The prevention of non-communicable diseases (NCDs) such as cardiovascular disease, cancer and type 2 diabetes is a major public health goal worldwide [1]. In 2012, NCDs were responsible for 38 million (68%) of the world’s 56 million deaths [1]. Lifestyle behaviours including diet, physical activity and sedentary behaviour are key modifiable risk factors for these diseases and improving these behaviours is considered essential to reducing the financial and health burden of these NCDs [1]. Unhealthy diet, physical inactivity and sedentary behaviour are known to track from childhood into adulthood and are difficult to change later in life [2, 3]. This exacerbates associated health problems and demonstrates why preventing the development of these health risk factors throughout the lifespan is important.

Given the global scale of NCDs, preventative interventions that can reach large populations at low cost are needed. Smartphones and tablets, including the software applications (apps) that run on these devices, have become an integral part of people’s life with large increases in usage rates since their introduction in 2007 [4, 5]. For example, 70% of Americans, 61% of Europeans and 74% of Australians regularly use a smartphone and/or tablet [5, 6]. The growth in mobile technologies has also stimulated the growth in health and fitness apps to provide behavioural interventions that can reach large populations [7]. Clever use of apps in health promotion and prevention of disease has enabled researchers to apply health behaviour changes techniques (e.g., goal setting, self-monitoring, performance feedback) that have proven to facilitate health behaviour change across diverse population groups and settings [7, 8].

Despite the increasing use of apps in health behaviour change studies their efficacy is unclear, particularly for different lifestyle behaviours such as diet, physical activity and sedentary behaviour, as well as for specific population groups including children and adults. Some systematic reviews [913] have addressed the potential of health apps to improve diet, physical activity and/or sedentary behaviour. However, the scope of these reviews has been broad and many questions remain. Firstly, most previous reviews [911, 14] were on electronic and mobile health (e-& mHealth) interventions, not specifically on app-based interventions. Secondly, many reviews [7, 8, 1518] have focused on app design, the incorporation of established behaviour change techniques and they examined intervention feasibility rather than efficacy. Thirdly, many previous reviews [1922] have focused on the use of apps for the treatment of obesity and chronic diseases. As such, little is known about the potential of using apps to change health behaviours for disease prevention. Finally, no reviews have distinguished between app interventions for children versus adults. This is important as app features appealing to children and adolescents may differ from those used by adults, and the choice of app features will likely determine user engagement, retention and ultimately intervention efficacy [22].

This review aimed to address these gaps in the literature by systematically synthesising evidence for the efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour for NCD prevention. Since app features used by children may differ from those used by adults, we distinguished between app interventions targeted to children and adults.


Literature search

This review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Additional file 1) [23]. Systematic searches were conducted between November and December 2015 in five databases: Scopus, CINAHL, SportDiscuss, PsycINFO and Web of Science. The search was limited to English language literature, humans, and the year of publication between 01 January 2006 and 31 October 2016. It was considered unlikely that app interventions were developed before 2006/2007 when smartphones were introduced. Systematic search strategies were designed using a combination of thesaurus and free terms covering the following terms: application, app, smartphone, smart phone, tablet, mobile game, game, physical activity, walk, physical fitness, leisure activity, motor activity, exercise, sport, sedentary, sedentary behaviour, sedentary behavior, sitting, screen time, inactive, diet, dietary, nutrition, nutritional, healthy eating, food, fruit, vegetable, snack, soft drink, carbonated beverage, intervention, program, programme, health promotion, prevention and trial. The detailed search strategies used for each database are presented in the Additional file 2. Additionally, articles were identified via hand-searching and reviewing reference lists of relevant papers.

Inclusion criteria and study selection

Studies were included in the present review if (1) they used an app in an intervention to influence at least one of the following lifestyle behaviours: dietary intake, physical activity, sedentary behaviour; (2) targeted children and/or adults; (3) focused on behaviour change for disease prevention (i.e., not specifically aiming to treat and manage health conditions such as obesity, hypertension and NCDs); and (4) reported data regarding efficacy for behaviour change (e.g., change in daily minutes of physical activity). In addition, studies could also report (but were not required) other relevant outcomes that may have conceivably been impacted by health behaviour change (i.e., fitness, body weight, blood pressure, glucose, cholesterol, quality of life). All types and units of measurements for the lifestyle behaviours and related health outcomes were acceptable (e.g., objective measure, self-report, minutes, steps, servings, calories, kilograms). The app intervention could be a stand-alone intervention using apps only, or a multi-component intervention where the use of an app was one of several intervention components (e.g., physical education, provision of physical activity equipment, parental education, face-to-face counselling). Studies were excluded from the review if: (1) non-experimental study designs were utilised (i.e., observational or case studies, studies reporting prevalence or trend data, feasibility studies, measurement studies, theoretical papers); and (2) the publication was not a peer-reviewed primary study (i.e., letters, commentaries, conference proceedings, reviews, narrative articles). Initially, titles and abstracts were screened for inclusion by a single reviewer (SS). As per best practice for systematic reviews [23], two reviewers (SA, WVL) independently reviewed the eligibility of studies for inclusion in the review, with disagreement resolved by discussion and consensus with a third reviewer (SS).

Data extraction

Data extraction was conducted using a standardised form developed specifically for this review (Table 1); similar to those used in other systematic reviews [12, 24, 25]. For all included studies, data were extracted for author, year, country, study design, intervention duration, measurement time points, attrition rate, sample, behaviour change theory, app features including behaviour change techniques, intervention components, outcomes, measures and main study results. The primary outcome measures extracted for main study results were dietary intake, physical activity and sedentary behaviour. Other relevant outcome measures closely related to these behaviours included weight status (body mass index, body weight, waist circumference), fitness, blood pressure, cholesterol, glucose and quality of life. To determine whether the interventions had a significant effect on behavioural and health outcomes, data on significance and magnitude of within- and between-group differences was extracted. For each included study, two reviewers independently extracted data (NAB and either SS, SA or WVL). Reviewers one and two agreed on the data extraction in over 70% of the studies. Disagreement was easily resolved by discussion and consensus with a third reviewer (either SS, SA or WVL).

Table 1 Characteristics of the app-based intervention studies included in the review

Study quality assessment

The quality of the included studies was assessed using 25-point criteria adapted from the CONSORT checklists for the reporting of randomised controlled trials [26]. While the CONSORT checklist is intended for controlled trials, most criteria are applicable to other study designs and the weaker study designs justifiably received a lower score than studies using a controlled trial design. This approach has been used in other reviews [24]. Each criterion was rated as 1 (fulfilled), 0.5 (not all sub-items making up the criterion were fulfilled), 0 (not fulfilled or unclear), or not applicable (criterion was not applicable to the study design). Not applicable criteria were discounted from the ‘overall study quality score’ (sum of points). Hence, the highest attainable quality score was not 25 for all studies. Adapted from previous reviews [27, 28], the obtained study quality score for each study was divided by the highest attainable score and multiplied by 100 to give a percentage of fulfilled criteria; and studies were then grouped into high (>66.7%), fair (50–66.6%) or low (<50%) study quality (Additional file 3). The study quality assessment was conducted independently by two reviewers (SS, SA), with disagreement resolved by discussion and consensus with a third reviewer (WVL). Percent agreement between reviewers one and two for the scoring of the CONSORT criteria was 89%, with the most common points of discrepancy relating to recruitment methods, outcomes reporting and blinding procedures.


Study selection

A flowchart of the study selection process is presented in Fig. 1. A total of 6926 publications were identified from the database search. After removal of duplicates, 4945 publication titles and abstracts were screened, and 194 full-text articles were considered potentially eligible for inclusion. Of these, 30 articles reporting data on the efficacy of an app-based intervention to improve diet, physical activity and/or sedentary behaviour for prevention were included for final review. Some articles [2934] reported on the same studies, and as such, a total of 30 articles describing 27 studies were included in this review.

Fig. 1
figure 1

Flowchart of study selection process

Study characteristics

Characteristics of the app intervention studies included in this review are presented in Table 1.

Twelve studies were conducted in North America [3546], seven studies in Australia/New Zealand [2932, 4751], seven studies in Europe [33, 34, 5257], and one study in the Middle-East [58]. Most studies were randomised controlled trials (n = 19) with 2-group [29, 31, 35, 40, 4346, 48, 50, 5255, 58] or 3-group [33, 34, 42, 47, 56, 57] study designs. The remaining studies were controlled trials (n = 3) [33, 37, 49], randomised trials (n = 1) [39] or pre-post studies (n = 4) [36, 38, 41]. Twenty-three studies [3136, 38, 39, 4146, 4858] targeted adults (mean age: 41.5 years, range: 18–71 years) and four studies [29, 37, 40, 47] targeted children or adolescents (mean age: 12.4 years, range: 8–17 years). The total number of participants across the 27 studies was 2699 (510 children/adolescents, 2189 adults). Samples sizes ranged from 17 to 502 (mean sample size: 100). The duration of interventions ranged from 1–24 weeks, with an average intervention duration of 10 weeks. Follow-up assessments were at 4 weeks (n = 5), 8 weeks (n = 8), 12 weeks (n = 9), 20 weeks (n = 2), 6 months (n = 2), 9 months (n = 1) and 18 months (n = 1). Attrition rates ranged from 0% [36] to 57% [51], with an average attrition rate of 17%. The majority of studies [31, 33, 3545, 47, 48, 50, 53, 5558] reported higher rates of female participation (on average 64% of participants were female). The targeted health behaviours were dietary intake (n = 13), physical activity (n = 21) and sedentary behaviour (n = 5). Other reported lifestyle-related health outcomes were: weight status (n = 11); fitness (n = 3); blood pressure (n = 4); blood glucose (n = 1); cholesterol (n = 1); and quality of life (n = 2). Ten studies [33, 3638, 4345, 49, 50, 55, 57] targeted a single health behaviour, whilst 17 studies [29, 31, 35, 3942, 4648, 5056, 58] targeted multiple health behaviours and related health outcomes. Fourteen studies [33, 3740, 42, 4547, 51, 52, 5557] involved interventions delivered solely via an app (stand-alone intervention) and 13 studies [29, 31, 35, 36, 41, 43, 44, 4850, 53, 54, 58] involved interventions that used apps in conjunction with other intervention strategies (multi-component intervention), such as physical education, parental education, counselling sessions, printed materials, motivational emails, websites and pedometer use. Fifteen studies [29, 31, 3639, 45, 46, 4850, 52, 5658] used a newly designed app in the intervention and 12 studies [33, 35, 4044, 47, 51, 5355] used a commercially available app. Further, 15 of the 27 studies reported intervention designs based on behaviour change theories, such as Self-determination Theory (n = 3) [29, 35, 37], Transtheoretical Model (n = 4) [31, 33, 41, 48], Social Cognitive Theory (n = 4) [29, 39, 44, 46], Theory of Planned Behaviour (n = 2) [45, 50], Control Systems Theory of Self-regulation (n = 1) [58], and the Behaviour Change Wheel (n = 1) [55].

Study quality

A detailed summary of quality assessments of included studies is presented in the Additional file 3. Overall, study quality ranged from high (n = 11) [29, 31, 4345, 47, 48, 50, 53, 54, 58], to fair (n = 8) [35, 37, 46, 49, 5557], and low (n = 8) [33, 36, 38, 39, 41, 42, 51, 52]. Study quality of interventions targeted to children/adolescents was high (n = 2) [29, 47] and fair (n = 2) [37, 40], and study quality of interventions targeted to adults ranged from high (n = 9) [31, 44, 45, 48, 50, 53, 54, 58], to fair (n = 6) [35, 46, 49, 5557], and low (n = 8) [33, 36, 38, 39, 41, 42, 51, 52]. Most of the 13 interventions that used an app in combination with other intervention strategies were of high quality (n = 9) [29, 31, 43, 44, 48, 50, 53, 54, 58], whilst most of the 14 stand-alone app interventions were of fair (n = 6) [37, 40, 46, 5557] or low quality (n = 6) [33, 38, 39, 42, 51, 52]. Study quality did not differ markedly between app interventions targeting multiple health behaviours and related health outcomes (high: n = 8, fair: n = 4, low: n = 5) and those targeting a single health behaviour (high: n = 3, fair: n = 4, low: n = 3). On average, the included studies fulfilled 61% of the assessment criteria (range: 34–92%). Most studies met the CONSORT requirements to provide a strong scientific rationale and described their participant eligibility, statistical methods and interventions clearly. Fewer studies reported sample size calculations [29, 31, 35, 43, 47, 50, 5355, 57, 58] and included randomisation [29, 31, 35, 37, 39, 4348, 50, 5255, 58] and blinding procedures [31, 4450, 53] in their study design. Attrition rates were reported or could be calculated for the majority of studies [29, 31, 33, 3551, 5358].

Intervention efficacy

A summary of intervention effects for the included lifestyle behaviour outcomes (diet, physical activity, sedentary behaviour) and related health outcomes (weight status, fitness, blood pressure, glucose, cholesterol, quality of life) are presented in Table 2. Overall, a slightly larger proportion of single health behaviour interventions (5 out of 10; 50%) [33, 45, 49, 55, 57] showed significant between-group improvements than multiple health behaviour interventions (7 out of 17; 41%) [29, 31, 48, 50, 53, 54, 58]. Further, a larger proportion of interventions that used an app in conjunction with other intervention strategies (8 out of 13; 62%) demonstrated significant between-group improvements in the behavioural and health outcomes [29, 31, 4850, 53, 54, 58] compared to stand-alone app interventions (5 out of 14; 36%) [33, 40, 45, 55, 57].

Table 2 Summary of intervention effects on behaviour outcomes and related health outcomes

Children and adolescents

Of the four studies that specifically targeted children and/or adolescents, one study [29] reported significant between-group improvements in diet, sedentary behaviour and fitness in the app intervention group. Another study [37] reported a significant within-group increase in physical activity, but no significant difference between groups. The remaining two studies [40, 47] reported no significant changes in the behavioural or related health outcomes.


Of the 23 studies that targeted adults, 17 studies reported significant improvements in diet (n = 6) [31, 38, 45, 54, 57, 58], physical activity (n = 13) [31, 33, 36, 37, 39, 41, 43, 4850, 5355, 58], sedentary behaviour (n = 1) [39], and other improved outcomes including weight status (n = 4) [31, 41, 42, 58], 49], fitness (n = 1) [56], blood pressure (n = 2) [41, 56] and cholesterol (n = 1) [41]. Of the studies reporting significant findings, 11 studies detected significant between-group differences in diet (n = 5) [31, 45, 54, 57, 58], physical activity (n = 9) [31, 33, 4850, 5355, 58] and weight status (n = 2) [31, 58] in favour of the app intervention group. Seven studies found significant within-group improvements in diet (n = 1) [38], physical activity (n = 4) [33, 36, 37, 39, 41, 43], sedentary behaviour (n = 1) [39] and weight status (n = 2) [41, 42, 56], blood pressure (n = 2) [41, 56] and cholesterol levels (n = 1) [41]. Five studies [35, 44, 46, 51, 52] reported no significant changes in the health outcomes of interest, and no significant findings were found in relation to the outcome glucose levels (assessed in one study).

Characteristics of efficacious interventions

App interventions showing significant between-group improvements in the behavioural and health outcomes tended to be multi-component interventions [31, 36, 41, 43, 4850, 53, 54, 58], with sample sizes above 90 participants [35, 43, 44, 4749, 53] and intervention durations longer than 8 weeks [37, 4245, 47, 49]. A slightly larger proportion of single health behaviour [33, 45, 49, 55, 57] versus multiple health behaviour interventions [29, 31, 48, 50, 53, 54, 58] demonstrated between-group improvements (50% versus 41%, respectively). Further, most of the interventions [29, 31, 33, 39, 42, 45, 4850, 53, 55, 56, 58] showing significant improvements in the behavioural and health outcomes included goal-setting, self-monitoring and performance feedback in the app design. Some efficacious interventions also incorporated other behaviour change techniques, such as motivational messages [29, 36, 57], health education/tailored advice [29, 31, 38, 48, 54, 57], reinforcement [33, 39, 40, 45], gamification in the form of exergames, award and rewards [3740, 50], social support through interaction with peers [33, 37, 50] and friendly team challenges [29, 37, 39, 45]. There was not enough data to identify which behaviour change techniques determined intervention efficacy. Moreover, there was no difference in the behaviour change techniques incorporated in apps for children compared to those used in apps for adults. Eleven studies [31, 38, 40, 43, 45, 4850, 54, 5658] out of the 19 studies showing significant improvements in behavioural and health outcomes reported usage statistics to determine participants’ engagement with the app. Three of these studies [38, 43, 49] examined associations between app usage and changes in the behavioural and health outcomes. Their findings showed that higher app usage was associated with improvements in physical activity and healthy eating [38, 43, 49].


This systematic review found modest evidence for the efficacy of app interventions to improve diet, physical activity and sedentary behaviours for NCD prevention. Overall, 19 out of the 27 identified studies reported significant improvements in behavioural and related health outcomes. Most of these studies reported significant between-group improvements in the app intervention group versus comparison group, which is considered the gold standard to demonstrate intervention efficacy [59]. Notwithstanding study limitations, the findings from this review indicate that apps can be an effective tool to improve health behaviours. The advantages of smartphone apps over other intervention delivery modes such as websites, face-to-face counselling and group sessions may partially explain the efficacy of app interventions. Given that many people have busy lifestyles, they value convenient access to health behaviour change programs that provide information and advice, real-time self-monitoring, feedback, reinforcement, social support, and rewards ‘on the go’ [60]. The appeal of smartphones for assistance in health promotion concurs with the trend that more people are seeking health information via mobile devices [61, 62]. In this context, apps provide the opportunity to bring behavioural interventions into real life situations where people make decisions about their health.

Despite the potential of apps, half of the interventions identified in this review used apps in conjunction with other intervention strategies. The remaining interventions were stand-alone interventions where the app was the sole intervention component. Importantly, however, is the observation that most of the multi-component interventions demonstrated significant between-group improvements in behavioural and health outcomes, whereas fewer stand-alone app interventions reported significant between-group improvements. This raises the question whether multi-component interventions yield stronger intervention effects than stand-alone app interventions. Many reviews of health behaviour change interventions [6367] recommend the use of multiple intervention strategies to achieve long-term health behaviour change. Reviews of website-delivered interventions [9, 28, 68] have also shown stronger behaviour change effects when combined with other intervention strategies such as SMS, telephone coaching and motivational emails. As such, it is likely that the integration of apps in multi-component interventions produces stronger health outcomes compared to stand-alone app interventions; however, this remains to be tested in future trials.

The majority of app interventions were targeted to adults; only four out of the 27 identified app interventions were specifically aimed at children or adolescents. This is consistent with previous e & mHealth reviews [11, 12, 25, 69] showing that internet and mobile phone delivered interventions have mainly targeted adult populations. Although smartphones and tablets have become an integral part of children’s lives [70], surprisingly few interventions have yet utilised app technology in pediatric health behaviour change programs [12]. However, as exergame and serious game apps are becoming increasingly popular among children and adolescents [71], it is likely that more app studies focussing on children will be conducted in the near future.

The average attrition rate (i.e., participant loss to follow-up) reported in the app interventions was 17%, which is lower compared average attrition rates of 23–27% found in web-based interventions [28, 68, 72]. Lower participant attrition indicates less bias in the estimated intervention effects [73]. However, participant attrition does not capture participants’ engagement with the app intervention which also determines intervention effects. For example, web- and app-based interventions that have examined participant engagement found that higher levels of website and app usage were associated with increased intervention efficacy [38, 43, 68]. Despite this, less than half of the studies reported usage statistics to determine participants’ engagement with the app. This is consistent with previous reviews [24, 28, 68] reporting that few internet and mobile interventions recorded participants’ engagement with the intervention technologies. Given the relative ease by which app usage statistics can be tracked, it is unfortunate that this data is not being collected and published for all studies in this area. We know that participant engagement, measured by number of website logins, usually declines after the first few weeks in website and social media interventions [24, 28, 68]. An example is the Australian 10,000 Steps program, a successful, freely available web- and app-based intervention to promote physical activity [61]. Participants’ engagement with the 10,000 Steps website and app lasts on average 5–6 weeks, with longer usage duration (on average 8 weeks) being observed in people who use both the 10,000 Steps website and app [61]. This rapid decline in usage concurs with qualitative research showing that people often lack commitment to using any particular app and they tend to engage in only transient, casual app use [60]. Since participant engagement determines intervention exposure, and level of intervention exposure determines intervention efficacy [28, 61, 74], better understanding of factors that improve participant engagement and retention is needed.

To increase user engagement with health behaviour change apps, more information is needed about what app features and behaviour change techniques people value and use. Recent focus group data showed that [60, 75] that young people value health behaviour apps that require low effort, are pleasant to use, are developed by credential experts, enable self-monitoring, provide advice on how to change behaviour, include positively framed alerts/reminders (but not too frequent), provide accurate tracking functions, incorporate adequate privacy settings, and clearly show what the app will do (no surprises). Some of these user preferences were part of the efficacious apps identified in this review. For example, most apps included goal-setting, self-monitoring and performance feedback [29, 31, 33, 39, 42, 45, 4850, 53, 55, 56, 58]. Additionally, some apps incorporated tailored advice, motivational prompts and reinforcement, gamification, social support or friendly team challenges [29, 33, 3740, 45, 48, 50, 54, 57]. Reviews of web-based interventions have demonstrated that interventions including more behaviour change techniques are more effective [74, 76]. This may also apply to app-based interventions. However, it remains unclear what the optimal number and combination of app features and behaviour change techniques is to increase user retention and ultimately intervention efficacy. It is also possible that efficacy declines when too many features or techniques are implemented. Furthermore, socio-demographic factors (e.g., sex, age, education) and psychosocial factors (e.g., attitudes, perceived benefits, enjoyment) may also influence app usage [60]. Emerging research on the adoption of app technology showed that higher app usage is associated with being female and of younger age [61, 77], as well as with personal interest in new technologies, positive attitudes towards smartphone apps and perceived benefit of use [77]. Therefore, targeting and tailoring smartphones apps to specific population groups may also enhance the efficacy of app-based interventions.

Strengths and limitations

Strengths of this systematic review are that it was conducted and reported according to PRISMA guidelines [23], and study quality was systematically assessed using the CONSORT checklist [26]. The search strategy was comprehensive, and study selection, data extraction, and quality rating were completed by two independent reviewers, as is standard practice for high quality systematic review [23]. These procedures ensure accuracy of the reviewed data. The scope of this review was limited to app interventions improving behavioural and related health outcomes for prevention; hence app interventions relating to chronic disease treatment were not captured in this review. Few app interventions identified in this review focused on dietary and sedentary behaviours which makes it more difficult to draw conclusions on the efficacy of app interventions targeting these behaviours, as opposed to those targeting physical activity behaviour. Furthermore, the included studies varied widely in terms of methodological quality, with some studies scoring very poorly, thereby reducing the trust that can be placed in their findings. Finally, the possibility of publication bias should also be acknowledged. As with all systematic reviews examining the efficacy of interventions, it is possible that some studies with null findings have not been published [78].

Recommendations for future research

Based on this review, it is recommended that future studies:

  • Test the efficacy of specific app features and behaviour change techniques in high quality controlled trials to distinguish effective from ineffective intervention components.

  • Directly compare the efficacy of stand-alone app intervention compared to multi-component interventions that use apps in combination with other intervention strategies.

  • Compare the efficacy of app interventions to other intervention delivery modes, such as website, print-based and face-to-face interventions.

  • Utilise larger sample sizes to ensure they are sufficiently powered to detect significant intervention effects.

  • Tailor app interventions to specific population groups (e.g., women, young people) in whom usage and adoption of app technology is high.

  • Report app usage statistics using objective and self-report measures to examine levels of and reasons for participant (dis)engagement and intervention exposure.

  • Explore the optimal duration and intensity of app interventions to ensure user engagement and retention as the intervention progresses.

  • Identify factors that increase user engagement and retention in app interventions to sustain behavioural health improvments in the long-term.

  • Investigate the relationship between user engagement and intervention efficacy, whilst taking into account socio-demographic and psychosocial factors.


Interventions using apps to improve diet, physical activity and sedentary behaviour for prevention show promise for effective behaviour change in children and adults. The evidence base is largest for the use of apps to increase physical activity in adults. Fewer interventions have used apps to improve dietary and sedentary behaviours, and very few app interventions have targeted children and adolescents. Multi-component interventions that combine apps with other intervention strategies appear to be more effective than stand-alone app interventions, however, this remains to be tested further in controlled trials. Overall, there is still considerable scope to improve the efficacy of app-based interventions. In particular, intervention studies should gather more app usage statistics to identify factors that improve user engagement and retention, and its relationship with intervention efficacy. In addition, more formative research is needed to determine the optimal number and combination of app features, behaviour change techniques, and level of participant contact needed to maximise user engagement and ultimately intervention efficacy.