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Abstract

Regular physical exercise lowers the risk of all-cause mortality and various chronic diseases. New technologies, such as smartphones and social media, have been used successfully as health promotion tools in college populations. The purpose of this study was to conduct a systematic review of studies examining the effectiveness of interventions that used modern technologies, as with social media or text messaging, to promote physical activity or reducing sedentary behavior in college students. The systematic review was conducted on the PubMed and SCOPUS databases, considering studies published from 2012 to 2022. For a total of 19 articles selected, an evidence table was drawn up, and the quality of the studies was assessed using the PRISMA checklist. The interventions differed enormously in design, from the strategies implemented to the types of outcome considered. Fifteen of the 19 studies demonstrated an improvement in participants’ physical activity levels, 3 studies found no such improvement, and 1 reported a worsening of baseline activity levels. Interventions to improve college students’ physical activity levels through the use of social media and/or text messaging tend to be effective. However, many factors can influence the effectiveness of such interventions. For example, a gender-related difference emerged in student participation, and the interventions proved more effective if they were accompanied by the creation of social groups.

Keywords

Physical activity university students health promotion text messaging social media systematic review

What do we already know about this topic?

A sedentary lifestyle was found to be common among highly educated young people such as university students, in a critical period of life, building their own lifestyle choices

How does your research contribute to the field?

The present study collects evidences about the efficacy of intervention studies regarding physical activity promotion in college students through the use of new technologies.

What are your research’s implications toward theory, practice, or policy?

The use of mHealth and similar technologies is a successful type of intervention in increasing physical activity among university students.

Introduction

Physical inactivity is an increasing global health problem causing more than 10 000 deaths in Europe¹ and approximately 5 million deaths around the world every year.²

Low levels of physical activity are common in the general population, including young people.³ A sedentary lifestyle was found to be common among highly educated young people such as university students.⁴ Indeed, a study on Italian university students⁵ reported that 25.8% never do any type of physical activity and 4.9% do sport just once per month. These results agree with a cross-sectional survey involving university students from 23 countries, which demonstrated that the overall prevalence of physical inactivity is substantially higher in women (45.8%) than in men (33%),⁶ indicating that a significant part of university students do not reach the minimum levels of physical activity recommended by the WHO. This subset of the population is in a critical period of life, taking the first steps toward independence and building their own lifestyle choices as adults. Moving to a new city and a new house, changing habits, and meeting new people can all affect their ability to maintain a healthy lifestyle during this transition period. Consequently the specific life of college and university can induce an increase in sedentary behaviors and a decrease in active behaviors.⁷

The habits adopted during high school years also influence adult lifestyles. In fact, weight gain or sedentary behavior in young adulthood have been linked to problems during university life, such as a reluctance to increase physical activity.⁸

Using mass communication technologies for health-related applications (i.e., mobile Health (mHealth)) is an area of fervent development. An American study⁹ reported that 94% of young adults possess a smartphone. The near ubiquity of this device shows how it could be used in planning health promotion interventions that easily reach almost all of the population with minimal problems and at a relatively low cost. In terms of physical activity, mHealth behavioral interventions were implemented through text messages or social media groups.¹⁰

The aim of this systematic review is gathering evidences about the effectiveness of new technologies, as with social media or text messaging, in promoting physical activity or reducing sedentary behavior among college students.

Methods

Search Strategy

A comprehensive and systematic literature search was conducted in the PubMed and Scopus databases to identify randomized-experimental and quasi-experimental studies investigating the efficacy of interventions using text messaging or social media for the promotion of physical activity in university students. Using Boolean operators, the search process involved a search string obtained by combining the terms:

“social media” OR “text messag* OR “cell phone messag*” OR “mobile phone messag*” OR “SMS messag*” OR “MMS messag*” OR “physical activ*” OR “gym” OR “physical exerc*” OR “sedentar* OR “physical inactiv*” OR “fitness” OR “walking” OR “exercise” OR “universit* OR “colleg*” OR “facult*.”

The search strings are reported in Appendix 1.

The records retrieved from the databases were imported into Endnote and duplicates were removed. Search hits were checked by reading the article titles and abstracts. If the results of a study were published more than once, just the most complete article was considered in the analysis. The authors also checked the reference lists of the papers included in the review for any articles that were not already considered.

In conducting this systematic review, the authors conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement.

The following data was extracted from each study: the number of the articles; author, year, and journal of publication; the efficacy of the intervention; the study aim; the study design; the methods of recruitment; the sample characteristics; the intervention applied; the measures of the outcomes; the results; and the conclusions.

Eligibility Criteria

The studies included in the review had to meet the following inclusion criteria:

The population studied was university students.

Multi-component interventions were used with only partial physical activity.

A clearly defined measure of efficacy in terms of physical activity level variation (both subjective by means of questionnaires or objective by means of pedometers, accelerometers, or other equivalent methods).

Published in English.

Published between January 2012 and July 2022.

The studies excluded for the purposes of this review had:

No measure of physical activity.

No intervention to promote physical activity among university students.

Data Synthesis

Authors in each study choose the measures to evaluate physical activity, determine the timing to assess intervention efficacy, and select digital platforms for delivering effective physical activity promotion messages to participants. As a result of this heterogeneity and lack of standardization, it was not feasible to employ a quantitative approach to summarize data and accurately represent the diverse intervention strategies. Therefore, the extracted data were not utilized for quantitative synthesis via meta-analysis. Instead, a qualitative summarizing approach was adopted to assess the effectiveness of each intervention independently and in relation to others included in the studies.

Methodological Assessment

Two authors judged the methodological quality of the studies. RCT studies were evaluated by the CONSORT checklist. There are 25 items on this checklist that focus on reporting how randomized trials was designed, analyzed, and interpreted.¹¹ Alternatively, the quasi-experimental studies or non-experimental studies were evaluated with the TREND Statement Checklist (22 items).¹² The scoring system conforms to a “yes,” “no,” or “not applicable” design.

The studies included in the review were categorized into 3 groups to determine the quality score: 0.8 to 1 (high quality), 0.5 to 0.8 (intermediate level), and <0.5 (low level). The quality score was calculated by dividing the number of positive items by the total study score, where the total study score is the sum of the checklist items that were applicable for each study.

Appendix 2 outlines the methodological quality of the studies.

Figure 1 shows the flow chart of the article selection process.

Figure 1.

Flowchart on the selection of studies.

Results

Included Studies

The study selection process led to the identification of a total of 19 studies (Table 1) that met the inclusion criteria. One article¹³ was discarded after the selection phase because it lacked measures of post-intervention physical activity due to an attrition rate of 91%, making it impossible to conduct a post-intervention evaluation.

Table 1.

Overview of Studies Reviewed.

No.	AuthorYearJournal	Efficacy of intervention	Study aim	Study design	Recruitment	Sample characteristics
1.	O. Castro¹² 2021 Frontiers in Psychology	Improvement	To examine the feasibility and preliminary, short term effects of a theory-based intervention aimed at reducing total and prolonged sedentary behavior.	Quasi-experimental 1 group pre-post pilot study	Participants were invited to participate via an announcement on the University-wide online portal.	University of Southern Queensland, Australia February 2020. 9 ambulatory undergraduate students aged 18 years or older (mean age = 22 ± 2.32). Inclusion criteria: aged 18 years or over, ambulatory, study on-campus and full-time at the USQ’s Faculty of Business, Education, Law and Arts
2.	E. Cotten¹³ 2016 JMIR Mhealth Uhealth	No improvement	To determine the effectiveness of a text message-based intervention in increasing non-sedentary behavior.	RCT	Mass emails to various faculties at Western University. The study was also advertised through an article in the university newspaper.	University of Western Ontario, Canada. January 2015-March 2015. 82 students. Inclusion criteria: age between 18 and 65, be able to write in English, own a mobile phone with free unlimited incoming text messages, be a student at Western University.
3.	K. Dillon¹⁴ 2022 Psychology and Health	Improvement	To evaluate the effectiveness of a Health Action Process Approach (HAPA) based planning intervention with text messages to reduce student-related sitting time and increase specific non-sedentary behaviors.	Two-arm, repeated measure, single-blinded, RCT	Participants were primarily recruited through various faculty-specific student social media groups/pages. Participants were also recruited by contacting professors and course instructors at specific universities via email, requesting they post and advertise the study recruitment poster on their course’s online portal.	Canada. From January to April 2019 and from January to April 2020. 61 students from universities across Canada (mean age = 21.13 ± 4.81 years, 80.3% female). Inclusion criteria: aged 18 or older, be a full-time university student, be fluent in English, have access to a computer with Internet, own a mobile phone with free unlimited incoming text messages. Exclusion criteria: self-declared medical conditions or any physical limitation that prevents physical activity.
4.	C. A. Figueroa¹⁵ 2021 Annals of Behavioral Medicine	Improvement	To assess the effects of sending daily motivational and feedback text messages on changes in step count.	Microrandomized clinical trial	Participants were recruited through the Social and Experimental Research Lab (Xlab) app from the University of California, Berkeley, advertised during campus events and on Facebook.	University of California, Berkeley, USA. September-October 2019. 93 students from the university. Exclusion criteria: not having a smartphone, unable to exercise due to disability, planned to leave the country during the study.
No.	AuthorYearJournal	Intervention	Outcome measures	Results	Conclusion
1.	O. Castro 2021 Frontiers in Psychology	A 45 to 60 min one-on-one session, including an educational component around the health effects of sedentary behavior and 3 distinct activities: review of accelerometer-assessed sedentary behavior from the 6-day baseline assessment and feedback, “pros and cons” exercise, and suggested behavior change strategies. In addition, automated daily text messages targeting sedentary behavior were sent for 6 days (4 messages per day at fixed intervals)	Outcomes were assessed over 6 days (4 weekdays and 2 weekend days) in pre- and post-intervention periods and included accelerometer-based (activPAL) and self-reported (questionnaire) total sedentary time, as well as accelerometer-based number of steps and prolonged sedentary time (time spent in sedentary bout durations of ≥30 min)	From baseline to post intervention there was a significant reduction in accelerometer-based total and prolonged sedentary time during weekend days. In addition, there was a significant increase in accelerometer-based standing time and stepping during weekend days. There was a statistically significant reduction in total self-reported sedentary time across the whole 6-day period and during weekend days.	Findings from this small, short-term intervention suggest that a single one-on-one session, together with automated text messages, may help University students reduce sedentary behavior and enhance movement during weekend days.
2.	E. Cotten 2016 JMIR Mhealth Uhealth	Participants in the intervention group received daily text messages scheduled by the researcher encouraging breaks from sitting, standing, light- and moderate-intensity physical activity. The control group received daily text messages about random health or nutrition facts.	Primary outcomes were measured using questionnaires: frequency of breaks from sitting, length of breaks from sitting, time spent standing and light-intensity physical activity, moderate intensity physical activity. A questionnaire was also used to assess secondary outcome measures of self-efficacy. Outcomes were measured at 4 time points: baseline, 2, 4, and 6 weeks.	Small to moderate effects that did not reach significance were found that consistently favored the text intervention group for all primary outcome behaviors. Self-efficacy beliefs favored the text intervention group and reached significance for sitting less. Significant relations were found between the self-efficacy constructs and breaks, standing, and light or moderate physical activity.	Text messages have the potential to increase non-sedentary behavior in university students. These messages can increase self-efficacy beliefs to take more breaks and reduce sitting time. Efficacious beliefs can predict actual sedentary behavior and to a lesser extent light- and moderate-intensity PA.
3.	K. Dillon 2022 Psychology and Health	Participants randomized into the Health Action Process Approach (HAPA) intervention group received a single, one-on-one behavioral counseling session, an informational booklet, a planning sheet, and text messages once a day for 6 weeks, at a specified time identified by each individual based on their preferences and schedule. Participants randomly assigned to the control group received no information or intervention of any kind.	Study-related sitting time and time spent in specific non-sedentary behaviors were assessed at baseline, weeks 2, 4, 6 (post-intervention), and 8 (follow-up). Time spent sitting (primary outcome), standing, walking, and stretching during study-related activities were measured using a validated 3-item modified occupational Sitting and Physical Activity Questionnaire. Frequency and duration of breaks from sitting (secondary outcomes) were measured with a modified, more domain specific, version of the SIT-Q 7d questionnaire.	Significant group by time interaction effects favoring the intervention group were found for sitting time, walking time, and stretching time.	Combining a HAPA-based planning intervention with text messages can reduce student-related sitting time in university students while increasing specific non-sedentary behaviors during study-related activities.
4.	C. A. Figueroa 2021 Annals of Behavioral Medicine	Participants used a physical activity app, “DIAMANTE” for a period of 6 weeks. They were microrandomized to receive different types of motivational messages, based on a cognitive-behavioral framework, and given feedback on their steps.	At baseline and follow-up, students completed a survey on demographics, socioeconomic status, health status, PA, and psychological questionnaires. Every day during the intervention their phone pedometer passively tracked participants’ steps.	Sending any vs no text message initially resulted in an increase in daily steps, but this effect decreased over time. A multivariate analysis evaluating each text message category separately showed that the initial positive effect was driven by the motivational messages though the effect was small and trend-wise significant, but not the feedback messages.	Sending motivational physical activity text messages based on a cognitive-behavioral framework may have a positive effect on increasing steps, but this decreases with time.
No.	AuthorYearJournal	Efficacy of intervention	Study aim	Study design	Recruitment	Sample characteristics
5.	K. Hardan-Khalil¹⁶ 2021 Journal of American College Health	No improvement	To examine the effect of receiving daily motivational text messages on the change in college students’ nutritional choices and levels of physical activity.	Nonequivalent control group	Participants were recruited through flyers posted in the School of Nursing bulletin boards, student union, and the on-campus student wellness Center. An announcement was posted on the School of Nursing website, and a mass email was sent to all university students.	California State University, Long Beach, USA. 2019. 201 students. Inclusion criteria: be enrolled in one of the university’s academic programs and to be age 18 years old or older.
6.	R. Keahey¹⁷ 2021 Health Education Journal	Decrease	To evaluate the feasibility and acceptability of a theory-grounded, text message-based intervention targeting sedentary behavior among university students.	Single-group repeated measures design.	Recruitment consisted of in-person or poster-based information sessions presented at the start of lectures across a variety of classes and fields of study during 2 weeks in autumn 2018.	British Columbia, Canada. 2018. 99 full-time students aged 17-25 at a post-secondary institution in British Columbia, Canada.
7.	P.L. Lua¹⁸ 2013 Mal J Nutr	Improvement	To evaluate the effectiveness of implementing nutrition education intervention (NEI) that targeted at incremental reduction of body weight and increased physical activity level among university students.	Cluster randomized controlled study	Participants were randomly selected from 16 classes in all 4 universities.	Terengganu, Malaysia. September 2011 - February 2012. 417 university students from 4 public universities in Terengganu (UniSZA, UMT, UiTM Dungun, UiTM Kuala Terengganu), mean age 19.14 years (SD = 1.2). Inclusion criteria: Malaysian university students aged between 18 and 24 years, actively using a mobile telephone, first or second year diploma or degree from management studies, generally healthy and able to read, write, speak, and understand Malay or English. Exclusion criteria: age below 18 years or above 24 years, did not have mobile phones, were in the final year and in other studies, have been diagnosed with any diseases, and were unable to read, write, speak, or understand Malay or English.
8.	J. Sandrick¹⁹ 2017 JMIR Mhealth Uhealth	Improvement	To test the effects of coaching and text messages on self-selected health on diet, exercise, stress, and sleep.	RCT	Participants were recruited using various strategies such as classroom announcements, advertisements, flyers, and booths located in student common areas on the university campus.	Pennsylvania, USA. January-May 2015. 60 full time students (aged 18-30 years) of a small university in western Pennsylvania. Exclusion criteria: age <18 years or >30 years, pregnancy, eating disorders, chronic medical diagnoses, prescription medications other than birth control.
No.	AuthorYearJournal	Intervention	Outcome measures	Results	Conclusion
5.	K. Hardan-Khalil 2021 Journal of American College Health	All participants received a brochure that contained nutritional and physical activity information. The experimental group participants received a daily motivational text message.	Participants filled in a survey (demographic information, health and diet, and physical activity) at the beginning and the end of the study. The physical activity portion was assessed using the International Physical Activity Questionnaire (IPAQ).	There were significant interactions between group and time on most health and diet items, but there were no significant interactions on the physical activity items.	This study suggests that motivational texts are an effective method to increase awareness about nutrition and physical activity.
6.	R. Keahey 2021 Health Education Journal	The 6-week intervention comprised 4 weekly text messages delivered to participants’ mobile devices.	Data concerning students’ sedentary behavior and physical activity were collected via online survey completed at 3 time points over the course of 1 university semester: baseline (T1), post-intervention (T2), and 2-week follow-up (T3).	Hours per week of sedentary behavior did not change across time points, whereas minutes per week of physical activity decreased significantly from baseline to follow-up.	Findings suggest minor modifications to the intervention may improve participants’ engagement, but the study demonstrated overall that a theory-grounded, text message-based intervention to reduce sedentary behavior can be feasibly implemented.
7.	P.L. Lua 2013 Mal J Nutr	The Intervention Group (IG) received 10 weeks intervention focused on nutrition education intervention (NEI) promotion using 3 modes: conventional lectures, 3 brochures as take-home messages, and 1 text message every 5 days for intervention reinforcement. The Control Group (CG) did not receive any intervention.	Body weight, height, waist and hip circumference, and physical activity level (IPAQ short form) were assessed before and after intervention.	No significant changes in body weight or BMI were observed among both groups. Nevertheless, physical activity level improved significantly among IG participants compared to CG with increased metabolic equivalent (MET) min/week spent for walking, moderate and vigorous activities, and significantly decreased sitting time.	The multimodal Nutrition Education Intervention had a positive influence on physical activity outcomes among university students. NEI should be continuously implemented in this particular group.
8.	J. Sandrick 2017 JMIR Mhealth Uhealth	30 students were randomized to receive a single face-to-face meeting with a health coach to review results of behavioral questionnaires and to set a health behavior goal for the 8-week study period. The face-to-face meeting was followed by text messages designed to encourage achievement of the behavioral goal. 30 control subjects underwent the same health and behavioral assessments at intake and program end but did not receive coaching or text messages.	At baseline and following the 8-week intervention period, participants completed validated behavioral surveys assessing dietary habits, exercise activity, levels of perceived stress, sleep practices, daytime sleepiness, and fatigue. Blood testing for fasting glucose and cholesterol was also performed before and after the intervention.	Among intervention participants, 73% showed improvement in health behavior goal attainment, with the whole group showing a mean improvement of 88%. Mean improvement in behavioral domains was not seen in the control group. Intervention participants also increased their exercise significantly compared with controls, regardless of their self-selected goal category. The increased exercise was paralleled by significantly lower fasting glucose levels.	The health coaching session plus tailored text messages improved self-selected health behavior with a modest ripple effect to include unselected health behavior. Face-to-face feedback and goal setting along with timely motivational text messages as described in this report are of value in promoting healthy behavior in college students.
No.	AuthorYearJournal	Efficacy of intervention	Study aim	Study design	Recruitment	Sample characteristics
9.	S. J. Nam²⁰ 2019 Journal of Psychosomatic Obstetrics and Gynecology	Improvement	To examine the effects of social-media-based support on premenstrual syndrome (PMS) and physical activity among female South Korean university students.	Quasi-experimental study with an equivalent-control-group pretest-posttest design	Participants were recruited using flyers on campus.	South Korea. September-December 2016. 64 female menstruating students with PMS, not on hormonal therapy.
10.	Z. C. Pope²¹ 2019 International Journal of Environmental Research and Public Health	Improvement from baseline, but no difference between groups	To evaluate the feasibility of a combined smartwatch and theoretically based, social media-delivered health education intervention vs a comparison on improving health behaviors/outcomes.	Two-arm, randomized pilot study.	Participants were recruited using flyer/email communication and in-person recruitment presentations.	University of Minnesota, USA. 2017-2018. 38 students from a large metropolitan Midwest University Inclusion criteria: 18-35 years old, BMI ≥18.5 kg/m², PA levels below national recommendations over the last month, currently eating less than the recommended daily amount of fruit and vegetables, no self-reported diagnosed physical/mental disability, completed physical activity readiness questionnaire, willing to be randomized.
11.	A. Alshahrani²² 2021 Eastern Mediterranean Health Journal	Improvement	To measure the effect of a WhatsApp-based intervention for promoting physical activity among female college students in Abha, Saudi Arabia.	RCT pretest/post-test randomized controlled open label experimental design	Not specified.	Saudi Arabia. November 2019-January 2020. 110 female students from King Khalid University, faculties of medicine, dentistry, pharmacy, nursing, applied medical science, and information systems. Inclusion criteria: age between 18-28 years, owning a smartphone with internet access and WhatsApp. Exclusion criteria: not being able to exercise (physical disability, morbid obesity), pregnancy.
12.	D. N. Cavallo²³ 2012 American Journal of Preventive Medicine	No difference between groups	To test the efficacy of a physical activity intervention that combined education, physical activity monitoring, and online social networking to increase social support for physical activity compared to an education-only control.	RCT	Recruitment was done through print and electronic communications including e-mail, Facebook, and Twitter	University of North Carolina, USA. 2010-2011. 134 female undergraduate students at a large Southeastern public university. Inclusion criteria: aged <25 years, reported <30 min of daily physical activity, and >30 min of daily use of Facebook. Participants that answered yes to one or more questions on the Physical Activity Readiness Questionnaire were required to submit physician approval. Exclusion criteria: answered yes to 3 or more questions on the SCOFF disordered eating questionnaire.
No.	AuthorYearJournal	Intervention	Outcome measures	Results	Conclusion
9	S. J. Nam 2019 Journal of Psychosomatic Obstetrics and Gynecology	The experimental group received social-media-based support through a Fitbit Flex, a smartphone application, text messaging, and e-mail for 1 menstrual cycle. Social-media-based support was of 3 types: appraisal support, belonging support, and self-esteem support. The control group was provided with information about PMS and physical activity for ethical considerations.	PMS and physical activity level were collected on the first day of menstruation and at the next menstruation. PMS symptoms and levels were measured using the Korean form of the Premenstrual Assessment Form-Past Cycle Version. Physical activity was measured using the Korean version of the International Physical Activity Questionnaire-Short Form.	The total mean score for PMS decreased significantly in the experimental group compared to the control group. The experimental group had a significantly greater increase in physical activity whereas the control group had slightly decreased physical activity. The differences between the 2 groups for physical activity were significant.	Female university students with PMS experienced decreased premenstrual symptoms and increased physical activity with social-media-based support, which could be an efficacious, accessible, and widely available nursing intervention to manage PMS and physical activity.
10	Z. C. Pope 2019 International Journal of Environmental Research and Public Health	Participants were randomized into: (a) experimental: Polar M400 smartwatch use and twice-weekly health education intervention (social cognitive theory-based and self-determination theory-based); or (b) comparison: enrollment in a separate, content-identical intervention. The study lasted 12 weeks.	Primary outcomes pertained to intervention feasibility and were evaluated mainly via surveys. PA was evaluated using accelerometer data at baseline, 6 and 12 weeks; YMCA 3-min step test at baseline and 12 weeks. Other secondary outcomes included, physiological/psychosocial outcomes, and dietary behavior that were measured mainly via questionnaires.	In both groups there were an increase in moderate-to-vigorous PA, a decrease in body weight, an increase in self-efficacy, social support, and intrinsic motivation. There was also a small decrease in daily caloric consumption over time.	Both interventions were feasible/of interest to college students and demonstrated initial effectiveness at improving health behaviors/outcomes. However, smartwatch provision may not result in an additional benefit.
11.	A. Alshahrani 2021 Eastern Mediterranean Health Journal	The intervention group received a 15-min orientation on exercise and the benefits of keeping a healthy life pattern and up to 4 physical activity promotion messages per week via a WhatsApp group for 10 weeks. The control participants’ group was used only for communications between them and the main investigator, and they maintained their usual activity for 10 weeks.	Physical activity was assessed at baseline and at 10-weeks’ follow-up using the WHO Global Physical Activity Questionnaire.	Compared with the control group, mean metabolic equivalents/week of the intervention group improved significantly. The mean difference in total physical activity before and after intervention was significant in all domains and in all categories of activity. The proportion of participants who met the WHO criteria for minimum physical activity per week increased from 69.8% to 90.5% after intervention.	Social-network-based interventions improve physical activity and may be incorporated into youth-targeted health programs.
12.	D. N. Cavallo 2012 American Journal of Preventive Medicine	The intervention group received access to the Internet Support for Healthy Associations Promoting Exercise (INSHAPE) website which provided educational information related to physical activity and a self-monitoring tool and were enrolled in a Facebook group. Participants were encouraged through e-mails, website instructions, and moderator communications to solicit and provide social support related to increasing physical activity in the Facebook group. The control group (education-only) received access to a limited version of the INSHAPE website, which didn’t include the self-monitoring tool.	Perceived social support (primary outcome) for physical activity was assessed at baseline and 10 weeks using a questionnaire. Physical activity (secondary outcome) was assessed at baseline and 12 weeks using a version of the Paffenbarger activity questionnaire adapted for online use.	Participants experienced increases in social support and physical activity over time but there were no differences in perceived social support or physical activity between groups.	Use of an online social networking group plus self-monitoring did not produce greater perceptions of social support or physical activity as compared to education-only controls. Given their promising features and potential reach, efforts to further understand how online social networks can be used in health promotion should be pursued.
No.	AuthorYearJournal	Efficacy of intervention	Study aim	Study design	Recruitment	Sample characteristics
13.	G. Gilbert²⁴ 2021 International Journal of Environmental Research and Public Health	No improvement.	To investigate the effects on motivation and PA behavior of framed messaging on social media in university students.	RCT, 3-arm feasibility study.	Participants were recruited from UK universities by convenience sampling through course announcements, emails, flyers, posters, and social media platforms. A prize draw of 50 GBP was advertised to maximize recruitment.	United Kingdom. February-March 2019. 148 students from UK universities. Inclusion criteria: being aged 18-26, a student at a UK university, and have a Facebook account.
14.	D. Mandic²⁵ 2020 PeerJ	Improvement	To investigate how effective a Web-based approach is among university students when it comes to physical activity in the long-term.	Non-randomized prospective cohort study.	All first year students at the Faculty of Medicine in Belgrade were invited to participate through flyers or noticeboard announcements.	University of Belgrade, Serbia. 2016-2017. 254 first year students at the Faculty of Medicine in Belgrade Average age 20.34 years (SD 0.57).
15.	Z. C. Pope²⁶ 2020 Journal of American College Health	Improvement	To evaluate the feasibility of a 10-week program combining a smartphone application and theory-based, social media-delivered health education intervention to improve college students’ health behaviors and outcomes.	Two-arm parallel randomized pilot trial	Participants were recruited via university-wide mass emails, online postings, and word of mouth in August 2015, with a second recruitment period in January 2016.	University of Minnesota, USA. September 2015-June 2016. 44 students from the University of Minnesota. Inclusion criteria: 18-35 years old, body mass index (BMI) ≥18.5 kg/m², self-reported participation in <150 min of moderate-intensity or 75 min of vigorous-intensity PA weekly on a consistent basis over the previous 2 months, owned an Android or Apple smartphone, possessed no self-reported diagnosed physical or mental disorder, were willing to be randomized, completed the Physical Activity Readiness Questionnaire. Exclusion criteria: planned leave of absence from school during study period, not a full-time undergraduate/graduate student at the University.
16.	A. E. Rote²⁷ 2015 Journal of Physical Activity and Health	Improvement	To examine the efficacy of a Facebook social support group to increase steps/day in young women.	RCT	Recruitment occurred through announcements made in large university classes and via individual e-mails gained through a university list-serve.	USA. 2013. 63 female college freshmen at a large, urban university in the Midwest. Inclusion criteria: currently living in on-campus dormitories, a Facebook user, insufficiently active (defined as obtaining less than 7500 steps/day).
No.	AuthorYearJournal	Intervention	Outcome measures	Results	Conclusion
13.	G. Gilbert 2021 International Journal of Environmental Research and Public Health	Participants were randomized into Gain-Framed (GF), Loss-Framed (LF), or control groups. All participants received 2 messages per day (9 a.m. and 1 p.m.) from their Facebook group. While the participants in the GF and LF groups received motivating messages regarding PA, participants in the control group received messages involving random health facts, unrelated to PA.	Motivation for PA, exercise, active travel, and PA levels were assessed at baseline and after the intervention using online questionnaires. Motivation outcomes were measured using an adapted version of the Behavioral Regulation in Exercise Questionnaire 3 (BREQ-3), while PA levels were assessed using the IPAQ-S.	Both GF and LF messages effectively increased average motivation for PA, exercise, and active travel in comparison to controls, but not PA levels. No meaningful differences between GF or LF messages were observed.	Framed messages regarding mental health outcomes of PA delivered via social media could be effective for increasing PA motivation in university students, but there is no gain- or loss-framed advantage.
14.	D. Mandic 2020 PeerJ	Participants selected a 6-month intervention according to their preference: Intervention through social media (Facebook) (Group 1) or combined with a motivational interview (Group 2). Group 3 consisted of students without any intervention.	At baseline, participants filled a questionnaire and underwent measurement of weight, height, and waist circumference. PA was evaluated through the International Physical Activity Questionnaire-Short Version (IPAQ-SV). One year after completion of the 6-month intervention period, all students were invited to a second comprehensive assessment.	Comparison of physical activity 12 months after the intervention showed a significant difference between both groups with the intervention compared to Group 3 without any intervention. Students in the second group were 3.25 times more likely to increase physical activity.	The study confirmed the presence of low-level physical activity among medicine students and showed that multicomponent interventions have significant potential for positive change. The desirable effects of the web-based intervention are higher if an additional booster is involved, such as a motivational interview.
15.	Z. C. Pope 2020 Journal of American College Health	The experimental group used the MapMyFitness smartphone application to log and track physical activity (PA) and participated in a Social Cognitive Theory-based, Facebook-delivered health education intervention that lasted 10 weeks. The comparison group was only included in a separate, but content-identical, Facebook intervention.	Average daily minutes of sedentary behavior and moderate-to-vigorous PA (MVPA) was measured via Actigraph GT3X at baseline, 5 weeks, and 10 weeks. Participants height, weight, and body fat percentage were also measured at baseline and at 10 weeks. The YMCA 3-min Step Test40 was administered to evaluate cardiovascular fitness at baseline and at 10 weeks.	A modest sedentary behavior reduction in the experimental group was observed. Additionally, both groups demonstrated slight reductions in weight and body fat percentage.	A low-burden and well-integrated social media-based intervention is feasible and of interest to college students, possibly improving select health behaviors and outcomes. PA-oriented smartphone application offered limited additional benefit.
16.	A. E. Rote 2015 Journal of Physical Activity and Health	Participants were randomized to one of two 8-week interventions: a Facebook Social Support (FSS) Group or a Standard Walking Intervention (SWI). The SWI group received weekly step goals and tracked steps/day with a pedometer. The FSS group did the same and were also enrolled in a Facebook group and asked to post information about their steps/day and provide feedback to one another.	Steps/day was measured objectively at baseline and at the end of the 8-week intervention period via sealed spring-loaded pedometers.	Women in both intervention arms significantly increased steps/day pre- to postintervention. However, women in the FSS Group increased steps/day significantly more than women in the SWI Group.	The study demonstrates the potential effectiveness of using Facebook to offer a social support group to increase physical activity in young women.
No.	AuthorYearJournal	Efficacy of intervention	Study aim	Study design	Recruitment	Sample characteristics
17.	J. Todorovic²⁸ 2019 Public Health	Increased likelihood	To assess physical activity levels among first- and fifth-year medical students and social media intervention with the aim to improve physical activity.	Quasi-experimental prospective longitudinal study.	Not specified.	University of Belgrade, Serbia. October 2016. 375 first- and fifth-year students from the Faculty of Medicine at the University of Belgrade.
18.	M. Wang²⁹ 2021 European Journal of Clinical Nutrition	Improvement	To evolve traditional health intervention by using integrated methods based on social media and multiple mobile tools.	Non-randomized control intervention.	Students in the Application of Nutrition and Health Care course at Zhejiang University were eligible to participate. 110 volunteered to join the program. The score of the course was related to their engagement over the 21 days.	Zhejiang University, Hangzhou, China. October 2019. 110 undergraduate students from Zhejiang University.
19.	J. Zhang³⁰ 2015 Preventive Medicine Reports	Improvement	To identify what features of social media—promotional messaging or peer networks—can increase physical activity.	RCT	Participants were recruited through advertisements on the University’s website, the student email list, and the Facebook page of a graduate student organization. In addition, flyers were put up on billboards in campus buildings.	Philadelphia, PA, USA. 2014. 217 graduate students from a large Northeastern university in Philadelphia, PA.

Note. Intervention, results, and conclusions.

Populations

The 19 studies examined include samples for a total of 2579 university students. They were conducted in various parts of the world, including Canada (3), Serbia (2), Australia (1), Malaysia (1), South Korea (1), Saudi Arabia (1), UK (1), and China (1).

Many studies enrolled more females than males, and 4 (Cavallo et al,¹⁴ Rote et al,¹⁵ Nam and Cha,¹⁶ and Alshahrani et al¹⁷) recruited only women. The starting level of physical activity was used as a selection criterion at the beginning of 4 studies (Cavallo et al,¹⁴ Rote et al,¹⁵ Pope et al,¹⁸ and Pope and Gao¹⁹), not aiming to intervene on the general student population, but instead specifically targeting inactive students.

Interventions

The types and characteristics of the interventions in the 19 selected studies are very different (see Table 1), both for the presence of a control group, as well as for the technology type and combinations of strategies used. The most common intervention type combines an educational component (e.g., lectures, leaflets) and reinforcement through instant messages. Six studies (Nam and Cha,¹⁶ Lua et al,²⁰ Sandrick et al,²¹ Castro et al,²², Dillon et al,²³ and Hardan-Khalil et al²⁴) adopted this kind of intervention, 3 (Nam and Cha,¹⁶ Lua et al,²⁰ and Dillon et al²³) of which with a passive control that did not receive any intervention (this makes it impossible to separate the educational component from the messages), while 2 (Sandrick et al²¹ and Hardan-Khalil et al²⁴) had a control group that received the educational portion of the intervention but not the messages and 1 (Castro et al²²) did not have a control group (i.e., a “before and after” study).

Four studies (Castro et al,²² Dillon et al,²³ Cotten and Prapavessis,²⁵ and Figueroa et al²⁶) only used messaging, but with varying characteristics of the control group. Two studies (Castro et al²² and Figueroa et al²⁶) had no control at all (micro-randomized type and “before and after”), 1 article (Dillon et al²³) had a control that received no intervention, while the comparison group of the last study (Cotten and Prapavessis²⁵) received messages containing generic topics about health, but not specifically about physical activity.

Two interventions (Mandic et al²⁷ and Wang et al²⁸) only used social media (Facebook in 1 case, and WeChat in the other), both with passive controls. Two studies (Rote et al¹⁵ and Pope et al¹⁸) combined the use of social media (Facebook in both cases) with devices for monitoring physical activity (smartwatch or pedometer), and in both cases the control received the Facebook media intervention but not the devices.

The other 5 studies have a unique intervention design:

One study (Gilbert et al²⁹) combined the educational component with the use of social media (Facebook) and messages on mobile phones. The intervention group was compared with the control group which received just the educational intervention.

Another study (Cavallo et al¹⁴) combined social media (Facebook) together with an educational program. The control group only received the educational program.

In a 3-arm study (Mandic et al²⁷): a group used a social strategy (Facebook), in another group the social media strategy was accompanied by an individual motivational interview and the last group received no intervention.

One study (Pope and Gao¹⁹) combined a physical activity monitoring app with a social media intervention (Facebook). Control group participants received only social media interventions.

Finally, 1 study (Zhang et al³⁰) provided students with free access to a variety of physical activities offered by the institution, promoted using a social media versus a motivational strategy.

Outcome Measures

Throughout the 19 studies, the outcomes of our interest, as well as the methods for measuring them, were very varied.

Physical activity was the only outcome measure in twelve (Cavallo et al,¹⁴ Nam and Cha,¹⁶ Alshahrani et al,¹⁷ Pope et al,¹⁸ Pope and Gao,¹⁹ Lua et al,²⁰ Sandrick et al,²¹ Hardan-Khalil et al,²⁴ Mandic et al²⁷, Wang et al,²⁸ Gilbert et al²⁹, and Todorovic et al³¹) of the 19 studies. In 2 articles (Rote et al¹⁵ and Figueroa et al²⁶), participants’ daily steps were measured, which could also be considered as a form of physical activity measurement. An independent measure of sedentary behavior was used in 1 study (Castro et al²²), while 4 studies (Dillon et al,²³ Cotten and Prapavessis,²⁵ Zhang et al,³⁰ and Keahey et al³²) combined the measurement of sedentary behavior with physical activity.

The most common method of measuring physical activity or sedentary behavior was self-reporting through questionnaires. This method was used in 14 studies (Cavallo et al,¹⁴ Nam and Cha,¹⁶ Alshahrani et al,¹⁷ Lua et al,²⁰ Sandrick et al,²¹ Castro et al,²² Dillon et al,²³ Hardan-Khalil et al,²⁴ Cotten and Prapavessis,²⁵ Figueroa et al,²⁶ Wang et al,²⁸ Gilbert et al,²⁹ Todorovic et al,³¹ and Keahey et al³²), that is, nearly three-quarters of all studies reviewed. The most frequently used questionnaire is the International Physical Activity Questionnaire (IPAQ), often in its short form (IPAQ Short Form or IPAQ-S).

Two studies (Pope et al¹⁸ and Castro et al²²) combined questionnaires with objective measures, using devices such as activ-Pal, pedometers, smartwatches, and even pedometer embedded in the students’ smartphones (Figueroa et al²⁶), while 3 (Rote et al,¹⁵ Pope et al,¹⁸ and Pope and Gao¹⁹) just used these objective measures (without associating them with a self-reported measure).

In 14 (Cavallo et al,¹⁴ Rote et al,¹⁵ Nam and Cha,¹⁶ Alshahrani et al,¹⁷ Pope et al,¹⁸ Lua et al,²⁰ Sandrick et al,²¹ Castro et al,²² Hardan-Khalil et al,²⁴ Figueroa et al²⁶, Wang et al,²⁸ Gilbert et al,²⁹ Zhang et al,³⁰ and Todorovic et al³¹) out of the 19 studies (approximately 74%), outcomes were measured exclusively at baseline and at the end of the intervention. Three studies (Pope et al,¹⁸ Pope and Gao,¹⁹ and Cotten and Prapavessis²⁵) added other interim measurements taken at regular intervals during the course of the intervention period, allowing assessment of the changes over time. Three other studies (Dillon et al,²³ Mandic et al²⁷, and Keahey et al³²) also included a follow-up measurement, allowing an assessment of the degree of persistence of behavioral changes. Two cases (Dillon et al²³ and Keahey et al³²) used a very short follow-up (2 weeks after the intervention finished) while only 1 study (Mandic et al²⁷) presented a longer-term assessment (1 year after the end of the intervention).

Study Designs

More than half of the studies (11 out of 19) were randomized controlled trials (RCTs), while the others were non-RCTs (8 out of 19).

Some studies did not implement interventions focused only on physical activity promotion, but instead were multi-component interventions. Five studies (Lua et al,²⁰ Sandrick et al,²¹ Hardan-Khalil et al,²⁴ Cotten and Prapavessis²⁵, and Wang et al²⁸) included interventions to change eating behavior in order to promote healthier eating patterns. One study (Nam and Cha¹⁶) evaluated the component of the physical activity intervention as part of a series of strategies aimed at reducing the symptoms of Premenstrual Syndrome in students. Lastly, 1 study (Sandrick et al²¹) allowed participants to choose a behavioral style to follow (dealing with physical activity, diet, stress, and sleep), and then an intervention was designed to promote the health style chosen.

Effectiveness

Thirteen articles (Rote et al,¹⁵ Nam and Cha,¹⁶ Alshahrani et al,¹⁷ Pope et al,¹⁸ Pope and Gao,¹⁹ Lua et al,²⁰ Sandrick et al,²¹ Castro et al,²² Dillon et al,²³ Figueroa et al,²⁶ Mandic et al,²⁷ Wang et al,²⁸ and Zhang et al³⁰) showed an improvement in physical activity in the group that underwent the intervention. Three studies (Hardan-Khalil et al,²⁴ Cotten and Prapavessis²⁵, and Gilbert et al²⁹) did not show any improvement, while in 1 case (Keahey et al³²), a post-intervention worsening of physical activity was observed.

Finally, 2 studies (Cavallo et al¹⁴ and Pope et al¹⁸) did not find a significant association with increased physical activity when comparing the experimental and control groups on the application of the single versus the multi-component intervention strategy. The lack of significance should not be interpreted as a failure of the intervention, but rather as the absence of an advantage in the multi-component context compared with its single-strategy control.

Quality Assessment

Regarding RCT studies, the quality scores ranged from the highest value of 0.92 for (Sandrick et al²¹) and the lowest of 0.67 for (Rote et al¹⁵). More than 70% of the articles had a quality score of 0.8 or higher.

Analyzing the non-RCT studies, their scores ranged from a maximum of 0.93 for (Castro et al²²) and a minimum of 0.67 for (Todorovic et al³¹). More than 60% of the articles had a quality score of 0.8 or higher.

Discussion

A total of 13 (Rote et al,¹⁵ Nam and Cha,¹⁶ Alshahrani et al,¹⁷ Pope et al,¹⁸ Pope and Gao,¹⁹ Lua et al,²⁰ Sandrick et al,²¹ Castro et al,²² Dillon et al,²³ Figueroa et al,²⁶ Mandic et al,²⁷ Wang et al,²⁸ and Zhang et al³⁰) out of 19 intervention studies included in the present systematic review found an improvement in physical activity level, suggesting that intervention studies promoting physical activity in college students through the use of social media and/or text messaging could be effective.

Types of Interventions

Only 8 of the included interventions were based on a theoretical framework of behavior change (mentioned in the results section). According to Castro,²² scientific studies often rely on “common sense” to define the research design, instead of using pre-existing evidence or conducting a thorough analysis of the determinants relevant to the study. In most cases, the protocol includes both an initial education and training component, which teaches participants the necessary skills and knowledge to motivate them to physical activity, and a reinforcement component. For example, attention and involvement of the subject were supported by messaging used as a reminder (Lua et al,²⁰ Sandrick et al,²¹ Castro et al,²² Dillon et al,²³ Hardan-Khalil et al,²⁴ Cotten and Prapavessis,²⁵ Figueroa et al,²⁶ and Keahey et al³²), or alternatively through the use of social media platforms (Cavallo et al,¹⁴ Rote et al,¹⁵ Nam and Cha,¹⁶ Alshahrani et al,¹⁷ Pope et al,¹⁸ Pope and Gao,¹⁹ Mandic et al,²⁷ Wang et al,²⁸ Gilbert et al²⁹, Zhang et al,³⁰ and Todorovic et al³¹). Several new studies are needed to assess how different components contribute to the overall efficacy of the protocol.

Technologies Adopted and Their Implications

Smartphones were used in most interventions, since they are now almost ubiquitous among college students.³³ A literature review supports the use of instant messaging over other methods of communication. An American study³⁴ revealed that, among respondents aged 18 to 23, text messaging was the preferred method of communication, over voice calls, email, or even face-to-face interactions. Among the studies carrying out interventions through social media, only a few used specialized platforms, such as INSHAPE in Cavallo et al¹⁴ and a plugin for WeChat in Wang et al²⁸, while the rest used Facebook. However, study results were published between 2012 and 2022, and it is important to note how young people’s perceptions of social media have changed. Using platforms which are the most popular and trendy in the specific historical period of the intervention is always important to cater to user preferences, which will of course change over time. Well-designed interventions may fail if they are implemented on platforms that are not liked or highly used by the target population. Based on the 2018 PewResearch’s survey³⁵ on 743 U.S. teens, Facebook is no longer the social media of choice in this demographic. According to the survey, YouTube was the most preferred online platform, selected by 85% of respondents, followed by Instagram with 72%, Snapchat with 69%, and Facebook with 51%. This means that 1 in 2 individuals could potentially be excluded from a youth health promotion intervention using this platform.

Generalizability of Results

Some notable factors and design choices may have influenced study results. First of all, timing of the intervention (Dillon et al²³), since the cyclical nature of health behaviors in college students can lead to bias in the measurements. For instance, Pope and Gao¹⁹ acknowledged that because assessment sessions often coincide with holiday breaks (such as Christmas and Thanksgiving) or with examination periods, participants’ physical activity-related behavior may not reflect their usual behavior. Moreover, inferring long term treatment effectiveness is difficult, since most studies are short-term and lack post-intervention evaluations. One study (Keahey et al³²), conducted over the course of 1 semester, found that physical activity actually decreased post-intervention. It is possible to explain this decline in physical activity levels by the increased workload associated with the academic semester and by the inability of the intervention to provide coping strategies that should make it possible for the students to remain physically active. According to Castro²² 89% of respondents reported being sedentary during the examination period, based on the interviews conducted among study participants.

Communication contents is another factor which greatly varies among different studies. According to a study³⁶ of 1460 adults conducted in Australia in 2019, messages containing specific and achievable goals may be more effective in reducing sedentary behavior. The process of customizing messages to a population or individual may result in increased costs in the planning phase, but adds to the intervention’s effectiveness. Participants in a Danish study³⁷ of 2030 smokers between the ages of 15 and 25 were randomized between receiving personalized or non-personalized messages in the context of an online program for quitting smoking. Patients who received personalized messages were more likely to give up smoking for a long time.

The high proportion of female participants also reinforces the well-known difficulties in recruiting and engaging men in interventions that aim to promote healthy lifestyles. Achieving important population-level outcomes requires strategies that increase recruitment of male subjects in health promotion studies. In line with our results, a previous systematic review,³⁸ which analyzed 10 studies looking at interventions to promote healthy behavior, found 83.3% women in the studies on average.³⁹ Another study⁴⁰ was actually performed in order to evaluate possible methods to increase the recruitment of men in a physical activity promotion studies. They found that (consistent with the pre-existing literature) men are much less likely to respond to unisex communication campaigns,⁴¹ instead the production of content geared toward males increases their involvement. There was a much greater likelihood for study participants to interact with advertisements containing concise and clear captions, especially below 35 words and images representing masculine traits, such as strength and leadership. Several studies came to the same conclusion: representing typically masculine ideals may be an effective way to boost male recruitment in health promotion programs. The difficulty in recruiting male subjects is a theme known in the context of health behavior studies, in fact males have increased risk for some chronic noncommunicable diseases that can be prevented.⁴² A systematic literature review of 57 studies conducted in 2015⁴³ examined clustering patterns between health risk behaviors (such as alcohol abuse, smoking, and inactivity) and sociodemographic characteristics, finding that males were more frequently associated with those clusters. According to another systematic review published in 2014,⁴⁴ being male is associated with reduced “help-seeking behaviors,” highlighting their tendency to delay and avoid seeking medical care. As a result, disease diagnosis is delayed, mortality is higher, leading to a higher burden on the healthcare system. Special care should be taken when designing future studies, in order to increase the proportion of men as study subjects.

Finally, a geographical contextualization of each intervention is necessary in order to fully understand its generalizability and applicability outside the research environment. Thankfully, although technological and financial constraints may limit the effectiveness of mHealth, there is a growing body of literature that reassures of the potential of these interventions even in developing countries and in marginalized communities.^45
-47 Of the studies included in the review, 3 took place in low- or middle-income countries (Lua et al,²⁰ Mandic et al,²⁷ and Todorovic et al³¹) and all concluded in favor of the effectiveness of the intervention employed. In these studies, the inclusion criteria selected students who actively used digital platforms, but it should be noted that, unlike in developed countries, smartphone ownership is higher among adults in emerging economies. On the other hand, a limited number of studies conducted in developing countries on the topic of mHealth are of high quality.

Standardizing Outcome Measures

Physical activity outcome measures were very heterogeneous among the included studies, nevertheless they can essentially be divided into 2 macrocategories: direct and self-reported measures. The choice between these 2 measurement methods depends on the available evidence and the logistical and practical requirements of the specific intervention. 187 studies were analyzed in a Canadian systematic review⁴⁸ published in 2008, which found that the measurement method chosen can significantly influence the observed levels of physical activity.

Furthermore, problems in direct measurement standardization still exist. Thanks to technological advancements, we now have easy access to devices that are increasingly miniaturized and require no cumbersome wiring, making them less intrusive in the lives of participants. A Harvard Medical School article⁴⁹ published in 2014 describes the use of accelerometers (Actigraph GT3X model) in a long-term longitudinal study involving approximately 18 000 sedentary women over 62 years old. A number of challenges were encountered during the development of this project, including:

Logistical difficulties associated with retrieving and delivering devices. Approximately 2.1% of participants who received accelerometers did not return them, resulting in a substantial expense given the number of participants and cost (approximately $200-250 per person).

Data management, cleaning, and analysis difficulties. This long-term data collection results in a large amount of data (estimated in the article at 20 terabytes).

Another study⁵⁰ published in Medicine & Science in Sports & Exercise (2015) outlines the heterogeneity of physical activity measures. The study focuses on the importance of assessing the total volume of physical activity (TAC/d, Total Activity Counts per day), which is a standardized measure to summarizes the frequency, intensity, and duration of activity sessions. The widespread use of this measure would lead to greater comparability among different studies. To produce solid evidence, it is essential to use direct measurements, particularly in the analysis of behavioral sedentary and low-intensity physical activity, which are much more difficult to quantify than moderate- or high-intensity physical activity. The evidence on the health benefits of these types of physical activity is significantly less established than on moderate- or vigorous-intensity physical activity. Furthermore, the amount of data provided by these technologies, when combined with algorithms based on Machine Learning and artificial intelligence, may provide new insight into the effects of certain physical activity characteristics (such as intensity, frequency, duration, and volume) on health outcomes in the future.

Feasibility and Accessibility of Studies

The feasibility and/or acceptability of the interventions was considered in many studies. One study included in the current review (Castro et al²²), mentioned that the intervention protocol was “feasible and acceptable.” In fact, a questionnaire was administered at the end of the study to measure exactly this. Feedback from the students was substantially positive, while the only possible negative factor was the number of days the subjects were expected to wear the monitoring devices per week. Participants in Keahey et al’s³² article who received messages liked the intervention (only 5.6% disliked the intervention).

Although many studies, including some in this review, have emphasized that the implemented interventions are low-cost (Wang 2021²⁸ and Keahey et al³²), cost-effectiveness related to different components of these interventions specific to different target populations has not yet been validated scientifically. It is crucial that this information be collected in order to plan the appropriate intervention for the right group of people, how to implement it, and in what context.

Synergistic Interventions with Environmental Changes

Based on the current holistic concept of health, which draws upon the socio-ecological theory,⁵¹ the adoption of any health behavior, including an active lifestyle, is not solely influenced by individual factors, but also by environmental factors.

Several studies have been published with the aim of showing possible environmental intervention strategies in university students. One⁵² attempted to translate the knowledge derived from studies that have successfully reduced sedentary lifestyles in pre-university students and office workers by introducing “sit-stand” desks, that is, that are capable of changing from a sitting height to a standing height with relative ease. Another study⁵³ conducted in Germany in 2019 combined the implementation of these “sit-stand” desks with “decisional cues,” placing posters and pictures in common university study areas. The study indicates that these reminders can promote the reduction of sedentary behavior and increase active behavior in its place. This shows how interventions that act on reducing environmental barriers can be highly effective.

One study (Castro et al²²) evidenced that the increase in physical activity was associated with an improvement in physical activity levels exclusively during weekends, because students are often away from the campus and in a different context. Again, this shows how the environment can influence adherence to the intervention and thus effectiveness.

To date, there are no studies in the literature combining individual-targeted strategies with environmental modification in university students. There is a need for more research on how these two components interact with each other, since the two can have a synergistic effect when combined.

Limitations

Heterogeneity of the interventions used and outcome measures meant that a meta-analysis was not possible. Many studies have been described as multi-component interventions that, in many cases, did not involve a control group necessary to evaluate the contribution of each component separately, limiting the understanding of the effectiveness of individual strategies and the impact of their interaction (Cavallo et al¹⁴, Nam and Cha,¹⁶ Pope et al,¹⁸ Pope and Gao¹⁹, Lua et al²⁰, Sandrick et al,²¹ Hardan-Khalil et al,²⁴ Cotton and Prapavessis,²⁵ Figueroa et al²⁶, Mandic et al²⁷, and Wang et al²⁸). Moreover, there was an observable tendency for these interventions to include mainly female individuals, and 4 studies actually recruited only female subjects. A further consideration is that in some research, inclusion criteria were based on the level of physical activity at baseline, which limited access to participants that were more active. The goal in these cases was to avoid bias created by a ceiling effect that prevents some subjects from increasing their physical activity levels due to their already high baseline levels.

Conclusion

The systematic review of literature revealed that new technologies, such as text messaging and social media, can effectively been used to improve physical activity habits among university students. The study highlighted the persistence of some key limitations to the existing body of evidence and just as many opportunities to enhance and improve the applicability of these interventions. First, the lack of standardization of the theoretical framework and study design, which reduces the possibility of replicating and adapting interventions in other settings. Second, the need for greater equity in scientific research on the topic, particularly with respect to gender and geographic setting. Finally, the opportunity to better tailor interventions to students through an analysis of their needs and habits and, in addition, by combining direct health promotion with environmental changes. In conclusion, it is important for researchers to act to shape health promotion interventions, basing their designs on past models and data to increase the likelihood that interventions will be effective when implemented.

Footnotes

Appendix 1 Acknowledgements

None to declare.

Author Contributions

Conceptualization, A.B.; methodology, A.B.; data curation, R.L.B., F.M., A.M., C.Z., and G.L.; writing—original draft preparation, R.L.B., A.M., C.Z., and A.B.; supervision, G.L. All authors have read and agreed to the published version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

This study is a systematic review and it does not require any ethical compliance.

ORCID iD

Alessandra Buja

References

WHO. Steps to health: a European framework to promote physical activity for health. WHO Regional Office for Europe Scherfigsvej 8 DK-2100 Copenhagen Ø. 2007. Accessed December 20, 2022. https://Www.Euro.Who.Int/__data/Assets/Pdf_file/0020/101684/E90191.pdf

Park

Moon

Kim

Kong

YH.

Sedentary lifestyle: overview of updated evidence of potential health risks. Korean J Fam Med. 2020;41(6):365-373. doi:10.4082/kjfm.20.0165

World Health Organization. Global action plan on physical activity 2018–2030: more active people for a healthier world. World Health Organization. 2018. Accessed December 20, 2022. https://apps.who.int/iris/handle/10665/272722

Edelmann

Pfirrmann

Heller

, et al. Physical activity and sedentary behavior in university students–the role of gender, age, field of study, targeted degree, and study semester. Front Public Health. 2022;10: 821703. Accessed February 12, 2024. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.821703

Teleman

de Waure

Soffiani

Poscia

Di Pietro

ML.

Physical activity and health promotion in Italian university students. Ann Ist Super Sanita. 2015;51(2):106-110. doi:10.4415/ANN_15_02_06

Haase

Steptoe

Sallis

Wardle

Leisure-time physical activity in university students from 23 countries: associations with health beliefs, risk awareness, and national economic development. Prev Med. 2004;39(1):182-190. doi:10.1016/j.ypmed.2004.01.028

Sparling

Snow

TK.

Physical activity patterns in recent college alumni. Res Q Exerc Sport. 2002;73(2):200-205. doi:10.1080/02701367.2002.10609009

Votruba

Thearle

Piaggi

Knowler

Hanson

Krakoff

Weight maintenance from young adult weight predicts better health outcomes. Obesity. 2014;22(11):2361-2369. doi:10.1002/oby.20854

Warnick

Pfammatter

Champion

Galluzzi

Spring

Perceptions of health behaviors and mobile health applications in an academically elite college population to inform a targeted health promotion program. Int J Behav Med. 2019;26(2):165-174. doi:10.1007/s12529-018-09767-y

10.

Rose

Barker

Maria Jacob

, et al. A systematic review of digital interventions for improving the diet and physical activity behaviors of adolescents. J Adolesc Health Off Publ Soc Adolesc Med. 2017;61(6):669-677. doi:10.1016/j.jadohealth.2017.05.024

11.

Schulz

Altman

Moher

, CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Ann Intern Med. 2010;152(11):726-732. doi:10.7326/0003-4819-152-11-201006010-00232

12.

Des Jarlais

Lyles

Crepaz

TREND

Group.

Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. Am J Public Health. 2004;94(3):361-366. doi:10.2105/ajph.94.3.361

13.

Olofintuyi

Roberson

Ilesanmi

, et al. The use of text messaging to promote physical activity in African-American college students: a feasibility study. J Natl Soc Allied Health. 2018;15(1):30-44.

14.

Cavallo

Tate

Ries

Brown

DeVellis

Ammerman

AS.

A social media–based physical activity intervention. Am J Prev Med. 2012;43(5):527-532. doi:10.1016/j.amepre.2012.07.019

15.

Rote

Klos

Brondino

Harley

Swartz

AM.

The efficacy of a walking intervention using social media to increase physical activity: a randomized trial. J Phys Act Health. 2015;12(S1):S18-S25. doi:10.1123/jpah.2014-0279

16.

Nam

Cha

Effects of a social-media-based support on premenstrual syndrome and physical activity among female university students in South Korea. J Psychosom Obstet Gynecol. 2020;41(1):47-53. doi:10.1080/0167482X.2018.1559811

17.

Alshahrani

Siddiqui

Khalil

, et al. WhatsApp-based intervention for promoting physical activity among female college students, Saudi Arabia: a randomized controlled trial. East Mediterr Health J. 2021;27(8):782-789. doi:10.26719/emhj.21.012

18.

Pope

Barr-Anderson

Lewis

Pereira

Gao

Use of wearable technology and social media to improve physical activity and dietary behaviors among college students: a 12-week randomized pilot study. Int J Environ Res Public Health. 2019;16(19):3579. doi:10.3390/ijerph16193579

19.

Pope

Gao

Feasibility of smartphone application- and social media-based intervention on college students’ health outcomes: a pilot randomized trial. J Am Coll Health. 2022;70(1):89-98. doi:10.1080/07448481.2020.1726925

20.

Lua

Dali

WPEW

Shahril

MR.

Multimodal nutrition education intervention: a cluster randomised controlled trial study on weight gain and physical activity pattern among university students in Terengganu, Malaysia. Malays J Nutr. 2013;19:339-352. Accessed February 19, 2024. https://nutriweb.org.my/mjn/publication/19-3/g.pdf

21.

Sandrick

Tracy

Eliasson

, et al. Effect of a counseling session bolstered by text messaging on self-selected health behaviors in college students: a preliminary randomized controlled trial. JMIR MHealth UHealth. 2017;5(5):e67. doi:10.2196/mhealth.6638

22.

Castro

Vergeer

Bennie

Biddle

SJH

. Feasibility of reducing and breaking up university students’ sedentary behaviour: pilot trial and process evaluation. Front Psychol. 2021;12:661994. doi:10.3389/fpsyg.2021.661994

23.

Dillon

Rollo

Prapavessis

A combined health action process approach and mHealth intervention to reduce sedentary behaviour in university students – a randomized controlled trial. Psychol Health. 2022;37(6):692-711. doi:10.1080/08870446.2021.1900574

24.

Hardan-Khalil

Costa

Fisher

DG.

Daily motivational text messages impact on college students’ nutritional awareness and physical activity levels. J Am Coll Health. 2022;70(8):2511-2518. doi:10.1080/07448481.2020.1869748

25.

Cotten

Prapavessis

Increasing nonsedentary behaviors in university students using text messages: randomized controlled trial. JMIR MHealth UHealth. 2016;4(3):e99. doi:10.2196/mhealth.5411

26.

Figueroa

Deliu

Chakraborty

, et al. Daily motivational text messages to promote physical activity in university students: results from a microrandomized trial. Ann Behav Med. 2022;56(2):212-218. doi:10.1093/abm/kaab028

27.

Mandic

Bjegovic-Mikanovic

Vukovic

Djikanovic

Stamenkovic

Lalic

NM.

Successful promotion of physical activity among students of medicine through motivational interview and Web-based intervention. PeerJ. 2020;8:e9495. doi:10.7717/peerj.9495

28.

Wang

Guo

Zhang

, et al. Promoting healthy lifestyle in Chinese college students: evaluation of a social media-based intervention applying the RE-AIM framework. Eur J Clin Nutr. 2021;75(2):335-344. doi:10.1038/s41430-020-0643-2

29.

Gilbert

Williamson

Richards

Collyer

Kelly

Do framed mental health messages on social media influence university students’ motivation for physical activity?

Int J Environ Res Public Health. 2021;18(16):8671. doi:10.3390/ijerph18168671

30.

Zhang

Brackbill

Yang

Centola

Efficacy and causal mechanism of an online social media intervention to increase physical activity: results of a randomized controlled trial. Prev Med Rep. 2015;2:651-657. doi:10.1016/j.pmedr.2015.08.005

31.

Todorovic

Terzic-Supic

Djikanovic

Nesic

Piperac

Stamenkovic

Can social media intervention improve physical activity of medical students?

Public Health. 2019;174:69-73. doi:10.1016/j.puhe.2019.05.030

32.

Keahey

White

Duchesne

Pelletier

CA.

A theory-grounded text message–based intervention to reduce sedentary behaviour in university students. Health Educ J. 2021;80(6):672-685. doi:10.1177/00178969211007163

33.

Starri M. DIGITAL 2022 – I DATI ITALIANI. We are social Italy. February 9, 2022. Accessed December 20, 2022. https://wearesocial.com/it/blog/2022/02/digital-2022-i-dati-italiani/

34.

Skierkowski

Wood

RM.

To text or not to text? The importance of text messaging among college-aged youth. Comput Hum Behav. 2012;28(2):744-756. doi:10.1016/j.chb.2011.11.023

35.

Anderson

Teens, social media and technology 2018. Pew Research Center: Internet, Science & Tech. May 31, 2018. Accessed December 20, 2022. https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/

36.

Alley

Vandelanotte

Duncan

, et al. Should I sit or stand: likelihood of adherence to messages about reducing sitting time. BMC Public Health. 2019;19(1):871. doi:10.1186/s12889-019-7189-z

37.

Skov-Ettrup

Ringgaard

Dalum

Flensborg-Madsen

Thygesen

Tolstrup

JS.

Comparing tailored and untailored text messages for smoking cessation: a randomized controlled trial among adolescent and young adult smokers. Health Educ Res. 2014;29(2):195-205. doi:10.1093/her/cyt112

38.

Maher

Lewis

Ferrar

Marshall

De Bourdeaudhuij

Vandelanotte

Are health behavior change interventions that use online social networks effective? A systematic review. J Med Internet Res. 2014;16(2):e40. doi:10.2196/jmir.2952

39.

Robertson

Douglas

Ludbrook

Reid

van Teijlingen

What works with men? A systematic review of health promoting interventions targeting men. BMC Health Serv Res. 2008;8:141. doi:10.1186/1472-6963-8-141

40.

Ryan

Lopian

, et al. It’s not raining men: a mixed-methods study investigating methods of improving male recruitment to health behaviour research. BMC Public Health. 2019;19(1):814. doi:10.1186/s12889-019-7087-4

41.

Bottorff

Seaton

Johnson

, et al. An updated review of interventions that include promotion of physical activity for adult men. Sports Med. 2015;45(6):775-800. doi:10.1007/s40279-014-0286-3

42.

World Health Organization. Gender and noncommunicable diseases in Europe. Analysis of STEPS data. World Health Organization .

43.

Noble

Paul

Turon

Oldmeadow

Which modifiable health risk behaviours are related? A systematic review of the clustering of Smoking, nutrition, alcohol and physical activity (‘SNAP’) health risk factors. Prev Med. 2015;81:16-41. doi:10.1016/j.ypmed.2015.07.003

44.

Yousaf

Grunfeld

Hunter

MS.

A systematic review of the factors associated with delays in medical and psychological help-seeking among men. Health Psychol Rev. 2015;9(2):264-276. doi:10.1080/17437199.2013.840954

45.

Stephani

Opoku

Quentin

A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries. BMC Public Health. 2016;16(1):572. doi:10.1186/s12889-016-3226-3

46.

Hurt

Walker

Campbell

Egede

LE.

MHealth interventions in low and middle-income countries: a systematic review. Glob J Health Sci. 2016;8(9):183-193. doi:10.5539/gjhs.v8n9p183

47.

McCool

Dobson

Whittaker

Paton

Mobile health (mHealth) in low- and middle-income countries. Annu Rev Public Health. 2022;43(1):525-539. doi:10.1146/annurev-publhealth-052620-093850

48.

Prince

Adamo

Hamel

Hardt

Connor Gorber

Tremblay

A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5:56. doi:10.1186/1479-5868-5-56

49.

Lee

Shiroma

EJ.

Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sports Med. 2014;48(3):197-201. doi:10.1136/bjsports-2013-093154

50.

Bassett

Troiano

McClain

Wolff

DL.

Accelerometer-based physical activity: total volume per day and standardized measures. Med Sci Sports Exerc. 2015;47(4):833-838. doi:10.1249/MSS.0000000000000468

51.

Gebel

Bauman

Petticrew

The physical environment and physical activity: a critical appraisal of review articles. Am J Prev Med. 2007;32(5):361-369. doi:10.1016/j.amepre.2007.01.020

52.

Jerome

Janz

Baquero

Carr

LJ.

Introducing sit-stand desks increases classroom standing time among university students. Prev Med Rep. 2017;8:232-237. doi:10.1016/j.pmedr.2017.10.019

53.

Mnich

Bachert

Kunkel

Wäsche

Neumann

Nigg

CR.

Stand up, students! Decisional cues reduce sedentary behavior in university students. Front Public Health. 2019;7:230. doi:10.3389/fpubh.2019.00230

Promotion of Physical Activity Among University Students With Social Media Or Text Messaging: A Systematic Review