Determinants of physical activity behaviour in the cardiac population: a systematic review

doi:10.21203/rs.3.rs-3581333/v1

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Determinants of physical activity behaviour in the cardiac population: a systematic review

https://doi.org/10.21203/rs.3.rs-3581333/v1

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Background: Physical inactivity is one of the most detrimental modifiable cardiovascular risk factors. Despite the well-established benefits of physical activity (PA) on the overall health of people with cardiovascular disease (CVD), challenges remain in maintaining heart-healthy PA behaviour after completing a cardiac rehabilitation programme. This study aimed to collect and analyse observational data on the relative importance of determinants for heart-healthy PA levels in people with CVD, to inform behaviour change research and intervention development in CVD secondary prevention.

Methods: This systematic review included studies reporting bivariate association statistics of determinants and PA behaviour in adults fulfilling the medical indications for cardiac rehabilitation. Included were peer-reviewed journal articles published in English, Dutch, or German since 2005. Five electronic databases (CINAHL, Embase, MEDLINE via PubMed, PsycINFO, Web of Science Core Collection) were last searched in April 2023. Risk of bias of included studies was assessed using the National Heart, Lung, and Blood Institute's Study Quality Assessment Tool for Observational Studies. Data were analysed using descriptive analysis and narrative synthesis.

Results:Included were 57 articles reporting on 54 primary studies from 23 countries, with the majority from North America (n=22), Europe (n=18) and Asia (n=10). Twenty-nine studies were rated good quality and 25 studies fair quality. The studies report on altogether 17,639 individual participants, providing collated empirical evidence for 51 determinants and their patterns of association with PA behaviour in the cardiac population. Those determinants showing distinct positive associations with PA behaviour are self-efficacy, life attitude, intention, exercise belief, education, weather, exercise behaviour, habit formation, social support, psychological wellbeing and employment; whereas those determinants showing distinct negative associations with PA behaviour are age, emotion (including anxiety and kinesiophobia), depression and comorbidity.

Conclusions: This systematic review has identified determinants of PA behaviour that bear particular relevance for the cardiac population. Developers of behaviour change interventions may draw on these findings to inform intervention concepts, methods and designs. Further research in underrepresented geographic regions, and studies into underrepresented determinants of PA and dynamic changes of PA behaviours over time are warranted.

Systematic review registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42020206637

Associations

cardiac rehabilitation

cardiovascular disease

correlates

habit formation

secondary prevention

Cardiovascular disease (CVD) is the leading cause of premature death and disability worldwide, accounting for approximately 17.9 million deaths each year (1). Although disability-adjusted life years of ischaemic heart disease have declined over the past three decades, for over 50-year-olds, CVD still remains the leading cause of disease burden (2), and the global prevalence of CVD and its associated burden, both in terms of health as well as socio-economically, are currently rising due to aging populations (3,4). Various modifiable risk factors (e.g., obesity, smoking and physical inactivity) are linked to an unhealthy lifestyle and contribute to CVD. A recent epidemiological study including 155,000 participants across 21 high-, middle-, and low-income countries found that modifiable risk factors account for approximately 70% of CVD-related cases and deaths (5).

Physical inactivity is one of the most detrimental modifiable cardiovascular risk factors, yet heart-healthy physical activity (PA) levels remain considerably lower than recommended (6,7). The World Health Organization and international medical guidelines currently recommend 150-300 minutes of moderate-intensity or 75-150 minutes of vigorous-intensity PA (or an equivalent combination of both) per week for the prevention and treatment of CVD (8,9).

Exercise-based cardiac rehabilitation programmes stimulate and guide people with CVD to be physically active as part of secondary prevention. The concept of cardiac rehabilitation refers to interventions that mitigate the impact of underlying cardiac diseases (i.e., minimise the risk of morbidity and mortality) and restore a person’s physiological, psychological, and societal functioning (10,11). However, despite the well-established benefits of PA on the overall health of people with CVD, challenges remain in maintaining heart-healthy PA behaviour after completing a cardiac rehabilitation programme (12). For example, a survey including 8,261 patients with coronary artery disease from 27 European countries found that 6 to 24 months following a cardiac event, only 35% of the respondents were carrying out regular PA for at least 150 minutes per week, whilst only 16% of the respondents were carrying out vigorous PA for at least 60 minutes per week (13). These findings reflect a general and urgent need for effective measures to increase PA levels in the long run and thus reduce the risk of recurring cardiovascular events and premature death in people with CVD.

Reasons for low levels of PA after cardiac events are diverse. Previous studies suggest that determinants such as health beliefs (14–16), health conditions and comorbidities (17–19), health literacy (20,21), illness cognition (22–24), and socio-demographic characteristics (25,26) impact PA behaviour in the CVD population. Many previous studies have collected quantitative data on various determinants of PA in the CVD population. Yet, no systematic reviews and meta-analyses have been performed to provide collated insights on the quantified associations between determinants and PA behaviour. Therefore, this systematic review aimed to collect and analyse observational data on the relative importance of determinants for heart-healthy PA levels in people with CVD, to inform behaviour change research and intervention development in CVD prevention.

This systematic review aimed to identify and report on studies that collected observational data quantifying the associations between determinants and PA behaviour in the cardiac population. The objectives were to (1) identify relevant determinants of PA behaviour in the cardiac population; and (2) to describe the strength of the associations between the identified determinants and PA behaviour.

This article follows the Preferred Reporting Items of Systematic Reviews and Meta-analysis (PRISMA) statement (27). The PRISMA checklist is appended in an online supplement, see Additional File 1.

Protocol and registration

The full protocol for this systematic review has been published elsewhere (28). The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO, registration number CRD42020206637).

Eligibility criteria

Search domains

The search domains comprised of population, determinants of PA, and outcome behaviour (PA).

Population

This systematic review focused on an adult study population aged 18 years or over, fulfilling the medical indications for cardiac rehabilitation. These medical indications vary between countries but commonly include (6,9,29–31):

Acute coronary syndrome, e.g., ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (NSTEMI)
Bypass surgery
Other heart surgeries
Heart and lung transplantation
Chronic heart failure
Percutaneous coronary intervention
Chronic coronary heart disease
Pulmonary hypertension
Condition after an electrophysiological intervention
Cardiac pacemaker or a defibrillator
Haemodynamically stable arrhythmia

The rationale for defining the population description by these medical indications for cardiac rehabilitation is that those imply an evidence-based recommendation for people with CVD to carry out heart-healthy PA as part of their lifelong secondary CVD risk prevention.

Determinants

Determinants of PA behaviour include non-modifiable (e.g., demographics, comorbidity) and modifiable (e.g., cognitive, health behaviour, psychological) aspects. Authors use various terminology to describe such determinants, e.g., barriers, causes, contributors, correlates, determinants, enablers, facilitators, factors, influences, mediators, obstacles, predictors, reasons, and triggers. This broad range of terms was included in the search strategy to account for authors’ differing terminology.

Outcome behaviour

Outcome data regarding PA behaviour included both self-reported (subjective) and tracked (objective) PA data. Self-reported data refers to information provided by self-completion of a questionnaire or by answering questions posed by a researcher or healthcare professional. Tracked data refers to data collected through sensor devices, e.g., consumer-grade devices such as smartphones, smartwatches, pedometers, wearable activity trackers, or research-grade sensors such as inertial measurement units and accelerometers.

Different outcomes describing individuals’ average or regular levels of PA (e.g., daily/weekly minutes of moderate and vigorous PA, step count, energy expenditure, and instrument-specific activity scores or categorisation to activity levels) were considered as well as descriptions of dynamic subbehaviours of PA over time (starting, stopping, increasing, reducing, and switching the type of PA). Outcomes describing attendance at, or completion of cardiac rehabilitation programmes were excluded, as these are not representative of ongoing, habitual and independent heart-healthy PA behaviour.

Additional criteria

This review included only studies that either reported bivariate associations statistics and their 95% confidence intervals, or that presented data which allowed the calculation of such association statistics and their 95% confidence intervals. Studies were included when they reported observational data, regardless of whether the study design was cross-sectional or longitudinal. Data from experimental studies were also eligible in principle, if these data concerned a ‘usual care’ control group, which was then taken as observational data. Included were peer-reviewed journal articles published in English, Dutch, or German since 2005.

Information sources

Systematic literature searches were set up and conducted in cooperation with a trained library information expert. Five bibliographic databases were used: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica dataBASE (Embase), Medical Literature Analysis and Retrieval System Online (MEDLINE via PubMed), PsycINFO, and Web of Science Core Collection. A preparatory search of the Cochrane Database of Systematic Reviews (CDSR) and PROSPERO registry assured that no similar systematic review had previously been conducted or was underway.

Search strategy

The search strategy is given in an online supplement, see Additional File 2. The search was first conducted in August 2020 (last date of search 20.08.2020) and updated in April 2023 (last date of update search 27.04.2023).

Study selection process

Screening of search results was performed in parallel and independently by pairs of reviewers (RC, JG, STK, HM, BM, MS). In case of inconsistencies or disagreements, a third reviewer was consulted.

Data extraction

Data extraction was conducted in parallel and independently by pairs of reviewers (RC, JG, STK, HM, BM, MS) to ensure high accuracy. The following data were extracted: authors, journal, study title, language, year of publication, study aim, study design, sample description (characteristics and size), setting and study country, determinants, method(s) of assessing determinants (e.g., validated questionnaire), type of PA behaviour, PA behaviour assessment method(s), timepoint or follow-up period since hospitalisation or cardiac rehabilitation, effect sizes for associations between determinants and PA behaviour, i.e., correlation coefficient (r), odds ratio (OR), and Cohens'd (d), as well as their 95% confidence interval. When no bivariate effect size and/or 95% confidence interval was reported, it was calculated from the available data whenever possible using RStudio (2022.07.2).

Quality assessment

Pairs of reviewers (RC, JG, STK, HM, BM, MS) independently rated the methodological quality of all included studies using the National Heart, Lung, and Blood Institute's (NHLBI) Study Quality Assessment Tool for Observational Studies (32) (Additional File 3). Disagreements between two reviewers were discussed and, if necessary, resolved by a third reviewer.

Data synthesis

Descriptive analysis and narrative synthesis were conducted. Due to the heterogeneity of included studies with regard to participant characteristics (cardiac diagnoses and time since diagnosis or index event), determinants (conceptualisation, measurement methods), and PA behaviour (measurement methods, follow-up period), a quantitative synthesis (meta-analysis) was not performed.

The descriptive analysis was conducted using text and tables to present for each individual determinant, the corresponding study, sample size, PA outcome, timepoint or follow-up period since hospitalisation or cardiac rehabilitation, and the effect size and 95% confidence interval concerning the associations between the determinant and PA behaviour. To structure the descriptive analysis, determinants were categorised according to the OPTImAL ontology (33). The OPTImAL ontology offers a system with pre-formalised sets of 320 interrelated predictors formed from 60 multifaceted aspects that may determine PA in the CVD population. It offers a coherent and comprehensive semantic structure and is therefore a valuable tool for organising the determinants of PA into meaningful categories.

For ease of interpretation, the directions of association statistics were aligned across all determinant categories, so that positive associations (+) indicate that a higher value of the determinant is associated with more PA (e.g., higher self-efficacy is associated with more PA); and negative associations (-) express that higher value of the determinant is associated with less PA (e.g., more depressive symptoms are associated with less PA). The strength of association statistics was classified as none, weak, moderate, strong, or very strong according to the qualitative descriptors and cut-points described by Rosenthal (34) (table 1). Vote counting was used to summarise the number of statistics expressing positive, negative or no associations (35).

To support the narratitive synthesis, data were visualised in a global heat map (RStudio 2022.07.2) and in a diverging stacked bar chart (Microsoft Excel Version 2307, 2019).

Table 1. Qualitative descriptors for strength of associations according to Rosenthal (34).

Association statistic	None	Weak	Moderate	Strong	Very strong
Correlation coefficient (r)	-0.10 < r <0.10	0.10 ≤ r < 0.30 -0.30 < r ≤ -0.10	0.30 ≤ r < 0.50 -0.50 < r ≤ -0.30	0.50 ≤ r < 0.70 -0.70 < r ≤ -0.50	0.70 ≤ r r ≤ -0.70
Odds ratio (OR)	0.67 < OR < 1.50	1.50 ≤ OR < 2.50 0.40 < OR ≤ 0.67	2.50 ≤ OR < 4.00 0.25 < OR ≤ 0.40	4.00 ≤ OR < 10.00 0.10 < OR ≤ 0.25	10.00 ≤ OR OR ≤ 0.10
Cohen’s d (d)	-0.20 < d < 0.20	0.20 ≤ d < 0.50 -0.50 < d ≤ -0.20	0.50 ≤ d < 0.80 -0.80 < d ≤ -0.50	0.80 ≤ d < 1.30 -1.30 < d ≤ -0.80	1.30 ≤ d d ≤ -1.30

The literature searches (first search and update) yielded a total of 14,429 search results. After de-duplication, 10,222 search results remained for title and abstract screening. Two hundred seventy-two articles were retrieved for full-text assessment, of which 215 were excluded. Reasons for exclusion are given in the PRISMA flow diagramme (figure 1), with the main reason for exclusion being that studies reported adjusted and multivariate statistical models but not their underlying bivariate associations (n=101). Finally included were 57 articles reporting on 54 primary studies (figure 1).

PRISMA 2020 flow diagram for new systematic reviews, including searches of databases and registers only

[insert Figure 1]

Figure 1. PRISMA flow diagram. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71 (36). For more information, visit: http://www.prisma-statement.org/

Study characteristics

Study characteristics are summarised in table 2. The 54 included studies (37–93) were conducted in 23 countries, with the majority from North America (n=22), Europe (n=18) and Asia (n=10). Three studies were conducted in Oceania, one in South America, and there were no studies from African countries. Most studies had either an observational longitudinal design (n=28) or an observational cross-sectional design (n=25). One experimental study was included, from which only usual care control group data was extracted. Individual study sample sizes ranged from 25 to 3,128, with 17,639 study participants included in total. The global distribution of study participants is shown in figure 2.

[insert Figure 2]

Figure 2. Global distribution of study participants

The majority of study participants were male (54.6% to 100% male in 49 studies). The mean age of study samples ranged from 54 to 75 years, except for one study in patients with congenital heart disease with a mean age of 29 years. With regard to cardiac diagnoses, study samples represented either coronary heart disease across various degrees of severity (descriptors coronary heart disease and ascertained heart disease, n=19), specific groups such as bypass surgery, heart valve surgery, or heart transplantation (n=18), or a mix of cardiac conditions (descriptors cardiac rehabilitation and mixed cardiac diagnoses, n=17). The time point of data collection in relation to an acute cardiac event or in relation to a cardiac rehabilitation programme was up to 3 months in 18 studies, 3 to 6 months in 9 studies, 6 to 12 months in 7 studies, 12 to 24 months in 4 studies, over 24 months in 8 studies, and not reported in 8 studies (table 2).

Table 2. Study characteristics

Author	Country	Study design	Study population	N	Gender (% male)	Age (mean, SD)	Time-point/follow-up^k
Ali 2017 (37)	Pakistan	Cross-sectional	Bypass surgery	265	59.2	67.2 (13.4)	Mean 5.7 (SD 1.3) weeks post surgery
Ali 2017 (38) Ali 2018 (39)	Canada	Longitudinal	Cardiac rehabilitation	411	70.1	65.0 (10.4)	2 years after CR start
Alsaleh 2023 (82)	Jordan	Cross-sectional	Coronary heart disease^c	400	64.7	54.3 (8.9)	≥4 months after diagnosis
Arthur 2013 (78)	Canada	Longitudinal	Cardiac rehabilitation	203	0.0	63.1 (11.0)	12 months after CR completion
Bernardo 2013 (40)	Brazil	Cross-sectional	Cardiac rehabilitation	69	71.0	67.2 (9.2)	At CR start
Blanchard 2008 (41) Blanchard 2009 (42)	Canada	Longitudinal	Ascertained heart disease^a	76	76.0	63.0 (11.0)	6 months after CR start
Blanchard 2010 (43) Blanchard 2011 (44)	Canada	Longitudinal	Ascertained heart disease^a	280	72.5	63.0 (11.5)	3 months after CR start
Bray 2006 (45)	Canada	Longitudinal	Cardiac rehabilitation	44	63.6	59.4 (13.5)	6 weeks after CR completion
Chien 2014 (46)	Taiwan	Longitudinal	Heart failure	111	62.2	63.2 (11.5)	1 month after hospital discharge
Crane 2005 (47)	USA	Cross-sectional	Myocardial infarction^b	84	0.0	75.0 (5.8)	6-12 months after MI
Dąbek 2020 (48)	Poland	Cross-sectional	Coronary heart disease^c	217	56.7	67.4 (8.5)^e	During hospitalisation
de Melo Ghisi 2015 (49)	Canada	Experimental	Cardiac rehabilitation	146	78.8	64.3 (12.0)	22-24 weeks after CR start
de Melo Ghisi 2015 (50)	Canada	Longitudinal	Cardiac rehabilitation	214	67.2	66.1 (9.3)	6 months after CR start
Dontje 2014 (51)	Netherlands	Cross-sectional	Heart failure	68	71.0	62.0 (14)	Stable outpatients recruited from HF clinics^l
Dunn 2018 (52)	USA	Longitudinal	Coronary heart disease^c	122	65.6	65.0 (9.1)	During hospitalisation
Esnaasharieh 2022 (83)	Iran	Cross-sectional	Heart Failure	103	59.2	55.9 (11.4)	Mean 7.2 (SD 5.0) years disease duration
Fahmi 2022 (84)	Indonesia	Cross-sectional	Post-treatment AMI	150	78.8	n.r.	Patients recruited from a cardiac clinic^l
Foster 2021 (85)	Scotland	Cross-sectional	Cardiac rehabilitation	285	75.9	68.7 (10.5)	Patients referred to CR^l
Funaki 2023 (86)	Japan	Longitudinal	Ascertained heart disease^a	577	86.5	64 (58 - 68)^h	1 month after PCI
Goodwin 2019 (53)	USA	Cross-sectional	Acute coronary syndrome	151	69.5	64.0 (11.4)	35 days after hospital discharge
Holahan 2008 (54)	USA	Cross-sectional	Mixed cardiac diagnoses^d	130	49.0	60.2 (12.4)	Outpatients recruited through medical settings^l
Huang 2022 (80)	Taiwan	Cross-sectional	Coronary heart disease^c	215	62.8	68.8 (10.8)	Mean 9.4 (SD 6.9) years disease duration
Kałka 2021 (87)	Poland	Cross-sectional	Coronary heart disease^c	751	100	59.5 (9.4)	After CR completion
Kanning 2010 (55)	Germany	Longitudinal	Coronary heart disease^c	108	91.0	55.0 (10.0)	6 months after CR completion
Kim 2022 (88)	USA	Longitudinal	Cardiac rehabilitation	25	64.0	64.6 (10.5)	9 months after CR completion
Klompstra 2018 (56)	Sweden	Cross-sectional	Heart Failure	100	62.0	67.0 (13.0)	Median 37 (range 2-192) months after diagnosis
Knapik 2019 (57)	Poland	Cross-sectional	Coronary heart disease^c	135	56.3	71.9 (4.8)	Stable CAD patients recruited from cardiologyclinic^l
Kronish 2006 (58)	USA	Longitudinal	Acute coronary syndrome	492	58.9	60.6 (12.2)	3 months after hospital discharge
Le Grande 2006 (59)	Australia	Longitudinal	Ascertained heart disease^a	200	74.0	59.0 (10.1)	6 months after PCI
Le Grande 2015 (60)	Australia	Longitudinal	Ascertained heart disease^a	133	84.0	60.0 (9.3)	6 weeks after hospital discharge
Liu 2022 (89)	China	Cross-sectional	Ascertained heart disease^a	260	65.8	57.2 (9.6)	>1 year after PCI
Lund 2016 (61)	Denmark	Longitudinal	Heart valve surgery	524	64.7	70.0^f	6-12 months after surgery
Luszczynska 2006 (62)	Poland	Longitudinal	Myocardial infarction^b	114	64.0	54.3 (7.0)	8 months after MI
Marques-Sule 2022 (79)	Spain	Cross-sectional	Heart transplantation	117	56.4	56.0 (12.1)	Mean 79.5 (SD 65.6) months since transplantation
Millen 2008 (63)	Canada	Longitudinal	Cardiac rehabilitation	50	62.0	62.0 (13.0)	12 weeks after CR completion
Murray 2012 (64)	Canada	Longitudinal	Cardiac rehabilitation	129	85.3	59.5 (10.3)	1 month after CR completion
Peersen 2020 (65)	Norway	Cross-sectional	Ascertained heart disease^a	1101	79.2	62.0 (10.0)	Median 16 (range 2-36) months after cardiac event
Peláez 2010 (66)	Canada	Cross-sectional	Coronary heart disease^c	756	69.0	60.0 (10.0)	Patients at a tertiary care cardiac center^l
Prapavessis 2005 (67)	New Zealand	Cross-sectional	Congenital heart disease	64	47.0	28.8 (9.9)	Surgical repair of cardiac defect in childhood
Racodon 2020 (68)	France	Cross-sectional	Cardiac rehabilitation	100	76.0	59.9 (10.5)	1 year after CR completion
Rodgers 2013 (69)	Canada	Longitudinal	Cardiac rehabilitation	114	78.1	57.6 (9.8)	1 month after CR completion
Russell 2009 (70)	Canada	Longitudinal	Cardiac rehabilitation	68	86.8	64.9 (8.9)	6 weeks after CR completion
Russell 2010 (71)	Canada	Longitudinal	Cardiac rehabilitation	53	100	62.8 (10.8)	2 weeks after CR completion
Salman 2019 (72)	Scotland	Cross-sectional	Mixed cardiac diagnoses^d	3128	49.4	63.3^f	Patients recruited by survey^l
Schmitz 2022 (90)	Germany	Longitudinal	Psycho-Cardiological rehabilitation	164	54.6	54.6 (7.5)	6 months after CR completion
Setny 2022 (91)	Poland	Longitudinal	Acute coronary syndrome	1003ⁱ	71.0^j	64.5^g(8.0)^e	6-24 months after cardiac event
Slovinec D'Angelo 2014 (73)	Canada	Longitudinal	Ascertained heart disease^a	801	75.4	61.4 (10.0)	12 months after hopitalisation
Sweet 2014 (74)	Canada	Longitudinal	Cardiac rehabilitation	109	68.0	62.3 (9.6)	4 months after CR start
Teleki 2022 (92)	Hungary	Longitudinal	Ascertained heart disease^a	117	66.0	62.5 (6.2)	6 months after hospital discharge
Tokunaga-Nakawatase 2012 (75)	Japan	Longitudinal	Coronary heart disease^c	76	100	67.0 (7.8)	Patients recruited from an outpatient clinic^l
Van Der Wal 2006 (76)	Netherlands	Cross-sectional	Heart failure	501	60.0	72.0 (11.0)	Median 40 (IQR 11-79) months of HF symptoms
Wang 2023 (93)	China	Longitudinal	Coronary heart disease^c	279	63.0	64.7 (13.2)	1 week after hospital discharge
Werren 2022 (81)	Italy	Cross-sectional	Acute coronary syndrome	372	86.3^j	65.4^gf	Median 31 (IQR 24 – 38) months after CR start
Zullo 2010 (77)	USA	Cross-sectional	Myocardial infarction^b	1374	53.0	64.4 (12.1)^e	≤10 years after MI
^aincludes post-myocardial infarction, percutaneous coronary intervention, coronary artery bypass grafting ^bincludes STEMI and NSTEMI ^cincludes descriptors coronary artery disease, ischemic heart disease, and stable coronary heart disease ^d includes patients with a variety of cardiac diagnoses but not recruited from a cardiac rehabilitation programme ^e SD calculated by authors ^f SD not reported ^g weighted mean calculated by authors ^hmedian reported ⁱsample size calculated by authors ^jpercentage calculated by authors ^kif a longitudinal study, furthest time point of physical activity measurement chosen ^ltime since cardiac event not reported AMI, acute myocardial infarction MI, myocardial infarction CR, cardiac rehabilitation HF, heart failure CAD, coronary artery disease PCI, percutaneous coronary intervention SD, standard deviation IQR, interquartile range

Methodological quality assessment

Twenty nine studies were rated good quality and 25 studies fair quality. There were no study exclusions because of poor methodological quality. Individual quality ratings for all included studies are given in a supplementary table, see Additional file 4.

It is notable that for the majority of studies a statistical sample size justification was not conducted (n=34) or not reported (n=5), which lowers statistical confidence in accuracy of parameter estimates being taken into account during study design. A large number of studies did not measure the exposure (determinant) prior to the outcome (n=22) or more than once over time (n=24). For some types of determinants this might weaken the interpretation of direction of causality, but not necessarily for all types of determinants (e.g., for modifiable cognitive factors such as self-efficacy a concurrent temporal association is appropriate). The assessment item concerning measurement and statistical adjustment for potential confounding variables was considered not applicable throughout, because this systematic review sought to extract bivariate (unadjusted) association data only.

With regard to risk of bias, it is noted that 26 studies did not report on the participation rate of eligible persons, and 13 studies reported a loss to follow-up of more than 20%, which raises the possibility of selection bias in these studies. Masking of outcome assessors was not conducted in 16 studies in which masking would have been feasible within their study designs, indicating the possibility of observer bias in these studies.

Physical activity behaviour

The majority of studies measured PA behaviour based on self-reported questionnaires (n=49), one study (75) employed both a self-reported questionnaire and a wearable sensor device (pedometer), and four studies (51,53,86,88) used wearable sensor devices (accelerometers) only.

Of those studies using self-reported questionnaires, eleven studies (48,61,68,75,79,82,84,85,89,90,93) employed the International Physical Activity Questionnaire (IPAQ) (94), thirteen studies (38,39,41–44,50,64,67,69,73,74,78) the Godin Leisure-Time Exercise Questionnaire (GLTEQ) (95), three studies (66,70,71) the 7-Day Physical Activity Recall (PAR) (96,97), and three studies (45,63,80) the Physical Activity Scale for the Elderly (PASE) (98).The following questionnaires were used in one study each: the Baecke Physical Activity Questionnaire (40), the Active Australia Survey (60), a revised version of the Heart Failure Compliance Scale (76), the Allied Dunbar National Fitness Survey (72), the Nord-Trøndelag Health Study questionnaire (65), the Behavioural Risk Factor Surveillance questionnaire (47), the German Freiburger questionnaire (55), the Rapid Asssessment of Physical Activity (RAPA) questionnaire (83), the Health Action Process Approach Scale (92), and the Daily Activity Questionnaire in Heart Failure Scale (DAQIHF)(46). Thirteen studies (37,49,52,54,56–59,62,77,81,87,91) collected PA data from self-designed or adapted questionnaires.

Collectively, these different methods of measuring the outcome PA behaviour capture activities such as walking, cycling, housework, gardeninig, exercise and training, including indoors, outdoors, leisure and work-related physical activities. The units of measurements include amount of light, moderate, and/or vigorous PA (minutes and hours per week), metabolic equivalents of tasks (METs), weekly frequency of exercise, step counts, or instrument-specific scores. In several studies, participants were dichotomised into those meeting the PA recommendations versus those not meeting the PA recommendations. The specific units of measurement for each included study are given in a supplementary file, see Additional File 5.

Determinants of physical activity behaviour

The 54 studies included in this review yielded 545 bivariate association statistics between various determinants and PA behaviour. Most determinants were self-reported, except for clinical measurements and demographic determinants which were mainly extracted from medical records. Determinants represent 17 overarching categories (factors) and 51 sub-categories mapped to the OPTImAL ontology (table 3). Most data were mapped to the overarching determinant categories cognitive factors (27.3%), demographic factors (18.2%), and psychological factors (12.0%). All other overarching determinant categories inlcude between 0.2% and 7.0% of data. The sub-categories for which most data could be extracted were self-efficacy (12.5%), social support (6.1%), exercise behaviour (5.7%), motivation (5.1%), age (5.1%), comorbidity (4.8%), emotion (4.8%), gender (4.4%), education (4.0%), and psychological wellbeing (4.0%), with all other sub-categories ranging between 0.2% and 2.9% (table 3).

Table 3. Categories of the OPTImAL ontology for which bivariate association statistics (N=543) were extracted.

Overarching category (factor)	Number of extracted association statistics (n, %)	Sub-category (determinant)	Number of extracted association statistics (n, %)
Anthropometric factors	13 (2.4%)	Body mass index	13 (2.4%)
Cardiovascular disease factors	17 (3.1%)	Cardiovascular risk score	4 (0.7%)
		NYHA class	7 (1.3%)
		History of heart disease	6 (1.1%)
Cognitive factors	148 (27.2%)	Self-efficacy	68 (12.5%)
		Self-regulation / monitoring	6 (1.1%)
		Motivation / exercise motivation	28 (5.1%)
		Life attitude	13 (2.4%)
		Subjective norm	5 (0.9%)
		Intention	10 (1.8%)
		Illness perception	5 (0.9%)
		Health belief	2 (0.4%)
		Exercise belief	11 (2.0%)
Morbidity factors	26 (4.8%)	Comorbidity	26 (4.8%)
Demographic factors	99 (18.2%)	Age	28 (5.1%)
		Education	22 (4.0%)
		Ethnicity	7 (1.3%)
		Gender	24 (4.4%)
		Marital status	13 (2.4%)
		Socioeconomic status	5 (0.9%)
Environment factors	27 (5.3%)	Physical environment	4 (0.7%)
		Weather	8 (1.5%)
		Residence	3 (0.6%)
		Transport	4 (0.7%)
		Living arrangements	9 (1.7%)
		Home exercise equipment	1 (0.2%)
Health behaviour factors	38 (7.0%)	Exercise behaviour	31 (5.7%)
		Habit formation	6 (1.1%)
		Patient activation	1 (0.2%)
Healthcare service factors	20 (3.7%)	Exercise recommendation	2 (0.4%)
		Cardiac rehabilitation attendance	15 (2.8%)
		Access to care	1 (0.2%)
		Treatment	2 (0.4%)
Health literacy factors	8 (1.5%)	Knowledge / information	8 (1.5%)
Insurance factors	1 (0.2%)	Insurance	1 (0.2%)
Lab test	2 (0.4%)	Lipid profile	2 (0.4%)
Lifestyle factors	11 (2.0%)	Smoking	7 (1.3%)
		Alcohol	1 (0.2%)
		Stress	1 (0.2%)
		Lack of time	2 (0.4%)
Patient physiology factors	8 (1.5%)	Arterial hypertension	2 (0.4%)
		Left ventriular ejection fraction	6 (1.1%)
Symptom factors	9 (1.7%)	Fatigue / sleepiness	9 (1.7%)
Social support factors	33 (6.1%)	Social support	33 (6.1%)
Psychological factors	65 (11.9%)	Loneliness	1 (0.2%)
		Emotion	26 (4.8%)
		Depression	16 (2.9%)
		Psychological well-being	22 (4.0%)
Social environment factors	18 (3.3%)	Family circumstances	4 (0.7%)
		Dog ownership / dog walking	2 (0.4%)
		Employment	12 (2.2%)

Details of all extracted association statistics are given in online supplement, under Additional File 5. Figure 3 presents a descriptive graphical summary of all bivariate association statistics. For each determinant sub-category, this allows a visual comparison of the relative amount of data (number of bivariate association statistics and number of studies from which these were extracted) and the directions and strengths of associations (excluding those associations which were categorised as 'none').

Out of the 51 determinant sub-categories, 26 (51.0%) showed predominantly positive associations with PA behaviour, 11 (21.7%) showed predominantly negative associations, 8 (15.7%) showed mixed positive and negative associations, and for 6 (11.8%) determinant sub-categories all associations were categorised as 'none'.

Out of those determinants which were associated predominantly positively with PA behaviour, self-efficacy, life attitude, intention for PA, exercise belief, education, weather, (prior) exercise behaviour, habit formation, social support, psychological wellbeing and employment provide the strongest indicators, based on the number of respective association statistics and the distribution of strengths of associations. Self-efficacy represents by far the strongest indicator (30 weak, 29 moderate, 3 strong and 1 very strong positive associations as compared to only 5 associations which were categorised as 'none'), followed by exercise behaviour (9 weak, 14 moderate, 3 strong, and 2 very strong positive associations as compared to only 3 associations which were categorised as 'none') and social support (16 weak, 6 moderate, and 1 strong positive associations as compared to 2 weak negative associations and 8 associations which were categorised as 'none'). The remaining determinants which were also associated positively with PA behaviour were self-regulation, subjective norm, illness perception, health belief, socioeconomic status, physical environment, transport (i.e., availability of transportation), home exercise equipment, patient activation, treatment, knowledge, insurance, lipid profile, occurrence of CVD in the family, and dog ownership (figure 3).

The determinants which were associated predominantly negatively with PA behaviour are New York Heart Association (NYHA) class, history of heart disease, comorbidity, age, smoking, alcohol, lack of time, fatigue, loneliness, emotion and depression. Out of these, the strongest indicators were age (12 weak, 5 moderate, 1 strong, and 1 very strong negative associations as compared to 1 weak positive association and 8 associations which were categorised as 'none'), emotion (6 weak, 3 moderate, 6 strong and 1 very strong negative associations as compared to 10 associations which were categorised as 'none'), and comorbidity (15 weak negative associations as compared to 2 weak positive associations and 9 associations which were categorised as 'none'); followed by NYHA class (5 weak, 1 moderate, and 1 strong negative associations), depression (5 weak, 2 moderate and 1 strong negative associations as compared to 1 moderate positive association and 7 associations which were categorised as 'none'), and fatigue (3 weak, 2 moderate and 1 strong negative associations as compared to 3 associations which were categorised as 'none') (figure 3).

The determinants which showed mixed associations were body mass index, motivation, ethnicity, gender, marital status, living arrangements, cardiac rehabilitation attendance, and left ventricular ejection fraction. The determinants for which all associations were categorised as 'none' include cardiovascular risk score, residence, exercise recommendation, access to care, stress, and arterial hypertension (figure 3).

[insert Figure 3]

Figure 3. Associations for 51 determinants of physical activity behaviour. Shown are the number, direction and strength of associations, and the number of studies from which association statistics were extracted for each determinant.

This systematic review extracted 545 bivariate association statistics from 54 primary studies with altogether 17,639 study participants. These observational data provide collated empirical evidence for 51 determinants and their patterns of association with PA behaviour in the cardiac population. Those determinants showing distinct positive associations with PA behaviour are self-efficacy, life attitude, intention, exercise belief, education, weather, exercise behaviour, habit formation, social support, psychological wellbeing and employment; whereas those determinants showing distinct negative associations with PA behaviour are age, emotion (including anxiety and kinesiophobia) and comorbidity.

Determinants with particular relevance to the cardiac population

This study provides a valuable overview of determinants of PA behaviour based on empirical data and specifically for people with CVD. While many of the included determinants are also relevant to PA behaviour in the general population (e.g., self-efficacy, intention, social support) (99,100) , several determinants bear particular importance in the context of CVD: kinesiophobia, psychological wellbeing and depression, cardiac rehabilitation attendance and prior exercise behaviour.

Kinesiophobia in the context of our systematic review describes the fear of PA and exercise in the presence of CVD. Its reported prevalence in patients with coronary artery disease ranges from 19% to 25%, with a tendency to reduce over time(101,102). Kinesiophobia can lead to maladaptive avoidance behaviour such as lower levels of PA and reduced attendance at exercise-based cardiac rehabilitation programmes, resulting in negative health consequences, disability and depression (103). It is therefore important that CVD secondary prevention interventions include processes to detect and address kinesiophobia.

Similarly, psychological wellbeing and depression can have considerable influence over patients’ adherence to PA recommendations and other CVD secondary prevention measures (104). Depressive symptoms are prevalent in up to 45% of patients with coronary heart disease and are associated with increased morbidity and mortality as well as decreased quality of life (105,106). For example, in a global study of 14,577 patients with stable coronary heart disease, psychosocial stress – including depressive symptoms – was associated with increased cardiovascular mortality despite optimal medical secondary prevention treatment (106). Accordingly, the importance of addressing psychological wellbeing and depression in CVD secondary prevention is gaining increased attention (104,105).

Exercise-based cardiac rehabilitation should constitute a gateway for CVD patients to establishing lifelong heart-healthy PA habits, but cardiac rehabilitation attendance is only moderately predictive of longer-term PA behaviour. This was shown in a systematic review that summarised evidence from 26 randomised controlled trials and investigated the impact of cardiac rehabilitation on mid-term (up to 6 months) and long-term (more than 6 months) PA behaviour. Completion of a cardiac rehabilitation programme as compared to non-attendance showed that despite initial success in the short run, there was no clear positive effect on longer-term PA habit formation of cardiac patients (107). Another systematic review of 40 randomised controlled trials corroborates these findings, with only 9 included studies showing that cardiac rehabilitation increased the proportion of physically active participants at median 6 months follow-up. Only 3 studies showed that cardiac rehabilitation increased the proportion of physically active participants at median 5 years follow-up, and only 26% of PA outcomes across all included studies showed statistically significant improvement compared to control (108). This is also reflected in the mixed pattern of associations found for the determinant cardiac rehabilitation attendance in this review.

After self-efficacy, exercise behaviour (referring to exercise behaviour prior to the cardiac index event, as well as shortly thereafter) was the determinant with the second-most consistent pattern of positive associations in this review, demonstrating that those who exercise more are also more likely to be physically active several months later. This emphasises the role of physical acticvity promotion in the general population, in clinical practice in general (109,110), and specifically early on in the CVD secondary prevention pathway, including through cardiac rehabilitation programmes (111,112).

In sum, this review identifies kinesiophobia, psychological wellbeing and depression, cardiac rehabilitation attendance and prior exercise behaviour as determinants which warrant particular attention in the context of supporting PA in the cardiac population. Further relevant determinants emerging from this review are age and comorbidity which both show distinct and consistent negative associations with PA behaviour. As these are non-modifiable factors, this confirms the need for adapting PA opportunities to the capabilities and preferences of older patients and those with greater comorbidity, and for raising awareness of the benefits of PA and minimising the perceived risks of PA in older age, as has been highlighted by others (113).

Underrepresented determinants in this review

Most empirical evidence in this review was found for cognitive, demographic and psychological determinant categories, but limited data were available for other determinant categories and sub-categories which are also of interest in this context.

Surprisingly few studies included data on determinants concerning the environment, despite a relatively large literature describing associations between, e.g., the built environment and PA in the general population (114,115) and across the life span (116). In the environment category, several singular statistics in this review indicate interesting associations, such as the availability of PA resources (e.g., exercise facilities at work, community infrastructure, such as jogging trails, bike paths, swimming pools and financial means) (moderate positive association in comparison with lack of resources)(85) and possession of a bike or home exercise equipment (weak positive associations)(44,85), as well as several weak and moderate positive associations for adapting to adverse weather conditions (75). Overall, however, this evidence primarily indicates a lack of extractable data and research gaps specific to the cardiac population. For example, the availability and vicinity of supervised exercise sessions for CVD patients, the distribution and density of public access defibrillators, the usability and appropriateness of public infrastructure for PA (fitness trails, hiking routes, etc.) for people with CVD, or the availability of emergency alert systems for people exercising in remote rural locations could be more focused aspects of interest for the cardiac population.

The same applies to other underrepresented determinants in this review. The sub-category health literacy, for example, only includes data on knowledge (46,50,76) and lacks the more differentiated elements that are represented in current conceptualisations of health literacy, such as the ability to actively engage with healthcare providers and the ability to find good health information (117). Patient activation describes a more recent concept of the extent to which an individual actively engages in self-management behaviours (118), which is a relevant aspect in the secondary prevention of CVD but was only represented in one study (39). Exercise recommendation by healthcare professionals was addressed in only two studies (82,86), although this has been identified as an issue requiring attention, with 46% of coronary patients reporting that they did not recall having received personal advice on PA (13). The impact of (different models of) health insurance on PA behaviour presents another point of interest (119). One study in this review included data showing a moderate positive association between private health coverage and PA behaviour (60), but more differentiated aspects could be investigated, such as comparisons between private and universal health coverage, impact of proportion and size of co-payments (e.g., for participation in cardiac rehabilitation programmes), and impact of PA promotion schemes offered by health insurances (119).

These are some examples of underrepresented determinants in this review. Readers are referred to Additional File 5 for further singular or underrepresented determinants which may bear particular relevance to the cardiac population.

Related research

A discussion of the methodological choices made for this review in comparison with related research (120–123), and a discussion of the respective advantages and disadvantages of self-reported (subjective) versus tracked (objective) measurement methods for PA behaviour are provided in the protocol for this review (28). Further to this, it is noticeable that all studies included in this review measured individuals’ PA levels at certain time points or during short time periods, or with respect to an assumed general average PA level. But the dynamic aspects of PA behaviour over time, including adoption, maintenance, drop-out and resumption of PA (99), and determinants relating to these aspects, were not represented. One previous systematic review and meta-analysis of 34 studies examined determinants of specifically PA maintenance in previously sedentary individuals (samples recruited from the general population and from different diagnostic groups, including CVD in two studies), by comparison of 'maintainers' with 'relapsers' (122). This study showed that self-efficacy and intention were higher among maintainers compared with relapsers, and that maintainers also held more positive attitudes and perceived more positive and less negative consequences of PA compared with individuals who relapsed. While these findings are in general alignment with data from the present review, further investigation into the dynamics of PA behaviour specifically in the cardiac population is warranted.

Strengths and limitations

A strength of this review was the completion of review steps (screening and eligibility assessment, data extraction, quality assessment) by independent parallel reviewers, which supported the quality of screening decisions, data extraction and study appraisals. A limitation of this review was that – despite the use of the OPTImAL ontology, which provided a helpful framework for categorisation of determinants – some choices had to be made when one determinant could match two or more OPTImAL categories. This relates to the variability and multiplicity that can be observed in the underlying constructs of variables, for example, in the definitions of psychological constructs and approaches for their measurement (124). Another limitation concerns the lack of meta-analysis, which was not warranted due to the heterogeneity of included studies. Vote counting provided an acceptable alternative method, although it does not convey information on the magnitude of effects, and it does not account for differences in the relative sizes of the included studies (35). Lastly, this review was limited by the (lack of) reporting of bivariate association statistics in the literature, as 101 articles had to be excluded because they contained only multivariate models or adjusted analyses. A trial to contact corresponding authors and request raw data for this review was discontinued due to high administrative burden and low response. The lack of reporting of unadjusted bivariate association statistics in the literature is deplorable, because studies such as the present review could be enriched by additional already collected empirical data. Although the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline already encourages authors to report unadjusted estimates (item 16a) (125), the current trajectory towards research data sharing may enable greater access to data in the future (126).

Future directions

This review provides intervention developers with an overview of determinants and their relative strengths and consistency of associations, which may be taken into account when designing behaviour change interventions. This is particularly useful for those intervention development approaches that take behavioural determinants as their conceptual starting point (127). Intervention developers may directly target those determinants which emerge as strong indicators from this review, including determinants that are important for the general population as well as determinants with specific relevance to the cardiac population. Alternatively, or additionally, intervention delvelopers may consider investigating and generating new evidence for determinants which are underrepresented in this review but which have potential impact on the PA behaviour of people with CVD.

Future research should address the imbalance in the geographical distribution of evidence, as most studies included in this review contributed data from the Global North. Moreover, future research should strive to capture more continuous and long-term (over 24 months) patterns of PA behaviour, to provide insight into the dynamic changes of PA behaviour and inform about long-term habit formation. Methodological challenges around logistics, costs and participant burden are acknowledged, but rapid advances in digital technology and wearable and implantable sensor devices promise future solutions for realising a vision of ubiquitous sensing and big data analysis (128).

This systematic review highlights several determinants that emerge as strong indicators for PA behaviour in the cardiac population: self-efficacy, (prior) exercise behaviour, social support, emotion (including anxiety and kinesiophobia), psychological wellbeing, depression, age and comorbidity. These findings align with determinants of PA identified in the general population and in other clinical populations, but also identify determinants that bear particular relevance for the cardiac population. Developers of behaviour change interventions may draw on these findings to inform intervention concepts, methods and designs. Further research in underrepresented geographic regions, and studies into underrepresented determinants of PA and dynamic changes of PA behaviours over time specific to the cardiac population are warranted.

CINAHL, Cumulative Index to Nursing and Allied Health Literature

CVD, Cardiovascular Disease

Embase, Excerpta Medica dataBASE

MEDLINE, Medical Literature Analysis and Retrieval System Online

NHLBI, National Heart, Lung, and Blood Institute

NSTEMI, Non-ST-Elevation Myocardial Infarction

PA, Physical Activity

PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analysis

PROSPERO, International Prospective Register of Systematic Reviews

STEMI, ST-Elevation Myocardial Infarction

IPAQ, International Physical Activity Questionnaire

GLTEQ, Godin Leisure-Time Exercise Questionnaire

PAR, 7-Day Physical Activity Recall

PASE, Physical Activity Scale for the Elderly

RAPA, Rapid Assessment of Physical Activity

DAQIHF, Daily Activity Questionnaire in Heart Failure Scale

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable

Availability of data and materials

All data and materials generated during this systematic review are included in this published article [and its supplementary information files].

Competing interests

The authors declare that they have no competing interests.

Funding

The authors have not declared a specific grant for this research from any funding agency in public, commercial or not-for-profit sectors.

Authors' contributions

JG, STK, JN, and RC conceptualized this study. JG, STK, and RC designed the methodology. STK and RC were the project leads. JG and HM conducted the literature searches. JG, HM, MS, BM, STK and RC screened search results, extracted data, and conducted quality appraisals of included studies. JG, HM and STK curated the data and provided visualisations for the study. HM, JG, and STK wrote the original draft. All authors reviewed and edited the manuscript.

Acknowledgements

We are grateful to information specialist Georg Franssen (Maastricht University Library, Maastricht University, The Netherlands) for supporting the development of the search strategy, and to statistician Dr. Georg Zimmermann (Paracelsus Medical University, Salzburg, Austria) for statistical advice at the conceptualisation stage of this review. We thank Dr. Miroslav Sili (Jugendinitiative Triestingtal, Berndorf, Austria) für support at the screening stage of this review, statistician Dr. Eleonora Carrozzo (Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria) for assistance with the extraction and calculation of association statistics, and Barbara Bauer (FH Campus Wien – University of Applied Sciences, Vienna, Austria) for support with data visualisation.

Corresponding author

Correspondence to Stefan Tino Kulnik: [email protected]

Author details

¹Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria

²CAPHRI Department of Health Promotion, Maastricht University, Maastricht, The Netherlands

³University Institute of Sports Medicine, Prevention and Rehabilitation and Research Institute of Molecular Sports Medicine and Rehabilitation, Paracelsus Medical University, Salzburg, Austria

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Additionalfile1.docx
Additional file 1 Microsoft word document (.docx) Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) checklist (27) A complete 27-item checklist of the PRISMA guidelines for conducting a systematic review.
Additionalfile2.docx
Additional file 2 Microsoft word document (.docx) Search strategy A complete list of the search terms used for the screening process for all five databases (MEDLINE/PubMed, Embase, Web of Science Core Collection, PsychINFO, CINAHL)
Additionalfile3.docx
Additional file 3 Microsoft word document (.docx) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies of the National Health Institute/National Heart, Lung, and Blood Institute (32). An overview of the 14-item quality assessment tool, quality rating options, and names of the raters for the quality appraisal step.
Additionalfile4.docx
Additional file 4 Microsoft word document (.docx) Quality assessment of included studies. A complete list of all 54 included studies’ quality ratings. Possible rating options were poor, fair and good.
Additionalfile5.docx
Additional file 5 Microsoft word document (.docx) Associations between determinants of physical activity and physical activity behaviour. A full list of all 545 bivariate associations categorised according to OPTImAL ontology – a pre-formarlised set of 320 interrrelated predictors that may determine physical activity in the cardiac population (33).

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Reviewers invited by journal
23 Nov, 2023
Editor assigned by journal
23 Nov, 2023
First submitted to journal
07 Nov, 2023

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Determinants of physical activity behaviour in the cardiac population: a systematic review

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Abstract

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Background

Methods

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Eligibility criteria

Search domains

Population

Determinants

Outcome behaviour

Additional criteria

Information sources

Search strategy

Study selection process

Data extraction

Quality assessment

Data synthesis

Results

Study characteristics

Methodological quality assessment

Physical activity behaviour

Determinants of physical activity behaviour

Discussion

Determinants with particular relevance to the cardiac population

Underrepresented determinants in this review

Related research

Strengths and limitations

Future directions

Conclusions

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