Effectiveness of behavior change and self-management theoretically-informed telehealth interventions for stroke secondary prevention: An overview of systematic reviews

Abstract

Introduction

Despite the increased use of telehealth interventions, low-level evidence supports their use for behavior change and self-management in stroke secondary prevention. Therefore, this overview of systematic reviews (SRs) critically appraises and consolidates the evidence about theoretically-informed telehealth interventions in stroke secondary prevention.

Methods

Two phases were conducted independently by two reviewers. Phase-1 included SRs contemplating randomized controlled trials (RCTs) implementing telehealth interventions with individuals post-stroke, targeting cardiovascular events, risk-reducing health behaviors or physiological risk factors. Phase-2 interrogated RCTs from these SRs that implemented theoretically-informed interventions. Best-evidence synthesis of published meta-analyses and new meta-analyses of theoretically-informed interventions were conducted. GRADE evidence was applied.

Results

In Phase-1 (15 SRs), best-evidence synthesis identified telehealth interventions as effective in reducing recurrent angina and recurrent stroke rates (both with very low GRADE), improving medication adherence (low GRADE), physical activity participation (very low GRADE), and blood pressure targets (very low GRADE), reducing systolic blood pressure (SBP) (moderate GRADE) and low-density lipoprotein levels (very low GRADE). In Phase-2 (14 RCTs), new meta-analyses identified theoretically-informed telehealth interventions as effective in improving medication adherence (SMD: 0.38; 95%CI: 0.13–0.64; I²: 72%, low GRADE) and healthy eating (SMD: 0.38; 95%CI: 0.15–0.60; I²: 38%, low GRADE), and decreasing SBP (MD: −9.19; 95%CI: −5.49 to −12.89; I²: 0%, moderate GRADE).

Discussion

Telehealth demonstrates utility in stroke secondary prevention, notably in SBP reduction. High-quality RCTs are required given the lack of current evidence supporting theoretically-informed telehealth interventions addressing primary outcomes of secondary prevention, and the low certainty evidence identified for health behavior change.

Keywords

Stroke self-management health behavior telehealth secondary prevention systematic review‌

Introduction

Stroke has a high burden worldwide and is the third leading cause of death and disability combined.^1,2 Recurrent stroke contributes to the high burden of this disease, where recurrence rates vary between 7.7% and 39.7% from three months to 12 years after a first stroke.³ Recurrent strokes are associated with greater functional disability than first stroke episodes^4,5 and are considered a leading cause of hospital readmissions during the first year post-stroke.⁶ Consequently, stroke secondary prevention has been identified as one of the top 10 priority research areas to reduce stroke burden by individuals post-stroke and other key stakeholders.⁷

Stroke secondary prevention based on non-surgical and non-pharmacological (e.g., behavioral) interventions comprises the use of theoretically-informed interventions to address and self-manage lifestyle-related risk factors, contextualized and individualized to the needs of individuals post-stroke and their families.⁸ While stroke secondary prevention guidelines recommend the use of behavior change and self-management interventions to promote a healthy lifestyle including healthy eating, physical activity, cessation of smoking, safe alcohol consumption, and management of psychosocial stress, recommendations are often drawn from primary prevention study evidence.^9–11 Better evidence supporting interventions delivered post-stroke is therefore required.¹⁰

Theoretically-informed behavioral interventions to self-manage lifestyle-related risk factors post-stroke⁸ can be provided remotely through telehealth.⁹ Telehealth refers to the provision of health care services using information and communication technologies (e.g., cellphones and internet) where patients and providers are separated by distance.¹² Telehealth interventions are becoming more frequent in health service delivery and were widely adopted during the Coronavirus pandemic.^13,14 There is evidence by meta-analysis that their use in clinical populations is at least as good as usual care,¹⁵ alongside a growing body of research in stroke care.^16,17 However, an updated practice guideline for stroke secondary prevention pointed that telehealth use in practice is yet supported by limited research evidence and low level evidence such as professional consensus.^9,18 Therefore, better evidence supporting the effectiveness of theoretically-informed telehealth interventions in stroke secondary prevention is required.

In recent years, systematic reviews (SRs) of telehealth interventions targeting stroke secondary prevention outcomes have been published.^19–21 However, some specifically addressed one outcome (e.g., medication adherence)^19,22,23 while others did not specify stroke populations (e.g., included individuals with arterial occlusive events or cardiovascular disease).^19,20 These methodological differences do not allow consolidated evidence about the effectiveness of telehealth interventions for stroke secondary prevention outcomes to be determined. The purpose of this overview, therefore, was to critically appraise and consolidate evidence from existing SRs on the effectiveness of behavior change and self-management telehealth interventions delivered to individuals for the purposes of reducing risk of recurrent stroke and to provide new synthesis from primary studies that were clearly informed by behavior change theory and/or techniques, as per best-practice recommendations.

Methods

Design and registration

An overview of SRs was conducted following the Cochrane Guidelines for Overviews of Reviews²⁴ and adhering to the preferred reporting guideline for overviews of reviews of healthcare interventions (PRIOR) statement (checklist available in Supplementary Files).²⁵ This overview involved two phases: identification, screening, selection and synthesis of high-level evidence from eligible SRs of telehealth interventions addressing stroke secondary prevention outcomes (phase-1), and identification, screening, selection and synthesis of eligible primary studies that used specified behavior change theory and/or techniques from these SRs (phase-2). The protocol for this overview was previously published²⁶ as a public record of the review. The current overview describes telehealth interventions as distinct from interventions delivered face-to-face previously published.²⁷ Each step, in both overview phases, was conducted independently by two reviewers (PCP and CDCMF). A third reviewer (OL) was consulted where no consensus was initially reached through discussion.

Search methods

Electronic databases Embase, Epistemonikos, Medline and Cochrane were searched for published SRs (current to March 2023) using a comprehensive search strategy (available in Supplementary Files). Searches were supplemented by hand-searching reference lists of relevant SRs and reference lists of relevant guideline and policy documents.^10,28,29 No language or time restriction for publications were applied. Given the multiplicity of terms often used to describe telehealth interventions (e.g., telemedicine, telerehabilitation, eHealth, mHealth) no search string was employed related to telehealth. Rather all SRs on stroke secondary prevention were screened against our inclusion criteria. Peer review of the electronic search strategy was conducted in line with best practice PRESS guidelines.³⁰

Phase-1: systematic reviews (SRs)

Selection criteria

Inclusion criteria were guided by the Population, Intervention, Comparator, Outcome and Study Type (PICOS) construct²⁴:

Population: individuals post-stroke or transient ischemic attack (TIA), or cardiovascular disease populations where stroke was included as a discrete subpopulation;

Intervention: telehealth interventions¹² targeting risk reduction by changing health behaviors and/or by self-management;

Comparator: usual care or alternate active intervention;

Outcomes:

Primary outcomes related to cardiovascular events: mortality (all cause and cardiovascular), recurrent stroke and other cardiovascular events;

Secondary outcomes related to risk-reducing health behaviors: medication adherence (anti-hypertensive agents, cholesterol lowering agents, antiplatelet/anticoagulant agents), healthy eating, physical activity participation, smoking cessation, safe alcohol consumption and emotional self-regulation of psychosocial stress;

Tertiary outcomes related to physiological risk factors: systolic blood pressure (SBP), diastolic blood pressure (DBP), lipid profiles and blood glucose.

Study type: SRs pooling randomized controlled trials (RCTs) or cluster randomized controlled trials (CRCTs) of telehealth interventions to improve risk-reducing health behaviors post-stroke and reporting outcomes of interest. If reviews included other study designs, we included reviews from which it was possible to extract meta-analysis of RCTs or CRCTs data. For the study to be considered a systematic review, it was required to have identified itself as SRs, stated the objectives, named all databases searched, and detailed inclusion and exclusion criteria.³¹

SRs were excluded if they included only studies that aimed to change the behaviors of health professionals in secondary prevention practice or health systems interventions which aim to improve secondary prevention protocols.

Identification of SRs and data extraction

Identified published SRs were independently screened for inclusion by two reviewers. Data were extracted from included SRs independently by two reviewers using an agreed proforma. Graphical Representation of Overlap for OVErviews (GROOVE) tool was used to identify the degree of overlap between identified SRs.³² Telehealth interventions were classified according to the Center for Connected Health Policy's four domains of application: live videoconferencing (synchronous), store-and-forward (asynchronous), remote patient monitoring, and mobile health.^33,34

Quality appraisal of included SRs

Each SR was independently rated for quality by two reviewers using the risk of bias in SRs (ROBIS) tool.³⁵

Best-evidence synthesis of reported SRs results

A best-evidence synthesis was conducted for all published meta-analyses affecting significant change in pre-identified outcomes of interest. Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria³⁶ were applied to establish the certainty of evidence they provided. Judgement of GRADE quality for each comparison was agreed by consensus of all authors, with consideration of risk of bias of included studies,³⁷ inconsistency,³⁸ indirectness,³⁹ imprecision⁴⁰ and publication bias.⁴¹ Where two or more reviews reported meta-analysis for the same outcome and intervention, the best-evidence was determined by consensus of authors, based on the quality appraisal of the SR of origin, the degree of overlapping studies and the timeframe in which the review was conducted. High-level evidence knowledge gaps relating to telehealth interventions for stroke secondary prevention were identified.

Phase-2: primary studies (RCTs or CRCTs)

Selection criteria and identification of the primary studies

Unique primary studies identified from included SRs during phase-1 were next screened independently by two reviewers. Initial screening used the study description provided by the SRs of origin and then full text review was conducted to ensure the identified primary studies met specific study-level inclusion criteria: an RCTs or CRCTs study design; stroke or TIA population; theoretically-informed telehealth interventions delivery only (e.g., that described the underpinning theory and/or referenced behavior change techniques)^42,43; and included primary, secondary or tertiary outcomes related to this overview. Studies which included interventions designed to alter care delivery processes or health professional education were excluded. Following screening, the interventions, as described in the included primary studies (and associated protocol paper, where relevant), were independently examined and extracted using domains 1 and 2 of TIDieR's 10 item checklist.⁴⁴

Data extraction from primary studies

Included primary studies had the following data items extracted independently by two reviewers: characteristics of the participants (e.g., number, age, sex, and time post-stroke), additional characteristics relating to the intervention (using domains 3–10 of the TIDieR checklist),⁴⁴ primary, secondary and tertiary outcomes, and results of between group comparisons. Telehealth interventions were again classified according to the Center for Connected Health Policy's four domains of application.^33,34 Where any inconsistency between the information provided by the SR and the primary study was identified, the information provided by the primary study was considered.

Quality appraisal of primary studies

Two reviewers assessed the quality of each primary study using the Cochrane Risk of Bias Tool.⁴⁵

Data synthesis

Data from primary studies were grouped by technologies used to implement telehealth interventions and by outcomes of interest. Where data type and outcomes reported from two or more primary studies permitted, a meta-analysis was conducted. For continuous data, mean differences (MD) and 95% confidence intervals (95%CI) or standardized mean differences (SMD) were calculated from pooled data. For dichotomous variables, risk ratios (RR) with 95%CI using the Mantel-Haenszel method were employed. The certainty of the evidence for each meta-analysis by outcome of interest was reported based on GRADE criteria.³⁶ Again, the judgement of GRADE certainty for each comparison was agreed by consensus of all authors.

Results

Search

Figure 1 depicts the flowchart for the screening and selection of eligible studies across the two overview phases. The initial search applied across the electronic databases identified 14,721 records. In phase-1, 15 SRs were included.^{19–23,46–55} In phase-2, 229 primary studies were identified from these SRs, of which only 14 could be included, as RCTs that employed specified behavior change theory and/or techniques in the telehealth intervention delivered for individuals post-stroke.^56–69

Figure 1.

Flowchart of the identification, screening, and selection of eligible studies in the two phases of the overview.

Phase-1: systematic reviews (SRs)

Table 1 details the 15 included SRs. They dated from 2016–2022 and they searched across 19 different databases. The most employed electronic databases in >10% of all included SRs were Medical Literature Analysis and Retrievel System Online (Medline) (16%), Cochrane (18%), Excerpta Medica Database (Embase) (16%), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) (12%). Six (33%) SRs^{19,20,46,51,54,55} registered or published the review protocol in advance. Seven (47%) SRs^{20,46,48–50,52,54} reported adherence to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement.⁷⁰

Table 1.

Description and methodological quality of systematic reviews included in the overview.

Systematic review	Main objective; population; telehealth application domain	Search range	Review: 1. registered in PROSPERO; 2. published protocol; 3. followed the PRISMA(yes or no)	Primary studies included: in the review; in the overview (n; %)	Outcomes of interest	Methodological quality*
Adler et al.¹⁹	To determine whether mobile phone text messaging is effective in enhancing adherence to recommended medication Individuals with established arterial occlusive Mobile Health	Cochrane CPCI-S EMBASE MEDLINE Studies published until November 2016	1. No information 2. Yes 3. No information (only flow diagram)	7; 1 (14.3%)	Primary: medication adherence, fatal, non-fatal and combined cardiovascular events Secondary: surrogate outcomes for secondary prevention (e.g., blood pressure), adverse effects	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Al-Arkee et al.²⁰	To assess the effects of mobile health care apps on medication adherence and health-related outcomes Individuals with CVD Mobile Health	CINAHL Cochrane EMBASE Google Scholar MEDLINE PsycINFO Studies published until January 2020	1. Yes (CRD42019121385) 2. No information 3. Yes	16; 1 (6.3%)	Primary: medication adherence Secondary: SBP, DBP, TC, LDL	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Armitage et al.⁴⁶	To establish whether apps designed to support medication adherence demonstrate efficacy and to identify the intervention characteristics and BCT associated with efficacy Individuals with any health condition Mobile Health	CINAHL EMBASE MEDLINE PsycINFO Web of Science Studies published from 1990 to November 2018	1. Yes (CRD42017080150) 2. No information 3. Yes	9; 0 (0%)	Medication adherence	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Freund et al.⁴⁷	To examine the effectiveness of self-directed, information and communications technology-based interventions that aimed to improve outcomes Individuals after stroke or TIA Remote patient monitoring and mobile health	Cochrane EMBASE MEDLINE Studies published from January 2006 to June 2020	1. No information 2. No information 3. No information (only flow diagram)	19; 4 (21.1%)	Stroke outcomes (e.g., speech, hemiparesis, cognition), physical outcomes (e.g., blood pressure), behavioral outcomes (e.g., medication adherence, physical activity), psychosocial outcomes (e.g., depression, quality of life) and health service use	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Gandapur et al.²²	To review the role of mobile health tools for improving medication adherence Individuals with CVD Mobile Health	MEDLINE Studies published from January 1966 to December 2015	1. No information 2. No information 3. No information	10; 1 (10.0%)	Medication adherence	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Gandhi et al.⁴⁸	To provide further insight as to whether mobile health technology can provide added benefit in the contemporary management of CVD Individuals with CVD Mobile Health	CINAHL CNKI Cochrane EMBASE MEDLINE PsycINFO WANFANG Studies published from 1950 to January 2016	1. No information 2. No information 3. Yes	27; 0 (0%)	Primary: medication adherence Secondary: adherence to either pharmacologic or nonpharmacologic recommendations, and the ability to meet secondary prevention targets (blood pressure, LDL, BMI, exercise, or smoking cessation) Tertiary: blood pressure, LDL, BMI, exercise, smoking cessation, and clinical events (e.g., TIA/stroke, myocardial infarction, and mortality)	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Hwang et al.⁴⁹	To identify the components the of telehealth-self-management intervention's focus and self-management support components, the telehealth delivery type, and the telehealth-self-management support effects Individuals after stroke Videoconferencing, store-and-forward, remote patient monitoring, and mobile health	CINAHL EMBASE MEDLINE PsycINFO Web of Science Studies published from 2005 to 2020	1. No information 2. No information 3. Yes	10; 1 (10.0%)	Goal attainment, self-care, activities of daily living, self-efficacy, quality of life, lifestyle behavior, participation, function, psychosocial, disability, and adherence	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Kassavou et al.²³	To assess the effectiveness of interactive voice response and short message service interventions to support medication adherence, and to identify the BCT and other intervention characteristics that are positively associated with greater intervention effectiveness Individuals with diagnosis of or at risk of a cardio-metabolic condition Mobile Health	CINAHL Cochrane EMBASE MEDLINE PsycINFO (from January 1992 to April 2016) Data related to the search date were not provided for the other databases	1. No information 2. No information 3. No information (only flow diagram)	17; 1 (5.9)%	Medication adherence	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Kraft et al.⁵⁰	To systematically assess the principles of telemedical-supported strategies to improve secondary prevention after stroke or TIA currently used and to analyze the potential implications of telemedical support in terms of long-term outcome Individuals after stroke or TIA Videoconferencing, store-and-forward, remote patient monitoring, and mobile health	Cochrane Google Scholar MEDLINE Studies published until May 2016	1. No information 2. No information 3. Yes	13; 4 (30.8%)	Primary: SBP (e.g., adherence to SBP recommendations, achievement of health behavior targets) Secondary: consequences of the primary outcomes (e.g., mortality, recurrent events, laboratory or vital parameters, health service utilization, re-admission to hospital, and quality of life)	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Laver et al.⁵¹	To determine whether the use of telerehabilitation leads to improved ability to perform activities of daily living when compared with in-person rehabilitation or no rehabilitation or usual care Individuals after stroke Videoconferencing, store-and-forward, remote patient monitoring, and mobile health	AMED CINAHL Cochrane EBSCO EMBASE HTA MEDLINE OTseeker PEDro PsycBITE PsycINFO REHABDATA Studies published until June 2019	1. No information 2. Yes 3. No information	22; 2 (9.1%)	Primary: independence in activities of daily living Secondary: self-care and domestic life, mobility, balance, participant satisfaction with the intervention, self-reported health-related quality of life, depression	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Lv et al.⁵²	To estimate the effects of telemedicine and mobile health interventions on the control of systolic blood pressure Individuals after stroke Videoconferencing, store-and-forward, remote patient monitoring, and mobile health	Cochrane EMBASE MEDLINE Web of Science Studies published until June 2020	1. No information 2. No information 3. Yes	9; 4 (44.4%)	SBP	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Park et al.⁵³	To identify the potential of mobile phones features to be used as effective interventions in the secondary prevention of CVD Individuals with CVD Mobile Health	CINAHL Cochrane MEDLINE PsycINFO Studies published from January 2002 to January 2016	1. No information 2. No information 3. No information	28; 1 (3.6%)	Secondary prevention of CVD	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Patterson et al.⁵⁴	To determine if smartphone applications are effective at changing physical activity and sedentary behavior Individuals with CVD Mobile Health	CINAHL Cochrane EMBASE MEDLINE SCOPUS Sports Discus Studies published from 2007 to October 2020	1. Yes (CRD42020189046) 2. No information 3. Yes	19; 0 (0%)	Primary: physical activity and sedentary behavior Secondary: participant characteristics (e.g., age, gender, diagnoses) and the factors which influence uptake (including requirement to own a smartphone), engagement and adherence to such interventions and retention rates	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Rintala et al.⁵⁵	To investigate the effectiveness of technology-based distance physical rehabilitation interventions on physical functioning compared to a combination of traditional treatments such as similar treatment, other treatment, and usual care Individuals after stroke Videoconferencing, store-and-forward, remote patient monitoring, and mobile health	CINAHL Cochrane EMBASE PEDro Web of Science Studies published from January 2000 to May 2018	1. Yes (CRD42017065918) 2. No information 3. No information	13; 1 (7.7%)	Measures of physical functioning	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall
Zeng et al.²¹	To evaluate the effects of mobile health and telehealth technology and usual care on medication adherence Individuals after stroke Mobile Health	Cochrane EMBASE MEDLINE Web of Science Studies published until September 2021	1. No information 2. No information 3. No information	10; 5 (50%)	Medication adherence	Domain 1
						Domain 2
						Domain 3
						Domain 4
						Overall

Legend: AMED - Allied and Complementary Medicine; BCT - behavior change technique; BMI - body mass index; CINAHL - Cumulative Index to Nursing and Allied Health Literature; CNKI - China National Knowledge Infrastructure; CPCI-S - Conference Proceedings Citation Index - Science; CVD – cardiovascular disease. DBP - diastolic blood pressure; EBSCO - Business Source Complete; EMBASE - Excerpta Medica Database; HbA1c - glycated haemoglobin; HTA - Health Technology Assessment Database; INSPEC - ISI Web of Knowledge; LDL - low-density lipoprotein; MEDLINE - Medical Literature Analysis and Retrievel System Online; OTseeker - Occupational Therapy System Evaluation of Evidence; PEDro - Physiotherapy Evidence Database; PRISMA - Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROSPERO - International Prospective Register of Systematic Reviews; PsycBITE – Psychological Database for Brain Impairment Treatment Efficacy; PsycINFO - Psychological Abstracts; SBP - systolic blood pressure; TC – total cholesterol; TIA - transient ischemic attack.

*Risk of bias in systematic reviews ROBIS tool (Domain 1: study eligibility criteria; Domain 2: identification and selection of studies; Domain 3: data collection and study appraisal; Domain 4: synthesis and findings; Overall - Risk of bias in the review. Colors: green - low risk of bias; red - high risk of bias; yellow - unclear)

Telehealth intervention domains permitted in the inclusion criteria of these SRs were: all domains (videoconferencing, store-and-forward, remote patient monitoring, and mobile health),^49–52,55 remote patient monitoring and mobile health,⁴⁷ and only mobile health.^{19–23,46,48,53,54} Eight (53%) SRs^{21–23,47,49,50,52,53} rated as at high risk of bias, primarily because of concerns related to study eligibility criteria.

Nine SRs (60%)^{20,21,23,46,48,50–52,54} conducted meta-analyses addressing outcomes of interest to this overview (available in Supplementary Files). For primary outcomes related to cardiovascular events, one SR pooled data addressing mortality, recurrent stroke and other cardiovascular events.⁴⁸ For secondary outcomes related to risk-reducing health behaviors: five SRs pooled data addressing medication adherence^{20,21,23,46,48}; two pooled data addressing physical activity participation^48,54; one pooled data addressing smoking cessation⁴⁸; one pooled data addressing emotional self-regulation of psychosocial stress (depressive symptoms)⁵¹; and none addressed healthy eating or safe alcohol consumption. For tertiary outcomes related to physiological risk factors: four SRs pooled data addressing blood pressure (recommended blood pressure targets, reduction in SBP and/or DBP)^20,48,50,52; two pooled data addressing lipid profile (lipid targets, total cholesterol and low density lipoprotein (LDL))^20,48 and none addressed blood glucose.

Table 2 depicts the best-evidence synthesis supporting telehealth interventions by GRADE of evidence for each meta-analysis by outcome demonstrating statistically significant effect sizes. For outcomes related to cardiovascular events, very low GRADE certainty of evidence was identified for telehealth interventions to reduce rates of recurrent angina and recurrent stroke and/or TIA.⁴⁸ For outcomes related to risk-reducing health behaviors, low GRADE certainty of evidence was identified for telehealth interventions to improve medication adherence^21,46 and very low GRADE certainty of evidence to improve physical activity participation.⁵⁴ For outcomes related to physiological risk factors, very low GRADE certainty of evidence was identified for telehealth interventions to meet recommended blood pressure targets,⁴⁸ moderate GRADE certainty of evidence to reduce SBP⁵² and a very low GRADE certainty of evidence to control LDL levels.²⁰

Table 2.

GRADE of evidence supporting telehealth interventions with statistically significant effects (meta-analyses from the included systematic reviews).

Outcome	Systematic review	Interventions compared	Studies	N	Effect Estimate [95%CI]	I2	Quality of evidence (GRADE)	Reasons for down-grade
Recurrent stroke/TIA	Gandhi et al.⁴⁸	Mobile health intervention versus usual care	2	176	OR: 0.18 [0.08 to 0.41]	NR	⊕◯◯◯ Very Low	ROB and imprecision; Inconsistency domain was not rated considering the absence of data
Angina	Gandhi et al.⁴⁸	Mobile health intervention versus usual care	3	305	OR: 0.23 [0.08 to 0.64]	NR	⊕◯◯◯ Very Low	ROB and imprecision; Inconsistency domain was not rated considering the absence of data
Medication adherence	Al-Arkee et al.²⁰	App-based interventions versus usual care	6	820	MD: 0.90 [0.03 to 1.78]	93%	⊕◯◯◯ Very Low	ROB, inconsistency, indirectness, and imprecision
	Armitage et al.⁴⁶	App-based interventions versus usual care	9	988	OR: 2.12 [1.64 to 2.75]	9.8%	⊕⊕◯◯ Low	ROB and imprecision
	Gandhi et al.⁴⁸	Mobile health interventions versus usual care	9	1355	OR: 4.51 [2.38 to 8.57]	61%	⊕◯◯◯ Very Low	ROB and inconsistency
	Kassavou et al.²³	Interactive voice response or SMS interventions versus usual care	17	25,101	OR: 1.89 [1.51 to 2.36]	89%	⊕◯◯◯ Very Low	ROB and inconsistency
	Zeng et al.²¹	Mobile health intervention versus usual care (scores)	4	498	SMD: 0.67 [0.49 to 0.85]	0%	⊕⊕◯◯ Low	ROB
	Zeng et al.²¹	Mobile health intervention versus usual care (ratios)	6	1653	OR: 2.81 [1.35 to 5.85]	77%	⊕◯◯◯ Very Low	ROB and inconsistency
Physical activity participation	Patterson et al.⁵⁴	Smartphone apps interventions versus usual care (MVPA minutes/week)	7	600	MD: 40.35 [1.03 to 79.67]	51%	⊕◯◯◯ Very Low	ROB, indirectness, and imprecision
Physical activity participation	Patterson et al.⁵⁴	Smartphone apps interventions versus usual care (steps/day)	3	67	MD: 2390 [1006.9 to 3791.2]	0%	⊕◯◯◯ Very Low	ROB, indirectness, and imprecision
Blood pressure targets	Gandhi et al.⁴⁸	Mobile health interventions versus usual care	2	913	OR: 2.80 [2.04 to 3.86]	NR	⊕◯◯◯ Very Low	ROB, inconsistency and publication bias; Inconsistency domain was not rated considering the absence of data
SBP	Kraft et al.⁵⁰	Telemedical interventions versus control treatment without telemedical approach	4	840	MD: −6.14 [-10.41 to -1.87]	53%	⊕◯◯◯ Very Low	ROB, inconsistency and imprecision
SBP	Lv et al.⁵²	Telemedicine and mobile health interventions versus usual care	9	1583	MD: -5.49 [-7.87 to -3.10]	24%	⨁⨁⨁◯ Moderate	ROB
LDL	Al-Arkee et al.²⁰	App-based interventions versus usual care	2	192	MD: -0.46 [-0.79 to -0.12]	0%	⊕◯◯◯ Very Low	ROB, indirectness, imprecision, and publication bias

Legend: 95%CI - 95% confidence interval; DBP - diastolic blood pressure; GRADE - Grading of Recommendations Assessment, Development and Evaluation; LDL - low-density lipoprotein; MD - mean difference; NR – not reported; RCT - randomized controlled trial; ROB - Risk of bias; RR - Risk Ratio; SBP - systolic blood pressure; SMD - standardized mean difference; SMS - short message service.

Figure 2 depicts only slight overlap of primary studies between the 15 SRs identified, with a 2.15% degree of corrected covered area (CCA). The citation matrix for primary studies (available in Supplementary Files) identifies 6 (43%) key primary studies^{56,60,62,66–68} cited in more than one systematic review.

Figure 2.

Phase 1: graphical representation of overlap for OVErviews (GROOVE) and overall results.

Phase-2: primary studies

Table 3 summarizes the 14 primary studies included following screening of RCTs. Included interventions were all broadly categorized under the Center for Connected Health Policy's domain^33,34 of mobile health, with one study including both mobile heath and remote patient monitoring.⁶⁶ No primary studies identified used the live videoconferencing (synchronous) or store-and-forward (asynchronous) telehealth domains. The interventions identified were implemented through telephone/cellphone for call-based delivery^{56,58,59,61,63,67} or utilized a combination of technologies: cellphone and internet for app use, alongside a home blood pressure monitor⁶⁶; cellphone/telephone for call-based delivery alongside a home blood pressure monitor⁶⁴; computer and internet for web-site access⁶²; computer and internet for web-site access combined with a cellphone/telephone for call-based delivery⁶⁵; computer and internet to support sending SMS combined with a cellphone to receive SMS/call.^57,60,68,69

Table 3.

Description of primary studies included in the overview considering the used technology and domain of application of theoretically-informed telehealth interventions

Primary article; country (systematic review source)	Sample size (n); % female	Age (years): mean (SD)	Time after stroke	Experimental group	Control group	Outcomes related to stroke secondary prevention	Main findings
Cellphone and internet for app use, alongside a home blood pressure monitor: remote patient monitoring and mobile health
Sarfo et al.⁶⁶; Ghana (Al-Arkee et al.²⁰; Freund et al.⁴⁷; Zeng et al.²¹)	60 (IC: 30; CG: 30); 35% (EG: 40%; CG: 30%)	EG: 54.3 (11.9); CG: 55.9 (13.7)	≤ 1 month	• Phone-based intervention for blood pressure control (home monitoring and reporting blood pressure, daily) + tailored motivational SMS text message based on medication adherence levels • App: NR • Blood pressure monitor: Blue-toothed UA-767Plus • BCT: self-determination theory • Professional: nurse • Length of intervention: 3 months (follow-up: at 6 and 9 months)	SMS text messages on healthy lifestyle behaviors (not related to medication adherence)	• SBP (proportion of individuals with <140 mmHg) • DBP (proportion of individuals with <90 mmHg) • Medication adherence (medication possession ratio)	• SBP and DBP: intragroup change was not reported; no significant change between groups • Medication adherence: significant increase in the EG compared to CG (at 3 months); increase in both groups and no significant change between groups at 9 months
Cellphone/telephone for call-based delivery alongside a home blood pressure monitor: mobile health
Mackenzie et al.⁶⁴; Canada (Lv et al.⁵²)	56 (EG: 29; CG: 27); 32.1%	NR	NR	• Medication counselling, provision of home blood pressure monitoring equipment and medication dosette, and telephone calls (monthly intervals for 6 months) to deliver motivational interviewing • Blood pressure monitor: NR • BCT: motivational interviewing, self-efficacy and self-managed care theories • Professional: nurse • Length of intervention: 6 months	Stroke physician specialist assessment, initiation and titration of blood pressure medication, adherence and risk factor counselling at clinic visits and follow-up by family physicians	• SBP (mmHg) • DBP (mmHg) • Medication adherence (%)	Increase in EG and decrease in CG for all measures. No significant change between groups for all measures
Computer and internet for web-site access: mobile health
Kim J et al.⁶²; Korea (Freund et al.⁴⁷; Kraft et al.⁵⁰)	36 (EG: 18; CG: 18); 41.7% (EG: 37.5%; CG: 44.4%)	EG: 67.4 (7.3); CG: 63.9 (7.4)	Within 1 year	• Web-based stroke education program on recurrence prevention behaviors (video lectures, automated quizzes, reliable external links to websites; 9 session) • BCT: social cognitive theory • Professional: doctor and nurse • Length of intervention: 2 months and 1 week (follow-up: at 3 months)	Usual care (prescribed by physician and no further recurrence prevention advice)	• Lipid profile (total cholesterol and triglyceride; mg/dl) • Health diet (amount of fruit and vegetable consumed/week; salty food consumed: none, occasionally of very often) • Physical activity participation (yes or no; changed positively) • Smoking cessation (yes or no; changed positively) • Safe alcohol consumption (yes or no; changed positively) • Medication adherence (adhere fully, missed occasionally, or changed medication dose; changed positively)	• Lipid profile: triglyceride (decrease in both groups); total cholesterol (increase in EG and decrease in CG); no significant change between groups • Medication adherence, smoking cessation and safe alcohol consumption: increase of individuals who change positively in both groups; no significant change between groups • Health diet and physical activity participation: significant increase of individuals who change positively in the EG compared to CG
Computer and internet for web-site access combined with a cellphone/telephone for call-based delivery: mobile health
Reeves et al.⁶⁵; United States (Freund et al.⁴⁷)	265 (EG-1: 88; EG-2: 90; CG: 87); 49% (EG-1: 46%; EG-2: 55%; CG: 48%)	EG-1: 67.5 (13.0); EG-2: 66.1 (12.7); CG: 65.2 (13.9)	1 month after discharge	• EG-1: Home-based social worker-led case (home visits and follow-up telephone call) • EG-2: Home-based social worker-led case + MISTT website (patient-orientated information on stroke education, prevention, recovery, and community resources management) • BCT: self-management support, community resources, and decision support • Professional: social workers • Length of intervention: 3 months	Usual care	• Psychosocial stress management (depression symptom: PHQ-9; anxiety: Neuro-QOL) • Recurrent stroke and TIA (frequency)	• Psychosocial stress management: decrease in all groups for both measures • Recurrence rate of stroke and TIA: n = 5 (EG-1); n = 12 (EG-2); n = 7 (CG); no significant difference between groups No significant change between groups for all measures
Computer and internet to support sending SMS combined with a cellphone to receive SMS/call: mobile health
Cadilhac et al.⁵⁷; Australia (Hwang et al.⁴⁹)	54 (EG: 25; CG: 29); 38.9% (EG: 40%; CG: 38%)	EG: 69 (11); CG: 68 (10)	12–24 months	• Comprehensive post-discharge support eHealth program (SMS daily) ± goal-setting assistance for 2–3 goals and 2–3 administrative messages • Support messaging system: iVERVE system • BCT: social cognitive theory, information-motivation-behavioral theory and operant condition • Professional: NR • Length of intervention: 1 month	Usual care in the community, goal-setting assistance for 2–3 goals and 2–3 administrative messages	Psychosocial stress management (heiQ, domain emotional well-being; HADS, domains depression and anxiety; status; EQ-5D, domain anxiety or depression, changed positively)	• heiQ: increase in both groups and preliminary evidence of potential improvement in favour of the intervention group* • HADS: increase in both groups and preliminary evidence of potential improvement in favour of the intervention group* • EQ-5D: increase in both groups and preliminary evidence of potential improvement in favour of the intervention group*
Kamal et al.⁶⁰; Pakistan (Adler et al.¹⁹; Freund et al.⁴⁷; Gandapur et al.²²; Kassavou et al.²³; Park et al.⁵³; Zeng et al.²¹)	200 (EC: 100; CG: 100); 32.5% (EG: 36%; CG: 29%)	EG: 56.1 (1.5); CG: 57.6 (1.3)	> 1 month	• SMS for Stroke (daily customized reminders and twice weekly health information) + usual care • BCT: health belief model and social cognitive theory • Professional: NR • Length of intervention: 2 months	Usual care	• Medication adherence (MMAS-8) • SBP (mmHg) • DBP (mmHg)	• Medication adherence: significant increase in the EG compared to CG • SBP: intragroup change was not reported; no significant change between groups • DBP: significant decrease in the EG compared to the CG
Wan et al.⁶⁸; China (Lv et al.⁵²; Zeng et al.²¹)	158 (EG: 80; CG: 78); 26.2 (EG: 36.3; CG: 33.3)	NR	NR	• Health belief model comprehensive reminder system on health behaviors and blood pressure (patient calendar handbook; in-person health belief education; telephone health belief education; automated SMS) + health education during hospitalization, a stroke prevention handout, follow-up by doctors at the outpatient department, and telephone follow-up by nurses at 1 week and 1 month after discharge • BCT: goal-setting • Professional: nurse • Length of intervention: 3 months	Usual care (health education during hospitalization, a stroke prevention handout, follow-up by doctors at the outpatient department, and telephone follow-up by nurses at 1 week and 1 month after discharge)	• Physical activity participation (HPLP-II) • Health diet (nutrition and low-salt diet: HPLP-II) • Safe alcohol consumption (limiting alcohol use, HPLP-II) • Medication adherence (HPLP-II) • SBP (mmHg) • DBP (mmHg)	• Physical activity participation, health diet, medication adherence: significant increase in the EG compared to CG • Safe alcohol consumption: increase in both groups; no significant change between groups • SBP and DBP: significant decrease in the EG compared to CG
Wang et al.⁶⁸; China (Lv et al.⁵²)	151; (EG: 76; CG: 75); 33.8% (EG: 34.2%; CG: 33.3%)	NR	NR	• Comprehensive reminder system based on the health belief model (health belief education, calendar handbook, automated SMS, telephone follow-up interviews) + health education • BCT: goal-setting • Professional: nurse • Length of intervention: 6 months	Usual care (health education during hospitalization, a stroke prevention handout, telephone follow-up by nurses at 1 week and 1 month, and follow-up by doctors at the outpatient department after discharge)	• Medication adherence (HPLP-II) • SBP (mmHg) • DPB (mmHg) • Recurrent stroke (frequency)	• Medication adherence: significant increase in the EG compared to CG • SBP: significant decrease in the EG compared to CG • DBP: decrease in both groups (at 3 months) and increase in both groups (at 6 months); no significant change between groups • Recurrence rate of stroke: n = 2 for both groups; no significant difference between groups
Telephone/cellphone for call-based delivery: mobile health
Adie et al.⁵⁶; United Kingdom (Kraft et al.⁵⁰; Lv et al.⁵²)	56 (EG: 29; CG: 27); 50% (EG: 58.6%; CG: 40.7%)	72.5 (8.9); EG: 73.6 (8.0); CG: 71.2 (9.7)	Within 1 month; 16 (8) days	• Telephone-based follow-up (20-min telephone counseling intervention; 7–10 days, 1, 2 and 4 months) ± educational material ± usual care • BCT: social cognitive theory and motivational interviewing • Professional: NR • Length of intervention: 6 months	Usual care (did not include follow-up visits in secondary care; involved instructions for follow-up with their general practitioner)	• SBP (mmHg) • DBP (mmHg) • Lipid profile (total cholesterol; mg/dl) • Physical activity participation (minutes/week) • Smoking cessation (yes or no; changed positively)	• SBP: no change in EG; decreased in the CG • DBP, lipid profile, and smoking cessation: increase in both groups • Physical activity participation: no change in both groups • No significant difference between groups for all measures
Gillham et al.⁵⁸; United Kingdom (Kraft et al.⁵⁰)	52 (EG: 26; CG: 26); 28.8%	68.3; EG: 67.7 (12.0); CG: 68.9 (13.2)	NR	• Enhanced secondary prevention (information about stroke pathology and individual stroke risk factors, a motivational interviewing about behavior change, and a plan for behavior change ± telephone support and follow-up to discuss progress at two weeks and six weeks) ± conventional care • BCT: motivational interviewing • Professional: NR • Length of intervention: 3 months	Usual care (advice given during routine care; no further secondary prevention advice)	• Psychosocial stress management (HADS: anxiety and depression) • Safe alcohol consumption (units/week) • Smoking cessation (yes or no; changed positively) • Physical activity participation (number of 20-min sessions/week) • Health diet (portions of fruit and vegetable consumed/week)	• Psychosocial stress management, safe alcohol consumption: increase in both groups; no significant change between groups • Smoking cessation: intragroup change was not reported; no significant change between groups • Physical activity participation and health diet: significant increase in the EG compared to the CG
Kim H et al.⁶¹; Korea (Kraft et al.⁵⁰)	61 (EG: 32; CG: 29); 37.7% (EG: 40.6%; CG: 34.5%)	EG: 67.4 (8.5); CG: 66.7 (9.4)	EG: 24 (2–124) months; CG: 36 (2–188) months	• Lifestyle modification tele-coaching program for secondary stroke prevention (10–20 min/week) ± in-person education • BCT: coaching • Professional: nurse • Length of intervention: 2 months	In-person education	• Safe alcohol consumption (total consumption/month) • Lipid profile (total cholesterol; triglyceride; HDL; LDL; mg/dl) • Physical activity participation (IPAQ) • SBP (mmHg) • DBP (mmHg)	• Safe alcohol consumption: increase in EG and no change in CG; no significant change between groups • Lipid profile: total cholesterol and LDL (decrease in both groups), triglyceride and HDL (decrease in EG and increase in CG); no significant change between groups • Physical activity participation: significant increase in the EG compared to the CG • SBP and DPB: significant decrease in the EG compared to CG
Kirkness et al.⁶³; United States (Laver et al.⁵¹)	100 (EG Telephone: 37; EG In-person: 35; CG: 28); 50% (EG Telephone: 48.6%; EG In-person: 51.4%; CG: 50%)	EG Telephone: 61.7 (31–85); EG In-person: 58.5 (23–83); CG: 60.7 (32–88)	Within 4 months	• EG Telephone: Living well with stroke 2 brief psychosocial intervention (6 session, average 26 min ± one in-person orientation session) • EG In-person: Living well with stroke 2 brief psychosocial intervention (6 session, average 38 min ± one in-person orientation session) • BCT: cognitive behavioral therapy and problem-solving approaches • Professional: nurse • Length of intervention: 2 months (follow-up: at 21 weeks, and at 12 months)	Usual care (visits in homes at 8, 21 weeks, and 12 months following entry to the study; prescriptions of antidepressants)	Psychosocial stress management (depression, HDRS)	Intragroup change was not reported; no significant change between groups
Hedegaard et al.⁵⁹; Denmark (Zeng et al.²¹)	203 (EG: 102; CG: 101); 38.9% (EG: 40.2%; CG: 37.6%)	EG: 64 (56–73), CG: 68 (61–73)	< 1 month	• A focused medication review followed by dialogue to support adherence and lifestyle changes + a patient interview followed by a list of their own goals and agreed actions + 3 follow-up telephone calls to the participant (1 week, 2 months and 6 months) • BCT: motivational interviewing • Professional: pharmacist • Length of intervention: 6 months (follow-up: 24 months)	Usual care (standard care provided by the managing medical team without any enhancement)	Medication adherence (medication adherence ratio)	Increase in both groups; no significant change between groups
Wan et al.⁶⁷; China (Laver et al.⁵¹; Rintala et al.⁵⁵; Zeng et al.²¹)	80 (EG: 40; CG: 40); 28.8% (EG: 25.0%; CG: 32.5%)	EG: 59.1 (12.4); CG: 60.2 (12.6)	< 1 month	• Goal-setting telephone follow-up on health behaviors (3 telephone follow-up calls at 1 week and at 1 and 3 months after discharge, each lasting 15–20 min) + usual stroke education • BCT: goal-setting • Professional: nurse • Length of intervention: 3 months (follow-up: at 6 months)	Usual stroke education and appointments with doctors for routine blood pressure measurements and medication adjustment	• Physical activity participation (HPLP-II) • Health diet (nutrition and low-salt diet: HPLP-II) • Safe alcohol consumption (unhealthy use of alcohol: HPLP-II) • Medication adherence (HPLP-II)	• Physical activity participation: increase in EG (at 3 and 6 months); increase (at 3 months) and decrease (at 6 months) in CG; no significant change between groups • Health diet: increase in both groups (at 3 and 6 months); no significant change between groups • Safe alcohol consumption: increase in EG (at 3 and 6 months); increase (at 3 months) and decrease (at 6 months) in CG; no significant change between groups • Medication adherence: significant increase in the EG compared to CG (at 6 months)

Legend: unmarked - information taken from systematic reviews; underlined - information taken from primary studies; BCT - behavior change technique; CG - control group; DBP - diastolic blood pressure; EG - experimental group; EQ-5D - EuroQoL-5 dimension; HDL - high density lipoprotein; HDRS - Hamilton Depression Rating Scale; heiQ - Health Education Impact Questionnaire; IPAQ - International Physical Activity Questionnaire; LDL – low density lipoprotein; MISTT - Michigan Stroke Transitions Trial; MMAS-8 - Morisky Medication Adherence Scale; mmHg - millimeter of mercury; PHQ-9 - Patient Health Questionnaire-9; SBP - systolic blood pressure; HADS - Hospital Anxiety Depression Scale; SMS - short message service.

*Between groups comparisons were not performed. After an intra-groups comparisons, median or logistic regression models were used for assessing potential within group differences.

The majority of primary studies (n = 9, 64%) were conducted in high income countries (Australia, Canada, Denmark, Korea, United Kingdom, and United States), with 36% (n = 5) performed in middle-income countries: China,^67–69 Ghana⁶⁶ and Pakistan.⁶⁰ Sample sizes in primary studies ranged from n = 36⁶² to n = 265.⁶⁵ Time post-stroke at trial entry ranged from less than one month^56,59,66,67 to 188 months.^56,61 Four studies did not provide information about time post-stroke.^58,64,68,69

Behavior change theories and/or techniques implemented varied across the studies. Many were implemented as a single strategy in the included studies: coaching,⁶¹ goal-setting,^67–69 motivational interviewing,^58,59 self-determination theory,⁶⁶ and social cognitive theory.⁶² Other primary studies implemented reported theories and techniques in combination: cognitive behavioral therapy and problem-solving approaches,⁶³ health belief model and social cognitive theory,⁶⁰ motivational interviewing, self-efficacy and self-managed care theories,⁶⁴ self-management support, community resources, and decision support,⁶⁵ social cognitive theory and motivational interviewing,⁵⁶ and social cognitive theory, information-motivation-behavioral theory and operant condition.⁵⁷

The professional delivering the telehealth interventions, where clearly identified (n = 10, 71%), comprised: a nurse (n = 7, 50%),^{61,63,64,66–69} doctor and nurse combination (n = 1, 7%),⁶² a pharmacist (n = 1, 7%)⁵⁹ and social workers (7%, n = 1).⁶⁵ The length of identified interventions varied from one⁵⁷ to six months.^56,59,64,69 The majority of studies (n = 10, 71%) reported usual care as the control condition,^{56–60,62,63,65,68,69} although all control interventions reported were devoid of behavior change theory and/or techniques.

The methodological quality of RCTs included in phase-2 is summarized in Table 4. Domains with the highest risk of bias were noted to be blinding of participants and personnel (n = 14, 100%) and blinding of outcome assessment (n = 3, 21%).

Table 4.

Methodological quality of primary studies included in the overview (phase-2) based on cochrane risk of bias.

Primary studies	Cochrane risk of bias
Primary studies	1 Random sequence generation	2 Allocation concealment	3 Blinding of participants and personnel	4 Blinding of outcome assessment	5 Incomplete outcome data	6 Selective reporting	7 Anything else, ideally prespecified
Adie et al.⁵⁶	Low	Low	High	Unclear	Low	High	Low
Cadilhac et al.⁵⁷	Low	Low	High	Low	Low	Low	Low
Gillham et al.⁵⁸	Low	Unclear	High	High	Low	Low	Low
Hedegaard et al.⁵⁹	Low	Low	High	Low	Low	Low	Low
Kamal et al.⁶⁰	Low	Low	High	Low	Low	Low	Low
Kim H et al.⁶¹	Low	Unclear	High	Unclear	Low	Low	Low
Kim J et al.⁶²	Low	Unclear	High	High	Low	Low	Low
Kirkness et al.⁶³	Low	Unclear	High	Low	Low	Low	Low
Mackenzie et al.⁶⁴	Low	Unclear	High	Unclear	Low	Low	Low
Reeves et al.⁶⁵	Low	High	High	High	Low	Low	High
Sarfo et al.⁶⁶	Low	Low	High	Low	Low	Low	Low
Wan et al.⁶⁷	Low	Low	High	Low	Low	Low	Low
Wan et al.⁶⁸	Low	Low	High	Low	Low	Low	Low
Wang et al.⁶⁹	Low	Low	High	Low	Low	Low	Low

Of the primary outcomes of interest, two studies reported recurrent stroke rates as an outcome.^65,69 Secondary outcomes of interest reported included medication adherence (n = 8),^{59,60,62,64,66–69} healthy eating (n = 4),^58,62,67,68 physical activity participation (n = 6),^{56,58,61,62,67,68} smoking cessation (n = 3),^56,58,62 safe alcohol consumption (n = 5),^{58,61,62,67,68} and emotional self-regulation of psychosocial stress (anxiety and depression) (n = 4).^57,58,63,65 Tertiary outcomes of interest reported included SBP and DBP control (n = 7 for both),^{56,60,61,64,66,68,69} total cholesterol levels (n = 3),^56,61,62 triglyceride levels (n = 2),^61,62 LDL levels (n = 1)⁶¹ and HDL levels (n = 1).⁶¹

Overview of behavior change and self-management theoretically-informed telehealth interventions for stroke secondary prevention

Table 5 depicts GRADE of evidence supporting theoretically-informed telehealth interventions for each meta-analysis conducted by outcome.

Table 5.

GRADE of evidence supporting telehealth interventions (meta-analyses of primary studies) (phase-2)

Outcome	RCT Studies	Behavior change theories and/or techniques implemented	Technologies used	Participants	Effect estimate [95%CI]	I²	Quality of evidence (GRADE)	Reasons for down-grade
Primary outcomes
Recurrent stroke	Reeves et al.⁶⁵	Self-management support, community resources, and decision support	Computer and internet to access web-site and cellphone or telephone to receive call	416	RR: 0.63 [0.33 to 1.19]	0%	⊕◯◯◯ Very Low	ROB and imprecision
Recurrent stroke	Wang et al.⁶⁸	Goal-setting		416	RR: 0.63 [0.33 to 1.19]	0%	⊕◯◯◯ Very Low	ROB and imprecision
Secondary outcomes
Medication adherence	Hedegaard et al.⁵⁹	Motivational interviewing	Telephone or cellphone to receive call	968	SMD: 0.38 [0.13 to 0.64]	72%	⊕⊕◯◯ Low	Inconsistency
	Kamal et al.⁶⁰	Health belief model and social cognitive theory	Computer and internet to support sending SMS and cellphone to receive SMS/call
	Mackenzie et al.⁶⁴	Motivational interviewing, self-efficacy and self-managed care theories	Cellphone or telephone to receive call and blood pressure monitor
	Sarfo et al.⁶⁶	Self-determination theory	Cellphone and internet to use app and blood pressure monitor
	Wan et al.⁶⁷	Goal-setting	Telephone or cellphone to receive call
	Wan et al.⁶⁸		Computer and internet to support sending SMS and cellphone to receive SMS/call
	Wang et al.⁶⁹
Healthy eating	Gillham et al.⁵⁸	Motivational interviewing	Telephone or cellphone to receive call	562	SMD: 0.38 [0.15 to 0.60]	38%	⊕⊕◯◯ Low	ROB and inconsistency
	Kim J et al.⁶²	Social cognitive theory	Computer and internet to access web-site
	Wan et al.⁶⁷	Goal-setting	Computer and internet to support sending SMS and cellphone to receive SMS/call
	Wan et al.⁶⁸	Goal-setting
Physical activity participation	Gillham et al.⁵⁸	Motivational interviewing	Telephone or cellphone to receive call	349	SMD: 0.10 [-0.68 to 0.88]	92%	⊕◯◯◯ Very Low	ROB, inconsistency and imprecision
	Kim H et al.⁶¹	Coaching	Telephone or cellphone to receive call
	Wan et al.⁶⁷	Goal-setting	Computer and internet to support sending SMS and cellphone to receive SMS/call
	Wan et al.⁶⁸	Goal-setting
Smoking cessation	Adie et al.⁵⁶	Social cognitive theory and motivational interviewing	Telephone or cellphone to receive call	92	RR: -0.00 [-0.12 to 0.11]	0%	⊕◯◯◯ Very Low	ROB, inconsistency and imprecision
Smoking cessation	Kim J et al.⁶²	Social cognitive theory	Computer and internet to access web-site	92	RR: -0.00 [-0.12 to 0.11]	0%	⊕◯◯◯ Very Low	ROB, inconsistency and imprecision
Safe alcohol consumption	Wan et al.⁶⁷	Goal-setting	Computer and internet to support sending SMS and cellphone to receive SMS/call	138	SMD: -0.94 [-2.60 to 0.72]	97%	⊕◯◯◯ Very Low	Inconsistency and imprecision
Safe alcohol consumption	Wan et al.⁶⁸	Goal-setting		138	SMD: -0.94 [-2.60 to 0.72]	97%	⊕◯◯◯ Very Low	Inconsistency and imprecision
Psychosocial stress management	Cadilhac et al.⁵⁷	Social cognitive theory, information-motivation-behavioral theory and operant condition	Computer and internet to support sending SMS combined with a cellphone to receive SMS/call	423	SMD: 0.05 [-0.16 to 0.26]	10%	⊕◯◯◯ Very Low	ROB and imprecision
	Gillham et al.⁵⁸	Motivational interviewing	Telephone or cellphone to receive call
	Reeves et al.⁶⁵	Self-management support, community resources, and decision support	Computer and internet to access web-site and cellphone or telephone to receive call
Tertiary outcomes
SBP (mmHg)	Kim H et al.⁶¹	Coaching	Telephone or cellphone to receive call	426	MD: -9.19 [-12.89 to -5.49]	0%	⨁⨁⨁◯ Moderate	ROB and indirectness
	Mackenzie et al.⁶⁴	Motivational interviewing, self-efficacy and self-managed care theories	Cellphone or telephone to receive call and blood pressure monitor
	Wan et al.⁶⁷	Goal-setting	Computer and internet to support sending SMS and cellphone to receive SMS/call
	Wang et al.⁶⁸	Goal-setting
DBP (mmHg)	Adie et al.⁵⁶	Social cognitive theory and motivational interviewing	Telephone or cellphone to receive call	331	MD: -6.34 [-15.73 to 3.06]	91%	⊕◯◯◯ Very Low	ROB, inconsistency and indirectness
	Kim H et al.⁶¹	Coaching	Telephone or cellphone to receive call
	Mackenzie et al.⁶⁴	Motivational interviewing, self-efficacy and self-managed care theories	Cellphone or telephone to receive call and blood pressure monitor
	Wan et al.⁶⁷	Goal-setting	Computer and internet to support sending SMS and cellphone to receive SMS/call
Total cholesterol (mg/dl)	Adie et al.⁵⁶	Social cognitive theory and motivational interviewing	Telephone or cellphone to receive call	153	MD: 7.42 [-3.83 to 18.66]	0%	⊕◯◯◯ Very Low	ROB and imprecision
	Kim H et al.⁶¹	Coaching	Telephone or cellphone to receive call
	Kim J et al.⁶²	Social cognitive theory	Computer and internet to access web-site
Triglycerides (mg/dl)	Kim H et al.⁶¹	Coaching	Telephone or cellphone to receive call	97	MD: -6.02 [-31.86 to 19.82]	0%	⊕◯◯◯ Very Low	ROB and imprecision
Triglycerides (mg/dl)	Kim J et al.⁶²	Social cognitive theory	Computer and internet to access web-site	97	MD: -6.02 [-31.86 to 19.82]	0%	⊕◯◯◯ Very Low	ROB and imprecision

Legend: 95%CI - 95% confidence interval; DBP – diastolic blood pressure; GRADE - Grading of Recommendations Assessment, Development and Evaluation; MD - mean difference; ROB - Risk of bias; RCT - randomized controlled trial; RR - Risk Ratio; SBP - systolic blood pressure; SMD - standardized mean difference; SMS - short message service.

Primary outcomes related to cardiovascular events

Data presented permitted pooled analysis from the two studies that reported recurrent stroke outcomes measured as the number of events.^65,69 Meta-analysis demonstrated a nonsignificant reduction in recurrent stroke risk (RR: 0.63, 95%CI: 0.33 to 1.19, I²: 0%, random-effect) in favor of telehealth interventions (available in Supplementary Files), with very low GRADE certainty of evidence.

Secondary outcomes related to risk-reducing health behaviors

Medication adherence

Data presented permitted pooled analysis from seven studies that reported medication adherence outcomes.^{59,60,64,66–69} The following measures were used in the studies pooled to inform this outcome: Health Promoting Lifestyle Profile II (HPLP II) (medication adherence subcategory),^67–69 medication possession ratio score,^59,66 Morisky Medication Adherence Scale (MMAS),^60,66 and percentage compliance for one to three medications.⁶⁴ Meta-analysis demonstrated a significant improvement in medication adherence (SMD: 0.38; 95%CI: 0.13 to 0.64; I²: 72% random-effect) in favor of telehealth interventions (Figure 3), with low GRADE certainty of evidence. Interventions contributing to this evidence-base used telephone/cellphone for call-based delivery,^59,67 computer and internet to support sending SMS combined with a cellphone to receive SMS/call,^60,68,69 cellphone/telephone for call-based delivery alongside a home blood pressure monitor,⁶⁴ cellphone and internet for app use alongside a home blood pressure monitor.⁶⁶ The interventions were informed by motivational interviewing,⁵⁹ health belief model and social cognitive theory,⁶⁰ motivational interviewing, self-efficacy and self-managed care theories,⁶⁴ self-determination theory,⁶⁶ and goal-setting.^67–69

Figure 3.

Medication adherence outcome for the comparison of telehealth interventions versus usual care.

Healthy eating

Data presented permitted pooled analysis from all four studies that reported healthy eating outcomes.^58,62,67,68 Data were pooled from studies employing the following measures: number of servings of fruits and vegetables consumed per week,⁵⁸ amount of fruits and vegetables consumed per week,⁶² and HPLP II (nutrition and low-salt diet subcategories).^67,68 Meta-analysis demonstrated a significant improvement in healthy eating (SMD: 0.38; 95%CI: 0.15 to 0.60; I²: 38%, random-effect) in favor of telehealth interventions (Figure 4), with low GRADE certainty of evidence. Interventions contributing to this evidence-base used telephone/cellphone for call-based delivery,^58,67 computer and internet for web-site access,⁶² computer and internet to support sending SMS combined with a cellphone to receive SMS/call⁶⁸ and were informed by motivational interviewing,⁵⁸ social cognitive theory,⁶² and goal-setting.^67,68

Figure 4.

Healthy eating outcome for the comparison of telehealth interventions versus usual care.

Physical activity participation

Data presented permitted pooled analysis from four studies that reported outcomes measuring physical activity participation.^58,61,67,68 Data were pooled from studies that used the following measures: number of 20-min sessions per week,⁵⁸ International Physical Activity Questionnaire (IPAQ),⁶¹ and HPLP II (physical activity subcategory).^67,68 Meta-analysis demonstrated a nonsignificant increase in physical activity participation (SMD: 0.10; 95%CI: −0.68 to 0.88; I²: 92%, random-effect) (available in Supplementary Files), with very low GRADE certainty of evidence.

Smoking cessation

Data presented permitted pooled analysis from two studies that reported smoking cessation measured as the number of individuals who stopped smoking.^56,62 Meta-analysis demonstrated a nonsignificant increase in smoking cessation rates (RR: −0.00, 95%CI: −0.12 to 0.11, I²: 0% random-effect) (available in Supplementary Files), with very low GRADE certainty of evidence.

Safe alcohol consumption

Data presented permitted pooled analysis from two studies that reported outcome measures addressing safe alcohol consumption (HPLP II; unhealthy use of alcohol subcategory).^67,68 Meta-analysis demonstrated a nonsignificant reduction in alcohol consumption (SMD: −0.94; 95%CI: −2.60 to 0.72; I²: 97%, random-effect) (available in Supplementary Files), with very low GRADE certainty of evidence.

Emotional self-regulation of psychosocial stress

Data presented permitted pooled analysis from two studies that reported outcomes addressing emotional self-regulation (anxiety and depression).^57,58,65 The following measures were reported in studies addressing this outcome: Hospital Anxiety and Depression Scale (HADS)^57,58 and Patient Health Questionnaire-9 (PHQ-9).⁶⁵ Meta-analysis demonstrated a nonsignificant improvement in emotional self-regulation of psychosocial stress (anxiety and depression) (SMD: 0.05; 95%CI: −0.16 to 0.26; I²: 10%, random-effect) in favor of the telehealth intervention (available in Supplementary Files), with very low GRADE certainty of evidence.

Tertiary outcomes related to physiological risk factors

Systolic blood pressure

Data presented permitted pooled analysis from four studies that reported SBP (mmHg) as an outcome measure.^61,64,68,69 Meta-analysis demonstrated a significant reduction in SBP (MD: −9.19; 95%CI: −5.49 to −12.89; I²: 0%, random-effect) in favor of telehealth interventions (Figure 5), with moderate GRADE certainty of evidence. Interventions contributing to this evidence-base used telephone/cellphone for call-based delivery,⁶¹ cellphone or telephone for call-based delivery alongside a home blood pressure monitor,⁶⁴ computer and internet to support sending SMS combined with a cellphone to receive SMS/call.^68,69 The interventions were informed by coaching,⁶¹ motivational interviewing, self-efficacy and self-managed care theories,⁶⁴ and goal-setting.^68,69

Figure 5.

Systolic blood pressure outcome (mmHg) for the comparison of telehealth interventions versus usual care.

Diastolic blood pressure

Data presented permitted pooled analysis from four studies that reported DBP (mmHg) as an outcome of interest.^56,61,64,68 Meta-analysis demonstrated a nonsignificant reduction in DBP (MD: −6.34; 95%CI: 3.06 to −15.73; I²: 91%, random-effect) in favor of the telehealth interventions (available in Supplementary Files), with very low GRADE certainty of evidence.

Lipid profiles: total cholesterol and triglyceride control

Data presented permitted pooled analysis from all three studies that reported an outcome of total cholesterol (mg/dl)^56,61,62 and from two studies that reported an outcome of triglyceride (mg/dl).^61,62 Meta-analyses demonstrated a nonsignificant increase in total cholesterol levels (MD: 7.42; 95%CI: −3.83 to 18.66; I²: 0%, random-effect) (available in Supplementary Files) and a nonsignificant reduction in triglyceride levels (MD: −6.02; 95%CI: −31.86 to 19.82; I²: 0%, random-effect) in favor of telehealth interventions (available in Supplementary Files), with very low GRADE certainty of evidence for both outcomes.

Discussion

To the best of our knowledge, this overview is the first to critically appraise and consolidate evidence from SRs on the effectiveness of telehealth interventions designed to affect behavior change and self-management, delivered to individuals for stroke secondary prevention and the first to provide high-level pooled data for theoretically-informed telehealth interventions. The best-evidence synthesis conducted from fifteen published SRs supports telehealth interventions to affect significant reduction in rates of recurrent angina and recurrent stroke and/or TIA (both with very low GRADE certainty), improvement in medication adherence (low GRADE certainty), physical activity participation (very low GRADE certainty), and achievement of recommended blood pressure targets (low GRADE certainty), reduction in SBP (moderate GRADE certainty) and LDL levels (very low GRADE certainty). Phase-2, which conducted new meta-analyses of data from 14 primary studies which had a theoretical underpinning, in line with best-practice, also demonstrated significant improvements in medication adherence (low GRADE certainty) and SBP reduction (moderate GRADE certainty) and identified new evidence, albeit with low GRADE certainty, for the lifestyle-related outcome of health eating, in favor of telehealth interventions. With the exception of SBP, greater certainty of evidence is required to confirm the utility of telehealth interventions in stroke secondary prevention. These results direct what can now be done and what should be carefully considered regarding theoretically-informed telehealth interventions for stroke secondary prevention. Notably they indicate the need for studies to measure and document cardiovascular events outcomes for secondary prevention and the need for further high quality, well conceptualized trials in this area.

The most interesting result of this overview concerns the lack of evidence for primary outcomes in stroke secondary prevention related to cardiovascular events. Only one published SR performed a meta-analysis addressing the outcome of recurrent cardiovascular events,⁴⁸ with very low GRADE certainty of evidence found. Only two studies were included in this meta-analysis.⁴⁸ Context and populations varied between the two studies included in this meta-analysis. One was conducted in a hospital setting⁷¹ and one included a mixed population of individuals with cardiovascular disease.⁷² Thus, this meta-analysis does not strengthen the evidence on telehealth interventions for stroke secondary prevention alone. In phase-2, it was possible to perform a new meta-analysis with data from two studies^65,69 that included only participants post-stroke and that had recurrent stroke as an outcome. Here, a nonsignificant reduction in recurrent stroke, in favor of the telehealth intervention (very low GRADE certainty), was identified, downgraded primarily because of imprecision related to small participant numbers and risk of bias issues. Both studies from which data were drawn had short intervention times (3 and 6 months, respectively),^65,69 neither included longer-term follow up post intervention and only one study detailed time from stroke at trial entry.⁶⁵ Considering the variability of stroke recurrence rates by time post-stroke,³ it is important that future studies present a longer follow-up time and provide clearer information about the participants time from stroke at trial entry.

Medication is considered a cornerstone of stroke secondary prevention.¹⁰ However, it is well established that up to 40% of individuals post-stroke have poor medication adherence⁷³ and medication persistence.⁷⁴ Across both phases of the present overview, low GRADE certainty of evidence for telehealth interventions targeting improved medication adherence was identified. Methodological concerns in both cases required the evidence to be downgraded. Therefore, future studies that aim to improve medication adherence post-stroke using telehealth interventions should prioritize the methodological quality of the study and ensure adequate sample sizes. Providing detailed intervention descriptions that include the theoretical underpinning and/or techniques may facilitate meta-analyses grouping by behavior change techniques/theory in the future to increase the certainty of evidence supporting theoretically-informed telehealth interventions and direct evidence-based intervention delivery in practice.

Ten potentially modifiable risk factors are collectively associated with 90% of the population attributable risk (PAR) for stroke worldwide.⁷⁵ Hypertension constitutes the largest modifiable risk factor⁷⁵ and this overview of reviews identifies that telehealth interventions post-stroke have the strongest certainty of evidence (moderate GRADE) to reduce SBP by over 9 mmHg. This finding has clinical relevance in stroke secondary prevention as a 10 mmHg reduction in SBP is reported by meta-analysis to significantly reduce the risk of coronary heart disease, stroke and heart failure and is associated with a 13% reduction in all-cause mortality.⁷⁶ Physical inactivity is the second largest PAR for stroke.⁷⁵ Activity participation alongside other lifestyle related PAR for stroke including diet, smoking and unsafe alcohol consumption are also important targets for stroke prevention interventions.⁷⁵ Only one published SR included in this overview provided meta-analyses addressing physical activity participation outcomes, again, with very low GRADE certainty for this finding. When interrogated at primary study level (in phase-2 of this overview), new meta-analyses, albeit with limited study numbers, was permitted for each of these outcomes. Only the outcome of healthy eating provided evidence (low GRADE certainty) in favor of telehealth interventions. Again, high risk of bias in the majority of RCTs and varied effect sizes across studies necessitated a downgrading of the evidence. Once again, high quality, theoretically-informed telehealth interventions post-stroke that address lifestyle related risk factors are required to advance the secondary prevention evidence base in this area.

The gaps highlighted by this overview can help shape future research priorities, and direct future research in this field. There is a need for telehealth trials, with longer follow-up time, that are designed, adequately powered and conducted to address outcomes of stroke recurrence, as well as mortality (all cause and cardiovascular) and other cardiovascular events. Additionally, studies designed to investigate the effectiveness of theoretically-informed (theory based or employing specified behavior change techniques) telehealth interventions with individuals post-stroke are needed to address outcomes related to risk-reducing health behaviors, including the promotion of physical activity, which is the second largest PAR for stroke.⁷⁵ No published meta-analysis or primary study identified in this review addressed the outcome of blood glucose levels. Considering that diabetes mellitus is also one of the ten potentially modifiable risk factors of stroke worldwide⁷⁵ and that the estimated prevalence of diabetes in individuals post-stroke is 28%,⁷⁷ reducing blood glucose levels is a legitimate target for theoretically-informed telehealth interventions post-stroke.

Strengths and limitations

This overview of reviews, adhering to the PRIOR guidelines, critically appraised and consolidated evidence from published SRs of telehealth interventions delivered to address modification of recurrent risk through behavior change and self-management post-stroke. By applying GRADE criteria to published meta-analyses and to new syntheses conducted on primary study level data, the current certainty of evidence associated with telehealth interventions was elucidated to aid in interpretation and application of the findings, where relevant. Furthermore, care was taken at primary study level to include only interventions that were theoretically-informed by behavior change theory and/or techniques, in line with best practice recommendations,⁸ that acknowledge the complexity in implementing behavior change and self-management interventions. This narrowed the critical lens provided by previous SRs, where only slight overlap in studies existed across reviews. Furthermore, it allows better replication of the interventions in clinical practice by professionals who wish to provide telehealth interventions for stroke secondary prevention.

As phase-2 of this overview utilized primary studies included in the published SRs, it is possible that more recent RCTs may have been excluded. As SRs of RCTs were the sole focus of this overview, the acceptability and uptake of telehealth interventions, alongside their perceived utility were not considered from the perspective of individuals with stroke. Although high levels of satisfaction and acceptance of telehealth interventions for health professional and individuals post-stroke have been reported in the literature,⁷⁸ future studies are required to specifically examine end-users’ experiences of telehealth interventions for behavior change and self-management post-stroke and to explore the more effective mechanisms of delivery. Limited data were available for the majority of outcomes of interest in this overview, and no RCTs were identified for a number of outcomes of interest in stroke secondary prevention. Results presented for the majority of outcomes reported need to be interpreted with caution due to the low GRADE certainty of evidence assigned. In addition, limited data provided in study descriptions in many primary studies prevented examination of the findings in relation to the optimal time to deliver interventions of this nature post-stroke, the impact of age of the participants, or the professionals who implemented the telehealth interventions.

Conclusions

Insufficient evidence currently exists by published SRs to support the role of telehealth interventions post-stroke to reduce cardiovascular mortality. Evidence presented does support telehealth interventions to make a meaningful improvement in medication adherence and reduction in SBP, but does not allow replication of the intervention due to notable heterogeneity in the interventions currently pooled in published meta-analyses. Consolidation of evidence on the effectiveness of theoretically-informed telehealth interventions for behavior change and self-management delivered to individuals for stroke secondary prevention provided more granular detail, but only in the telehealth domain of mobile interventions. Here, evidence supports coaching, motivational interviewing, self-efficacy, goal-setting and self-managed care in affecting significant reductions in SBP in comparison to usual care, with moderate GRADE certainty of evidence. Evidence supporting theoretically-informed telehealth interventions to improve medication adherence and health eating currently has too low certainty of evidence to inform practice. High quality RCTs that implement theoretically-informed telehealth interventions addressing both cardiovascular and lifestyle related outcomes and with longer-term follow-up are required.

Supplemental Material

sj-docx-1-jtt-10.1177_1357633X241238779 - Supplemental material for Effectiveness of behavior change and self-management theoretically-informed telehealth interventions for stroke secondary prevention: An overview of systematic reviews

Supplemental material, sj-docx-1-jtt-10.1177_1357633X241238779 for Effectiveness of behavior change and self-management theoretically-informed telehealth interventions for stroke secondary prevention: An overview of systematic reviews by Paula da Cruz Peniche, Christina Danielli Coelho de Morais Faria, Patricia Hall, and Olive Lennon in Journal of Telemedicine and Telecare

Footnotes

Data availability statement

The study, as a review of reviews, uses only published data. All included studies are referenced. Important data that validates the results of the present study are presented as supplementary materials, including the search strategy employed and the list of articles that were excluded as they did not meet the eligibility criteria.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Financial support was provided by the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-Finance code 001), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ), Pró-reitoria de Pesquisa da Universidade Federal de Minas Gerais (PRPq/UFMG) and Worldwide Universities Network (WUN).

ORCID iDs

Paula da Cruz Peniche

Olive Lennon

Supplemental material

Supplemental material for this article is available online.

References

Feigin

Brainin

Norrving

, et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. Int J Stroke 2022; 17: 18–29.

GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 2021; 20: 795–820.

Lin

Zhang

Mei

, et al. Cumulative risk of stroke recurrence over the last 10 years: A systematic review and meta-analysis. Neurol Sci 2021; 42: 61–71.

Wang

, et al. Effect of recurrent stroke on poor functional outcome in transient ischemic attack or minor stroke. Int J Stroke 2016; 11: NP80.

Modrego

Pina

Fraj

, et al. Type, causes, and prognosis of stroke recurrence in the province of Teruel, Spain. A 5-year analysis. Neurol Sci 2000; 21: 355–360.

Zhong

Geng

Wang

, et al. Prevalence, causes and risk factors of hospital readmissions after acute stroke and transient ischemic attack: A systematic review and meta-analysis. Neurol Sci 2016; 37: 1195–1202.

Stroke Association, James Lind Alliance. Priorities in stroke rehabilitation and long-term care. Stroke Association. Available from: https://www.jla.nihr.ac.uk/priority-setting-partnerships/Stroke/downloads/Stroke-PSP-Top-Ten-list-of-results-rehab.pdf (2021, accessed 09 August 2023).

Lawrence

Asaba

Duncan

, et al. Stroke secondary prevention, a non-surgical and non-pharmacological consensus definition: results of a Delphi study. BMC Res Notes 2019; 12: 823.

Gladstone

Lindsay

Douketis

, et al. Canadian Stroke Best Practice Recommendations: Secondary Prevention of Stroke Update 2020. Can J Neurol Sci 2022; 49: 315–337.

10.

Kleindorfer

Towfighi

Chaturvedi

, et al. Guideline for the prevention of stroke in patients with stroke and transient ischemic attack: A guideline from the American Heart Association/American Stroke Association. Stroke 2021; 2021(52): e364–e467.

11.

Harshfield

Georgakis

Malik

, et al. Modifiable lifestyle factors and risk of stroke: A Mendelian randomization analysis. Stroke 2021; 52: 931–936.

12.

World Health Organization, International Telecommunication Union. WHO-ITU global standard for accessibility of telehealth services. Geneva: World Health Organization and International Telecommunication Union, 2022. Available from: https://www.who.int/publications/i/item/9789240050464 (accessed 09 August 2023).

13.

Liu

Wang

, et al. Trends and frontiers of research on telemedicine from 1971 to 2022: A scientometric and visualisation analysis. J Telemed Telecare 2023; 29: 731–746.

14.

Schulz

Long

Kanhutu

, et al. Telehealth during the coronavirus disease 2019 pandemic: Rapid expansion of telehealth outpatient use during a pandemic is possible if the programme is previously established. J Telemed Telecare 2022; 28: 445–451.

15.

Snoswell

Chelberg

Guzman

, et al. The clinical effectiveness of telehealth: A systematic review of meta-analyses from 2010 to 2019. J Telemed Telecare 2023; 29: 669–684.

16.

Bloem

Dorsey

Okun

. The coronavirus disease 2019 crisis as catalyst for telemedicine for chronic neurological disorders. JAMA Neurol 2020; 77: 927–928.

17.

Lai

Woo

Hui

, et al. Telerehabilitation - a new model for community-based stroke rehabilitation. J Telemed Telecare 2004; 10: 199–205.

18.

Wein

Lindsay

Côté

, et al. Canadian Stroke best practice recommendations: Secondary prevention of stroke, sixth edition practice guidelines, update 2017. Int J Stroke 2018; 13: 420–443.

19.

Adler

Martin

Mariani

, et al. Mobile phone text messaging to improve medication adherence in secondary prevention of cardiovascular disease. Cochrane Database Syst Rev 2017; 4: Cd011851.

20.

Al-Arkee

Mason

Lane

, et al. Mobile apps to improve medication adherence in cardiovascular disease: Systematic review and meta-analysis. J Med Internet Res 2021; 23: e24190.

21.

Zeng

, et al. Impact of mobile health and telehealth technology on medication adherence of stroke patients: A systematic review and meta-analysis of randomized controlled trials. Int J Clin Pharm 2022; 44: 4–14.

22.

Gandapur

Kianoush

Kelli

, et al. The role of mHealth for improving medication adherence in patients with cardiovascular disease: A systematic review. Eur Heart J Qual Care Clin Outcomes 2016; 2: 237–244.

23.

Kassavou

Sutton

. Automated telecommunication interventions to promote adherence to cardio-metabolic medications: Meta-analysis of effectiveness and meta-regression of behaviour change techniques. Health Psychol Rev 2018; 12: 25–42.

24.

Higgins

JPT

Thomas

Chandler

, et al. Cochrane handbook for systematic reviews of interventions version 6.3. Cochrane, 2022. Available from: www.training.cochrane.org/handbook (accessed 09 August 2023).

25.

Gates

Pieper

, et al. Reporting guideline for overviews of reviews of healthcare interventions: Development of the PRIOR statement. Br Med J 2022; 378: e070849.

26.

Lennon

Blake

Booth

, et al. Interventions for behaviour change and self-management in stroke secondary prevention: Protocol for an overview of reviews. Syst Rev 2018; 7: 231.

27.

Hall

Lawrence

Blake

, et al. Interventions for behaviour change and self-management of risk in stroke secondary prevention: an overview of reviews. Cerebrovasc Dis 2024; 53: 1–13.

28.

Scottish Intercollegiate Guidelines Network. SIGN 149: Risk estimation and the prevention of cardiovascular disease. Edinburgh: Scottish Intercollegiate Guidelines Network, 2017. Available at: https://www.sign.ac.uk/assets/sign149.pdf (accessed 09 August 2023).

29.

European Stroke Organisation (ESO) Executive Committee, ESO Writing Committee. Guidelines for management of ischaemic stroke and transient ischaemic attack 2008. Cerebrovasc Dis 2008; 25: 457–507.

30.

McGowan

Sampson

Salzwedel

, et al. PRESS Peer review of electronic search strategies: 2015 Guideline Statement. J Clin Epidemiol 2016; 75: 40–46.

31.

Martinic

Pieper

Glatt

, et al. Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks. BMC Med Res Methodol 2019; 19: 203.

32.

Pérez-Bracchiglione

Meza

Bangdiwala

, et al. Graphical representation of overlap for OVErviews: GROOVE tool. Res Synth Methods 2022; 13: 381–388.

33.

Center for Connected Health Policy (CCHP). A Framework for Defining Telehealth Center for Connected Health Policy. Center for Connected Health Policy. Available from: https://cchp.nyc3.digitaloceanspaces.com/2021/04/Telehealth-Definintion-Framework-for-TRCs_0.pdf (2021, accessed 09 August 2023).

34.

Center for Connected Health Policy (CCHP). What is telehealth? Center for Connected Health Policy. Available from: https://www.cchpca.org/what-is-telehealth/ (2023, accessed 09 August 2023).

35.

Whiting

Savović

Higgins

, et al. ROBIS: A new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol 2016; 69: 225–234.

36.

Guyatt

Oxman

Vist

, et al. GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. Br Med J 2008; 336: 924–926.

37.

Guyatt

Oxman

Vist

, et al. GRADE Guidelines: 4. Rating the quality of evidence–study limitations (risk of bias). J Clin Epidemiol 2011; 64: 407–415.

38.

Guyatt

Oxman

Kunz

, et al. GRADE Guidelines: 7. Rating the quality of evidence–inconsistency. J Clin Epidemiol 2011; 64: 1294–1302.

39.

Guyatt

Oxman

Kunz

, et al. GRADE Guidelines: 8. Rating the quality of evidence–indirectness. J Clin Epidemiol 2011; 64: 1303–1310.

40.

Guyatt

Oxman

Kunz

, et al. GRADE Guidelines 6. Rating the quality of evidence–imprecision. J Clin Epidemiol 2011; 64: 1283–1293.

41.

Guyatt

Oxman

Montori

, et al. GRADE Guidelines: 5. Rating the quality of evidence–publication bias. J Clin Epidemiol 2011; 64: 1277–1282.

42.

Michie

Richardson

Johnston

, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013; 46: 81–95.

43.

Hagger

Cameron

Hamilton

, et al. The handbook of behavior change. United Kingdom: Cambridge University Press, 2020. Available from: https://www.cambridge.org/core/books/handbook-of-behavior-change/BD76D0E53DB1BC2EE8C53C1603D45E6A (accessed 09 August 2023).

44.

Hoffmann

Glasziou

Boutron

, et al. Better reporting of interventions: Template for intervention description and replication (TIDieR) checklist and guide. Br Med J 2014; 348: g1687.

45.

Higgins

Altman

Gøtzsche

, et al. The Cochrane collaboration's tool for assessing risk of bias in randomised trials. Br Med J 2011; 343: d5928–d5928.

46.

Armitage

Kassavou

Sutton

. Do mobile device apps designed to support medication adherence demonstrate efficacy? A systematic review of randomised controlled trials, with meta-analysis. BMJ Open 2020; 10: e032045.

47.

Freund

Carey

Dilworth

, et al. Effectiveness of information and communications technology interventions for stroke survivors and their support people: A systematic review. Disabil Rehabil 2022; 44: 4563–4578.

48.

Gandhi

Chen

Hong

, et al. Effect of Mobile health interventions on the secondary prevention of cardiovascular disease: Systematic review and meta-analysis. Can J Cardiol 2017; 33: 219–231.

49.

Hwang

Park

Chang

. Telehealth interventions to support self-management in stroke survivors: A systematic review. Healthcare (Basel) 2021; 9: 472.

50.

Kraft

Hillmann

Rücker

, et al. Telemedical strategies for the improvement of secondary prevention in patients with cerebrovascular events-A systematic review and meta-analysis. Int J Stroke 2017; 12: 597–605.

51.

Laver

Adey-Wakeling

Crotty

, et al. Telerehabilitation services for stroke. Cochrane Database Syst Rev 2020; 1: Cd010255.

52.

Jiang

, et al. Effects of telemedicine and mHealth on systolic blood pressure management in stroke patients: Systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth 2021; 9: e24116.

53.

Park

Beatty

Stafford

, et al. Mobile phone interventions for the secondary prevention of cardiovascular disease. Prog Cardiovasc Dis 2016; 58: 639–650.

54.

Patterson

Davey

Keegan

, et al. Smartphone applications for physical activity and sedentary behaviour change in people with cardiovascular disease: A systematic review and meta-analysis. PLoS One 2021; 16: e0258460.

55.

Rintala

Päivärinne

Hakala

, et al. Effectiveness of technology-based distance physical rehabilitation interventions for improving physical functioning in stroke: A systematic review and meta-analysis of randomized controlled trials. Arch Phys Med Rehabil 2019; 100: 1339–1358.

56.

Adie

James

. Does telephone follow-up improve blood pressure after minor stroke or TIA? Age Ageing 2010; 39: 598–603.

57.

Cadilhac

Andrew

Busingye

, et al. Pilot randomised clinical trial of an eHealth, self-management support intervention (iVERVE) for stroke: Feasibility assessment in survivors 12–24 months post-event. Pilot Feasibility Stud 2020; 6: 172.

58.

Gillham

Endacott

. Impact of enhanced secondary prevention on health behaviour in patients following minor stroke and transient ischaemic attack: A randomized controlled trial. Clin Rehabil 2010; 24: 822–830.

59.

Hedegaard

Kjeldsen

Pottegård

, et al. Multifaceted intervention including motivational interviewing to support medication adherence after stroke/transient ischemic attack: A randomized trial. Cerebrovasc Dis Extra 2014; 4: 221–234.

60.

Kamal

Shaikh

Pasha

, et al. 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; 15: 212.

61.

Kim

. [The lifestyle modification coaching program for secondary stroke prevention]. J Korean Acad Nurs 2013; 43: 331–340.

62.

Kim

Lee

Kim

. Effects of a web-based stroke education program on recurrence prevention behaviors among stroke patients: A pilot study. Health Educ Res 2013; 28: 488–501.

63.

Kirkness

Cain

Becker

, et al. Randomized trial of telephone versus in-person delivery of a brief psychosocial intervention in post-stroke depression. BMC Res Notes 2017; 10: 500.

64.

Mackenzie

Ireland

Moore

, et al. Tailored interventions to improve hypertension management after stroke or TIA–phase II (TIMS II). Can J Neurosci Nurs 2013; 35: 27–34.

65.

Reeves

Fritz

Woodward

, et al. Michigan Stroke Transitions Trial. Circ Cardiovasc Qual Outcomes 2019; 12: e005493.

66.

Sarfo

Treiber

Gebregziabher

, et al. Phone-based intervention for blood pressure control among Ghanaian stroke survivors: A pilot randomized controlled trial. Int J Stroke 2019; 14: 630–638.

67.

Wan

Zhang

, et al. Effectiveness of goal-setting telephone follow-up on health behaviors of patients with ischemic stroke: A randomized controlled trial. J Stroke Cerebrovasc Dis 2016; 25: 2259–2270.

68.

Wan

Zhang

You

, et al. 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; 33: 509–517.

69.

Wang

Shen

Wan

, et al. 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; 35: 156–164.

70.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Br Med J 2021; 372: 71.

71.

Chen

. [The influence of strengthening health education on declining the rate of recurrence and stroke]. Journal of Qiqihar University of Medicine 2011; 32: 2515–2516.

72.

Hong

. The observation of WeChat improvement on self-efficiency of patients who received the carotid artery stent implantation. Chin Dig Med 2015; 6: 54–56.

73.

Kronish

Diefenbach

Edmondson

, et al. Key barriers to medication adherence in survivors of strokes and transient ischemic attacks. J Gen Intern Med 2013; 28: 675–682.

74.

Jiang

Yang

, et al. Persistence of secondary prevention medication and related factors for acute ischemic stroke and transient ischemic attack in China. Neurol Res 2017; 39: 492–497.

75.

O'Donnell

Chin

Rangarajan

, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): A case-control study. Lancet 2016; 388: 761–775.

76.

Ettehad

Emdin

Kiran

, et al. Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis. Lancet 2016; 387: 957–967.

77.

Lau

Lew

Borschmann

, et al. Prevalence of diabetes and its effects on stroke outcomes: A meta-analysis and literature review. J Diabetes Investig 2019; 10: 780–792.

78.

Johansson

Wild

. Telerehabilitation in stroke care–a systematic review. J Telemed Telecare 2011; 17: 1–6.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.17 MB