Mobile Technology–Based Interventions for Stroke Self-Management Support: Scoping Review

Background: There is growing interest in enhancing stroke self-management support using mobile health (mHealth) technology (eg, smartphones and apps). Despite this growing interest, “self-management support” is inconsistently defined and applied in the poststroke mHealth intervention literature, which limits efforts to synthesize and compare evidence. To address this gap in conceptual clarity, a scoping review was conducted. Objective: The objectives were to (1) identify and describe the types of poststroke mHealth interventions evaluated using a randomized controlled trial design, (2) determine whether (and how) such interventions align with well-accepted conceptualizations of self-management support (the theory by Lorig and Holman and the Practical Reviews in Self-Management Support [PRISMS] taxonomy by Pearce and


Background
Managing the chronic effects of stroke (eg, mobility problems, cognitive impairment, and depression) has become a global health priority because of its enormous burden on health care systems [1,2].In Canada, >400,000 people live with the effects of stroke, and by 2038, this number is expected to increase to nearly 700,000 [3].To meet the needs of this growing population and address international priorities, self-management support interventions for stroke are of growing interest to researchers and health care professionals.Broadly defined, self-management support is a complex intervention that provides people with knowledge, confidence, and skills to manage their chronic condition [4].Self-management support interventions have been shown to improve a variety of health outcomes after stroke, including risk factor control [5], functional ability [6], participation [6], and quality of life [7].They have also been recommended in recent clinical practice guidelines [8].Unfortunately, however, because of limited health care budgets and unequal access to rehabilitation, few Canadians have the opportunity to participate in self-management support interventions following stroke [8,9].Increased access to timely, effective, and low-cost stroke self-management support could be provided through mobile health (mHealth) technology-based (eg, smartphone app-based) interventions.
Despite the growing potential, need, and interest in enhancing stroke self-management support interventions with mHealth, the evidence for its effectiveness remains unclear.In previous reviews of poststroke mHealth interventions, connections were drawn to "self-management support"; however, the concept was never explicitly defined or operationalized [10][11][12][13][14].In these previous reviews, self-management support was discussed in a way that suggests that it is a newly emerging concept in the literature on poststroke mHealth interventions.Specifically, in the abstract and introduction of 3 reviews, self-management was framed as a key concept in the rationale for the review [10,12,14].For example, in 1 review, mHealth for self-management was described as a "new strategy for stroke rehabilitation" [10].In the discussion of 2 reviews, improved self-management was highlighted as an important outcome of mHealth use [11,13].In the conclusion of 1 review, identifying literature on mHealth interventions to support self-management was stated as the purpose of the study [11].Although clearly emphasizing an interest in the concept, without explicit definitions or operationalizations, the literature remains challenging to synthesize and compare, which may lead future reviews to draw incorrect conclusions about intervention effectiveness [15].To our knowledge, no review has addressed this lack of conceptual clarity; that is, no review has aimed to map the literature on poststroke mHealth interventions according to well-accepted conceptualizations of self-management support.

Objectives
To address this gap in the literature, we conducted a scoping review.This method was selected for its utility in clarifying key concepts in the literature, identifying key characteristics related to a concept, and identifying and analyzing knowledge gaps in an emerging field [16].The objectives were to (1) identify and describe the types of poststroke mHealth interventions evaluated using a randomized controlled trial (RCT) design, (2) determine whether (and how) such interventions align with well-accepted conceptualizations (theory [17] and taxonomy [18]) of self-management support, and (3) identify the mHealth functions that facilitate self-management.The purpose of this study was to identify gaps in the literature and recommendations for future research related to mHealth-enhanced stroke self-management support.

Design
Using well-established methods [19,20], a scoping review was conducted.The protocol was not registered.A critical appraisal of the included studies was not conducted as the aim of this review was to map the breadth and depth of conceptualizations, not to draw conclusions about intervention effectiveness [16].The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist is provided in Multimedia Appendix 1 [21].

Identifying Relevant Studies
In consultation with 2 librarians, ANT searched MEDLINE, Embase, PsycINFO, CINAHL, AMED, Scopus, and ProQuest Dissertations and Theses Global 3 times (October 2-3, 2020, February 28, 2022, and July 10, 2023).The second and third searches were conducted to identify new literature published between 2020 and 2022 and between 2022 and 2023.The search terms captured 2 search concepts: stroke and mHealth (see Multimedia Appendix 2 for the full Ovid search strategy).

Selecting Studies
ANT, JML-M, NC, CT, VN, JR, and SJ conducted level-1 (title and abstract) and level-2 (full-text) screening in duplicate using Covidence (Veritas Health Innovation).Disagreements were resolved through consensus-based discussion.Studies were included if the article reported original research, the study included human participants with stroke or transient ischemic attack, the study evaluated an mHealth intervention (mHealth defined using 2 definitions: those of the World Health Organization-"[the] medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants [PDAs], and other wireless devices" [22]-and Akter et al [23]-"focusing on any wireless technologies [e.g., Bluetooth, GSM, GPRS/3G, Wi-Fi, WiMAX] to transmit various health-related data content and services through mobile devices, including mobile phones, smartphones, PDAs, laptops and Tablet PCs"), and the study was an RCT.The search was limited to RCTs as a preliminary search identified a large number of studies using an RCT design.In addition, as RCTs are typically regarded as the highest in quality and presumably are the farthest along in the technology development process, their influence on research and practice was thought to be the most significant.Studies were excluded if the sample was mixed (eg, acquired brain injury), the intervention included client (person with stroke)-facing technology or equipment that was not clearly mobile and wireless, the article did not report any outcome measures related to intervention effectiveness, and the article was not written in English.

Charting the Data
ANT developed the data-charting form in collaboration with DRD and EN.ANT charted the data verbatim and then JML-M, NC, CT, VN, JR, and SJ verified the data.Data were charted from the included articles as well as from supplementary materials and protocol papers when referenced.The data-charting form included study characteristics (eg, study aims and outcome measures), participant characteristics (eg, time since stroke and sex or gender), and intervention characteristics (based on the Template for Intervention Description and Replication checklist [24]).Visual information related to the intervention characteristics was also charted (eg, screenshots of apps).

Objective 1: Types of Poststroke mHealth Interventions
Multimedia Appendix 5  summarizes the interventions individually and the following sections summarize the interventions collectively, according to selected items from the Template for Intervention Description and Replication checklist [24].

What: Describe Any Physical or Informational Materials Used and Each of the Procedures, Activities, or Processes Used in the Intervention
A total of 7 focal areas were identified: physical exercise (

How: Describe the Modes of Delivery of the Intervention and Whether It Was Provided Individually or in a Group
A total of 85% (22/26) of the interventions were delivered both virtually (eg, via videoconferencing or SMS text messaging) and in-person (eg, in-person orientation or clinic visits).In total, 77% (20/26) were individual based (delivered to the individual with stroke), 38% (10/26) were dyad based (delivered to the individual with stroke and their caregiver or family member), and 8% (2/26) were group based (delivered to groups of people with stroke).

Where: Describe the Types of Locations Where the Intervention Occurred
In total, 58% (15/26) of the interventions occurred both at the hospital or clinic (in-person component) and the participants' home (virtual component).

When and How Much: Describe the Number of Times the Intervention Was Delivered and Over What Period
Intervention delivery time ranged from 14 days to 1 year, with the most common being 4 weeks (5/26, 19%) and 6 months (5/26, 19%).Session frequency varied (twice/d to once every 2-3 mo), as did session length (5 min to 1 h).This variability reflects a wide range of session types (eg, exercise sessions, education sessions, blood pressure self-monitoring, and clinic visits).There was also variability in the dosage of technology used, such as the schedule for sending and receiving messages (twice/d to once/wk) and the amount of time connected to the devices (eg, 1 intervention required participants to wear a pedometer at all times except when sleeping, bathing, or swimming).

Tailoring: If the Intervention Was Planned to be Personalized, Titrated, or Adapted, Describe What, Why, When, and How
A total of 69% (18/26) of the interventions involved tailoring to the person with stroke (eg, abilities, goals, or preferred music).In total, 8% (2/26) of the interventions involved tailoring to the caregiver or family member (eg, preferred ADLs) [40,50].A total of 12% (3/26) of the interventions involved self-tailoring by the person with stroke (eg, education topics [52] or exercises [53,64,65]).

Objective 2: Alignment With Self-Management Support Theory and Taxonomy
Of the 29 conceptual variables, 26 (90%) were coded at least once.The number of interventions coded per variable ranged from 0 to 25 (mean 8.55).The number of variables coded per intervention ranged from 2 to 15 (mean 9.54).Figure 2 [17,18, presents the extent and range of alignment, and Table 2 presents the nature of alignment.

Core self-management skills
Clients developed skills in problem-solving through training focused on the Target-Plan-Perform-Prove strategy [50].

Medical self-management
Clients practiced the emotional task of self-monitoring mood through app-based questionnaires [44].Emotional self-management Clients practiced maintaining or changing behaviors related to ADLs or life roles (eg, dressing or cooking) in the context of their home environment [40,50].

Mechanism of change
Self-efficacy was used as an outcome measure [50,53].

Characteristics of self-management support
Interventions were based on client-identified problems, concerns, or goals [40,45,50,59,63,66,67].Patient-perceived problems Self-tailoring was encouraged by instructing clients to select their own educational content based on their time and needs [52], practice the exercises as often as they wished [53], and gradually modify the exercises based on preference and perceived strenuous intensity [64,65].

Performance mastery
Modeling was offered through group-based formats [44,59]; demonstration videos (eg, of the therapist or client performing the exercises or of animated characters taking medications [41,42,49,53,58]); fictional and nonfictional Modeling stories about people with stroke that are age appropriate, country specific, and culturally relevant (eg, Mahatma Gandhi was a character in a story created for an Indian audience [60]); distorted mirror reflections via an app that allowed clients to watch the reflection of the unaffected half of their face as if it were the affected half [51]; and culturally competent counseling by clinicians of clients' ethnic groups [46].

Not reported Interpretation of symptoms
Social persuasion was promoted through group-based (eg, a WhatsApp group was created for clients to motivate each other to maintain an active lifestyle [44,59]) and dyad (client-caregiver or family member)-based (eg, caregivers Social persuasion were instructed to support the client at home in using the technology and adhering to the program [40][41][42][43]46,[48][49][50]57,60]) formats.

Not reported Information about available resources
Clients were provided with specific, tailored instruction on how to perform physical or linguistic exercises [41,42,53,57].

Provision of equipment
Clients were provided with a 24/7 stroke helpline number [49].Provision of easy access to advice or support when needed Clients developed communication skills by using apps to communicate their needs with nursing staff [43] and participate in shared decision-making with occupational therapists for goal setting [63].

Training or rehearsal for practical self-management activities
Clients developed psychological skills in goal setting [46,50,63,66,67] and problem-solving [50].Training or rehearsal for psychological strategies Social support was facilitated through group-based (eg, a WhatsApp group was created for clients to facilitate peer support [44,59]) and dyad (client-caregiver or family member)-based (eg, caregivers were encouraged to watch educational videos with the client and engage in a follow-up discussion with a professional afterward or instructed to support the client at home in using the technology and adhering to the program [40][41][42][43]46,[48][49][50]57,60]) formats.

Social support
Lifestyle advice and support were provided to clients through a peer-based WhatsApp group [44] and nurse-led education and goal-setting sessions [66,67].
Lifestyle advice and support a ADL: activity of daily living.

Objective 3: mHealth Functions That Facilitate Self-Management
Across all conceptual variables and interventions, 7 mHealth functions were identified as facilitating self-management: educating, communicating, goal setting, monitoring, providing feedback, reminding, and motivating.

Overview
To our knowledge, this is the first scoping review to map the literature on poststroke mHealth interventions according to a self-management support theory and taxonomy.A total of 29 studies describing 26 interventions were included.Overall, we found that the interventions addressed 7 focal areas, 5 types of mobile devices, and 7 mHealth functions.Collectively, the interventions aligned well with the concept of self-management support.However, on an individual basis (per intervention), the alignment was less strong.The following sections further explain how this review extends previous reviews on poststroke mHealth [10][11][12][13][14][69][70][71][72] and telehealth [73] interventions in relation to the study objectives and interventions included.

Focal Areas: Current Trends and Gaps
Our first objective was to identify and describe the types of poststroke mHealth interventions evaluated using an RCT design.Speaking to such types, 7 focal areas were identified: physical exercise, risk factor management, linguistic exercise, ADLs training, medication adherence, stroke education, and weight management.These 7 focal areas have been identified in previous reviews on poststroke mHealth interventions [10][11][12][13][14][69][70][71][72]; however, the included interventions varied.Compared with previous reviews, 45% (13/29) of the studies included in our review (12 interventions) had not been previously identified.Similar to previous reviews, this review found the most common focal area to be physical exercise, likely reflecting the rising trend within the general population of using mobile technology to promote physical fitness in everyday life [74,75].Hence, the literature clearly supports continued research on poststroke mHealth interventions for physical exercise.Also consistent with previous reviews, this review did not identify any interventions focused on mood or fatigue.Considering the high prevalence of poststroke depression, anxiety, and fatigue, this is a serious gap that should be addressed in future research [76].Surprisingly, unlike 5 previous reviews [10,11,13,70,71], this review did not identify any interventions focused on cognition.This difference was due to the varying eligibility criteria (eg, study design).Given this difference across reviews as well as the high prevalence of poststroke cognitive impairment [76], future research on poststroke mHealth interventions for cognition is encouraged to progress toward the level of RCTs.

mHealth Technology: Positioning on the Spectrum of Definitions
Regarding the types of technology used in the interventions, our review identified 5 types of mobile devices (mobile phones or smartphones, tablets, wearable sensors, wireless monitoring devices, and laptops) and 6 features within these devices (apps, messaging, phone calling, videos, videoconferencing, and email).This wide range of technologies resulted from our novel approach to defining mHealth.Previous reviews on poststroke mHealth interventions have defined mHealth either very narrowly, focusing on a few specific mobile devices or features (eg, mobile phones [72], wearable activity monitors [69], or mobile apps for phones [10,11,14,69,71] and tablets [10,11,14,70,71]), or very broadly, focusing on mHealth in general and including devices and features that may not be entirely mobile and wireless (eg, computer programs [10,12,71], telephone calls [12], and web-based applications [13]).Our review was interested in the literature between these 2 ends of the narrow-broad spectrum of mHealth definitions.We followed the recommendation of Cameron et al [26] to define mHealth in a way that captures the "combinatorial complexity" of the mobile system and used 2 open-ended definitions of mHealth [22,23].Thus, we captured additional literature on poststroke mHealth interventions by focusing on entirely mobile systems (technology and equipment) of any type (devices and features).As the field of mHealth continues to grow, we suggest that future reviews explicitly position themselves on this narrow-broad spectrum of mHealth definitions so that the literature can be more readily interpreted and applied.In addition, future work should build on that by Cameron et al [26] to further deepen our understanding of the mobile system.

Objective 2: Alignment With Self-Management Support Theory and Taxonomy
Our second objective was to determine whether (and how) the included interventions aligned with well-accepted conceptualizations (theory [17] and taxonomy [18]) of self-management support.Collectively, the interventions addressed 90% (26/29) of the conceptual variables, whereas individual interventions only addressed an average of 33% (9.54/29) of the conceptual variables.This discrepancy speaks to the potential for improvements in the alignment between poststroke mHealth interventions and the concept of self-management support.The results also revealed key conceptual variables missing from the literature, such as "emotional self-management" and "information about available resources."Hence, the results suggest that future research should be more closely aligned with the theory and taxonomy of self-management support.Previous reviews on poststroke mHealth interventions [10][11][12][13][14][69][70][71][72] have not mapped the literature in this way.However, a review of poststroke telehealth interventions [73] used the PRISMS taxonomy [18] in a similar way, further validating the relevance of this approach.

Objective 3: mHealth Functions That Facilitate Self-Management
Our third objective was to identify the mHealth functions that facilitate self-management.A total of 7 mHealth functions were identified: educating, communicating, goal setting, monitoring, providing feedback, reminding, and motivating.These 7 functions, although together framed as facilitating self-management, are not inherently specific to self-management support interventions as they speak generally to what the intervention does, not specifically to what the intervention is about.Viewing mHealth functions in this way, as generic "verbs describing the behavior of the system" [26] or as action words that link technology capabilities with intervention components, has not been done in past reviews on poststroke mHealth interventions [10][11][12][13][14][69][70][71][72].However, this approach to conceptualizing mHealth functions does align with other work in the broader field of mHealth [26,30].Future research is encouraged to build on this approach and use the identified functions to describe how specific technology capabilities are linked to specific intervention components.Specifically linking technology capabilities with intervention components is important as it would allow for more systematic examinations as to what it is about delivery through mHealth that may be superior or not to other intervention delivery modalities (eg, is educating on sensitive topics via mHealth better than via in-person groups?).

Recommendations for Future Research
The purpose of this study was to identify gaps in the literature and recommendations for future research related to mHealth-enhanced stroke self-management support.In total, 3 overarching recommendations for future research were identified.First, future research should be more explicit about the theories their interventions are based on as well as their conceptualizations of self-management support.Using theory and other conceptualizations in this way would help promote a common language of self-management support and ensure that all conceptual variables are considered, which could ultimately improve intervention adherence, effectiveness, replicability, and uptake in clinical practice.Second, future research should be more multidisciplinary so that a wider range of conceptual variables can be addressed per intervention.This multidisciplinary approach to improving alignment would likely lead to more comprehensive, holistic, and effective interventions.Third, future research should use larger sample sizes and consider using pragmatic trial designs to establish real-world effectiveness.

Limitations
The search was limited to the English language, so the findings may be biased toward English-speaking countries, although 15 countries were represented.Directed content analysis, as with any qualitative approach, involves subjectivity; to address this, operational definitions for codes were used and reported.Finally, this review focused on RCTs, so the findings may be biased toward more traditional or RCT-suited interventions.Given the challenges associated with conducting RCTs on technology-based interventions [77], future reviews should consider including other study designs.

Conclusions
This scoping review clarified the concept of self-management support in the literature on poststroke mHealth interventions by mapping studies according to well-accepted conceptualizations of self-management support.On the basis of the results, it is recommended that future research on poststroke mHealth interventions be more theoretically driven, more multidisciplinary, and larger in scale.

Figure 1 .
Figure 1.PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.

Figure 2 .
Figure 2. Extent and range of alignment between poststroke mobile technology-based interventions and self-management support theory and taxonomy.Please see Multimedia Appendix 6 for a larger version.

Table 1 .
Study and participant characteristics.

Table 2 .
Nature of alignment between poststroke mobile technology-based interventions and self-management support theory and taxonomy.