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Perioperative digital behaviour change interventions for reducing alcohol consumption, improving dietary intake, increasing physical activity and smoking cessation: a scoping review

Abstract

Background

Evidence suggests that unhealthy lifestyle behaviours are modifiable risk factors for postoperative complications. Digital behaviour change interventions (DBCIs), for instance text messaging programs and smartphone apps, have shown promise in achieving lifestyle behaviour change in a wide range of clinical populations, and it may therefore be possible to reduce postoperative complications by supporting behaviour change perioperatively using digital interventions. This scoping review was conducted in order to identify existing research done in the area of perioperative DBCIs for reducing alcohol consumption, improving dietary intake, increasing physical activity and smoking cessation.

Main text

This scoping review included eleven studies covering a range of surgeries: bariatric, orthopaedic, cancer, transplantation and elective surgery. The studies were both randomised controlled trials and feasibility studies and investigated a diverse set of interventions: one game, three smartphone apps, one web-based program and five text message interventions. Feasibility studies reported user acceptability and satisfaction with the behaviour change support. Engagement data showed participation rates ranged from 40 to 90%, with more participants being actively engaged early in the intervention period. In conclusion, the only full-scale randomised controlled trial (RCT), text messaging ahead of bariatric surgery did not reveal any benefits with respect to adherence to preoperative exercise advice when compared to a control group. Two of the pilot studies, one text message intervention, one game, indicated change in a positive direction with respect to alcohol and tobacco outcomes, but between group comparisons were not done due to small sample sizes. The third pilot-study, a smartphone app, found between group changes for physical activity and alcohol, but not with respect to smoking cessation outcomes.

Conclusion

This review found high participant satisfaction, but shows recruitment and timing-delivery issues, as well as low retention to interventions post-surgery. Small sample sizes and the use of a variety of feasibility outcome measures prevent the synthesis of results and makes generalisation difficult. Future research should focus on defining standardised outcome measures, enhancing patient engagement and improving adherence to behaviour change prior to scheduled surgery.

Introduction

Patients undergoing surgery are at risk of postoperative complications, which may result in increased postoperative morbidity and mortality, extended hospital stay and increased societal costs (Wakeam et al., 2015; Tevis et al., 2016). While the surgical procedure itself may be an unavoidable risk factor, evidence suggests that unhealthy lifestyle behaviours, such as alcohol, diet, physical activity and smoking are modifiable risk factors for postoperative complications (Thomsen et al., 2009; Eliasen et al., 2013; Steffens et al., 2018; Schwegler et al., 2010; Levett et al., 2016; Moller et al., 2003). Digital behaviour change interventions (DBCIs) are interventions that employ computer technology, usually websites, mobile phones or smartphone applications (apps), to encourage and support behaviour change with the goal of improving or maintaining health. DBCIs may be automated without the need for health professional input, but can be used in combination with digital and face-to-face support (Yardley et al., 2016). DBCIs have shown promise in achieving lifestyle behaviour change in a wide range of populations (Dorri et al., 2020; Villinger et al., 2019; Santo et al., 2018; Suffoletto et al., 2014; Hall et al., 2015; Müssener et al., 2016; Bendtsen et al., 2015; McCambridge et al., 2013; Bendtsen et al., 2012; Thomas et al., 2018; Balk-Moller et al., 2017; Buckingham et al., 2019), and it may therefore be possible to reduce postoperative complications by supporting behaviour change using digital interventions.

Perioperative medicine has been defined by Grocott and Mythen, 2015 (Grocott & Mythen, 2015) as “a patient-focused, multidisciplinary, and integrated approach to delivering the best possible health care throughout the perioperative journey from the moment of contemplation of surgery until full recovery”. Much of the perioperative medicine occurs outside the operating room such as behaviour change, prehabilitation and management of long-term health problems (Miller et al., 2016). In recent years, studies focusing on how to improve patients’ lifestyle behaviour preoperatively, with a view to improve postoperative recovery, have become more common (Gillis et al., 2018; Santa Mina et al., 2014; Wong et al., 2012; Mills et al., 2011; Martindale et al., 2013). For instance, a systematic review (Thomsen et al., 2014) concluded that preoperative smoking interventions may support short-term smoking cessation, which is important as smokers have been shown to be at a higher risk of respiratory complications during anaesthesia, as well as having increased risk of cardiopulmonary and wound-related postoperative complications (Thomsen et al., 2009; Moller & Tonnesen, 2006). Similarly, excessive preoperative alcohol consumption may increase the risk of postoperative morbidity, infections and wound and pulmonary complications (Eliasen et al., 2013). Intensive alcohol and smoking cessation interventions 6–8 weeks before surgery have been shown to reduce postoperative morbidity (Thomsen et al., 2014; Moller et al., 2002; Oppedal et al., 2012), which supports the causal argument. In addition, a recent systematic review found that intensive alcohol abstinence interventions may reduce the incidence of postoperative complications (Egholm et al., 2018).

Smoking and alcohol consumption are not the only lifestyle-related risk factors of postoperative complications. Low levels of physical activity may also increase the risk, and preoperative exercise has been associated with a reduced number of cases of postoperative pulmonary complications in lung cancer (Steffens et al., 2018) and cardiac patients (Snowdon et al., 2014; Marmelo et al., 2018). A systematic review concluded that preoperative physical therapy may reduce postoperative pulmonary complications and length of hospital stay in patients undergoing cardiac surgery (Hulzebos et al., 2012). There was also evidence that malnourished patients have a significantly higher postoperative mortality and morbidity (Schwegler et al., 2010; Goiburu et al., 2006; Wischmeyer et al., 2018).

Digital behaviour change interventions which support lifestyle behaviour change have shown promise in a range of clinical populations, for example among cancer and stroke patients (Zhang et al., 2018; Choi & Paik, 2018), and among others (Brewer et al., 2015; Burkow et al., 2018). While studies of lifestyle behaviour interventions in the surgical context are fewer, the research field of perioperative digital lifestyle interventions is growing, and it is therefore timely to take stock on the current state of the field.

Objectives

As the body of literature on perioperative digital lifestyle behaviour change interventions has not been comprehensively reviewed, and since the topic is heterogenous and not amenable to more precise systematic review, we conducted a scoping review (Peters et al., 2015) in order to identify existing research done in the area of perioperative DBCIs for reducing alcohol consumption, improving diet, increasing physical activity and smoking cessation. This scoping review aims to describe the existing studies by population, intervention, comparator, outcomes and study design (PICOS), and findings may determine the value of a full systematic review (Munn et al., 2018).

Methods

This review includes the items recommended by the PRISMA extension for Scoping reviews (Moher et al., 2009). A search strategy and plan for data extraction and synthesis was produced in advance but no protocol was published.

Eligibility criteria

Eligibility criteria were developed using the PICOS format. Reports in peer-reviewed journals of quantitative, qualitative and mixed method studies were eligible for inclusion. Studies were included if they were published in English, without any restriction on publication date.

Participants

Studies including patients from any surgical specialty were included, with no restriction on type of surgery, participant gender or age. As there is no standardised definition of the duration of the perioperative period (Thompson et al., 2016), and in order to achieve a broad scope, this review covers all phases of perioperative medicine—from decision to perform surgery (e.g. referral or pre-assessment) until early postoperative inpatient hospital recovery period.

Interventions

Included interventions consisted of perioperative DBCIs. That is, interventions which employ computer technology, usually websites, mobile phones or smartphone applications (apps) to encourage and support behaviour change. The content of included interventions focused on supporting behaviour change with respect to at least one of: alcohol consumption, diet, physical activity or smoking. Interventions included were in principle unguided, and components of the interventions were delivered directly to participants via a digital device. Studies where an intervention was used only to schedule or remind participants of other activities (e.g. to take medicines) were not included, neither were disease- or recovery management systems such as physiotherapy or at home monitoring.

Outcomes

Studies were included if either behaviour change outcomes were reported (alcohol, diet, physical activity or smoking) or usability measures (including engagement, accessibility and user ratings).

Information sources and search

We searched for literature in PubMed, PubMed Central; Cochrane Central Register of Controlled Trials (CENTRAL); Database of Abstracts of Reviews of Effects (DARE); Scopus; PsycINFO; PsycARTICLES; and Web of Science. A search strategy was created for PubMed, which was then adapted to the other information sources. The final search strategy for PubMed can be found in Additional file 1: Appendix A.

Selection of sources of evidence and data charting process

The search results were exported into Mendeley and duplicates were removed. KÅ initially screened titles and abstracts of the identified articles and removed those clearly deemed irrelevant for the objective according to the predefined eligibility criteria. KÅ and MB thereafter independently screened the full texts of the identified articles and together reached a final decision on which studies to include. Data were extracted from the included studies by KÅ using the TIDieR checklist (Template for Intervention Description and Replication) (Hoffmann et al., 2014) and an author created form for extracting characteristics of studies (including PICOS, follow-up rates, results and funding). Table 1 shows the extracted data items. Finally, MB independently screened the data extraction for accuracy. Both KÅ and MB synthesized results. Qualitative findings were extracted as reported, and no attempt to further analyse findings were made.

Table 1 Items from the TIDieR checklist extracted from reports, and items extracted from studies via the study-specific form

Synthesis of results

The following sections were defined to aid the synthesis of results: populations and interventions, theoretical frameworks, feasibility studies, engagement data, behaviour change outcomes and protocols. KÅ synthesized the extracted data under each section in a descriptive overview. MB critically screened the process and synthesis, and findings were discussed in multiple sessions involving both KÅ and MB.

Results

Selection of sources of evidence

The search for records was conducted at three different dates in 2019: September 25 (PubMed), October 17 (CENTRAL, Scopus, Web of Science) and November 7 (PsycINFO, PsycARTICLES, DARE NHS-EED). On February 16, 2021, the search for records was repeated. Figure 1 shows a PRISMA flow diagram of the record selection process. Eleven records fulfilled the eligibility criteria and were subsequently included in the review.

Fig. 1
figure1

PRISMA flow diagram of record selection process

Characteristics and results of individual sources of evidence

A summary of included studies and their characteristics can be found in Table 2. Intervention descriptions including relevant items from the TIDieR checklist can be found in Additional file 2: Appendix B. Of the eleven studies included in this review, five (Lemanu et al., 2018; DeMartini et al., 2018; Krebs et al., 2019; van der Velde et al., 2021; Bendtsen et al., 2019) reported on randomised controlled trials (RCT) (full-scale trials = 1, pilot trials = 3, and study protocols = 1), and six (Mundi et al., 2015; McCrabb et al., 2017; Nolan et al., 2019; Low et al., 2020; Kulinski & Smith, 2020; Thomas et al., 2020) reported on feasibility studies. Most studies were conducted in the USA (n = 5), and the remaining in Australia, New Zealand, Netherlands and Sweden. The mean age of participants was 51.1 years (SD 9.0 years) (n = 385), and 57% were female (n = 413).

Table 2 Summary of included studies evaluating perioperative digital lifestyle interventions

Synthesis of results

Populations and interventions

The included studies covered a range of surgery types: bariatric (Lemanu et al., 2018; Mundi et al., 2015), orthopaedic (McCrabb et al., 2017), cancer (Krebs et al., 2019), transplantation (DeMartini et al., 2018) and non-specific elective surgery (van der Velde et al., 2021; Bendtsen et al., 2019; Nolan et al., 2019; Low et al., 2020; Kulinski & Smith, 2020). In the majority of studies, interventions were presented to patients prior to surgery, for instance at the preoperative planning meeting (Lemanu et al., 2018; DeMartini et al., 2018; van der Velde et al., 2021; Bendtsen et al., 2019; Mundi et al., 2015; Nolan et al., 2019; Low et al., 2020; Kulinski & Smith, 2020), and in two studies the interventions were introduced to patients in the ward post-surgery (Krebs et al., 2019; McCrabb et al., 2017). The majority of the interventions targeted smoking cessation (Krebs et al., 2019; Bendtsen et al., 2019; McCrabb et al., 2017; Nolan et al., 2019), physical activity (Lemanu et al., 2018; Low et al., 2020), or a combination of physical activity and diet (Mundi et al., 2015; Kulinski & Smith, 2020). Only two studies targeted either alcohol alone (DeMartini et al., 2018), or all four lifestyle behaviours at once (alcohol, diet, physical activity and smoking) (van der Velde et al., 2021). Studies varied in format: one game (Krebs et al., 2019), one web-based program (McCrabb et al., 2017), three smartphone apps (van der Velde et al., 2021; Mundi et al., 2015; Low et al., 2020) and five text message interventions (Lemanu et al., 2018; DeMartini et al., 2018; Bendtsen et al., 2019; Nolan et al., 2019; Kulinski & Smith, 2020). The text message interventions consisted of either texts only, or combined with web-based modules.

Half of the studies utilised tailoring or personalisation of the interventions (DeMartini et al., 2018; van der Velde et al., 2021; Mundi et al., 2015; Nolan et al., 2019; Low et al., 2020). In one study (Low et al., 2020), the intervention was completely based on personalised data gathered via a smartwatch, and in another the intervention was dynamic based on patients’ operation date (van der Velde et al., 2021). Two of the text message interventions allowed participants to send messages to the system and receive automated tailored responses (DeMartini et al., 2018; Nolan et al., 2019), and one intervention included tailored ecological momentary assessment messages based on baseline characteristics (Mundi et al., 2015).

Theoretical frameworks

Three reports explicitly mentioned that interventions were based on theories for behaviour change (DeMartini et al., 2018; Krebs et al., 2019; McCrabb et al., 2017). The theories mentioned were social cognitive theory (Bandura, 1989), the transtheoretical model (Prochaska, 1979) and the relapse prevention model (Lowman et al., 1996). Other reports mentioned that interventions were based on previous research, clinical best practice, guidelines and expert knowledge (Bendtsen et al., 2019; Nolan et al., 2019). Van der Velde et al. (van der Velde et al., 2021) reported that their app were based on behaviour change techniques (BCTs), for example goalsetting, social support and feedback on behaviour. Also, McCrabb et al. (McCrabb et al., 2017) reported that their intervention employed a number of BCTs, but these were not specified. For additional details of the interventions, please see Additional file 2: Appendix B.

Feasibility studies

The six feasibility studies measured subjects’ knowledge, experience, usability, acceptability, satisfaction and perceived helpfulness with respect to the digital interventions (Mundi et al., 2015; McCrabb et al., 2017; Nolan et al., 2019; Low et al., 2020; Kulinski & Smith, 2020; Thomas et al., 2020). All studies utilised study specific questionnaires, resulting in high heterogeneity of measurements. Overall, the feasibility studies reported mixed findings with high user acceptability and satisfaction with the support offered, along with challenges in engaging users. Nolan et al. (Nolan et al., 2019) reported that a majority (82%) of the participants were satisfied with the text messaging smoking cessation program, and that many (68%) expressed that they would be interested in using it again for future surgeries. Similar results were found for the text message intervention by Kulinski et al. (Kulinski & Smith, 2020), who tested a prehabilitation program for patients with obesity awaiting elective surgery. Fifteen of 18 participants (83%) found the programme useful and all participants stated that they would recommend the programme to others in the lead-up to surgery. Mundi et al. (Mundi et al., 2015) found that bariatric surgical participants felt that the intervention helped them understand behaviours of long-term weight loss, and made them feel more connected to the care team. McCrabb et al. (McCrabb et al., 2017) reported that only 11/20 orthopaedic trauma patients remembered using the smoking cessation support, likely due to effects of medication, and very few participants accessed the intervention after hospital discharge, which made evaluation of the intervention problematic. Qualitative data from Low et al. (Low et al., 2020) reported that participants found physical activity prompts to be motivating before abdominal cancer surgery, but also reported that it was especially difficult to walk in the hospital immediately after surgery when they were too weak to walk unassisted, were in the middle of tests or other care procedures, or were on medications that made it difficult to get up and walk. The qualitative study from Thomas et al. (Thomas et al., 2020) showed that elective surgery patients were open and receptive to receive smoking cessation support in a digital format from a health care provider ahead of surgery. Data showed that patients having elective surgery had a strong motivation to quit smoking, where for instance one participant reported: “And then I walked down to her at the unit straight away from meeting with the surgeon to speak with her. And then I said, “Let’s throw away the cigarettes”.

Engagement data

Six of the studies (DeMartini et al., 2018; Krebs et al., 2019; Mundi et al., 2015; McCrabb et al., 2017; Nolan et al., 2019; Low et al., 2020) reported on engagement data, defined by Perski et al. (Perski et al., 2017) as “Engagement with DBCIs is (1) the extent (e.g. amount, frequency, duration, depth) of usage and (2) a subjective experience characterised by attention, interest and affect”. Among text message interventions where participants were expected to respond to messages (DeMartini et al., 2018; Mundi et al., 2015; Nolan et al., 2019), total response rates ranged from 31 to 81%, with more participants responding early in the intervention period (Nolan et al., 2019). Interventions not using text messages (game and web-based program) (Krebs et al., 2019; McCrabb et al., 2017) showed participation rates of 40–90% with respect to engaging with the intervention at least once. Overall, a majority of studies found a severe drop-off in usage of the interventions after surgery, for instance Low et al. (Low et al., 2020) reported a significant decline from 91% before surgery to 36% during inpatient time to 65% post-discharge.

Behaviour change outcomes

The RCT studies (Lemanu et al., 2018; DeMartini et al., 2018; Krebs et al., 2019; van der Velde et al., 2021) examined the effects on behaviour change with respect to physical activity, alcohol and smoking. Lemanu et al. (Lemanu et al., 2018) measured adherence to exercise advice (30 min of light to moderate exercise a day, 5 days a week) via the International Physical Activity Questionnaire (IPAQ) among patients on waiting list for laparoscopic sleeve gastrectomy. Result showed that adherence to exercise advice was significantly higher in exposure group than control group (p = 0.041), but there was no evidence suggesting a difference between the two groups at 6 weeks post-operative follow-up. DeMartini et al. (DeMartini et al., 2018) objectively measured alcohol consumption (e.g., ethyl glucuronide) at 8 weeks among pre-liver transplant candidates with alcoholic liver disease. Result showed that none of the individuals tested positive for alcohol consumption at 8 weeks in the intervention group; however, 2 out of 6 participants (33%) in the standard care group did test positive. Krebs et al. (Krebs et al., 2019) measured smoking abstinence (biochemically verified) at 1 month following hospital discharge among cancer patients undergoing surgery. Confirmed smoking abstinence was higher in the intervention group (4/13) in relation to control group (2/11) at 1 month-follow up. Finally, van der Velde et al. (van der Velde et al., 2021) measured change in health risk behaviours (physical activity, alcohol, and smoking) 3 days prior to surgery, based on the recommendations of the Dutch Health Council (questionnaire not disclosed). Compared with the control group, the intervention group showed an increase in self-reported physical activity and muscle strengthening activities prior to surgery. Also, 2 of 2 frequent alcohol users in the intervention group versus 1 of 9 in the control group drank less alcohol prior to surgery. No difference was found in change of smoking cessation.

Difficulties of recruitment were apparent across studies, and sample sizes were relatively small, (n = 102) for the full-scale RCT (Lemanu et al., 2018) and (n = 15–86) for the pilot-studies (DeMartini et al., 2018; Krebs et al., 2019; van der Velde et al., 2021). Krebs et al. (Krebs et al., 2019) reported not meeting sufficient number of eligible patients despite screening for 2 years.

Protocols

Bendtsen et al., 2019. (Bendtsen et al., 2019) reports a protocol for an ongoing RCT of a text message based smoking cessation intervention for patients prior to elective surgery. A total of 434 participants are planned to be randomised, and outcomes are measured subjectively via questionnaires.

Discussion

Summary of evidence

This scoping review aimed to identify existing research done in the area of perioperative DBCIs, in particular with respect to alcohol, diet, physical activity and smoking. We identified a limited number of studies (11 studies) across five different surgery contexts (including unspecified elective surgery). Most interventions targeted smoking cessation, were administered preoperatively, and were delivered via text messages. Overall, the findings of this scoping review indicate a paucity of research on the effects of perioperative digital interventions for lifestyle behaviour change. Also, small sample sizes and the use of a variety of outcome measures prevent formal synthesis of results in for instance meta-analyses and make generalisation of findings difficult.

In a wider context, patients’ have been found to have a substantial desire to modify behaviours for postoperative benefit (McDonald et al., 2019), but also a need for structured preoperative support. In addition, patients have been found to have positive expectations of e-Health in preoperative care (van der Meij et al., 2017). However, despite patient acceptance and willingness, small sample sizes due to difficulties of study recruitment prior to surgery causes issues with validity of findings. Thus, there seems to be a discrepancy between patient expectations and acceptance of study procedures. Most studies included in this scoping review had small sample sizes, and outcome and engagement findings should therefore be viewed with high scepticism. Also, some studies have relied on pre-post measurements (Mundi et al., 2015; Nolan et al., 2019; Kulinski & Smith, 2020) to measure behaviour change, a practice that is vulnerable to regression to the mean and other confounding (Vickers & Altman, 2001).

Another issue identified is the timing of intervention delivery, and adherence to the intervention postsurgery. McCrabb et al. (McCrabb et al., 2017) reported that due to effects from medication, only half of the participants remembered using the intervention in hospital after surgery, and very few participants accessed the intervention after hospital discharge. Also, a majority of the studies included in this review found a severe drop-off in usage of the interventions after surgery, potentially indicating a loss of motivation once the main reason for their behaviour change was removed. This is unfortunate since a healthy lifestyle can improve postoperative recovery (Li et al., 2013; Minnella et al., 2016; Mayo et al., 2011; van Rooijen et al., 2019a; van Rooijen et al., 2019b), and reduces the risk of future health issues (World Health Organization, 2018). It is also possible that the reason for surgery motivates behaviour change differently, e.g. obesity surgery is more tightly coupled with change in diet and physical activity in comparison to smoking and orthopaedic surgery. However, findings of this scoping review cannot support such conclusions, thus we leave this for future research to investigate.

Use of e-health in perioperative care, including replacement of face-to-face consultations with telerehabilitation and telemonitoring, has shown promise (van der Meij et al., 2016). Thus, digitalisation has made progress in the surgical context, and research on DBCIs as part of perioperative care should learn from these successes. Perhaps a blended care model where DBCIs become a part of the current treatment is a future model. Although there are indications to assume that the preoperative period might be a window of opportunity to change behaviour, one must bear in mind that the preoperative period is a stressful and sometimes painful period for many patients. However, it has been argued (Robinson et al., 2020) that if a surgical teachable moment is exploited correctly, the situation can trigger sustainable behaviour change – just as the decision to undergo surgery can be life-changing itself.

Limitations

We took a broad approach in this review, aiming to identify existing research in the field rather than addressing any specific question. It should be noted that we only searched for reports published in English in peer-reviewed journals, which means that grey literature, conference proceedings and abstracts have not been considered for inclusion. Due to this limitation, there may for instance be local government reports written in other languages than English which are not included in this scoping review.

Overall, the included studies had relatively few participants and suffered from attrition and loss of engagement, a main finding of this scoping review, thus the overall findings with respect to satisfaction and usability should be carefully considered in light of this. Also, use of study specific questionnaires and pre-post measurements questions both the internal and external validity of findings.

Conclusions

This scoping review highlights the relatively new research field of DBCIs for perioperative behaviour change. Included studies indicate participant satisfaction, but also show recruitment and timing-delivery issues, as well as low retention to the intervention post-surgery. Small sample sizes and the use of a variety of feasibility outcome measures hinders the synthesis of results and makes generalisation difficult.

Availability of data and materials

Not applicable.

Abbreviations

RCT:

Randomised controlled trial

BCT:

Behaviour change techniques

PICOS:

Population, intervention, comparator, outcomes and study design

DBCI:

Digital behaviour change interventions

IPAQ:

International Physical Activity Questionnaire

TIDieR:

Template for Intervention Description and Replication

References

  1. Balk-Moller NC, Poulsen SK, Larsen TM. Effect of a nine-month web- and app-based workplace intervention to promote healthy lifestyle and weight loss for employees in the social welfare and health care sector: a randomized controlled trial. J Med Internet Res. 2017;19(4): e108. doi:https://doi.org/10.2196/jmir.6196, PMID:28396303

  2. Bandura A. Human agency in social cognitive theory. Am Psychol. 1989;44(9):1175-1184. doi:https://doi.org/10.1037/0003-066x.44.9.1175, PMID:2782727

  3. Bendtsen M, Linderoth C, Bendtsen P. Mobile phone–based smoking-cessation intervention for patients undergoing elective surgery: protocol for a randomized controlled trial. JMIR Res Protoc. 2019;8(3):e12511. https://doi.org/10.2196/12511.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bendtsen P, Bendtsen M, Karlsson N, White IR, McCambridge J. Online alcohol assessment and feedback for hazardous and harmful drinkers: findings from the AMADEUS-2 randomized controlled trial of routine practice in Swedish universities. J Med Internet Res. 2015;17(7):e170. https://doi.org/10.2196/jmir.4020.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bendtsen P, McCambridge J, Bendtsen M, Karlsson N, Nilsen P. Effectiveness of a proactive mail-based alcohol internet intervention for university students: dismantling the assessment and feedback components in a randomized controlled trial. J Med Internet Res. 2012;14(5):e142. https://doi.org/10.2196/jmir.2062.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Brewer LC, Kaihoi B, Zarling KK, Squires RW, Thomas R, Kopecky S. The use of virtual world-based cardiac rehabilitation to encourage healthy lifestyle choices among cardiac patients: intervention development and pilot study protocol. JMIR Res Protoc. 2015;4(2): e39. doi:https://doi.org/10.2196/resprot.4285, PMID:25857331

  7. Buckingham SA, Williams AJ, Morrissey K, Price L, Harrison J. Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: a systematic review. Digit Heal. 2019;5:2055207619839883. doi:https://doi.org/10.1177/2055207619839883, PMID:30944728

  8. Burkow TM, Vognild LK, Johnsen E, Bratvold A, Risberg MJ. Promoting exercise training and physical activity in daily life: a feasibility study of a virtual group intervention for behaviour change in COPD. BMC Med Inform Decis Mak. 2018;18(1):136. doi:https://doi.org/10.1186/s12911-018-0721-8, PMID:30563507

  9. Choi Y-H, Paik N-J. Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation. J Vis Exp. 2018;(133). doi:https://doi.org/10.3791/56241, PMID:29578520, 133

  10. DeMartini KS, Schilsky ML, Palmer A, Fehon DC, Zimbrean P, O’Malley SS, et al. Text messaging to reduce alcohol relapse in prelisting liver transplant candidates: a pilot feasibility study. Alcohol Clin Exp Res. 2018;42(4):761–9. https://doi.org/10.1111/acer.13603.

  11. Dorri S, Asadi F, Olfatbakhsh A, Kazemi A. A systematic review of electronic health (eHealth) interventions to improve physical activity in patients with breast cancer. Breast Cancer. 2020;27(1):25-46. doi:https://doi.org/10.1007/s12282-019-00982-3, PMID:31187411

  12. Egholm JWM, Pedersen B, Møller AM, Adami J, Juhl CB, Tønnesen H. Perioperative alcohol cessation intervention for postoperative complications. Cochrane Database Syst Rev. 2018. https://doi.org/10.1002/14651858.CD008343.pub3.

  13. Eliasen M, Gronkjaer M, Skov-Ettrup LS, Mikkelsen SS, Becker U, Tolstrup JS, Flensborg-Madsen T. Preoperative alcohol consumption and postoperative complications: a systematic review and meta-analysis. Ann Surg. 2013;258(6):930-942. doi:https://doi.org/10.1097/SLA.0b013e3182988d59, PMID:23732268

  14. Gillis C, Buhler K, Bresee L, Carli F, Gramlich L, Culos-Reed N, Sajobi TT, Fenton TR. Effects of nutritional prehabilitation, with and without exercise, on outcomes of patients who undergo colorectal surgery: a systematic review and meta-analysis. Gastroenterology. 2018; 155(2): 391-410.e4. doi:https://doi.org/10.1053/j.gastro.2018.05.012, PMID:29750973

  15. Goiburu ME, Goiburu MMJ, Bianco H, Diaz JR, Alderete F, Palacios MC, Cabral V, Escobar D, Lopez R, Waitzberg DL. The impact of malnutrition on morbidity, mortality and length of hospital stay in trauma patients. Nutr Hosp. 2006;21(5):604-610. , PMID:17044607

  16. Grocott MPW, Mythen MG. Perioperative medicine: the value proposition for anesthesia?: A UK Perspective on Delivering Value from Anesthesiology. Anesthesiol Clin. 2015;33(4):617-628. doi:https://doi.org/10.1016/j.anclin.2015.07.003, PMID:26610619

  17. Hall AK, Cole-Lewis H, Bernhardt JM. Mobile text messaging for health: a systematic review of reviews. Annu Rev Public Health. 2015;36:393-415. doi:https://doi.org/10.1146/annurev-publhealth-031914-122855, PMID:25785892, 1

  18. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, Altman DG, Barbour V, Macdonald H, Johnston M, Lamb SE, Dixon-Woods M, McCulloch P, Wyatt JC, Chan A-W, Michie S. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. doi:https://doi.org/10.1136/bmj.g1687, PMID:24609605, mar07 3

  19. Hulzebos EHJ, Smit Y, Helders PPJM, van Meeteren NLU. Preoperative physical therapy for elective cardiac surgery patients. Cochrane database Syst Rev. 2012. doi:https://doi.org/10.1002/14651858.CD010118.pub2, PMID:23152283

  20. Krebs P, Burkhalter J, Fiske J, Snow H, Schofield E, Iocolano M, Borderud S, Ostroff JS. The QuitIT Coping skills game for promoting tobacco cessation among smokers diagnosed with cancer: pilot randomized controlled trial. JMIR mHealth uHealth. 2019;7(1): e10071. doi:https://doi.org/10.2196/10071, PMID:30632971

  21. Kulinski K, Smith NA. Surgical prehabilitation using mobile health coaching in patients with obesity: a pilot study. Anaesth Intensive Care. 2020;48(5):373-380. doi:https://doi.org/10.1177/0310057X20947731, PMID:33104444

  22. Lemanu DP, Singh PP, Shao RY, Pollock TT, MacCormick AD, Arroll B, Hill AG. Text messaging improves preoperative exercise in patients undergoing bariatric surgery. ANZ J Surg. 2018. doi:https://doi.org/10.1111/ans.14418, PMID:29943447, 88, 7-8, 733, 738

  23. Levett DZH, Edwards M, Grocott M, Mythen M. Preparing the patient for surgery to improve outcomes. Best Pract Res Clin Anaesthesiol. 2016;30(2):145-157. doi:https://doi.org/10.1016/j.bpa.2016.04.002, PMID:27396803

  24. Li C, Carli F, Lee L, Charlebois P, Stein B, Liberman AS, et al. Impact of a trimodal prehabilitation program on functional recovery after colorectal cancer surgery: a pilot study. Surg Endosc. 2013;27(4):1072–82. https://doi.org/10.1007/s00464-012-2560-5.

  25. Low CA, Danko M, Durica KC, Kunta AR, Mulukutla R, Ren Y, Bartlett DL, Bovbjerg DH, Dey AK, Jakicic JM. A real-time mobile intervention to reduce sedentary behavior before and after cancer surgery: usability and feasibility study. JMIR Perioper Med. 2020;3(1): e17292. doi:https://doi.org/10.2196/17292, PMID:33393915

  26. Lowman C, Allen J, Stout RL. Replication and extension of Marlatt’s taxonomy of relapse precipitants: overview of procedures and results. The Relapse Research Group. Addiction. 1996; 91 Suppl: S51-S71. PMID:8997781

  27. Marmelo F, Rocha V, Moreira-Goncalves D. The impact of prehabilitation on post-surgical complications in patients undergoing non-urgent cardiovascular surgical intervention: Systematic review and meta-analysis. Eur J Prev Cardiol. 2018;25(4):404-417. doi:https://doi.org/10.1177/2047487317752373, PMID:29338307

  28. Martindale RG, McClave SA, Taylor B, Lawson CM. Perioperative nutrition: what is the current landscape? JPEN J Parenter Enteral Nutr. 2013;37(5 Suppl):5S–20S. 24009250. https://doi.org/10.1177/0148607113496821.

    Article  PubMed  Google Scholar 

  29. Mayo NE, Feldman L, Scott S, Zavorsky G, Kim DJ, Charlebois P, Stein B, Carli F. Impact of preoperative change in physical function on postoperative recovery: argument supporting prehabilitation for colorectal surgery. Surgery. 2011;150(3):505-514. doi:https://doi.org/10.1016/j.surg.2011.07.045, PMID:21878237

  30. McCambridge J, Bendtsen M, Karlsson N, White IR, Nilsen P, Bendtsen P. Alcohol assessment and feedback by email for university students: Main findings from a randomised controlled trial. Br J Psychiatr 2013;203(5):334-340. doi:https://doi.org/10.1192/bjp.bp.113.128660, PMID:24072758

  31. McCrabb S, Baker AL, Attia J, Balogh ZJ, Lott N, Naylor J, Harris IA, Doran CM, George J, Wolfenden L, Skelton E, Bonevski B. Smoke-free recovery from trauma surgery: a pilot trial of an online smoking cessation program for orthopaedic trauma patients. Int J Environ Res Public Health. 2017; 14(8). doi: https://doi.org/10.3390/ijerph14080847, PMID:28788089

  32. McDonald S, Yates D, Durrand JW, Kothmann E, Sniehotta FF, Habgood A, Colling K, Hollingsworth A, Danjoux G. Exploring patient attitudes to behaviour change before surgery to reduce peri-operative risk: preferences for short- vs. long-term behaviour change. Anaesthesia. 2019;74(12):1580-1588. doi:https://doi.org/10.1111/anae.14826, PMID:31637700

  33. Miller TE, Shaw AD, Mythen MG, Gan TJ, Workgroup F. The PQI (POQI) I. Evidence-based perioperative medicine comes of age: the perioperative quality initiative (POQI). Perioper Med. 2016;5(1):26. https://doi.org/10.1186/s13741-016-0055-y.

    Article  Google Scholar 

  34. Mills E, Eyawo O, Lockhart I, Kelly S, Wu P, Ebbert JO. Smoking cessation reduces postoperative complications: a systematic review and meta-analysis. Am J Med. 2011; 124(2): 144-154.e8. doi: https://doi.org/10.1016/j.amjmed.2010.09.013, PMID:21295194

  35. Minnella EM, Awasthi R, Gillis C, Fiore JF, Liberman AS, Charlebois P, et al. Patients with poor baseline walking capacity are most likely to improve their functional status with multimodal prehabilitation. Surgery. 2016;160(4):1070–9. https://doi.org/10.1016/j.surg.2016.05.036.

    Article  PubMed  Google Scholar 

  36. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097.

  37. Moller A, Tonnesen H. Risk reduction: perioperative smoking intervention. Best Pract Res Clin Anaesthesiol. 2006;20(2):237–48. 16850775. https://doi.org/10.1016/j.bpa.2005.10.008.

    Article  PubMed  Google Scholar 

  38. Moller AM, Pedersen T, Villebro N, Norgaard P. Impact of lifestyle on perioperative smoking cessation and postoperative complication rate. Prev Med (Baltim). 2003;36(6):704-709. doi:https://doi.org/10.1016/s0091-7435(03)00012-4, PMID:12744914

  39. Moller AM, Villebro N, Pedersen T, Tonnesen H. Effect of preoperative smoking intervention on postoperative complications: a randomised clinical trial. Lancet (London, Engl). 2002; 359(9301): 114-117. doi: https://doi.org/10.1016/S0140-6736(02)07369-5, PMID:11809253

  40. Mundi MS, Lorentz PA, Grothe K, Kellogg TA, Collazo-Clavell ML. Feasibility of smartphone-based education modules and ecological momentary assessment/intervention in pre-bariatric surgery patients. Obes Surg. 2015;25(10):1875-1881. doi:https://doi.org/10.1007/s11695-015-1617-7, PMID:25702141

  41. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143. doi:https://doi.org/10.1186/s12874-018-0611-x, PMID:30453902

  42. Müssener U, Bendtsen M, Karlsson N, White IR, McCambridge J, Bendtsen P. Effectiveness of Short Message Service Text-Based Smoking Cessation Intervention Among University Students. JAMA Intern Med. 2016;176(3):321–8. https://doi.org/10.1001/jamainternmed.2015.8260.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Nolan MB, Warner MA, Jacobs MA, Amato MS, Graham AL, Warner DO. Feasibility of a perioperative text messaging smoking cessation program for surgical patients. Anesth Analg. 2019; 129(3): e73-e76. doi: https://doi.org/10.1213/ANE.0000000000003715, PMID:31425205

  44. Oppedal K, Møller AM, Pedersen B, Tønnesen H. Preoperative alcohol cessation prior to elective surgery. Cochrane Database Syst Rev. 2012. https://doi.org/10.1002/14651858.CD008343.pub2.

  45. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017;7(2):254-267. doi:https://doi.org/10.1007/s13142-016-0453-1, PMID:27966189

  46. Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13(3):141-146. doi:https://doi.org/10.1097/XEB.0000000000000050, PMID:26134548

  47. Prochaska JO. Systems of Psychotherapy. Homewood: The Dorsey Press; 1979.

    Google Scholar 

  48. Robinson A, Slight R, Husband A, Slight S. The value of teachable moments in surgical patient care and the supportive role of digital technologies. Perioper Med (London, England). 2020; 9: 2. doi: https://doi.org/10.1186/s13741-019-0133-z, PMID:32042404

  49. Rollnick S, Heather N, Gold R, Hall W. Development of a short 'readiness to change' questionnaire for use in brief, opportunistic interventions among excessive drinkers. Br J Addict. 1992;87(5):743–54. https://doi.org/10.1111/j.1360-0443.1992.tb02720.x. PMID: 1591525.

  50. Santa Mina D, Clarke H, Ritvo P, Leung YW, Matthew AG, Katz J, Trachtenberg J, Alibhai SMH. Effect of total-body prehabilitation on postoperative outcomes: a systematic review and meta-analysis. Physiotherapy. 2014;100(3):196-207. doi:https://doi.org/10.1016/j.physio.2013.08.008, PMID:24439570

  51. Santo K, Hyun K, de Keizer L, Thiagalingam A, Hillis GS, Chalmers J, Redfern J, Chow CK. The effects of a lifestyle-focused text-messaging intervention on adherence to dietary guideline recommendations in patients with coronary heart disease: an analysis of the TEXT ME study. Int J Behav Nutr Phys Act. 2018;15(1):45. doi:https://doi.org/10.1186/s12966-018-0677-1, PMID:29792202

  52. Schwegler I, von Holzen A, Gutzwiller J-P, Schlumpf R, Muhlebach S, Stanga Z. Nutritional risk is a clinical predictor of postoperative mortality and morbidity in surgery for colorectal cancer. Br J Surg. 2010;97(1):92-97. doi:https://doi.org/10.1002/bjs.6805, PMID:20013933

  53. Snowdon D, Haines TP, Skinner EH. Preoperative intervention reduces postoperative pulmonary complications but not length of stay in cardiac surgical patients: a systematic review. J Physiother. 2014;60(2):66-77. doi:https://doi.org/10.1016/j.jphys.2014.04.002, PMID:24952833

  54. Steffens D, Beckenkamp PR, Hancock M, Solomon M, Young J. Preoperative exercise halves the postoperative complication rate in patients with lung cancer: a systematic review of the effect of exercise on complications, length of stay and quality of life in patients with cancer. Br J Sports Med. 2018;52(5):344. doi:https://doi.org/10.1136/bjsports-2017-098032, PMID:29437041

  55. Suffoletto B, Kristan J, Callaway C, Kim KH, Chung T, Monti PM, Clark DB. A text message alcohol intervention for young adult emergency department patients: a randomized clinical trial. Ann Emerg Med. 2014; 64(6): 664-672.e4. doi:https://doi.org/10.1016/j.annemergmed.2014.06.010

  56. Tevis SE, Cobian AG, Truong HP, Craven MW, Kennedy GD. Implications of multiple complications on the postoperative recovery of general surgery patients. Ann Surg. 2016;263(6):1213-1218. doi:https://doi.org/10.1097/SLA.0000000000001390, PMID:27167563

  57. Thomas K, Bendtsen M, Linderoth C, Bendtsen P. Implementing facilitated access to a text messaging, smoking cessation intervention among swedish patients having elective surgery: qualitative study of patients’ and health care professionals’ perspectives. JMIR mHealth uHealth. 2020;8(9):e17563. https://doi.org/10.2196/17563.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Thomas K, Müssener U, Linderoth C, Karlsson N, Bendtsen P, Bendtsen M. Effectiveness of a text messaging–based intervention targeting alcohol consumption among university students: randomized controlled trial. JMIR mHealth uHealth. 2018;6(6):e146. https://doi.org/10.2196/mhealth.9642.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Thompson BM, Stearns JD, Apsey HA, Schlinkert RT, Cook CB. Perioperative management of patients with diabetes and hyperglycemia undergoing elective surgery. Curr Diab Rep. 2016;16(1):2. doi:https://doi.org/10.1007/s11892-015-0700-8, PMID:26699765

  60. Thomsen T, Tonnesen H, Moller AM. Effect of preoperative smoking cessation interventions on postoperative complications and smoking cessation. Br J Surg. 2009;96(5):451-461. doi:https://doi.org/10.1002/bjs.6591, PMID:19358172

  61. Thomsen T, Villebro N, Møller AM. Interventions for preoperative smoking cessation. Cochrane Database Syst Rev. 2014. https://doi.org/10.1002/14651858.CD002294.pub4.

  62. van der Meij E, Anema JR, Otten RHJ, Huirne JAF, Schaafsma FG. The effect of perioperative E-health interventions on the postoperative course: a systematic review of randomised and non-randomised controlled trials. PLoS One. 2016;11(7): e0158612. doi:https://doi.org/10.1371/journal.pone.0158612, PMID:27383239

  63. van der Meij E, Bouwsma EVA, van den Heuvel B, Bonjer HJ, Anema JR, Huirne JAF. Using e-health in perioperative care: a survey study investigating shortcomings in current perioperative care and possible future solutions. BMC Surg. 2017;17(1):61. doi:https://doi.org/10.1186/s12893-017-0254-6, PMID:28535763

  64. van der Velde M, Valkenet K, Geleijn E, Kruisselbrink M, Marsman M, Janssen LM, Ruurda JP, van der Peet DL, Aarden JJ, Veenhof C, van der Leeden M. Usability and preliminary effectiveness of a preoperative mhealth app for people undergoing major surgery: pilot randomized controlled trial. JMIR mHealth uHealth. 2021;9(1): e23402. doi:https://doi.org/10.2196/23402, PMID:33410758

  65. van Rooijen S, Carli F, Dalton S, Thomas G, Bojesen R, Le Guen M, et al. Multimodal prehabilitation in colorectal cancer patients to improve functional capacity and reduce postoperative complications: the first international randomized controlled trial for multimodal prehabilitation. BMC Cancer. 2019A;19(1):98. https://doi.org/10.1186/s12885-018-5232-6.

  66. van Rooijen SJ, Molenaar CJL, Schep G, van Lieshout RHMA, Beijer S, Dubbers R, Rademakers N, Papen-Botterhuis NE, van Kempen S, Carli F, Roumen RMH, Slooter GD. Making patients fit for surgery: introducing a four pillar multimodal prehabilitation program in colorectal cancer. Am J Phys Med Rehabil. 2019B;98(10):888-896. doi:https://doi.org/10.1097/PHM.0000000000001221, PMID:31090551

  67. Vickers AJ, Altman DG. Statistics notes: Analysing controlled trials with baseline and follow up measurements. BMJ. 2001;323(7321):1123-1124. doi:https://doi.org/10.1136/bmj.323.7321.1123, PMID:11701584

  68. Villinger K, Wahl DR, Boeing H, Schupp HT, Renner B. The effectiveness of app-based mobile interventions on nutrition behaviours and nutrition-related health outcomes: A systematic review and meta-analysis. Obes Rev. 2019;20(10):1465-1484. doi:https://doi.org/10.1111/obr.12903, PMID:31353783

  69. Wakeam E, Hyder JA, Tsai TC, Lipsitz SR, Orgill DP, Finlayson SRG. Complication timing and association with mortality in the American College of Surgeons’ National Surgical Quality Improvement Program database. J Surg Res. 2015;193(1):77-87. doi:https://doi.org/10.1016/j.jss.2014.08.025, PMID:25260955

  70. Wischmeyer PE, Carli F, Evans DC, Guilbert S, Kozar R, Pryor A, Thiele RH, Everett S, Grocott M, Gan TJ, Shaw AD, Thacker JKM, Miller TE, Hedrick TL, McEvoy MD, Mythen MG, Bergamaschi R, Gupta R, Holubar SD, Senagore AJ, Abola RE, Bennett-Guerrero E, Kent ML, Feldman LS, Fiore JF Jr, Perioperative Quality Initiative (POQI) 2 Workgroup American Society for Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on Nutrition Screening and Therapy Within a Surgical Enhanced Recovery Pathway. Anesth Analg. 2018;126(6):1883-1895. doi:https://doi.org/10.1213/ANE.0000000000002743, PMID:29369092

  71. Wong J, Lam DP, Abrishami A, Chan MT V, Chung F. Short-term preoperative smoking cessation and postoperative complications: a systematic review and meta-analysis. Can J Anaesth. 2012;59(3):268-279. doi:https://doi.org/10.1007/s12630-011-9652-x, PMID:22187226

  72. World Health Organization. Noncommunicable Diseases. Fact Sheet. 2018.; 2018. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

  73. Yardley L, Choudhury T, Patrick K, Michie S. Current issues and future directions for research into digital behavior change interventions. Am J Prev Med. 2016;51(5):814-815. doi:https://doi.org/10.1016/j.amepre.2016.07.019, PMID:27745680

  74. Zhang X, Deng Z, Parvinzamir F, Dong F. MyHealthAvatar lifestyle management support for cancer patients. Ecancermedicalscience. 2018;12:849. doi:https://doi.org/10.3332/ecancer.2018.849, PMID:30079111

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Funding

This scoping review was funded by Region Östergötland (Strategiområdet Sjukvård och Välfärd, LIO-858631, PI: Dr. Marcus Bendtsen), and conducted within the MoBILE research program which is funded by the Swedish Research Council for Health, Working Life and Welfare (FORTE, Dnr: 2018-01410, PI: Professor Marie Löf), and by the Swedish Cancer Society (Cancerfonden, 20 0883 Pj, PI: Dr. Marcus Bendtsen). Open Access funding provided by Linköping University.

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Co-author Marcus Bendtsen owns a private company (Alexit AB) which develops and disseminates eHealth applications to health organizations and professionals in both the private and public sector. Alexit AB had no part in the funding, planning, execution or analysis of this scoping review.

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Supplementary Information

Additional file 1: Appendix A.

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Additional file 2.

Appendix B. TIDieR checklist.

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Åsberg, K., Bendtsen, M. Perioperative digital behaviour change interventions for reducing alcohol consumption, improving dietary intake, increasing physical activity and smoking cessation: a scoping review. Perioper Med 10, 18 (2021). https://doi.org/10.1186/s13741-021-00189-1

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Keywords

  • Perioperative
  • Digital behaviour change interventions
  • Lifestyle behaviour
  • Feasibility
  • Randomised controlled trial
  • Scoping review