
==== Front
J Exerc Sci Fit
J Exerc Sci Fit
Journal of Exercise Science and Fitness
1728-869X
2226-5104
The Society of Chinese Scholars on Exercise Physiology and Fitness

S1728-869X(24)00061-3
10.1016/j.jesf.2024.09.002
Article
The effect of electronic health (eHealth) interventions for promoting physical activity self-efficacy in children: A systematic review and meta-analysis
Lu Nike 23481595@life.hkbu.edu.hk
a
Lau Patrick W.C. wclau@hkbu.edu.hk
a⁎
Song Huiqi huiqisong@cuhk.edu.hk
b
Zhang Yuxin yuxin.zhang@deakin.edu.au
c
Ghani Ruhina Binta A. 23481048@life.hkbu.edu.hk
a
Wang Chenglong wangchenglong@ciss.cn
d
a Department of Sport, Physical Education and Health, Hong Kong Baptist University, China
b JC School of Public Health and Primary Care, The Chinese University of Hong Kong, China
c Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
d China Institute of Sport Science, Beijing, China
⁎ Corresponding author. wclau@hkbu.edu.hk
11 9 2024
10 2024
11 9 2024
22 4 417428
18 6 2024
10 9 2024
10 9 2024
© 2024 The Society of Chinese Scholars on Exercise Physiology and Fitness. Published by Elsevier (Singapore) Pte Ltd.
2024
The Society of Chinese Scholars on Exercise Physiology and Fitness
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background/objective

Physical activity (PA) self-efficacy plays a crucial role in maintaining and enhancing PA behaviors in children. However, the effectiveness of eHealth interventions in boosting PA self-efficacy among children remains uncertain. Furthermore, which behavior change techniques (BCTs) used in eHealth interventions can positively influence children's PA self-efficacy needs further exploration for designing tailored eHealth interventions. Therefore, this systematic review and meta-analysis aimed to identify the effectiveness of eHealth interventions and BCTs in promoting children's PA self-efficacy.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across six databases (PubMed, Web of Science, EBSCOhost, Ovid, SPORTDiscus, PsycINFO) up to January 8, 2024. Inclusion criteria included randomized controlled trials (RCT), quasi-experimental, and two-group experiments that examined the effect of eHealth interventions on PA self-efficacy among healthy children aged 0–18 years. The Physiotherapy Evidence Database (PEDro) scale was utilized to assess the risk of bias. Random effects meta-analysis was performed to determine the effectiveness of eHealth interventions and BCTs in selected studies.

Results

Sixteen studies were screened, including 6020 participants with an average age of 11.58 years (SD = 2.87). The result showed small but significant intervention effects with high heterogeneity (I2 = 92.34 %) for postintervention PA self-efficacy (Hedges’ g = 0.315; 95 % CI = 0.069, 0.562, p = .012). Two BCTs were significantly associated with enhanced PA self-efficacy: instruction on performing the behavior (p = .003) and behavior demonstration (p = .036). Additionally, studies that adopted social support (unspecified) a nd prompt/cues were significantly less effective than studies that did not use these BCTs (p = .001).

Conclusions

The findings showed that eHealth interventions positively affect children's PA self-efficacy. This review is the pioneer in focusing on BCTs in eHealth interventions for children. The insights gained provide valuable knowledge about tailored BCTs incorporated into eHealth interventions that promote children's PA self-efficacy.

Trial registration

International Prospective Register of Systematic Review (PROSPERO): CRD42024512058.

Keywords

Self-efficacy
Physical activity
eHealth
Children
==== Body
pmc1 Background

Childhood regular physical activity (PA) has many well-documented benefits, such as improving cardiovascular health, mental well-being, and overall quality of life.1, 2, 3 An active childhood lays the foundation for chronic disease prevention in later years.4 Yet, a cross-sectional survey conducted in 2016 indicated that 80 % of 1.6 million children and adolescents aged 11 to 17 in 146 countries fail to meet the World Health Organization's (WHO) guideline of at least 60 min of moderate-to-vigorous PA (MVPA) daily.5,6 More than 90 % of girls in 27 countries were insufficiently active. For boys, the prevalence of insufficient PA increased significantly between 2001 and 2016, and this trend continues.5 To encourage children's PA, it seems important to intervene from a psychological perspective. A longitudinal study found that autonomous motivation and self-efficacy had a significant relation to PA.7 Another longitudinal study also indicated that intrinsic motivation can positively influence children's PA participation.8

Social cognitive theory (10.13039/501100022514 SCT ), self-determination theory (SDT), and theory of planned behavior (TPB)9, 10, 11 are the three most common psychological theories in promoting 10.13039/100006131 PA .12 These theories have been found to be highly correlated with children's behavioral intention to engage in 10.13039/100006131 PA , emphasizing the importance of motivation and perceived autonomy in influencing children's 10.13039/100006131 PA .10,13 Autonomous motivation is a key part of SDT and strongly predicts children's intentions and behaviors regarding 10.13039/100006131 PA .14 It can be shaped by perceived autonomy support from peers or teachers, mediating the link between social support and activity intentions.13 In terms of TPB, attitudes, subjective norms, and perceived behavioral control also predict these intentions and mediate the impact of autonomous motivation on activity behaviors.15 10.13039/501100022514 SCT emphasizes self-efficacy as an essential element in affecting individuals' behaviors.16 A study analyzed the relationship between the elements of 10.13039/501100022514 SCT and 10.13039/100006131 PA concluded that self-efficacy and goal were consistently associated with 10.13039/100006131 PA .17 A meta-analysis identified attitudes, norms, and self-efficacy as important determinants of intentions and behaviors.18 However, only self-efficacy can directly affect individuals' behavioral choices.16 Intervention in self-efficacy seems to be more efficient in children's 10.13039/100006131 PA . In addition, a systematic review pointed out self-efficacy was the only one positively associated with increased PA in both children and adolescents among various psychological factors.19 A cross-sectional survey of 4779 children in Canada showed that children with higher self-efficacy tend to be more physically active.20 Enhancing children's self-efficacy makes them more likely to participate in and maintain PA actively.21

Bandura defined self-efficacy as an individual's belief in their ability to perform a certain behavior or achieve a desired outcome.22 In health promotion, self-efficacy has been widely recognized as a precondition and consequence of PA.23, 24, 25 Voskuil and Robbins defined children's PA self-efficacy as their belief in their ability to participate in PA and choose PA in the presence of obstacles.26 Higher PA self-efficacy can effectively regulate emotions and help children overcome difficulties in physical activities.27 It is also closely related to the confidence in completing physical activities and the intrinsic motivation to enjoy them.28 An experimental study showed that when children's self-efficacy is enhanced, they are more like to reduce their reliance on external motivation and enhance their confidence in their physical ability, thus increasing PA.29 In another study, PA self-efficacy was found to have the largest total effect on PA in a structural equation model. It can directly or indirectly affect individuals' 10.13039/100006131 PA through self-regulation or social support and is an important factor in predicting and promoting 10.13039/100006131 PA .30

Many studies demonstrated the effectiveness of utilizing behavior change techniques (BCTs) in promoting PA self-efficacy. BCTs are common elements used in behavior change interventions, defined as 'indivisible, observable, and reproducible parts of an intervention intended to alter behavior'.31 Three systematic reviews and meta-analyses showed that BCTs including "action planning," "providing guidance," "enhancing behavioral effort," "time management," "immediate self-monitoring of behavioral outcomes" and "planning social support/social change," "alternative experience," and "feedback" were significantly associated with higher 10.13039/100006131 PA self-efficacy.32, 33, 34 However, the effectiveness of different BCTs in altering 10.13039/100006131 PA self-efficacy may vary across populations.34 While existing literature primarily focuses on BCTs in adult populations’ 10.13039/100006131 PA self-efficacy, there are limited insights into their impact on children.

The emergence of electronic health (eHealth) interventions plays a significant role in promoting healthy lifestyles for children.35, 36, 37 One systematic review revealed that eHealth active games positively influence children's PA self-efficacy.38 Additionally, diverse eHealth strategies such as wearable devices, online social networks, and smartphone applications35 show the ability to create customized PA content, offer real-time feedback, and adjust to individual needs positioning eHealth interventions as a promising method for enhancing children's PA self-efficacy.36,39, 40, 41 Furthermore, studies pointed out that behavior change strategies as fundamental in designing digital health interventions for children.42,43 Nonetheless, there is a lack of clarity regarding the effectiveness of eHealth interventions on PA self-efficacy in children and which specific BCTs adopted in current eHealth interventions contribute the most. Accordingly, this review is structured to address the following research questions.(1) How effective are eHealth interventions at enhancing PA self-efficacy in children?

(2) What BCTs have been used in eHealth interventions are closely associated with increased PA self-efficacy in children?

2 Method

The review protocol was registered in PROSPERO (registration ID: CRD42024512058). Reporting of this review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.44

2.1 Eligibility criteria

Studies written in English and published in English peer-reviewed journals were included. The selection criteria for studies were structured around the PICOS (participants, intervention, comparator, outcomes, and study design) framework, which is detailed as follows:

(a) Participants: healthy children aged 0–18 years; (b) Intervention: eHealth intervention (such as mobile phone, web-based, messages, video games); (c) Comparator: experiments with experimental and control groups (without any interventions) were included. (d) Outcomes: the main result must include PA self-efficacy; (e) Study design: Randomized controlled trial, quasi-experimental, two-group experiment, and the quantitative components of mixed-method studies.

Articles were excluded if: (a) Participants: studies with participants who were not aged 0–18 years or had mental or physical disabilities/disorders/condition; (b) Intervention: studies did not apply any eHealth approach in the intervention; (c) Comparator: studies without appropriate control conditions or groups. (d) Outcomes: the authors did not present pre-and post-test data on PA self-efficacy.

2.2 Search strategy

Article searches were conducted on January 8, 2024, utilizing six databases: PubMed, Web of Science, EBSCOhost, Ovid, SPORTDiscus, and PsycINFO. The range of publication dates covered was from inception through January 8, 2024. For details on the search strategy terms, refer to Table 1.Table 1 Search strategy terms.

Table 1eHealth	Physical Activity	Self-efficacy	Children	
Electronic health OR eHealth OR mobile health OR mHealth OR Digital health OR Telehealth OR Online* OR Virtual* OR Web* OR Internet* OR Smartphone OR phone OR APP OR chatbot OR “conversational agent” OR “social media” OR Facebook OR Exergame OR “technology” OR “video”	Physical activity OR Active* OR Fitness OR Exercise* OR Sport OR Active lifestyle OR Sports participation OR Workout	Self-efficacy OR Self-confidence OR Exercise confidence OR Physical activity Confidence OR PA confidence OR Self-belief	Pre-schooler* OR Schoolchild* OR School-age OR Child* OR Pediatric* OR Adolescent* OR Youngster* OR Teen* OR Minor* OR Youth OR Young person OR Juvenile*	

2.3 Data extraction

The principal and co-authors carried out the data extractions using Microsoft Office Excel 2019 (Microsoft, USA). The extracted data included the author, publication year, location, sample size, demographic characteristics, theoretical framework, study design, details of the intervention, information on experimental and control groups, measurements of PA self-efficacy, BCTs, and study results.

2.4 Quality assessment

This review examined the quality of experimental methods using the Physiotherapy Evidence Database (PEDro) scale.45 The PEDro scale contains 11 criteria to evaluate an experiment's internal and external validity. The principal and two co-authors evaluated the quality of each experiment on a scale of yes (1) and no (0). Studies with PEDro scores between 8 and 10 were rated methodologically excellent in quality; A score between 5 and 7 is high quality; A score between 3 and 4 is moderate quality; Those that score less than 3 are low-quality.45

2.5 Statistical analysis

The meta-analysis used data extracted from intervention and control/comparison groups across pre- and post-intervention periods. The standardized mean difference, accounting for sample sizes, means, and standard deviations (SD) in each group, was computed and converted to Hedges' g to estimate effect sizes.46 Follow-up assessment data were not considered. Meta-analyses for PA self-efficacy and BCTs were conducted provided that at least three studies reported interventions addressing the same components and supplied adequate data for effect size calculation.

All statistical analyses were performed under the umbrella of a random-effects model, acknowledging the potential differences between studies that could influence the treatment effect,47 and were conducted via the Comprehensive Meta-Analysis software (version 3; Biostat, Englewood, NJ, USA). Sensitivity analyses were conducted using random-effects models, with each study being removed from the pooled analysis in each instance. The effect size values are presented alongside their respective 95 % Confidence Intervals (CIs). Calculated effect sizes were interpreted using the following thresholds: small (g < 0.40), moderate (g = 0.40−0.70), and large (g > 0.70), according to the Cochrane Handbook.48 Heterogeneity was quantified using the I-squared (I2) statistic, with values of <25 %, 25−75 %, and >75 % interpreted as low, moderate, and high heterogeneity, respectively.49 Publication bias was assessed through Egger's regression tests and the visual inspection of funnel plots.50 All statistical tests were conducted at a significance level of p < .05.

3 Results

3.1 Preferred reporting items for systematic review and meta-analyses flowchart

The initial search found 2651 studies. After removing the duplicates, 1913 articles remained, of which 1531 were excluded due to not meeting the participant criteria. Of the remaining 380 articles, 364 articles were ineligible. Finally, 16 articles were selected for this review (see Fig. 1).Fig. 1 Flow diagram of each stage of the study selection.

Fig. 1

3.2 Study characteristics

A total of 6020 participants were included in this study. The sample size ranges from 40 to 3036 (see Table 2). The mean age of participants was 11.58 years old (SD = 2.87), and the average number of females included in each study was 50.91 %. There were four RCTs,51, 52, 53, 54 two cluster RCTs,55,56 five quasi-experimental,57, 58, 59, 60, 61 and five two-group experiments.62, 63, 64, 65, 66 In addition, 12 different PA self-efficacy scales were employed in selected studies, and the scale developed by Mok et al. (2015) was the most frequently utilized instrument, appearing in four studies.51,62,63,66 See Table 3 for a summary of all included studies.Table 2 Summary of the study characteristics.

Table 2Study characteristics	Frequencies	
Participants numbers	
Mean number	376	
Sum of number	6020	
Range of number	51–3036	
Number of studies	16	
Participant characteristics	
Mean age	11.58 ± 2.87 years	
Mean age range	8.24–17.28 years	
Mean percentage of females	50.91 %	
Intervention duration	
Mean duration	16 weeks	
Range of duration	0.1–48 weeks	
Study design	
RCT	4	
Cluster-RCT	2	
Quasi-experimental	5	
Two-group experiment	5	

Table 3 Study information including research participants, research design, sample size, intervention, and results.

Table 3Study and information	Study design	Intervention	Result (EG)	
1. Mok et al. (2020)● Croatia, Lithuania, Macedonia, Poland, Romania, Serbia, South Africa, and Turkey

● S: N = 3036 (Female: 50.7 %)

● HC

● Age: 8–11 years

	● SD: RCT

● TF: Not mentioned

● Duration: 4 months

● Setting: School

● Delivered by: Teachers

	● EG (N = 1914): BBPAS

● CG (N = 1122): Standard teaching and materials

● PA self-efficacy measurement: The Attitudes toward Physical Activity Scale (Mok et al., 2015)

● Data were collected before and after the intervention

	● Self-efficacy in selecting video exercises (F = 366.258**, η2 = 0.145)

● Interest in doing PA (F = 9.227**, η2 = 0.003)

● Confidence in own fitness (ns)

● Perceived benefits of PA (F=53.175**, η2 = 0.018)

● Orientation to personal best goals when engaging in PA (F = 25.539**, η2 = 0.009)

● BMI (ns)

	
2. Kennedy et al. (2018)● Australia

● S: N = 607 (Female: 50.1 %)

● HC

● Mean age: 14.1 ± 0.5 years

	● SD: Cluster RCT

● TF: SCT, SDT

● Duration: 12 months

● Setting: Secondary schools

● Delivered by: Teachers

	● EG (N = 353): Smartphone app

● CG (N = 254): Regular scheduled PE and curricular school sport

● PA self-efficacy measurement: The Behavioral Regulations in Exercise Questionnaire-2 (Markland & Tobin, 2004), Resistance training self-efficacy scale (Lubans et al., 2011)

● Data were collected at baseline, 6 months (post-program), and 12 months (follow-up)

	● Self-efficacy for RT (6M: p = .002, 12M: p = .220)

● Muscular fitness

Push-ups (6M: p = .001, 12M: p = .011)

Standing long jump (6M: p = .397, 12M: p = .258)

● Weekday MVPA (6M: p = .953, 12M: p = .143)

● Autonomous motivation for PA (6M: p = .836, 12M: p = .043)

● Motivation for RT (6M: p = .184, 12M: p = .251)

● BMI z-score (6M: p = .061, 12M: p = .313)

	
3. Staiano et al. (2013)● United States

● S: N = 54 (female: 55.6 %)

● BMI at or above the 75th percentile

● Age: 15–19 years

	● SD: RCT

● TF: SCT

● Duration: 20 weeks

● Setting: The school-based wellness clinic

● Delivered by: Researchers

	● Competitive exergame (N = 19): The Wii Active game (to compete against their opponent)

● Cooperative exergame (N = 19): The Wii Active game (to cooperate with their partner)

● CG (N = 16): Continued usual daily activities

● PA self-efficacy measurement: Exercise Confidence Survey (Sallis et al., 1988)

● Data were collected at baseline, 10 weeks, and 20 weeks

	● PA self-efficacy cooperative group (p = .005) competitive group (p = .083)

● Peer support cooperative group (p = .010)

competitive group (p = .001)

	
4. Direito et al. (2015)● New Zealand

● S: N = 51 (female: 57 %)

● HC

● Age: 14–17 years

	● SD: RCT

● TF: Not mentioned

● Duration: 8 weeks

● Setting: Home

● Delivered by: Researchers

	● EG1 (N = 17): use of an immersive app (Zombies, Run)

● EG2 (N = 17): use of a nonimmersive app (Get Running)

● CG (N = 17): usual behavior

● PA self-efficacy measurement: Physical Activity Self-Efficacy Scale (Batholomew et al., 2006)

● Data were collected before and after the intervention

	● PA self-efficacy

EG1 (p = .99)

EG2 (p = .96)

● Average daily time spent in MVPA (min)

EG1 (p = .98)

EG2 (p = .99)

● Average daily time spent in vigorous PA (min)

EG1 (p = .90)

EG2 (p = .99)

● Average daily time spent in moderate PA (min)

EG1 (p = .98)

EG2 (p = .96)

● Average daily time spent in light PA (min)

EG1 (p = .91)

EG2 (p = .99)

● Average daily time spent in sedentary activities (min)

EG1 (p = .96)

EG2 (p = .99)

	
5. Chen et al. (2011)● United States

● S: N = 228 (female: 64.8 %)

● HC

● Mean age: 12.52 ± 3.15 years

	● SD: RCT

● TF: SCT, the Transtheoretical Model–Stages of Change

● Duration: 8 months

● Setting: Community

● Delivered by: Researchers

	● EG (N = 103): Web-based behavior program

● CG (N = 125): received general health information and not tailored

● PA self-efficacy measurement: Health Behavior Questionnaire (Edmundson et al., 1996; Matheson et al., 2004)

● Data were collected at baseline, 2 months, 6 months, and 8 months

	● PA self-efficacy (p = .49)

● Physical activity knowledge (p = .008)

● Nutrition self-efficacy (p = .55)

● Nutrition knowledge (p = .001)

● BMI (p = .84)

	
6. Spook et al. (2016)● Netherlands

● S: N = 231 (female: 62.8 %)

● HC

● Mean age: 17.28 ± 1.26 years

	● SD: Cluster RCT

● TF: Self-regulation theory

● Duration: 4 weeks

● Setting: Online

● Delivered by: Researchers

	● EG (N = 105): Balance It app

● CG (N = 126): No interventions were offered

● PA self-efficacy measurement: PA self-efficacy scale (Van der Horst et al., 2008)

● Data were collected before and after the intervention

	● PA self-efficacy (R2 = 0.02)

● Moderate PA (days) (R2 = 0.00)

● Vigorous PA (days) (R2 = 0.00)

● Active transport (days) (R2 = 0.02)

	
7. Lee & Gao (2020)● United States

● S: N = 157 (Female: 46.5 %)

● HC

● Age: 9–11 years

	● SD: Quasi-experimental

● TF: SCT

● Duration: 2 weeks

● Setting: Elementary schools

● Delivered by: Teachers

	● EG (N = 77): Apps: Educreation, Coach's Eye, Scoreboard, GarageBand, Interval Timer and Stopwatch, Team Shake

● CG (N = 80): Traditional PE class

● PA self-efficacy measurement: A six-item self-efficacy on PA questionnaire (Gao et al., 2010)

● Data were collected before and after the intervention

	● SCT-related psychosocial beliefs

Self-efficacy (p=.75)

Outcome expectancy (p=.13)

Social support (p=.94)

Enjoyment (p=.43)

● Light PA (p<.001)

● MVPA (p=.004)

● Sedentary bahavior (p=.07)

	
8. Liang et al. (2020)● Hong Kong

● S: N = 87 (Female: 37.9 %)

● HC

● Mean age: EG = 10.5 ± 0.7 years, CG = 10.4 ± 0.8 years

	● SD: Quasi-experimental

● TF: Not mentioned

● Duration: 8 weeks

● Setting: Primary school

● Delivered by: Researchers

	● EG (N = 30): Kinect® and Xbox 360® gaming console

● CG (N = 57): Continued with their usual activities

● PA self-efficacy measurement: PA self-efficacy questionnaire (Liang et al., 2014)

● Data were collected before and after the intervention

	● Psychosocial variables (enjoyment, PA self-efficacy, and social support) (ns)

● Waking time PA and sedentary behavior

MVPA (p=.12)

MPA (p=.06)

VPA (p=.86)

LPA (p=.01)

Sedentary time (p=.07)

● After-school time PA and sedentary behavior

MVPA (p=.07)

MPA (p=.08)

VPA (p=.16)

LPA (p<.01)

Sedentary time (p=.01)

● Percentage body fat (p=.71)

● zBMI (p=.42)

	
9. Balasekaran et al. (2021)● Singapore

● S: N = 113 (Female: 58.4 %)

● HC

● Mean age: EG: 9.71 ± 0.99 years, CG: 9.66 ± 0.94 years

	● SD: Quasi-experimental

● TF: Not mentioned

● Duration: 10 weeks

● Setting: School

● Delivered by: Teachers

	● EG (N = 48): BBPAS

● CG (N = 65): Continued their academic lessons without video intervention

● PA self-efficacy measurement: Attitudes toward Physical Activity Scale questionnaire (Motl et al., 2000)

● Data were collected before and after the intervention

	● Self-efficacy in Learning with Video Exercises (p < .001)

● Self-efficacy in Selecting Video Exercises (p < .001)

● Importance of Exercise Habit (p=.001)

● Exercise Motivation and Enjoyment (p < .001)

● Self-confidence on Physical Fitness (p < .001)

● Trying to do Personal Best (p=.003)

	
10. Robbins et al. (2020)● Hong Kong

● S: N = 84 (Female: 50.6 %)

● HC

● Mean age: EG = 11.3 ± 0.8 years, CG = 11.9 ± 0.8 years

	● SD: Quasi-experimental

● TF: SDT

● Duration: 12 weeks

● Setting: School

● Delivered by: Researchers

	● EG (N = 39): Private Facebook group

● CG (N = 45): Usual activities

● PA self-efficacy measurement: The 6-item Perceived PA Self-Efficacy Scale (Dishman et al., 2010)

● Data were collected before and after the intervention

	● PA Self-efficacy (p = .58)

● Percent body fat (p = .68)

● PA

Measured MVPA (p = .17)

Social support (p = .10)

Autonomous motivation (p = .01)

Controlled motivation (p = .15)

Amotivation (p = .35)

● Diet quality (ns)

● Healthy eating self-efficacy (p = .03)

● BMI (ns)

	
11. Rizal et al. (2019)● Malaysia

● S: N = 322 (Female: 50.6 %)

● HC

● Mean age: 10.53 ± 0.5 years

	● SD: Quasi-experimental

● TF: The transtheoretical model

● Duration: 12 weeks

● Setting: Schools

● Delivered by: Teachers

	● EG (N = 177): BBPAS

● CG (N = 145): Not involved in the BBPAS intervention

● PA self-efficacy measurement: 18-item exercise self-efficacy scale (Kim, 2007)

● Data were collected before and after the intervention

	● PA Self-efficacy

Internal feelings (p = .031)

Competing demand (p = .240)

Situational (p = .748)

● PA behavior (p = .007)

	
12. Emeljanovas et al. (2018)● Lithuania

● S: N = 181 (Female: 45.9 %)

● HC

● Mean age: 8.24 ± 1.10 years

	● SD: Two-group experiment

● TF: Not mentioned

● Duration: 3 months

● Setting: Primary school

● Delivered by: Teachers

	● EG (N = 93): BBPAS

● CG (N = 88): Continued their regularly scheduled physical education for 45 min twice weekly

● PA self-efficacy measurement: The attitude toward physical activity scale (Mok et al., 2015)

● Data were collected before and after the intervention

	● PA Self-efficacy (p < .01)

● Fitness (p < .01)

● Personal Best (p < .01)

● Interest (p < .01)

● Importance (p < .01)

● Benefits (p < .01)

● Learning (p < .01)

● Health (p < .01)

	
13. Glapa et al. (2018)● Poland

● S: N = 326 (Female: 47.9 %)

● HC

● Mean age: 9.7 ± 1.06 years

	● SD: Two-group experiment

● TF: Not mentioned

● Duration: 4 consecutive months

● Setting: Schools

● Delivered by: Teachers

	● EG (N = 264): BBPAS

● CG (N = 62): did not have breaks with Brain Brakes videos

● PA self-efficacy measurement: The attitude toward physical activity scale (Mok et al., 2015)

● Data were collected before and after the intervention

	● Self-efficacy on learning with video exercises (p < .01)

● Promoting the holistic health (p = .09)

● Importance of exercise habit (p = .49)

● Exercise motivation and enjoyment (p = .22)

● Self-confidence on physical fitness (p = .67)

● Trying to do personal best (p = .13)

	
14. Gao et al. (2019)● United States

● S: N = 81(Female: 48.1 %)

● HC

● Mean age: EG: 9.42 ± 0.77 years, CG: 9.09 ± 0.42 years

	● SD: Two-group experiment

● TF: SCT

● Duration:9 months

● Setting: Elementary schools

● Delivered by: Teachers

	● EG (N = 36): Kinect® and Xbox 360® gaming console

● CG (N = 45): Continued their regular recess activities

● PA self-efficacy measurement: A six question survey assessed children's self-efficacy (Sallis et al., 1999)

● Data were collected at baseline and at the 4th and 9th months

	● Self-efficacy (p=.90)

● METs (p < .01)

● Kilocalories per day (p < .01)

● Outcome expectancy (p < .05)

● Social support (p = .08)

	
15. Wang et al. (2017)● Hong Kong

● S: N = 179 (42.5 %)

● HC

● Mean age: 10.2 years

	● SD: Two-group experiment

● TF: SCT, SDT, Elaboration likelihood models

● Duration: 8–10 weeks

● Setting: Primary schools

● Delivered by: Researchers

	● EG (N = 95): Escape from Diab (exergame)

● CG (N = 84): Adopted general diet and PA information and behaviours as usual

● PA self-efficacy measurement: PA self-efficacy scale (Jago et al., 2009)

● Data were collected at baseline, about 8–10 weeks after baseline (post 1), and 8–10 weeks after the game (post 2)

	● Self-efficacy for PA (p < .01)

● Self-reported PA (p < .05)

● Objective PA (p> .05)

● PA motivation (p = .04)

● PA preference (p < .05)

● Intrinsic motivation for FVW (p < .05)

● Self-efficacy for FVW (p < .05)

● FVW preference (p < .05)

	
16. Popeska et al. (2018)● The Republic of Macedonia

● S: N = 283 (Female: 45.2 %)

● HC

● Mean age: EG = 9.18 ± 1.13 years, CG = 9.24 ± 0.82 years

	● SD: Two-group experiment

● TF: Not mentioned

● Duration: 3 months

● Setting: School

● Delivered by: Teachers

	● EG (N = 152): BBPAS

● CG (N = 131): With no intervention

● PA self-efficacy measurement: Attitudes toward Physical Activity Scale (Mok et al., 2015)

● Data were collected before and after the intervention

	● Self-efficacy in learning with video exercises (p = .009)

● Self-confidence on physical fitness (p = .000)

● Exercise motivation and enjoyment (p = .002)

● Importance of exercise habit for health (p = .003)

● Training for personal best and motivating others (p = .001)

● Promoting holistic health (p = .041)

● Knowledge and self-awareness for individual (p = .102)

	
Note. S = sample; TF = theoretical foundation; SD = study design; EG = experiment group; CG = control group; RCT = randomized controlled trial; PA = physical activity; PE = physical education; HC = healthy children; ES = effect size; SCT = social cognitive theory; SDT = self-determination theory; FVW = motivation for fruit, vegetables, and water; AVG = active video games; BBPAS = Brain Breaks Physical Activity Solutions; RT = resistance training; BMI = body mass index; CBT = cognitive and behavioral treatment; Intent to treat: ITT; Without outlier: WO; LPA = light physical activity; MVPA = moderate to vigorous physical activity; VPA = vigorous physical activity; ns = not significant at 0.05; MET = metabolic equivalent task; PAS = perceived autonomy support; PCS = perceived competence support; PRS = Perceived relatedness support.

4 Frequencies of BCTs used in studies

BCTs developed by Michie et al. (2015) include 16 clusters.67 The clusters presented in this review are goals and planning, feedback and monitoring, social support, shaping knowledge, natural consequences, comparison of behavior, associations, reward and threat, and antecedents. In total, 20 BCTs were used in selected studies. The most used BCT is "Instruction on how to perform the behavior," applied in 68.75 % of the studies. Other frequently utilized techniques include “Goal setting (behavior),” “Social support (unspecified),” and “Adding objects to the environment,” applied in over 50 % of studies. See Table 4 for the frequency of BCTs included in the intervention studies and see Appendix 1 for details.Table 4 Frequencies of BCTs used in studies.

Table 4Behavior Change Techniques	PA Self-efficacy (k = 16)	
N	%	
1. Goals and planning	
1.1 Goal setting (behaviour)	9	55.25 %	
1.2 Problem solving	1	6.25 %	
1.3 Goal setting (outcome)	1	6.25 %	
1.4 Action planning	5	31.25 %	
2. Feedback and monitoring	
2.1 Monitoring of behaviour by others without feedback	4	25 %	
2.2 Feedback on behavior	6	37.5 %	
2.3 Self-monitoring of behaviour	4	25 %	
2.4 Self-monitoring of outcome(s) of behavior	1	6.25 %	
2.7 Feedback on outcome(s) of behaviour	3	18.75 %	
3. Social support	
3.1 Social support (unspecifed)	10	62.5 %	
4. Shaping knowledge	
4.1 Instruction on how to perform the behaviour	11	68.75 %	
5. Natural consequences	
5.1 Information about health consequences	3	18.75 %	
6. Comparison of behaviour	
6.1 Demonstration of the behaviour	5	31.25 %	
7. Associations	
7.1 Prompts/cues	6	37.5 %	
8. Repetition and substitution	
8.1 Behavioural practice/rehearsal	2	12.5 %	
8.7 Graded tasks	2	12.5 %	
10. Reward and threat	
10.3 Non-specific reward	1	6.25 %	
10.8 Incentive (outcome)	1	6.25 %	
12. Antecedents	
12.1 Restructuring the physical environment	5	31.25 %	
12.5 Adding objects to the environment	8	50 %	

4.1 Methodological quality

The methodological quality of the included studies was assessed using the PEDro Scale and presented in Table 5. It consists of 11 items that evaluate various aspects of study quality, including randomization, blinding, and statistical reporting. Each criterion the study meets receives a score of 1, while criteria not met are scored 0. The first criterion relates to external validity and is not included in the total score; thus, the maximum achievable score is 10.Table 5 PEDro score.

Table 5Author	Criteria	Total	
1	2	3	4	5	6	7	8	9	10	11	score (10)	
Wang et al. (2017)	1	0	0	1	0	0	0	1	0	1	1	4	
Robbins et al. (2020)	1	1	1	1	0	0	0	1	1	1	1	7	
Rizal et al. (2019)	1	0	0	1	0	0	0	1	0	1	1	4	
Popeska et al. (2018)	0	0	0	1	0	0	0	1	0	1	1	4	
Kennedy et al. (2018)	1	1	1	1	0	0	0	1	1	1	1	7	
Lee & Gao, (2020)	1	0	0	1	0	0	0	1	0	1	1	4	
Liang et al. (2020)	1	0	0	1	0	0	0	1	0	1	1	4	
Mok et al. (2020)	0	1	1	1	0	0	0	1	0	1	1	6	
Emeljanovas et al. (2018)	0	0	0	0	0	1	1	1	1	1	1	6	
Glapa et al. (2018)	0	1	0	1	0	0	0	1	0	1	1	5	
Gao et al. (2019)	1	0	0	0	0	0	0	1	0	1	1	3	
Balasekaran et al. (2021)	1	1	0	1	0	0	0	1	0	1	1	5	
Staiano et al. (2013)	1	1	0	1	0	0	0	1	1	1	1	7	
Direito et al. (2015)	1	1	1	1	0	0	0	1	1	1	1	8	
Spook et al. (2016)	1	1	1	1	0	0	0	1	1	1	1	8	
Chen et al. (2011)	1	1	0	1	0	0	0	1	1	1	1	7	
Item score: 1 = meets criteria, 0 = does not meet criteria; Criteria: 1 = Eligibility criteria, 2 = Random allocation, 3 = Concealed allocation, 4 = Baseline comparability, 5 = Blind subjects, 6 = Blind therapists, 7 = Blind assessors, 8 = Adequate follow-up, 9 = Intention to Treat Analysis, 10 = Between-group comparisons, 11 = Point estimates and variability; NR = Not Reported.

The study by Direito et al. (2015) and Spook et al. (2016) indicated excellent methodology quality. Eight studies showed high-quality.51,52,54,55,59,60,62,63 These studies generally succeeded in meeting criteria related to random allocation, baseline comparability, and providing point estimates and variability. Conversely, the studies with the lowest scores which indicated moderate quality57,61,64, 65, 66,68 failed to meet several key criteria, such as concealed allocation, blinding of subjects, therapists and assessors, and intention to treat analysis. Overall, 62.5 % of studies were categorized as high in methodological quality.

4.2 Meta-analysis

A total of 18 experimental groups with Direito et al. (2015) and Staiano et al. (2013) had two eHealth intervention groups compared with the control group, reported changes in PA self-efficacy. eHealth interventions (Hedges' g = 0.315; 95 % CI = 0.069, 0.562) with high heterogeneity (I2 = 92.34 %, p < .01) moderated PA self-efficacy. See Table 6 for the meta-analytic result of the effects on PA self-efficacy, and Fig. 2 for a forest plot of all included studies. This indicates that eHealth interventions are positively associated with enhanced children's PA self-efficacy, while the high heterogeneity warrants cautious interpretation.Table 6 Meta-analytic results of the effects of eHealth interventions on PA self-efficacy.

Table 6Outcome	Number of studies	Meta-analytic effect size	Heterogeneity	
Hedges's g	95%CI	p value	I2%	Q	p value	
PA self-efficacy	18	0.315	[0.069, 0.562]	0.012	92.34	221.98	<0.001	

Fig. 2 Forest plot showing PA self-efficacy effect sizes with 95 % CI.

Fig. 2

4.3 BCTs associated with changes in PA self-efficacy

13 out of 20 BCTs were analyzed, other BCTs were used in less than 3 articles and could not conduct valid analysis. “Instruction on how to perform the behavior” showed a significant association with PA self-efficacy with moderate effect (Hedges's g = 0.436, p = .003), and showed considerable effectiveness compared to studies not including this BCT. Additionally, "Demonstration of the behavior" was found to have a significant association with PA self-efficacy, also showing a moderate effect size (Hedges's g = 0.527, p = .036), however no superior effect than studies not including this BCT. Specifically, studies that adopted social support (unspecified) and prompt/cues were significantly less effective than studies that did not use these BCTs (Hedges's g = 0.645, p = .001; Hedges's g = 0.475, p = .001). See Table 7 for the details.Table 7 Comparison between PA self-efficacy, according to whether specific techniques are included in the eHealth intervention or not.

Table 7BCT	BCT included	BCT not included	Test of moderators	
k	g	Lower 95 % CI	Upper 95 % CI	Test of null (p-value)	k	g	Lower 95 % CI	Upper 95 % CI	Test of null (p-value)	QMP-value	
1. Goals and planning	
1.1 Goal setting (behavior)	11	0.309	−0.014	0.632	0.060	7	0.328	−0.073	0.728	0.109	0.944	
1.4 Action planning	5	0.175	−0.484	0.835	0.602	13	0.367	0.102	0.633	0.007*	0.597	
2. Feedback and monitoring	
2.1 Monitoring of behaviour by others without feedback	4	0.435	−0.210	1.079	0.186	14	0.279	0.004	0.554	0.047*	0.663	
2.2 Feedback on behavior	7	0.224	−0.171	0.619	0.267	11	0.380	0.053	0.708	0.023*	0.549	
2.3 Self-monitoring of behaviour	5	0.259	−0.186	0.705	0.254	13	0.335	0.064	0.607	0.016*	0.776	
2.4Feedback on outcome(s) of behaviour	3	0.667	−0.033	1.367	0.062	15	0.248	0.034	0.461	0.023*	0.262	
3. Social support	
3.1 Social support (unspecified)	12	0.082	−0.088	0.252	0.344	6	0.645	0.358	0.931	0.000**	0.001*	
4. Shaping knowledge	
4.1 Instruction on how to perform the behavior	12	0.436	0.145	0.728	0.003*	6	0.036	−0.123	0.195	0.656	0.018*	
5. Natural consequences	
5.1 Information about health consequences	3	0.232	−0.071	0.535	0.133	15	0.336	0.058	0.613	0.018*	0.622	
6. Comparison of behavior	
6.1 Demonstration of the behavior	5	0.527	0.035	1.019	0.036*	13	0.207	0.020	0.394	0.030*	0.234	
7. Associations	
7.1 Prompts/cues	6	0.043	−0.165	0.250	0.688	12	0.475	0.196	0.755	0.001**	0.015*	
12. Antecedents	
12.1 Restructuring the physical environment	5	0.043	−0.205	0.290	0.736	13	0.397	0.118	0.675	0.005*	0.063	
12.5 Adding objects to the environment	9	0.389	−0.013	0.790	0.058	9	0.253	−0.084	0.589	0.142	0.611	

BCT 1.1 and 12.5 had a small, non-significant effect on PA self-efficacy. The remaining BCTs (1.4, 2.1, 2.2, 2.3, 2.4, 5.1, 12.1) showed no significant association with increased PA self-efficacy; however, studies not including these BCTs had a significant effect on PA self-efficacy. See Table 7 for the details.

4.4 Sensitivity analysis

When the sensitivity analysis was carried out by excluding the studies one by one, the heterogeneity still remained high. After removing the five articles with the highest bias,33,36,41,44,45 the heterogeneity was reduced to I2 = 34.84 %, while the heterogeneity of RCT studies was 0 and that of non-RCTs was 61.75 %. See details in Appendix 2.

5 Discussion

This systematic review and meta-analysis aimed to examine the effectiveness of eHealth interventions on PA self-efficacy. Notably, this study was a pioneer in employing the BCT taxonomy to explain the specific effects of eHealth interventions on children's PA self-efficacy. The findings indicated that eHealth interventions had a small yet statistically significant effect on children's PA self-efficacy improvement. Among the 13 BCTs analyzed, Instruction on how to perform the behavior and Demonstration of the behavior were significantly associated with increased PA self-efficacy. The remaining BCTs showed small to moderate effect sizes and were not significantly linked to changes in self-efficacy.

The high heterogeneity observed could be attributed to varied widely in participant numbers (ranging from 51 to 3036), intervention durations (ranging from 0.1 to 48 weeks), different study designs, and twelve PA self-efficacy scales used across the sixteen studies. These findings were strengthened by performing the sensitivity analysis.

6 eHealth interventions and PA self-efficacy

Pakarinen et al. (2017) initially explored the potential of eHealth interventions on children's PA self-efficacy and discovered eHealth active games can positively improve PA self-efficacy. This review further found a significant association between eHealth interventions and improvements in children's PA self-efficacy. Specifically, 62.5 % of studies in this review reported a significant enhancement in children's PA self-efficacy.51,52,55,56,59,61, 62, 63,65,66 This result aligns with previous studies, highlighting the importance of eHealth in improving PA self-efficacy. A cross-sectional study noted that using eHealth for physical activities positively impacts self-efficacy.69 Another survey indicated that self-efficacy mediates the relationship between eHealth use and healthy behaviors, eHealth can boost self-efficacy in turn.70 A longitudinal study has also shown that digital interventions help improve self-efficacy by capturing experience, social persuasion, emotional and physical factors, providing cues and challenges for healthy behaviors.71 Another longitudinal study pointed out that social support from family members has a stimulating effect on self-efficacy in using eHealth interventions.72 However, these studies were all research on adults. This systematic review and meta-analysis systematically described the role of eHealth in promoting children's PA self-efficacy.

In this review, the results showed integrating face-to-face interactions with eHealth elements for children seems essential, as all the studies in this review were conducted in schools, homes, or clinics, except one study.56 Interestingly, Brain Breaks Physical Activity Solutions (BBPAS), which adopted web-based structured 10.13039/100006131 PA breaks and short classroom videos, included the largest number of participants and countries, 3935 participants and 11 countries, and all six studies that adopted it showed a significant increase in 10.13039/100006131 PA self-efficacy.51,59,61, 62, 63,66 Previous studies also highlighted the need to incorporate real-world settings into eHealth or mHealth interventions to promote health behaviors in children.73 Individual (e.g., feedback, goal-setting, reward) and social interaction (e.g., social sharing, competition) are the two kinds of support for eHealth interventions that affect self-efficacy.74,75 Feedback and other ways can help individuals understand the progress of physical activities, enhance the awareness of their own abilities, and promote self-efficacy.76,77 For social interaction, peer encouragement positively influences children's self-efficacy and physical performance.78 Children who received peer encouragement regularly reported higher self-efficacy and performed better in physical tasks compared to those who did not receive encouragement.78 Interestingly, peer support and self-efficacy can interact to predict 10.13039/100006131 PA .79 Children who perceive strong social support are more likely to overcome barriers to 10.13039/100006131 PA .80

Furthermore, the mobile application (Balance It) used by Spook et al. (2016) also demonstrated significant improvements in children's PA self-efficacy.56 The design utilized various motivational strategies such as goal setting, feedback, and reinforcement through virtual rewards which likely contributed to its success. This aligns with findings from Schwarz et al. (2023) and Johnson et al. (2022), which highlighted that mobile app features such as rewards, social interaction, personalized challenges, self-monitoring, and customization options can enhance user engagement.42,81 With the increasing frequency of smartphone usage among children,82 it is important to transform smartphones into positive intervention tools. Future research could focus on integrating real-world settings with effective mobile health interventions to maximize the effectiveness of interventions.

7 BCTs and PA self-efficacy

The effectiveness of BCTs in increasing PA self-efficacy in adults and obese individuals in both digital and non-digital interventions is documented in many studies.83, 84, 85, 86 However, no previous review has systematically and quantitatively examined effective BCTs specifically in eHealth interventions for promoting PA self-efficacy in children.

In this review, two BCTs were significantly associated with improvements in PA self-efficacy, Instruction on how to perform the behavior and Demonstration of the behavior. When referring to exercise, Michie et al. (2015) code these two and behavioral practice/rehearsal as a series. These two are also the most used in other studies of BCTs on health behavior change among children.87, 88, 89 This prevalence may be due to children's developing cognitive skills,90 necessitating clear instructions and demonstrations to help form healthy behaviors.91 Demonstrations by teachers or in eHealth tools can aid skill acquisition,92 and serve as persuasive cues for PA opportunities.93

Aside from instructions on behavior, goal setting (behavior) and social support (unspecified) were most frequently used. Prior review reported on eHealth-based interventions identified goal setting as an effective technique to promote health behavior change.86,94 To boost self-efficacy, goal-setting theories suggest that specific and challenging goals lead to better performance than vague objectives,95 and specific goal setting is crucial for turning intentions into actions.32

In the social support cluster, 10 studies adopted social support (unspecified). However, the studies that did not include this 10.13039/100014691 BCT showed significant results, which is inconsistent with the findings of previous studies.96,97 It is commonly believed that social support from parents could be a positive mediator that impacts children's 10.13039/100006131 PA self-efficacy. However, the impact is complex for adolescents in puberty, who want to escape the restrictions set by their parents but also crave their help when they encounter difficulties.97 In this review, children aged 11.58 ± 2.87 years, maybe in puberty or pre-puberty,98 could be why social support (unspecified) is not significantly related to 10.13039/100006131 PA self-efficacy. Another study also pointed out that perceived parental control is negatively related to self-efficacy beliefs and enjoyment of 10.13039/100006131 PA among adolescents.99 Cheng et al. (2020) suggested that parental co-participation, transport, or positive comments can improve adolescents' self-efficacy.100 This requires that the design of social support should consider the differences in physical and mental changes across different age ranges of children.

Compared with BCTs adopted in promoting adults' 10.13039/100006131 PA self-efficacy, most BCTs for children require the involvement of others to guide and support children's 10.13039/100006131 PA behaviors. However, BCTs for adults require relatively high individual ability, and stimulating adult behaviors also needs to be approached from multiple dimensions, such as through self-monitoring behaviors, graded tasks, and social incentives.84,86 Considering the differences in cognition, behavior and understanding ability, BCT selection for children may require more direct rewards and clear guidance, which also emphasizes the need to pay attention to age-appropriate BCT selection. In addition, Michie et al. (2009) cautioned that combinations of large numbers of techniques could dilute the impact of the most effective ones and compromise delivery fidelity.101 Therefore, BCTs should be selected carefully, considering the determinants and each BCT's effectiveness.102

8 Strengths and limitations

This systematic review pioneers a thorough examination of the effects of eHealth interventions on children's PA self-efficacy, analyzing the employed BCTs and identifying the most effective components. Strengths include a rigorous search strategy, involvement of at least three independent researchers at critical stages, and the high methodological quality of the included studies (62.5 % high-quality).

The limitations include the limited number of articles and the high heterogeneity. The small number of studies in the meta-analysis reduces its power, and some studies lacked sufficient intervention descriptions, making it difficult to identify the BCTs used. Furthermore, the considerable heterogeneity may be due to varied widely in their participant numbers and intervention durations, as well as diverse PA self-efficacy measurement tools used across studies. Future research should design more rigorous studies and adopt standardized measures of PA self-efficacy to enhance comparability and reliability, improving understanding of eHealth interventions' impact on children's PA self-efficacy.

9 Conclusion

This systematic review and meta-analysis investigated the effect of eHealth intervention and specific BCTs on enhancing PA self-efficacy in children. The findings show there is small but significant eHealth intervention effects were found for postintervention PA self-efficacy. Instruction on performing the behavior and behavior demonstration were significantly associated with the improvement of children's PA self-efficacy. Given the limited number of studies and insufficient intervention descriptions in some studies, these findings need to be interpreted with caution. Overall, this review is the pioneer to focus on BCTs in eHealth interventions for children. The insights gained provide valuable knowledge about tailored BCTs incorporated into eHealth interventions designed for children.

Funding

Not applicable.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article.Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jesf.2024.09.002.
==== Refs
References

1 Eddolls W.T.B. McNarry M.A. Lester L. Winn C.O.N. Stratton G. Mackintosh K.A. The association between physical activity, fitness and body mass index on mental well-being and quality of life in adolescents Qual Life Res 27 9 2018 2313 2320 10.1007/s11136-018-1915-3 29948603
2 Stabelini Neto A. Santos G.C.D. Silva J.M.D. Improving physical activity behaviors, physical fitness, cardiometabolic and mental health in adolescents - ActTeens Program: a protocol for a randomized controlled trial PLoS One 17 8 2022 e0272629 10.1371/journal.pone.0272629
3 Tambalis K.D. Sidossis L.S. Physical activity and cardiometabolic health benefits in children Cardiorespiratory Fitness in Cardiometabolic Diseases: Prevention and Management in Clinical Practice 2019 405 423
4 Loprinzi P.D. Cardinal B.J. Loprinzi K.L. Lee H. Benefits and environmental determinants of physical activity in children and adolescents Obes Facts 5 4 2012 597 610 10.1159/000342684 22986648
5 Guthold R. Stevens G.A. Riley L.M. Bull F.C. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1· 6 million participants The lancet child & adolescent health 4 1 2020 23 35 10.1016/S2352-4642(19)30323-2 31761562
6 World Health Organization WHO Guidelines on Physical Activity and Sedentary Behaviour 2020 World Health Organization
7 Henning L. Dreiskämper D. Pauly H. Filz S. Tietjens M. What influences children's physical activity? Investigating the effects of physical self-concept, physical self-guides, self-efficacy, and motivation J Sport Exerc Psychol 44 6 2022 393 408 10.1123/jsep.2021-0270 36265839
8 Liu Y. Ge X. Li H. Physical activity maintenance and increase in Chinese children and adolescents: the role of intrinsic motivation and parental support Front Public Health 11 2023 1175439 10.3389/fpubh.2023.1175439
9 Owen K.B. Smith J. Lubans D.R. Ng J.Y. Lonsdale C. Self-determined motivation and physical activity in children and adolescents: a systematic review and meta-analysis Prev Med 67 2014 270 279 10.1016/j.ypmed.2014.07.033 25073077
10 Liu J. Zeng M. Wang D. Zhang Y. Shang B. Ma X. Applying social cognitive theory in predicting physical activity among Chinese adolescents: a cross-sectional study with multigroup structural equation model Front Psychol 12 2022 695241 10.3389/fpsyg.2021.695241
11 Nogg K.A. Vaughn A.A. Levy S.S. Blashill A.J. Motivation for physical activity among US adolescents: a self-determination theory perspective Ann Behav Med 55 2 2021 133 143 10.1093/abm/kaaa037 32756874
12 Kwan M.Y.W. Li Y.-C. Cairney J. Theory-based correlates of physical activity among children with developmental coordination disorder: a scoping review Current Developmental Disorders Reports 9 4 2022 105 109 10.1007/s40474-022-00254-4
13 Pasi H. Lintunen T. Leskinen E. Hagger M.S. Predicting school students' physical activity intentions in leisure-time and school recess contexts: testing an integrated model based on self-determination theory and theory of planned behavior PLoS One 16 3 2021 e0249019 10.1371/journal.pone.0249019
14 Chicote‐López J. Abarca‐Sos A. Gallardo L.O. García‐González L. Social antecedents in physical activity: tracking the self‐determination theory sequence in adolescents J Community Psychol 46 3 2018 356 373 10.1002/jcop.21945
15 Gholidahaneh M. Ghorbani S. Esfahaninia A. Causal relationship between autonomous motivation and leisure time physical activity in primary school students: applying the theory of planned behavior International Journal of School Health 8 2021 184 191
16 Bandura A. Health promotion by social cognitive means Health Educ Behav 31 2 2004 143 164 10.1177/1090198104263660 15090118
17 Young M.D. Plotnikoff R. Collins C. Callister R. Morgan P. Social cognitive theory and physical activity: a systematic review and meta‐analysis Obes Rev 15 12 2014 983 995 10.1111/obr.12225 25428600
18 Sheeran P. Maki A. Montanaro E. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: a meta-analysis Health Psychol 35 11 Nov 2016 1178 1188 10.1037/hea0000387 27280365
19 Van Der Horst K. Paw M.J.C.A. Twisk J.W. Van Mechelen W. A brief review on correlates of physical activity and sedentariness in youth Med Sci Sports Exerc 39 8 2007 1241 1250 10.1249/mss.0b013e318059bf35 17762356
20 Spence J.C. Blanchard C.M. Clark M. Plotnikoff R.C. Storey K.E. McCargar L. The role of self-efficacy in explaining gender differences in physical activity among adolescents: a multilevel analysis J Phys Act Health 7 2 Mar 2010 176 183 10.1123/jpah.7.2.176 20484756
21 Li Y.-C. Joshi D. King-Dowling S. Hay J. Faught B.E. Cairney J. The longitudinal relationship between generalized self-efficacy and physical activity in school-aged children Eur J Sport Sci 18 4 2018 569 578 10.1080/17461391.2018.1430852 29400618
22 Bandura A. Self-efficacy: toward a unifying theory of behavioral change Psychol Rev 84 2 Mar 1977 191 215 10.1037//0033-295x.84.2.191 847061
23 Bauman A.E. Reis R.S. Sallis J.F. Wells J.C. Loos R.J. Martin B.W. Correlates of physical activity: why are some people physically active and others not? Lancet 380 9838 2012 258 271 10.1016/s0140-6736(12)60735-1 22818938
24 McAuley E. Blissmer B. Self-efficacy determinants and consequences of physical activity Exerc Sport Sci Rev 28 2 2000 85 88 10902091
25 Dishman R.K. Motl R.W. Saunders R. Self-efficacy partially mediates the effect of a school-based physical-activity intervention among adolescent girls Prev Med 38 5 2004 628 636 10.1016/j.ypmed.2003.12.007 15066366
26 Voskuil V. Robbins L. Youth physical activity self-efficacy: a concept analysis J Adv Nurs 71 9 2015 2002 2019 10.1111/jan.12658 25823520
27 Wing E.K. Bélanger M. Brunet J. Linking parental influences and youth participation in physical activity in- and out-of-school: the mediating role of self-efficacy and enjoyment Am J Health Behav 40 1 2016 31 37 10.5993/AJHB.40.1.4 26685811
28 Schroeder K. Kubik M.Y. Lee J. Sirard J.R. Fulkerson J.A. Self-efficacy, not peer or parent support, is associated with more physical activity and less sedentary time among 8- to 12-year-old youth with elevated body mass index J Phys Activ Health 17 1 2020 74 79 10.1123/jpah.2019-0108
29 Efrat M.W. Exploring strategies that influence children's physical activity self-efficacy Contemp Issues Educ Res 10 2 2017 87 94
30 Rovniak L. Bill E. Wientt R. Stephens R. Social cognitive determinants of physical activity in young adults: a prospective structural equation analysis Annals of behavioral medicine : a publication of the Society of Behavioral Medicine 24 2002 149 156 10.1207/S15324796ABM2402_12 12054320
31 Michie S. Abraham C. Eccles M.P. Francis J.J. Hardeman W. Johnston M. Strengthening evaluation and implementation by specifying components of behaviour change interventions: a study protocol Implement Sci 6 2011 10 10.1186/1748-5908-6-10 21299860
32 Williams S.L. French D.P. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour--and are they the same? Health Educ Res 26 2 2011 308 322 10.1093/her/cyr005 21321008
33 Ashford S. Edmunds J. French D.P. What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis Br J Health Psychol 15 Pt 2 2010 265 288 10.1348/135910709x461752 19586583
34 Olander E.K. Fletcher H. Williams S. Atkinson L. Turner A. French D.P. What are the most effective techniques in changing obese individuals' physical activity self-efficacy and behaviour: a systematic review and meta-analysis Int J Behav Nutr Phys Act 10 2013 29 10.1186/1479-5868-10-29 23452345
35 Kracht C.L. Hutchesson M. Ahmed M. E-&mHealth interventions targeting nutrition, physical activity, sedentary behavior, and/or obesity among children: a scoping review of systematic reviews and meta-analyses Obes Rev 22 12 2021 e13331 10.1111/obr.13331
36 McIntosh J. Jay S. Hadden N. Whittaker P. Do E-health interventions improve physical activity in young people: a systematic review Publ Health 148 2017 140 148 10.1016/j.puhe.2017.04.001
37 Sequí-Domínguez I. Cavero-Redondo I. Alvarez-Bueno C. López-Gil J.F. Martinez Vizcaino V. Pascual-Morena C. Effectiveness of eHealth interventions promoting physical activity in children and adolescents: systematic review and meta-analysis J Med Internet Res 26 2024 e41649 10.2196/41649
38 Pakarinen A. Parisod H. Smed J. Salanterä S. Health game interventions to enhance physical activity self-efficacy of children: a quantitative systematic review J Adv Nurs 73 4 2017 794 811 10.1111/jan.13160 27688056
39 Vandelanotte C. Müller A.M. Short C.E. Past, present, and future of eHealth and mHealth research to improve physical activity and dietary behaviors J Nutr Educ Behav 48 3 2016 219 228.e1 10.1016/j.jneb.2015.12.006 26965100
40 Meerits P.R. Tilga H. Koka A. Web-based need-supportive parenting program to promote physical activity in secondary school students: a randomized controlled pilot trial BMC Publ Health 23 1 2023 1627 10.1186/s12889-023-16528-4
41 Staiano A.E. Beyl R.A. Guan W. Hendrick C.A. Hsia D.S. Newton R.L. Jr. Home-based exergaming among children with overweight and obesity: a randomized clinical trial Pediatr Obes 13 11 2018 724 733 10.1111/ijpo.12438 30027607
42 Johnson R.W. White B.K. Gucciardi D.F. Gibson N. Williams S.A. Intervention mapping of a gamified therapy prescription app for children with disabilities: user-centered design approach JMIR Pediatr Parent 5 3 2022 e34588 10.2196/34588
43 Cushing C.C. Steele R.G. A meta-analytic review of eHealth interventions for pediatric health promoting and maintaining behaviors J Pediatr Psychol 35 9 2010 937 949 10.1093/jpepsy/jsq023 20392790
44 Shamseer L. Moher D. Clarke M. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation Bmj 350 2015 g7647 10.1136/bmj.g7647
45 Albanese E. Bütikofer L. Armijo-Olivo S. Ha C. Egger M. Construct validity of the Physiotherapy Evidence Database (PEDro) quality scale for randomized trials: item response theory and factor analyses Res Synth Methods 11 2 2020 227 236 10.1002/jrsm.1385 31733091
46 Hedges L.V. Distribution theory for glass's estimator of effect size and related estimators J Educ Stat 6 2 1981 107 128 10.3102/10769986006002107
47 Kontopantelis E. Springate D.A. Reeves D. A re-analysis of the Cochrane Library data: the dangers of unobserved heterogeneity in meta-analyses PLoS One 8 7 2013 e69930 10.1371/journal.pone.0069930
48 Higgins J.P.T. Thomas J. Chandler J. Higgins Julian P.T. Thomas James Chandler Jacqueline Cochrane Handbook for Systematic Reviews of Interventions second ed. Cochrane Book Series 2019 Wiley-Blackwell
49 Huedo-Medina T.B. Sánchez-Meca J. Marín-Martínez F. Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods 11 2 2006 193 206 10.1037/1082-989X.11.2.193 16784338
50 Egger M. Smith G.D. Schneider M. Minder C. Bias in meta-analysis detected by a simple, graphical test BMJ 315 7109 1997 629 634 10.1136/bmj.315.7109.629 9310563
51 Mok M.M.C. Chin M.K. Korcz A. Brain Breaks® physical activity Solutions in the classroom and on attitudes toward physical activity: a randomized controlled trial among primary students from eight countries Int J Environ Res Public Health 17 5 2020 10.3390/ijerph17051666
52 Staiano A.E. Abraham A.A. Calvert S.L. Adolescent exergame play for weight loss and psychosocial improvement: a controlled physical activity intervention Obesity 21 3 2013 598 601 10.1002/oby.20282 23592669
53 Direito A. Jiang Y.N. Whittaker R. Maddison R. Apps for IMproving FITness and increasing physical activity among young people: the AIMFIT pragmatic randomized controlled trial J Med Internet Res 17 8 2015 e210 10.2196/jmir.4568
54 Chen J.-L. Weiss S. Heyman M.B. Cooper B. Lustig R.H. The efficacy of the web-based childhood obesity prevention program in Chinese American adolescents (Web ABC study) The Journal of adolescent health : official publication of the Society for Adolescent Medicine 49 2 2011 148 154 10.1016/j.jadohealth.2010.11.243 21783046
55 Kennedy S.G. Smith J.J. Morgan P.J. Implementing resistance training in secondary schools: a cluster randomized controlled trial Med Sci Sports Exerc 50 1 2018 62 72 10.1249/mss.0000000000001410 29251687
56 Spook J. Paulussen T. Kok G. van Empelen P. Evaluation of a serious self-regulation game intervention for overweight-related behaviors ("Balance it"): a pilot study J Med Internet Res 18 9 2016 e225 10.2196/jmir.4964 27670222
57 Lee J.E. Gao Z. Effects of the iPad and mobile application-integrated physical education on children's physical activity and psychosocial beliefs Phys Educ Sport Pedagog 25 6 2020 567 584 10.1080/17408989.2020.1761953
58 Liang Y. Lau P.W.C. Jiang Y. Maddison R. Getting active with active video games: a quasi-experimental study Int J Environ Res Public Health 17 21 2020 10.3390/ijerph17217984
59 Balasekaran G. Ibrahim A.A.B. Cheo N.Y. Using brain-breaks(®) as a technology tool to increase attitude towards physical activity among students in Singapore Brain Sci 11 6 2021 10.3390/brainsci11060784
60 Robbins L.B. Ling J. Clevenger K. A school- and home-based intervention to improve adolescents' physical activity and healthy eating: a pilot study J Sch Nurs 36 2 2020 121 134 10.1177/1059840518791290 30068245
61 Rizal H. Hajar M.S. Muhamad A.S. Kueh Y.C. Kuan G. The effect of Brain breaks on physical activity behaviour among primary school children: a transtheoretical perspective Int J Environ Res Public Health 16 21 2019 10.3390/ijerph16214283
62 Emeljanovas A. Miežienė B. MoChingMok M. The effect of an interactive program during school breaks on attitudes toward physical activity in primary school children An Psicolog 34 2018 580 586 10.3390/ijerph15020368
63 Glapa A. Grzesiak J. Laudanska-Krzeminska I. The impact of Brain breaks classroom-based physical activities on attitudes toward physical activity in polish school children in third to fifth grade Int J Environ Res Public Health 15 2 2018 10.3390/ijerph15020368
64 Gao Z. Pope Z.C. Lee J.E. Quan M. Effects of active video games on children's psychosocial beliefs and school day energy expenditure J Clin Med 8 9 2019 10.3390/jcm8091268
65 Wang J.J. Baranowski T. Lau P.W.C. Buday R. Gao Y. Story immersion may Be effective in promoting diet and physical activity in Chinese children J Nutr Educ Behav 49 4 2017 321 329.e1 10.1016/j.jneb.2017.01.001 28391798
66 Popeska B. Jovanova-Mitkovska S. Chin M.K. Edginton C.R. Mo Ching Mok M. Gontarev S. Implementation of Brain Breaks(®) in the classroom and effects on attitudes toward physical activity in a Macedonian school setting Int J Environ Res Public Health 15 6 2018 10.3390/ijerph15061127
67 Michie S. Wood C.E. Johnston M. Abraham C. Francis J.J. Hardeman W. Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data) Health Technol Assess 19 99 2015 1 188 10.3310/hta19990
68 Liang Y. Lau P.W. Huang W.Y. Maddison R. Baranowski T. Validity and reliability of questionnaires measuring physical activity self-efficacy, enjoyment, social support among Hong Kong Chinese children Prev Med Rep 1 2014 48 52 10.1016/j.pmedr.2014.09.005 26844039
69 Marchant G. Bonaiuto F. Bonaiuto M. Guillet Descas E. Exercise and physical activity eHealth in COVID-19 pandemic: a cross-sectional study of effects on motivations, behavior change mechanisms, and behavior Front Psychol 12 2021 618362 10.3389/fpsyg.2021.618362
70 Choi M. Association of eHealth use, literacy, informational social support, and health-promoting behaviors: mediation of health self-efficacy Int J Environ Res Publ Health 17 21 2020 7890 10.3390/ijerph17217890
71 Olsen C. Lungu D.A. Effectiveness of a smartphone app (heia meg) in improving decisions about nutrition and physical activity: prospective longitudinal study JMIR Form Res 8 2024 e48185 10.2196/48185
72 McCormack G.R. Petersen J. Ghoneim D. Blackstaffe A. Naish C. Doyle-Baker P.K. Effectiveness of an 8-week physical activity intervention involving wearable activity trackers and an eHealth app: mixed methods study JMIR Form Res 6 5 2022 e37348 10.2196/37348
73 Kracht C.L. Hutchesson M. Ahmed M. E‐&mHealth interventions targeting nutrition, physical activity, sedentary behavior, and/or obesity among children: a scoping review of systematic reviews and meta‐analyses Obes Rev 22 12 2021 e13331 10.1111/obr.13331 n/a
74 Hosseinpour M. Terlutter R. Your personal motivator is with you: a systematic review of mobile phone applications aiming at increasing physical activity Sports Med 49 9 2019 1425 1447 10.1007/s40279-019-01128-3 31144235
75 Marchant G. Bonaiuto F. Bonaiuto M. Guillet Descas E. Exercise and physical activity eHealth in COVID-19 pandemic: a cross-sectional study of effects on motivations, behavior change mechanisms, and behavior Front Psychol 12 2021 618362 10.3389/fpsyg.2021.618362
76 Prestwich A. Conner M. Hurling R. Ayres K. Morris B. An experimental test of control theory-based interventions for physical activity Br J Health Psychol 21 4 2016 812 826 10.1111/bjhp.12198 27169809
77 Lubans D.R. Smith J.J. Skinner G. Morgan P.J. Development and implementation of a smartphone application to promote physical activity and reduce screen-time in adolescent boys Front Public Health 2 2014 42 10.3389/fpubh.2014.00042 24904909
78 Innes K.L. Graham J.D. Bray S.R. Effects of peer encouragement on efficacy perceptions and physical performance in children J Sport Exerc Psychol 42 4 2020 314 322 10.1123/jsep.2019-0280 32711396
79 Prochnow T. Patterson M.S. Umstattd Meyer M.R. A social network analysis approach to group and individual perceptions of child physical activity Health Educ Res 35 6 2020 564 573 10.1093/her/cyaa035 32918452
80 Laird Y. Fawkner S. Niven A. A grounded theory of how social support influences physical activity in adolescent girls Int J Qual Stud Health Well-Being 13 1 2018 1435099 10.1080/17482631.2018.1435099
81 Schwarz A. Winkens L.H.H. de Vet E. Ossendrijver D. Bouwsema K. Simons M. Design features associated with engagement in mobile health physical activity interventions among youth: systematic review of qualitative and quantitative studies JMIR Mhealth Uhealth 11 2023 e40898 10.2196/40898
82 He Z. Wu H. Yu F. Effects of smartphone-based interventions on physical activity in children and adolescents: systematic review and meta-analysis JMIR Mhealth Uhealth 9 2 2021 e22601 10.2196/22601
83 Dombrowski S.U. Sniehotta F.F. Avenell A. Johnston M. MacLennan G. Araújo-Soares V. Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review Health Psychol Rev 6 1 2012 7 32 10.1080/17437199.2010.513298
84 Williams S.L. French D.P. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour—and are they the same? Health Educ Res 26 2 2011 308 322 10.1093/her/cyr005 21321008
85 Ashford S. Edmunds J. French D.P. What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis Br J Health Psychol 15 2 2010 265 288 10.1348/135910709X461752 19586583
86 Carraça E. Encantado J. Battista F. Effective behavior change techniques to promote physical activity in adults with overweight or obesity: a systematic review and meta-analysis Obes Rev 22 suppl 4 2021 e13258 10.1111/obr.13258 Suppl 4
87 Al-Walah M.A. Donnelly M. Cunningham C. Heron N. Which behaviour change techniques are associated with interventions that increase physical activity in pre-school children? A systematic review BMC Publ Health 23 1 2023 2013 10.1186/s12889-023-16885-0
88 Schoeppe S. Alley S. Rebar A.L. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques Int J Behav Nutr Phys Activ 14 1 2017 83 10.1186/s12966-017-0538-3 83
89 Anselma M. Chinapaw M.J.M. Kornet-van der Aa D.A. Altenburg T.M. Effectiveness and promising behavior change techniques of interventions targeting energy balance related behaviors in children from lower socioeconomic environments: a systematic review PLoS One 15 9 2020 e0237969 10.1371/journal.pone.0237969
90 Lewis L. Povey R. Rose S. What behavior change techniques are associated with effective interventions to reduce screen time in 0-5 year olds? A narrative systematic review Prev Med Rep 23 2021 101429 10.1016/j.pmedr.2021.101429
91 Watson J. Cumming O. MacDougall A. Czerniewska A. Dreibelbis R. Effectiveness of behaviour change techniques used in hand hygiene interventions targeting older children - a systematic review Soc Sci Med 281 2021 114090 10.1016/j.socscimed.2021.114090
92 Robinson L.E. Palmer K.K. Wang L. Protocol for a cluster randomized clinical trial of a mastery-climate motor skills intervention, Children's Health Activity and Motor Program (CHAMP), on self-regulation in preschoolers PLoS One 18 3 2023 e0282199 10.1371/journal.pone.0282199
93 Wang H. Blake H. Chattopadhyay K. Development of a school-based intervention to increase physical activity levels among Chinese children: a systematic iterative process based on behavior change wheel and theoretical domains framework Front Public Health 9 2021 610245 10.3389/fpubh.2021.610245
94 Webb T.L. Joseph J. Yardley L. Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy J Med Internet Res 12 1 2010 e4 10.2196/jmir.1376
95 Locke E.A. Latham G.P. Building a practically useful theory of goal setting and task motivation. A 35-year odyssey Am Psychol 57 9 2002 705 717 10.1037//0003-066x.57.9.705 12237980
96 Campos J.G. Bacil E.D.A. Piola T.S. Silva MPd Pacífico A.B. Campos Wd Social support, self-efficacy and level of physical activity of students aged 13-15 years Revista brasileira de cineantropometria & desempenho humano 21 1 2019 1 10.1590/1980-0037.2019v21e58684
97 Ren Z. Hu L. Yu J.J. The influence of social support on physical activity in Chinese adolescents: the mediating role of exercise self-efficacy Children 7 3 2020 10.3390/children7030023
98 Lee P.A. Guo S.S. Kulin H.E. Age of puberty: data from the United States of America Apmis 109 2 2001 81 88 10.1034/j.1600-0463.2001.d01-107.x 11398998
99 Wing E.K. Bélanger M. Brunet J. Linking parental influences and youth participation in physical activity in-and out-of-school: the mediating role of self-efficacy and enjoyment Am J Health Behav 40 1 2016 31 37 10.5993/AJHB.40.1.4 26685811
100 Cheng L.A. Mendonça G. Lucena J. Rech C.R. Farias Júnior J.C. Is the association between sociodemographic variables and physical activity levels in adolescents mediated by social support and self-efficacy? J Pediatr 96 2020 46 52 10.1016/j.jped.2018.08.003
101 Michie S. Abraham C. Whittington C. McAteer J. Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression Health Psychol 28 6 2009 690 701 10.1037/a0016136 19916637
102 Kok G. Gottlieb N.H. Peters G.-J.Y. A taxonomy of behaviour change methods: an Intervention Mapping approach Health Psychol Rev 10 3 2016 297 312 10.1080/17437199.2015.1077155 26262912
