The state-of-the-art of eHealth self-management interventions for people with Chronic Obstructive Pulmonary Disease: a scoping review

doi:10.21203/rs.3.rs-3787842/v1

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The state-of-the-art of eHealth self-management interventions for people with Chronic Obstructive Pulmonary Disease: a scoping review

https://doi.org/10.21203/rs.3.rs-3787842/v1

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Introduction:

eHealth self-management interventions may help patients with COPD to engage in their self-management. However, little is known about the actual content of these eHealth interventions. Therefore, this review investigates the state-of-the-art of eHealth self-management interventions for COPD. More specifically, we research the functionality and modality, the positive health dimensions addressed, the target population characteristics, and the self-management processes and behavioural change techniques (BCTs).

Methods

A scoping review was performed to investigate current literature. Parts of the PRISMA-ScR protocol were followed and tailored to this study. The databases: PUBMED, SCOPING, PsychINFO (EBSCO), and Wiley were searched for literature.

Results

This review found that most eHealth technologies enable patients to (self-)monitor their symptoms by using (smart)measuring devices and/or smartphones/tablets. The self-management process ‘taking ownership of health needs’, the BCT ‘feedback and monitoring’, and the positive health dimension ‘bodily functioning’ were most often addressed. The inclusion criteria of studies in combination with the population reached when carrying out the studies show that a subset of COPD patients participate in such eHealth research.

Discussion/Conclusion:

The current body of literature related to eHealth interventions addresses mainly the physical aspect of COPD self-management. The necessity to specify inclusion criteria to control variables combined with the practical challenges to recruit diverse participants leads to people with COPD being included in eHealth studies that only represent a subgroup of the whole population. These findings showcase the gaps in current literature. Therefore, future developments should aim to develop eHealth technologies more inclusively and need to address multiple dimensions of the positive health paradigm.

eHealth

self-management

interventions

COPD

review

Chronic Obstructive Pulmonary Disease (COPD) is a common disabling lung condition characterized by chronic respiratory symptoms that cause persistent, mostly progressive, airflow limitations [1]. It is one of the major issues of public health and its prevalence, mortality, and morbidity are constantly increasing [1]–[3]. COPD was listed as the third leading cause of death worldwide in 2019 [4]. Of these deaths, 90% transpire in low-and middle-income countries [5]. Although COPD is chronic and thus not curable, it is, however, treatable and disease progression is preventable (e.g., by smoking cessation) [3], [6]. Therefore, an important aspect of treating COPD is chronic disease management.

An essential component of chronic disease management is self-management [7]. It requires an active role of the patient in managing their symptoms, treatment, lifestyle changes, and physical, emotional, and social consequences [7]. To support patients to engage in self-management, self-management interventions or programs are developed, and their effectiveness is investigated in research.

Self-management interventions or programs are shown to have positive effects, for example, in supporting patients to develop and/or improve their self-management skills and disease knowledge [8]–[10]. Camus-García found in their study that self-management interventions may improve clinical outcomes in COPD (e.g., improvements in Health-Related Quality of Life (HRQoL), and lower probability of hospital admissions [11]. The actual content of such self-management interventions programs for COPD is diverse [8] and it is not well known, which elements are crucial to design a successful intervention program. In the following, some elements that can be considered when designing an intervention program will be shortly described: Content for COPD self-management, processes of self-management, behaviour change techniques.

The diversity of content may be explained by the numerous objectives and endpoints that self-management intervention programs have [12]. Interventions focus on acute exacerbation management and admission avoidance by incorporating exacerbation action plans [13], and often also include education, exercise training, and breathing strategies [14]. However, research suggests that intervention programs that only include education or action plans alone may not result in behavioral change, increased patient’s confidence, or new skills that patients learn or practice [12], [15].

Besides the content of self-management intervention programs, the design of the intervention program should also reflect that self-management consists of different processes. Schulman-Green et al., (2012) identified different self-management processes for chronic illnesses such as ‘learning’ ‘taking ownership of health needs’, and ‘performing health promotion activities’. All processes are divided in specific self-management tasks (e.g., 'learning about condition and health needs', 'changing behaviour to minimize health impact’) and skills (e.g., ‘acquiring information’, ‘reducing stress’) [16]. Schulman-Green et al., (2012) concluded that the identification of such processes may help support and guide future self-management intervention programs. In addition, they also showed that the various processes need to be considered in the context of the whole and their importance to the patients may differ according to their place in their patient journey [16]. Therefore, more knowledge about such self-management processes within self-management eHealth intervention programs is needed to support the development of such interventions.

Self-management interventions may also aim to change a certain behaviour of the patient, so that the incorporation of Behavioural change techniques (BCT) can be beneficial to design a successful intervention program. BCTs are ‘a specific observable, replicable, and irreducible component of an intervention program designed to alter or redirect causal processes that regulate behaviour’ [17], and can be included the design of any type of self-management intervention program. By adding these ‘active ingredients’ (for example, ‘feedback’, and ‘self-monitoring’) chances for achieving behavioural change may be increased [17]. Thus, combining self-management processes and BCTs in intervention programs may lead to positive results for one’s self-management. However, to the best of our knowledge, no research is dedicated towards investigating the presence of BCTs, and self-management processes in current self-management interventions for COPD.

One way of supporting people with COPD to engage in self-management is by using eHealth interventions. eHealth interventions can be defined as: ‘An eHealth technology specifically focused on intervening in an existing context by changing behaviours and/or cognitions’ [18]. eHealth interventions to support self-management may help people with chronic diseases to be more independent and empowered, by for example, gaining knowledge about their disease, monitor and report on daily symptoms, and learning specific self-management skills [19]–[21]. Therefore, the use of eHealth interventions in COPD care represents a promising way of delivering health services such as support in self-management [22].

In the current literature, a diverse range of eHealth interventions aim to support patients in their self-management [7]. Self-management eHealth interventions specifically for people with COPD are increasingly provided to support patients in health communication, self-monitoring, and their medical treatment [23]. A review from [24] revealed that eHealth interventions for COPD in the Netherlands included focused on: COPD care, medication adherence, smoking cessation, and information about COPD-related topics. In addition, [25] revealed in their review that most self-management eHealth interventions for people with COPD focus on education and increasing motivation related to topics such as smoking cessation, exercise, diet, and symptom management. The strong focus on exacerbation prevention, smoking cessation, following action plans, and physical activity in the interventions indicates a tendency towards managing the physical aspect of COPD in self-management eHealth interventions. However, the physical aspect of one’s disease is only one dimension of the positive health paradigm. As was conceptualized by [26]: “Health includes the ability to adapt, and self manage in the face of social, physical, and emotional challenges”, also referred to as ‘positive health’. [27] stated that positive health as a concept has several important health indicators, categorised into six dimensions: ‘bodily functions’, ‘mental well-being’, ‘meaningfulness’, ‘quality of life’, ‘social participation’, and ‘daily functioning’. They stressed the fact that attention to these indicators should be paid because in this way, shared decision-making (SDM) may be supported and the gap between healthcare and the social context may be closed. Therefore, these dimensions are all important to consider when (self-)managing one’s disease. However, no research is available regarding to what extent positive health dimensions are addressed in current self-management eHealth interventions for COPD.

Furthermore, using eHealth to support people with COPD might also entail some challenges as low health literacy is prevalent among people with COPD [28], [29]. In addition, moderate levels of self-reported eHealth literacy are also common among people with COPD [30]. Some studies revealed that people with COPD experienced technical barriers when using eHealth interventions for self-management [31]. Yet, [32] indicated few technical issues experienced by people with COPD when using eHealth to support self-management, leading to uncertainty about whether such eHealth technologies are suitable for the whole COPD population. Although some information about eHealth usage for this population is available [23–27], little research is dedicated towards investigating whether current eHealth interventions account for the wider population of people with COPD (such as those with (e)Health literacy).

To summarize, little is known about the actual content and design of self-management eHealth interventions for people with COPD. Therefore, the current scoping review investigates the state-of-the-art of current eHealth interventions for COPD self-management, and identifies potential gaps in literature, which may give insight or serve as inspiration into how future eHealth self-management interventions could be developed. Due to the variation in the literature regarding the definition of self-management in COPD, this paper defines self-management as: “The ability of an individual to manage one’s symptoms, treatment, physical, social, and emotional consequences, and lifestyle changes. It includes means of empowerment, educating oneself, being autonomous, learning and adapting to new behaviours, acceptance, and adapting to a new balance in life”. More specifically, we want to unravel the state-of-the-art of eHealth self management interventions by the following sub questions:

What is the e in eHealth self-management? What is the function of the eHealth technology and what is the modality of the technology?

What is the health in eHealth self-management? Which of the 6 dimensions in the model of ‘positive health’ are addressed by the eHealth technology?

Who is the self in self-management? What is the intended target population of the eHealth technology, what is the actual target population extracted from the in- and exclusion criteria, and what is the population included in the studies?

What is the management in eHealth self-management? Which self-management processes and behavioural change techniques (BCT) are addressed within the eHealth technology?

A scoping review was performed to investigate the current available literature, which was deemed the most suitable method for providing an overview of existing literature on a given topic. Parts of the PRISMA-ScR protocol (items 1–7, 9–11, 13–21 ) as proposed by[33] were followed and tailored to this specific study.

Search strategy

The first reviewer was responsible for identifying relevant articles in the databases PUBMED, SCOPUS, PsycINFO (EBSCO), and Wiley. Combinations of the search terms ‘self-management’, ‘COPD’, and ‘eHealth’ were used to generate the search string.

Study selection

Studies were considered eligible if they were original research and portrayed an eHealth intervention supporting the self-management of COPD. The eHealth self-management intervention should involve and engage people with COPD, in other words, patients should be able to obtain a personal gain from their self-management that is supported or encouraged by the intervention. The complete list of assessment and eligibility criteria is presented in Table 1.

Table 1

Assessment and eligibility criteria for studies.
Characteristics	Inclusion	Exclusion
Concept	Describing an eHealth intervention supporting the self-management of COPD.	-
Population	Adults ≥ 18 years diagnosed with COPD [and other chronic conditions provided that the eHealth technology has a dedicated part towards COPD].	Older adults, rural patients, communities in general, unspecified multimorbidity or chronic conditions.
eHealth technology	eHealth technologies to support people with COPD to engage in self-management that involve patients in their intervention: - At least one self-management process as defined by [16] - In case of solely monitoring, patients should be able to see their data.	Collecting data solely for research purposes, to train machine learning or artificial intelligence algorithms without any further patient engagement.
Study design	Original research	Review, protocol, abstract, letters, conference proceedings, commentary, note, short survey, erratum.
Language	English	-
Year published	Between 1st of January 2012 and 1st of June 2022	-

Procedure

The screening was performed using Rayyan.ai [34]. To screen for title and abstract, both reviewers adhered to the eligibility criteria that were discussed before the start of the screening (Table 1). One reviewer screened all articles for title and abstract. The second reviewer screened 20% of title and abstracts of those studies. After this first screening, a discussion took place to compare discrepancies and come to a consensus between reviewers. For the full-text screening, the same process was applied. Reasons for excluding articles during the full-text screening were recorded. Before extracting the data, a data extraction form was developed, discussed, and agreed upon with three authors. This form was piloted after the full-text screening to reduce errors during data extraction. Data extraction was performed by the first author using Atlas.ti version 9.1.7.0 ([35]) following the data extraction form to answer the proposed research questions. Table 2 shows the overview of how the articles were extracted and charted.

Table 2

Overview of extraction and charting details.
Extraction categories	Extraction details	Reference	Way of extracting	Way of charting data
General	a) Year of study b) Type of study c) Definition of self-management	-	Directly from data	Data clustered in bar graph [b], table [c], or presented descriptively [a]
SQ1: What is the e in eHealth self-management?	a) Functionality b) Modality c) TRL-level d) eHealth development details	c) [36]	Both directly from data [a, b, d] and assessed/ categorized by reviewer(s) [b, c]	Counted for separately [a, d] and available information about eHealth development status was categorized in the different TRL-levels [c]. Data is mapped into bar graph [b, c], table [d] or presented descriptively [a, d]
SQ2: What is the health in eHealth self-management?	a) Positive health dimensions	a) [27]	Interpretation and assessed/ categorized by reviewer(s)	Each dimension is counted for separately and mapped in radar and bar chart
SQ3: Who is the self in self-management?	a) Intended target population b) Included target population c) Actual target population	-	Both directly from data [a, b, c] and assessed/categorized by reviewer(s) [c]	When information on education of participants is available in the demographics, this is categorized in low, medium, or high education [c] and clustered in flow chart [a, b, c].
SQ4: What is the management in eHealth self-management?	a) Self-management processes b) Behavioural change techniques (BCTs)	a) [16] b) [17]	Both directly from data [b] and assessed/ categorized by reviewer(s) [a, b]	Each process and behavioural change technique is counted for separately and mapped in bar graph [a, b]

Search results

Figure 1 shows the detailed search strategy. A total of 893 articles were identified during the initial search. After 306 duplicates were removed, 588 articles were screened on title and abstract. This screening phase resulted in 189 articles that could be assessed for full text. After full-text screening, 88 articles were excluded. This resulted in 101 articles being included in this scoping review.

Study characteristics

The included papers represented 100 unique studies. Most articles (N = 18) were published in 2021, followed by 2020 (N = 15), and 2017 (N = 13). As shown in Fig. 2, the most common study types were either Randomised Controlled Trials (RCTs) (N = 18) or pilot studies (N = 16).

Only 8 articles provided a definition of the concept of self-management (Table 3). One article described that self-management implies that ‘people are in charge of their own lives with their disease and its treatment, enabling motivation to change’ [37].

Table 3

Self-management definitions.
Self-management definitions	Reference
A person’s conviction in his or her ability to manage challenges and complete a task successfully.	[38]
Self-management implies that people are in charge of their own lives with their disease and its treatment, enabling motivation to change.	[37]
The individual’s ability to manage the symptoms, treatments, physical, and psychosocial consequences and lifestyle changes inherent in living with a chronic condition.	[39]–[41]
The ability of the patient to deal with all the aspects of a chronic disease condition.	[42]
The actions taken by an individual to manage symptoms, treatment, emotions, and lifestyle changes as part of living with a chronic condition.	[43]
A process that facilitates an individual’s confidence and capability to engage in health-promoting behaviours in order to deal with the impact of their condition on all aspects of their health-namely, a sense of self, physical, emotional, social and medical domains so as to maximize function and quality of life.	[44]

1. The e in eHealth

This section focusses on the functionality, modality, TRL-level, and eHealth development details of the used technologies. Details about the functionality and modality of the eHealth interventions can be found in supplementary material 1. Of all included studies, 76 studies mentioned the name of their eHealth technologies. Some articles reported on studies using the same eHealth technologies (e.g., ‘EDGE’[32], [45]–[48], ‘It’s Life!’[37], [49], [50], ‘MasterYourBreath’[51]–[54], ‘COMET’[55], [56]). Fifty unique eHealth technologies were found in this review.

Most articles (N = 91) included (self-)monitoring (e.g., monitoring of symptoms) as function of their technology. 69 articles included the function of education or information (e.g., education on COPD), and 27 articles supported communication (e.g., eConsults with HCPs, peer-to-peers support chats). Most articles (N = 68) included more than one function within their technology.

Figure 3 shows that a (smart) measurement device (e.g., wearable or monitoring system) was most common (N = 39) modality, followed by a smartphone (N = 27), and tablet (N = 25). If studies used more than one device, the combination of (smart) measurement device with a tablet (N = 19) or smartphone (N = 8) was most often made.

This review found no article which explicitly stated their TRL. According to our assessment and categorization, 47 eHealth technologies in the articles were assessed to be in the development phase (TRL4-6), N = 53 in the deployment phase (TRL7-9), and no technologies within the research phase (TRL1-3).

Details about the eHealth development process showed that only 14 studies explicitly mentioned to have used either a user-centred design, participatory design, scenario-based methods, reflective life-world research, or action research approach. Furthermore, 18 studies reported details about the theories on which their self-management intervention was based on. Some of which were targeted towards behavioural change techniques independent of technology use, others were technology related and more targeted towards technological adoption or persuasive design. Table 4 shows the different theories that were mentioned. The social cognitive theory was most often used (N = 5).

Table 4

Used theories within the eHealth self-management intervention.
Category	Theory	Reference
Behavioural change	Health Belief Model (HBM)	[57], [54]
	Social Cognitive Theory	[54], [58]–[61]
	Self-care theory	[62]
	Transtheoretical Model	[58], [54]
	Five A’s Model	[50]
	Attitude-Social influence Self-efficacy model (ASE)	[54]
	Self-efficacy theory	[63]
	I-Change model	[51]–[54]
	Self-Determination model	[64]
	Tech to Goal (TGG)	[65]
	Theory of planned behaviour	[54]
Technological adoption/ Persuasive design	Technology Acceptance Model (TAM)	[66]
	Unified Theory of acceptance and use of technology (UTAUT)	[67]
	eHealth based Person-Centred Care (PCC)	[68]
Unspecified	Goal setting theories unspecified	[54]
	Implementation theory unspecified	[54]
	Health promotion unspecified	[63]

2. The health in eHealth technologies for self-management

Figure 4 shows how many eHealth technologies used in the studies addressed the different positive health dimensions. All included articles (N = 101) addressed (at least) the dimension bodily functioning, 45 daily functioning, 13 participation, and 12 articles mental well-being. We were not able to identify any indications that the dimensions meaningfulness and quality of life were explicitly addressed in any of the eHealth technologies supporting self-management. Details about the positive health dimensions can be found in Supplementary material 2.

Most studies (N = 48) focussed on one specific dimension namely, bodily functioning. Others (N = 42) focussed on two dimensions, 11 articles on three dimensions, and only 3 articles focussed on four dimensions within their eHealth technology. The combination of the dimensions bodily functioning and daily functioning was most often made (N = 33). Followed by the combinations bodily functioning, daily functioning and mental well-being (N = 5), bodily functioning and participation (N = 4), bodily functioning, daily functioning, and participation (N = 3), Bodily functioning, mental well-being and participation (N = 3), body functioning, mental well-being, participation, and daily functioning (N = 3), and bodily functioning and mental well-being (N = 1).

When comparing the presence of the dimensions with the years of the studies (Fig. 5), we found in the years 2013 to 2015 and 2017 to 2018, the dimension bodily functioning is dominantly present, followed by daily functioning. From 2017 to 2021, a small increase in the presence of the dimension mental well-being can be seen over the years. In the years 2020 and 2021, the presences of the dimensions daily functioning and participation is almost equal compared to bodily functioning.

3. The self in self-management

All 101 included papers (partly) described who was the intended population for the intervention (as stated in the intervention description), the included population (as stated in the in- and exclusion criteria), and the final actual study population (stated in the demographics of study participants). In some studies, certain inclusion criteria were required to participate, thereby restricting the group of eligible participants (i.e., the actual population). This scoping review extracted the following inclusion criteria: disease specific (needing to have a certain severity of COPD), capability related (needing to be cognitive capable, able to read and write, understand certain language, willing/able to provide consent), age related (needing to have a minimum or maximum age), smoking (history) related (being a (former) smoker), and technology related (needing to have digital skills, access to internet, own a certain device). Details about the self in self-management can be found in Supplementary material 5.

Intended population. There was some variation in the specific intended populations targeted in the articles. As shown in Fig. 6, the majority of the articles N = 59 were targeted at persons with COPD in general, N = 23 studies focused on one or more specific COPD severities, and N = 19 studies on COPD in combination with other chronic conditions. Some articles included more than one comorbidity.

Included population. Figure 7 presents an overview of the identified included population. More studies (N = 50) than outlined in the section “intended population (N = 23)”, had disease specific inclusion criteria (focusing on one or more COPD severities). 50 articles had capability related inclusion criteria, reflected in that participants needed to, for example, be cognitive capable and/or able to write and/or read to be eligible for participation. Furthermore, in 38 articles participants needed to have a certain minimum age, with a minimum of 40 years old being the most common. In eight articles, the age needed to be below a certain maximum. The maximum of 70 years old was most common and thereby, four times mentioned as inclusion criteria. A total of twelve articles had inclusion criteria regarding smoking (history) in which participants needed to be for example, an (ex-)smoker. Finally, 39 articles had technology related inclusion criteria. Participants needed for example, to own a smartphone or tablet and/or have digital skills in order to participate. Only one study explicitly mentioned to have no exclusion criteria based on age, comorbidities, and previous participation in pulmonary rehabilitation (PR). Also, in this same study, participants did not need to have previous experience using digital technology.

Actual population. Figure 8 shows the actual population included in the studies. Of the 25 articles that mentioned the severity of their participants, most participants had a moderate or severe COPD. Out of the 101 articles, only 21 shared a clear description of the education level of their participants which were then categorized for this article. The educational level of participants could be categorised in low, medium and high education which were almost equally distributed. Of the 71 articles that shared the mean age of their participants, we calculated the combined means which resulted in 64,85 years old. The gender of participants was clearly mentioned in 88 articles and were almost equally distributed. Of the 30 articles that shared the smoking history of their participants, almost half of the participants (51%) were reported as current or former smokers. In the 11 articles that described technology related experience, 89% of the participants had experience with technology.

4. The management in self-management

This section describes which self-management processes and BCTs were found within the different eHealth technologies. Details about this section can be found in Supplementary materials 4 and 5.

Self-management processes

Figure 9 shows the self-management processes found in the articles. No article explicitly described which self-management processes were reflected in the intervention design. When analysing how the self-management process was supported within the different studies, we identified that most studies (N = 94) addressed the process of taking ownership towards health needs (e.g., by including self-monitoring of symptoms or setting goals). 71 focussed on the process of learning (e.g., by including education within their technology), 27 on healthcare resources (e.g., by enabling communication with healthcare professionals within the technology), 23 on performing health promotion activities (e.g., by performing exercise or skill training), and 17 on social resources (e.g., by involving caregiver/family or peer-to-peer support), 1 on adjusting (e.g., ways to cope), and 1 on integrating illness into daily life (e.g., alternating daily live to conserve energy). We found no eHealth technologies specifically focussing on the self-management processes: meaning making, spiritual resources, psychological resources, processing emotions or community resources.

Behaviour Change Techniques

Figure 10 shows the BCTs extracted in this study. Only two studies explicitly stated which behaviour change techniques they used. When analysing the descriptions in the studies, we identified that feedback and monitoring were mostly used in the different articles (N = 88) (e.g., monitoring activity status). This is followed by shaping knowledge (N = 66) (e.g., receiving education), goals and planning (N = 38) (e.g., action planning), associations (N = 23) (e.g., receiving status updates), social support (N = 14) (e.g., communication with other people with COPD), regulation (N = 11) (e.g., addressing medication adherence), repetition and substitution (N = 10) (e.g., habit formation), rewards and threat (N = 6) (e.g., receiving visual rewards), natural consequences (N = 5) (e.g., information about health consequences), Self-belief (N = 5) (e.g., increasing self-efficacy), comparison of behaviour (N = 5) (e.g., follow along exercise video), Comparison of outcomes (N = 2) (e.g., information about effect physical activity), antecedents (N = 1 (e.g., adding objects to the environment), and identity (N = 1) (e.g., prompt identification as a role model). The BCTs covert learning and scheduled consequences were not observed to be present within the studies.

The results of this scoping review outline the state-of-the-art of eHealth self-management interventions for COPD. This review revealed that in current literature, most eHealth technologies for COPD self-management focus on (self-)monitoring one’s disease by utilizing the use of measurement devices, smartphones and/or tablets. These eHealth technologies mostly underpin the self-management process ‘taking ownership of health needs’, the BCT ‘feedback and monitoring’, and the dimension ‘bodily functioning’.

1. Self-management of COPD

Definitions of self-management

Only 8 studies provided a definition of self-management. One can assume that the definition of a paper determines where the focus of the self-management intervention lies as this often serves as a basis of the work. Because the majority of articles did not provide a definition, one may assume that self-management is a reasonable well-known concept in the field and does not require any further explanation. However, we question that assumption, as [15] found much diversity in definitions of self-management. We therefore suggest that articles include a clear definition of which self-management concept they aim to operationalise in their interventions.

Underlying theories, techniques, and processes.

Only few studies reported on using underlying theories, and specific BCTs supporting their self-management eHealth interventions. No article explicitly mentioned to focus on certain self-management processes. This is surprising given the fact that all studies aim to improve self-management and thus, aim to achieve some sort of behavioural change. As the concept of self-management varies in literature, reporting on the use of such processes, techniques and theories may be beneficial for understanding underlying structures and processes that will initiate behaviour to improve self-management. Building on these processes, technique and theories and then reporting in more detail on what was perceived as useful, beneficial, and desirable with this target population can advance the field and contribute to the body of work. This may simultaneously be valuable for informing future eHealth self-management initiatives as they can take into account these theories, techniques and processes in their developments. When looking at the literature, the lack of reporting on underlying theories was also prevalent in the review of Heimer et al. (2023) [134] in which only three of the included studies reported specific BCTs. Also, other studies encountered the problem of low reporting on BCTs, Hardeman et al. (2008) [135] and Lorencatto et al. (2013) [136] concluded that fewer than half of the planned BCTs were specified in the later published article. In addition, a review from de Bruin et al. (2023) [137] revealed that reporting about the active content of behavioural interventions varies considerably between studies. This limits the readers’ ability to compare, interpret, and generalise the effects of these studies [137]. Thus, including such theories in eHealth interventions and transparency in later reporting may lead to opportunities for achieving sustainable behavioural change.

The physical aspects of self-management

As we could show in this review, there is a tendency towards managing the physical aspect of one’s disease in current eHealth technologies for COPD self-management. This is reflected throughout different findings of this review. First of all, the functionality ‘(self-)monitoring’, and the BCT ‘feedback and monitoring’ were most often addressed. Although (self-)monitoring is very valuable and exacerbations may be detected in an early stage, this nonetheless demonstrates that the main focus lies on what happens with or inside the body. Secondly, the dominant physical aspect also manifests itself when looking at the self-management processes that are supported by the different technologies: ‘psychological resources’, ‘spiritual resources’, and ‘community resources’ were not found to be present. The self-management process of ‘taking ownership of health needs’ was mostly present followed by ‘learning’. Other processes, however, such as ‘integrating illness into daily life’ and ‘adjusting’, were only observed once, even though the target group has to deal with these aspects every single day [138]. We think that not addressing these processes is a missed opportunity, given that supporting people with COPD during their day-to-day activities might lead to even more improved outcomes of self-management. Finally, the dimensions of bodily- and daily functioning was most frequently used. This illustrates the current underrepresentation of other dimensions within current eHealth technologies for COPD self-management. Other dimensions (participation, mental well-being) were not as dominantly represented, or not observed at all (like meaningfulness and quality of life). As we’ve seen a small increase of dimensions over the past few years, we might see a small change of focus. However, this is not as fundamental and still leaves a lot of room for improvements on this matter. When looking at other chronic diseases (e.g., rheumatoid arthritis), a review of Seppen et al. (2020) [139] identified four different types of eHealth interventions used in their included articles. Although not explicitly stated, interventions were all related to the physical aspect (medication adherence, activity plan, information, disease monitoring, and activity monitoring) [139]. Thus, the tendency of the physical domain may not only be limited towards COPD. Therefore, future studies should investigate whether this view is also present in other chronic diseases.

2. Inclusiveness and representation of people with COPD

As all eHealth technologies target towards people with COPD as end-users, only 14 articles reported on involving the patient perspective in their design or development process. This raises the question whether and/or how the needs and perspectives of patients were taken into account. As including the perspective of end-users lead to a better fit and increase chances of successful adoption and sustained use [140], researchers should consider using such design principles when developing future eHealth technologies. This may give many opportunities for improvements for self-management eHealth technologies for COPD.

Furthermore, it appeared that even though articles outlined the target group to be the general population of patients with COPD, they often recruited a specific subset of people with COPD. Certain inclusion criteria are made within the studies (e.g., needing to own a smartphone, needing to have a certain disease severity). The consequence of such inclusion criteria leads to a restriction, in that only a selected group of individuals are included in the studies. While this may be due to practicalities (e.g., the complexity of COPD as a progressive lung disease), the question remains whether the intervention is generalizable or transferable to the wider population of people with COPD, if they were not part of the studies in the first place. Even when the narrowing of patients may have good reasons for the study purposes, it still affects the generalizability of study results. Therefore, awareness and transparency should be given about these potential restrictions made related to the patient group within such studies.

Given that the most often used device used is a (smart) measurement device (in combination with), smartphones and/or tablets, the group of people eligible for using these technologies in daily life narrows even more. Effectively, this means that certain groups of people (e.g., those who lack resources to buy these devices) are not included in the research and therefore the intervention might not be tested on people who are unfamiliar with smart devices or have low digital literacy. Previous studies showed that moderate levels of eHealth literacy, and low levels of health literacy are prevalent among the COPD population [28], [30]. Thus, we cannot assume that this population has access to eHealth technologies and master the skills to manage such interventions without any support. Therefore, guidance should be made available to help those who need support in using these eHealth technologies. Furthermore, even though there might be some practical reasoning behind the inclusion criteria (e.g., no budget to buy devices for all participants), it may simultaneously cause an even larger gap between people included in eHealth technology studies and the group of people who might need the support the most. Thus, even though we recognize the challenge to successfully recruit participants who are representative of the population as a whole, and it is well-known that this is extremely difficult to achieve, we nevertheless recommend future studies to strive towards reaching those people who are underrepresented and difficult to reach.

Limitations

This review also has its limitations. Albeit being a scoping review, we attempted a very systematic approach. However, data extraction and categorization were challenging due to the style of writing in articles (which comes with certain formats and limits such as word count), the lack of explicit reporting on certain aspects, and the overlap between some processes and dimensions. It might be the case that through incomplete reporting in articles, certain self-management processes, BCTs, health dimensions, aspects about the technology, or details about the ‘self’ could not be extracted. However, through systematic analysis we tried to the best of our ability to give a complete picture of current literature about eHealth technologies for COPD self-management.

Another limitation of this study is that although we extracted the different dimensions of positive health and self-management processes, some dimensions and processes are closely related, intertwined with or support each other. For example, the dimension ‘quality of life’ was not observed to be explicitly addressed within the eHealth interventions. However, interventions may have as overarching goal to increase quality of life of people through the use of their intervention.

This scoping review provides an overview of the state-of-the-art eHealth technologies for COPD self-management interventions. We showed that current eHealth technologies tend to address the physical aspect of COPD self-management. These findings reveal a gap in available literature, as many dimensions of the positive health paradigm and self-management processes are not addressed in current eHealth interventions for COPD self-management. However, as COPD is a chronic disease and exerts its impact on all aspects of one’s life, the underrepresented dimensions and processes might be very important to include. This might give people with COPD the tools needed to be able to adapt towards a new balance in life and as this would consider the person as a whole instead of only the bodily representation in the context of a disease. Our review also showcases another gap, namely the effect of inclusion criteria that leads to a subgroup of people with COPD included in eHealth technology studies. For this reason, one should be cautious when interpreting results, as this may give a distorted view of the COPD population within these studies. These gaps demonstrate the need for more inclusive research and design of eHealth self-management interventions for people with COPD focussing on multiple dimensions of the health paradigm.

Ethical Approval

Not applicable.

Funding

The research is supported by the European project RE-SAMPLE. RE-SAMPLE is funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 965315.

Author Contribution

All authors had a significant contribution to the work. E.T.B and R.V were involved in the screening process. E.T.B, C.G and M.T were involved in several data extraction discussions. E.T.B drafted the first version of the manuscript. All authors were involved in revising and finalizing the manuscript and approved the final version.

Availability of data and materials

Not applicable.

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No competing interests reported.

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The state-of-the-art of eHealth self-management interventions for people with Chronic Obstructive Pulmonary Disease: a scoping review

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