Primary care prevention of cardiovascular risk behaviors in adolescents: a systematic review

(1)

Article

Reference

Primary care prevention of cardiovascular risk behaviors in adolescents: a systematic review

TISSOT, Hervé, PFARRWALLER, Eva, HALLER, Dagmar M.

Abstract

Adolescence is associated with behavioral changes offering opportunities for prevention of cardiovascular risk behaviors. Primary care physicians are ideally placed to deliver preventive interventions to adolescents. The objective was to systematically review the evidence about effectiveness of primary care-led interventions addressing the main cardiovascular risk behaviors in adolescents: physical activity, sedentary behaviors, diet and smoking. PubMed, Embase, PsycINFO, CINAHL, Cochrane, ClinicalTrials.gov, and ISRCTN registry were searched from January 1990 to April 2020. Randomized controlled trials of interventions in primary care contexts on at least one of the cardiovascular behaviors were included, targeting 10–19-year old adolescents, according to the World Health Organization's definition. Two authors independently assessed risk of bias. Twenty-two papers were included in the narrative synthesis, reporting on 18 different studies. Interventions targeting smoking uptake seemed more effective than interventions targeting established smoking or the three other risk behaviors. Intervention components or intensity [...]

TISSOT, Hervé, PFARRWALLER, Eva, HALLER, Dagmar M. Primary care prevention of

cardiovascular risk behaviors in adolescents: a systematic review. Preventive Medicine , 2020, vol. 142, p. 106346

PMID : 33275966

DOI : 10.1016/j.ypmed.2020.106346

Available at:

http://archive-ouverte.unige.ch/unige:147405

Disclaimer: layout of this document may differ from the published version.

(2)

Preventive Medicine 142 (2021) 106346

Available online 1 December 2020

Review Article

Primary care prevention of cardiovascular risk behaviors in adolescents: A systematic review

Herv ´ e Tissot

^a^,¹^,²

, Eva Pfarrwaller

^a^,¹^,^*

, Dagmar M. Haller

^a^,^b

aPrimary Care Unit, Faculty of Medicine, University of Geneva, Geneva, Switzerland

bDepartment of General Practice, The University of Melbourne, Melbourne, Australia

A R T I C L E I N F O Keywords:

Adolescent

Primary care physicians Cardiovascular disease Primary prevention

A B S T R A C T

Adolescence is associated with behavioral changes offering opportunities for prevention of cardiovascular risk behaviors. Primary care physicians are ideally placed to deliver preventive interventions to adolescents. The objective was to systematically review the evidence about effectiveness of primary care-led interventions addressing the main cardiovascular risk behaviors in adolescents: physical activity, sedentary behaviors, diet and smoking. PubMed, Embase, PsycINFO, CINAHL, Cochrane, ClinicalTrials.gov, and ISRCTN registry were searched from January 1990 to April 2020. Randomized controlled trials of interventions in primary care contexts on at least one of the cardiovascular behaviors were included, targeting 10–19-year old adolescents, according to the World Health Organization’s definition. Two authors independently assessed risk of bias. Twenty- two papers were included in the narrative synthesis, reporting on 18 different studies. Interventions targeting smoking uptake seemed more effective than interventions targeting established smoking or the three other risk behaviors. Intervention components or intensity were not clearly associated with effectiveness. Risk of bias was mostly unclear for most studies. There is little evidence for specific interventions on adolescents’ cardiovascular risk behaviors in primary care, mainly due to studies’ methodological limitations. Further research should investigate the effectiveness of opportunistic primary care-based interventions as compared to more complex interventions, and address the methodological shortcomings identified in this review.

1. Introduction

Cardiovascular diseases (CVDs) are the number one cause of deaths in the world, with an estimated 17.9 million deaths from CVDs in 2016, representing 31% of all global deaths (World Health Organization, 2017). Most CVDs could be prevented by addressing behavioral risk factors, namely tobacco use, unhealthy diet and obesity, and physical inactivity. Besides population-based prevention, individual approaches, such as motivational interviewing, are recommended to facilitate lifestyle changes, and primary care remains essential for providing these

interventions (Piepoli et al., 2020). Whereas many habits develop in early childhood, it is in adolescence that behavioral patterns change (e.

g. eating habits, smoking uptake, sedentary behaviors) and lifestyle habits consolidate, influencing cardiovascular health in adulthood (Haas et al., 2014; Laitinen et al., 2012). The results of the Health Behavior in School-aged Children study showed that at the age of 15, only one third of adolescents eat daily fruit, two-thirds watch television more than 2 h per day, almost four out of five adolescents exercise less than an hour per week, and 20% report smoking cigarettes at least once a week (Currie et al., 2012). This is associated with 10% of girls and 20% of boys being

Abbreviations: BMI, Body mass index; CINAHL, Cumulative Index to Nursing and Allied Health Literature; CVD, Cardiovascular disease; ISRCTN, International Standard Randomized Controlled Trial Number; MVPA, Moderate-to-vigorous physical activity; PC, Primary care; PCP, Primary care physician; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RCT, Randomized controlled trial; RoB, Risk of bias; USPSTF, United States Preventive Services Task Force.

* Corresponding author at: Unité des Internistes Généralistes et Pédiatres (UIGP), Centre Médical Universitaire, Av. Michel-Servet 1, CH-1211 Genève 4, Switzerland.

E-mail addresses: herve.tissot@unige.ch (H. Tissot), eva.pfarrwaller@unige.ch (E. Pfarrwaller), dagmar.haller-hester@unige.ch (D.M. Haller).

1 Contributed equally as co-first authors.

2 Present address: Unit´e de Psychologie Clinique des Relations Interpersonnelles (UPCRI), Faculty of Psychology and Sciences of Education, University of Geneva, Switzerland.

Contents lists available at ScienceDirect

Preventive Medicine

journal homepage: www.elsevier.com/locate/ypmed

https://doi.org/10.1016/j.ypmed.2020.106346

Received 2 June 2020; Received in revised form 28 October 2020; Accepted 24 November 2020

(3)

overweight or obese.

Although adolescence is a period of risk, it is also a period of op- portunity for intervention and prevention of health-compromising behaviors. Systematic reviews have provided evidence that large-scale population-based prevention programs are potentially effective to limit the development of CVD risk behaviors in adolescents (Kelishadi and Azizi-Soleiman, 2014; Richardson et al., 2009). Nevertheless, individual approaches are important to individualize public health messages, target at-risk individuals, and encourage behavior change (Piepoli et al., 2020). We know that most young people are in contact with a primary care physician (PCP) at least once a year (Zimmer-Gembeck et al., 1997;

Yassaee et al., 2017; Jeannin et al., 2005). PCPs are thus in an ideal position to deliver such individualized interventions to adolescents (Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: summary report, 2011).

Prevention is widely considered to be an essential part of adolescent health care (Prado et al., 2015). Since prevention can be time- consuming it is necessary to build empirical evidence about its effectiveness to help PCPs prioritize effective preventive interventions (Pol- lak et al., 2008).

There is evidence in favor of screening children and adolescents for obesity and offering brief behavioral interventions (Sim et al., 2016; US Preventive Services Task Force, 2017). Yet we still know very little about the effectiveness of primary care preventive interventions on CVD risk behaviors in an unselected population of adolescents. Moreover, most reviews have focused on the effect of interventions on anthropometric measures, and less on behavioral outcomes, despite the insight they may provide for PCPs to target CVD risk behaviors.

The objective of the present study was to systematically review randomized trials for the effectiveness of interventions conducted in primary care settings, targeting 10–19 year old adolescents (following the World Health Organizations’ definition of adolescence) and the main cardiovascular risk behaviors: smoking, physical activity, sedentary behaviors, and diet, as defined by national and international entities, for example in terms of fruit and vegetable consumption or fat intake (World Health Organization, 2020; U.S. Department of Health and Human Services and U.S. Department of Agriculture, 2020). We were particularly interested in describing the components of effective interventions, because the type and content of interventions may influence effects on behaviors. For example, for young people considered as digital natives, the use of mobile phone technology may offer an interesting addition to PCP-led interventions (Wickham and Carbone, 2015). We excluded population-level interventions as we wanted to focus on interventions applicable to the primary care context where individual, one-to-one contacts are the norm. Although we focused on interventions aimed at a general, unscreened adolescent population, we also included studies targeting overweight or obese adolescents because of the high prevalence in the general population.

We thus conducted a systematic review of randomized controlled trials to answer the following questions: (i) Can interventions conducted in primary care settings reduce the development of the main cardiovascular risk behaviors in 10–19 year old adolescents, and (ii) can characteristics associated with the effectiveness of interventions be identified?

2. Methods

2.1. Protocol and registration

This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009). The protocol for this systematic review has been registered (PROSPERO CRD42016028045 available from: http://www.crd.york.

ac.uk/PROSPERO/display_record.php?ID=CRD42016028045) and published (Haller et al., 2016).

2.2. Eligibility criteria

We limited the literature search to randomized controlled trials (RCTs) and cluster RCTs published in any language (but with an abstract in English) between January 1990 and April 2020. We considered studies published in peer-reviewed journals, as well as completed, but still un- published, trials identified through trial registration platforms when complete results were available. Conference abstracts were excluded.

Eligible studies had to target adolescents from 10 to 19 years old recruited in primary care. Studies also including children or young adults outside this age range were eligible if the 10–19-year-old subjects could be analyzed separately. Eligible studies’ interventions had to target adolescents’ behaviors related to the main cardiovascular risk factors (smoking, low physical activity/sedentary behavior and/or unhealthy diet) and be delivered by a PCP or another professional working within the primary care practice. Interventions involving electronic tools targeting patients identified in the primary care practice were also eligible. We defined the PCP as a physician providing first-contact care to the undifferentiated patient, including adolescents (American Academy of Family Physicians, 2020). Thus, we included the specialties of family medicine, general practice, general internal medicine, and general pediatrics. School-based interventions were only considered if they were conducted in the context of a school-based health center comparable to a primary care setting providing individual care.

Studies comparing the intervention to an active or passive control were eligible. Finally, eligible studies had to include measures of at least one of our review’s primary outcomes: smoking, physical activity, sedentary behaviors, or diet, each measured either subjectively (self- reported) and/or objectively (such as accelerometers or cotinine levels).

For studies meeting our eligibility criteria, we also recorded body mass index (BMI), waist circumference, cholesterol, blood pressure, fasting glucose and blood insulin levels, considered secondary outcomes of the review.

2.3. Information sources

We performed a search of published papers and RCT protocols in the following databases: PubMed, Embase, PsycINFO, CINAHL, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and ISRCTN registry. We conducted the initial searches in the databases between January 31, 2017 and March 26, 2017, and last updated them on April 30, 2020.

We developed a search strategy for each database by combining the key terms “adolescence”, “diet “, “physical activity”, “sedentary behaviors”, “smoking”, “primary care”, and “randomized controlled trials”, as well as synonyms and related subject headings. Our full search strategy for Pubmed/MEDLINE is presented in Appendix A. Adjusted search strategies for the other databases are available on request.

2.4. Study selection

One reviewer (HT) performed the search, imported the citations into a reference management program (EndNote version X8; Thomson Reuters, New York, NY, USA) and discarded duplicates. We applied a two-step strategy to select studies: First, one of the authors (HT) screened all ti- tles and abstracts for inclusion criteria. If in doubt, the study was included for full-text review. Then, two authors (HT and EP) independently reviewed all full-text articles for inclusion criteria and documented rea- sons for exclusion. Discrepancies were resolved through discussion with another reviewer (DMH), who took the final decision for inclusion. If the database information about a research protocol was insufficient to allow a decision, we contacted the authors of these studies by e-mail to obtain articles that we might have missed in our search (three authors were contacted leading to one included paper).

(4)

2.5. Data extraction

We created an online data extraction template allowing the data to be exported to a single Excel sheet. The template was based on the data items in the Cochrane data extraction form (The Cochrane Collaboration, 2014). Extracted data included trial characteristics, setting, sampling, population characteristics, intervention details, and outcome measures.

We also recorded whether the intervention was informed by a psycho- logical behavior change theory or a model. One of the authors (HT) extracted the data for all studies, whereas a second author (EP) separately extracted the data for five studies to confirm the reliability of the data extraction process. We contacted five study authors to obtain missing essential information.

2.6. Analysis and synthesis

Results were grouped according to our review’s outcomes and syn- thetized narratively. Given the heterogeneity in outcome measures reported in the studies meeting our eligibility criteria, we did not perform quantitative meta-analyses.

2.7. Risk of bias

Two authors (HT and EP) independently assessed risk of bias (RoB) using the Cochrane Risk of Bias Tool for Randomized Controlled Trials (Higgins et al., 2011). Differences of opinion were resolved by discussion with a third reviewer (DMH). Each study was attributed a global risk of bias based on this assessment, according to which assessment was the most frequent. We did not exclude any articles from the review based on RoB, but rather chose to describe the overall study quality and most

frequent threats to validity.

3. Results 3.1. Study selection

Our search strategy yielded 5145 unique records (Fig. 1), of which 28 met our inclusion criteria, including 22 papers published in peer-reviewed journals and 6 research protocols. All studies described in the protocols were already included in the published papers, thus the records representing the protocols were only used to obtain methodological information, but otherwise not included in the review. The 22 papers described 18 different research projects, as some study results were published in more than one paper. We report the results at a study level.

3.2. Risk of bias assessment

In general, risk of bias was considered “unclear” across most cate- gories (see Fig. 2 and Appendix B Figure). Most studies’ global RoB was rated “mostly unclear” because of frequent missing information about the study methods. High risk of bias assessment was most frequently linked to issues with randomization, missing allocation concealment, or absence of blinding of outcomes assessors.

3.3. Study characteristics

The details of included studies are presented in Table 1. Most studies (N =13) were conducted in the Unites States. Intervention characteristics are summarized in Table 2. They varied largely, from counselling consultations with a physician, to multicomponent interventions

Records idenﬁed through database searching

(n = 6732)

Screening Included Eligibility Idenﬁcaon

Addional records idenﬁed through other sources

(n = 5)

Records aer duplicates removed (n = 5145)

Records screened (n = 5145)

Records excluded (n = 4797)

Full-text arcles assessed for eligibility

(n = 348)

Full-text arcles excluded (n = 326) Age group (n = 123) Intervenon type and context (n = 118) Protocols of studies already included in full-text arcles (n = 6) Other (n = 79) 22 records included in

qualitave synthesis, describing 18 studies

Fig. 1.PRISMA diagram for systematic review of randomized controlled trials of primary care interventions to reduce cardiovascular risk factors in adolescents, published 1990–2020.

(5)

including group sessions, electronic devices or websites. Duration was also variable, from single sessions to several months’ duration. Studies focused either on the general adolescent patient population consulting at the PC practice (N =8), or on a more targeted population of overweight or obese patients, based on BMI cut-offs which varied between studies.

3.4. Outcome measures in included studies

Outcome measures relied mostly on self-reported data. Six studies used validated physical activity recall instruments, from which a stan- dardized measure of physical activity was derived. Only three trials studying physical activity and sedentary behavior also used objective data obtained through accelerometers, and one used a fitness test.

Outcomes related to diet were always self-reported: Four studies used a validated dietary recall instrument (generally over the last 24 h), and three used versions of a validated food frequency questionnaire. All six studies addressing cigarette smoking only measured self-reported outcomes, of which one used a telephone questionnaire, four used postal questionnaires, and one study did not specify the type of questionnaire used.

3.5. Use of theory

Of the 18 included studies, only six stated that the intervention was informed by behavioral theory: one used Social Cognitive Theory (Chen et al., 2019), one used the Transtheoretical Model of Behavior Change (Kong et al., 2013), and four studies used the same intervention (PACE+) which was based on a combination of both theories (Patrick et al., 2006; Patrick et al., 2013; Saelens et al., 2002; Patrick et al., 2001). Two studies mentioned a model of behavioral counselling as a basis for the intervention (the FRAMES model (De Micheli et al., 2004) and the 5A Model (Pbert et al., 2008)). Use of theory was not associated with evidence of an effective intervention.

3.6. Description of interventions and their effects on primary outcomes Most studies measured multiple behavioral outcomes relevant for our review (see Table 1 for details). Outcome measures and effects of interventions on each outcome are summarized in Appendix C. Most papers lacked information about effect sizes, and outcome measures were heterogenous. We thus classified interventions as “showing some effectiveness” if statistically significant between-group differences were observed on at least one primary outcome, based on a p-value cutoff of 0.05. Interventions without significant effects and those with obvious important biases were classified as “showing no or unclear effectiveness”.

3.6.1. Physical activity

Thirteen studies measured physical activity outcomes (Table C1) (Chen et al., 2019; Kong et al., 2013; Patrick et al., 2006; Patrick et al., 2013; Saelens et al., 2002; Patrick et al., 2001; Oreskovic et al., 2016;

Ortega-Sanchez et al., 2004; Rosenberg et al., 2007; Chen et al., 2017;

DeBar et al., 2012; Eddy Ives et al., 2012; Fleischman et al., 2016; Love- Osborne et al., 2014; Walker et al., 2002). Reported outcomes varied across studies and included moderate-to-vigorous physical activity (MVPA), proportion of adolescents meeting physical activity guidelines, number of weekly active days, and duration of sports activities. Only two studies found significant changes in physical activity due to the intervention: In one study, daily MVPA increased by a mean of 7.7 min (Oreskovic et al., 2016), whereas another study found an increase in adolescents self-defining as “physically active”, which was a composite measure of duration, frequency and intensity of physical activity (Ortega-Sanchez et al., 2004). A third study found a significant increase in the number of weekly active days only in boys (mean increase of 0.3 active days/week), but not in girls or in the total sample (Patrick et al., 2006). All of these interventions started off with counselling by a PC provider (physician or practice nurse), based on a baseline survey which allowed for individualized goal setting, and followed by reminders (text messages (Oreskovic et al., 2016), mail or phone contact (Patrick et al., 2006), or a second primary care consultation (Ortega-Sanchez et al., 2004)). Outcomes were measured 4–6 months after the end of the intervention in two of the studies (Oreskovic et al., 2016; Ortega-San- chez et al., 2004) and at intervention end in one of them (Patrick et al., 2006). Two studies using a similar multicomponent intervention (the

“PACE+” intervention) did not find a significant effect (Saelens et al., 2002; Patrick et al., 2001). One study that evaluated an exclusively electronic intervention in adolescents recruited at a primary care clinic (smartphone app and connected fitness device along with an online educational program) did not find significant effects (Chen et al., 2019).

Except for one trial, which studied a single-session intervention (Walker et al., 2002), the studies that did not find significant effects evaluated interventions of several months’ duration, including multicomponent interventions and repeated motivational interviewing sessions, and were relatively small-scale studies (maximum 208 participants).

3.6.2. Sedentary behaviors

Eight studies measured outcomes related to sedentary behaviors (Table C2) (Chen et al., 2019; Kong et al., 2013; Patrick et al., 2006;

Patrick et al., 2013; Saelens et al., 2002; Oreskovic et al., 2016;

Rosenberg et al., 2007; Chen et al., 2017; DeBar et al., 2012; Eddy Ives et al., 2012). All of them also measured physical activity outcomes.

Sedentary behavior outcomes were self-reported (with the exception of one study (Oreskovic et al., 2016)). The most frequent measure was time spent in front of screens, including television, video games, and com- puters. One study also included time spent talking on the phone and

Fig. 2. Risk of bias across all studies included in a systematic review of primary care interventions to reduce cardiovascular risk factors in adolescents: Review authors’ assessment of each risk of bias item (percentages across all included studies).

(6)

Table 1

Characteristics of studies included in a systematic review of primary care interventions to reduce cardiovascular risk behaviors in adolescents.

Study Country Years of

study Study

type Target population Setting N Study

population characteristics

Outcomes relevant for

the review (icon)^a Global RoB Effectiveness of

intervention (color)^b Chen et al.

(2017), Chen et al.

(2019)^c

USA 2015–2016 RCT

(pilot) Chinese American overweight or obese adolescents (13–18-year-old clinic patients with BMI ≥ 85th percentile).

Primary care providers at 2 large community clinics in northern California.

40 Female: 42.5%

Mean age: 14.9 years (SD = 1.67) Mean BMI:

28.27 (SD = 4.65)

Unclear

DeBar et al.

(2012) USA 2005–2009 RCT Female health plan members aged 12–17 years with a BMI ≥90th percentile

One health maintenance organization (HMO) in the Pacific northwest

208 Female: 100%

Mean age: 14.1 years (SD =1.4) Mean BMI percentile:

97.09 (SD = 2.27)

Unclear

De Micheli

et al. (2004) Brazil 2001–2002 RCT 10–19-year old males and females consulting at the clinic

One adolescent health clinic in a pediatric university department (4 pediatricians)

108 Female: 49.5%

Mean age:

14.25 years

Unclear

Eddy Ives

et al. (2012) Spain 2006–2007 RCT 10–14-year old males and females with BMI >85th percentile consulting at the clinic

48 pediatric primary care clinics in Catalonia, Spain

174 Female: 50%

Mean age:

11.81 years (SD

=1.21) Overweight:

26.4%

Obese: 73.6%

Unclear

Fleischman

et al. (2016) USA 2013–2014 RCT

(pilot) 10–17-year old males and females with a BMI ≥95th percentile

One pediatric primary care clinic +a children’s hospital

40 Female: 77.5%

Mean age: 14.3 years (SD =1.9)

Unclear

Fidler and Lambert (2001)

UK Not stated RCT 10–15-year old, non- smoking males and females from the practices’ patient lists.

14 primary health

care centers 2942 Female: 52.8%

Mean age: Not stated Nonsmokers:

100%

Low

Hollis et al.

(2005), Huang et al.

(2005)

USA 1997–2001 RCT 14–17-year old males and females consulting at the health center.

7 medical centers (pediatrics and family medicine departments)

2526 Female: 59.2%

Mean age: 15.4 years Nonsmokers:

77.8%

Low

Kong et al.

(2013) USA 2009–2010 cRCT 9–11th grade students with

BMI ≥85th percentile 2 school-based

health centers 60 Female: 58.8%

Mean age: 14.8 years

Unclear

Love-Osborne

et al. (2014) USA 2010–2011 RCT Adolescents with BMI ≥ 85th percentile from the school-based health center population

2 school-based

health centers 165 Female: 51.5%

Unclear

Oreskovic

et al. (2016) USA 2013–2014 RCT

(pilot) Adolescents aged 10–16 years, residing in low- and middle-income towns, with a BMI ≥85th percentile and followed at an academic outpatient community health center.

1 academic outpatient community health center

60 Female: 47%

Mean age: 11.9 years Mean BMI percentile: 94

High

Ortega- Sanchez et al. (2004)

Spain Not stated RCT Patients aged 12–21 years consulting at the physicians’

offices over a 6-month period.

6 family physicians’

offices 448 Female: 41.7%

Unclear

Patrick et al.

(2001) USA Not stated RCT

(pilot) Patients aged 11–18 years, scheduled for an examination or consulting for a well-care visit.

3 pediatric or adolescent medicine outpatient clinics

148 Female: 39.2%

Unclear

Kong et al.

(2013), Rosenberg et al. (2007)

USA 2001–2002 RCT Adolescents 11–15 years, scheduled for a well-child visit or contacted for the study.

45 primary care providers from 6 private clinic sites

878 Female: 53.5%

Mean age: 12.7 (SD =1.3) Overweight:

18.4%

Obese: 27.4%

Unclear

Patrick et al.

(2013) USA Not stated RCT 12–16-year old patients, due for their annual visit or having an existing

18 pediatric

practices 101 Female: 63.4%

Unclear

(continued on next page)

(7)

listening to music (Patrick et al., 2006), and two studies differentiated between school and non-school days (Kong et al., 2013; Patrick et al., 2013). Four of them reported some significant effects of the intervention in terms of self-reported screen time (Chen et al., 2019; Kong et al., 2013; Patrick et al., 2006; Patrick et al., 2013). These interventions were of longer duration (8–12 months) when compared to the ineffective interventions (5 months or shorter, except for one intervention consisting of several PCP visits over 12 months (Eddy Ives et al., 2012)).

Effective interventions contained varied components, such as frequent telephone or face-to-face contacts or electronic devices, but these components did not differentiate them from the ineffective interventions.

Also, outcomes in effective interventions were only measured immediately after the end of the intervention, therefore not allowing for longer- term conclusions.

3.6.3. Diet

Ten studies described the effect of interventions on diet (Table C3) (Chen et al., 2019; Kong et al., 2013; Patrick et al., 2006; Patrick et al., 2001; DeBar et al., 2012; Eddy Ives et al., 2012; Fleischman et al., 2016).

The most frequently measured outcomes were related to the consumption of specific food classes, such as daily servings of fruit and vegetables (six studies), or estimates of quantities of food consumed, such as total calories per day (four studies) or percent of daily calories from fat (five

studies). All outcomes were self-reported and measured with various types of questionnaires (details see Table C3). Only in two studies, participants were asked about dietary habits considered as proxies for a healthy diet, such as the number of family meals (DeBar et al., 2012) or eating when feeling bored (Eddy Ives et al., 2012). None of the ten studies showed a clear-cut effect of the intervention. Two studies observed partially positive results: In a small pilot study, the intervention reduced sweetened beverage consumption by 1.08 glasses per day (volume measurement of the glasses not reported) (Chen et al., 2019).

Another study observed a decrease in fast-food consumption and an increase in family meals, but no other significant effects on other outcomes (DeBar et al., 2012). Contrary to the other interventions, the two partially effective interventions both included a component of interactive electronic equipment (exergaming equipment and activity tracker linked to a smartphone app, respectively).

3.6.4. Smoking

Six studies described the effect of an intervention on cigarette smoking (Table C4) (De Micheli et al., 2004; Pbert et al., 2008; Walker et al., 2002; Fidler and Lambert, 2001; Hollis et al., 2005; Huang et al., 2005; Stevens et al., 2002; Jones et al., 2005). All outcome measures were self-reported. Four of these studies observed a significant decrease in smoking uptake in non-smokers (De Micheli et al., 2004; Pbert et al., Table 1 (continued)

Study Country Years of

study Study

type Target population Setting N Study

population characteristics

Outcomes relevant for

the review (icon)^a Global RoB Effectiveness of

intervention (color)^b appointment for a physical

exam or well visit, at high risk for diabetes (BMI >85th percentile or weight and height >85th percentile or weight >120% of ideal for height, plus any two of the following risk factors:

Family history of T2DM, race/ethnicity, signs of insulin resistance).

Mean BMI percentile: 97.6 (SD =0.023)

Pbert et al.

(2008) USA 2000–2004 cRCT 13–17-year-old patients scheduled for routine or acute care visits

8 pediatric

practices 2709 Female: 54%

Mean age: 16.8 years (SD =1.4) Smokers: 9.7%

Unclear

Saelens et al.

(2002) USA Not stated RCT Adolescents 12–16 years old, 20–100% above the 50th percentile for BMI and interested in weight control.

2 pediatric primary

care clinics 44 Female: 40.9%

Mean age: 14.2 years (SD =1.2) Mean BMI: 30.7 kg/m2 (SD = 3.1)

Unclear

Stevens et al.

(2002), Jones et al.

(2005)

USA Not stated cRCT 5th or 6th grade children presenting with their parents for a well-child visit (mean age: 11 years)

12 pediatric primary care practices

3145 Female: 48%

Mean age: 11 years (SD =0.9) Children who ever smoked:

5%

Unclear

Walker et al.

(2002) UK 1999 RCT 14–15-year old practice

patients 8 general practices

(12 nurses) 1488 Female: 51%

Mean age: 14.8 years Current smokers: 23%

High

Abbreviations: RCT =Randomized controlled trial, cRCT =cluster-randomized controlled trial, BMI =Body mass index, MVPA =Moderate to vigorous physical activity, PA =physical activity, PCP =Primary care physician.

aOutcomes relevant for the review are summarized with the following icons: =physical activity, =sedentary behaviors, =diet, =cigarette smoking, =secondary outcomes of the review (anthropometric and biological outcomes).

b The color of the icons relates to the overall effectiveness of the intervention on the outcome: Green =effective (statistically significant difference between intervention and control group), red =not effective (no significant difference between intervention and control group), blue =partial effectiveness or unclear/

unknown.

cStudy reported in two different publications with partly different outcomes. Therefore, only the results reported in the more recent paper are reported in this review.

(8)

Table 2

Characteristics of interventions described in studies included in a systematic review of primary care interventions to reduce cardiovascular risk behaviors in adolescents.

Study Intervention description

(content, components) Modes of delivery Delivered by Total duration of intervention

Parents

involved Theory or model used to design the intervention

Control condition

Chen et al.

(2017), Chen et al.

(2019)^a

(1) Use of wearable sensor (Fitbit flex) for 6 months (2) 8 online educational

modules for 3 months, followed by (3) Tailored, biweekly

text messages for 3 months.

The Fitbit flex app and the online program were used to track PA, sedentary activity, and dietary intake progress, set individual goals, monitor progress related to attaining goals, and provide tips and strategies.

Online, text

messages N/A

(pediatricians involved in recruitment only)

6 months No Social cognitive theory Concepts used in intervention: Self- efficacy, outcome expectation, skill mastery, self- regulation capabilities.

Pedometer and food- and-activity-diary (use for 3 months to track PA, sedentary activity, and food intake), plus online program consisting of 8 modules related to general adolescent health issues.

DeBar et al.

(2012) Teen intervention:

- 16 group meetings (90 min duration) over 5 months (weekly for 3 months and biweekly during months 4 and 5), including weight measurement and review of dietary and PA self-monitoring records.

- Yoga-based stretching and strength training during sessions, yoga practice resources for home use

- Exergaming equipment provided for use at home

Parent intervention: 12 weekly group sessions for 3 months

PCP involvement:

- Training of PCPs in motivational enhancement techniques for behavior change

- Individual sessions for participants with PCP at study onset and 6 months later - PCPs provided with

summaries of teens’ current health habits, to assist with selecting behavioral targets

Group sessions Written and electronic materials Consultation with PCP

Nutritionists, health educators, clinical psychologists PCPs (pediatricians)

5 months Yes None mentioned Usual care +packet of

written materials Meeting with PCP at study onset to encourage healthy lifestyle changes

De Micheli et al.

(2004)

Intervention depending on substance use status:

- Information sheets (all participants) - If no substance use

during past month:

Short (2–3 min) preventive intervention - If substance use during

past month: Brief intervention (20 min).

Individual session Pediatricians Single

session No FRAMES model

(feedback, responsibility, advice, menu of options, empathy, self-efficacy)

Usual care

(9)

Table 2 (continued)

Parents

Control condition

Training of pediatricians in questionnaire use and brief intervention techniques.

Eddy Ives et al.

(2012)

- Lifestyle counselling by PCP at baseline (diet, exercise habits), with parents present - Five follow-up visits at

1, 3, 6, 9 and 12 months for evaluation of adher- ence to

recommendations.

Individual sessions PCPs 12 months Yes None mentioned Only baseline visit

(lifestyle counselling) + follow-up at 12 months

Fleischman et al.

(2016)

Use of telehealth to promote collaboration between PCPs and obesity specialists:

- Five PCP consultations (30 min) every 3 months, using a booklet containing dietary messages and discussing dietary behaviors and goal achievement.

- Weekly tele- consultation between obesity specialists and PCPs.

- 12 tele-visits with obesity specialists (dietitian and psychologist, 30–60 min) over 6 months for patients and parents.

In-person sessions with PCP Teleconsultations with specialists

PCPs Obesity specialists Dietitian Psychologist

6 months Yes None mentioned PCP consultation and

tele-consultation for PCPs (same as intervention, but without tele-visits with obesity specialists).

Fidler and Lambert (2001)

Printed material about the advantages of remaining a non-smoker, sent by post every 3 months.

Non-smoker certificate.

Written materials No direct contact (PCP as sender of information)

12 months No None mentioned Only baseline and final

questionnaires

Hollis et al.

(2005), Huang et al.

(2005)

(1) Initial consultation with PCP giving brief advice about quitting smoking or to not start.

(2) 10–12-min computer session (Pathways to Change interactive computer program).

(3) 3–5-min motivational counselling with health counselors (4) Handout of stage- relevant advice, information sheets, quit kits.

(5) 2 individual booster sessions within the next 11 months with health (mostly by phone), using the Pathways to Change program.

In-person sessions Computer session Written materials Telephone sessions

PCPs Health counselors

12 months No None mentioned

(authors only mention that intervention was

“theory-driven”;

computer program based on stages of readiness).

Diet intervention (only health counselling)

Kong et al.

(2013) Adolescents committed to improvement of nutrition and physical activity (ACTION):

- 8 encounters with family medicine nurse practitioner every 2–3 weeks, with use of motivational interviewing.

In-person sessions Written and electronic materials

Family medicine nurse practitioner

8 months (one school year)

Yes Transtheoretical

model of behavior change (no details given)

One visit with family physician similar to IG + booklet

(10)

Parents

Control condition

- Toolkit (DVD and print materials) with obesity risk reduction strategies.

- Newsletter and telephone updates for parents.

Love- Osborne et al.

(2014)

- Visits with health educator (frequency adapted to participant), using the

HeartSmartKids tool, motivational interviewing, and goal setting (diet and physical activity goals).

- Self-monitoring though log-sheets.

- Two weekly text- messages for first and second semester (half of IG) or second semester (other half of IG).

- Medical visit if none in the previous 2 years.

In-person sessions

Text messages Health educator (incorporated into school-based health center team with supervising physician)

6–8 months (one school year)

No None mentioned Medical visit if none in the previous 2 years.

Oreskovic et al.

(2016)

- 30-min meeting with pediatrician and individualized counselling based on MVPA baseline data (obtained through global positioning system) and fixing of individualized PA goal.

- 15–20 weekly reminders (text message and/or phone call).

- PA promoting gift and small financial incentive for adolescent and family for meeting their goal.

In-person session Text messages and/

or phone calls

Pediatrician 3–4 months Yes None mentioned Handout with feedback on current MVPA level, with standard-of-care diet and PA recommendation.

Ortega- Sanchez et al.

(2004)

Counselling by PCP during medical visit, based on baseline PA questionnaire:

Reinforcement counselling, increase counselling or initiation counselling.

Written guidelines provided to physicians.

In-person session PCP Single

session with follow-up visit at 12 months

No None mentioned No specific intervention (only planned office visit).

Patrick et al.

(2001) PACE+intervention:

(1) Interactive computerized assessment in the waiting room, resulting in a tailored action plan.

(2) PA/diet counselling from health care provider (physician or nurse practitioner) based on the action plan.

(3) Extended intervention (3 different types):

Frequent mail, infrequent mail and telephone, frequent mail and telephone.

Computer session In-person session Telephone calls and/or postal mail

PCP or nurse practitioner Research staff (extended intervention)

4 months Yes (extent of parents’

involvement determined by each patient)

Transtheoretical model of behavior change and social cognitive theory (basis for PACE+

intervention) Relapse prevention model (basis for extended interventions)

Computerized assessment in waiting room plus PA/diet counselling, without extended intervention.

12 months Yes

(11)

Parents

Control condition

Patrick et al.

(2006), Rosenberg et al.

(2007)

PACE+intervention:

Computer-supported intervention (see above), printed take-home manual, 11 telephone counselling calls (10–15 min) and mail contact.

Parent intervention:

Support, phone contact as needed, manual

Computer session In-person session Telephone calls Mailed written materials

Primary care provider (not detailed)

Transtheoretical model of behavior change and social cognitive theory (basis for PACE+

intervention)

Sun protection behavior program (SunSmart)

Patrick et al.

(2013) Program website with tutorials, challenges and feedback, based on

“stoplight approach”, plus monthly mailed tip- sheets.

Study arms: Website only (+reminders), website + monthly 90-min group sessions (with parents), website +SMS (min. 3 text messages per week).

Website Written materials Depending on intervention arm:

Group sessions or text messages

Pediatricians involved in recruitment only Health counselors for group sessions

12 months Yes Behavioral

determinants model and Transtheoretical model of behavior change

Main concepts used in intervention:

Education about behavioral goals, promoting use of evidence-based behavior change strategies

Usual care: Printed material, encouraged to attend 3 group sessions, monthly tip sheets by mail (reflecting the prevailing standard of care for this population).

Pbert et al.

(2008) (1) Provider-delivered component:

Individual session based on an algorithm (2) Peer counselling:

Motivational interviewing and behavior change counselling (15–30- min session plus follow-up with 10- min telephone calls after 2, 6, 12, and 21 weeks)

In-person sessions

Telephone calls Pediatric providers (physicians, nurse practitioners, physician’s assistants, pediatric residents) Trained peer counselors

5 months No Provider-delivered

component based on the 5A model (recommended by the US public health service and the American Academy of Pediatrics)

Usual care

Saelens et al.

(2002) (1) Computer program adapted from PACE+ (see above), generating a tailored action plan (2) Counselling by

pediatrician to discuss action plan (3) 11 telephone calls

with counselor (10–20 min per session) over 14–16 weeks

(4) Participant manual to help acquire behavioral skills (5) Self-monitoring of

food intake (booklet based on stoplight diet)

(6) Self-monitoring of PA (7) Awards for meeting

goals (lottery) (8) Information sheets

for parents

Computer session In-person session Telephone calls Written materials

Pediatricians Counselors (degree in psychology or nutrition)

4 months Yes See above (PACE+

intervention) Typical care: Visit with pediatrician (non- tailored discussion based on pediatric obesity recommendations).

Stevens et al.

(2002), Jones et al.

(2005)

(1) Focus of health supervision visit on tobacco and alcohol use, contract signed between child, parents and clinician;

training of pediatricians and nurse practitioners.

(2) Letter signed by pediatrician to

In-person session

Written materials Pediatricians Nurse practitioners

36 months Yes None mentioned Same as intervention,

but focusing on safety (bicycle helmet, seatbelt use, safe gun storage).

(12)

2008; Fidler and Lambert, 2001; Hollis et al., 2005). Whereas one of these studies targeted only non-smokers at baseline (Fidler and Lambert, 2001), the three others targeted a general adolescent population and also found significant effects on the proportion of heavy smokers (De Micheli et al., 2004) and smoking quit rate (Pbert et al., 2008; Hollis et al., 2005), respectively. The four effective interventions were different from each other: One was a single brief intervention (De Micheli et al., 2004), one consisted of handing out printed materials (Fidler and Lambert, 2001), and two had several components (consultation, computer session, and repeated motivational counselling (Hollis et al., 2005) and consultation, peer counselling and follow-up telephone calls (Pbert et al., 2008)). These two interventions were somewhat individualized according to participants’ current stage of change.

3.7. The effects of interventions on secondary outcomes of the review Of the studies investigating physical activity, sedentary behaviors and/or diet, nine studies also included outcomes considered as secondary for this review (Table C5) (Chen et al., 2019; Kong et al., 2013;

Patrick et al., 2006; Patrick et al., 2013; Saelens et al., 2002; Chen et al., 2017; DeBar et al., 2012; Eddy Ives et al., 2012; Fleischman et al., 2016;

Love-Osborne et al., 2014). All of them measured BMI outcomes. Four of them observed significant differences in BMI percentile or z-score in favor of the intervention (Chen et al., 2019; Kong et al., 2013; Saelens et al., 2002; DeBar et al., 2012), which were due to a decrease of BMI in the intervention group and/or an increase of BMI in the control group.

Three additional studies observed significant decreases in BMI z-score in one or both groups, but without statistically significant between-group differences (Eddy Ives et al., 2012; Fleischman et al., 2016; Love- Osborne et al., 2014). BMI z-score reductions that were considered clinically significant (ranging from − 0.1 to − 0.31) were mostly short- term effects, as outcomes were measured immediately at the end of the intervention. The most extended effect was observed on outcomes measured 7 months after the intervention (DeBar et al., 2012). Other

secondary outcomes included waist circumference or waist-to-hip-ratio (three studies (Kong et al., 2013; Eddy Ives et al., 2012; Fleischman et al., 2016)), blood pressure (two studies (Kong et al., 2013; Fleischman et al., 2016)), blood cholesterol (two studies (Kong et al., 2013; DeBar et al., 2012)) and blood glucose (two studies (Kong et al., 2013; DeBar et al., 2012)). Only one study found significant effects for one of these outcomes (significant between-group difference in waist circumference due to an increase in the control group) (Kong et al., 2013). In two studies, significant differences in some of the behavioral outcomes were observed along with significant differences in BMI. Both were small- scale studies targeting overweight or obese adolescents (Chen et al., 2019; Kong et al., 2013).

4. Discussion

In this literature review, we synthesized the evidence about the effectiveness of interventions conducted in PC settings to improve adolescents’ main cardiovascular risk behaviors. Overall, we found little evidence of the effectiveness of primary care-based interventions targeting adolescents’ cardiovascular risk behaviors. This was due to the lack of coherent results, but also to methodological shortcomings identified in most studies, limiting the quality of the available evidence.

Interventions aiming to reduce smoking or to decrease sedentary time tended to have more significant effects on adolescents’ behaviors than interventions targeting physical activity or diet. Interventions focusing on smoking behaviors seemed to be mainly effective to limit smoking uptake in non-smokers. This finding is in line with recent evidence from the United States Preventive Services Task Force (USPSTF) showing that behavioral interventions in primary care settings may reduce the likelihood of smoking initiation in children and adolescents (Selph et al., 2020). Similarly to the USPSTF, we also found insufficient evidence for interventions for the cessation of tobacco use. In our review, we only identified four studies showing effectiveness on smoking cessation, and they varied from a simple handout of printed materials to Table 2 (continued)

Parents

Control condition

reinforce the agreement (10 days later)

(3) Brochure on effective communication (4) Reminders at

subsequent office visits for 36 months (5) Reinforcement

through 12 newsletters for parents and children sent over 36 months Walker et al.

(2002) 20-min consultation with practice nurse to discuss health and health related behavior focusing on topics of the adolescents’

choice.

In-person session General practice nurse (general practitioners involved in recruitment)

Single

session No No theory mentioned

Authors state that “the intervention was based on models of self-efficacy and behavior change” and

“followed the structure suggested by the American Medical Association for consultations promoting self- efficacy for health lifestyles with adolescents”.

Usual care, baseline questionnaires sent by mail.

Abbreviations: MVPA =Moderate to vigorous physical activity, PA =physical activity, PCP =Primary care physician.

aStudy reported in two different publications with partly different outcomes. Therefore, only the results reported in the more recent paper are reported in this review.