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Published on 20.05.20 in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/15818, first published Aug 08, 2019.

This paper is in the following e-collection/theme issue:

    Original Paper

    Selecting Evidence-Based Content for Inclusion in Self-Management Apps for Pressure Injuries in Individuals With Spinal Cord Injury: Participatory Design Study

    1Institute of Communication and Health, Faculty of Communication Science, Università della Svizzera italiana, Lugano, Switzerland

    2Swiss Paraplegic Research, Nottwil, Switzerland

    3Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland

    4Department of Health Sciences and Technology, Health Ethics and Policy Lab, ETH Zürich, Zürich, Switzerland

    5Swiss Paraplegic Centre, Nottwil, Switzerland

    Corresponding Author:

    Maddalena Fiordelli, PhD

    Institute of Communication and Health

    Faculty of Communication Science

    Università della Svizzera italiana

    Via Buffi 13

    Lugano, 6900

    Switzerland

    Phone: 41 58 666 47 57

    Email: maddalena.fiordelli@usi.ch


    ABSTRACT

    Background: Technological solutions, particularly mobile health (mHealth), have been shown to be potentially viable approaches for sustaining individuals’ self-management of chronic health conditions. Theory-based interventions are more successful, as evidence-based information is an essential prerequisite for appropriate self-management. However, several reviews have shown that many existing mobile apps fail to be either theoretically grounded or based on evidence. Although some authors have attempted to address these two issues by focusing on the design and development processes of apps, concrete efforts to systematically select evidence-based content are scant.

    Objective: The objective of this study was to present a procedure for the participatory identification of evidence-based content to ground the development of a self-management app.

    Methods: To illustrate the procedure, we focused on the prevention and management of pressure injuries (PIs) in individuals with spinal cord injury (SCI). The procedure involves the following three steps: (1) identification of existing evidence through review and synthesis of existing recommendations on the prevention and self-management of PIs in SCI; (2) a consensus meeting with experts from the field of SCI and individuals with SCI to select the recommendations that are relevant and applicable to community-dwelling individuals in their daily lives; and (3) consolidation of the results of the study.

    Results: In this case study, at the end of the three-step procedure, the content for an mHealth intervention was selected in the form of 98 recommendations.

    Conclusions: This study describes a procedure for the participatory identification and selection of disease-specific evidence and professional best practices to inform self-management interventions. This procedure might be especially useful in cases of complex chronic health conditions, as every recommendation in these cases needs to be evaluated and considered in light of all other self-management requirements. Hence, the agreement of experts and affected individuals is essential to ensure the selection of evidence-based content that is considered to be relevant and applicable.

    JMIR Mhealth Uhealth 2020;8(5):e15818

    doi:10.2196/15818

    KEYWORDS



    Introduction

    Background

    Ever since communication technologies were adopted for health care purposes and defined under the umbrella term electronic health (eHealth), the concept of empowerment and the use of technological solutions have become intertwined [1]. As technological devices became more personal and connected, this relationship took on a new relevance. In particular, mobile health (mHealth) solutions, commonly defined as the use of mobile and wireless technologies to support the achievement of health objectives [2], have been used to enhance the self-management of various chronic conditions [3], such as diabetes [4] and asthma [5]. Evidence indicates that these mHealth solutions can foster self-management by addressing multiple risk factors [6] and sustaining long-term adherence to prevention measures, which remains a major issue [7].

    Studies have examined not only the effectiveness of mHealth [8], but also its design and development process. Even though there is great potential for using mobile technologies for health purposes, findings show that many of the existing mobile apps are not theoretically grounded [9] and their contents are not based on evidence [3,7,9,10]. This is problematic because studies mentioned that theory-driven health interventions are more effective than those without theoretical grounding [11]. It is only recently that mHealth has started to adopt strategies informed by behavior change theories, but this adoption has not been systematic [12]. Some apps are only partially applying the principles of behavior change theories [13], whereas others have defined the app’s features or its mechanism (ie, goal setting) based on a set of different theories or models, but without clear reference to them [14-16]. Additionally, some authors have attempted to create a framework to develop digital behavior change interventions that integrate, for instance, behavior theories, design thinking, and user-centered design [17,18]. Despite these efforts, to date, many apps are still not based on theory, as attested by recent systematic reviews [19,20].

    Another flaw of many existing apps lies in the quality of their content, which does not reflect the latest scientific evidence. Indeed, some ex-post examinations of mHealth apps underlined that their content rarely adheres to evidence-based knowledge [21-24]. Some authors have based the content of their interventions on the results of systematic reviews or additional participatory efforts (ie, involving different stakeholders) [25,26]. However, their procedures are not detailed and cannot be replicated. So far, the efforts to develop a framework for integrating evidence-based content into mHealth interventions have been limited [27]. Hence, it remains unclear how disease-specific recommendations and professional best practices should be selected to inform mHealth interventions. This is problematic as evidence-based information can enhance health literacy [28], which is a precondition for patient participation and informed decision-making [29]. Consequently, apps that are based on outdated or inaccurate content might negatively affect the users’ health and safety [30-32]. Considering the huge amount of incorrect and misleading information available on the internet, as well as in leaflets and other lay publications [33,34], it is of utmost importance that new mHealth interventions tackle the issue of content quality.

    Participatory design is a democratic process involving different stakeholders from the early phases of the design process [35-37]. At least the following two premises provide the basis for different participatory design approaches: all stakeholders should be involved in the design phase to inform the approach and this will increase the likelihood of technology acceptance because it will help set clear expectations [38]. It is for a very good reason that many authors underscored the potential of a participatory design approach throughout various steps, such as requirement analysis, definition of features, and user interface design [39-41], but without providing much clarity on the most appropriate involvement of experts and other stakeholders for the selection of content. Participatory design could be a viable approach for achieving the evidence basis of an app. Several guidelines exist, but they are mostly designed for health care professionals rather than for community-dwelling individuals or patients. Selecting the content of an app through a participatory design approach involves understanding which of the recommendations are not only impactful in terms of prevention, but also feasible and applicable for people living in the community.

    The objective of this study was to fill this gap by describing a structured procedure for the participatory identification of evidence-based content to ground the development of a self-management app. To illustrate the procedure, we used a project based in Switzerland aiming to develop an app for the prevention and self-management of pressure injuries (PIs) in individuals with spinal cord injury (SCI).

    A Case in Point: Spinal Cord Injury

    SCI is a complex chronic condition affecting human functioning in all aspects [42], and it is associated with a number of complications [43,44]. People with SCI have a high risk of developing PIs [42]. The incidence of PIs in the SCI population is 25% to 66% [45], and approximately 85% of individuals with SCI will experience PIs at some point in their lifetimes [46]. PIs impact the quality of life of the affected individuals, as their treatment necessitates prolonged inactivity, which often results in a loss of income and a feeling of social isolation [47]. Moreover, evidence shows that PIs can account for approximately one-fourth of the cost of care for individuals with SCI [48].

    There is general agreement on the fact that PIs might be often preventable in individuals with SCI [49] and that prevention is more cost-effective than treatment [50]. Prevention is possible through active self-management. However, this self-management remains challenging owing to the many different factors that need to be taken into account. Indeed, individuals with SCI have to play an active role in the prevention of PIs by, for instance, adapting their behavior, which includes repositioning, performing pressure-relieving movements, and keeping the skin clean [51]. The prevention and management of PIs in individuals with SCI could benefit from the development of an evidence-based mobile app that supports individuals in performing the many preventive measures, as well as monitoring and treating early stage PIs.


    Methods

    Study Design

    We used a consensus method for the participatory identification of evidence-based content to ground the development of a self-management app for PIs in individuals with SCI. Indeed, to ensure that individuals with SCI have access to sources of information that are credible, of good quality, and up to date, the information provided in the app should be consistent with the latest available clinical recommendations, including those that are indeed the best available evidence for pressure ulcer prevention and remain the foundation of a prevention program [52].

    The recommendations were identified through a three-stage research procedure developed following the main steps of the consensus development method [53]. First, a review of existing recommendations for the prevention and management of PIs in individuals with SCI was conducted. Second, a consensus meeting [54] to select the most important recommendations that individuals with SCI should apply in their daily lives was performed. Finally, the results were consolidated by the expert team. Both the review of the recommendations and the consensus meeting were conducted at the end of 2017, while the third phase was performed during the first quarter of 2018.

    Stage 1: Review and Categorization of Existing Recommendations

    Published recommendations on the prevention and management of PIs were identified through an electronic search and consultation with experts between March and July 2017. Keywords for the search were combined from three different domains. The first was related to PIs (ie, pressure ulcers, pressure injuries, decubitus, pressure sores, bedsores, and skin problems), the second was related to self-management (ie, prevention, detection, treatment, self-management, reduction, and risk factors), and the third was related to SCI (ie, spinal cord injury, tetraplegia, quadriplegia, and paraplegia). The online search applied these keywords in both English and German languages. The search was performed in Google as well as PubMed. The research team extracted all recommendations for the prevention and management of PIs that were directed toward or could be applied by community-dwelling individuals with SCI.

    The review obtained a comprehensive collection of recommendations that were screened and synthesized (ie, similar recommendations from different sources were merged). The results of stage 1 were presented in a document that was sent to all participants of the consensus meeting for preparation.

    Stage 2: Consensus Meeting

    A purposive sample of health professionals and community-dwelling individuals with SCI were invited to participate in a consensus meeting [53]. With the help of SCI-specialized medical doctors, we identified health professionals who may have experience with PIs in individuals with SCI. For the recruitment of those working in the inpatient setting, we contacted the different departments of the four SCI rehabilitation centers in Switzerland and requested for collaboration. For the recruitment of health professionals working in the outpatient setting and individuals with SCI, we relied on informal networks. Through this process we contacted a total of 35 individuals, and they were offered two possible dates for the consensus meeting.

    The final sample of 15 participants [53] included SCI-specialized medical doctors, nurses, wound experts, psychologists, occupational therapists, physiotherapists, and nutritionists who were from different parts of Switzerland and working in SCI rehabilitation centers in Switzerland, as well as home care providers, home care counsellors, representatives from an accident insurance fund, and individuals with SCI. Table 1 presents the participants’ characteristics.

    The consensus meeting was grounded in a systematic consensus planning process that helps to prioritize issues of a different kind during experts’ discussions [55]. The meeting lasted one day and was structured in two parts. A person experienced in consensus meetings moderated the plenary sessions. Three persons facilitated the working groups. They were trained for the technical tasks (eg, dealing with the voting system) and were knowledgeable about the project.

    Table 1. Characteristics of participants in the consensus meeting.
    View this table
    Consensus Meeting Part I: Recommendations Selection

    The participants were divided into two working groups (whenever possible, a representative for every profession and a person with SCI were included in each working group). Moreover, professionals who worked together were included in different groups. They were asked to discuss one by one the recommendations derived from stage 1 and to vote by show of hands in favor of or against their inclusion in the set of recommendations to be implemented in the app. The vote should be based on the relevance and applicability of the recommendations for community-dwelling individuals with SCI. The facilitator of each group was in charge of taking notes on the discussions and carrying out the vote with the help of an ad-hoc technological infrastructure. A Microsoft Access (2010, version 14.0; Microsoft Corp, Redmond, Washington, USA) database containing the list of recommendations resulting from stage 1 was developed prior to the consensus meeting. Every participant voted in favor or against inclusion of each of the recommendations. The facilitator entered the sum of individual votes into the Access database. Based on this sum, a percentage of agreement for including each recommendation was computed. After this first vote (vote A), it was possible to merge the votes of the working groups and retrieve from the system the list of recommendations divided into recommendations to be included, recommendations to be excluded, and recommendations that were ambiguous. As the consensus method is based on a democratic debate and judicial model [53], the recommendations voted on below 40% were excluded, the recommendations voted on above 75% were included, and the recommendations voted on between 40% and 75% were considered ambiguous. These thresholds have been defined based on the experience of previous consensus meetings. The last group of recommendations was discussed in a plenary session in which all participants could argue in favor of or against their choice. After this exchange, the working groups met again to vote on the ambiguous recommendations (vote B). The recommendations were included, excluded, or considered ambiguous following the same rules as in vote A.

    Moreover, during the group discussions, the participants had the opportunity to indicate that a recommendation needed specification. This was mostly the case when the recommendation was deemed to be too generic or when its applicability for community-dwelling individuals with SCI was considered unclear or vague. The recommendations that needed specification were collected in a list and further elaborated on in an afternoon session (part II).

    Consensus Meeting Part II: Recommendations Specification

    The participants were divided into three working groups that were stratified by profession, workplace, and affiliation with the previous working groups. As for the morning working groups, whenever possible, we distributed the participants so that at least one representative of every profession and of people with SCI was present in each group. Moreover, professionals who worked together were included in different groups. We also differently mixed the participants with respect to the morning working groups.

    Participants further specified the recommendations that were indicated during the previous session as being too vague or unclear to be implemented by community-dwelling individuals with SCI in their daily lives. Each of the three working groups received a list of 20 or 21 recommendations to specify (total 62) and was assigned a sheet of paper presenting a research-based user persona. User personas (Multimedia Appendix 1), which are fictional characters with concrete characteristics and behaviors that are intended to represent different user types, have been used in the user-centered design process for designing software [56,57]. They helped make the specification process concrete, as each group could refer to a vivid portrait.

    Stage 3: Consolidation of Results

    After the consensus meeting, the research team together with two experts from the project scientific advisory board consolidated the results by refining them and taking into consideration the input of the participants. For instance, special attention was devoted to the recommendations that remained ambiguous after stage 2. They were screened and sorted out by the research team based on eight logical rules for their inclusion or exclusion. The rules (Textbox 1) referred to the size of the discrepancy between the results of vote A and vote B, and between the two working groups. Additionally, new recommendations were developed for domains that, according to the participants, were insufficiently covered by the existing recommendations. The consolidation stage resulted in a newer and more complete set of recommendations. These recommendations were shared with all the participants of the consensus meeting via email. Feedback from the participants was collected and integrated.


    Textbox 1. Logical rules for the inclusion or exclusion of ambiguous recommendations.
    View this box

    Results

    Stage 1: Review and Categorization of Existing Recommendations

    The sources presented in Table 2 have been identified, and their documents have been systematically reviewed [58-65]. The recommendations extracted from the documents were categorized by applying a deductive-inductive approach. At first, the recommendations were ordered according to the four categories defined by Keast et al (ie, appropriate support surfaces, regular repositioning of the patient, optimizing nutrition, and skin care) [66]. This categorization, however, was not exhaustive. We therefore started an inductive process by grouping together those recommendations that were not covered by the categories defined by Keast et al. We created new categories until all recommendations belonged to one category. We then revised the categories with the aim of reducing their number. We compared among each other the recommendations included in every category and with those included in other categories, and when possible, we merged the categories. Following this procedure, we reached the number of 12 categories. This procedure is similar to the basic rule of the constant comparative method often used in qualitative research, namely the comparison of a new incident with the previous incidents coded in the same category [67].

    The result of the review and recommendation categorization was a list of 130 recommendations for the prevention and management of PIs by individuals with SCI organized in relation to the following topics: (1) Support surface (code A); (2) Repositioning (code B); (3) Nutrition (code C); (4) Skin care (code D); (5) Skin assessment (code E); (6) Exercising (code F); (7) Collaboration with health professionals or caregivers (code G); (8) Transfers (code H); (9) Clothing (code I); (10) Body function and structure (code J); (11) Personal factors (code K); and (12) General (code L). The orders of the categories and recommendations within a category do not reflect a priority order. The recommendations were then collected in a preparatory document, which was sent to the participants prior to the consensus meeting.

    Table 2. Documents reviewed for the identification of recommendations.
    View this table

    Stage 2: Consensus Meeting

    Consensus Meeting Part I: Recommendations Selection

    Figure 1 shows the results of vote A. From the original list of 130 recommendations, 15 were excluded and 60 were included in the final set of recommendations for implementation in the app. The remaining 55 recommendations, with votes ranging between 40% and 74%, fell into the category of “ambiguous” and were subject to a second vote (vote B).

    Figure 2 shows the results of vote B. Of 55 recommendations, nine were excluded and 25 were included in the final set of recommendations for implementation in the app. Twenty-one recommendations again fell into the category of “ambiguous.” These recommendations were no longer discussed by the participants during the consensus meeting, but were later examined by the research team.

    Consensus Meeting Part II: Recommendations Specification

    A total of 62 recommendations needed specification. The list was composed of recommendations indicated by the working groups as well as recommendations indicated a priori by the research team. During the specification phase, different solutions for further clarification of the recommendations were defined by the groups. Most of the recommendations were specified by adding a further explanation of the action to take or by referring to additional criteria for the correct implementation of the recommendation. Approximately one-quarter of the specifications referred to the need to combine complementary recommendations. Experts suggested that a few of the recommendations should be specified by having a dedicated information section about the topic in the app. The specifications were gathered by the research team and further used in the development of evidence-based content for the app.

    Figure 1. Results from vote A.
    View this figure
    Figure 2. Results from vote B.
    View this figure

    Stage 3: Consolidation of Results

    Decision on Ambiguous Recommendations

    The research team examined the 21 recommendations that remained ambiguous after vote B. The ex-post examination resulted in the inclusion of seven recommendations in the final set and the exclusion of 14.

    Expert Consultations on the Category of Nutrition

    The review of existing recommendations (stage 1) resulted in three recommendations for the category of nutrition. These recommendations were debated considerably in the working groups during the consensus meeting (stage 2), as they were considered unsatisfactory. Although participants recognized the importance of nutrition as a risk factor for PIs, they criticized the incompleteness of the presented recommendations and their inability to depict the complexity of nutrition advice in relation to the prevention and management of PIs in individuals with SCI. Hence, the participants agreed with the research team to set up a working group composed of nutritionists and SCI-specialized medical doctors to develop new comprehensive nutrition recommendations. Table 3 provides details of the characteristics of the health professionals who were consulted to develop the recommendations about nutrition.

    Table 3. Characteristics of the participants consulted for recommendations on nutrition.
    View this table

    Multiple sessions of expert consultation were conducted with the aim of developing nutrition recommendations that account for the complex interaction between SCI management and PI prevention. These consultations took place between the end of 2017 and the middle of 2018. Based on previously examined and new sources [62,68-74], the nutritionists developed a new set of recommendations that encompassed information on drinking, weight, and nutrition. The proposed recommendations were then discussed and finalized in collaboration with SCI-specialized medical doctors. In total, six new nutrition recommendations were developed. They were circulated among the participants in the consensus meeting before being added to the final set of recommendations.

    The final set of recommendations is presented in Multimedia Appendix 2. It includes 98 recommendations that synthesize evidence-based recommendations for the prevention and management of PIs for community-dwelling individuals with SCI.

    A checklist for the process of participatory selection of the evidence to ground a self-management app is presented in Textbox 2.


    Textbox 2. Checklist of the process of participatory selection of the evidence to ground a self-management app.
    View this box

    Discussion

    Principal Findings

    This article proposes a procedure for the participatory identification of evidence-based content to ground the development of a self-management app. To our knowledge, this is one of the first attempts to apply a structured procedure for the participatory identification of evidence-based content for a self-management app in the field of SCI. The procedure consists of the following three steps: review of the literature, consensus meeting, and consolidation of the results (including, for instance, a set of expert consultations, if needed).

    Our methodological approach raises two challenges that can hinder the development of evidence-based mHealth interventions. First, it has to be noted that sometimes the literature itself presents contradictory evidence [52,75,76], as the field of medicine is in continuous evolution. This underscores the challenge for clinicians and app developers in terms of identifying evidence-based knowledge on a topic. Thus, the involvement of experienced health care professionals might be a valuable means to assess the available evidence, contextualize evidence and recommendations, identify gaps, and suggest pragmatic solutions [77-79].

    The second challenge is to select relevant and applicable evidence for people living in the community. In particular, this study stresses the challenge of selecting the evidence base for the prevention of a complication in the context of a complex chronic condition. Indeed, when selecting the prevention measures for PIs, experts have to take into consideration all aspects of self-management as well as feasibility issues. For instance, in the case of the prevention of PIs in individuals with SCI, it was mentioned during the working group that hydration is very important for preventing PIs; however, liquid intake often requires catheterization, which, in turn, can increase the risk of bladder infections. Similarly, doing pushup exercise to relieve the skin is good for preventing PIs, but it could cause damage to the shoulders in the long term. These examples illustrate the complexity and sometimes conflicting nature of evidence-based recommendations that are feasible for community-dwelling individuals and that ensure a comprehensive approach to the self-management of SCI. Indeed, systematic reviews and meta-analysis offer valuable synthesis of the evidence [80], but they often have a narrow focus (eg, one complication), and in many cases, they only report on experimental studies, which, owing to their rigor, avoid biases (eg, confounding factors and selection bias), but do not consider real-life situations [81]. In order to overcome these limitations and achieve a comprehensive approach to self-management, it is fundamental for experts from all relevant specialties as well as the persons affected by the health condition to be involved in the selection of the evidence for mHealth interventions. The combination of interdisciplinarity and lived experiences ensures that all perspectives are represented in the discussion. However, for the discussion to be constructive and achieve agreement on a shared decision, a structured process is needed. A consensus meeting represents a valid method to synthesize information and enable decisions to be made when published information is inconsistent or inadequate [82], and it is widely used in medical and health services research [83-85].

    Limitations

    We have to acknowledge a few limitations of our study. The first one is related to the selection of recommendations, as we searched only for recommendations in English and German. We also focused on recommendations specific to SCI and PIs, not considering, for instance, other recommendations on SCI in general or on PIs in other populations. The second limitation is linked to the participants in the consensus meeting. All relevant stakeholders were represented; however, the participant mix could have been more balanced (eg, there were many nurses and only one occupational therapist). In addition, the consensus meeting was held on only one day. This resulted in focused discussions on many relevant aspects of the recommendations, but it was very intense for all participants. Having more time at our disposal could have also allowed an additional discussion and voting round to avoid concluding the meeting while still having some ambiguous recommendations, which the research team later needed to clarify. We also acknowledge that the procedure used has not been compared with another procedure and has not been evaluated. However, the commitment of the participants during the procedure showed that the participatory approach was positively received.

    Strengths

    Although this study had the above-mentioned limitations, it is important to acknowledge some of its strengths. The methodological choice of holding a consensus meeting has been proven to be highly valuable, as its structured process guarantees a democratic discussion and a judicial model [53]; hence, it provides a viable and transparent option for a true participatory design process. Three other strengths that we want to highlight helped the procedures of stage 2 to run more smoothly. First, the selection of experts through other professionals and an informal network proved to be highly valuable, and it provided credibility to our invitation. Moreover, being aware of time constraints, we condensed the consensus meeting activities in one day and provided stakeholders with two dates as options. Second, it should be noted that for constructive discussions during the consensus meeting, participant preparation for the session was extremely important (ie, having read the preparatory document describing the procedure and the list of recommendations resulting from stage 1). Third, having an efficient and automated voting system was essential for ensuring that the results of one vote were quickly available for the next round (plenary or group discussion). This case study proved the value of the presented procedure; however, as this was a demonstration study, there is a requirement for further studies to validate the approach.

    Conclusion

    Considering that people need evidence-based information to make informed decisions and participate in health [29], this study may be valuable for improving the quality of mHealth interventions as it detailed the participatory procedure needed for the selection of the scientific evidence that forms the basis of mHealth content. In particular, this procedure might be useful in the selection of evidence-based content in the case of complex chronic health conditions, for which every recommendation needs to be evaluated and considered in light of all other self-management requirements. Hence, agreement among all experts and affected individuals on which evidence is to be included is essential.

    Acknowledgments

    The authors would like to thank the Swiss Accident Insurance Fund (Suva) for funding the project this paper was based on. We are also very grateful to Nadia Lustenberger and the master’s students who supported us in the literature review, Melissa Selb and Wolfgang Segerer for their advice and invaluable support in the organization and conduction of the consensus meeting, and other colleagues who assisted as note takers and facilitators. We would also like to thank all the participants for their valuable insights and collaboration.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    User personas.

    PDF File (Adobe PDF File), 616 KB

    Multimedia Appendix 2

    Final set of recommendations for pressure injury prevention and management.

    DOCX File , 24 KB

    References

    1. Eysenbach G. What is e-health? J Med Internet Res 2001;3(2):E20 [FREE Full text] [CrossRef] [Medline]
    2. Olla P, Shimskey C. mHealth taxonomy: a literature survey of mobile health applications. Health Technol 2015 Jan 30;4(4):299-308. [CrossRef]
    3. Fiordelli M, Diviani N, Schulz PJ. Mapping mHealth research: a decade of evolution. J Med Internet Res 2013;15(5):e95 [FREE Full text] [CrossRef] [Medline]
    4. El-Gayar O, Timsina P, Nawar N, Eid W. Mobile applications for diabetes self-management: status and potential. J Diabetes Sci Technol 2013 Jan 01;7(1):247-262 [FREE Full text] [CrossRef] [Medline]
    5. Marcano Belisario JS, Huckvale K, Greenfield G, Car J, Gunn LH. Smartphone and tablet self management apps for asthma. Cochrane Database Syst Rev 2013 Nov 27(11):CD010013 [FREE Full text] [CrossRef] [Medline]
    6. Tung JY, Stead B, Mann W, Pham B, Popovic MR. Assistive technologies for self-managed pressure ulcer prevention in spinal cord injury: A scoping review. J Rehabil Res Dev 2015;52(2):131-146. [CrossRef]
    7. Breland JY, Yeh VM, Yu J. Adherence to evidence-based guidelines among diabetes self-management apps. Transl Behav Med 2013 Sep 1;3(3):277-286 [FREE Full text] [CrossRef] [Medline]
    8. Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 2013;10(1):e1001362 [FREE Full text] [CrossRef] [Medline]
    9. Azar KM, Lesser LI, Laing BY, Stephens J, Aurora MS, Burke LE, et al. Mobile applications for weight management: theory-based content analysis. Am J Prev Med 2013 Nov;45(5):583-589. [CrossRef] [Medline]
    10. Subhi Y, Bube SH, Rolskov Bojsen S, Skou Thomsen AS, Konge L. Expert Involvement and Adherence to Medical Evidence in Medical Mobile Phone Apps: A Systematic Review. JMIR Mhealth Uhealth 2015 Jul 27;3(3):e79 [FREE Full text] [CrossRef] [Medline]
    11. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M, Medical Research Council Guidance. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ 2008 Sep 29;337:a1655 [FREE Full text] [CrossRef] [Medline]
    12. Aromatario O, Van Hoye A, Vuillemin A, Foucaut A, Crozet C, Pommier J, et al. How do mobile health applications support behaviour changes? A scoping review of mobile health applications relating to physical activity and eating behaviours. Public Health 2019 Oct;175:8-18 [FREE Full text] [CrossRef] [Medline]
    13. Hartin PJ, Nugent CD, McClean SI, Cleland I, Tschanz JT, Clark CJ, et al. The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial. JMIR Mhealth Uhealth 2016 Aug 02;4(3):e93 [FREE Full text] [CrossRef] [Medline]
    14. Block G, Azar KM, Romanelli RJ, Block TJ, Hopkins D, Carpenter HA, et al. Diabetes Prevention and Weight Loss with a Fully Automated Behavioral Intervention by Email, Web, and Mobile Phone: A Randomized Controlled Trial Among Persons with Prediabetes. J Med Internet Res 2015 Oct 23;17(10):e240 [FREE Full text] [CrossRef] [Medline]
    15. Tombor I, Shahab L, Brown J, Crane D, Michie S, West R. Development of SmokeFree Baby: a smoking cessation smartphone app for pregnant smokers. Transl Behav Med 2016 Dec;6(4):533-545 [FREE Full text] [CrossRef] [Medline]
    16. Mummah SA, King AC, Gardner CD, Sutton S. Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption. Int J Behav Nutr Phys Act 2016 Aug 08;13:90 [FREE Full text] [CrossRef] [Medline]
    17. Mummah SA, Robinson TN, King AC, Gardner CD, Sutton S. IDEAS (Integrate, Design, Assess, and Share): A Framework and Toolkit of Strategies for the Development of More Effective Digital Interventions to Change Health Behavior. J Med Internet Res 2016 Dec 16;18(12):e317 [FREE Full text] [CrossRef] [Medline]
    18. Becker S, Miron-Shatz T, Schumacher N, Krocza J, Diamantidis C, Albrecht U. mHealth 2.0: Experiences, Possibilities, and Perspectives. JMIR Mhealth Uhealth 2014 May 16;2(2):e24 [FREE Full text] [CrossRef] [Medline]
    19. Cho Y, Lee S, Islam SM, Kim S. Theories Applied to m-Health Interventions for Behavior Change in Low- and Middle-Income Countries: A Systematic Review. Telemed J E Health 2018 Oct;24(10):727-741 [FREE Full text] [CrossRef] [Medline]
    20. Kaner EF, Beyer FR, Garnett C, Crane D, Brown J, Muirhead C, et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst Rev 2017 Sep 25;9:CD011479 [FREE Full text] [CrossRef] [Medline]
    21. Modave F, Bian J, Leavitt T, Bromwell J, Harris Iii C, Vincent H. Low Quality of Free Coaching Apps With Respect to the American College of Sports Medicine Guidelines: A Review of Current Mobile Apps. JMIR Mhealth Uhealth 2015 Jul 24;3(3):e77 [FREE Full text] [CrossRef] [Medline]
    22. Kebede M, Steenbock B, Helmer SM, Sill J, Möllers T, Pischke CR. Identifying Evidence-Informed Physical Activity Apps: Content Analysis. JMIR Mhealth Uhealth 2018 Dec 18;6(12):e10314 [FREE Full text] [CrossRef] [Medline]
    23. Knight E, Stuckey MI, Prapavessis H, Petrella RJ. Public health guidelines for physical activity: is there an app for that? A review of android and apple app stores. JMIR Mhealth Uhealth 2015 May 21;3(2):e43 [FREE Full text] [CrossRef] [Medline]
    24. Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! But does it adhere to evidence-informed practices? Transl Behav Med 2011 Dec;1(4):523-529 [FREE Full text] [CrossRef] [Medline]
    25. Lau Y, Cheng LJ, Chi C, Tsai C, Ong KW, Ho-Lim SS, et al. Development of a Healthy Lifestyle Mobile App for Overweight Pregnant Women: Qualitative Study. JMIR Mhealth Uhealth 2018 Apr 23;6(4):e91 [FREE Full text] [CrossRef] [Medline]
    26. White BK, Martin A, White JA, Burns SK, Maycock BR, Giglia RC, et al. Theory-Based Design and Development of a Socially Connected, Gamified Mobile App for Men About Breastfeeding (Milk Man). JMIR Mhealth Uhealth 2016 Jun 27;4(2):e81 [FREE Full text] [CrossRef] [Medline]
    27. Wilhide Iii CC, Peeples MM, Anthony Kouyaté RC. Evidence-Based mHealth Chronic Disease Mobile App Intervention Design: Development of a Framework. JMIR Res Protoc 2016 Feb 16;5(1):e25 [FREE Full text] [CrossRef] [Medline]
    28. Gellert P, Tille F. What do we know so far? The role of health knowledge within theories of health literacy. The European Health Psychologist 2015;17(6):266-274 [FREE Full text]
    29. Edwards A, Elwyn G. Evidence-based Patient Choice. Oxford: Oxford University Press; 2001.
    30. Haffey F, Brady RR, Maxwell S. A comparison of the reliability of smartphone apps for opioid conversion. Drug Saf 2013 Feb;36(2):111-117. [CrossRef] [Medline]
    31. Huckvale K, Car M, Morrison C, Car J. Apps for asthma self-management: a systematic assessment of content and tools. BMC Med 2012;10:144 [FREE Full text] [CrossRef] [Medline]
    32. Kalz M, Lenssen N, Felzen M, Rossaint R, Tabuenca B, Specht M, et al. Smartphone apps for cardiopulmonary resuscitation training and real incident support: a mixed-methods evaluation study. J Med Internet Res 2014;16(3):e89 [FREE Full text] [CrossRef] [Medline]
    33. Biermann JS, Golladay GJ, Greenfield ML, Baker LH. Evaluation of cancer information on the Internet. Cancer 1999 Aug 01;86(3):381-390. [Medline]
    34. Smith H, Gooding S, Brown R, Frew A. Evaluation of readability and accuracy of information leaflets in general practice for patients with asthma. BMJ 1998 Jul 25;317(7153):264-265 [FREE Full text] [CrossRef] [Medline]
    35. Groussard P, Pigot H, Giroux S. From conception to evaluation of mobile services for people with head injury: A participatory design perspective. Neuropsychol Rehabil 2018 Jul;28(5):667-688. [CrossRef] [Medline]
    36. Noergaard B, Sandvei M, Rottmann N, Johannessen H, Wiil U, Schmidt T, et al. Development of a Web-Based Health Care Intervention for Patients With Heart Disease: Lessons Learned From a Participatory Design Study. JMIR Res Protoc 2017 May 17;6(5):e75 [FREE Full text] [CrossRef] [Medline]
    37. Hendriks N, Slegers K, Wilkinson A. Lessons Learned from Participatory Design in Dementia Care: Placing Care Partners at the Centre. Stud Health Technol Inform 2017;233:63-77. [Medline]
    38. Konnerup U. Engaging People with Aphasia in Design of Rehabilitation Through Participatory Design: A Way to Learn what They Really Want. Stud Health Technol Inform 2017;233:148-157. [Medline]
    39. Newman C, Shankar R, Hanna J, McLean B, Osland A, Milligan C, et al. Developing an Evidence-Based Epilepsy Risk Assessment eHealth Solution: From Concept to Market. JMIR Res Protoc 2016 Jun 07;5(2):e82 [FREE Full text] [CrossRef] [Medline]
    40. Baskerville NB, Struik LL, Dash D. Crush the Crave: Development and Formative Evaluation of a Smartphone App for Smoking Cessation. JMIR Mhealth Uhealth 2018 Mar 02;6(3):e52 [FREE Full text] [CrossRef] [Medline]
    41. Birrell L, Deen H, Champion KE, Newton NC, Stapinski LA, Kay-Lambkin F, et al. A Mobile App to Provide Evidence-Based Information About Crystal Methamphetamine (Ice) to the Community (Cracks in the Ice): Co-Design and Beta Testing. JMIR Mhealth Uhealth 2018 Dec 20;6(12):e11107 [FREE Full text] [CrossRef] [Medline]
    42. International Spinal Cord Society. World Health Organization. Geneva: World Health Organization; 2013. International perspectives on spinal cord injury   URL: https://www.who.int/disabilities/policies/spinal_cord_injury/en/ [accessed 2020-05-12]
    43. Sweis R, Biller J. Systemic Complications of Spinal Cord Injury. Curr Neurol Neurosci Rep 2017 Feb;17(2):8. [CrossRef] [Medline]
    44. Brinkhof M, Al-Khodairy A, Eriks-Hoogland I, Fekete C, Hinrichs T, Hund-Georgiadis M, SwiSCI Study Group. Health conditions in people with spinal cord injury: Contemporary evidence from a population-based community survey in Switzerland. J Rehabil Med 2016 Feb;48(2):197-209 [FREE Full text] [CrossRef] [Medline]
    45. Regan MA, Teasell RW, Wolfe DL, Keast D, Mortenson WB, Aubut JL, Spinal Cord Injury Rehabilitation Evidence Research Team. A systematic review of therapeutic interventions for pressure ulcers after spinal cord injury. Arch Phys Med Rehabil 2009 Feb;90(2):213-231 [FREE Full text] [CrossRef] [Medline]
    46. Byrne DW, Salzberg CA. Major risk factors for pressure ulcers in the spinal cord disabled: a literature review. Spinal Cord 1996 May;34(5):255-263. [CrossRef] [Medline]
    47. Consortium for Spinal Cord Medicine Clinical Practice Guidelines. Pressure ulcer prevention and treatment following spinal cord injury: a clinical practice guideline for health-care professionals. J Spinal Cord Med 2001;24 Suppl 1:S40-101. [CrossRef] [Medline]
    48. Bogie KM, Reger SI, Levine SP, Sahgal V. Electrical stimulation for pressure sore prevention and wound healing. Assist Technol 2000;12(1):50-66. [CrossRef] [Medline]
    49. Houlihan BV, Jette A, Paasche-Orlow M, Wierbicky J, Ducharme S, Zazula J, et al. A telerehabilitation intervention for persons with spinal cord dysfunction. Am J Phys Med Rehabil 2011 Sep;90(9):756-764. [CrossRef] [Medline]
    50. Kruger EA, Pires M, Ngann Y, Sterling M, Rubayi S. Comprehensive management of pressure ulcers in spinal cord injury: current concepts and future trends. J Spinal Cord Med 2013 Nov;36(6):572-585 [FREE Full text] [CrossRef] [Medline]
    51. Schubart JR, Hilgart M, Lyder C. Pressure ulcer prevention and management in spinal cord-injured adults: analysis of educational needs. Adv Skin Wound Care 2008 Jul;21(7):322-329. [CrossRef] [Medline]
    52. Cogan AM, Blanchard J, Garber SL, Vigen CL, Carlson M, Clark FA. Systematic review of behavioral and educational interventions to prevent pressure ulcers in adults with spinal cord injury. Clin Rehabil 2017 Jul;31(7):871-880. [CrossRef] [Medline]
    53. Bourrée F, Michel P, Salmi L. Consensus methods: Review of original methods and their main alternatives used in public health. Revue d'Épidémiologie et de Santé Publique 2008 Dec;56(6):e13-e21. [CrossRef]
    54. Holden MA, Haywood KL, Potia TA, Gee M, McLean S. Recommendations for exercise adherence measures in musculoskeletal settings: a systematic review and consensus meeting (protocol). Syst Rev 2014 Feb 10;3(1):10 [FREE Full text] [CrossRef] [Medline]
    55. Stucki A, Cieza A, Michel F, Stucki G, Bentley A, Culebras A, et al. Developing ICF Core Sets for persons with sleep disorders based on the International Classification of Functioning, Disability and Health. Sleep Med 2008 Jan;9(2):191-198. [CrossRef] [Medline]
    56. Woods L, Cummings E, Duff J, Walker K, editors. The development use of personas in a user-centred mHealth design project. In: Proceedings of the 29th Australian Conference on Computer-Human Interaction. 2017 Nov Presented at: Australian Conference on Computer-Human Interaction: ACM; 2017; Australia p. 560-565. [CrossRef]
    57. Barberà-Guillem R, Campos N, Biel S, Erdt S, Payá J, Ganzarain J. User involvement: how we integrated users into the innovation process and what we learned from it. In: Assistive Technologies for the Interaction of the Elderly. Berlin, Germany: Springer; 2014:33-47.
    58. Böthig R, Domurath B, Kaufmann A, Bremer J, Vance W, Kurze I. [Neuro-urological diagnosis and therapy of lower urinary tract dysfunction in patients with spinal cord injury : S2k Guideline of the German-Speaking Medical Society of Paraplegia (DMGP), AWMF register no. 179/001]. Urologe A 2017 Jun 17;56(6):785-792. [CrossRef] [Medline]
    59. Eisenhuth J, Geyh S, Gottschalk S, Kues S, Neikes M, Nüsslein ST. Psychologische Aspekte in der Dekubitusprophylaxe. Empfehlungen des Arbeitskreises Psychologie der Deutschsprachigen Medizinischen Gesellschaft für Paraplegie (DMGP) 2012.
    60. Houghton P, Campbell K. Canadian best practice guidelines for the prevention and management of pressure ulcers in people with Spinal Cord Injury: a resource handbook for clinicians. Ontario: Ontario Neurotrauma Foundation; 2013:a.
    61. Bowman T. Ontario Neurotrauma Foundation. Preventing and treating pressure sores: A guide for people with spinal cord injury   URL: https://onf.org/wp-content/uploads/2019/04/Pressure_Ulcer_Guide_medium-res_single_pages.pdf [accessed 2017-10-20]
    62. National Pressure Ulcer Advisory Panel. Prevention and treatment of pressure ulcers: quick reference guide. Cambridge: Cambridge Media; 2014.
    63. Hsieh J, McIntyre A, Wolfe D, Lala D, Titus L, Campbell K. Pressure ulcers following spinal cord injury. Spinal Cord Injury Rehabilitation Evidence Version 2014;5:1-90.
    64. Jain A. ISCOS - Textbook on comprehensive management of spinal cord injuries. Indian J Orthop 2016;50(2):223. [CrossRef]
    65. Schweizer Paraplegiker Zentrum. Patient education, Druckstellen-Dekubitus v1. Schweizer Peraplegiker Zentrum education 2017. [CrossRef]
    66. Keast DH, Parslow N, Houghton PE, Norton L, Fraser C. Best Practice Recommendations for the Prevention and Treatment of Pressure Ulcers. Advances in Skin & Wound Care 2007;20(8):447-460. [CrossRef]
    67. Glaser BG. The Constant Comparative Method of Qualitative Analysis. Social Problems 1965 Apr;12(4):436-445. [CrossRef]
    68. Laughton GE, Buchholz AC, Martin Ginis KA, Goy RE, SHAPE SCI Research Group. Lowering body mass index cutoffs better identifies obese persons with spinal cord injury. Spinal Cord 2009 Oct;47(10):757-762. [CrossRef] [Medline]
    69. Sopher R, Nixon J, Gorecki C, Gefen A. Exposure to internal muscle tissue loads under the ischial tuberosities during sitting is elevated at abnormally high or low body mass indices. J Biomech 2010 Jan 19;43(2):280-286. [CrossRef] [Medline]
    70. Spreyermann R. Vor-und Nachsorge II: Gesundheitscoaching bei querschnittgelähmten Patientinnen und Patienten. 2013.   URL: https://ssop.ch/assets/Uploads/Vorsorge-u-Nachsorge-II.pdf [accessed 2017-10-20]
    71. Weaver FM, Collins EG, Kurichi J, Miskevics S, Smith B, Rajan S, et al. Prevalence of obesity and high blood pressure in veterans with spinal cord injuries and disorders: a retrospective review. Am J Phys Med Rehabil 2007 Jan;86(1):22-29. [CrossRef] [Medline]
    72. Wong S, Derry F, Jamous A, Hirani SP, Grimble G, Forbes A. Validation of the spinal nutrition screening tool (SNST) in patients with spinal cord injuries (SCI): result from a multicentre study. Eur J Clin Nutr 2012 Mar 14;66(3):382-387. [CrossRef] [Medline]
    73. American Diabetes Association. Academy of Nutrition and Dietetics. Evidence-Based Nutrition Practice Guideline   URL: https://www.andeal.org/category.cfm?cid=14 [accessed 2018-02-11]
    74. Schweizerische Gesellschaft für Ernährung. 2019.   URL: http://www.sge-ssn.ch/ [accessed 2018-02-11]
    75. Groah SL, Schladen M, Pineda CG, Hsieh CJ. Prevention of Pressure Ulcers Among People With Spinal Cord Injury: A Systematic Review. PM R 2015 Jun 18;7(6):613-636. [CrossRef] [Medline]
    76. Atkinson RA, Cullum NA. Interventions for pressure ulcers: a summary of evidence for prevention and treatment. Spinal Cord 2018 Mar 25;56(3):186-198. [CrossRef] [Medline]
    77. Hofmeijer J. Evidence-based medical knowledge: the neglected role of expert opinion. J Eval Clin Pract 2014 Dec;20(6):803-808. [CrossRef] [Medline]
    78. Ehrich J, Somekh E, Pettoello-Mantovani M. The Importance of Expert Opinion-Based Data: Lessons from the European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA) Research on European Child Healthcare Services. J Pediatr 2018 Apr;195:310-311.e1. [CrossRef] [Medline]
    79. Ponce OJ, Alvarez-Villalobos N, Shah R, Mohammed K, Morgan RL, Sultan S, et al. What does expert opinion in guidelines mean? A meta-epidemiological study. Evid Based Med 2017 Oct 18;22(5):164-169. [CrossRef] [Medline]
    80. Bartolucci AA, Hillegass WB. Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses. In: Chiappelli F, editor. Evidence-Based Practice: Toward Optimizing Clinical Outcomes. Berlin, Heidelberg: Springer; 2010:17-33.
    81. Wallace J, Nwosu B, Clarke M. Barriers to the uptake of evidence from systematic reviews and meta-analyses: a systematic review of decision makers' perceptions. BMJ Open 2012 Sep 01;2(5):e001220 [FREE Full text] [CrossRef] [Medline]
    82. Jones J, Hunter D. Consensus methods for medical and health services research. BMJ 1995 Aug 05;311(7001):376-380 [FREE Full text] [CrossRef] [Medline]
    83. European Society of Paedriatric Endocrinology. 2017. Consensus Meeting and Guideline Development   URL: https://www.eurospe.org/clinical-practice/consensus-meeting-and-guideline-development/ [accessed 2019-11-12]
    84. World Health Organization. 2017. Consensus meeting report: development of a target product profile (‎TPP)‎ and a framework for evaluation for a test for predicting progression from tuberculosis infection to active disease   URL: https://apps.who.int/iris/handle/10665/259176 [accessed 2020-04-19]
    85. Morris C, Janssens A, Allard A, Thompson CJ, Shilling V, Tomlinson R. Informing the NHS Outcomes Framework: evaluating meaningful health outcomes for children with neurodisability using multiple methods including systematic review, qualitative research, Delphi survey and consensus meeting. In: Health Services and Delivery Research. Southampton: NIHR Journals Library; 2014.


    Abbreviations

    eHealth: electronic health
    mHealth: mobile health
    PI: pressure injury
    SCI: spinal cord injury


    Edited by G Eysenbach; submitted 08.08.19; peer-reviewed by F Beyer, A Berrocal, S Lee, H Kondylakis; comments to author 21.01.20; revised version received 17.03.20; accepted 25.03.20; published 20.05.20

    ©Maddalena Fiordelli, Claudia Zanini, Julia Amann, Anke Scheel-Sailer, Mirjam Brach, Gerold Stucki, Sara Rubinelli. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 20.05.2020.

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