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Guided online self-management interventions in primary care: a survey on use, facilitators, and barriers
© van der Vaart et al. 2016
Received: 26 October 2015
Accepted: 29 February 2016
Published: 9 March 2016
Guided online psychological self-management interventions offer broad prospects for the treatment of people with mild to moderate mental health problems, but implementation is challenging. The aims of this study are (1) to gain insight into use of and intention to use these interventions among primary care health professionals, (2) to determine the main barriers to use such interventions among non-users.
An online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was disseminated among mental health counsellors (MHCs; in Dutch POHs) in GP practices and primary care psychologists (PCP) in mental health care practices. The survey covered the current use of online interventions, the intention to use these in the future, and an operationalization of the UTAUT concepts: performance expectancy, effort expectancy, social influence, and facilitating conditions.
In total, 481 MHCs and 290 PCPs responded (24 %). Of them, 49 % of MHCs and 21 % of PCPs currently use online interventions in their treatments. A further 40 % of MHCs and 27 % of PCPs plan to introduce such interventions within the next year. Both groups were moderately positive about the presence of eHealth facilitators in their daily practice. Among current non-users, performance expectancy and facilitating conditions were significant predictors of usage intention in both groups of health professionals.
Use of and intention to use online interventions is relatively high in Dutch primary care. Non-users, particularly, experience several barriers which need attention to enhance implementation. There is a need for further efforts regarding facilitation of and education on eHealth, as well as for research directed to its normalization in daily practice.
KeywordsPrimary care Mental care Online interventions Self-management Implementation
The use of information and communication technology (ICT) in health care (eHealth) is evolving rapidly. ICT is used both to support regular care and care systems and as a means to actually provide care online. eHealth offers prospects for regulating the demand on health care, which is growing due to the aging population, the increase in chronic diseases, and higher treatment rates due to better diagnostics and more widely available effective interventions. Where mental health care is concerned, integrating health services into primary care is increasingly considered the most viable way to respond to this growing demand and to ensure broad access to mental health care . In the Netherlands, primary care physicians are therefore urged to adopt an increasing role in treating people with mild to moderate mental health problems and to refrain from referring patients to specialized mental health services as much as possible. This system is based on a stepped care principle, which aims to provide the best fit between the severity of a patient’s problems and the intensity of the treatment he or she receives .
Guided online psychological self-management interventions (often based on cognitive behavioural therapy principles ) offer good prospects for the treatment of people with mild to moderate mental health problems in an accessible manner. These web-based interventions tend to combine the technological possibilities of online programs with the tailored approach of face-to-face interventions, resulting in a larger scalability with lower costs per additional user . Numerous studies and systematic reviews have shown these online interventions to be effective in both primary and secondary care, for a broad range of problems such as mood disorders, anxiety, and adaptation to chronic somatic conditions [5–9]. Generally patients follow a set of modules (often consisting of, for instance, psycho-education, assignments, registrations, and relaxation training), while they receive feedback and coaching from a therapist through online messages. In this way, people with a non-severe or non-complex diagnosis can, in a relatively short time and with limited assistance, learn essential skills to self-manage their problems.
In spite of the great potential of online interventions and their value for alleviating the pressure on mental health care, the adoption of new forms of care is complex, especially when it comes to eHealth [10–12]. In implementing health technologies, a variety of economic, political, and organizational factors are key. In the Netherlands, policy changes on these matters are becoming established, and the use of eHealth in primary care has been being stimulated, largely top down, for some years now . This means that eHealth technology is becoming increasingly available, and health care professionals are encouraged to use such interventions. However, this brings about a variety of implementation challenges on the level of the individual professional and the social context in which they work; health professionals need to learn new skills and alter the way they work to incorporate online care into their daily routines. These factors are represented in the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. . This model provides an overview of aspects that play a large role among individuals when implementing health technology. This model shows that the efforts that can be expected to be invested and the benefits that could be gained from technology are essential predictors for the behavioural intention to use health technology. Furthermore, the social influence and facilitating conditions experienced on the work floor can also have significant influence on the success of a new technology.
In order to gain insight into the implementation of guided online psychological self-management interventions in primary care, this paper studies the extent to which they are actually used in routine clinical practice by mental health counsellors (MHC) working in GP practices, and by primary care psychologists (PCP). These two groups of health professionals both deliver primary care, but in a different context and with a different treatment intensity . The MHC is an official collaborative position within a GP practice. These health professionals are mostly trained as psychologists or psychiatric nurse practitioners, and are governed by the GP or seconded from larger mental health care institutions. They offer short-term counselling to patients with mild psychological problems (with or without DSM diagnosis). A PCP on the other hand treats patients with mild to moderate problems (a DSM diagnosis is required); these patients are referred to the PCP by the GP. If these forms of primary care prove insufficient, the patient can be referred to specialized mental health care in secondary care. Both groups of primary care professionals could benefit from using online interventions, but they might use such interventions differently due to the nature of their care and the type and severity of problems they encounter.
The present study used an online survey among these two large groups of mental health professionals to gain insight into their current use of technology and online interventions and the behavioural intention to use them. The study used the UTAUT model to investigate the main facilitators and barriers among non-users of online interventions, in order to map essential variables that could enhance successful implementation.
Participants and procedure
Both groups of participants were recruited by (personal) e-mail. The MHCs were selected using a national health care portal (https://www.zorgkaartnederland.nl/) which provides an overview of all general practitioners in the Netherlands. Via this portal, websites of GP practices were checked to find the names and e-mail address of MHCs. If it was unclear which individual held the position of MHC, or if no e-mail address was found, the practice was contacted by phone to ask for the required information. This resulted in 1655 e-mail addresses. In addition, several national mental health care institutions which were known to second MHCs to GP practices were approached and asked to forward the survey invitation to all their practising MHCs. To recruit the PCPs, the website of the Dutch national association of independent psychologists (LVVP) was used, which presented the contact information of all associated primary care psychologists. This resulted in 1332 e-mail addresses. Additionally, Google was used to find further PCPs: using the search terms “primary care psychologist” and “psychologist basic health care” (“eerstelijnspsycholoog” and “psycholoog gezondheidszorg basis” in Dutch). This led to an additional 371 addresses. In the first mailing, some e-mails failed to deliver, for reasons such as addresses that no longer existed, respondents that were no longer employed at that practice, or out-of-office replies that reported the addressee would not return before the closure-date of the survey. Two weeks after the first mailing a reminder e-mail was sent to all known addresses. In total, 1601 MHC and 1641 PCP addresses were contacted. In a few cases these may not have been unique addresses, since it is possible that the same health professional was e-mailed at multiple addresses (if employed at several institutions). Some may also have received the survey via multiple routes (since it was also distributed to larger groups of professionals, for instance when sent to a practice e-mail address).
The e-mail contained a personal invitation addressed to the health professional by name (when known); the e-mail included an information letter and a link to the online survey in Qualtrics (2015 Qualtrics, LLC). The information letter explained the purpose of the study and its voluntary nature, the use and anonymization of the data, the estimated time needed to participate (10 min), and the reward for participating in the study (four gift certificates of €50 were raffled among the participants). The online survey itself also contained this information letter and an informed consent form. Not responding to the survey or opting out in the informed consent form were considered as choosing not to participate in the study. MHCs who work at several GP practices were asked to fill in the questionnaire with reference to the GP practice in which they were working at that time. The study was approved by the Psychology Research Ethics Committee (PREC) of Leiden University.
The study consisted of a questionnaire in two versions: one for the MHCs and one for the PCPs. The questionnaires were virtually identical, with the exception of detailed questions on the respondent’s professional background and workplace.
The questionnaire (see ‘Additional file 1’) consisted of three parts: (I) background information about the health care professional and the practice; (II) general and practice-related use of the internet, and (III) experience with using and/or intention to use online psychological self-management interventions. The background information of the health professional included gender, age, professional background, and hours employed. The background information about the health care practice included number of staff, average number of consultations per patient and time invested per consultation, and characteristics of the care mostly provided. In part two, internet use and experience with online care was measured; this included the presence of different types of eHealth technologies already in use at the practice.
Subscales, number of items, and example items used in the UTAUT survey
No. of items
I expect/perceive that online psychological self-management interventions will be effective for our patients
I expect/perceive that guided online psychological self-management interventions require a lot of new skills
I expect/perceive that online psychological self-management interventions will be used a lot by my colleagues
I expect/perceive that online psychological self-management interventions match with the current state of technology in my practice
I intend to use/continue using online self management programs in my work.
Analyses were performed using the Statistical Package for the Social Sciences (IBM SPSS Statistics 21). Scale scores were calculated and Cronbach’s alphas of the internal reliability were analysed. Descriptive statistics were performed to describe the study sample, their internet experience, and their scores on the UTAUT items. Chi-square tests and t-tests were applied as appropriate to analyse whether reported differences between the two groups of health professionals and between users and non-users within these groups were significant.
To gain insight into the constructs that predict behavioural intention among non-users, multiple regression analyses were performed. Since the distribution of scores on the scale ‘behavioural intention’ deviated from normality, non-parametric Spearman’s correlations were calculated to determine the relationship between the UTAUT variables performance expectancy, effort expectancy, social influence, and facilitation conditions, and the dependent variable behavioural intention. Based on the statistical significance of these relations, multiple regression analyses were carried out to examine the further nature of these relations for both groups.
Background information about the professional and the practice (N = 771)
n = 481 (%)
n = 290 (%)
Age (M, SD)
Psychiatric nurse practitioner
Health care psychologist (GZ-psycholoog)
Other (pedagogue, nurse, counsellor)
Hours employed on weekly basis (M, SD)
Number of peer colleagues in the practice (M, SD)
Health care practice
Number new patients each month
Number of consultations during one complete treatment
Number of minutes spent per therapy session (M, SD)
With regard to the characteristics of the care provided, MHCs reported mostly treating patients with stress/burn-out (91 % of health professionals reported seeing patients with these problems), mood disorders (88 %), and anxiety (62 %). PCPs also saw a large number of patients who suffered from these types of problems: anxiety (98 %), mood disorders (97 %) and stress/burn-out (88 %). However, they also reported seeing patients with a variety of other problems, such as fatigue (74 %), problems in a social context (73 %), coping with somatic symptoms and chronic diseases (66 %), sleeping problems (66 %), and pain (55 %). The type of care the MHCs provided for their patients was mostly clarification of the problem and diagnostics (81 %), interventions/treatment (70 %), psycho-education (68 %), and coaching patients in self-management (61 %). The main focus in care provision among PCPs was on interventions/treatment (98 %), followed by clarification of the problem and diagnostics (71 %), and psycho-education (54 %).
General and practice-related usage of the internet (N = 771)
n = 481 (%)
n = 290 (%)
General internet usage
(Virtually) every day
Several times a week
1 day a week to (virtually) never
Subjective internet skills
Work-related internet usage
Searching for referral information
Searching for medical information
E-mail communication with patients
Searching for insurances and reimbursements
Available technologies in health care practice
Electronic medical records
Website with patient information
Online self-management modules
Webportal with patient records
Online appointment tool
Experience with online psychological self-management interventions and intention to use it
Experience with online psychological self-management interventions and intention to use them (N = 771)
Experience and intention
n = 481 (%)
n = 290 (%)
Has seen such interventions
Has been trained to use such interventions
Has used such interventions in treatments
Currently uses online interventions in treatment
Expected time frame of usage among non-users
Within the next 6 months
Within the next year
Within 2 to 5 year
Not within the next 5 year
Intention to use (α = .95 for MHC and .96 for PCP)
In the complete sample (M, SD)b
Among current users (M, SD) (n = 296)c
Among current non-users (M, SD) (n = 472)c
Facilitators and barriers
Scores on the UTAUT constructs (N = 771)
n = 481 (M, SD)
n = 290b (M, SD)
When studying the relationships between the UTAUT concepts and non-users’ intention to use online psychological self-management interventions in the future, all UTAUT concepts showed significant correlations (mostly moderate to high) among both the MHCs and PCPs surveyed (Performance expectancy rho = .66 and .64; Effort expectancy rho = .56 and .40; Social influence rho = .42 and .46; and Facilitating conditions rho = .62 and .68 respectively for MHCs and PCPs, all with P < .001).
Results of multiple regression analyses of UTAUT variables on behavioral intention
Guided online self-management interventions have a high potential to enhance psychological care in primary care; however, the implementation of these interventions is often complex. The primary aim of this study was to investigate to what extent these interventions are used among mental health professionals in primary care. The results showed that health technologies in general are quite integrated into clinical practice, as many health professionals use such technologies to search for information, communicate with their patients, and keep medical records. Among the health care professionals surveyed, online self-management interventions are currently used by almost half of the mental health counsellors in GP practices and by a fifth of primary care psychologists in independent mental health care practices. These numbers are high in comparison with previous studies, which have shown that the take-up of online care in clinical practice is often disappointing [3, 9, 15]. Moreover, the intention that both groups report to use guided online psychological self-management interventions in the future seems positive, especially among MHCs. In this group, 40 % expect to use online interventions within the next year. With 49 % already using them, this leaves only 11 %, many of whom expect to use online interventions in a more distant timeframe of 1–5 years. Among the PCPs, the group of those who not expect to use online interventions in the near future is larger: more than half report that this will take at least 2 years.
The second aim was to study the facilitating and inhibiting factors associated with use of and intention to use guided online psychological self-management interventions; here some significant differences were found between the two groups of health professionals. MHCs more often reported agreeing that the facilitating conditions for the use of online interventions in their practice were sufficient, that the use of online interventions matched their current way of working, and that their management encouraged them to use such interventions. In both groups this factor was shown to be an essential predictor for the behavioural intention among non-users to start using the interventions in the future. This might explain the differences between MHCs and PCPs, both in current use and expected use, since GPs are actively encouraged to use eHealth applications in Dutch policy. Furthermore, a difference in social influence was found, which shows that the two groups of health professionals have differing perceptions of how positively their colleagues and organizations view online interventions. PCPs often work more independently (which is reflected in a lower response rate on the items of social influence) and therefore might experience less encouragement or support from fellow health professionals.
Another difference was found in performance expectancy, which also proved an important predictor of intention in both groups of non-users. MHCs were more positive on the usefulness they expected (or already noticed) of online interventions in terms of perceived effectivity, usefulness in their patient population, enhancement of quality and diversity of care, and the increase of their own productivity. A possible explanation for this difference lies in the patient population of the two groups. MHCs may see online interventions as a valuable addition to their current care provision, to use them for psycho-education and support of patients with mild problems. There are many primary prevention interventions -on promoting self-help with mental problems, coping with somatic diseases, or adapting one’s lifestyle, for instance- which could all be very usable in a GP practice as a first step in treatment. PCPs, on the other hand, regularly see people with moderate mental health problems, for which they might deem online treatment unsuitable. While research on online CBT shows that it is effective for patients with more severe problems [5–7], previous research has also shown that health professionals are hesitant about the suitability of online care for their patients. A study by Hermens et al. , for example, showed that health professionals reported that they were uncertain about the appropriateness of eHealth interventions for depressed patients and about how to ensure a working relationship with a patient during an eHealth intervention.
Although these findings show that online interventions are quite widely available in Dutch primary care, the results also correspond with studies on the core challenges of eHealth implementation. The scores on the UTAUT factors, which reflect the extent to which health professionals agree that facilitators are present, show that health professionals who are not yet using online interventions are somewhat hesitant about implementation of these interventions. Facilitating conditions, performance expectancy, and effort expectancy proved to be predictors of behavioural intention among non-users. In other studies too these predictors emerged as a key issue in ensuring successful implementation [17–19]. The scores on UTAUT concepts do not reflect a great deal of optimism among current users either, which might raise questions on how health professionals actually use online interventions in treatment, and to what extent such interventions are part of daily practice, even when they are available. Murray et al.  show in a qualitative study that it is essential for health professionals to feel that health technologies have a positive impact on their interactions with patients, and match their skills and the goals of their organization. When making the step to implementation in practice, more research is needed on whether and why interventions are effective and useful, and for whom, when, and how they can best be used [10, 16, 21]. Furthermore, uptake and normalization should be investigated, with a focus on acceptance and use of, and attitudes to online interventions, to tackle the follow-up challenges in the implementation of online interventions in clinical practice [22–24].
In this study a very broad sample of both MHCs and PCPs were invited to participate, and the recruitment database covered a large part of the entire population of both groups. Nevertheless, it should be taken into account that a response bias could be present in the final sample. The estimated response rate is 24 %, which is quite normal for such a large survey study. Still, by sending out one more reminder, we could have increased the number of responses . Furthermore, it is possible that health care professionals who are already interested in eHealth or working with online interventions might be more inclined to fill in the survey, resulting in a somewhat overly optimistic view. Also, respondents could have answered the questions in a socially desirable manner, since the government stimulates health professionals to use eHealth technologies. This might also explain the higher response rate among MHCs, who are expected to deliver a certain percentage of their care via online technologies. No data is available on non-responders, so it is impossible to examine to what extent responders and non-responders differ.
To enhance the use of online interventions in clinical practice and to overcome barriers concerning performance and effort expectancy, more attention could be paid to the competences and skills of health professionals. Education in online care should become a structural part of professional training (both initial training and post- and in-service training), to teach health care professionals what online care entails and how they can use it in daily practice [26, 27]. This also includes e-therapy skills to learn how to communicate with patients via online media and empower patients to self-manage their treatment .
From these results it can be concluded that guided online psychological self-management interventions are widely available in Dutch primary care, especially in GP practices. Almost 90 % of the mental health counsellors in GP practices and almost half of the primary care psychologists in independent mental health care practices surveyed already use these kind of interventions or have the intention to do so within the next year. As regards the UTAUT factors, both groups are moderately positive about the presence of facilitators in their daily clinical practice. Performance expectancy, effort expectancy, and facilitating conditions are significant predictors of the intention to start using online interventions in the future. Further effort and research is essential to enhance these factors and to focus on the acceptance and normalization of online interventions in daily practice.
We thank all the health professionals who participated in the study. We thank Hanne Zwarts, Cindy Hartman, Steffany Cornelissen, Anne van Olffen, Emma Lamers, Janneke Ketelaars, Mandy van der Walle, Birgit Klomps, Linda Renes, Marjolein Zevenhoven, Zohra Hamid, He Jie Ding, Paulien Jansen, and Arjen Korevaar for their contribution to the data collection.
Funding was provided by the Institute of Psychology of Leiden University. This funding source had no influence on the collection, analysis, and interpretation of data; on the writing of the report; or on the decision to submit the article for publication.
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- World Health Organization/Wonca. Integrating mental health into primary care: a global perspective. Geneva: World Health Organization; 2008.Google Scholar
- Bower P, Gilbody S. Stepped care in psychological therapies: access, effectiveness and efficiency. Narrative literature review. Brit J Psychiat. 2005;186:11–7.View ArticlePubMedGoogle Scholar
- Andersson G, Carlbring P, Berger T, Almlov J, Cuijpers P. What makes Internet therapy work? Cogn Behav Ther. 2009;38:55–60.View ArticlePubMedGoogle Scholar
- Murray E. Web-based interventions for behavior change and self-management: potential, pitfalls, and progress. Med 20. 2012;1:e3. doi:10.2196/med20.1741.Google Scholar
- Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One. 2010;5, e13196.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersson G, Cuijpers P, Carlbring P, Riper H, Hedman E. Guided Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis. World Psychiatry. 2014;13:288–95.View ArticlePubMedPubMed CentralGoogle Scholar
- van Beugen S, Ferwerda M, Hoeve D, Rovers MM, Spillekom-van Koulil S, van Middendorp H, et al. Internet-based cognitive behavioral therapy for patients with chronic somatic conditions: a meta-analytic review. J Med Internet Res. 2014;16, e88. doi:10.2196/jmir.2777.View ArticlePubMedPubMed CentralGoogle Scholar
- Høifødt R, Strøm C, Kolstrup N, Eisemann M, Waterloo K. Effectiveness of cognitive behavioural therapy in primary health care: a review. Fam Pract. 2011;28:489–504.View ArticlePubMedGoogle Scholar
- Williams AD, Andrews G. The effectiveness of internet cognitive behavioural therapy (iCBT) for depression in primary care: a quality assurance study. PLoS One. 2013;8, e57447.View ArticlePubMedPubMed CentralGoogle Scholar
- 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 Medicine. 2011;8:1–16.View ArticleGoogle Scholar
- Mair FS, May C, O’Donnell C, Finch T, Sullivan F, Murray E. Factors that promote or inhibit the implementation of e-health systems: an explanatory systematic review. Bull World Health Organ. 2012;90:357–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Van Gemert-Pijnen JEWC, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011;13, e111. doi:10.2196/jmir.1672.View ArticlePubMedPubMed CentralGoogle Scholar
- Trimbos-instituut. Trendrapportage GGZ: Versterking van de GGZ in de huisartsenpraktijk. [Trend report mental health care: reinforcing mental health care in general practice]. ’s-Hertogenbosch: Canon Nederland N.V; 2014.Google Scholar
- Venkatesh V, Morris MG, Davis BG, Davis FD. User acceptance of information technology: toward a unified view. MIS Quart. 2003;27:425–78.Google Scholar
- Demiris G, Afrin LB, Speedie S, Courtney KL, Sondhi M, Vimarlund V, et al. Patient-centered applications: use of information technology to promote disease management and wellness. A white paper by the AMIA knowledge in motion working group. J Am Med Inform Assoc. 2008;15:8–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Hermens MLM, Muntingh A, Franx G, van Splunteren PT, Nuyen J. Stepped care for depression is easy to recommend, but harder to implement: results of an explorative study within primary care in the Netherlands. BMC Fam Pract. 2014;15.Google Scholar
- Wilhelmsen M, Høifødt RS, Kolstrup N, Waterloo K, Eisemann M, Chenhall R, et al. Norwegian general practitioners’ perspectives on implementation of a guided web-based cognitive behavioral therapy for depression: a qualitative study. J Med Internet Res. 2014;16, e208. doi:10.2196/jmir.3556.View ArticlePubMedPubMed CentralGoogle Scholar
- Griebel L, Sedlmayr B, Prokosch HU, Criegee-Rieck M, Sedlmayr M. Key factors for a successful implementation of personalized e-health services. Stud Health Technol Inform. 2013;192:965.PubMedGoogle Scholar
- Li J, Talaei-Khoei A, Seale H, Ray P, Macintyre CR. Health care provider adoption of eHealth: systematic literature review. Interact J Med Res. 2013;2, e7.PubMedPubMed CentralGoogle Scholar
- Murray E, Burns J, May C, Finch T, O’Donnell C, Wallace P, et al. Why is it difficult to implement e-health initiatives? A qualitative study. Implement Sci. 2011. doi:10.1186/1748-5908-6-6.Google Scholar
- Mohr DC, Schueller SM, Montague E, Burns MN, Rashidi P. The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res. 2014;16, e146.View ArticlePubMedPubMed CentralGoogle Scholar
- Finch TL, Mair FS, O’Donnell C, Murray E, May CR. From theory to ‘measurement’ in complex interventions: methodological lessons from the development of an e-health normalisation instrument. BMC Med Res Methodol. 2012;12:69.View ArticlePubMedPubMed CentralGoogle Scholar
- May CR, Finch TL, Cornford J, Exley C, Gately C, Kirk S, et al. Integrating telecare for chronic disease management in the community: what needs to be done? BMC Health Serv Res. 2011;11:131.View ArticlePubMedPubMed CentralGoogle Scholar
- Waller R, Gilbody S. Barriers to the uptake of computerized cognitive behavioural therapy: a systematic review of the quantitative and qualitative evidence. Psychol Med. 2009;39:705–12.View ArticlePubMedGoogle Scholar
- Sauermann H, Roach M. Increasing web survey response rates in innovation research: An experimental study of static and dynamic contact design features. Res Policy. 2013;42:273–86.View ArticleGoogle Scholar
- Hanna L, May C, Fairhurst K. The place of information and communication technology-mediated consultations in primary care: GPs’ perspectives. Fam Pract. 2012;29:361–6.View ArticlePubMedGoogle Scholar
- Mannan R, Murphy J, Jones M. Is primary care ready to embrace e-health? A qualitative study of staff in a London primary care trust. Inform Prim Care. 2006;14:121–31.PubMedGoogle Scholar
- Barazzone N, Cavanagh K, Richards DA. Computerized cognitive behavioural therapy and the therapeutic alliance: a qualitative enquiry. Br J Clin Psychol. 2012;51:396–417.View ArticlePubMedGoogle Scholar