# Electronic screening and brief interventions promoting healthy diet and physical activity among adult patients in medical settings: A systematic review

Camille Forcier, Aymery Constant, David Val-Laillet, Ronan Thibault,
Romain Moirand

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## To cite this version:

Camille Forcier, Aymery Constant, David Val-Laillet, Ronan Thibault, Romain Moirand. Electronic screening and brief interventions promoting healthy diet and physical activity among adult patients in medical settings: A systematic review. Clinical Nutrition ESPEN, 2024, 64, pp.509-518. 10.1016/j.clnesp.2024.10.164. hal-04782736

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Meta-analysis

Electronic screening and brief interventions promoting healthy diet and physical activity among adult patients in medical settings: A systematic review

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Camille Forcier<sup>a</sup>, Aymery Constant<sup>a, b, *</sup>, David Val-Laillet<sup>a</sup>, Ronan Thibault<sup>a, c</sup>, Romain Moirand<sup>a, d</sup>

ARTICLE INFO

Article history:

Received 21 May 2024

Accepted 26 October 2024

Keywords:

Screening and brief interventions

e-health

Health promotion

Diet

Physical activity

SUMMARY

**Background & aims:** Electronic screening and brief interventions (eSBIs) are promising health promoting approaches but their utilization towards nutrition outcomes has been little documented so far. The aim of this systematic review is to examine the nature and findings of eSBIs targeting diet and/or physical activity (PA) among adult patients in healthcare settings since smartphone technologies emerged.

**Methods:** Three systematic searches in PubMed, ScienceDirect and PsycINFO were conducted from January 2010 to May 2023. We include all studies assessing eSBI towards lifestyle habits (dietary habits and/or PA at least), implemented in healthcare settings and targeting adult participants.

**Results:** Of 1981 articles identified, 15 studies met inclusion criteria. Most of them documented eSBIs implemented in primary care (n = 11), targeting PA (n = 11) and diet (n = 9) alone or combined (n = 5). Some eSBIs targeted additional behaviors (e.g. smoking, drinking). Delivery modes included computer (n = 8), printed documents (n = 8) or phone (n = 2) but only four eSBIs were interactive (or partially automated). In addition to personalized feedback, most common behavior change techniques were "information about social and environmental consequences" (n = 9), "goal setting" (n = 7), and "information about health consequences" (n = 7). Most studies assessing behavior change had a control group (8 in 10), and eight studies found positive changes in behavior.

**Conclusions:** Although implementing eSBIs towards PA/diet among adult patients in medical settings seems promising for primary, secondary and tertiary prevention, common conceptual framework, intervention and outcomes are severely lacking, and the extent to which they follow existing SBI guidelines remains unclear. Demonstrating their effectiveness requires fixing these issues before conducting prospective assessment studies in medical settings.

© 2024 The Authors. Published by Elsevier Ltd on behalf of European Society for Clinical Nutrition and Metabolism. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Despite the pivotal role of healthy lifestyle habits in chronic disease prevention and management [1,2], adherence to guidelines remain suboptimal for the majority of the adult population. For example, in Europe, only 12 % of the population consume five fruits and vegetables (F&V) a day and more than 45 % never exercise or

play sport [3,4]. Healthcare professionals often have frequent interactions with individuals in various settings, such as clinics, hospitals, community outreach programs, and even in everyday encounters. By benefitting from these interactions effectively, health providers could "Make Every Contact Count" (MECC), i.e. improve public health through supporting behavior change in the millions of people with whom they come into contact. Each medical and/or paramedical visit could therefore constitute a "teachable moment" for health promotion [5,6]. Unfortunately, prevention advices are often overlooked during routine medical visit, and tend to cover only a specific pathology or risk factor [7,8]. A systematic review of 36 systematic reviews exploring barriers to delivering

<footnote><sup>a</sup> INRAE, INSERM, Univ Rennes, Nutrition Metabolisms and Cancer, NuMeCan, F-35000 Rennes, France</footnote>

<footnote><sup>b</sup> EHESP, School of Public Health, F-35043 Rennes, France</footnote>

<footnote><sup>c</sup> Endocrinology-Diabetology-Nutrition Department, Home Parenteral Nutrition Centre, CHU Rennes, F-35000 Rennes, France</footnote>

<footnote><sup>d</sup> Liver Diseases Department and Addictology Unit, CHU Rennes, F-35000 Rennes, France</footnote>

<footnote>* Corresponding author. Ecole des Hautes Etudes en Santé Publique, Avenue du Prof. Leon Bernard, 35043 Rennes Cedex, France.</footnote>

<footnote>E-mail address: aymery constant@ehesp.fr (A. Constant).</footnote>
behavior change interventions for behavioral risk factors identified several, such as perceived lack of time, perceived lack of prioritization, negative perceptions of knowledge and skills, and availability of resources and support [9].

One of the most promising approaches to implement the MECC in medical settings consists in providing time-limited, focused conversation or counseling sessions designed to offer quick support and guidance to individuals who may benefit from targeted assistance [10–12]. Screening and Brief Interventions (SBIs) are cost-effective, require less training time for professionals, and can be adapted to many situations or problems, including diet, physical activity (PA), smoking, drinking, and other types of substance (ab) use [11,13–19]. Recently, the WHO confirmed their considerable potential for improving public health outcomes by publishing SBIs implementation guidelines for primary care practitioners (PCP) [20], making the advice more relevant and actionable. In the recent years, SBIs increasingly incorporated digital tools to provide more accessible, cost-effective, personalized and engaging interventions. Smartphone technologies combining the portability of traditional mobile devices with advanced computing capabilities have the potential to provide platforms for delivering SBIs with increased accessibility, scalability, personalization and engagement [21].

According to available evidence, some elements are pivotal for SBI design, implementation and effectiveness. The FRAMES model (Feedback, Responsibility, Advice, Menu, Empathy, and Self-efficacy) [22] offers a blueprint of six elements that produce change following brief intervention (BI), and the inclusion of behavior change techniques (BCTs) may enhance their effectiveness, tailor interventions to individual needs, and empower individuals in taking control over their behavior and habits. Since they were firstly developed to reduce alcohol consumption for decades [23–25], their utilization towards other behaviors in adults is just starting to emerge and has been little documented so far [26–29]. The aim of this systematic review is to examine the nature and findings of eSBIs that have sought to improve nutritional counseling among adult patients in medical settings since smartphone technologies emerged. Specific objectives are 1) to identify and describe eSBIs targeting diet and/or PA among adult patients in healthcare settings, 2) to investigate the behavior change techniques used; and 3) to estimate the effectiveness of these interventions.

<table><caption>**Table 1**
Keywords used in the database search.</caption><thead><tr><th>Screening and brief intervention</th><th>Physical activity/diet</th><th>Electronic component</th><th>Healthcare setting</th></tr></thead><tbody><tr><td>- SBI</td><td>- Life style</td><td>- Internet</td><td>- Hospital</td></tr><tr><td>- Screening and brief intervention*</td><td>- Nutrition*<br/>- Food intake</td><td>- Website*<br/>- Web-based</td><td>- Medical center*<br/>- Health center*</td></tr><tr><td>- Brief intervention*</td><td>- Food habits</td><td>- Online-treatment*</td><td>- Medical institution</td></tr><tr><td>- SBIRT</td><td>- Feeding behaviors</td><td>- Computer*</td><td>- Medical set*</td></tr><tr><td>- Screening, brief Intervnetio interventions and referral to treatment</td><td>- Eating</td><td>- Telephon*</td><td>- Primary care</td></tr><tr><td>- Health screen*</td><td>- Diet* intake<br/>- Diet* behavior*</td><td>- Computer-delivered intervention*</td><td>- Health set*<br/>- Health facilities</td></tr><tr><td>- Motivational interviewing</td><td>- Diet* quality</td><td>- Website-interactivity</td><td></td></tr><tr><td>- Personalized feedback</td><td>- Diet* pattern*</td><td>- Computer-based</td><td></td></tr><tr><td>- Personalized feedback</td><td>- Diet* habit*</td><td>- Online</td><td></td></tr><tr><td>- PFI</td><td>- Diet* guideline*</td><td>- Interactive technolog*</td><td></td></tr><tr><td>- Personalized feedback In intervention*</td><td>- Eating behavior*</td><td>- App*</td><td></td></tr><tr><td>- Tailored feedback</td><td>- Eating pattern*</td><td>- m-health</td><td></td></tr><tr><td>- Feedback</td><td>- Eating habit*</td><td>- e-health</td><td></td></tr><tr><td>- Brief motivational Intervention*</td><td>- Physical activit*</td><td></td><td></td></tr><tr><td>- BFI</td><td>- Exercise*</td><td></td><td></td></tr><tr><td>- Brief feedback intervention*</td><td>- Sedentary behavior</td><td></td><td></td></tr><tr><td>- Screen* intervention*</td><td>- Health risk behavior</td><td></td><td></td></tr><tr><td>- Screen* feedback</td><td></td><td></td><td></td></tr></tbody></table>

## 2. Methods

We conducted a systematic review according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [30].

### 2.1. Search strategy

In the present review, the term “eSBI” labelled an intervention targeting lifestyle habits (dietary habits and/or PA at least) comprising screening, BI and providing personalized feedback. The eSBI must use at least one electronic device (e.g. phones, smartphones, computers, tablets, apps) for the screening and/or the brief intervention. BI can be fully automated (e.g. computer-based) or interactive (provided by a person). In the absence of standard definition, the intervention is considered brief if including less than four sessions without a time limit [15,25]. We retrieved studies written in English or French from three databases (PubMed, ScienceDirect, PsycINFO) published from January 2010 (when smartphone technology first appeared) to May 2023. No registration was made and no protocol was prepared. The search terms (Table 1) combined keywords and MeSH terms describing SBI (n = 20), digital tools (n = 14), PA/diet (n = 20) and healthcare settings (n = 8).

### 2.2. Eligibility criteria

We included studies meeting the following criteria: Original research on eSBI towards lifestyle habits (dietary habits and/or PA at least), implemented in healthcare settings and targeting adult participants.

### 2.3. Study selection and data extraction

Firstly, the first author (CF) examined titles and abstracts to remove obviously irrelevant materials. Secondly, the same author examined the full text for compliance with eligibility criteria, leading to the flowchart for study inclusion and exclusion (Fig. 1). Information on participants, setting, study design, outcomes, screening and intervention, comparator and follow-up duration are presented in Table 2, together with main results of studies. The first
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Flow-chart for study inclusion.</figcaption></figure>

author (CF) attended the online training on behavior change tech-
niques (BCTs) taxonomy (www.bct-taxonomy.com/) to classify BCTs
according to this framework.

3. Results

3.1. Description of studies included

Out of the 1981 studies identified at first step, fifteen meeting
the inclusion criteria were included (Fig. 1), conducted mainly in
primary care (n = 11). Nine studies were randomized controlled
trials (RCT) and thirteen were prospective with a median follow-up
of 4 months (IQR: [1–6]). Eleven studies had a comparator group
consisting of a routine care control group (n = 6), a target behavior
different from the outcome (n = 2), or a standard non-personalized
intervention (n = 3). All these studies included 12,533 participants
(median: 200; IQR: [113–1235]).

3.2. Targeted behaviors

Four studies had a single outcome, either PA [31–33] or F&V
consumption [34] while the others (n = 11) had multiple outcomes.
The most common behaviors targeted were PA (n = 11), smoking
(n = 7), alcohol consumption (n = 5) and diet (n = 9). Additionally,
two studies targeted outcomes such as sleep or drug use. Compo-
nents of eating behaviors in eSBIs towards diet varied from 1 to 12
(median: 3; IQR: [1.50–8.25]), and one study omitted their detailed
description [35]. For the most part (n = 7), eating components were
types of food/nutrients (e.g. F&V = 7, fat intake = 3), but also calorie
intake (n = 1) and chemical elements (e.g. phosphorus, sodium) in
one study including patients with kidney disease [36].

3.3. Description of the e-SBI delivery mode

Screening used e-surveys in eleven studies, sent *via* e-mail or
completed on a website, completed at home or in healthcare set-
tings (on a tablet or computer). The survey of Parekh et al.'s inter-
vention [37] was sent by mail and Mc Clish et al. [38] conducted the
intervention by phone. Information on the screening delivery mode
was lacking in two studies [39,40]. When it comes to BI, which
includes at least a personalized feedback, eight studies used
computer-based brief interventions, while three of them were
interactive, i.e. including face-to-face interactions with a healthcare
provider [34,39,40]. Eight studies used printed BI that could be
provided in the healthcare setting or sent by mail (n = 2), and one
of them involved an interview with PCP [31]. Two studies used
telephone calls to provide follow-up interventions, four days and
five weeks after baseline [40], or two weeks after baseline [38].
Several studies used multimodal interventions [31,41].

3.4. Description of brief intervention with behavior change
technique (BCTs) used

The majority of studies reported single-session eSBIs. The
printed intervention of Agarwal et al. [31] was provided to patient
at baseline, and sent by mail or e-mail two weeks later. Concerning
Katz et al.'s intervention [40], the BI at baseline focused on
personalized feedback, goal setting and action planning while
follow-up phone calls (3–5 days and 4–6 weeks after baseline)
focused on reviewing behavioral goals and problem solving. In
Parekh et al.'s study [37], the number of sessions depended on the
group: The “dual intervention” group received the same interven-
tion as the “single intervention” group but twice (at baseline and 3
<table><caption>**Table 2**
Summary of included articles.</caption><thead><tr><th rowspan="2">Study</th><th rowspan="2">Participants and setting</th><th rowspan="2">Target behaviors</th><th colspan="2">Mode of provision (Duration)</th><th rowspan="2">Interactive Bl, provider</th><th rowspan="2">Design (Sample size)</th><th rowspan="2">Follow-up</th><th rowspan="2">Comparator</th><th rowspan="2">Main outcomes</th><th rowspan="2">Results</th></tr><tr><th>Screening</th><th>Intervention</th></tr></thead><tbody><tr><td>Agarwal et al., 2020<br>Canada</td><td>All patients<br>Primary care</td><td>PA</td><td>E-mail or on<br>tablet in the<br>waiting room</td><td>Printed<br>onsite, then<br>sent by email<br>or mail</td><td>Yes, PCP</td><td>Step wedge<br>CRT (437)</td><td>M4</td><td>Usual care</td><td>MET minutes per<br>week</td><td>No difference<br>between groups.</td></tr><tr><td>Almeida et al., 2015<br>US</td><td>Patients of a<br>health care visit<br>Treadmill center</td><td>PA</td><td>Computer<br>interactive<br>session</td><td>Computer<br>interactive<br>session<br>(20 min with<br>screening)</td><td>No</td><td>RCT (415)</td><td>M1</td><td>C1: eSBI on<br>healthy eating<br>C2: Fitness<br>facilities voucher</td><td>Moderate and<br>vigorous PA per<br>week<br>% Of participants<br>meeting PA<br>guidelines</td><td>Intervention and<br>intervention + C2<br>increased weekly<br>PA participation<br>compared to the<br>C1 and C2. No<br>difference<br>between C2 and<br>C1 or between<br>intervention and<br>intervention + C2.<br>Intervention and<br>intervention + C2<br>groups had<br>significantly<br>higher prevalence<br>of meeting PA<br>guidelines than<br>C1 and C2.<br>No difference<br>between groups.</td></tr><tr><td>Hess et al., 2014<br>US</td><td>All patients<br>Primary care</td><td>PA<br>Smoking</td><td>Tablet</td><td>Printed</td><td>No</td><td>CRT (98)</td><td>BL</td><td>Usual care</td><td>% Of patient<br>reporting<br>discussion with<br>PCP (about PA,<br>smoking)</td><td>No difference<br>between groups.</td></tr><tr><td>Hrolfsdottir et al., 2019<br>Iceland</td><td>Overweight<br>pregnant women<br>University<br>hospital</td><td>12 food<br>groups</td><td>Not specified<br>(5–10 min)</td><td>Web-based</td><td>Yes,<br>nutritionist</td><td>RCT (88)</td><td>M3, M6</td><td>Usual care</td><td>Dairy products (g/<br>d)<br>Vegetables (g/d)<br>Fruits (g/d)<br>Fish (% ≥300 g/<br>week)<br>Processed meat<br>(g/d)<br>Soft drinks (g/d)<br>French fries, chips<br>(% ≥100 g/week),<br>Cakes, biscuits,<br>sweets (g/d)</td><td>Only soft drink<br>consumption was<br>significantly<br>lower in the<br>intervention<br>group.</td></tr><tr><td>Katz et al., 2017<br>US</td><td>Patients with CVD<br>risk factor<br>Emergency unit</td><td>Fat<br>F&V<br>Cholesterol<br>PA<br>Smoking</td><td>Not specified</td><td>Computer<br>session (1 h)<br>Follow-up<br>calls (30 min)</td><td>Yes,<br>Health<br>educator</td><td>RCT (124)</td><td>M2, M6</td><td>Minimal<br>counseling</td><td>Saturated fat<br>calories<br>F&V servings/day<br>PA hours/week<br>Number of<br>cigarettes/day</td><td>No difference<br>between groups.</td></tr></tbody></table>
<table><tr><td>Krist et al., 2016<br>US</td><td>All patients<br>Primary care</td><td>F&amp;V<br>Sweetened beverage<br>Fast food<br>PA<br>Smoking<br>Drinking<br>Drug use<br>Sleep</td><td>Online<br>survey/phone<br>before the<br>visit<br>Or printed<br>onsite</td><td>Printed</td><td>No</td><td>CRT (2913)</td><td>W2</td><td>Usual care</td><td>Self-reported<br>goals and<br>improved<br>behaviors</td><td>Increases in self-<br>reported goal<br>settings on diet,<br>PA, drinking and<br>sleep.<br>No difference for<br>drug use.<br>More<br>improvement<br>reported in diet,<br>PA, and sleep. No<br>difference in<br>alcohol, smoking<br>and drug use.<br>Increased PA days<br>at follow-up.</td></tr><tr><td>Leijon et al., 2011<br>Sweden</td><td>All patients<br>Primary care</td><td>PA</td><td>Touch-screen<br>kiosk in or<br>close to the<br>waiting room</td><td>Printed</td><td>No</td><td>Prospective<br>(311)</td><td>M3</td><td>N/A</td><td>Number of PA<br>days</td><td>Improvements in<br>fat, fiber and F&amp;V<br>intake at follow-<br>up.</td></tr><tr><td>McClish et al.,<br>2014<br>US</td><td>All patients<br>Primary care</td><td>Fat<br>Fiber<br>F&amp;V</td><td>Phone</td><td>Printed, and<br>phone call<br>(15 min)</td><td>No</td><td>Prospective<br>(113)</td><td>M1</td><td>N/A</td><td>For fat and fiber<br>intake: Score of<br>FFB<br>F&amp;V servings/day</td><td>No change in self-<br>efficacy levels<br>before and after<br>exposure.</td></tr><tr><td>Murali et al., 2013<br>US</td><td>Patients with<br>chronic kidney<br>disease<br>Nephrology<br>department</td><td>Na, P, K<br>Protein<br>Calorie<br>Carb<br>Fat<br>Saturated fat<br>Cholesterol</td><td>Web-based<br>(20 min)</td><td>Web-based</td><td>No</td><td>QE, pre-post<br>trial (12)</td><td>BL</td><td>None</td><td>Self-efficacy</td><td></td></tr><tr><td>Nagykaldi et al.,<br>2013<br>US</td><td>Middle-aged and<br>older adults, with<br>multiple health<br>conditions<br>Primary care</td><td>Diet<br>PA<br>Smoking<br>Drinking<br>Drug use</td><td>Web-based<br>(45 min)</td><td>Web-based</td><td>No</td><td>QE CT (200)</td><td>M12</td><td>Usual care</td><td>Estimated life<br>expectancy<br>Wellness score</td><td>Increase in<br>estimated life<br>expectancy<br>within the<br>intervention<br>group and<br>compared to the<br>control group.<br>Increase in<br>wellness score in<br>the intervention<br>group and<br>compared to the<br>control group.<br>Increase in the<br>likelihood of<br>discussing PA<br>with physician.<br>No differences for<br>drinking.</td></tr><tr><td>Ozanne et al.,<br>2019<br>US</td><td>Women aged 40-<br>74<br>Primary care</td><td>Drinking<br>PA</td><td>On tablet in<br>the waiting<br>room (5 min)</td><td>Printed</td><td>No</td><td>RCT (1235)</td><td>W1</td><td>Usual care</td><td>Discussion with<br>the physician<br>about alcohol and<br>PA</td><td>OR of meeting<br>recommendations<br>higher in dual<br>intervention<br>group for all food<br>except spreads<br>and meat intake,<br>physical activity<br>and smoking as<br>compared to dual</td></tr><tr><td>Parekh et al., 2014<br>Australia</td><td>All patients<br>Primary care</td><td>Fish<br>Spreads<br>Salt<br>F&amp;V<br>Milk<br>Meat<br>Smoking<br>PA<br>Drinking</td><td>Printed, sent<br>by mail</td><td>Printed at<br>baseline and<br>M3 for dual<br>intervention</td><td>No</td><td>RCT (3065)</td><td>M12</td><td>eSBI on other<br>behaviors at<br>baseline and M3<br>for dual control</td><td>Adherence to<br>guidelines</td><td></td></tr></table>

(continued on next page)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th rowspan="2">Study</th><th rowspan="2">Participants and setting</th><th rowspan="2">Target behaviors</th><th colspan="2">Mode of provision (Duration)</th><th rowspan="2">Interactive Bl. provider</th><th rowspan="2">Design (Sample size)</th><th rowspan="2">Follow-up</th><th rowspan="2">Comparator</th><th rowspan="2">Main outcomes</th><th rowspan="2">Results</th></tr><tr><th>Screening</th><th>Intervention</th></tr></thead><tbody><tr><td>Plaete et al., 2016<br>Belgium</td><td>Patients recruited either by PCP or researcher</td><td>F&amp;V</td><td>Tablet in waiting room or website</td><td>Web-based</td><td>Yes, only for PCP's patients</td><td>QE CT (139)</td><td>M1</td><td>Feedback with general information</td><td>F&amp;V intake</td><td>control.<br>OR of meeting recommendations higher in single intervention group for all foods, alcohol (but not for salt and meat intake, physical activity and smoking) as compared to single control.</td></tr><tr><td>Ruffin et al., 2011<br>US</td><td>Healthy adults aged 35–65 yrs</td><td>F&amp;V PA Smoking</td><td>Web-based or physician office computer or by phone</td><td>Mail or e-mail or during visit</td><td>No</td><td>CRT (3254)</td><td>M6</td><td>Standardized prevention messages</td><td>Adherence to guidelines</td><td>Increase in F&amp;V intake in the PCP and the researchers' intervention group as compared to control group.<br>The intervention group was more likely than the control group to move from not at goal status to at goal for both fruit and vegetable intake and physical activity.</td></tr><tr><td>van den brekel-Dijkstra et al., 2016<br>Netherlands</td><td>Healthy aged 45–70</td><td>PA Smoking Drinking</td><td>Web-based</td><td>Web-based</td><td>No</td><td>Prospective pilot (129)</td><td>M6</td><td>N/A</td><td>Initiation of behavior change (single-item) for diet, PA, smoking, alcohol use</td><td>There was no intervention effect on smoking cessation.<br>32 % reported to have improved PA, 28 % to have improved their diet, 24 % of current drinkers reported to have reduced alcohol intake, 44 % of current smokers reduced tobacco use.</td></tr></tbody></table>

Note: BL: baseline; CRT: cluster randomized trial; CT: cluster trial; CVD: cardiovascular diseases; eSBI: electronic screening and brief intervention; FFB: fat- and fiber-related behavior questionnaire; F&amp;V: fruit & vegetable; M: month; MET: metabolic equivalent of task; PA: physical activity; PCP: primary care physician; QE: quasi-experimental; RCT: randomized controlled trial; W: week; %: percentage.
months after). The same applies to the "dual control" group that received the control intervention twice. The intervention included a printed personalized feedback indicating poor adherence to guidelines. Finally, the intervention of Plaete et al. [34] comprised a baseline session focusing on feedback, action planning, problem solving and self-monitoring, and two follow-up sessions (one week and one month) focusing on feedback, goals and action plan revision.

A total of 75 BCTs [42] were used in the studies under review (Table 3), with a median value of 4 BCTs per intervention (IQR = [3–6]). The most common techniques (taxonomy#; name) used were "2.2 Feedback on behavior" (n = 15), "5.3 Information about social and environmental consequences" (n = 9) and "1.1 Goal setting" (n = 8).

## 3.5. Outcomes

### 3.5.1. Controlled studies assessing behavior change

In the two controlled studies focusing on PA, the study by Almeida et al. [32] reported an increase in PA time per week in groups with an interactive computer session. This session focuses on action planning and problem solving (with or without gym membership voucher), as compared with the same computer intervention focused on healthy eating, and a group with only a one-year voucher. One study showed no difference in the number of Metabolic Equivalent of Task (MET) per minute between two groups (intervention and usual care) at 4-month follow-up [31]. When it comes to diet, Plaete et al. [34], using a website intervention to improve F&V intake, showed that the PCP intervention group (PCP recruited the participants) and the researchers' intervention group (researchers recruited the participants) increased their F&V intake, as compared to the control group. Hrolfsdottir et al. [39], using a website intervention providing personalized feedback on diet, showed a decrease in soft drinks consumption in the intervention group as compared to the control group, but no changes otherwise.

Four controlled studies targeted multiple behaviors, and two of them assessed adherence to guidelines. In the study by Parekh et al. [37], intervention targeted food choices (6 components), PA, smoking and alcohol. Estimates of adherence to guidelines were significantly higher for dual intervention group compared to dual control group for fish, F&V, milk, salt, alcohol, but not for spreads and meat, PA and smoking at 12 months. They were significantly higher for the single intervention group compared with control group for fish, spreads, F&V, milk and alcohol but not for salt and meat intake, PA and smoking at 12-month follow-up. In the study by Ruffin et al. [41], the intervention targeted F&V, PA and smoking. Estimates of adherence to guidelines were significantly higher in the intervention than in the control group for F&V and PA, but not for smoking. Katz et al. [40], found no difference in F&V, fat intake, PA or smoking between intervention and control group at 6-month follow-up. Nagykaldi et al. [35] showed that the estimated life expectancy in the intervention group was higher than in the control group at 12-month follow-up. The intervention focused on diet, PA, smoking, alcohol and drug use.

### 3.5.2. Uncontrolled studies assessing behavior change

Two prospective uncontrolled studies evaluated behavior change. One study aimed to increase fiber and F&V consumption while reducing fat consumption, with personalized feedback explaining how to improve these outcomes. Results showed an increase in fiber and F&V consumption and a decrease in fat

consumption at 3-month follow-up compared with baseline [38]. The other study used tailored printed feedback on the current PA level, and found an increase in the number of days meeting the PA guidelines at 3-month follow-up [33].

### 3.5.3. Others outcomes

Two studies assessed the rate of patients reporting discussion with the physician about the targeted behavior. The study by Hess et al. [43] targeting smoking and PA reported no difference between the e-SBI and the routine care group. The study by Ozanne et al. [44], targeting drinking and PA, reported a significant difference only for discussions about PA but not drinking between intervention and routine care group. Two others studies evaluated the rate of patients self-reporting behavior change. One uncontrolled study focused on smoking, alcohol and PA, showing that 32 % of patients reported improvements in PA, 28 % in diet (not targeted), 24 % of current drinkers reported reductions in alcohol consumption and 44 % of current smokers reduced smoking 6 months after baseline [45]. The other study targeted diet (three components), PA, smoking, drinking, drug use and sleep [46]. More patients in the intervention than in the control group reported substantial improvements in diet, PA, and sleep. Finally, a study using an intervention providing specific feedback for patients with chronic kidney disease assessed patients' self-efficacy to improve diet (i.e. belief on self-ability to achieve change) showed no difference over time [36].

## 4. Discussion

This review examined the nature and findings of eSBIs that have sought to improve nutritional counseling among adult patients in medical settings since smartphone technologies emerged. Out of the 1981 studies initially screened, fifteen met the inclusion criteria. Most of them documented eSBIs implemented in primary care (n = 11), targeting PA (n = 11) and diet (n = 9) alone or combined (n = 5). Some eSBIs targeted additional behaviors, mostly smoking (n = 7) and/or alcohol consumption (n = 5). Delivery modes included computer (n = 8), printed documents (n = 8) or phone (n = 2) but only four eSBIs were interactive (or partially automated). In addition to personalized feedback, most common BCTs were "information about social and environmental consequences" (n = 9), "goal setting" (n = 7), and "information about health consequences" (n = 7). Most studies assessing behavior change had a control group (8 in 10), and eight studies found positive changes in behavior.

Of the eight controlled studies in the review evaluating behavior change, six showed positive results for PA and/or diet, which concur with other reviews [15,18]. However, our findings showed an important clinical heterogeneity in the interventions and outcomes. Only three of the controlled studies showed improvement in PA [32,35,41], while others assessed other outcomes such as "discussing about PA", "wellness score" or "PA goals". For diet alone, indicators varied from one (F&V) to twelve different food/nutrient categories across studies. There was also a wide range of possible controls, such as usual care, non-personalized BI, or personalized interventions targeting other behaviors. Also, five studies evaluated outcomes other than behavior change [36,43–46], such as self-reported behavior change without baseline assessment, self-efficacy, and discussions with the physician.

Eight interventions targeted other behaviors besides PA/diet, mostly drinking alcohol and smoking, which are major risk factors for chronic diseases [2], and therefore constitute important target
<table><caption>**Table 3**
Behavior change techniques used according to the Behavior Change Taxonomy v1 [42] for each article.</caption><thead><tr><th>Study</th><th>1.1 Goal setting (behavior)</th><th>1.2 Problem solving</th><th>1.3 Goal setting (outcome)</th><th>1.4 Action planning</th><th>1.5 Review behavior goal(s)</th><th>1.6 Discrepancy between current behavior and goal</th><th>1.7 Review outcome goal(s)</th><th>2.2 Feedback on behavior</th><th>2.3 Self-monitoring of behavior</th><th>2.7 Feedback on outcome(s) of behavior</th></tr></thead><tbody><tr><td>Agarwal et al., 2020</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Almeida et al., 2015</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Hess et al., 2014</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Hrolfsdottir et al., 2019</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Katz et al., 2017</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Krist et al., 2016</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Leijon et al., 2011</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>McClish et al., 2014</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Murali et al., 2013</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Nagykaldi et al., 2013</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Ozanne et al., 2019</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Parekh et al., 2014</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Plaete et al., 2016</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td></tr><tr><td>Ruffin et al., 2011</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>van den brekel-Dijkstra et al., 2016</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

for eSBI in the adult population. Since interventions focusing on both diet and PA seem more effective than interventions focusing on just one of these two behaviors [47], adopting a multiple behavior approach in eSBI could improve health outcomes for patients [48].

While eSBIs delivery modes included phone, e-mail or web, the screening included mostly web-based tools on computer and tablet. Web-based screening produces immediate personalized report, saving time for the healthcare professional when delivering the intervention [21]. However, only five studies measured the screening durations [32,35,36,39,44], which varied from five to 45 min according to the number of behaviors assessed. While multicomponent interventions tend to be more effective than single-component interventions [47,48], a 45-min-long feedback could tire patients and jeopardize eSBI completions. Similarly, only three studies measured the duration of interventions [32,38,40], which varied from 15 to 20 min in primary care to 1 h for CVD risk-factor patients at emergency department. Since SBI are time-limited focused counseling sessions, undocumented time limit, or total durations exceeding 30 min is concerning for feasibility in medical settings. In addition to feedback, we identified 20 different other BCTs used in eSBIs. "information about social and environmental consequences" (N = 9) and "goal setting" (N = 8) were the most frequently used techniques, which is congruent with this type of intervention [49]. It must be noted that the effectiveness of PA/diet interventions seems unrelated to the number of BCTs [50,51]. After setting behavioral goals according to the individual's needs (feedback), providers should ensure that goals align with the person's values, priorities, and motivations using motivational counseling for optimal efficiency. Indeed, face-to-face interventions are more effective than digital interventions and mixed interventions, i.e. a combination of digital format and face-to-face contact with a professional [52]. Moreover, mobile technologies have the potential to increase the effectiveness of these interventions [53] by providing comprehensive personalized feedback on the individual's health behaviors in a limited time [54]. Nevertheless, most eSBIs were automated, and only a quarter of patients received face-to-face interventions in the study by Ozanne et al. [44], while personalized feedback failed to increase discussions about alcohol consumption in the study by Agarwal et al. [31]. These poor results

from interactive eSBIs contradict previous findings [52], and remain difficult to interpret given the ambiguous or poor descriptions of BI components. For instance, information is lacking on whether interactive Bls included low-level (i.e. general advice on how to change behavior) or high-level motivational feedback (i.e. provides more individually tailored messages based on factors such as readiness to change or developing personal goals) [25]. Despite the existence of a large body of knowledge, information about the use of BI conceptual frameworks such as FRAMES [22] is missing, and the extent to which eSBIs follow existing guidelines remains unclear. Since the necessity for proper training constitutes barrier to SBI for providers [9], addressing these problems before implementation could be pivotal for their success.

## 4.1. Limitations

### 4.1.1. Limitations of studies included

Some studies have methodological limitations, such as the absence of a control group or randomization. Moreover, many studies had small sample sizes and short or no follow-up, making it difficult to draw conclusions about the eSBIs long-term effectiveness. Some studies provided little detail on the BI, which precluded a comprehensive assessment of implemented BCTs. In addition, several studies failed to indicate eSBIs durations, although of utmost importance for feasibility and acceptability. Finally, the high number of missing data in studies increases the risk of bias, as the least satisfied participants had lower response rates than others.

### 4.1.2. Limitations of the review

To our knowledge, this is the first review analyzing evidence of eSBIs targeting diet/PA in healthcare setting. Despite the restrictive search terms applied in this review, we found important differences in types or timing of outcome measurements and intervention characteristics in the 15 studies included. This clinical heterogeneity makes their comparison difficult, and precludes understanding on what approaches are likely to be effective or not. Contrary to other outcomes though, adherence to PA/diet guidelines provides precise behavioral goals to be met for improving clinical conditions [55,56], and could therefore constitute the most relevant outcome for eSBI in medical settings.
<table><thead><tr><th>3.1 Social support (unspecified)</th><th>3.3 Social support (emotional)</th><th>4.1 Instruction on how to perform the behavior</th><th>5.1 Information about health consequences</th><th>5.2 Salience of consequences</th><th>5.3 Information about social and environmental consequences</th><th>9.1 Credible source</th><th>10.4 Social reward</th><th>10.5 Social incentive</th><th>12.5 Adding objects to the environment</th><th>13.2 Framing/reframing</th><th>15.4 Verbal persuasion about capability</th></tr></thead><tbody><tr><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td></tr><tr><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

## 5. Conclusion and future directions

Although implementing eSBIs towards PA/diet among adult patients in medical settings seems promising for primary, secondary and tertiary prevention, common conceptual framework, intervention and outcomes are severely lacking, and the extent to which they follow existing SBI guidelines remains unclear. Demonstrating their effectiveness requires fixing these issues before conducting prospective assessment studies in medical settings.

## Author contributions

**Camille Forcier:** Conceptualization, Formal analysis, Investigation, Data curation, Writing – Original Draft, Writing – Review and Editing, Visualization. **Aymery Constant:** Conceptualization, Data Curation, Writing – Original Draft, Writing – Review and Editing, Visualization, Supervision. **David Val-Laillet:** Writing – Review and Editing, Visualization. **Ronan Thibault:** Writing – Review and Editing, Visualization. **Romain Moirand:** Conceptualization, Writing – Review and Editing, Visualization, Supervision.

## Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of competing interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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