Quality of life (QoL) is an essential consideration when managing the wellbeing of patients and assists in interpretation of symptoms, functional status and perceptions. Atrial fibrillation (AF) and diabetes demand significant healthcare resources. Existing data demonstrate a negative impact on QoL as individual conditions, but there is less evidence relating to the impact of these disease groups in combination. This study therefore explores QoL in patients with AF and diabetes.
This cross-sectional, observational study required participants to complete the short form (SF)-36 survey via an online platform and was offered to people affected by AF alone and people with AF and diabetes in combination. The SF-36 provides a prevalidated tool with eight domains relating to physical and psychological health.
A total of 306 surveys were completed (231 AF group, 75 AF and diabetes group). The mean and standard deviation (SD) were calculated for each QoL domain, after re-coding in accordance with SF-36 guidance. Multi-variate analysis of variance (MANOVA) demonstrated an overall significant difference between the groups when considered jointly across all domains. There were significant differences between AF and AF with diabetes QoL responses in physical functioning, energy fatigue, emotional wellbeing, social functioning and pain. In these domains, the mean was highest in the AF group. There were no significant differences in the role physical, role emotional and general health domains.
In conclusion, this study demonstrates that diabetes and AF has a more detrimental effect on QoL than AF alone, in the majority of domains. Further research into the general AF population and where chronic conditions co-exist is important to comprehend the true impact this disease combination has on QoL.
It is important to consider quality of life (QoL) when managing the health and wellbeing of patients as it assists in the interpretation of symptoms, functional status, perceptions, experiences and patient expectations.1 Atrial fibrillation (AF) and diabetes are both long-term conditions that are increasing in prevalence. Both AF and diabetes can influence physical and psychological health and reduce QoL.1 Evidence has shown that in up to 40% of patients with diabetes, AF can co-exist,2 and little is known about how diabetes can further worsen QoL in AF. This comparison study, therefore, explores the QoL in these often co-existing long-term conditions.
QoL is a subjective phenomenon, based on individual perception and developed through experiences and beliefs.3 QoL has also been referred to as the relationship between actual and desired health and functioning with interchangeable broader terms including health status.4 Thus, the inclusion of symptoms, functional status, control, autonomy, general life satisfaction and health perceptions are all important and reflect necessary components when assessing QoL, meaning multi-morbidity may not simply equate to poor QoL.
Encouraging patients to assess their own QoL is vital in treatment decision-making as this may be incongruent with that of the health professional’s judgement.4 This can be through informed discussions central to understanding the impact of interventions on patient’s lives rather than just their bodies and medicalised outcomes.
QoL in AF
AF is the most common heart arrhythmia and has a significant impact on morbidity and mortality.2 Symptoms and complications can be debilitating, as can side effects of treatments, e.g. medication use. Rhythm control of AF is usually adopted to improve symptoms, yet evidence demonstrates that a heart rate control approach is often superior in terms of QoL.5 Furthermore, AF can induce anxiety, depression, emotional distress and a decline in cognitive function.6 The accumulation of interventions, effects or adverse outcomes, further compounds the overall wellbeing of patients with AF.
Most AF QoL studies have assessed symptomatic patients who are intolerant or refractory to anti-arrhythmic treatments, or who have undergone intervention, for example cardioversion or ablation.7,8 Rate or rhythm correction has generally been the focus of QoL studies and they have, therefore, been potentially biased towards selection of patients with symptoms or subgroups from clinical trials – baseline QoL scores have tended to be lower than the general population in these studies.1,7,9 AF stability can also impact on QoL scores and this was represented in a study investigating the impact of paroxysmal AF, demonstrating an inferior QoL when uncontrolled.10 When AF is permanent, an improvement psychologically and physically is often seen due to less anxiety and treatment stability.7 There has been less focus on assessing QoL in the general AF population who may not be undergoing intervention.
QoL in diabetes
QoL in patients with diabetes has also been reported to be worse than that of the general population, compounded by older age, concomitant chronic disease, poor diabetes control and polypharmacy.11,12 When diabetes co-exists with other chronic illnesses, the effect can be worse.13,14 Severe comorbidity (over five conditions) or the co-occurrence of two or more active health conditions that may or may not be linked by a causal relationship, has shown the greatest impact on people’s wellbeing.14,15
Study aims and hypotheses
The purpose of surveying people with AF is to provide further information on QoL in the general AF population rather than, as many QoL studies have been focused, immediately after treatment or intervention. Surveying people with both AF and diabetes will provide insight into the effects of these chronic diseases in combination while informing directed treatment and supporting patients’ wellbeing.
Hypothesis: people with comorbid AF and diabetes have poorer assessed QoL overall and in each of the eight domains within the short form (SF)-36 survey than people with only AF. The null hypothesis would be no difference.
Design and procedure
In this cross-sectional, observational comparison study, participants with either AF alone or AF and diabetes completed a QoL survey via an online platform, using the SF-36.16 This is a measure of functional health status, an important aspect of QoL, that relies upon patient self-reporting. It provides a pre-validated tool with previous application to both the AF and diabetic population but not specifically to these groups together. This tool includes eight domains relating to physical, social and emotional functioning, pain and general health; a high score denotes a more favourable outcome, that is, better QoL.16
The survey was available online and clearly signposted on the Arrhythmia Alliance (www.heartrhythmalliance.org/aa/uk) and Atrial Fibrillation Association websites (www.atrialfibrillation.org.uk). The website link opened the survey, with an introduction outlining the withdrawal procedure using a unique identification code that the participant added at the end of their survey. There were no external advertisements relating to this and visitors to either website voluntarily elected to complete the survey at their convenience. Consent was assumed by the self-initiation of completing the surveys.
A power analysis calculation was used (GPower®) to determine the appropriate sample size. A significance level of 0.05, a power of 80%, a signal-to-noise ratio of 0.4 (considered as ‘medium’) and testing differences between two groups, suggested a sample size of 128 participants. When applying the appropriate test, e.g. multi-variate analysis of variance (MANOVA) for testing the difference between the two groups in the eight domains, the sample size increased to 249 participants. It was anticipated that approximately half the number of surveys should be completed by people with AF and half by people with AF and diabetes.
Table 1. Eligibility criteria
|Inclusion criteria||Exclusion criteria|
|Able to read and understand the questions in English||Unable to read and understand the questions in English|
|Has AF or AF and diabetes||Does not have AF|
|≥18 years of age||<18 years of age|
|Key: AF = atrial fibrillation|
Eligibility criteria are set out in table 1. The necessity for being able to read and understand English was required to self-interpret the set questions and answer these as accurately as possible. The surveys were only made available in English due to the lack of immediate and adequate translation ability.
The study followed the SF-36 recommended analyses with scoring as a two-step process; first, pre-coded numeric values are recoded as per a standard scoring key.16 Items are scored and range from 1 to 100 – high scores denoting a more favourable QoL. Second, items in the same scale are averaged together to create the eight scale scores. Items left blank are not taken into account when calculating the scale scores.16
Scores from subscales often have skewed distributions but, despite the theoretical reasons why parametric approaches might not be the most appropriate, this approach is favoured in terms of simplicity and the ability to adjust for confounders, while facilitating comparisons with other datasets. Following a review of health-related QoL research, MANOVA was applied. In order to examine the hypothesis that people with comorbid AF and diabetes have poorer assessed QoL than people with AF alone, a one-way between-participants MANOVA was conducted to compare the SF-36 scores in each of the eight domains between the two groups. This was then applied to the overall difference in SF-36 score between the two groups.
A total of 306 surveys were completed over a four-month period: 231 (75.5%) with AF and 75 (24.5%) with AF and diabetes, perhaps reflecting the frequency of comorbidity. Data were entered and analysed using IBM SPSS Statistics for Windows (Version 26.0; SPSS, Inc.). A p value of <0.05 was considered statistically significant.
In order to make decisions on the treatment of missing data, the distributions of each domain were considered, such that the appropriateness of substituting the mean could be determined. Data were not normally distributed in all domains and missing data from unanswered questions were minimal with some variation across questions missed (i.e. it was not always the same question unanswered) and, therefore, these were left blank and the mean was not applied.
The distribution of scores illustrated the skewed nature of the domain scores in the two groups, as anticipated for role physical and role emotional, as these are inherently categorical and can, therefore, be misleading. This was the same in both the AF and the AF and diabetes groups.
Means and standard deviations (SDs) were calculated for the eight domains. These descriptive statistics suggest a more favourable QoL outcome when people have AF alone rather than AF and diabetes combined, in every domain besides the role emotional scores (figure 1). The domains that scored highest in terms of QoL for people with AF alone were pain, emotional wellbeing and physical functioning, respectively. The lowest scores and, therefore, poorest QoL, came from the energy fatigue domain in both groups.
The MANOVA demonstrated an overall significant difference between the AF and AF and diabetes groups when considered jointly on the variables, Wilkes Λ=0.777, F(8, 276)=10.113, p≤0.001, partial η²=0.227. Tests of between-subjects effects provided individual differences between groups in each domain and, where there was a significant difference, means were examined to describe the differences. There were significant differences between AF and AF and diabetes QoL responses in the physical functioning, energy fatigue, emotional wellbeing, social functioning and pain domains. In these domains, the mean was highest in the AF group. There were no significant differences in the role physical, role emotional and general health domains. Of these, the mean was highest in the AF and diabetes group combined in role emotional, but in general health, it was higher in the AF group but not significantly so.
Overall, there was a significant effect such that the AF only group had better QoL than the AF and diabetes comorbid group. There were no significant differences between groups in the role physical, role emotional and general health domains. This, therefore, does not support the hypothesis that QoL would be lower in all domains between groups. The questions that encompass the role physical domain centre around being able to perform physical duties, limitations and ability to perform work requiring effort. The means for role physical, both groups were not too dissimilar, albeit with a slightly higher mean in the AF group. The impact of diabetes on role physical may be related to other factors. For example, stability of diabetes can affect QoL and ability to function physically can be impaired where glucose control is more erratic. A study exploring QoL in people with diabetes alone (using SF-36) demonstrated lower mean scores across almost all domains when diabetes was poorly controlled with the largest difference noted in role physical, general health and energy fatigue.17 Similarly, stability of AF can also negatively impact QoL, and this has been recognised in research outside of this study.10
The lower QoL mean scores in the domains where there was a significant difference (physical functioning, energy fatigue, emotional wellbeing, social functioning, pain) were when AF and diabetes co-existed. This supports the hypothesis, in part, and also supports wider evidence whereby the co-existence of chronic disease results in a poorer QoL across physical and psychological domains,1,7,14 with AF and diabetes being linked specifically.1,18 However, this study did not obtain information on the co-existence of other chronic disease and this could have impacted results. Furthermore, age can impact QoL, but participants’ age was not recorded in this study.
Both AF and diabetes have been associated with anxiety and depression, and scores for emotional wellbeing and social functioning were lower when the conditions co-existed. Duration of diabetes has been shown to impact QoL, with longer duration diagnosis being detrimental to QoL.13 This impacts social functioning also, and the significantly lower scores for the AF and diabetes group can be further explained through the assumed influence of complications and treatment stability. Conversely, duration of AF has shown contradictions in terms of psychological wellbeing,7 and this might, in part, support the findings in this study. Longer duration AF has been shown to improve QoL in terms of emotional wellbeing and anxiety due to acceptance of the condition, fewer symptomatic episodes, less variation in therapies and reduced hospital visits.7,19 New-onset AF can negatively impact QoL due to anxiety around a new diagnosis and unfamiliarity of symptoms. Further research has shown that the earlier treatment can be initiated, the better outcomes patients have in terms of QoL.7,10,19
This study has also highlighted that the energy fatigue domain scored lowest in both groups. This is unsurprising when reduced energy and fatigue are commonly cited by patients with AF. It is also noteworthy that pain scores in the AF group are not too disparate from the guidance mean provided by SF-36 (70.77),16 but is scored significantly lower in the AF and diabetes group. As with other domains, this may be representative of the comorbid conditions often in existence alongside AF and diabetes, therefore, reflecting the poorer QoL when multi-morbidity exists.
While this study did not incorporate a third group of people with just diabetes, it is important to consider the impact on QoL, before combining with AF. Making direct comparisons is difficult due to varying study designs, but it is apparent that QoL in people with diabetes does not appear to be assessed quite so poorly (with higher mean scores) compared with when AF is present or indeed when AF exists alone.18 While statistical comparisons have not been made, it is acknowledged that QoL is negatively impacted in the presence of diabetes alone, but perhaps less so in physical functioning, pain and general health.11,17,20,21 It, therefore, appears that it is the existence of AF that has the biggest impact, whether as a lone diagnosis, or combined with diabetes.
Demographic data were not collected in this study and, therefore, contributing factors may not have been accounted for, e.g. age. Information on co-existing chronic conditions were also not obtained. Furthermore, duration of diabetes and type of AF might have been beneficial as both can impact on health-related QoL. It is also acknowledged that the survey was only available online and, therefore, precluded completion by those without access to the internet. It is also noteworthy that those accessing an AF website who voluntarily complete such surveys, may be more inclined to focus on symptoms with a perceived effect on QoL and, thus, may have impacted the outcomes of this study.
This study focused on people with AF with and without diabetes and has suggested that the co-existence of diabetes and AF has a more detrimental effect on peoples’ QoL than when AF exists alone in the majority of the domains. Both conditions are growing in prevalence and a negatively impacted QoL has detrimental effects on individuals, society, healthcare and the economy. Further research into the general AF population and where chronic conditions co-exist is important to comprehend the true impact, as it is recognised that the ageing population will more commonly have multiple comorbid conditions. In addition, understanding of QoL is also important to help inform and promote appropriate and targeted management of these patient groups, both medically and psychologically. The appraisal and re-appraisal of treatment decisions for patients with AF (and diabetes) requires focus in terms of QoL and should be central to treatment options when caring for these patients.
- Quality of life (QoL) is an essential consideration when managing the care of patients with chronic conditions. While research has explored the impact atrial fibrillation (AF) can have on QoL, this has not previously been explored when combined with diabetes – two common and increasingly prevalent long-term conditions
- This study suggests that when AF and diabetes co-exist, QoL is worsened in relation to physical functioning, energy and fatigue, emotional wellbeing, social functioning and pain
- This study did not show a significant difference in the role physical, role emotional and general health domains between groups
- Diabetes and AF often co-exist, and awareness of the impaired QoL, as demonstrated in this study, should guide targeted management of both conditions in order to prevent a worsening to patient QoL
Conflicts of interest
The author would like to acknowledge and thank the contribution of Trudie Lobban and her team at the Arrhythmia Alliance for their assistance with hosting the survey on their website and their support with collating the survey responses.
The study was approved by the Faculty of Health and Medicine Research Ethics Committee from the University of Lancaster (Ref: FHMREC18070) and also the Jersey Health and Community Service Research Ethics Committee.
The raw data required to reproduce these findings are available on request, held in a saved file by the lead author. This is, therefore, not available on a public repository.
1. Aliot E, Botto GL, Crijns HJ, Kirchhof P. Quality of life in patients with atrial fibrillation: how to assess it and how to improve it. Europace 2014;16:787–96. https://doi.org/10.1093/europace/eut369
2. Latini R, Staszewsky L, Sun J et al. Incidence of atrial fibrillation in a population with impaired glucose tolerance: the contribution of glucose metabolism and other risk factors. A post-hoc analysis of the nateglinide and valsartan in impaired glucose tolerance outcomes research trial. Am Heart J 2013;166:935–40. https://doi.org/10.1016/j.ahj.2013.08.012
3. Sazlina SG. Health screening for older people – what are the current recommendations? Malays Fam Physician 2015;10:2–10. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4567887/
4. Addington-Hall J, Kerla L. Who should measure quality of life? BMJ 2001;322:1417–20. https://doi.org/10.1136/bmj.322.7299.1417
5. Hagans VE, Ranchor AV, Van Sonderen E et al. Effect of rate or rhythm control on quality of life in persistent atrial fibrillation. Results from the Rate Control Versus Electrical Cardioversion (RACE) study. J Am Coll Cardiol 2004;43:241–7. https://doi.org/10.1016/j.jacc.2003.08.037
6. Thrall G, Lane D, Carroll D, Lip GY. Quality of life in patients with atrial fibrillation: a systematic review. Am J Med 2006;119:448.e1–448.e19. https://doi.org/10.1016/j.amjmed.2005.10.057
7. Bubien RS, Knotts-Dolson SM, Plumb VJ, Kay GN. Effect of radiofrequency catheter ablation on health-related quality of life and activities of daily living in patients with recurrent arrhythmias. Circulation 1996;94:1585–91. https://doi.org/10.1161/01.CIR.94.7.1585
8. Duff HJ, Raj SR, Exner DV et al. Randomized controlled trial of fixed rate versus rate responsive pacing after radiofrequency atrioventricular junction ablation: quality of life, ventricular refractoriness, and paced QT dispersion. J Cardiovasc Electrophysiol 2003;14:1163–70. https://doi.org/10.1046/j.1540-8167.2003.03168.x
9. Tse HF, Newman D, Ellenbogen KA et al. Effects of ventricular rate regularization pacing on quality of life and symptoms in patients with atrial fibrillation (atrial fibrillation symptoms mediated by pacing to mean rates [AF SYMPTOMS study]). Am J Cardiol 2004;94:938–41. https://doi.org/10.1016/j.amjcard.2004.06.034
10. Guedon-Moreau L, Capucci A, Denjoy I et al. Impact of the control of symptomatic paroxysmal atrial fibrillation on health-related quality of life. Europace 2010;12:634–42. https://doi.org/10.1093/europace/euq007
11. Papadopoulos A, Kontodimopoulos N, Frydas A et al. Predictors of health-related quality of life in type II diabetic patients in Greece. BMC Public Health 2007;7:186. https://doi.org/10.1186/1471-2458-7-186
12. Redekop W, Koopmanschap M, Stolk R et al. Health related quality of life and treatment satisfaction in Dutch patients with type 2 diabetes. Diabetes Care 2002;25:458–63. https://doi.org/10.2337/diacare.25.3.458
13. Trikkalinou A, Papazafiropoulou AK, Melidonis A. Type 2 diabetes and quality of life. World J Diabetes 2017;8:120–9. https://doi.org/10.4239/wjd.v8.i4.120
14. Brettschneider C, Leicht H, Bickel H. Relative impact of multimorbid chronic conditions on health-related quality of life: results from the MultiCare Cohort Study. PLoS One 2013;8:e66742. https://doi.org/10.1371/journal.pone.0066742
15. Adriaanse MC, Drewes HW, van der Heide I et al. The impact of comorbid chronic conditions on quality of life in type 2 diabetes patients. Qual Life Res 2016;25:175–82. https://doi.org/10.1007/s11136-015-1061-0
16. Ware JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36): I Conceptual framework and item selection. Medical Care 1992;30:473–83. https://doi.org/10.1097/00005650-199206000-00002
17. Engström M, Leksen J, Johanson U et al. Health-related quality of life and glycaemic control among adults with type 1 and type 2 diabetes – a nationwide cross-sectional study. Health Qual Life Outcomes 2019;17:141. https://doi.org/10.1186/s12955-019-1212-z
18. Echouffo-Tcheugui J, Shrader P, Thomas L et al. Care patterns and outcomes in atrial fibrillation patients with and without diabetes: ORBIT-AF registry. J Am Coll Cardiol 2017;70:1325–35. https://doi.org/10.1016/j.jacc.2017.07.755
19. Randolph T, Simon D, Thomas L et al. Patient factors associated with quality of life in atrial fibrillation. Am Heart J 2016;182:135–43. https://doi.org/10.1016/j.ahj.2016.08.003
20. Faria H, Veras V, Xavier A et al. Quality of life in patients with diabetes mellitus before and after their participation in an educational program. Rev Esc Enferm USP 2013;47:348–54. https://doi.org/10.1590/S0080-62342013000200011
21. Thommasen H, Zhang W. Health related quality of life and type 2 diabetes: a study of people living in the Bella Coola Valley. B C Med J 2006;48:272–8. Available from: https://bcmj.org/articles/health-related-quality-life-and-type-2-diabetes-study-people-living-bella-coola-valley