Do Chronic Obstructive Pulmonary Diseases (COPD) Self-Management Interventions Consider Health Literacy and Patient Activation? A Systematic Review

Self-management not only means to deal with the current condition, but also pursuing a holistic approach to mental and physical wellbeing. Self-management complements medical treatment to become more effective and successful. “Self-management has empowered me to better know and understand myself on so many levels” explains Jacqueline Bowman-Busato in her contribution.

For at least the past 23 years, I’ve been living with two complex chronic, relapsing diseases: Autoimmune Hashimoto’s and obesity. And yet, I can only say that it’s been the last 18 months where I have finally felt in control of my two diseases in any meaningful way. And this has been due to finally understanding and embracing responsible self-management.

Let me explain from a patient’s perspective. When I consciously started the journey of firstly realising that I had “a thyroid problem” which eventually was diagnosed as autoimmune hashimotos, I didn’t understand that a simple pill wasn’t enough to minimise symptoms. Critically, none of my medical specialists seemed to know or care about this fact either. The resultant search for energy in the wrong places aggravated my hashimotos symptoms (severe malabsorption of vitamin D and B as well as iron which all present as depression and severe anxiety). And all very quickly led to developing obesity. I never discussed obesity with my GP for 20 years (the average is 6 years according to a new study Action IO). I “dealt with it” by following holistic diets which always had a beginning, middle and very quick end!

Self-management has empowered me to better know and under-stand myself on so many levels.

It´s time to change

It was not until 18 months post bariatric surgery on 4 July 2016 that everything finally clicked into place for me. I realised that regardless of the good intentions of the public health environment, the sad fact of today’s chronic disease environment is medical treatment of physical manifestations rather than a holistic approach to mental as well as physical wellbeing, not to mention a lack of positive motivation to work together with health professionals in an empowering and empowered way.

Self-management has meant that I have had to take a very long and hard look at myself, the good, the bad and the very ugly truths in order to forge a personal pathway towards managing my life in such a way to optimise my mental health and wellbeing. Armed with my newly gained (and acknowledged) self-knowledge, I forged my own objectives-driven processes for achieving my goal of “mental clarity”. For me, brain fog has been my biggest barrier to sustainable management of both hashimotos and obesity. Having an objective of brain clarity rather than weight or specific blood values has meant that I’ve been able to take control of my health much more than if I solely relied on medication and then wondered why I was still malnourished to the point of continuing to seek energy in foods which are basically poison to me. Giving myself parameters with well-defined processes has significantly empowered me and raised my confidence levels to collaborate with my health care team. I am now listened to and heard.


Jacqueline Bowman-Busato

As a patient representative, Jacqueline has advised the Innovative Medicines Initiative (IMI) on patient engagement strategy, and provides expert advice to the European Commission on self-care policies. She works extensively on European as well as global projects bringing the key stakeholders together to build lasting consensus on global, regional and national levels.

Empowerment through self-management

Science very clearly states that obesity is a chronic relapsing disease. It‘s not the fault of one or other individual. In my world, that does not mean that I have to accept whatever medication I’m given in isolation. It means that I use the treatment (in my case the radical treatment of bariatric surgery) as a tool and I supplement with my own process for mental and physical wellbeing to put me on an even playing field to be able to optimise the medical treatment. Self-management empowers me to engage with the system and my health professionals. It allows me to give myself a bit of certainty which is not anxiety causing. It allows me to feel a partner in my own health. Self-management has empowered me to better know and understand myself on so many levels.

Exploring what self-management characteristics work (or do not work)

Standard network meta-analysis (NMA) focuses on assessing combination of components whereas component network meta-analysis (CNMA) focuses on estimating component effects and then proceeds to reconstruct the effects of self-management interventions (SMIs) based on the component effects. But how to explore what self-management characteristics work (or do not work)?

The aim of COMPAR-EU is to compare the effectiveness of SMIs in four chronic diseases. With hundreds of randomized controlled trials and a plethora of different SMIs available, NMA is the appropriate method for this aim. SMIs are not easy to define as they consist of multiple, possibly interacting components. These components are not limited just to what is the SMI (sharing information, skills training etc.) but also include the mode of delivery (e.g. face-to-face vs. remotely), the provider (physician, nurse, etc.), the location (primary care, hospital, etc.), the type of encounter (clinical visits, self-guided, etc.) and the time of communication (synchronous vs. asynchronous). Additionally, there may be variations in other characteristics (e.g. duration, intensity). One can easily see that heterogeneity of SMI may be an issue.

Methodological vehicles (NMA and CNMA)

At the synthesis level, there are two main approaches for handling such interventions:

  • Standard NMA, where each combination of components forms a distinct intervention.
  • Component NMA, that estimates the effect of each component and then estimates the effect of a SMI by summing the effects of the components comprising this SMI (additivity assumption).

The former will answer the question ‘which SMI work’ whereas the latter will answer the question ‘which components work’.

With multiple SMI components and outcomes, there is a high likelihood we will find patterns in the noise. Finding meaningful and important patterns between SMI components and effectiveness is challenging.

Evidence Base

In COMPAR-EU, we have identified 43 distinct characteristics (components) of SMIs and, after long discussions, grouped them down to 13 (plus two versions of a usual care). Ideally, we would like to estimate the effect of each of the 43 components, but we had to proceed to some clinically meaningful merging to avoid ending up with each study comparing different SMIs. There is a cost to that, information is lost by merging characteristics and this may lead to a substantial increase in heterogeneity. Subsequently, NMA assumptions such as transitivity (the ability to estimate effects indirectly) may be challenged.

To understand the challenges in the analysis with SMIs, even with a considerable merging of components, have a look at the network plot below (the outcome is all-cause hospital admissions, nodes/circles represents SMIs and edges/lines represent studies comparing the connected SMIs). There is a total of 60 studies. A meta-analyst would normally be delighted with this number. But these 60 studies compare 33 different SMIs (combination of different SMI components)!

We have observed similar patterns in all outcomes (more than 80) we have analyzed. More specifically:

  • Networks are sparse. Most SMIs are compared to usual care (UC) and not to each other. In this outcome, 52 of the 60 trials (87%) compare a SMI to UC.
  • For most comparisons (528 in this network!), information comes either only from direct evidence (studies directly comparing the SMIs of the comparison – lines in the network plot) or from indirect evidence (two SMIs not compared in any trial – no line in the network plot).
  • Studies are small and associated with much uncertainty. The relative effect of each SMI vs UC is informed mainly by the study including this comparison. We may end up with a SMI with a large effect in the NMA just because this SMI is compared in a single trial at high risk of bias. This has also implications for the main NMA assumptions (agreement between direct and indirect evidence).
  • There is substantial heterogeneity of the SMI effect.
  • In all outcomes there are studies not connected to the main network (in this outcome there is only one).

Dr Dimitris Mavridis

Dimitris has been involved in evidence synthesis for a decade now. He acts as a reviewer both for the Cochrane and the Campbell collaborations. He also serves as an associate editor for ‘Research Synthesis Methods’ and the series ‘statistics in practice’ in the ‘Evidence Based Mental Health’ journal. He has published many NMA and has been working mainly on statistical methodology surrounding NMA.

Interpretation of results

Apart from all these, interpretation of results is not straightforward. Partly due to imprecision in results and partly due to the fact that there is not a clear pattern between effect sizes and presence/absence of components in SMIs. Consider the table below. What are the conclusions? Adding component D worsens the outcome but adding both components D and E improves the outcome? It is tempting (and wrong) to focus on point estimates and statistical significance. The 95% CIs are huge and all the information comes from the small trials including these interventions. Hence, we additionally need to consider imprecision, the risk of bias and quality of these studies when interpreting the results. In this network, we have 30 relative effects of SMIs vs. UC and interpretation is much more complicated.

Component network meta-analysis attempts to solve some of these issues. The effect of a combination of components equals the sum of the effects of its components e.g.

This is known as the additivity assumption. Instead of estimating the effect of A+B+C from the couple trials comparing this SMI, you estimate it through the effects of its components, which are included in many trials each. As a result, we have

  • more precise effect estimates
  • effects closer to the null. Since components are included in trials with both small and large effects, when combining them to get the effect of the SMI, it is unlikely to see very large effects like those observed in standard NMA.

CNMA may give us information on which components are not working. A component included in SMIs with small effects is expected to have a negative impact on the SMIs in general. It also includes studies not connected to the main network, because all studies are connected at the component level.

Remaining challenges

Suppose now that the CNMA concludes that components A, B and C are effective, but these have never been combined in any of the trial arms. Is it rational to suggest a SMI with these components? Some content knowledge is probably useful. Additionally, the additivity assumption of CNMA is a hard one to defend. In practice, complex interventions such as SMIs are full of interdependencies (interactions) and non-linear responses. An interaction effect occurs when the effect of one component depends on the value of another component. We can express it like this

The interaction term can be positive (the two components act synergistically), negative (antagonistically) or zero (independently). The possible combinations of components are innumerable and we cannot just add interactions everywhere. Ideally, we would like content knowledge on where (which components) to add interaction terms. This is not an easy task, and ignoring interaction terms challenges the interpretation of the component effects.

So how are we supposed to analyze such a network? Albert Einstein said that if you have an hour to solve a problem, you should spend 55 minutes thinking about the problem and 5 minutes thinking about the solution. We have been thinking about the problem for some months now, trying to formulate the right questions, and according to my calculations we can afford going on like this for some extra months. But at some point we have to enter the five minute period of solving the problem. Yet, with such a complex dataset, with multiple components, outcomes and analyses, there is a high likelihood we will find patterns in the noise. Finding meaningful and important patterns in the data is the main challenge. To this end, we will try not even NMA and CNMA but a series of regression analyses and visual tools to understand interdependencies between components, with an aim to find SMI characteristics and their combinations that work, but also those that do not.

Formation of a panel to formulate recommendations for SMI for Type 2 Diabetes Mellitus

Sixteen participants from nine European countries conform this panel. There is a broad representation of stakeholders including health services researchers, endocrinologists, health economists, family practitioners, self-management experts, nurses, nutritionists, patient advocates and guideline methodologists. They are active since last week of October.

The panellists will:

  • Rate the importance of included outcomes
  • Propose the magnitude of effects thresholds.
  • Discuss and agree on draft evidence summaries prepared by the COMPAR-EU Consortium about the effects of interventions, the economic considerations, values and preferences of patients, and contextual factors of self-management interventions.
  • Discuss and agree on draft judgments for the different criteria relevant for the formulation of recommendations, included in the evidence to decision (EtD) framework.
  • Discuss, formulate and agree on draft recommendations, conclusions and other related contents (e.g. summary of findings tables, narrative summaries, etc.).

Launch of Self-Management Europe

Research partners set up a new European Research and Innovation Centre on Patient Empowerment and Self-Management: Self-Management Europe.

It is an exploitation initiative of the COMPAR-EU project with the aim of developing the potential of people, professionals, organisations, systems, and communities for creating a society that strengthens empowerment and self-management in people with chronic diseases. Through capacity building, the centre provides knowledge, skills, motivation, and competency to people to act as leaders of self-management and empowerment enhancement in everyday life to improve health and quality of life for all.

Whilst there are various ongoing initiatives in Europe to support self-help and self-management with whom the research centre will work in partnership. However, it addresses the critical gap of accelerating the translation of critical research findings in practical applications to benefit patients, organisational and clinical processes and health innovations.

The newly formed initiative recently published a press release which you can read here.