last year (p < 0.001).

Table 2 Prevalence of each long-term chronic condition and mean self-efficacy
(and standard deviation (SD)) by presence or absence of each condition
Full size table

QUALITY OF LIFE AND WELL-BEING SCORES

The mean scores for the EQ-5D -5L was 0.69 (SD 0.28), the EQ-VAS 65.44 (SD
23.66) and the LTCQ 69.31 (SD 21.77). For the EQ-5D-5L score, significant
differences were found for gender (p = 0.022), employment (p < 0.001), marital
status (p < 0.001), the presence of a mental health problem (p < 0.001), the
DBIS (p < 0.001) and deprivation score of the GP practice (p < 0.001). The
EQ-VAS was significantly different for gender (p = 0.043), age (p = 0.026),
employment status (p < 0.001), marital status (p < 0.001), presence of a mental
health condition (p < 0.001), the DBIS (p < 0.001), and deprivation score of the
respondent’s GP practice (p < 0.001). The LTCQ score was significantly different
for gender (p = 0.001), age (p < 0.001), marital status (p < 0.001), employment
(p < 0.001), presence of mental health condition (p < 0.001), the DBIS
(p < 0.001) and the deprivation score of the GP practice (p < 0.001). (Tables
with mean scores, standard deviation and level of significance can be found in
Additional file 1).

SELF-EFFICACY

The mean self-efficacy score for the total sample (n = 837) was 6.69 (SD 2.53).
Self-efficacy was significantly different for gender (p = 0.007), age
(p = 0.001), employment (p < 0.001), marital status (p < 0.001), presence of a
mental health condition (p < 0.001), and in those registered at a GP practice in
a more deprived area (p < 0.001). There was no significant difference for
ethnicity. Presence of a physical health problem did not show any significant
differences in self-efficacy, but there were only 6 people in the sample who did
not report a physical health condition. Many of the self-reported long term
conditions were associated with lower self-efficacy (see Table 2). Self-efficacy
was lower in participants reporting increasing disease burden (p < 0.001) and
those reporting lower EQ-5D-5 L scores, lower EQ-VAS and lower LTCQ scores (all
p < 0.001). The relationships between self-efficacy and EQ-5D-5 L, EQ-VAS, LTCQ
and disease burden are illustrated in Fig. 1. (Tables with mean scores, standard
deviation and level of significance can be found in Additional file 1).

Fig. 1

Self-efficacy (measured by the Self-efficacy for managing Chronic Disease Scale)
by EQ-5D-5L score (a), EQ-VAS (b), Long-term Condition Questionnaire (LTCQ) (c)
and disease burden (measured by the Disease Burden Impact Scale) (d)

Full size image

REGRESSION ANALYSIS

Linear regression was used to examine the impact of self-efficacy, controlled
for disease burden, other disease related factors, and demographics on quality
of life (EQ-5D-5L (Table 3), EQ-VAS (Table 4) and LTCQ (Table 5) of these
primary care patients with multi-morbidity. All three models were statistically
significant (all p < 0.001, with strong adjusted R2 of > 0.70). Significant
factors in all models, in addition to self-efficacy, were disease burden (DBIS
score) and being permanently sick or disabled. Other factors varied between
models, with the most notable being the presence of a mental health condition in
the LTCQ model.

Table 3 Linear regression for EQ-5D-5L (dependent variable) and self-efficacy
(measured by the Self-efficacy for Managing Chronic Disease Scale) controlled
for disease burden and demographics (p < 0.001, adjusted R2 = 0.70)
Full size table
Table 4 Linear regression for EQ-VAS (dependent variable) and self-efficacy
(measured by the Self-efficacy for Managing Chronic Disease Scale) controlled
for disease burden and demographics (p < 0.001, adjusted R2 = 0.71)
Full size table
Table 5 Linear regression for LTCQ (dependent variable) and self-efficacy
(measured by the Self-efficacy for Managing Chronic Disease Scale) controlled
for disease burden and demographics (p < 0.001, adjusted R2 = 0.78)
Full size table

DISCUSSION

Primary care patients with multi-morbidity in England experience lower quality
of life if their self-efficacy, i.e. their confidence to manage their diseases,
is lower. Furthermore, they experience higher personal burden of disease when
they reported lower self-efficacy. This is similar to previous US studies, which
also found lower self-efficacy with higher disease burden in multi-morbidity
[12, 38]. Differences in self-efficacy were found for the majority of conditions
(i.e. whether a specific disease was reported or not), although no significant
differences were found for some diseases including cancer, stroke and high
cholesterol. To the best of our knowledge, there is no evidence that may explain
these differences, and further research is needed to understand differences in
self-efficacy between different types of chronic long-term conditions.

The mean self-efficacy in this study was 6.69 (SD 2.53), which was higher than
the 5.17 reported in the original study on the self-efficacy scale [31].
Although some studies (e.g. [12, 38]) report the impact of self-efficacy on
disease burden or quality of life in multi-morbidity, they do not report mean
levels of self-efficacy nor factors associated with self-efficacy such as the
demographic or disease-related factors reported here. A German study on
multi-morbidity, using the same self-efficacy scale used in this study, reported
a similar mean self-efficacy of 6.69 (SD 2.32) [39].

The regression models used to investigate the impact of self-efficacy controlled
for disease burden; other disease-related factors and demographics on quality of
life were highly significant and also showed that lower self-efficacy was
related to lower quality of life. There is evidence that a more person-centred
care approach can enhance self-efficacy in single diseases (e.g. acute coronary
syndrome [40], stroke [41]) and that self-management support in chronic diseases
improves self-efficacy and patient outcomes [31]. As self-efficacy is
modifiable, patients with multi-morbidity could experience better quality of
life, and services may benefit from a reduction in use through effective
self-management support by health care professionals. Enhanced self-efficacy and
self-management may be achieved through teaching transferrable disease
management skills to patient with multi-morbidity. A lay-led intervention, which
involved teaching sessions on relaxation, diet, exercise, fatigue, breaking the
“symptom cycle”, managing pain and medication, and communication, led to
significantly enhanced self-efficacy [42]. More patient-centred communication
during consultations has been shown to be associated with higher self-efficacy
[38]. Health professionals should be encouraged to be aware of levels of
self-efficacy of their patients with multi-morbidity as it will enable them to
support patients more effectively. For example it has been shown in COPD
patients that better understanding of illness leads to higher self-efficacy
[43]. Other approaches to greater awareness of self-efficacy could range from
health professionals asking questions on self-efficacy during consultations to
more formal use of outcome measures such as those used in this study.

Some limitations of this study need to be acknowledged. The response rate was
31%, with 3% of those respondents needing to be excluded from this analysis.
Response rates in primary care surveys in England have ranged from 15.9 to 38%
[7, 44, 45], and hence the response rate in this study was not unusual. However
it does mean that the results need to be interpreted with caution as they may
not be representative for all primary care patients with multi-morbidity. The
study is based on a cross-sectional design and thus it is not possible to
establish cause and effect between quality of life and self-efficacy. Although
it is generally accepted that self-efficacy is a modifiable factor that can
enhance quality of life, clinical trials are necessary to provide definitive
answers on causality. Finally, the combination of diseases may also have an
impact on self-efficacy, but this was not investigated beyond the impact of the
co-existence of a mental health problem with a physical problem (the presence of
a mental health condition significantly lowers self-efficacy) and should be
investigated further. Studies that have investigated clustering of diseases have
found different clusters, for example Deruaz-Luyet et al. [46] identified four
clusters including (1) cardiovascular risk factors and conditions, (2) general
age-related and metabolic conditions, (3) tobacco and alcohol dependencies, and
(4) pain, musculoskeletal and psychological conditions. On the other hand,
Schafer et al. [47] identified three clusters 1) cardiovascular/metabolic, 2)
anxiety/depression/somatoform disorder and pain, and 3) neuropsychiatric
disorders. More evidence is needed on how to best cluster diseases for this type
of analysis, but it is an interesting area for future research.

CONCLUSION

Multi-morbidity is increasing, and self-management is considered essential for
its effective management. This study shows that primary care patients with lower
self-efficacy and higher disease burden have lower quality of life. Awareness of
health professionals of self-efficacy of their patients with multi-morbidity
would help to identify patients who are in need of enhanced self-management
support. Providing self-management support for chronic disease has been hailed
as a hallmark of good care [48]. Higher self-efficacy may lead to enhanced
quality of life for people with multi-morbidity.

ABBREVIATIONS

ANOVA:

Analysis of variance

COPD:

Chronic obstructive pulmonary disease

DBIS:

Disease burden impact scale

EQ-5D-5L:

EuroQOL 5 dimension 5 level

EQ-VAS:

EuroQOL visual analogue scale

GP:

General practitioner

IBS:

Irritable bowel syndrome

IHD:

Ischaemic heart disease

IMD:

Index of multiple deprivation

LTCQ:

Long-Term Conditions Questionnaire

MS:

Multiple sclerosis

NHS:

National Health Service

OA:

Osteoarthritis

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ACKNOWLEDGEMENTS

We thank the participants with long-term conditions for taking part in this
study and all of the organisations and colleagues who helped us recruit
participants and contributed to the overall study of validating the Long-Term
Conditions Questionnaire (LTCQ). We also thank Dr. Elizabeth Bayliss, University
of Colorado, for granting permission to use the Disease Burden Impact Score
questionnaire.

AVAILABILITY OF DATA AND MATERIAL

The datasets generated and/or analysed during the current study are not publicly
available due to ethical constraints. As stated in the study protocol approved
in by the ethics process, only members of the research team have access to the
study data. The full anonymised data set was shared between team members. Direct
access will be granted to authorised representatives from the sponsor or host
institution for monitoring and/or audit of the study to ensure compliance with
regulations.

FUNDING

This research was funded by the Policy Research Programme (PRP) in the
Department of Health England, which supports the Quality and Outcomes of
Person-centred Care Policy Research Unit (QORU), and by the National Institute
for Health Research (NIHR) Collaboration for Leadership in Applied Health
Research and Care (CLAHRC) Oxford at Oxford Health NHS Foundation Trust. The
views expressed are those of the authors and not necessarily those of the NHS,
the NIHR or the Department of Health and Social Care.

AUTHOR INFORMATION

AFFILIATIONS

1. Health Services Research Unit, Nuffield Department of Population Health,
University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK

Michele Peters, Caroline M. Potter, Laura Kelly & Ray Fitzpatrick

Authors
1. Michele Peters
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2. Caroline M. Potter
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3. Laura Kelly
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4. Ray Fitzpatrick
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CONTRIBUTIONS

RF and MP conceived the study. RF secured its funding and managed its overall
direction. MP led on securing ethics and other approvals for the study. MP, CMP
and LK were jointly responsible for participant recruitment (including working
with participating organisations and developing the database search protocol)
and for all aspects of data management (collection, entry, checking, cleaning,
computing scores). MP led the analysis and drafted the paper, which was
critically reviewed by all authors. All authors contributed to revisions and
approved the final version of the manuscript.

CORRESPONDING AUTHOR

Correspondence to Michele Peters.

ETHICS DECLARATIONS

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This study was reviewed by England’s National Research Ethics Service (NRES)
Committee East Midlands—Derby (reference 15/EM/0414) and approvals for the study
were granted by the Health Research Authority of England’s National Health
Service (NHS).

Participants’ consent was implied by return of the completed survey. This
process of consent was clearly explained in the Participant Information Sheet.

CONSENT FOR PUBLICATION

Not applicable.

COMPETING INTERESTS

The authors are developers and copyright holders of the Long-Term Conditions
Questionnaire (LTCQ).

PUBLISHER’S NOTE

Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.

ADDITIONAL FILE

ADDITIONAL FILE 1:

Table S1. Mean EQ-5D-5L scores (with a theoretical range of − 0.285 (a state
worse than death) to 1 (best possible health state) by demographic and
disease-related variables. Table S2. Mean EQ-VAS scores (range 1–100, with
higher scores indicating better health) by demographic and disease-related
variables. Table S3. Mean LTCQ scores (range 0–100, with higher scores
indicating ‘living well’) by demographic and disease-related variables. Table
S4. Self-efficacy score (measured by the Self-efficacy for Managing Chronic
Disease Scale with a score range of 1–10, with higher scores indicating higher
self-efficacy) by participants’ characteristics. (DOCX 29 kb)

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distribution, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The Creative Commons Public
Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/)
applies to the data made available in this article, unless otherwise stated.

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CITE THIS ARTICLE

Peters, M., Potter, C.M., Kelly, L. et al. Self-efficacy and health-related
quality of life: a cross-sectional study of primary care patients with
multi-morbidity. Health Qual Life Outcomes 17, 37 (2019).
https://doi.org/10.1186/s12955-019-1103-3

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* Received: 09 November 2018

* Accepted: 03 February 2019

* Published: 14 February 2019

* DOI: https://doi.org/10.1186/s12955-019-1103-3

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KEYWORDS

* Self-efficacy
* Quality of life
* Multi-morbidity
* Primary care

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* Background
* Methods
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