PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of wtpaEurope PMCEurope PMC Funders GroupSubmit a Manuscript
 
Patient Educ Couns. Author manuscript; available in PMC 2017 April 11.
Published in final edited form as:
PMCID: PMC5380218
EMSID: EMS70874

Using digital interventions for self-management of chronic physical health conditions: A meta-ethnography review of published studies

Abstract

Objectives

To understand the experiences of patients and healthcare professionals (HCPs) using self-management digital interventions (DIs) for chronic physical health conditions.

Methods

A systematic search was conducted in 6 electronic databases. Qualitative studies describing users’ experiences of self-management DIs were included, and authors’ interpretations were synthesised using meta-ethnography.

Results

30 papers met the inclusion criteria, covering a range of DIs and chronic conditions, including hypertension, asthma and heart disease. The review found that patients monitoring their health felt reassured by the insight this provided, and perceived they had more meaningful consultations with the HCP. These benefits were elicited by simple tele-monitoring systems as well as multifaceted DIs. Patients appeared to feel more reliant on HCPs if they received regular feedback from the HCP. HCPs focused mainly on their improved clinical control, and some also appreciated patients’ increased understanding of their condition.

Conclusions

Patients using self-management DIs tend to feel well cared for and perceive that they adopt a more active role in consultations, whilst HCPs focus on the clinical benefits provided by DIs.

Practice implications

DIs can simultaneously support patient condition management, and HCPs’ control of patient health. Tele-monitoring physiological data can promote complex behaviour change amongst patients.

Keywords: Self-management, Qualitative, Digital, Interventions

1. Introduction

With the increasing burden of chronic disease on health services, recent health policy has emphasised the central role of patient self-management in future healthcare [1]. Digital interventions (DIs) provide a potentially effective means to deliver self-management support to patients via technological media. DIs may use tools such as education or behaviour change support to promote activities which contribute to condition management, for example medication adherence or increasing physical activity. Systematic reviews of the impact of self-management DIs show small benefits to illness outcomes in asthma, diabetes and cardiovascular disease [25] although the evidence for these programmes remains inconsistent [6] and our understanding of what makes them more effective is still developing [7].

A distinction can be made between multifaceted DIs which incorporate several components to support self-management, and standalone tele-monitoring systems in which patients self-monitor health parameters (such as blood pressure) and transmit these data to a healthcare professional (HCP) or automated device to receive feedback on their health status and in some cases, advice on actions to respond to indicators of deteriorating health. Researchers have not always classified standalone tele-monitoring systems as self-management interventions [8], but there is evidence that just monitoring one’s own health data can prompt changes in behaviour [7]. The recognition of tele-monitoring as a form of self-management is consistent with Schermer [9] who proposed that tele-monitoring systems mainly facilitate ‘compliant self-management’, whereby patients adhere to clinical recommendations, but that systems could enhance more ‘concordant self-management’ whereby patients assimilate their own knowledge of their condition with clinical recommendations to adopt an integrated management regime.

Schermer’s distinction between compliance and concordance reflects a wider ambiguity over the goals of self-management in healthcare. It has been argued that DIs favour clinical outcomes over quality of life, offering a “one size fits all” solution at the cost of ignoring individual needs and dynamic management solutions that the patient has developed [10,11]. This conflict in the goals of self-management can present difficulties for HCPs in facilitating the patient to make their own decisions which can contradict clinical recommendations [12].

Recently, many self-management DIs have been developed and a number of studies have used qualitative methods to investigate users’ views, but these papers are distributed across different health conditions and types of DI. The current qualitative synthesis aimed to bring together findings from a diverse range of DIs and conditions to develop a detailed understanding of patient and HCP experiences of using self-management DIs [2]

2. Methods

2.1. Design

This systematic review adopted a meta-ethnography approach [13] to synthesise the findings of qualitative studies, as this inductive method allows an interpretive analysis [14] which fits well with the aim of developing our understanding of how digital self-management is experienced. The ENTREQ checklist (enhancing transparency in reporting the synthesis of qualitative research) was used to ensure systematic reporting of the review [15].

2.2. Criteria for including studies

Table 1 shows the review inclusion and exclusion criteria. We sought to identify qualitative studies investigating adult patients’ or HCPs’ experiences of using a self-management DI, excluding studies in which participants consider their views on a hypothetical DI. It was important that the primary components of the intervention were delivered digitally, as interventions delivered by telephone or video conference provide real-time interaction which is more akin to a face-to-face consultation. We used a broad definition of self-management to include any behaviour fostering increased responsibility for condition management or increasing confidence, as we held no prior assumptions about which types of intervention might affect patients’ self-management. Initial scoping searches indicated that some studies of standalone tele-monitoring DIs reported relevant reactions in terms of patients’ self-management behaviours, and thus we wanted to adopt an inclusive approach to defining self-management to incorporate a range of interventions.

Table 1
Inclusion and exclusion criteria.

2.3. Systematic search strategy

Systematic literature searches were conducted in August 2016. No date limits were applied to searches as we did not want to exclude potentially relevant studies. Thesaurus terms and abstract key word searches were used across four categories: E-health; intervention; qualitative methods; and chronic illness (see Appendix A). Searches were conducted using CINAHL; Embase; PsycINFO; MEDLINE; Web of Science; and The Cochrane Library. Initial key word search terms were identified by author consensus and in consultation with a specialist librarian. The terms were expanded through referral to a quantitative systematic review of asthma self-management DIs [2]; which added several e-health and self-management terms; and a qualitative meta-synthesis of e-health for depression and anxiety [16]; which added e-health and qualitative methods terms. The search terms were developed iteratively to ensure a balance between sensitivity and specificity; informed by the results in each database. The references of retrieved articles were searched; and a manual hand search of Journal of Medical Internet Research issues from the last five years was also conducted because early searches indicated that this was a consistently useful source for relevant articles.

The searches aimed to be exhaustive in terms of identifying all relevant papers relating to asthma and hypertension, as the synthesis was conducted in the context of a research programme investigating the integration of DIs into primary care for these conditions. The search terms ‘chronic disease/chronic illness’ were used in the thesaurus search and Web of Science key word search to identify papers from other chronic physical health conditions to determine whether the findings could be applied more widely (the decision of where to include these search terms was informed by the specificity of the results in each database). This approach is consistent with the literature on conducting searches for a meta-ethnography which advises that it is not necessary to conduct a thoroughly exhaustive search, but rather to select relevant papers which are likely to contribute to the development of new understanding [13,14].

2.4. Identification of studies

The title and abstract screening and full text screening were completed by the primary author (KM). All of the papers deemed eligible based on title/abstract screening were read in full by KM to decide whether they merited inclusion. 10% of these were also read in full by a second reviewer (LD), plus any papers that the primary author was uncertain about. Discrepancies were resolved through discussion (KM, LD, LY).

2.5. Data extraction

The following data were extracted into a database: author, year of publication, country, health condition, aims, DI, participants, target self-management behaviours, HCP involvement, data collection, data analysis, and main findings. The data extraction was performed by KM, and checked by LD.

2.6. Analysis and synthesis

The papers were initially grouped by condition and DI design to facilitate cross-comparison between contexts [14,17]. First order constructs (quotes from study participants) and second order constructs (study authors’ interpretations of their data) were compared within conditions and DI types and across all papers as an iterative process. This helped the authors become highly familiar with the data, and to organise the data coherently for the analysis whilst constantly bearing context in mind. Both the results and discussion sections of papers were included.

To synthesise the translations of the second order constructs, Noblit and Hare’s line of argument approach was used whereby similarities and differences were identified between groups of studies to compare findings across conditions and DIs [13], in order to gain an advanced understanding of the relationships between the key concepts and develop conceptual third order constructs. The primary author (KM) performed the analysis, facilitated by regular discussion within the research team. The research team have extensive experience in qualitative methods and synthesis, and include specialists within health psychology, digital interventions, and sociological implementation, as well as clinical expertise in Primary Care and hypertension.

The GRADE-CERQual approach [18] was used to evaluate confidence in the third order constructs developed in the review (Appendix B). This approach encourages transparency in qualitative syntheses by assessing each third order construct on four criteria: methodological limitations of the primary studies contributing to a finding; relevance of the studies in relation to the review question; coherence of the finding itself; and adequacy of the data contributing to a finding [18].

2.7. Quality appraisal

The eligible papers were appraised by KM against the well-established multi-dimensional National Institute for Health and Clinical Excellence (NICE) quality appraisal checklist for qualitative studies [19]. This covers domains of quality including theoretical approach, design, data collection, trustworthiness, analysis and ethics. This process enabled us to be transparent about any potential limitations in the primary studies which could affect confidence in the review findings [20]. Papers of low quality were not excluded or given less weight than high quality papers, but the findings were interpreted in the context of possible limitations [21].

3. Results

3.1. Searches

The searches identified 120 papers as potentially eligible based on the title and abstract screening. The PRISMA flow-chart (Fig. 1) shows that 30 papers met the inclusion criteria, and the most common reason for exclusion after full-text screening was insufficient qualitative analysis.

Fig. 1
PRISMA 2009 flow diagram.

3.2. Study characteristics

Table 2 shows the characteristics of the 30 studies included in the review. The health conditions addressed were: hypertension (n = 8 papers), diabetes (n = 7), chronic obstructive pulmonary disease (COPD, n = 7), asthma (n = 4), heart disease (n = 3) and chronic back pain (n = 1). The 30 studies described 25 different DIs; most were designed for use in Primary Care and involved interaction or support from the HCP.

Table 2
Characteristics of eligible studies (total n = 30).

Nine of the DIs were standalone tele-monitoring systems, which could be broken down into four categories: monitoring with a pre-defined algorithm for medication change (n = 1); monitoring with automated feedback (n = 1); monitoring with HCP feedback (n = 2); and monitoring with automated and HCP feedback (n = 5). Thirteen were multifaceted DIs with components such as education, behaviour change support, and forums. Two DIs were text-message reminder systems to prompt self-management behaviours, and one provided tailored questions for the patient’s next consultation.

Target self-management behaviours included self-monitoring of health readings (e.g. blood pressure, blood glucose), symptoms, or healthy lifestyle habits, engaging in physical activity or healthy diet changes, and adhering to recommended medication changes. Most studies collected data via semi-structured interviews (n = 26), though focus groups (n = 6) and ethnographic observations (n = 2) were also used.

3.3. Quality appraisal

The quality was high overall with 22 papers rated as high quality, 4 as medium [39,45,47,51], and 4 as low [22,24,40,42] (Appendix C). The most common criteria which papers failed to meet were reflection on the influence of the researcher, inclusion of ethical details, and justification of decisions about triangulating data. Some of these shortcomings did not necessarily indicate lack of rigour in data collection and interpretation, but may have been due to limited space for reporting [17].

3.4. Meta-ethnography analysis

Table 3 shows the key concepts from constant comparison, the first order constructs (primary quotes from the participants in the studies), second order constructs (study authors’ interpretations) and third-order constructs, which represent the new understanding derived from the meta-ethnography analysis. Due to the large number of studies in the review, Table 3 is based on a sub-sample of the studies contributing to each third order construct (purposively selected for richness, relevance and diversity of first and second order constructs), but the contribution of all studies is described in the line of argument. As almost half the studies included in the review used standalone tele-monitoring DIs, reactions to self-monitoring data became an important focus of the synthesis

Table 3
Meta-ethnography synthesis, including key concepts, first-order constructs from study participants’ quotes, second-order constructs from study authors’ interpretations, and third-order constructs from the meta-ethnography.

The CERQual evaluation found moderate confidence in all three third-order constructs, meaning it is likely that these findings are a reasonable representation of patient and HCP experiences of self-management DIs [18].

3.5. Line of argument

3.5.1. Perceived purpose of the DI: who is responsible?

Self-management DIs can facilitate HCPs to care for patients, or patients to care for themselves. The studies in this review showed that both goals can be achieved simultaneously. Patients using self-management DIs generally perceive that they are more aware of their condition [2326,28,3032,34,35,41,43,44,4649,51], better able to make decisions about their own health [23,25,28,32,34,35,39,44,45,47,49] and engage as an equal with the HCP in meaningful discussions [25,29,30,33,35,39,47] indicating that the DI facilitated self-management of their condition. Often in the same studies, HCPs focus on the improved clinical control afforded to them by self-management DIs, being able to track patients’ physiological data over time to detect exacerbations or change medication [26,28,30,32,36,43,4547]. This shows that these different goals of self-management DIs can operate in tandem, as both patients and HCPs perceive different benefits from the same DIs, and this was apparent across the various health conditions.

However, as well as improving self-management skills in patients, the same DIs can also initiate feelings of reliance on HCPs to manage their health. This reaction was particularly evident when HCPs contacted patients when their home readings were out-of-range. This led patients to feel that they were continually being monitored by their HCP [2528,32,43,45,46]. These patients still interpreted their own readings and used their data to inform decisions (indicating adoption of self-management), but at the same time relied on their HCP to detect when there was a problem. This DI design appeared to be more prevalent in conditions such as COPD and CHF, possibly because of the risk of deterioration or severe exacerbations in these conditions, and dependency increased when symptoms became worse. This feeling of ‘being monitored’ was a positive experience for patients, who felt reduced anxiety about their condition and were reassured by this level of care [25,26,28,32,43,45,46], but HCPs felt burdened by unrealistic patient expectations of continual monitoring and were concerned that this might lessen patients’ responsibility to detect exacerbations themselves [26,45,46]. In one study, COPD patients were responsible for contacting the HCP when their readings were high rather than the other way around, and they still benefited from a feeling of being well cared for just through knowing that the HCP had access to their readings and was using them to inform their care [49]. Therefore it seems beneficial for patients’ peace of mind to know that their home readings are being used by a HCP, but from a practical perspective, not necessarily to rely on HCP feedback for detecting problems. In some studies, patients and HCPs reported feeling uncertain about who was responsible for responding to out-of-range readings [31,32,38,45]. Careful use of appropriate feedback and ensuring that patients and HCPs have clear instructions about how to respond if a reading is out-of-range might help to prevent over-reliance on HCPs.

While HCPs tended to focus on their own responsibility to clinically control the patient’s condition rather than the patient’s self-management, in a few studies HCPs reported seeing the benefit for patients of increased self-awareness about their condition when using DIs [25,30,36,38,40,46,47] or wanting to act as the patients’ coach to encourage them to self-manage their condition [22,25,28]. Therefore self-management DIs promoted both patient self-management and HCP clinical control, and patients and HCPs each tended to focus mainly on their own improved control of the condition, although feedback expectations could influence patients’ perceived responsibility. HCPs seemed to weigh up the benefit of improved clinical control against the additional time required to process the patients’ data and make medical decisions [22,2628,30,32,36,38,43,45,46], and in some cases the poor integration of the DI with existing systems was highlighted as an issue for HCPs [27,38,46]. This was more of an issue for physicians/GPs than nurses, and implies that HCPs need an accessible format for reviewing patients’ data to minimise additional workload.

3.5.2. Perceiving meaning in self-monitored data

The other two third-order constructs identified in the meta-ethnography were focused on specific aspects of patient self-management, and therefore fall under the broader concept of patient responsibility described above. Patients’ reactions to self-monitoring their physiological data were complex. Understanding self-monitored physiological or symptom readings in the context of lifestyle behaviours such as medication adherence or physical activity appeared to give patients across conditions a sense of control over their condition and allowed them to assign meaning to their readings [24,25,29,32,39,43,44,47], which made the self-monitoring process more worthwhile to maintain over time. Perceiving an interaction between lifestyle activities and physiological data not only encouraged patients to continue self-monitoring, but also seemed to motivate them to engage in self-management behaviours in order to see an improvement in their readings, for example, to adhere to medication in order to reduce their blood pressure [24,2931,35,39], to better manage their diabetes through physical activity and diet [32,44,46], or to engage in more physical exercise to control their COPD [25,34]. This motivation to change behaviour based on physiological data was found even amongst patients using standalone tele-monitoring systems with no behaviour change support or educational tools [24,3032,35,39], indicating that just having access to the data was sufficient to trigger behaviour change. Hoaas gives a useful insight into patients’ motivation to engage in self-management behaviours over a longer period of time, as this study ran for 2 years [34]. They found that some patients lost motivation to continue engaging in physical activity when they could no longer see an improvement or after a spell of inactivity, but if patients adjusted their goals, e.g. to focus on duration rather than intensity of exercise, this helped to keep them motivated. Diabetic patients felt that feedback showing an improvement towards goals is a key source of motivation to self-manage their condition [44]. Therefore, self-monitoring data is motivating to patients, especially when they can detect an improvement, but careful goal-setting strategies may be needed in cases where improvement is not obvious.

Where diabetic patients had failed to adhere to a behaviour change to control their readings or felt that high readings were out of their control, they found self-monitoring to be a frustrating process [42,50]. Those who had stable readings which did not vary over time were less likely to feel a benefit from monitoring and this was the case across health conditions [25,29,46], as readings did not then convey any meaning about their condition.

3.5.3. Patients carefully consider recommended medication changes

Self-monitoring could also contribute to patients’ engagement with medication change if patients felt confident enough to change their medication based on their readings. Confidence appeared to be high in COPD patients [25,28,45] and some hypertensive patients [30,32], but lower for CHF patients who were concerned about taking diuretic medication in response to high readings, even when based on prior advice from their HCP [26,43]. It should be noted that age might also be a factor influencing confidence to adjust medication, as CHF patients are on average older than those with COPD.

As well as feeling confident, patients also needed to perceive that a recommended medication change was necessary. For example, hypertensive patients who felt that their readings were borderline were less likely to follow advice to change their medication because they didn’t feel their blood pressure was high enough to warrant a change [35]. Asthma patients could be reluctant to follow automated advice to change medication if this conflicted with their own beliefs about not needing steroid medication regularly [22]. However, when using a self-management diary to track symptoms and identify exacerbations, some asthma patients were happy to adjust their medication to control their symptoms [47]. Asthma patients in this intervention created personally defined health states and individual treatment plans, and it may be that this personal tailoring helped them to believe in the necessity of medication adjustment when they could see their symptoms were poorly controlled.

Fig. 2 shows a visual representation of the third order constructs.

Fig. 2
Visual representation of the third order constructs.

4. Discussion and conclusion

4.1. Discussion

This review provides an in-depth analysis of patients’ and HCPs’ experiences of using self-management DIs across common chronic health conditions. Patients and HCPs were found to perceive different benefits of using self-management DIs, showing that the same DI could facilitate both patient self-management and HCP clinical control. Some DIs were designed with an explicit focus on improving clinical control, but even without the tools to encourage self-management, patients tended to feel more involved in their condition management and better informed to make decisions. Appropriate feedback is important for managing patients’ expectations about the level of monitoring from their HCP, and for ensuring that both patients and HCPs know who is responsible for responding to out-of-range readings.

4.1.1. Interpretations in the context of current literature

This review extends our understanding of the self-care-dependency continuum referred to in a recent meta-synthesis on tele-health for COPD patients [52]. The present findings suggest that self-care and dependency are not necessarily incompatible, as both self-management and dependent patient behaviours can be promoted by DIs, although the style of feedback has an important influence on how much responsibility the patient adopts for self-management. Patients in all studies tend to describe increased awareness and improved decision-making skills when using a self-management DI, indicating more engagement in self-management. Receiving HCP feedback on physiological data encouraged patients to feel that they were being monitored and that responsibility remained with the HCP, implying increased dependency. Whereas dependency has been viewed as a negative outcome of self-management DIs [52], it was not a problem from the patient perspective as they felt very well looked after and reassured by the idea that HCPs were monitoring their health status, but it is more problematic for HCPs who are concerned about meeting patients’ expectations of continual monitoring. Therefore decisions about how and when patients using self-management DIs will receive feedback are important for optimising their experience of self-management and minimising over-reliance on HCPs.

In terms of evaluating perceived benefits of the DI, patients focused on the positive effects on their understanding and acceptance of their condition, whilst HCPs focused on the clinical benefits DIs offered them for managing the patients’ condition. As reported in the synthesis of COPD patients’ experiences of tele-health, HCPs were less positive about the use of self-management DIs than patients [52] and had concerns about the increased workload. This finding is also consistent with a recent synthesis which reported that clinicians can find it challenging to share control of condition management with the patient [53]. Explicit guidance for HCPs about how best to deliver support for patients using self-management DIs might help address these concerns.

Patients’ motivation to change their behaviour when they have access to their own data is in line with research on visualisation which shows that making health data visible can add meaning to activities which interact with these data [54]. Mamykina’s model of sense-making [55] describes how patients construct explanations of their health data based on their daily activities, which enables them to make lifestyle decisions in order to improve their health data. The feedback loop between actions and health status is more easily detected in some conditions than others, for example the benefits of adhering to asthma prevention medication are not immediate but accumulate over time [22]. This highlights the importance of designing digital tools with meaningful feedback systems to help patients review their data and develop a comprehensive understanding of these interactions [55]. The review found that where physiological data remained stable over time, patients were less motivated to engage with self-monitoring, and therefore where self-management behaviours are only likely to have a small impact on physiological data, other forms of encouragement may be needed to encourage patients to stay motivated.

The finding that standalone tele-monitoring DIs without behaviour change support promoted patient self-management supported the concept that tele-monitoring is a complex behaviour change process in itself [30]. This is consistent with a review of patient experiences of self-monitoring hypertension (with or without other intervention elements to support self-management) which found positive effects of self-monitoring behaviour on reassurance, patient empowerment and the HCP relationship [56].

The concerns patients expressed about medication changes in this review can be explained by the extended self-regulatory model [57], which incorporates beliefs about necessity of treatment and concerns about adverse treatment effects into the original self-regulatory model of illness perceptions [58]. Hypertensive patients’ non-adherence to recommended medication changes when their readings only slightly exceeded a threshold, and asthma patients’ decision not to increase regularity of steroid dose demonstrate the importance of beliefs in the necessity of treatment for adherence. Concerns about adverse effects of treatment were evident in the finding that CHF patients lacked confidence to change their medication and wanted responsibility to remain with their HCP. This suggests that in order to improve adherence to medication change advice DIs need to convince patients about the necessity of medication changes, and address their concerns about adverse treatment effects. Appropriate, reliable feedback could be essential for this, as differences in tailoring of automated feedback seemed able to influence patients’ acceptance of advice about medication changes [22,47].

Many of the findings which emerged from our inductive analysis mapped well on to the constructs from Normalisation Process Theory (NPT) [59], which provides a useful framework and standardised terminology for describing how interventions are adopted by HCPs and patients in routine practice [60]. Patients demonstrated cognitive participation by engaging in sense-making of their data, and their experience of a closer and more meaningful relationship with the HCP showed positive reflexive monitoring of intervention benefits. The uncertainty of some HCPs in how to respond to patients’ readings and the feeling that reviewing patient data was burdensome suggested low coherence for HCPs regarding the DI’s goals, as well as a lack of confidence in the resources available to them (collective action). Implementation into daily practice could be promoted through highlighting the dual benefits of self-management DIs to HCPs to increase coherence and reflexive monitoring.

4.1.2. Limitations of the current review

This review potentially represents a particularly positive patient perception of self-management DIs as it is based only on patients who volunteered to participate in trials and follow-up qualitative research, which is usually only a small sub-sample of those invited. This potential bias did not appear to be evident in the HCPs’ perspectives. The authors are also aware that their own preconceptions could have influenced the analysis of the data. We attempted to limit this by adopting an inductive approach, grounding our themes in the data, and we prioritised transparency by keeping a record of all emerging themes and discussing the analysis regularly to obtain shared viewpoints.

The CERQual evaluation of the review findings indicated moderate confidence in the three third-order constructs generated by the review, meaning that it is likely that these are a reasonable representation of patient and HCP experiences of self-management DIs.

4.2. Conclusion

The evidence from this review of qualitative research suggests that patients using self-management DIs perceived closer contact with HCPs, and felt better cared for. This is in line with previous findings that self-management does not replace professional care but rather enables patients to attain the best healthcare [1]. Monitoring their own health data gave patients a greater self-awareness of their condition and they were motivated to engage in lifestyle behaviours to help improve their data, even when using standalone tele-monitoring DIs without explicit behaviour change support. HCPs perceived clinical benefits to self-management DIs, but raised some concerns about the burden of monitoring patient data.

4.3. Practice implications

The finding that standalone tele-monitoring systems promoted feelings of motivation for condition management suggests that tele-monitoring could be more widely used to promote patient self-management and should not be regarded only as a clinical tool for tailoring treatment. Where physiological data are likely to remain stable over time, patients may need additional forms of encouragement to stay motivated to engage in self-management. Providing explicit guidance to patients and HCPs about responding to home readings might help to manage patient expectations and address HCPs’ concerns about the time involved in monitoring patients.

Supplementary Material

Appendix Tables

Acknowledgements

This independent research was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-1211-20001). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.

Footnotes

Conflicts of interest

Elizabeth Murray is the Managing Director of HeLP Digital, a not-for-profit Community Interest Company which disseminates digital health interventions to the NHS. She has not, and will not, receive any remuneration for this work.

Contributed by

Authors’ contributions

KM wrote the first and subsequent drafts of this manuscript, with initial feedback from LY and LD, followed by comments from CM and EM and then the remaining authors. KM completed the searches, title and abstract screening, and full text screening. LD screened a sub-sample of papers, and provided a second check on the CERQual evaluation of the review findings. LY and LD provided guidance on the qualitative synthesis analysis, and contributed to the development of third-order constructs. EM provided expert advice on the reporting of methods, CM advised on some of the theoretical aspects of the synthesis, whilst PL and RMcM provided clinical expertise and HCP experience. All authors read and approved the final manuscript.

References

[1] Taylor SJC, Pinnock H, Epiphaniou E, Pearce G, Parke HL, Schwappach A, et al. A rapid synthesis of the evidence on interventions supporting self-management for people with long-term conditions: PRISMS—Practical systematic Review of Self-Management Support for long-term conditions. 2014 [PubMed]
[2] Morrison D, Wyke S, Agur K, Cameron EJ, Docking RI, MacKenzie AM, et al. Digital asthma self-management interventions: a systematic review. J Med Internet Res. 2014;16:59–78. [PMC free article] [PubMed]
[3] Pal K, Eastwood SV, Michie S, Farmer A, Barnard ML, Peacock R, et al. Computer-based interventions to improve self-management in adults with type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2014;37:1759–1766. (8p) [PubMed]
[4] Pfaeffli Dale L, Dobson R, Whittaker R, Maddison R. The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: a systematic review. Eur J Prev Cardiol. 2015 [PubMed]
[5] Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive Health Communication Applications for people with chronic disease. Cochrane database of systematic reviews. 2005:CD004274. (Online) [PubMed]
[6] Black AD, Car J, Pagliari C, Anandan C, Cresswell K, Bokun T, et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8:188. [PMC free article] [PubMed]
[7] Salisbury C, Thomas C, O'Cathain A, Rogers A, Pope C, Yardley L, et al. TElehealth in CHronic disease: mixed-methods study to develop the TECH conceptual model for intervention design and evaluation. Br Med J Open. 2015;5:e006448. [PMC free article] [PubMed]
[8] McLean G, Murray E, Band R, Saunderson K, Hanlon P, Little P, et al. Digital interventions to promote self-management in adults with hypertension: protocol for systematic review and meta-analysis. JMIR Res Protoc. 2015;4:e133. [PMC free article] [PubMed]
[9] Schermer M. Telecare and self-management: opportunity to change the paradigm. J Med Ethics. 2009;35:688–691. [PubMed]
[10] Lawn S, McMillan J, Pulvirenti M. Chronic condition self-management: expectations of responsibility. Patient Educ Couns. 2011;84:e5–e8. [PubMed]
[11] Kendall E, Ehrlich C, Sunderland N, Muenchberger H, Rushton C. Self-managing versus self-management: reinvigorating the socio-political dimensions of self-management. Chronic Illn. 2011;7:87–98. [PubMed]
[12] Smith R. The discomfort of patient power: medical authorities will have to learn to live with irrational decisions by the public. Br Med J. 2002;324:497–498. [PMC free article] [PubMed]
[13] Noblit GW, Hare RD. Meta-ethnography: Synthesizing Qualitative Studies. Vol. 11. Sage; 1988.
[14] Campbell R, Pound P, Morgan M, Daker-White G, Britten N, Pill R, et al. Evaluating meta ethnography: systematic analysis and synthesis of qualitative research. Health Technol Assess. 2011;15(43):1–164. [PubMed]
[15] Tong A, Flemming K, McInnes E, Oliver S, Craig J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. 2012;12:1. [PMC free article] [PubMed]
[16] Knowles SE, Toms G, Sanders C, Bee P, Lovell K, Rennick-Egglestone S, et al. Qualitative meta-synthesis of user experience of computerised therapy for depression and anxiety. PLoS One. 2014;9:e84323. [PMC free article] [PubMed]
[17] Atkins S, Lewin S, Smith H, Engel M, Fretheim A, Volmink J. Conducting a meta-ethnography of qualitative literature: lessons learnt. BMC Med Res Methodol. 2008;8:21. [PMC free article] [PubMed]
[18] Lewin S, Glenton C, Munthe-Kaas H, Carlsen B, Colvin CJ, Gülmezoglu M, et al. Using qualitative evidence in decision making for health and social interventions: an approach to assess confidence in findings from qualitative evidence syntheses (GRADE-CERQual) PLoS Med. 2015;12:e1001895. [PMC free article] [PubMed]
[19] N.I.f.H.a.C. Excellence. Quality appraisal checklist–qualitative studies. [cited 2015 20/11/15];2012 Available from: https://www.nice.org.uk/article/pmg4/chapter/Appendix-H-Quality-appraisal-checklist-qualitative-studies#notes-on-the-use-of-the-qualitative-studies-checklist.
[20] Tong A, Palmer S, Craig JC, Strippoli GF. A guide to reading and using systematic reviews of qualitative research. Nephrol Dial Transplant. 2014:gfu354. [PubMed]
[21] Hannes K. Critical appraisal of qualitative research. In: Noyes BAJ, Hannes K, Harden A, Harris J, Lewin S, Lockwood C, editors. Supplementary Guidance for Inclusion of Qualitative Research in Cochrane Systematic Reviews of Interventions. Cochrane Collaboration Qualitative Methods Group; 2011.
[22] Anhøj J, Nielsen L. Quantitative and qualitative usage data of an Internet-based asthma monitoring tool. J Med Internet Res. 2004;6:e23–e. [PMC free article] [PubMed]
[23] Burner ER, Menchine MD, Kubicek K, Robles M, Arora S. Perceptions of successful cues to action and opportunities to augment behavioral triggers in diabetes self-management: qualitative analysis of a mobile intervention for low-income Latinos with diabetes. J Med Internet Res. 2014;16:e25–e. [PMC free article] [PubMed]
[24] Cottrell E, McMillan K, Chambers R. A cross-sectional survey and service evaluation of simple telehealth in primary care: what do patients think? BMJ Open. 2012;2 [PMC free article] [PubMed]
[25] Dinesen B, Huniche L, Toft E. Attitudes of COPD patients towards tele-rehabilitation: a cross-sector case study. Int J Environ Res Public Health. 2013;10:6184–6198. [PMC free article] [PubMed]
[26] Fairbrother P, Ure J, Hanley J, McCloughan L, Denvir M, Sheikh A, et al. Telemonitoring for chronic heart failure: the views of patients and healthcare professionals—a qualitative study. J Clin Nurs. 2014;23:132–144. [PubMed]
[27] Fairbrother P, Pinnock H, Hanley J, McCloughan L, Sheikh A, Pagliari C, et al. Continuity, but at what cost? The impact of telemonitoring COPD on continuities of care: a qualitative study. Prim Care Respir J: J Gen Pract Airways Group. 2012;21:322–328. [PubMed]
[28] Fairbrother P, Pinnock H, Hanley J, McCloughan L, Sheikh A, Pagliari C, et al. Exploring telemonitoring and self-management by patients with chronic obstructive pulmonary disease: a qualitative study embedded in a randomized controlled trial. Patient Educ Couns. 2013;93:403–410. [PubMed]
[29] Hallberg I, Ranerup A, Kjellgren K. Supporting the self-management of hypertension: patients’ experiences of using a mobile phone-based system. J Hum Hypertension. 2015 [PMC free article] [PubMed]
[30] Hanley J, Ure J, Pagliari C, Sheikh A, McKinstry B. Experiences of patients and professionals participating in the HITS home blood pressure telemonitoring trial: a qualitative study. Br Med J Open. 2013;3 [PMC free article] [PubMed]
[31] Hanley J, Fairbrother P, Krishan A, McCloughan L, Padfield P, Paterson M, et al. Mixed methods feasibility study for a trial of blood pressure telemonitoring for people who have had stroke/transient ischaemic attack (TIA) Trials. 2015;16:1. [PMC free article] [PubMed]
[32] Hanley J, Fairbrother P, McCloughan L, Pagliari C, Paterson M, Pinnock H, et al. Qualitative study of telemonitoring of blood glucose and blood pressure in type 2 diabetes. BMJ Open. 2015;5:e008896. [PMC free article] [PubMed]
[33] Hartmann CW, Sciamanna CN, Blanch DC, Mui S, Lawless H, Manocchia M, et al. A website to improve asthma care by suggesting patient questions for physicians: qualitative analysis of user experiences. J Med Internet Res. 2007;9:e3. [PMC free article] [PubMed]
[34] Hoaas H, Andreassen HK, Lien LA, Hjalmarsen A, Zanaboni P. Adherence and factors affecting satisfaction in long-term telerehabilitation for patients with chronic obstructive pulmonary disease: a mixed methods study. BMC Med Inform Decis Mak. 2016;16:1. [PMC free article] [PubMed]
[35] Jones MI, Greenfield SM, Bray EP, Baral-Grant S, Hobbs FDR, Holder R, et al. Patients’ experiences of self-monitoring blood pressure and self-titration of medication: the TASMINH2 trial qualitative study. Br Jo Gen Pract: J R Coll Gen Pract. 2012;62:e135–e142. [PMC free article] [PubMed]
[36] Jones MI, Greenfield SM, Bray EP, Hobbs FR, Holder R, Little P, et al. Patient self-monitoring of blood pressure and self-titration of medication in primary care: the TASMINH2 trial qualitative study of health professionals’ experiences. Br J Gen Pract. 2013;63:e378–e385. [PMC free article] [PubMed]
[37] Kerr C, Murray E, Noble L, Morris R, Bottomley C, Stevenson F, et al. The potential of web-based interventions for heart disease self-management: a mixed methods investigation. J Med Internet Res. 2010;12:e56–e. [PMC free article] [PubMed]
[38] Koopman RJ, Wakefield BJ, Johanning JL, Keplinger LE, Kruse RL, Bomar M, et al. Implementing home blood glucose and blood pressure telemonitoring in primary care practices for patients with diabetes: lessons learned. Telemed e-Health. 2014;20:253–260. [PMC free article] [PubMed]
[39] Lambert-Kerzner A, Havranek EP, Plomondon ME, Albright K, Moore A, Gryniewicz K, et al. Patients’ perspectives of a multifaceted intervention with a focus on technology: a qualitative analysis: circulation. Cardiovasc Qual Outcomes. 2010;3:668–674. [PubMed]
[40] Langstrup H. Making connections through online asthma monitoring. Chronic Illn. 2008;4:118–126. [PubMed]
[41] Leon N, Surender R, Bobrow K, Muller J, Farmer A. Improving treatment adherence for blood pressure lowering via mobile phone SMS-messages in South Africa: a qualitative evaluation of the SMS-text adherence suppoRt (StAR) trial. BMC Fam Pract. 2015;16:80. [PMC free article] [PubMed]
[42] Roblin DW. The potential of cellular technology to mediate social networks for support of chronic disease self-management. J Health Commun. 2011;16:59–76. [PubMed]
[43] Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Perceptions and experiences of heart failure patients and clinicians on the use of mobile phone-based telemonitoring. J Med Internet Res. 2012;14:e25–e. [PMC free article] [PubMed]
[44] Tatara N, Arsand E, Skrovseth SO, Hartvigsen G. Long-term engagement with a mobile self-management system for people with type 2 diabetes. JMIR Mhealth Uhealth. 2013;1:e1. [PMC free article] [PubMed]
[45] Ure J, Pinnock H, Hanley J, Kidd G, Smith EM, Tarling A, et al. Piloting tele-monitoring in COPD: a mixed methods exploration of issues in design and implementation. Prim Care Respir J. 2012;21:57–64. [PubMed]
[46] Urowitz S, Wiljer D, Dupak K, Kuehner Z, Leonard K, Lovrics E, et al. Improving diabetes management with a patient portal: a qualitative study of diabetes self-management portal. J Med Internet Res. 2012;14:e158–e. [PMC free article] [PubMed]
[47] van Kruijssen V, van Staa A, Dwarswaard J, tVeen JCCM, Mennema B, Adams SA. Use of online self-management diaries in asthma and COPD: a qualitative study of subjects’ and professionals’ perceptions and behaviors. Respir Care. 2015;60:1146–1156. [PubMed]
[48] Voncken-Brewster V, Tange H, Moser A, Nagykaldi Z, de Vries H, van der Weijden T. Integrating a tailored e-health self-management application for chronic obstructive pulmonary disease patients into primary care: a pilot study. BMC Fam Pract. 2014;15:4. [PMC free article] [PubMed]
[49] Williams V, Price J, Hardinge M, Tarassenko L, Farmer A. Using a mobile health application to support self-management in COPD: a qualitative study. Br J Gen Pract. 2014;64:e392–400. [PMC free article] [PubMed]
[50] Yu CH, Parsons JA, Mamdani M, Lebovic G, Hall S, Newton D, et al. A web-based intervention to support self-management of patients with type 2 diabetes mellitus: effect on self-efficacy, self-care and diabetes distress. BMC Med Inform Decis Mak. 2014;14:117. [PMC free article] [PubMed]
[51] Caiata Zufferey M, Schulz PJ. Self-management of chronic low back pain: an exploration of the impact of a patient-centered website. Patient Educ Couns. 2009;77:27–32. [PubMed]
[52] Brunton L, Bower P, Sanders C. The contradictions of telehealth user experience in chronic obstructive pulmonary disease (COPD): a qualitative meta-synthesis. PLoS One. 2015;10:e0139561. [PMC free article] [PubMed]
[53] Mudge S, Kayes N, McPherson K. Who is in control? Clinicians’ view on their role in self-management approaches: a qualitative metasynthesis. Br Med J Open. 2015;5:e007413. [PMC free article] [PubMed]
[54] Ruckenstein M. Visualized and interacted life: personal analytics and engagements with data doubles. Societies. 2014;4:68–84.
[55] Mamykina L, Smaldone AM, Bakken SR. Adopting the sensemaking perspective for chronic disease self-management. J Biomed Inform. 2015;56:406–417. [PMC free article] [PubMed]
[56] Fletcher BR, Hinton L, Hartmann-Boyce J, Roberts NW, Bobrovitz N, McManus RJ. Self-monitoring blood pressure in hypertension, patient and provider perspectives: a systematic review and thematic synthesis. Patient Edu Couns. 2016 [PubMed]
[57] Horne R, Weinman J. Self-regulation and self-management in asthma: exploring the role of illness perceptions and treatment beliefs in explaining non-adherence to preventer medication. Psychol Health. 2002;17:17–32.
[58] Leventhal H, Diefenbach M, Leventhal EA. Illness cognition: using common sense to understand treatment adherence and affect cognition interactions. Cognitive Ther Res. 1992;16:143–163.
[59] May C, Finch T, Mair F, Ballini L, Dowrick C, Eccles M, et al. Understanding the implementation of complex interventions in health care: the normalization process model. BMC Health Services Res. 2007;7:148. [PMC free article] [PubMed]
[60] Murray E, Treweek S, Pope C, MacFarlane A, Ballini L, Dowrick C, et al. Normalisation process theory: a framework for developing, evaluating and implementing complex interventions. BMC Med. 2010;8:63. [PMC free article] [PubMed]