The Influence of Healthy Lifestyle Technologies on Young People’s Physical Activity Participation and Health Learning: A Systematic Review

ABSTRACT

Healthy lifestyle technologies (HLT) have gained popularity in young people’s daily lives, but the long-term effect of these technologies on their physical activity (PA) participation and health learning is unclear. This systematic review (a) explores the effect of HLT use on the PA participation and health learning of young people (aged 13–18), and (b) determines whether the effects were long-term and/or sustainable. Literature searches were conducted in five databases to identify studies published in academic journals in English between 2011 to 2022 which explored young people’s use of HLT to support their PA participation and health learning. Twenty-six studies were identified. The reported impact of these technologies focused on three dimensions: behavior change, psychological responses, and facilitators and barriers to HLT use. This review concluded that HLT can have positive short-term effects on young people’s PA participation and health learning, but the long term or sustainable influence remains inconclusive.

KEYWORDS:

• Digital technologies
• physical activity
• health

Introduction

Digital technologies are used by young people almost everywhere and at almost any time in their daily lives (Lupton, 2021b; Rideout, 2015). Furthermore, digital technology is used for increasingly diverse purposes (Gard, 2014; Lupton, 2020), with young people’s use of such technologies not limited to communication and entertainment but also extending to supporting their physically active lifestyles and wellbeing. This includes searching for health content, motivating behavior, and personal surveillance (V. A. Goodyear & Armour, 2018; V. Goodyear & Armour, 2019; Kerner & Goodyear, 2017). Indeed, research on young people, health and digital technology has attracted much attention in the past two decades. Lupton (2017), for example, identified an increase in digital technology use for promoting and managing young people’s health, including the use of social media for discussing health-related topics, wearable devices for monitoring individuals’ lifestyle activities and health, and online websites for searching for health information. Moreover, various digital technologies promote young people’s physical activity (PA) and health-related learning in PA. These have been referred to as technologies for fostering healthy lifestyles (Chung et al., 2017; V. A. Goodyear et al., 2019; Kerner & Goodyear, 2017) and include technologies such as wearable sensors, health apps and Internet websites that are marketed as tools to enable users to move more, eat healthier and become more aware of the importance of healthy lifestyles (Lucivero & Prainsack, 2015). In the context of lifelong participation in PA, having the knowledge and understanding (of movement and health in terms of how to move, why, where, and with whom to engage in PA) is fundamental and an important component of a healthy active lifestyle (Bouchard et al., 2012; Cale & Harris, 2018; Harris, 2005).

For the purpose of this review, the various digital technologies which provide opportunities for facilitating healthy lifestyle behaviors are grouped under the term “healthy lifestyle technologies” (HLT). Such HLT include social media platforms (Facebook, YouTube, TikTok, WhatsApp, WeChat …), wearable devices (Fitbit, Apple Watch, Keep Band, Huawei Band …), internet health, health apps, etc. In addition, health learning in this review refers to any health information, messages or content related to developing knowledge and understanding of PA or health, including for example, of health literacy, health education, weight management, exercise, sports, or fitness. Given that 13 to 18 years of age is a vital time during which young people are informed, and inform themselves, about their physically active lifestyles and develop their health knowledge (Gard & Lupton, 2017; Lupton, 2021a; Rich, 2018), the effectiveness of various HLT used by young people both within and beyond formal institutions is an important area of research for policy, theory, and practice.

The literature on young people’s use of HLT has revealed conflicting views regarding the value of technologies in health education and in promoting PA participation. Gard (2014) raised concerns regarding the use of digital technologies in the Health and Physical Education (HPE) setting, most notably concerning students’ private and personal data and how this ran the risk of commercialization and exploitation by third parties. Similarly, Williamson (2015) expressed concern that governments and businesses could use technology to manage or market students’ personal health information. Casey et al. (2017) responded to Gard’s arguments and held that physical educators should regard digital health in HPE as an optional pedagogical tool that can be used in differing social, cultural, and educational contexts. Significantly, research with young people has shown that their perspectives and experiences of using HLT are more critical and rational than researchers might have first thought. This same research has also revealed that young people are aware and concerned about the risks regarding their personal data when using health-related digital technologies (Goodyear & Armour, 2018; Goodyear et al., 2019; Goodyear, Boardley, et al., 2021). Moreover, while some teachers and educators accept such technologies and recognize their pedagogical potential, others are reportedly resisting the digitization of HPE for various reasons including, for example, ethical concerns and fears over the potential harm from cyberbullying or misunderstandings of digital health content (Goodyear & Armour, 2021; Lupton, 2021a, 2021b). The above highlights the need for new or alternative ways to understand the connection between young people, HLT and health behaviors.

The influence and effects of HLT on young people’s health behaviors

Understanding the influence and effects of HLT on young people’s health behavior is a priority if we are to better support them to use such technologies. There is an urgent need to identify not only what PA young people are participating in, and from where they are acquiring their health knowledge, but the effects of HLT and whether these are sustainable in the long-term (i.e., ≥6 months). Work in this area, could contribute to enhanced awareness of the potential of such technologies for supporting young people’s healthy lifestyles as they move into adulthood. Indeed, while a healthy active lifestyle is frequently associated with improved quality of life and health, fostering knowledge about how to maintain healthy lifestyle behaviors is challenging. Further, this is a long-term educational goal not only of physical education (PE) (Haerens et al., 2010; Harris et al., 2018) but of other formal as well as informal settings. Importantly, HLT is one of the public pedagogy vehicles that can help promote young people’s engagement in PA (Goodyear et al., 2019b).

To date, several reviews have explored the influence or effects of HLT on young people’s PA participation and health learning with respect to PA and a healthy diet (Müller et al., 2016; Schoeppe et al., 2016; G. Williams et al., 2014). However, these reviews have focused on a broad age range from children to older adults, or young people in the clinical context rather than on young people aged 13–18 in the general population across a non-clinical context. In addition, whilst some systematic reviews have centered on younger age groups, they have focused on only one type of HLT rather than all, such as wearable activity trackers (Böhm et al., 2019; Ridgers et al., 2016), E-health (Mcintosh et al., 2017), mobile Apps (Rodríguez-González et al., 2022), online-based mediums (Goodyear et al., 2023), social media (Goodyear, Wood, et al., 2021), or smartphones (Domin et al., 2021). Meanwhile, other reviews have been narrower in other aspects (Goodyear et al., 2023; Lau et al., 2011; Modra et al., 2021). For example, Modra et al. (2021) limited their examination of digital technology to its influence on young people’s PA in PE. Similarly, whilst Goodyear et al. (2023) reviewed the effects of online interventions on children and young people’s behaviors, most of the papers they reviewed focused on exergames rather than other types of digital technologies. More importantly, only one review (Ridgers et al., 2016) has explored the sustainable influence of digital technology on young people’s PA. This review was, however, restricted to activity trackers. Further, it concluded that more research is needed to explore the long-term effects of using wearable activity trackers on young users’ PA behaviors. Consequently, a review of research which focuses on the influence, including the long-term effects, of various technologies on young people’s PA participation and health learning is warranted.

Research questions and objectives

The research question that guided this review was: do HLT influence young people’s physical activity participation and health learning? If so, how and is the influence sustained? Specifically, in an effort to address the limitations outlined above and establish a clear, current and broad picture of young people’s HLT use, the objectives of this systematic review were to: (1) update and synthesize the evidence base relating to young people’s HLT use, covering empirical studies published between January 2011 and January 2022; (2) analyze the influence of HLT on young people’s PA participation and health learning; (3) assess the sustainable/long-term influence of HLT on young people’s healthy lifestyle behaviors. The findings of this systematic review can be used to inform and potentially support the effective integration of HLT in PE in order to more effectively promote physically active lifestyles amongst young people.

Methods

The reporting of this systematic review was guided by the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement (Page et al., 2021). See supplementary materials for the PRISMA 2020 Checklist that was used.

Eligibility criteria

Studies were included if they adhered to the following inclusion criteria: (1) focused on young people aged 13–18 years old; (2) employed various HLT in relation to young people’s PA and/or health learning; (3) were empirical studies which adopted quantitative, qualitative, or mixed methods; (4) were published between January 2011 and January 2022; (5) were written in English.

This systematic review focused on young people aged 13–18 for two reasons: (1) many social media platforms or digital technologies have a minimum age for users of 13 years; and (2) most young people leave secondary education at the age of 18. In cases where the specific age of participants or the focus of the research were not given but referred to in general terms, the studies were discussed by the authors to reach consensus regarding their inclusion or exclusion from the review. For example, participants in some empirical studies were described as high school students with no specific age given. Given point two above regarding school leaving age, such studies were included in this review.

The timeframe for the review (between 2011–2022) was established primarily for three reasons: (1) the evidence from previous literature that young people have typically used digital technologies to promote their health and fitness over the past decade (e.g., Lupton, 2021b); (2) many new digital technologies for promoting or managing young people’s health have emerged in the last 10 years (e.g., Lau et al., 2011); (3) the earliest systematic review on young people’s use of various technologies on their physical activity was in 2011 (Lau et al., 2011). Whilst Lau and colleagues’ 2011 systematic review contributes to our understanding of how technologies can influence physical activity behavior change in young populations, the research needs to be brought up to date given the pace of change in digital technologies.

Conversely, exclusion criteria included: (1) studies on PA participation or health learning which did not use HLT; (2) studies which focused on areas of health or health behaviors other than PA (e.g., mental health, sex education, only diet/nutrition); (3) studies with specific target populations such as obese or overweight individuals or with other nonstandard population groups (e.g., young people with disabilities or with other medical conditions). Specific populations were not included in this review because it was felt different strategies for promoting a healthy active lifestyle amongst these populations may have been employed, thereby limiting their generalizability to all young people.

Search strategy

A systematic search was conducted to locate relevant studies in five databases in January 2022 using the university library online sources: SPOTD, Medline, PsycARTICLES, PsycINFO, and Scopus. Key search terms relating to the topic and research question were identified and included digital technology, social media, wearable device, physical activity, physical education, health information, health learning, young people, students, and variants of other search terms (see supplementary materials for a full list). In addition, a manual search for relevant studies that were not detected in the key words search was undertaken and the reference lists of articles and authors’ active google scholar profiles were searched.

The process of data extraction

Definitions of terms such as HLT, young people, and health learning were agreed following meetings between the three authors prior to data extraction. As noted earlier, in this review HLT refers to different types of digital technologies used by young people for health purposes, such as social media platforms (e.g., Facebook, YouTube), wearable devices (e.g., Fitbit, Apple Watch, Keep Band, Huawei Band), Internet health, health Apps, etc. Meanwhile, health learning refers to any health information, messages or content related to developing knowledge and understanding of PA or health.

A summary of the search process is shown in Figure 1. Three rounds of holistic searching were performed using Covidence (www.covidence.org) (a primary screening and data extraction tool for supporting all stages of systematic review management). The initial search returned 4733 papers based on the agreed search terms. These articles were screened according to their titles by the first author in the first round which led to 649 articles being abstract screened in the second round. At this stage, articles were excluded if they did not meet the inclusion criteria. If there were any articles that met some but not all criteria, the three authors met to discuss and make a decision regarding their inclusion or exclusion. Finally, the full text articles were read by the first author following which any further conflicts or uncertainties were highlighted and independently reviewed by the other two authors then discussed by all parties to arrive at a consensus. Following this, the manuscripts from other sources (i.e., from the reference lists and google scholar searches) were added. Twenty-six studies were included in the final review for analysis (see Figure 1).

Figure 1. PRISMA flow diagram (following the guidance in Page et al. (2021)).

Assessment of quality

The twenty-six included studies were critically assessed for the quality of their methodologies and outcomes using the Mixed Methods Appraisal Tool (MMAT) (Hong et al., 2018). The MMAT appraisal involves the rigorous assessment of quality in accordance with different criteria and the guidance provided. In this systematic review, an overall quality score is presented (using percentages) alongside a descriptive summary of the results. Whilst giving an overall rating score has been shown to be a weakness of MMAT (Noyes et al., 2019), a critical appraisal approach was adopted in a majority of systematic reviews between 1998 and 2008 (Hannes & Macaitis, 2012). Citing the overall score, in conjunction with a description of quality, seeks to overcome this weakness, adheres to the guidance from Hong and colleagues (Reporting the results of the MMAT, 2020), and provides both a useful summary indication as well as a more detailed picture of the quality of the included studies (see Supplementary File). If there was any uncertainty over the research design, then the studies were discussed, and their classification agreed, by the three authors (details of which can be found in the supplementary materials).

Over 60% of studies (n = 16) met 100% of the MMAT quality criteria (studies 1, 3, 6–8, 12, 14, 16–19, 21–23, 25, 26), whilst over a third (n = 9) met 80% of the quality criteria (2, 5, 9–11, 13, 15, 20, 24). One (study 4) met 60% of the quality criteria. A summary of the outcomes of the quality assessment is reported in Table 1. The studies that did not meet all the quality criteria failed to do so for various reasons including inadequate interpretation regarding the integration of qualitative and quantitative components in two mixed methods studies (4 and 11); inadequate detail concerning the research design for RTC (2, 9,10); and inadequate detail concerning the quality of the quantitative element in some mixed methods studies (4, 13, 15, 24).

Table 1. Quality assessment score of included studies.

Study	Author	Year	Methods	Responses	Quality (quality criteria met)
1	Schnall et al.	2013	Mixed	Yes to all 5	100%
2	Jauho et al.	2015	RCT	Yes to 2.1,2,3,5 and Can’t tell to 2.4	80%
3	Direito et al.	2015	RCT	Yes to all 2	100%
4	Pumper et al.	2015	Mixed	Yes to 5.1,2,4 and Can’t tell to 5.3,5	60%
5	Wartella et al.	2016	Online Survey	Yes to 4.1,2,4,5 and Can’t tell 4.3	80%
6	Turner et al.	2017	Pilot study	Yes to all 3	100%
7	Kerner and Goodyear	2017	Mixed	Yes to all 5	100%
8	Depper and Howe	2017	Qualitative	Yes to all 1	100%
9	Gaudet et al.	2017	RCT	Yes to 2.1,2,3,5 and Can’t tell to 2.4	80%
10	Leinonen et al.	2017	RCT	Yes to 2.1,2,3,5 and Can’t tell to 2.4	80%
11	Pope et al.	2017	Mixed	Yes to 5.1,2,4,5 and Can’t tell to 5.3	80%
12	Ridgers et al.	2018	Qualitative	Yes to all 1	100%
13	Goodyear et al.	2019a	Mixed	Yes to 5.1,2,3,4 and Can’t tell to 5.5	80%
14	Goodyear et al.	2019b	Qualitative	Yes to all 1	100%
15	Goodyear et al.	2019c	Mixed	Yes to 5.1,2,3,4 and Can’t tell to 5.5	80%
16	Booth et al.	2019	Qualitative	Yes to all 1	100%
17	Faust et al.	2019	Non-RCT	Yes to all 3	100%
18	Goodyear and Quennerstedt	2020	Qualitative	Yes to all 1	100%
19	Williams et al.	2020	Mixed	Yes to all 5	100%
20	Plaisime et al.	2020	Mixed	Yes to 5.1,2,3,4 and Can’t tell to 5.5	80%
21	Larsen et al.	2021	Qualitative	Yes to all 1	100%
22	Cushing et al.	2021	Non- RCT	Yes to all 3	100%
23	Harris et al.	2021	Qualitative	Yes to all 1	100%
24	Armstrong et al.	2021	Mixed	Yes to 5.1,2,3,4 and Can’t tell to 5.5	80%
25	Tümer and Sümen	2022	Quantitative	Yes to all 4	100%
26	Goodyear et al.	2022	Qualitative	Yes to all 1	100%

Results

Description of studies

A summary of the 26 studies is provided in Table 4 (see ntary File). Most (21 of 26) were published between 2017 to 2021 and were conducted in the United States (n = 11) and United Kingdom (n = 8), with the remaining studies being conducted in Finland (n = 2), Australia (n = 2), New Zealand, Canada, or Turkey (n = 1). In terms of design, nine studies were mixed methods, eight were qualitative, four were RCTs, two were non-RCT and quantitative descriptive studies respectively, and one was a pilot study. Twenty-one of the studies included males and females, with five targeting either males or females (3 male and 2 female). The studies were conducted in different contexts, with 17 taking place in schools, six online or in the community, while three studies were carried out across multiple contexts. In the case of the intervention studies (n = 15), most interventions lasted between 4 to 12 weeks, with the shortest being a 20-day intervention and the longest lasting 18 months.

The outcomes of HLT

In analyzing the findings, the influence of HLT on young people’s PA participation and health learning could be categorized into one of three main themes: (1) behavior change; (2) psychological responses; and (3) facilitators and barriers to HLT use.

Behavior change

This theme refers to the outcomes from the studies with respect to HLT and long- and short-term behavior change. Nine of the studies specifically reported an effect of HLT use on young people’s PA and health learning through different intervention designs. Seven studies (2, 3, 7, 9, 12, 14, 22) reported a short-term positive effect of wearable devices on young people’s PA with interventions ranging from 20 days to 18 months, and just one study (10) reported a long-term effect on young people’s PA following a six-month intervention. Three studies (9, 12, 17) highlighted a short-term effect and found that this effect was limited due to the reduced novelty of the wearable over time. For example, study 12 reported that feedback on activity from a wrist-worn monitor provided a short-term positive effect of 8 weeks on adolescents’ daily PA. To explore the long-term influence of such devices, study 10 examined the effects of a wearable device on young men’s PA and sedentary behavior, and a six-month formal intervention followed to examine whether there was a long-term effect. The results of the initial pilot showed a short-term positive effect on PA and sedentary behavior, while the formal intervention evidenced a long-term positive effect on young men’s daily moderate to vigorous physical activity (MVPA). Although study 17 reported a sustainable positive effect of a wrist-worn activity monitor on long-term PA change, the effect of this change was inconclusive after 18 months.

Some studies, of varying research designs, employed inferential statistics to establish participants’ behavior change in relation to their PA or both PA and fitness or PA and sedentary behavior (2, 3, 9, 10, 17, 22). Five of these measured the effects of wearable devices on behavior change (2, 9, 10, 17, 22), whereas one study (3), examined the influence of different apps on participants’ behavior change. Three of these studies reported no statistically significant intervention effect on participants’ PA (3, 9, 22) but two reported a statistically significant change in participants’ PA (2, 10). Study 17 drew on two case studies to demonstrate young people’s behavior change in relation to their attitudes.

Despite reporting differences or conflicting outcomes, all these intervention studies identified positive changes in terms of young people’s PA, fitness, or sedentary behavior. For example, study 2 reported a significant change in participants’ sedentary behavior between intervention and control groups. While study 22 reported no statistically significant difference in young people’s MVPA and sedentary time, it estimated the intervention group spent more time in MVPA and less time in sedentary activities each day. Meanwhile in study 3 fitness improved in both the immersive and non-immersive apps groups (intervention group), though there was no intervention effect on the outcomes of fitness and PA. In addition, study 17 reported that users with positive attitudes toward the Fitbit wearable device experienced a significant increase in MVPA, while users with negative attitudes experienced a decrease in MVPA.

Other types of studies also reported behavior change effects from HLT use. These included an online survey (5), nine mixed methods studies (1, 4, 7, 11, 13, 15, 19, 20, 24), eight qualitative studies (8, 12, 14, 16, 18, 21, 23, 26), and a quantitative descriptive study (25). Studies 5, 15, 24, and 25 gathered survey data/information regarding young people’s self-reported health-related behavior change and health knowledge. These studies found positive changes in health-related behavior among most participants during HLT use. For example, study 15 reported positive behavior changes in 43% of the young people following the health-related material (relating to PA, diet/nutrition and body image) they had seen on social media. More specifically, study 25 reported increased E-health literacy levels among high school students following the health sources they had accessed on the Internet, with 42.3% of the participants recognizing the Internet to be beneficial in helping them to make decisions about health behavior and knowledge. Studies 12, 13 and 14, on the other hand, focused on young people’s experiences and perceptions of Fitbit use to establish whether wearable devices could significantly impact behavior change as well as health knowledge. Study 12 reported both facilitators and barriers to young people’s day-to-day activities based on their user experiences. Study 13 identified three features of Fitbit and app use which influenced the promotion of young people’s health in relation to exercise and diet/nutrition: ease of use, personalized design, and social support. Finally, whilst study 14 reported that the participants predominantly used their Fitbit for monitoring their daily activities, the study also found that the device had a negative influence on young people due to peer comparison.

Psychological responses

In terms of attitude, young people were either positive, negative, or critical toward HLT. Twelve studies (1, 4, 5–7, 12, 14, 16, 19, 20, 21, 24) reported that participants had positive views of HLT as a health-facilitating tool. Their positive attitudes were linked to: (a) external factors based on the features of HLT (12, 19, 21), (b) internal factors focused on young people’s health needs (1, 5, 16, 19, 20, 24), and (c) user experiences (4, 6, 7, 14). For example, study 21 found that young people were attracted to social media to access health information and activity due to its colorful design. In addition, young users drew on online PA content to meet their personal health needs as well as to obtain personalized feedback (19) and fitness information (16). Study 6 reported that the participants found the experience of using a health-related app “relaxed, focused and peaceful” (“Results,” para. 4), specifically in relation to physical movement, mindful eating, and sensory practices.

In contrast, six studies (5, 7, 9, 14, 15, 26) reported young people to have a negative attitude to digital technologies and social media use with respect to their health needs, with some resisting using wearable devices and health apps. The main reasons young people gave for resisting using HLT related to their unsatisfactory features and design, with issues such as their inaccuracy in measuring daily activity (14), their irrelevance to their individual health needs (7), and their potential to lead to feelings such as boredom (14) and body shame (26) being highlighted.

In addition, and with respect to the users’ views, some studies (5, 8, 13–16, 18, 23) reported that young people were critical users of HLT and the health content they found, read and used. Specifically, most young people viewed health apps as an adult resource for promoting health rather than a personalized tool for their age group (13). Noteworthy is that young users in these eight studies had active strategies to evaluate the quality of health information on social media, for example, using advertisements as a means by which to evaluate the quality of health information (16), or identifying “negative, unrealistic, or dangerous” (p. 521) content on YouTube by avoiding videos with “obnoxious titles” (16). Equally some young people were critical of HLT because they saw them as commercial businesses (8) and were troubled by their commercial influence (23).

Most studies considered the cognitive aspects of behavior change which could ultimately influence young people’s long-term PA participation and health learning. In some studies (3, 7–9, 12, 14, 24, 26), intrinsic motivation was seen to be key in inspiring individuals to change their behaviors, although study 3 reported no change in young users’ intrinsic motivation through a health app intervention. In other studies, young people were externally motivated by rewards and the “gentle” pressure generated from using HLT (7–9, 12). For example, the intervention in study 7 was designed to encourage young people’s PA participation both intrinsically, by meeting young people’s psychological needs (e.g., competence and autonomy), and extrinsically, by means of competition and target setting (e.g., PA target of 10,000 steps per day). In addition, some studies measured various psychological variables associated with young people’s behavior change (3, 9, 17). Study 9 reported cognitive aspects of behavior change to be positively associated with young people’s PA. These positive behavior changes ranged from raising young people’s consciousness about PA to helping them to maintain their PA. However, another psychological variable, goal commitment, was not found to be associated with the participants’ levels of MVPA in this study.

Other psychological factors considered in the studies reviewed included mood states and/or self-esteem (17, 22, 24), with these studies reporting a positive association between these factors and HLT use. For example, case one in study 17 found increased self-reports of health, self-esteem and body image among wearable device users with a positive attitude, yet increased self-reports of anxiety and depression amongst negative attitude users. Case two reported that positive attitude users positively influenced their friends’ levels of PA, whereas negative attitude users perceived a decrease in both their own and friends’ levels of PA.

Facilitators and barriers to HLT use

Five overarching facilitators of HLT were found to be important or influential in meeting young people’s PA and health needs: acceptability, information, interaction, entertainment, and education. Across the review, 12 studies involved smartphone use with apps; 11 studies involved the use of wearable devices such as Fitbit charge, accelerometer, Apple watch, and other wrist-worn monitors; 11 studies used social media including Facebook, Instagram, health video and YouTube; and four studies involved the use of websites on the Internet. Several studies incorporated more than one type of HLT, whilst some (11 of 26) were based on only one type (7, 9, 11, 12, 15–19, 23, 26).

The acceptability of HLT by young people in this systematic review referred to how accessible the young people considered the HLT to be both in terms of the technology and health content. Several studies (1, 3, 6–8, 10–14, 21, 22) highlighted the importance of the acceptability of the device in encouraging young people to use the HLT at all, and in motivating young people to engage in PA and with health information and/or learning (7, 11–13, 15, 18, 25, 26).

Health information found through the Internet or social media (15, 18, 20) and which could be tailored to meet their health needs (1, 9–11, 13–16, 19–21) were primary reasons why many young people used HLT. In particular, there was a strong preference for personalized/tailored information through the intervention designs, that took into account young people’s differences such as gender (21), age (15, 21), stage of behavior change (9), and literacy needs (1). Indeed, when young people were asked about their expectations of HLT use, three studies (13, 14, 19) reported personalization to be a common response and expectation.

Many studies reported the interactive functions of HLT to be important in motivating young people to change their behavior, which mainly focused on social support (4, 8, 13, 15, 17–21, 26) and peer-comparison (4, 7, 8, 11, 12, 14, 18). These two dimensions of interaction were sometimes connected. For example, social media as a health content sharing platform was also seen to provide social support through online communities and promote competition and comparison with peers.

The entertainment afforded through HLT was another key element which attracted young people’s attention and interest in PA. Four sub-themes were reported across multiple studies, including fun videos on social media (14), game design which made the mobile health apps more interesting for individuals (10, 11), rewards for goal achievement (4, 6, 11, 12, 21) and PA improvement (11). Indeed, both gamification and reward design were the main HLT features found to increase young people’s motivation and engagement in competition and changing their behavior.

Education in the form of feedback was associated with goal setting in HLT and was found to promote young people’s PA and health behavior. Six studies reported goal setting to be an important feature of wearable devices and health apps to track young people’s PA progress. Feedback (3, 10, 12, 18, 19, 21), provided young people with knowledge of their fitness and health, workout instructions, and improved their levels of PA and health. For example, study 10 reported how on-going target setting and feedback on achieved goals motivated young people to sustain their HLT use and PA engagement. In addition, these studies reported that health feedback related to the goal achievement process helped young people to improve their levels of physical fitness and health through self-monitoring, self-motivation, or self-surveillance.

Across 20 studies, various barriers were identified to HLT use by the participants, which included both external and internal factors. External factors included functional issues, the credibility or reliability of the health information available, and the lack of appropriate guidance and support on HLT use. Internal factors included issues or concerns such as lack of time, purchasing issues, negative feelings, and lack of digital health literacy. Among the responses from the young users, the most common barrier to HLT use was function (1, 3, 8, 10, 12–14, 19, 21, 22). For example, studies 10, 12 and 22 highlighted the discomfort of wearing HLT devices, while study 3 reported the limitation of the one-size-fits all design of wearable devices and studies 13 and 14 reported a lack of personalization in the health apps used. Meanwhile others found the lack of feedback (12) and lack of social interaction with friends (8) to be limitations of HLT in terms of encouraging long-term health behavior engagement. With regards the credibility or reliability of the health information, the probability of young people receiving false, misleading, and/or low-quality health information was seen to influence their HLT use across many studies (1, 7, 14,16, 20, 23–25). Sixteen studies (1, 4–8, 12–18, 20, 23, 24, 26) reported that young people lacked appropriate guidance and support on HLT use from governments and health-related organizations, which it was highlighted could lead to cyber harm and to ridicule, insult, and bullying (26).

Lack of time was the most reported internal barrier to HLT use across five studies (3, 12, 13, 19, 21), with homework, feeling lazy or tired, and poor time management skills cited as reasons for this. Three studies (3, 12, 19) mentioned that young people did not typically have enough money to purchase digital products or purchase health apps on the app store. Moreover, young people in some studies had negative feelings toward the security of these commercial devices (7, 13, 14) and concerns about their personal health information (3, 12, 19). There was also evidence that poor health literacy negatively influenced young people’s use of HLT to search for health information and manage their PA participation. Nine studies (6, 8, 13, 14, 18–20, 23, 25) reported that young people had limited knowledge to be able to judge and interpret health information. For example, study 8 highlighted how young girls had a restricted understanding of health considering it to be purely a responsibility of individuals while study 14 reported that young people equated the idea of being healthy with not being fat. Equally, a lack of or limited E-literacy/digital literacy was considered to be an important barrier to young users’ continued use of technologies for health (1, 5, 6, 12, 14, 23–26).

Discussion

This systematic review aimed to identify the different influences of HLT on young people’s PA participation and health learning. Three themes were identified as key elements to explore young people’s HLT user experiences, including behavior change, psychological responses, and facilitators and barriers to HLT use.

The majority of the included studies provided evidence that HLT can have a short-term positive influence on young people’s behavior change and that this change is highly associated with the novelty effects of HLT use. This finding concurs with previous research which found that young people’s interests in using such devices to be sustained only for short periods because of the short-term novelty effect (Creaser et al., 2021; Shin et al., 2019). Nonetheless, the short-term appeal of HLT may still be a useful and important first step in raising or reinforcing young people’s awareness or understanding of the importance of physical activity and a healthy lifestyle, which could play a part in influencing their PA, fitness, or sedentary behavior in the future.

Several studies in this review highlighted the importance of digital health literacy in influencing young people’s behavior change. However, noteworthy is that these studies offered different and sometimes vague definitions of digital health literacy. Given the different critical claims that have been made regarding the impact of young people’s digital health literacy on their health and digital health and wellbeing (Auld et al., 2020; Bröder et al., 2017; Vamos et al., 2020; Vissenberg et al., 2022), it would seem important to develop a common understanding of this term. Dunn and Hazzard (2019) defined that digital health literacy is “an extension of health literacy and uses the same operational definition, but in the context of technology” (p. 294). Similarly, Smith and Magnani (2019) defined digital health literacy as “the ability to appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health-related problem” (p. 280). For the purpose of this review, digital health literacy was taken to refer to young people’s ability or competency in understanding and using health-related information from a wide range of digital sources.

This review further confirmed health knowledge and understanding to be at the heart of young people’s digital health literacy as well as their engagement in PA for a healthy active lifestyle (Cale & Harris, 2018; Hooper et al., 2021). Yet, the findings of the review suggest that young people have limited competence or knowledge to identify and articulate the essential qualities of health information in the digital context. Moreover, poor health literacy is arguably a significant barrier to young people’s health behaviors and to the long-term positive effect of their HLT use.

These two elements (the short-term positive influence of HLT on young people’s behavior change and the importance of digital health literacy), therefore, provide a convincing rationale for the need for future research which focuses on “emerging digital health” and how HLT can be used as a pedagogical tool to support the promotion of healthy active lifestyles amongst young people (Casey et al., 2017; Goodyear & Armour, 2021). Given these findings, supporting young people to use HLT critically and appropriately should arguably be a priority in schools and in PE. This would allow young people to better consider their individual digital and health learning needs and further improve their digital health literacy.

A further finding of this review was that young people’s motivation to use HLT was based on their basic psychological needs (i.e., autonomy, competence and relatedness), as well as their PA participation and health learning needs. Previous research supports the notion that motivation is a key psychological mechanism which supports and drives young people’s behavior change and healthy active lifestyle choices (Biddle & Wang, 2003; Parschau et al., 2013; Wang & Biddle, 2001). The young people were intrinsically motivated to engage in PA by the positive experiences and fun they had in using the HLT. Further, the young people’s motivation to use HLT was strongly associated with the functions and features of HLT. Features such as the colorful design of wearable devices or health apps were important in creating a novelty effect which, in turn, supported young people’s basic psychological needs. In addition, many external factors such as rewards, feedback, or goals, served as extrinsic motivation to maintain or increase the young people’s PA engagement, at least in the short-term. This finding generally supports the literature which has identified health information seeking or health learning to be key attributes in supporting the health needs of young people (Gray et al., 2005; Goodyear & Armour, 2019; Lupton, 2020). Further, and as these authors highlight, health information and learning are clearly needs which the Internet and/or social media can help to meet given the health information sources are readily and conveniently available to use anytime and anywhere.

The HLT functions of interaction, entertainment and feedback were also found to be extrinsically motivating and associated with young people’s HLT user experience and their attitudes and beliefs toward PA. In keeping with previous findings which have highlighted the importance of social support and the environment to young people’s motivation in traditional interventions (i.e., without HLT) (Atkin et al., 2015; Harris et al., 2020), this review found these variables to be similarly important when using HLT to influence young people’s PA participation. It was evident, across various quantitative and qualitative studies, that having a generally supportive environment and, a peer network and peer support for their PA, was important to young people’s HLT use and their PA achievement. On the other hand, a competitive environment for some young people may create isolation or negative feelings (e.g., pressure, guilt) from peers and therefore undermine relatedness and PA engagement. This suggests that perceived social relatedness as part of young people’s basic psychological needs can be linked, both indirectly and directly, to young people’s intrinsic motivation and its influence on PA and sustained behavior change (Beachboard et al., 2011; Ryan & Deci, 2017).

This review evidenced that young people could be critical HLT users. For example, they were aware how HLT operate as commercial businesses and expressed concerns regarding their commercial influence and their own privacy and the use of their personal data. As noted earlier, these concerns have been acknowledged in the literature (Gard, 2014; Lupton, 2015). Some young people were also critical of some of the health content they sourced and used via HLT and in response had identified some seemingly useful strategies to evaluate the quality of health information on social media such as relying on advertisements or avoiding material with “obnoxious titles.” Whilst this is encouraging, how widespread this is, the robustness of such strategies, as well as the extent and accuracy with which the young people are able to do this is not known. Indeed, this review reported that many young people still lack appropriate health-related knowledge suggesting they are not in a position to do this which could be potentially harmful. It has been argued that while digital literacy is often associated with “the ability to discern the quality of information found online” (Connolly & McGuinness, 2018, p. 77), young people are struggling with issues of risk and identity when using digital technologies (Connolly & McGuinness, 2018). Moreover, Harris et al. (2018, p. 418), explored young people’s health-related knowledge and suggested that “young people generally have simplistic knowledge and somewhat confused understanding of health, fitness and physical activity.” Hooper et al. (2021, p. 99) further asserted that young people’s health-related knowledge or conceptions of health are “rather limited, somewhat superficial and, at times, erroneous.”

From this perspective, the findings of this review further emphasize the need for relevant adults to provide support for young people in this space. Specifically, the time between 13 and 18 years of age is a critical developmental period for young people, cognitively, physically, and socially and is the time when health habits develop and transition into adulthood (Kelly et al., 2011). This coincides with a key period of learning for young people with schools providing a safe and supportive learning environment in which to develop tailored and teacher-supported health-related knowledge (Cale, 2021; Casey et al., 2017). Although some empirical studies evidence that schools and different curriculum subjects (such as PE) already support young people’s HLT use, for example, by using social media as a health-related learning tool to bridge informal and formal learning contexts (Goodyear & Armour, 2021; Lupton, 2022), the role PE plays in developing young people’s digital health literacy and the extent to which PE supports young people in becoming critical HLT users is still unclear.

Limitations

Whilst studies covering all study designs (quantitative, qualitative and mixed methods studies) and many types of HLT (e.g., social media, wearable devices, and health apps) have been included in this systematic review, some limitations of the review should be noted. Although the search used five databases and identified 5659 studies, 361 were randomly removed from the download process because of database download restrictions. Therefore, some studies which met the inclusion criteria may have been omitted. In addition, only 16 studies met 100% of the quality assessment criteria, with limited details regarding the research design and methodology employed being evident in some cases. Those studies lacking such details makes their replication difficult, meaning they provide limited direction or guidance for the future development of studies, interventions, and practice. More high quality studies with more robust research designs and methodologies are therefore needed to further confirm whether HLT can have a long-term influence on young people’s PA and health.

Conclusion

This systematic review has explored the influence of HLT use on young people’s PA participation and health learning. The results indicate that HLT can have positive effects on both. However, the findings evidenced only the short-term effects of HLT on young people’s PA and health learning with the long-term influence remaining inconclusive. Several psychological factors that supported young people’s HLT use, and which were strongly associated with the features/functions of HLT, and young people’s health behavior change were identified. These included, for example, motivation, goal commitment, and cognitive aspects of behavior change. Equally, the review highlighted several reported barriers to young people’s HLT use such as functionality issues, concerns about privacy and the credibility or reliability of the health information, and young people’s lack of time, digital literacy, and health-related knowledge.

Appropriate support from relevant adults, schools, and global bodies (e.g., WHO) is clearly needed in the drive to support young people’s use of HLT. In particular, the role of education is apparent. It is imperative that relevant adults, schools, and global bodies do not make assumptions about young people’s digital health literacy based solely on their use of these technologies. Many young people are digitally savvy and have developed their own strategies for ascertaining the quality and worth of information sourced through HLT, but many others lack the criticality needed to discern the good from the potentially harmful. Schools and teachers should support the development of young people’s digital health literacy and look to capitalize on the social relatedness and the positive effects of HLT.

Based on these findings, it is evident that further research is required to better understand HLT use and how the effects of HLT use can be sustained in order to maximize the benefits of its use on young people’s PA and health. Given the central role that schools and PE teachers could play in particular, it is important that future research explores their digital health literacy and understanding of HLT, and the ways in which they use it for their own PA participation. Only by doing this can the field of PE hope to support PE teachers to effectively support the young people in their care.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00336297.2023.2218038

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the China Scholarship Council [202108250016].