JMU JMIR Mhealth Uhealth JMIR mHealth and uHealth 2291-5222 JMIR Publications Toronto, Canada v11i1e46558 38055318 10.2196/46558 Review Review Mobile Technology–Based Interventions for Stroke Self-Management Support: Scoping Review Buis Lorraine Peeples Malinda Nabovati Ehsan Thompson Alexandra N MScOT 1 2 https://orcid.org/0000-0002-9539-6620 Dawson Deirdre R PhD 1 2 3 https://orcid.org/0000-0001-7517-6121 Legasto-Mulvale Jean Michelle MScPT 1 4 https://orcid.org/0000-0002-0311-0227 Chandran Nivetha HBSc 1 https://orcid.org/0009-0000-6151-7087 Tanchip Chelsea MSc 1 https://orcid.org/0000-0002-7054-3209 Niemczyk Veronika MScOT 5 https://orcid.org/0009-0003-4244-1322 Rashkovan Jillian 3 https://orcid.org/0009-0006-0423-7613 Jeyakumar Saisa HBSc 3 https://orcid.org/0009-0000-4451-6429 Wang Rosalie H PhD 1 3 6 https://orcid.org/0000-0001-7777-9989 Cameron Jill I PhD 1 3 6 https://orcid.org/0000-0003-4161-1572 Nalder Emily PhD 1
Rehabilitation Sciences Institute Temerty Faculty of Medicine University of Toronto Suite 160 500 University Avenue Toronto, ON, M5G 1V7 Canada 1 416 978 5937 emily.nalder@utoronto.ca
3 6 https://orcid.org/0000-0001-9612-9420 Rehabilitation Sciences Institute Temerty Faculty of Medicine University of Toronto Toronto, ON Canada Rotman Research Institute Baycrest Health Sciences Toronto, ON Canada Department of Occupational Science and Occupational Therapy Temerty Faculty of Medicine University of Toronto Toronto, ON Canada Department of Physical Therapy Temerty Faculty of Medicine University of Toronto Toronto, ON Canada School of Rehabilitation Science Faculty of Health Sciences McMaster University Hamilton, ON Canada KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto, ON Canada Corresponding Author: Emily Nalder emily.nalder@utoronto.ca 2023 6 12 2023 11 e46558 16 2 2023 3 7 2023 3 10 2023 18 10 2023 ©Alexandra N Thompson, Deirdre R Dawson, Jean Michelle Legasto-Mulvale, Nivetha Chandran, Chelsea Tanchip, Veronika Niemczyk, Jillian Rashkovan, Saisa Jeyakumar, Rosalie H Wang, Jill I Cameron, Emily Nalder. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 06.12.2023. 2023

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.

 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 colleagues), and (3) identify the mHealth functions that facilitate self-management.

 Methods

A scoping review was conducted according to the methodology by Arksey and O’Malley and Levac et al. In total, 7 databases were searched. Article screening and data extraction were performed by 2 reviewers. The data were analyzed using descriptive statistics and content analysis.

 Results

A total of 29 studies (26 interventions) were included. The interventions addressed 7 focal areas (physical exercise, risk factor management, linguistic exercise, activities of daily living training, medication adherence, stroke education, and weight management), 5 types of mobile devices (mobile phones or smartphones, tablets, wearable sensors, wireless monitoring devices, and laptops), and 7 mHealth functions (educating, communicating, goal setting, monitoring, providing feedback, reminding, and motivating). Collectively, the interventions aligned well with the concept of self-management support. However, on an individual basis (per intervention), the alignment was less strong.

 Conclusions

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.

 stroke chronic disease self-management rehabilitation mobile health mHealth eHealth telehealth telemedicine digital health mobile phone Introduction 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 [10-14]. 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.

 Methods 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).

 Collating, Summarizing, and Reporting the Results

ANT completed the data analysis in collaboration with DRD, EN, RHW, and JIC. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using conventional content analysis (objective 1) and directed content analysis (objectives 2 and 3) [25]. Directed content analysis for objective 2 was guided by the theory by Lorig and Holman [17] and the Practical Reviews in Self-Management Support (PRISMS) taxonomy by Pearce et al [18] as they are widely cited, slightly different conceptualizations of self-management support (see Multimedia Appendix 3 [17,18] for the operational definitions of codes). Directed content analysis for objective 3 was guided by the definition of mHealth functions by Cameron et al [26] (“the verbs describing the behavior of the system”), examples from previous research on mHealth functions [27-31], and dictionary definitions [32-38] (see Multimedia Appendix 4 [29-38] for the operational definitions of codes).

 Results Study Characteristics

A total of 29 studies describing 26 interventions were included (see Figure 1 for the PRISMA [Preferred Reporting Items for Systematic Reviews and Meta-Analyses] flow diagram [39]). The studies were published between 2007 and 2023 and were from Asia (13/29, 45%), Europe (8/29, 28%), North America (4/29, 14%), Africa (2/29, 7%), and Australia (2/29, 7%). Of the 29 studies, 1 (3%) was a doctoral dissertation [40] and the remaining 28 (97%) were peer-reviewed journal articles. A total of 34% (10/29) of the studies were considered pilot, proof-of-concept, or feasibility studies. The sample sizes ranged from 11 to 4298. Table 1 presents the study and participant characteristics.

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

Study and participant characteristics.

Study Country Study aims Outcome measures Sample size Stroke type Time since stroke or stroke stage Age (y) Sex or gender (male or men; %)
Asano et al [41] Singapore Effectiveness Late-Life Function and Disability Instrument; timed 5-Meter Walk Test; 2-minute walk test; Modified Barthel Index; Activities-Specific Balance Confidence Scale; EQ-5D 98 (IGa: 50; CGb: 48) 78% ischemic; 22% hemorrhagic Within 4 wk Mean 64.1 (range 40.5-89.6) 52
Emmerson et al [42] Australia Effectiveness Self-report logbook; Wolf Motor Function Test; customized questionnaire 58 (IG: 28; CG: 30) 86% ischemic; 15% hemorrhagic Median 120 (range 58-226) d Mean 66 (SD 16) 63
Ferrete Ruíz et al [43] Spain Effectiveness Mississippi Aphasia Screening Test; minutes of device use 30 (IG: 23; CG: 7) 100% ischemic Within 7 d IG: mean 73.20 (SD 9.53); CG: mean 72.40 (SD 2.79) 50
Grau-Pellicer et al [44] Spain Effectiveness Participant reports of community ambulation and sedentary behavior; 10-Meter Walk Test; 6-Minute Walk Test; Timed Up and Go test; Barthel Index; EQ-5D; satisfaction questionnaire 34 (IG: 21; CG: 13) Not reported IG: mean 18.92 (SD 27.6; range 1-96) mo; CG: mean 20.85 (SD 59.74; range 1-252) mo IG: mean 62.96 (SD 11.87; range 33-89); CG: mean 68.53 (SD 11.53; range 41-83) 51
Hankinson et al [45] Australia Pilot study; feasibility; effectiveness Adherence to intervention; Fugl-Meyer Assessment 15 (IG: 6; CG: 9) Not reported 0-180 d Not reported 59
Ifejika et al [46] United States Pilot; feasibility; preliminary effectiveness Reduction in total body weight; compliance with the weight loss intervention; Patient Health Questionnaire–9; systolic blood pressure; serum low-density lipoprotein value; proportion of total hemoglobin; proportion of serum coagulation factor VIII 25 (IG: 13; CG: 12) Not reported Acutely hospitalized Mean 54.1 (SD 9.4) 56
Jang and Jang [47] South Korea Effectiveness Manual Muscle Test; Manual Function Test; Purdue Pegboard Test 21 (IG: 10; CG: 11) 19% ischemic; 81% hemorrhagic Mean 963 (SD 799) d Mean 44.5 (SD 16.5) 71
Kamal et al [48] Pakistan Effectiveness Morisky Medication Adherence Scale; systolic and diastolic blood pressure; satisfaction questionnaires 162 (IG: 83; CG: 79) 84% ischemic; 17% hemorrhagic IG: median 2 (range 1-5) mo; CG: median 2 (range 1-4) mo IG: mean 56.07 (SD 1.5); CG: mean 57.62 (SD 1.3) 68
Kamal et al [49] Pakistan Effectiveness; safety Systolic and diastolic blood pressure; blood sugar (HbA1cc); blood cholesterol (low-density lipoprotein level); mortality; modified Rankin Scale; National Institutes of Health Stroke Scale; Barthel Index 277 (IG: 141; CG: 136) Not reported Not reported IG: mean 60.6 (SD 12); CG: mean 59.7 (SD 14.3) 67
Kamwesiga et al [50] Sweden Feasibility study; preliminary effectiveness Logbook to record adherence; Canadian Occupational Performance Measure; self-efficacy scale; Stroke Impact Scale 3.0 Uganda version; Barthel Index; Occupational Gaps Questionnaire, Ugandan version 28 (IG: 13; CG: 15) 75% ischemic; 21% hemorrhagic; 4% unspecified 3-6 mo—IG: 10 (76.9%) and CG: 10 (66.7%); 7-11 mo—IG: 3 (23.1%) and CG: 3 (20%); 1-2 y—IG: 0 and CG: 2 (13.3%) IG: mean 61.2 (SD 15); CG: mean 58.5 (SD 14) 25
Kang et al [51] South Korea Effectiveness Regional House-Brackmann Facial Nerve Grading System; length between the corner of the mouth and the earlobe 21 (IG: 10; CG: 11) 95% ischemic; 5% hemorrhagic Within 12 wk IG: mean 63.1 (SD 10.3); CG: mean 55.6 (SD 16) 62
Kang et al [52] Taiwan Effectiveness Stroke knowledge questionnaire; EQ-5D 63 (IG: 30; CG: 33) 43% ischemic; 57% hemorrhagic Not reported IG: mean 50.47 (SD 10.82); CG: mean 52.33 (SD 11.03) 68
Kenny et al [53] United Kingdom Feasibility; acceptability; preliminary effectiveness Motor Status Scale; Leeds Movement Performance Index; General Self-Efficacy Scale; diary to record time spent exercising 11 (IG: 5; CG: 6) 77% ischemic; 8% hemorrhagic; 15% unspecified Not reported Mean 73.46 (range 41-88) 46
Kim et al [54] South Korea Effectiveness Activities-Specific Balance Confidence Scale; Dynamic Gait Index; Four Square Step Test; Functional Ambulation Categories; Timed Up and Go test; up-stair and down-stair times; spatiotemporal parameters of gait (velocity and cadence) 18 (IG: 9; CG: 9) 40% ischemic; 60% hemorrhagic IG: mean 5.68 (SD 1.04) mo; CG: mean 4.76 (SD 2.65) mo IG: mean 58.3 (SD 11.8); CG: mean 51.8 (SD 13.7) 65
Labovitz et al [55] United States Effectiveness Pill count; plasma sampling; data from artificial intelligence platform 27 (IG: 15; CG: 12) 100% ischemic Not reported Mean 57 (SD 13.17); median 59 (range 30-79) 46
Lakshminarayan et al [56] United States Pilot, proof-of-concept study; feasibility; usability; acceptability; preliminary effectiveness Number of days blood pressure data were transmitted; systolic blood pressure; Morisky Medication Adherence Scale 50 (IG: 28; CG: 22) Not reported Acute IG: mean 63.1 (SD 9.7; range 42-81); CG: mean 68.3 (SD 10.0; range 46-85); withdrawn: mean 60.33 (SD 13.7; range 47-84) 68
Maresca et al [57] Italy Pilot study; effectiveness Token Test; Esame Neuropsicologico Per l’Afasia; Aphasic Depression Rating Scale; EQ-5D; Psychosocial Impact of Assistive Devices Scale 30 (IG: 15; CG: 15) 63% ischemic; 37% hemorrhagic Not reported Mean 51.2 (SD 11.3) 47
Moon et al [58] South Korea Effectiveness Functional Dysphagia Scale; penetration-aspiration scale; visual analog satisfaction scale 16 (IG: 8; CG: 8) 88% ischemic; 13% hemorrhagic IG: mean 22.75 (SD 9.21) d; CG: mean 21 (SD 9.02) d IG: mean 54.13 (SD 5.41); CG: mean 55.38 (SD 14.88) 56
Øra et al [59] Norway Pilot study; preliminary effectiveness Norwegian Basic Aphasia Assessment; Verb and Sentence Test; Communicative Effectiveness Index 62 (IG: 32; CG: 30) 69% ischemic; 18% hemorrhagic; 13% both ≤3 mo—IG: 16 (50%) and CG: 12 (40%); 3-12 mo—IG: 5 (15.6%) and CG: 4 (13.3%); >12 mo—IG: 11 (34.4%) and CG: 14 (46.7%) IG: mean 64.7 (SD 11.7); CG: mean 65 (SD 12.2) 66
Pandian et al [60] India Effectiveness Composite end point of recurrent stroke, high-risk transient ischemic attack, acute coronary syndrome, and all-cause mortality; change in BMI; physical activity total metabolic equivalent (min/wk); current smoking; current alcohol intake; modified Rankin Scale; medication noncompliance; systolic and diastolic blood pressure (mm Hg); fasting blood sugar (mg/dL); low-density lipoprotein cholesterol (mg/dL); triglycerides (mg/dL) 4298 (IG: 2148; CG: 2150) 83% ischemic; 17% hemorrhagic 2 d to 3 mo IG: median 56 (range 18-88); CG: median 56 (range 18-89) 73
Radomski [40] United States Effectiveness Everyday habit questionnaire; self-reported adherence to self-care checklist; Functional Independence Measure; Frenchay Activities Index; Caregiver’s Burden Scale; performance time for self-care task (seconds) 15 (IG: 5; CG 1: 5; CG 2: 5) Not reported Not reported Mean 59 (SD 14) 80
Sarfo et al [61] Ghana Pilot; feasibility; preliminary effectiveness Systolic and diastolic blood pressure; medication possession ratio; perceived confidence scale; Treatment Self-Regulation Questionnaire 56 (IG: 29; CG: 27) 77% ischemic; 23% hemorrhagic <1 mo IG: mean 54.3 (SD 11.9); CG: mean 55.9 (SD 13.7) 65
Sarfo et al [62] Ghana Pilot study; preliminary effectiveness Systolic and diastolic blood pressure; medication possession ratio score; Morisky Medication Adherence Scale; perceived confidence scale; Treatment Self-Regulation Questionnaire; Telemedicine Satisfaction and Usefulness Questionnaire; hypertension and stroke knowledge 14-item questionnaire 55 (IG: 28; CG: 27) 77% ischemic; 23% hemorrhagic <1 mo IG: mean 54.3 (SD 11.9); CG: mean 55.9 (SD 13.7) 65
Tomori et al [63] Japan Pilot study; feasibility; preliminary effectiveness 36-Item Short Form Health Survey; Brunnstrom recovery stages; Functional Independence Measure; Client Satisfaction Questionnaire; duration of stay 37 (IG: 16; CG: 21) Not reported ≥30 d; subacute Mean 66.22 (SD 10.64) 67
Vahlberg et al [64] Sweden Effectiveness 6-Minute Walk Test (m); chair stand test (s); 10-Meter Walk Test (m/s); Short Physical Performance Battery 79 (IG: 40; CG: 39) 72% ischemic; 11% hemorrhagic; 17% transient ischemic attack Mean 6 (SD 4.4) d IG: mean 63.9 (SD 10.1); CG: mean 63.9 (SD 10.8) 63
Vahlberg et al [65] Sweden Effectiveness Fat-free mass (kg); fat mass (kg); BMI; body weight (kg); HbA1c; serum insulin-like growth factor; low- and high-density lipoprotein cholesterol; self-reported health; mortality 71 (IG: 36; CG: 35) 72% ischemic; 11% hemorrhagic; 17% transient ischemic attack Median 5 d IG: mean 63.9 (SD 10); CG: mean 63.9 (SD 10) 63
Wan et al [66] China Effectiveness Systolic and diastolic blood pressure; Health-Promoting Lifestyle Profile II 158 (IG: 80; CG: 78) 100% ischemic Within 1 mo Median 63.81 65
Wang et al [67] China Effectiveness Health-Promoting Lifestyle Profile II; systolic and diastolic blood pressure; modified Rankin Scale; stroke recurrence 151 (IG: 76; CG: 75) 100% ischemic Within 1 mo Median 63.80 66
Wang et al [68] China Effectiveness Systolic blood pressure; Self-Management Ability Scale; Morisky Medication Adherence Scale; BMI; blood low-density lipoprotein 193 (IG: 98; CG: 95) 67% ischemic; 33% hemorrhagic Not reported IG: mean 42.75 (SD 0.16); CG: mean 41.32 (SD 2.16) 61

aIG: intervention group.

bCG: control group.

cHbA1c: glycated hemoglobin.

 Participant Characteristics

In total, 62% (18/29) of the studies included participants with both ischemic and hemorrhagic stroke, and 7% (2/29) also included transient ischemic attack. Of the 23 studies that reported participants’ stroke stage or time since stroke, 16 (70%) focused on the subacute stage (7 d to 6 mo after stroke). The average age of the participants ranged from 42 to 74 years (weighted average 57, weighted SD 4.46). No studies differentiated between sex and gender. A total of 83% (24/29) of the studies included more male participants or men than female participants or women, ranging from 25% to 80% of male participants or men. Some studies reported on participants’ education (15/29, 52%), marital status (8/29, 28%), employment status (6/29, 21%), and geographic location (5/29, 17%), and fewer studies reported on race (3/29, 10%), ethnicity (1/29, 3%), and income (2/29, 7%).

 Objective 1: Types of Poststroke mHealth Interventions

Multimedia Appendix 5 [40-68] 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].

Why: Describe Any Rationale, Theory, or Goal of the Elements Essential to the Intervention

mHealth technology was rationalized as a strategy to improve intervention effectiveness (18/26, 69%), access (13/26, 50%), convenience (6/26, 23%), and cost-effectiveness (5/26, 19%). A total of 5 interventions were explicitly based on a theory, model, framework, or taxonomy: self-determination theory (n=2, 40%); Health Belief Model (n=2, 40%); social cognitive theory (n=1, 20%); the International Classification of Functioning, Disability, and Health framework (n=1, 20%); the Coventry, Aberdeen, and London–Refined taxonomy of behavior change techniques (n=1, 20%); and a proposed ecological model of adherence to rehabilitation treatment recommendations (n=1, 20%). Common goals of the interventions were to improve outcomes related to treatment or medication adherence (10/26, 38%), motor or physical activity (8/26, 31%), functional ability or independence (5/26, 19%), speech, language, or swallowing (5/26, 19%), hypertension or blood pressure control (5/26, 19%), risk factor control (5/26, 19%), and quality of life (3/26, 12%).

 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 (10/26, 38%), risk factor management (5/26, 19%), linguistic exercise (3/26, 12%), activities of daily living (ADLs) training (3/26, 12%), medication adherence (2/26, 8%), stroke education (2/26, 8%), and weight management (1/26, 4%). In total, 5 types of mobile devices were used: mobile phones or smartphones (17/26, 65%), tablets (9/26, 35%), wearable sensors (5/26, 19%; eg, pedometers or wearable bracelets), wireless monitoring devices (4/26, 15%; eg, Bluetooth sphygmomanometers or Bluetooth blood glucose meters), and laptops (1/26, 4%). Within devices, the features used included: apps (15/26, 58%), messaging (12/26, 46%; eg, via an app or SMS text messaging), phone calling (7/26, 27%), videos (6/26, 23%), videoconferencing (3/26, 12%), and email (2/26, 8%). All but 4 interventions (22/26, 85%) were self-directed, and 8% (2/26) were gamified.

 Who Provided: For Each Category of Intervention Provider, Describe Their Background

The interventions were provided by researchers (9/26, 35%), occupational therapists (7/26, 27%), physical therapists (4/26, 15%), nurses (4/26, 15%), speech-language pathologists (2/26, 8%), physicians (2/26, 8%), pharmacists (1/26, 4%), neuropsychologists (1/26, 4%), brain and heart health managers (1/26, 4%), allied health professionals (1/26, 4%), clinicians (1/26, 4%), and clinic staff (1/26, 4%). In total, 12% (3/26) were provided by a multidisciplinary team of health care professionals.

 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 [17,18,40-68] presents the extent and range of alignment, and Table 2 presents the nature of alignment.

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.

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

Conceptual variable Intervention examples
Self-management support theory [17]
Core self-management skills
Problem-solving Clients developed skills in problem-solving through training focused on the Target-Plan-Perform-Prove strategy [50].
Decision-making Clients developed skills in decision-making through learning information about stroke, rehabilitation, medications, lifestyle, and risk factors (eg, stroke history, heart disease, atrial fibrillation, obesity, age, sleep patterns, diet, exercise, and blood pressure [40,46,48-50,52,60-62,66-68]), as well as by learning how to perform exercises [41-44,51,53,58,60,64,65], take medications [55], measure blood pressure [56,61,62,66-68], and measure blood glucose [68].
Resource utilization Not reported
Forming a patient-professional relationship Clients developed skills in forming a relationship with a professional through participating in virtual (eg, videoconference or phone call) and in-person sessions [40-46,48-60,63,66-68], as well as by sending and receiving messages (eg, via an app or SMS text messaging [40,44,48,50,60-62,64-68]).
Taking action Clients developed skills in taking action through goal-setting training focused on weight loss [46], ADLsa [50,63], and self-management (eg, medication adherence [66,67]).
Self-management tasks or behaviors
Medical self-management Clients practiced the medical tasks of performing self-directed physical or linguistic exercises [41-44,47,51,53,57,58,60,64,65], taking medications [48,55], and self-monitoring physical health data (weight, diet, and exercise [46,68]; sleep [68]; fatigue [44]; blood pressure [56,61,62,66-68]; and blood glucose [68]).
Emotional self-management Clients practiced the emotional task of self-monitoring mood through app-based questionnaires [44].
Role 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
Enhanced self-efficacy Self-efficacy was used as an outcome measure [50,53].
Characteristics of self-management support
Patient-perceived problems Interventions were based on client-identified problems, concerns, or goals [40,45,50,59,63,66,67].
Self-tailoring 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].
Efficacy enhancement
Performance mastery Performance mastery was promoted through feedback (eg, via an app or wearable sensor, through SMS text messaging, or from a therapist [40-47,49,54-57,61,62,64,65,68]) and self-reflection (eg, through checklists or diaries or from reminder messages prompting a response [40,43,47-50,53,60,64-67]).
Modeling 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 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].
Interpretation of symptoms Not reported
Social persuasion 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 were instructed to support the client at home in using the technology and adhering to the program [40-43,46,48-50,57,60]) formats.
Self-management support taxonomy [18]
Information about condition and/or its management Clients were provided with general information about stroke, rehabilitation, medications, lifestyle, and risk factors (eg, stroke history, heart disease, atrial fibrillation, obesity, age, sleep patterns, diet, exercise, and blood pressure [40,46,48-50,52,60-62,66-68]) as well as general instruction on how to perform exercises [41-44,51,53,58,60,64,65], take medications [55], measure blood pressure [56,61,62,66-68], and measure blood glucose [68].
Information about available resources Not reported
Provision of or agreement on specific clinical action plans and/or rescue medication Clients were provided with specific, tailored instruction on how to perform physical or linguistic exercises [41,42,53,57].
Regular clinical review Clients connected with health care professionals on a regular basis for a scheduled review of their condition and self-management (eg, via videoconference, phone call, messaging, or in-person sessions [40-42,44,46,50,55-58,66-68]).
Monitoring of condition with feedback Clients monitored symptoms, behaviors, or objective measures related to their condition (eg, exercise adherence data from wearable sensors, weight management data through tracking calories consumed, medication adherence data via app-based artificial intelligence, and blood pressure data from wireless monitoring devices [41,43-46,55-57,61,62,64-68]). Professionals also reviewed the monitored data and provided clients with feedback [41,44,55-57,61,62,68].
Practical support with adherence (medication or behavioral) Practical support with adherence was provided to clients in the form of reminder alarms [42], reminder messages (eg, via push notifications within an app or through SMS text messaging [40,46,48-50,55,61,62,64-68]), reminder phone calls [48,50,60], and reminder sheets (eg, checklists, diaries, and calendars [40,43,47,50,53,60,64-67]).
Provision of equipment Self-monitoring or self-management was enabled, assisted, or promoted through the provision of tablets [41-43,47,49,51,53,57], smartphones [40,56,58,61,62], wearable sensors (eg, pedometers or wearable bracelets [41,44,64,65,68]), wireless monitoring devices (eg, Bluetooth sphygmomanometers or Bluetooth blood glucose meters [41,56,61,62,68]), apps [43,44,46,47,49,51,52,55,56,61,62,68], and measuring cups [46].
Provision of easy access to advice or support when needed Clients were provided with a 24/7 stroke helpline number [49].
Training or rehearsal to communicate with health care professionals 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 everyday activities Clients developed skills to support ADLs (eg, dressing, cooking, or knitting [40,50,63]).
Training or rehearsal for practical self-management activities Clients developed specific, practical skills in performing self-directed physical or linguistic exercises [41-44,47,51,53,57,58,60,64,65], taking medications [48,55], and self-monitoring health data (weight, diet, and exercise [46,68]; sleep [68]; fatigue [44]; blood pressure [56,61,62,66-68]; blood glucose [68]; and mood [44]).
Training or rehearsal for psychological strategies Clients developed psychological skills in goal setting [46,50,63,66,67] and problem-solving [50].
Social support 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 [40-43,46,48-50,57,60]) formats.
Lifestyle advice and 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].

aADL: 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.

 Discussion Principal Findings 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 [10-14,69-72] and telehealth [73] interventions in relation to the study objectives and interventions included.

 Objective 1: Types of Poststroke mHealth Interventions 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 [10-14,69-72]; 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 [10-14,69-72] 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 [10-14,69-72]. 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.

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

Search strategy for the Ovid databases (MEDLINE, Embase, PsycINFO, and AMED).

Operational definitions used in the directed content analysis related to self-management support theory and taxonomy (objective 2).

Operational definitions used in the directed content analysis related to mobile health functions (objective 3).

Intervention characteristics according to selected items from the Template for Intervention Description and Replication checklist.

Extent and range of alignment between poststroke mobile technology–based interventions and self-management support theory and taxonomy.

 Abbreviations ADL

activity of daily living

 mHealth

mobile health

 PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

 PRISMA-ScR

Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

 PRISMS

Practical Reviews in Self-Management Support

 RCT

randomized controlled trial

This research is part of the doctoral work of ANT, which was funded by the Queen Elizabeth II Graduate Scholarship in Science and Technology, Ontario Graduate Scholarship, Ydessa Hendeles Graduate Scholarship at Baycrest Award, and Toronto Rehabilitation Institute Student Scholarship. EN is supported by a Canada Research Chair (Tier 2) in Resiliency and Rehabilitation funded by the Canada Research Chairs Program. The authors gratefully acknowledge Erica Nekolaichuk and Jessica Shiers-Hanley, librarians at the University of Toronto, for their assistance in developing the search strategy.

ANT conceptualized and designed the study under the guidance of DRD and EN. ANT searched the databases. ANT, JML-M, NC, CT, VN, JR, and SJ screened the articles. ANT extracted the data. JML-M, NC, CT, VN, JR, and SJ verified the data. ANT analyzed the data and wrote the manuscript in collaboration with DRD, EN, RHW, and JIC. All authors have reviewed the final manuscript.

None declared.

 Prince MJ Wu F Guo Y Gutierrez Robledo LM O'Donnell M Sullivan R Yusuf S The burden of disease in older people and implications for health policy and practice Lancet 2015 02 07 385 9967 549 62 10.1016/S0140-6736(14)61347-7 25468153 S0140-6736(14)61347-7 McKevitt C Fudge N Redfern J Sheldenkar A Crichton S Rudd AR Forster A Young J Nazareth I Silver LE Rothwell PM Wolfe CD Self-reported long-term needs after stroke Stroke 2011 05 42 5 1398 403 10.1161/STROKEAHA.110.598839 21441153 STROKEAHA.110.598839 Krueger H Koot J Hall RE O'Callaghan C Bayley M Corbett D Prevalence of individuals experiencing the effects of stroke in Canada: trends and projections Stroke 2015 08 46 8 2226 31 10.1161/STROKEAHA.115.009616 26205371 STROKEAHA.115.009616 Adams K Greiner AC Corrigan JM The 1st Annual Crossing the Quality Chasm Summit: A Focus on Communities 2004 Washington, DC National Academies Press Sakakibara BM Kim AJ Eng JJ A systematic review and meta-analysis on self-management for improving risk factor control in stroke patients Int J Behav Med 2017 02 24 1 42 53 10.1007/s12529-016-9582-7 27469998 10.1007/s12529-016-9582-7 PMC5762183 Warner G Packer T Villeneuve M Audulv A Versnel J A systematic review of the effectiveness of stroke self-management programs for improving function and participation outcomes: self-management programs for stroke survivors Disabil Rehabil 2015 01 12 37 23 2141 63 10.3109/09638288.2014.996674 25579669 Fryer C Luker J McDonnell M Hillier S Self management programmes for quality of life in people with stroke Cochrane Database Syst Rev 2016 08 22 2016 8 CD010442 10.1002/14651858.CD010442.pub2 27545611 PMC6450423 Mountain A Patrice Lindsay M Teasell R Salbach NM de Jong A Foley N Bhogal S Bains N Bowes R Cheung D Corriveau H Joseph L Lesko D Millar A Parappilly B Pikula A Scarfone D Rochette A Taylor T Vallentin T Dowlatshahi D Gubitz G Casaubon LK Cameron JI Canadian stroke best practice recommendations: rehabilitation, recovery, and community participation following stroke. Part two: transitions and community participation following stroke Int J Stroke 2020 10 27 15 7 789 806 10.1177/1747493019897847 31983292 Blacquiere D Lindsay MP Foley N Taralson C Alcock S Balg C Bhogal S Cole J Eustace M Gallagher P Ghanem A Hoechsmann A Hunter G Khan K Marrero A Moses B Rayner K Samis A Smitko E Vibe M Gubitz G Dowlatshahi D Phillips S Silver FL Heart and Stroke Foundation Canadian Stroke Best Practice Committees Canadian stroke best practice recommendations: Telestroke best practice guidelines update 2017 Int J Stroke 2017 10 26 12 8 886 95 10.1177/1747493017706239 28441928 Zhou X Du M Zhou L Use of mobile applications in post-stroke rehabilitation: a systematic review Top Stroke Rehabil 2018 09 13 25 7 1 11 10.1080/10749357.2018.1482446 30209991 Burns SP Terblanche M Perea J Lillard H DeLaPena C Grinage N MacKinen A Cox EE mHealth intervention applications for adults living with the effects of stroke: a scoping review Arch Rehabil Res Clin Transl 2021 03 3 1 100095 10.1016/j.arrct.2020.100095 33778470 S2590-1095(20)30088-4 PMC7984984 Adcock AK Haggerty T Crawford A Espinosa C mHealth impact on secondary stroke prevention: a scoping review of randomized controlled trials among stroke survivors between 2010-2020 Mhealth 2022 8 19 10.21037/mhealth-21-27 35449509 mh-08-21-27 PMC9014232 Marwaa MN Guidetti S Ytterberg C Kristensen HK Use of mobile/tablet and web-based applications to support rehabilitation after stroke: a scoping review J Rehabil Med 2022 04 07 54 jrm00269 10.2340/jrm.v54.452 35174871 PMC10348057 Rintala A Kossi O Bonnechère B Evers L Printemps E Feys P Mobile health applications for improving physical function, physical activity, and quality of life in stroke survivors: a systematic review Disabil Rehabil 2022 11 02 1 15 10.1080/09638288.2022.2140844 36325613 Jonkman NH Schuurmans MJ Jaarsma T Shortridge-Baggett LM Hoes AW Trappenburg JC Self-management interventions: proposal and validation of a new operational definition J Clin Epidemiol 2016 12 80 34 42 10.1016/j.jclinepi.2016.08.001 27531245 S0895-4356(16)30327-4 Munn Z Peters MD Stern C Tufanaru C McArthur A Aromataris E Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach BMC Med Res Methodol 2018 11 19 18 1 143 10.1186/s12874-018-0611-x 30453902 10.1186/s12874-018-0611-x PMC6245623 Lorig KR Holman HR Self-management education: history, definition, outcomes, and mechanisms Ann Behav Med 2003 08 26 1 1 7 10.1207/S15324796ABM2601_01 12867348 Pearce G Parke HL Pinnock H Epiphaniou E Bourne CL Sheikh A Taylor SJ The PRISMS taxonomy of self-management support: derivation of a novel taxonomy and initial testing of its utility J Health Serv Res Policy 2016 04 15 21 2 73 82 10.1177/1355819615602725 26377727 1355819615602725 Arksey H O'Malley L Scoping studies: towards a methodological framework Int J Soc Res Methodol 2005 02 8 1 19 32 10.1080/1364557032000119616 Levac D Colquhoun H O'Brien KK Scoping studies: advancing the methodology Implement Sci 2010 09 20 5 69 10.1186/1748-5908-5-69 20854677 1748-5908-5-69 PMC2954944 Tricco AC Lillie E Zarin W O'Brien KK Colquhoun H Levac D Moher D Peters MD Horsley T Weeks L Hempel S Akl EA Chang C McGowan J Stewart L Hartling L Aldcroft A Wilson MG Garritty C Lewin S Godfrey CM Macdonald MT Langlois EV Soares-Weiser K Moriarty J Clifford T Tunçalp Ö Straus SE PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation Ann Intern Med 2018 10 02 169 7 467 73 10.7326/M18-0850 30178033 2700389 mHealth: new horizons for health through mobile technologies: second global survey on eHealth World Health Organization 2011 2023-02-08 https://apps.who.int/iris/handle/10665/44607 Akter S D’Ambra J Ray P Development and validation of an instrument to measure user perceived service quality of mHealth Inf Manag 2013 6 50 4 181 95 10.1016/j.im.2013.03.001 Hoffmann TC Glasziou PP Boutron I Milne R Perera R Moher D Altman DG Barbour V Macdonald H Johnston M Lamb SE Dixon-Woods M McCulloch P Wyatt JC Chan A Michie S Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide BMJ 2014 03 07 348 mar07 3 g1687 10.1136/bmj.g1687 24609605 bmj.g1687 Hsieh H Shannon SE Three approaches to qualitative content analysis Qual Health Res 2005 11 15 9 1277 88 10.1177/1049732305276687 16204405 15/9/1277 Cameron JD Ramaprasad A Syn T An ontology of and roadmap for mHealth research Int J Med Inform 2017 04 100 16 25 10.1016/j.ijmedinf.2017.01.007 28241934 S1386-5056(17)30007-2 Ernsting C Dombrowski SU Oedekoven M O Sullivan JL Kanzler M Kuhlmey A Gellert P Using smartphones and health apps to change and manage health behaviors: a population-based survey J Med Internet Res 2017 04 05 19 4 e101 10.2196/jmir.6838 28381394 v19i4e101 PMC5399221 Conroy DE Yang C Maher JP Behavior change techniques in top-ranked mobile apps for physical activity Am J Prev Med 2014 06 46 6 649 52 10.1016/j.amepre.2014.01.010 24842742 S0749-3797(14)00040-3 Bhargava H Tanghetti J Mangal V Mandelli A Karmarkar U La Rocca A Mobile health technologies The UCLA Anderson Business and Information Technologies (BIT) Project 2016 Singapore, Singapore World Scientific 167 85 Smahel D Elavsky S Machackova H Functions of mHealth applications: a user's perspective Health Informatics J 2019 09 10 25 3 1065 75 10.1177/1460458217740725 29121831 Rivera J McPherson A Hamilton J Birken C Coons M Iyer S Agarwal A Lalloo C Stinson J Mobile apps for weight management: a scoping review JMIR Mhealth Uhealth 2016 07 26 4 3 e87 10.2196/mhealth.5115 27460502 v4i3e87 PMC4978862 Definition of educate Merriam-Webster 2023-02-08 https://www.merriam-webster.com/dictionary/educate Definition of communicate Merriam-Webster 2023-02-08 https://www.merriam-webster.com/dictionary/communicate Definition of goal setting Cambridge Dictionary 2023-02-08 https://dictionary.cambridge.org/dictionary/english/goal-setting Definition of monitor Merriam-Webster 2023-02-08 https://www.merriam-webster.com/dictionary/monitor Definition of feedback Merriam-Webster 2023-02-08 https://www.merriam-webster.com/dictionary/feedback Definition of remind Merriam-Webster 2023-02-08 https://www.merriam-webster.com/dictionary/remind Definition of motivate Merriam-Webster 2023-02-08 https://www.merriam-webster.com/dictionary/motivate Page MJ McKenzie JE Bossuyt PM Boutron I Hoffmann TC Mulrow CD Shamseer L Tetzlaff JM Akl EA Brennan SE Chou R Glanville J Grimshaw JM Hróbjartsson A Lalu MM Li T Loder EW Mayo-Wilson E McDonald S McGuinness LA Stewart LA Thomas J Tricco AC Welch VA Whiting P Moher D The PRISMA 2020 statement: an updated guideline for reporting systematic reviews BMJ 2021 03 29 372 n71 10.1136/bmj.n71 33782057 PMC8005924 Radomski MV Impact of post-discharge habit training of self care skills on independence, caregiver burden, and development of automaticity for survivors of recent stroke University of Minnesota 2007 2023-11-18 https://www.proquest.com/openview/3bc519083a10f08639f8d53a3ad21732/1?pq-origsite=gscholar&cbl=18750&diss=y Asano M Tai BC Yeo FY Yen SC Tay A Ng YS De Silva DA Caves K Chew E Hoenig H Koh GC Home-based tele-rehabilitation presents comparable positive impact on self-reported functional outcomes as usual care: the Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) randomised controlled trial J Telemed Telecare 2021 27 4 231 8 10.1177/1357633x19868905 Emmerson KB Harding KE Taylor NF Home exercise programmes supported by video and automated reminders compared with standard paper-based home exercise programmes in patients with stroke: a randomized controlled trial Clin Rehabil 2017 08 05 31 8 1068 77 10.1177/0269215516680856 27920262 0269215516680856 Ferrete Ruíz EJ de Torres Chacón MD Perez Sánchez S Domínguez Mayoral A Gamero García MA Barragan Prieto A Ruiz Bayo L Briales Grzib H Montaner Villalonga J Breaking the communication barrier using new technologies in aphasic patients post-stroke Rev Cient Soc Esp Enferm Neurol 2023 07 58 14 20 10.1016/j.sedeng.2022.06.002 Grau-Pellicer M Lalanza J Jovell-Fernández E Capdevila L Impact of mHealth technology on adherence to healthy PA after stroke: a randomized study Top Stroke Rehabil 2020 07 02 27 5 354 68 10.1080/10749357.2019.1691816 31790639 Hankinson K Shaykevich A Vallence A Rodger J Rosenberg M Etherton-Beer C A tailored music-motor therapy and real-time biofeedback mobile phone app ('GotRhythm') to promote rehabilitation following stroke: a pilot study Neurosci Insights 2022 05 19 17 26331055221100587 10.1177/26331055221100587 35615116 10.1177_26331055221100587 PMC9125048 Ifejika NL Bhadane M Cai CC Noser EA Grotta JC Savitz SI Use of a smartphone-based mobile app for weight management in obese minority stroke survivors: pilot randomized controlled trial with open blinded end point JMIR Mhealth Uhealth 2020 04 22 8 4 e17816 10.2196/17816 32319963 v8i4e17816 PMC7203620 Jang SH Jang WH The effect of a finger training application using a tablet PC in chronic hemiparetic stroke patients Somatosens Mot Res 2016 06 29 33 2 124 9 10.1080/08990220.2016.1197117 27357969 Kamal AK Shaikh Q Pasha O Azam I Islam M Memon AA Rehman H Akram MA Affan M Nazir S Aziz S Jan M Andani A Muqeet A Ahmed B Khoja S A randomized controlled behavioral intervention trial to improve medication adherence in adult stroke patients with prescription tailored Short Messaging Service (SMS)-SMS4Stroke study BMC Neurol 2015 10 21 15 212 10.1186/s12883-015-0471-5 26486857 10.1186/s12883-015-0471-5 PMC4618367 Kamal A Khoja A Usmani B Magsi S Malani A Peera Z Sattar S Ahmed Akram M Shahnawaz S Zulfiqar M Muqeet A Zaidi F Sayani S Artani A Azam I Saleem S Effect of 5-minute movies shown via a mobile phone app on risk factors and mortality after stroke in a low- to middle-income country: randomized controlled trial for the stroke caregiver dyad education intervention (Movies4Stroke) JMIR Mhealth Uhealth 2020 01 28 8 1 e12113 10.2196/12113 32012080 v8i1e12113 PMC7013656 Kamwesiga JT Eriksson GM Tham K Fors U Ndiwalana A von Koch L Guidetti S A feasibility study of a mobile phone supported family-centred ADL intervention, F@ce™, after stroke in Uganda Global Health 2018 08 15 14 1 82 10.1186/s12992-018-0400-7 30111333 10.1186/s12992-018-0400-7 PMC6094578 Kang J Chun MH Choi SJ Chang MC Yi YG Effects of mirror therapy using a tablet PC on central facial paresis in stroke patients Ann Rehabil Med 2017 06 41 3 347 53 10.5535/arm.2017.41.3.347 28758071 PMC5532339 Kang Y Shen H Lin C Elwyn G Huang S Wu T Hou W Does a mobile app improve patients' knowledge of stroke risk factors and health-related quality of life in patients with stroke? A randomized controlled trial BMC Med Inform Decis Mak 2019 12 21 19 1 282 10.1186/s12911-019-1000-z 31864348 10.1186/s12911-019-1000-z PMC6925878 Kenny M Gilmartin J Thompson C Video-guided exercise after stroke: a feasibility randomised controlled trial Physiother Theory Pract 2022 05 20 38 5 609 20 10.1080/09593985.2020.1790072 32684076 Kim JH Park SG Lim HJ Park GC Kim MH Lee BH Effects of the combination of rhythmic auditory stimulation and task-oriented training on functional recovery of subacute stroke patients J Phys Ther Sci 2012 24 12 1307 13 10.1589/jpts.24.1307 Labovitz DL Shafner L Reyes Gil M Virmani D Hanina A Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy Stroke 2017 05 48 5 1416 9 10.1161/STROKEAHA.116.016281 28386037 STROKEAHA.116.016281 PMC5432369 Lakshminarayan K Westberg S Northuis C Fuller CC Ikramuddin F Ezzeddine M Scherber J Speedie S A mHealth-based care model for improving hypertension control in stroke survivors: pilot RCT Contemp Clin Trials 2018 07 70 24 34 10.1016/j.cct.2018.05.005 29763657 S1551-7144(17)30836-4 PMC6317360 Maresca G Maggio MG Latella D Cannavò A De Cola MC Portaro S Stagnitti MC Silvestri G Torrisi M Bramanti A De Luca R Calabrò RS Toward improving poststroke aphasia: a pilot study on the growing use of telerehabilitation for the continuity of care J Stroke Cerebrovasc Dis 2019 10 28 10 104303 10.1016/j.jstrokecerebrovasdis.2019.104303 31371144 S1052-3057(19)30344-1 Moon JH Heo SJ Jung JH Effects of orofacial muscles exercise program on swallowing function and satisfaction in sub-acute stroke patients with dysphagia Med-Leg Update 2019 19 1 623 10.5958/0974-1283.2019.00111.7 Øra HP Kirmess M Brady MC Partee I Hognestad RB Johannessen BB Thommessen B Becker F The effect of augmented speech-language therapy delivered by telerehabilitation on poststroke aphasia-a pilot randomized controlled trial Clin Rehabil 2020 03 05 34 3 369 81 10.1177/0269215519896616 31903800 SPRINT INDIA trial collaborators Secondary prevention with a structured semi-interactive stroke prevention package in INDIA (SPRINT INDIA): a multicentre, randomised controlled trial Lancet Glob Health 2023 03 11 3 e425 35 10.1016/S2214-109X(22)00544-7 36796986 S2214-109X(22)00544-7 Sarfo F Treiber F Gebregziabher M Adamu S Patel S Nichols M Awuah D Sakyi A Adu-Darko N Singh A Tagge R Carolyn J Ovbiagele B PINGS (phone-based intervention under nurse guidance after stroke): interim results of a pilot randomized controlled trial Stroke 2018 01 49 1 236 9 10.1161/STROKEAHA.117.019591 29222227 STROKEAHA.117.019591 PMC5742065 Sarfo FS Treiber F Gebregziabher M Adamu S Nichols M Singh A Obese V Sarfo-Kantanka O Sakyi A Adu-Darko N Tagge R Agyei-Frimpong M Kwarteng N Badu E Mensah N Ampofo M Jenkins C Ovbiagele B PINGS Team Phone-based intervention for blood pressure control among Ghanaian stroke survivors: a pilot randomized controlled trial Int J Stroke 2019 08 22 14 6 630 8 10.1177/1747493018816423 30465630 Tomori K Nagayama H Ohno K Nagatani R Saito Y Takahashi K Sawada T Higashi T Comparison of occupation-based and impairment-based occupational therapy for subacute stroke: a randomized controlled feasibility study Clin Rehabil 2015 08 07 29 8 752 62 10.1177/0269215514555876 25381345 0269215514555876 Vahlberg B Lundström E Eriksson S Holmbäck U Cederholm T Effects on walking performance and lower body strength by short message service guided training after stroke or transient ischemic attack (The STROKEWALK Study): a randomized controlled trial Clin Rehabil 2021 02 35 2 276 87 10.1177/0269215520954346 32942914 PMC7874373 Vahlberg BM Lundström E Eriksson S Holmback U Cederholm T Potential effects on cardiometabolic risk factors and body composition by short message service (SMS)-guided training after recent minor stroke or transient ischaemic attack: post hoc analyses of the STROKEWALK randomised controlled trial BMJ Open 2021 10 18 11 10 e054851 10.1136/bmjopen-2021-054851 34663672 bmjopen-2021-054851 PMC8524288 Wan L Zhang X You L Ruan H Chen S The efficacy of a comprehensive reminder system to improve health behaviors and blood pressure control in hypertensive ischemic stroke patients: a randomized controlled trial J Cardiovasc Nurs 2018 11 33 6 509 17 10.1097/JCN.0000000000000496 29901484 Wang M Shen M Wan L Mo M Wu Z Li L Neidlinger SH Effects of a comprehensive reminder system based on the health belief model for patients who have had a stroke on health behaviors, blood pressure, disability, and recurrence from baseline to 6 months: a randomized controlled trial J Cardiovasc Nurs 2020 03 35 2 156 64 10.1097/JCN.0000000000000631 31904693 00005082-202003000-00010 Wang S Li Y Tian J Peng X Yi L Du C Feng C Liu C Deng R Liang X A randomized controlled trial of brain and heart health manager-led mHealth secondary stroke prevention Cardiovasc Diagn Ther 2020 10 10 5 1192 9 10.21037/cdt-20-423 33224743 cdt-10-05-1192 PMC7666930 Lynch EA Jones TM Simpson DB Fini NA Kuys SS Borschmann K Kramer S Johnson L Callisaya ML Mahendran N Janssen H English C ACTIOnS Collaboration Activity monitors for increasing physical activity in adult stroke survivors Cochrane Database Syst Rev 2018 07 27 7 7 CD012543 10.1002/14651858.CD012543.pub2 30051462 PMC6513611 Pugliese M Ramsay T Johnson D Dowlatshahi D Mobile tablet-based therapies following stroke: a systematic scoping review of administrative methods and patient experiences PLoS One 2018 1 23 13 1 e0191566 10.1371/journal.pone.0191566 29360872 PONE-D-17-24687 PMC5779660 Szeto SG Wan H Alavinia M Dukelow S MacNeill H Effect of mobile application types on stroke rehabilitation: a systematic review J Neuroeng Rehabil 2023 01 24 20 1 12 10.1186/s12984-023-01124-9 36694257 10.1186/s12984-023-01124-9 PMC9872745 Avcı A Gün M The effect of mobile phone-based interventions on blood pressure in stroke patients: a systematic review of randomized controlled trials Florence Nightingale J Nurs 2023 06 31 2 122 30 10.5152/FNJN.2023.22218 37404215 PMC10440970 Hwang N Park J Chang M Telehealth interventions to support self-management in stroke survivors: a systematic review Healthcare (Basel) 2021 04 15 9 4 472 10.3390/healthcare9040472 33921183 healthcare9040472 PMC8071480 Lupton D Quantifying the body: monitoring and measuring health in the age of mHealth technologies Critical Public Health 2013 12 23 4 393 403 10.1080/09581596.2013.794931 Lupton D Self-tracking, health and medicine Health Sociol Rev 2017 26 1 1 5 10.1080/14461242.2016.1228149 Lanctôt KL Lindsay MP Smith EE Sahlas DJ Foley N Gubitz G Austin M Ball K Bhogal S Blake T Herrmann N Hogan D Khan A Longman S King A Leonard C Shoniker T Taylor T Teed M de Jong A Mountain A Casaubon LK Dowlatshahi D Swartz RH Canadian stroke best practice recommendations: mood, cognition and fatigue following stroke, 6th edition update 2019 Int J Stroke 2020 08 21 15 6 668 88 10.1177/1747493019847334 31221036 Wang RH Kenyon LK McGilton KS Miller WC Hovanec N Boger J Viswanathan P Robillard JM Czarnuch SM The time is now: a FASTER approach to generate research evidence for technology-based interventions in the field of disability and rehabilitation Arch Phys Med Rehabil 2021 09 102 9 1848 59 10.1016/j.apmr.2021.04.009 33992634 S0003-9993(21)00366-X