Abstract
Introduction:
Leveraging every interaction between patients and healthcare professionals constitutes an opportunity to foster behavior change. We developed a mobile Screening and Brief Intervention (mSBI) designed to screen for and intervene with multiple health behaviors, based on a personalized feedback. The objectives of the present study were to assess its feasibility during consultations for chronic conditions, collect users’ opinions, and to investigate patients’ behaviors and intention to change.
Methods:
Research counselors provided the mSBI to patients from 2 departments at University Hospital. Socio-demographic, behavioral, and acceptability data were collected from patients together with feasibility data from counselors’ reporting.
Results:
A total of 259 participants were analyzed, aged 51 ± 17 years, with a majority of women (53%). The m-SBI averaged 20 min and most patients (92%) could complete the screening with minimal assistance. Medical doctors’ involvement facilitated referral to and uptake of the m-SBI, and limited adverse events. On average, patients adhered to 11 of the 18 guidelines screened. A majority of patients rated the personalized feedback as comprehensible and useful. Nearly half of them intended to change behavior.
Conclusions:
The m-SBI seems well-accepted and useful, but doctor referral, adapting the screening tool to patients with low health literacy, and app’s ability to send the feedback to patients/doctors are essential for feasibility.
Keywords: screening and brief intervention, mobile health, acceptability, feasibility, behavior change, medical setting
Introduction
The public health strategy “Make Every Contact Count” (MECC) emphasizes leveraging every interaction between patients and healthcare professionals as an opportunity to foster behavior change and improve health outcomes. 1 It aims at optimizing the effect of routine contacts by integrating health promotion messages and interventions into various settings, including healthcare environments. Generalizing such an approach could reduce healthcare costs, as unbalanced diet, sedentary lifestyle (physical inactivity and sitting time), smoking, and alcohol misuse have a broad and central role in the onset and development of chronic diseases.2,3 In line with this strategy, the WHO published the BRIEF Project, a manual to guide primary care practitioners in implementing brief interventions in their practices to prevent several behavioral risk factors amongst those previously cited. 4 However, a survey found that only between 31% and 56% of primary care physicians rated themselves as having significant expertise in behavior change counseling. 5
Screening and Brief Interventions (SBIs) are designed to deliver targeted messages within limited time, by using validated behavior change techniques,6 -9 and mobile-assisted SBIs (m-SBI) use mobile technologies to facilitate the delivery of key elements of traditional SBI. 10 At the minimum, they involve screening individuals for risk behaviors and delivering a brief intervention (BI), which provides personalized feedback about the risks and consequences of screened behaviors. Personalized feedback can be fully automated (eg, computer-based), interactive (provided by a person), or partially automated and interactive. The ensuing BI may include low-level (ie, general advice on how to change behavior) or high-level motivational feedback (ie, more individually-tailored messages based on factors such as readiness to change or developing personal goals). They tend however to focus on a single risk factor—typically addictive behaviors—whereas MECC and the BRIEF project adopt a broader and holistic approach by integrating all 4 main behavioral risk factors in the BI, that is tobacco use, alcohol use, unhealthy eating and physical inactivity. For example, dietary habits of problem drinkers are rarely accurately assessed during addiction consultations, whereas these patients are at risk of nutritional deficiencies.11,12
Consequently, we developed partially automated high-level motivational feedback designed to screen for and intervene with multiple health behaviors (dietary habits, physical activity, sedentary, alcohol, and smoking) in limited time, namely the m-SBI “CliniCAP®.” A systematic review including 9 studies on electronic SBIs showed that most of them lacked information about tool development processes and end-user involvement. 13 Investigating acceptability is also essential to favor users’ commitment, identify potential issues, tailor the intervention to user preferences, and ultimately enhance overall effectiveness,14,15 but may be challenging in primary care, at least in the first stage of testing.16,17 As a first step, we therefore conducted the preliminary investigations concerning the feasibility, resources, and management of this implementation in 2 specialized care departments managing large cohorts of diverse patients with lifestyle-related diseases (diabetes, overweight, obesity, alcoholic, cirrhosis, and non-alcoholic fatty liver disease) in a French university hospital. The main objective of the present study was to assess the feasibility of CliniCAP® in clinical consultations for chronic diseases at a university hospital. Secondary objectives were to collect users’ opinions to identify potential refinements, and to investigate patients’ behaviors and intention to change.
Methods
Design of the Mobile-Assisted Screening and Brief Intervention (m-SBI) CliniCAP®
A multidisciplinary research team with expertise in health psychology, addiction, nutrition, and behavioral neurosciences designed the m-SBI algorithm in collaboration with a mobile app development company. The tablet-based application (app) includes a validated food frequency questionnaire 18 ; the Alcohol Use Disorders Identification Test—Consumption; (AUDIT-C) 19 ; and the International Physical Activity Questionnaire 20 together with a question assessing smoking (presence or absence). Based on users’ input, the app produces color-coded feedback on the individual adherence to 18 health recommendations in the form of a cursor placed on a colored ruler (green indicates adherence to recommendations; orange indicates moderate excess or deficiency; red indicates severe excess or deficiency). This personalized feedback, which is presented individually, is likely to have a greater impact than general advice because it is more salient and explicit in revealing discrepancies between individual behavior and guidelines.21,22 The use of tablet-based app with local storage means that CliniCAP® may function without internet connection, and the text is characterized by easy words and short sentences to accommodate readers who have trouble understanding complicated words or formulations.
The ensuing BI is interactive (ie, provided by a person), and structured according to the motivational interviewing FRAMES blueprint (Feedback, Responsibility, Advice, Menu of options, Empathy, and Self efficacy) 23 and aims at setting behavioral goals to improve adherence to guidelines for items that were rated poor/very poor in the feedback. Accordingly, the research counselor expressed empathy, dealt with resistance to change and supported participant’s self-efficacy, 23 using predetermined open-ended questions that can be adapted to suit the individual’s stage of change 24 (precontemplation, contemplation, preparation): (1) “What is the importance of these results for you?” This question reveals patients who may be unaware of the problem or the need to find a solution (precontemplation). In case of steady “none to little importance” responses, the counselor may stop the m-SBI; (2) “What do you want to do with these results?” This question may raise patient’s awareness and foster consideration to change (contemplation); (3) “Which objective would you aim to prioritize?” This question fosters commitment to change 1 or several unhealthy behaviors by selecting the best behaviors to address (preparation); and (4) “What would be the means you could use in achieving this goal?” This question also fosters commitment to change by selecting most suitable strategies to be used (preparation). At each step, the counselor role is to reflect the patients’ assertions in order to help them clarifying their aspirations and boost their confidence in their own ability to change; he/she collaborates with them on a plan for change, starting from the patients’ ideas and possibly giving some counsels.
Study Design and Participants
To meet the study objectives, we conducted a single-arm feasibility study. Participants were recruited between January and April 2022 in the department of Liver Diseases (LD) and between April and October 2023 in the department of Endocrinology-Diabetology-Nutrition (EDN) during office visits to a French university hospital. Inclusion criteria were: patient aged >18 years; able to understand French and remember the component of the study; written informed consent. Non-inclusion criteria were: additional need for psychiatric or addiction evaluation; persons participant to major legal protection, persons deprived of liberty. Research counselors received training and provided the m-SBI CliniCAP® to in and out patients meeting the inclusion criteria. An institutional review board approved the study protocol.
Recruitment
In the LD department, research counselors proposed study participation directly to patients in the waiting room or in a hospital room, while medical doctors referred patients to the m-SBI after the medical visit in the EDN department. After signing a free and informed consent describing the study objectives and content, patients completed the tablet-based questionnaire and received the high-level motivational feedback from the research counselor. A study number was attributed to each patient for pseudonymization in a secured database to ensure confidentiality. Meetings were held in both departments to inform staff members about the study aim, protocol and procedure, and to organize the availability of private rooms devoted to the m-SBI during the study period.
Measures
Socio-demographics and behavioral characteristics
The app recorded automatically patients’ behaviors and adherence to each guideline together with the following variables: age (in years), gender (male or female), diploma (no high school degree, high school degree; and university degree), professional activity (active, unemployed, or retired), and main reason for consultation or hospitalization (liver disease, alcohol use disorder, post liver transplant follow-up, obesity, diabetes, and other disease).
Feasibility
Research counselors assessed the number of proposals to participate in the study (from either medical doctors or the research counselor) and refusals. They also rated patients’ need for assistance during the screening (ie, autonomy: 1 = important for all questions; 2 = important for some questions; 3 = moderate for some questions; 4 = no assistance needed); and the time necessary to complete each m-SBI (in min), both for the screening and intervention components.
Management
Research counselors reported any event jeopardizing the m-SBI uptake/completions. These could originate either from the social environment (interruption by staff member, lack of coordination with medical doctors, and negative reaction from staff member), the physical environment (room occupied just before the m-SBI and room taken during the m-SBI) or from the patient (left the room before completion, negative reactions to the m-SBI, and total incomprehension).
Acceptability
After the m-SBI, participants were presented with a printed questionnaire adapted from the Technology Acceptance Model, 25 one of the most influential models of technology acceptance. This questionnaire included the following statements: (1) nutrition is important for my health (eg, dietary habits; physical activity, and sedentarity); (2) the personalized feedback is comprehensible; (3) the personalized feedback is useful; (4) this tool would be useful during routine medical visits; (5) I plan to change my eating habits; (6) I would like to get the feedback at home; and (7) it would be useful to receive additional advices at home. The participants were asked to rate how much they agree or disagree with each proposed statement on a Likert-type response scale. The response options were ranked from 1 to 6, with the following description: strongly disagree = 1, disagree = 2; rather disagree = 3; rather agree = 4; agree = 5; and strongly agree = 6. In order to obtain conservative estimates, we considered that respondents agreed if they responded, “agree” or “strongly agree” to the statement.
Statistical Analysis
Categorical data are expressed as numbers (N) and percentages (%), whereas numerical data are expressed as means ± standard deviations (SDs) or as median and interquartile range [IQR]. Since our study outcomes were linear variables (m-SBI duration and intention to change eating habits), we used generalized linear regression models to estimate the strength of the association between outcomes and each covariate (univariate analysis). In order to determine variables independently associated with each outcome, all variables that demonstrated an association with P ≤ .05 in the unadjusted analysis were considered as candidate variables in the multiple linear regression model. Statistical analyses were performed using the SPSS statistical package, version 20 (SPSS, Chicago, IL, USA).
Results
Recruitment
Research counselors proposed study participation to 401 patients during the study period. Refusal rates reached 42% in the LD department and 22% in the EDN department (Figure 1).
Figure 1.
Study flowchart.
Description of Study Sample
A total of 259 participants were analyzed, aged 51 ± 17 years, with a majority of women (53%) and high school graduates (63%; Table 1). A minority was professionally active (44%) and the most common reason for specialized healthcare was liver diseases (40%). On average, patients adhered to 11 (IQR = [10-13]) guidelines of the 18 screened (Figure 2), with highest rates for fatty and processed foods (98%), incomplete grain products (85%), sweet beverages (85%), salty foods (84%), skipping meals (82%), white meat and eggs (80%), and physical activity (80%). Conversely, less than 1 in 4 patients adhered to guidelines on red meat (38%), dried vegetables (31%); eating between meals (31%), fishery products (25%), and fruits and vegetables (13%; Table 2).
Table 1.
Patients’ Characteristics (N = 259).
| Variables | N (%) or mean (±SD) |
|---|---|
| Female gender | 138 (53) |
| Male gender | 121 (47) |
| Age—years | 51 (±17) |
| Education level | |
| University degree | 99 (38) |
| High school graduate | 65 (25) |
| High school undergraduate | 95 (37) |
| Occupational status | |
| Active | 115 (44) |
| Unemployed | 61 (24) |
| Retired | 83 (32) |
| Main reason for treatment | |
| Liver disease | 104 (40) |
| Post liver transplant follow-up | 21 (8) |
| Alcohol use disorder | 24 (9) |
| Obesity | 46 (18) |
| Diabetes | 35 (14) |
| Other disease | 29 (11) |
| Need for assistance (External rating) | |
| None required | 128 (49) |
| Minimal for some questions | 110 (43) |
| Important for some questions | 13 (5) |
| Important for all questions | 8 (3) |
| Duration, in min | |
| Questionnaire completion | 10 (±7) |
| Interview | 10 (±7) |
| Number of followed guidelines [IQR] | 11 [10-13] |
Abbreviations: IQR, interquartile range; SD, standard deviation.
Figure 2.
Example of personalized feedback obtained with the CliniCAP sofware.
Table 2.
Patients’ Adherence to Health Guidelines According to CliniCAP Algorithm (N = 259).
| Adherence to guideline | |||
|---|---|---|---|
| Good (%) | Moderate excess/deficiency (%) | Severe excess/deficiency (%) | |
| Eating habits | |||
| Fatty and processed foods | 255 (98) | 1 (0) | 3 (1) |
| Incomplete grain products | 220 (85) | 23 (9) | 16 (6) |
| Salty foods | 218 (84) | 9 (3) | 32 (12) |
| Skip meals | 212 (82) | 21 (8) | 26 (10) |
| White meat and eggs | 208 (80) | 44 (17) | 7 (3) |
| Sweet foods | 182 (70) | 9 (3) | 68 (26) |
| Dairy products | 135 (52) | 105 (41) | 19 (7) |
| Complete grain products | 127 (49) | 31 (12) | 101 (39) |
| Red meat | 98 (38) | 89 (34) | 72 (28) |
| Dried vegetables | 81 (31) | 103 (40) | 75 (29) |
| Eat between meals | 80 (31) | 123 (47) | 56 (22) |
| Fishery products | 64 (25) | 129 (50) | 66 (25) |
| Fruits and vegetables | 33 (13) | 190 (73) | 36 (14) |
| Fluid intake | |||
| Sweet beverages | 219 (85) | 40 (15) | |
| Alcohol | 183 (71) | 26 (10) | 50 (19) |
| Sedentariness | 192 (74) | 50 (19) | 17 (7) |
| Physical activity | 206 (80) | 19 (7) | 34 (13) |
| Tobacco | 204 (79) | 55 (21) | |
Feasibility and Management
The inclusion rhythm was approximately 11 patients every week in the LD department and 8 patients every week in the EDN department. Research counselors reported a total of 146 challenging situations encountered over the study period, mostly in the LD department (n = 134; 92%). There, 91 patients refused to go in a private room, staff members interrupted 25 ongoing m-SBIs and 18 m-SBIs overlapped with medical visits. In addition, some doctors complained that patients had information unknown to them (ie, the personalized feedback) during the ensuing medical visit. Staff members interrupted 12 ongoing m-SBI in the EDN department. The m-SBI averaged 20 ± 13 min, with equal time spent completing the screening questionnaire and participating the motivational intervention, and research counselors rated the autonomy of most patients (92%) as satisfactory (ie, using the app with minimal or no help). In multivariate analysis (Table 3), the mean m-SBI duration was longer in patients with liver disease, post liver transplant, and alcohol use disorder treatment as compared to those with obesity, and shorter in patient with higher feedback comprehensibility score.
Table 3.
Factors Associated With Duration of Mobile Screening and Brief Intervention, Generalized Linear Model.
| Variables | Univariate estimates | Multivariate estimates | ||
|---|---|---|---|---|
| B | P-value | B | P-value | |
| Male gender | 3.607 | .009 | −1.093 | .402 |
| Age | 0.186 | <.001 | 0.076 | .059 |
| Education level | ||||
| University degree | −0.788 | .625 | ||
| High school graduate | −0.074 | .967 | ||
| High school undergraduate | Ref | |||
| Occupational status | ||||
| Active | −2.942 | .090 | ||
| Retired | 2.557 | .167 | ||
| Unemployed | Ref | |||
| Main reason for treatment | ||||
| Other disease | 1.773 | .450 | 1.470 | .526 |
| Diabetes | −2.391 | .282 | −0.281 | .900 |
| Liver disease | 8.296 | <.001 | 7.597 | <.001 |
| Post liver transplant follow-up | 9.200 | <.001 | 8.138 | .002 |
| Alcohol use disorder | 14.944 | <.001 | 15.623 | <.001 |
| Obesity | Ref | Ref | ||
| Number of green criteria | 0.004 | .990 | ||
| Acceptability and feasibility variables | ||||
| Importance of nutrition | 0.441 | .554 | ||
| Feedback comprehensibility | −2.505 | .006 | −2.265 | .005 |
| Feedback perceived usefulness | 0.632 | .411 | ||
| Perceived usefulness during consultations | 0.668 | .407 | ||
| Intention to change eating habits | 1.016 | .072 | ||
| Willingness to receive feedback | 0.707 | .173 | ||
| Perceived usefulness of home help | 0.152 | .729 | ||
| Need for assistance (external assessment) | −4.067 | <.001 | −1.687 | .090 |
Acceptability
At least 7 in 10 participants agreed or totally agreed with the following statements: “The personalized feedback is comprehensible” (n = 229; 88%), “the personalized feedback is useful” (n = 206; 79%) and “nutrition is important for my health” (n = 206; 79%). Less than half of patients agreed or totally agreed with the following statement: “I plan to change my habits” (n = 122; 47%), and “It would be useful to receive additional help at home” (n = 81; 32%; Table 4). In multivariate analysis (Table 5), the intention to change eating habits after the m-SBI was positively associated with female gender, perceived usefulness, willingness to receive feedback and the perceived usefulness of home help, and was lower in patients with diabetes compared to those with obesity.
Table 4.
Number and Percentage of Agreement With Statements Assessing the Brief Intervention Acceptability (N = 259).
| Statements | Totally disagree (%) | Disagree (%) | Mostly disagree (%) | Mostly agree (%) | Agree (%) | Totally agree (%) |
|---|---|---|---|---|---|---|
| 1. Nutrition is important for my health | 1 (0) | 5 (2) | 5 (2) | 42 (16) | 97 (37) | 109 (42) |
| 2. The personalized feedback is comprehensible | 0 (0) | 1 (0) | 4 (2) | 25 (10) | 89 (34) | 140 (54) |
| 3. The personalized feedback is useful | 3 (1) | 2 (1) | 6 (2) | 42 (16) | 125 (48) | 81 (31) |
| 4. This tool would be useful during routine medical visits | 1 (0) | 2 (1) | 5 (2) | 62 (24) | 112 (43) | 77 (30) |
| 5. I plan to change my eating habits | 8 (3) | 13 (5) | 22 (8) | 94 (36) | 67 (26) | 55 (21) |
| 6. I would like to get the feedback at home | 12 (5) | 14 (5) | 12 (5) | 34 (13) | 105 (41) | 82 (32) |
| 7. It would be useful to receive additional help at home (such as advice on internet) | 39 (15) | 37 (14) | 54 (21) | 48 (19) | 48 (19) | 33 (13) |
Table 5.
Factors Associated With Intention to Change Eating Habits, Generalized Linear Model.
| Univariate estimates | Multivariate estimates | |||
|---|---|---|---|---|
| Variables | B | P-value | B | P-value |
| Female gender | 0.513 | .001 | 0.410 | .005 |
| Age | −0.008 | .057 | ||
| Education level | ||||
| University degree | 0.119 | .501 | ||
| High school graduate | 0.177 | .371 | ||
| High school undergraduate | Ref | |||
| Occupational status | ||||
| Active | 0.315 | .101 | ||
| Retired | −0.143 | .484 | ||
| Unemployed | Ref | |||
| Main reason for treatment | ||||
| Other disease | −0.672 | .018 | −0.309 | .233 |
| Diabetes | −0.684 | .010 | −0.489 | .042 |
| Liver disease | −0.692 | .001 | −0.311 | .126 |
| Post liver transplant follow-up | −0.675 | .032 | −0.323 | .274 |
| Alcohol use disorder | −0.080 | .791 | 0.398 | .159 |
| Obesity | Ref | Ref | ||
| Duration | ||||
| Questionnaire completion | 0.015 | .170 | ||
| Interview | 0.019 | .101 | ||
| Acceptability and feasibility variables | ||||
| Importance of nutrition | 0.254 | .001 | 0.033 | .672 |
| Feedback comprehensibility | 0.215 | .032 | 0.062 | .514 |
| Feedback perceived usefulness | 0.448 | <.001 | 0.325 | <.001 |
| Perceived usefulness during consultations | 0.337 | <.001 | 0.084 | .338 |
| Willingness to receive feedback | 0.235 | <.001 | 0.111 | .045 |
| Perceived usefulness of home help | 0.201 | <.001 | 0.109 | .018 |
| Need for assistance (external assessment) | 0.286 | .013 | 0.111 | .305 |
Discussion
In this study, we investigated the feasibility and acceptability of a mobile-assisted screening for 5 major risk factors (dietary habits, physical activity, sedentary, alcohol, and smoking) followed by a motivational in-person BI. The feasibility was good, since this partially automated m-SBI averaged 20 min, half of this time without human assistance, and 9 patients in 10 were capable of completing the screening with minimal assistance. The involvement of medical doctors significantly facilitated referral to and uptake of the m-SBI because the proportion of refusals and interruptions was higher when the research counselor proposed participation in the waiting room before the medical consultation. A majority rated the feedback as comprehensible, useful, for medical visits and wanted to get it for home. Overall, patients adhered to 11 of the 18 guidelines screened particularly for liver diseases risk factors, and nearly half of them intended to change behavior, with higher intention positively associated with female gender, feedback perceived usefulness and willingness to take it back home. Intention to change was lower in patients with diabetes than in those with obesity.
A systematic review of 28 studies estimated non-participation levels in prevention programs between 10% and 99% with a median value of 38%, a result in line with the average estimate of 36% found in the present study. Several organizational, inter- and intrapersonal factors can explain such variability. 26 Many healthcare systems implemented lifestyle counseling, but competing priorities may lead to neglect of health promotion efforts.27,28 On one hand, current evidence suggests that medical practitioners tend to focus solely on patients’ medical issues.29,30 On the other hand, patients are often unaware of preventive care and expect doctors to provide prescription or medical treatment. Physicians’ endorsements lend credibility to prevention programs, as patients are more likely to enroll when recommended by a trusted healthcare provider. 31 In the present study, participants were more likely to participate when offered by a physician rather than a research counselor (78% vs 58% participation rate). In addition, proposing participation in the waiting room increased the number of interruptions, and jeopardized patients-doctor communication. These findings concur with evidence showing that active physician involvement is pivotal for referral to, and patient uptake of lifestyle modification programs,32,33 including the m-SBI CliniCAP®.
Dealing with time constraints is essential for the feasibility of m-SBI with multiple behaviors. According to the number of targeted behaviors, screening can take from 5 to 45 min34 -38 and BI from 15 to 60 min.34,39,40 Taking advantage of mobile technologies, 41 the entire m-SBI CliniCAP® averaged 20 min, with shorter duration for patients with obesity/diabetes, who already benefited from extensive dieticians’ nutritional management. Research counselors noted that these patients reported similarities between the information provided during dieticians’ consultations and the m-SBI.
A review on SBI with multiple health behaviors indicated that studies provided minimal information about the format and administration of the screening tools, suggesting that authors considered this component as incidental or less important. 13 In the present study, we thoroughly described the screening procedure, showing that most patients could complete and understand the tablet-based questionnaires without extensive assistance. Expectedly, patients’ feedback comprehensibility shortened the m-SBIs duration, highlighting the need for familiar user-friendly mobile platforms, especially for providers who identified the potential burden of introducing a new technology to patients. 42 Research counselors however noted some difficulties in the understanding of certain terms such as “wholegrain cereals” suggesting the need for assistance in patients with poor health and/or digital literacy.
The most recent trend in Technology Acceptance Model (TAM) on mobile technologies are characterized by involving patients as users. 43 Our study sample comprised patients with chronic diseases receiving regular counseling from specialized care physicians. They consequently considered nutrition as important and adhered, on average, to 11 of the 18 guidelines screened, especially as regards to liver diseases dietary risk factors such as fatty, processed, as salty food. 44 They nevertheless perceived the feedback as useful for medical visits and back home, and these opinions correlated positively with behavior change intention. Providing the personalized feedback to doctors and patients through email and/or printed document seem therefore important for the m-SBI acceptability. Since medical visits at hospital can be spaced 6 months apart, this personal report precisely indicating priorities for change may provide valuable help. However, we observed no relationship between behavioral intent and unhealthy lifestyle. According to implementation science, 45 interventions targeting patients’ motivation and capacity through self-regulatory techniques (such as CliniCAP®) should connect individuals to social and environmental resources to foster change in the long term. In addition, higher contact time and repeated interactions are important to obtain significant and clinically meaningful changes in physical activity and diet. 46 Transmitting feedback to allied health services (general practitioners, nurses, dieticians, and psychologists) and providing patients with tailored online resources (web-based platforms, dietary consultations, and self-help groups) could therefore extend the m-SBI effect over time. 47 However, only 1 in 3 patients wished access to additional help at home, probably because of question misunderstanding. Research counselors reported that some patients believed we proposed caregivers’ home visits, which they perceived as invasive. Finally, providing patients’ feedback to the primary care physician prior to the visit could facilitate lifestyle counseling in primary care settings, despite the short time available. If its efficacy is documented by further studies, its acceptability by primary care providers will need to be further investigated.
This study includes several limitations. First, CliniCAP® is suitable currently for people who have understanding in French. The reliance on written and visual information may also compromise the suitability for people with vision impairment. Second, the current findings are from a sample of patients who attended medical visit at a University Hospital, and while participants’ characteristics are comparable with others published studies on m-SBI in specialized care, the diversity of medical conditions and practices in both departments limited the homogeneity of study sample. Nevertheless, this situation reflects the clinical heterogeneity existing in healthcare settings, 48 to which prevention procedures need to adapt. Finally, the cross-sectional design of the study precludes positioning the findings within the often long-term, experience of behavior change. Our finding that 4 in 10 participants intended to change their lifestyle must be interpreted cautiously.
Conclusions
This study provides valuable insights on protocol and guidelines for implementing m-SBI with multiple risk behaviors in medical settings, and offers pathways of improvement and development. This semi-automated high-level motivational feedback seems well accepted and useful, but doctor referral to the m-SBI, adapting the screening tool to patients with low health literacy, and app’s ability to send the feedback to patients/doctors are essential for feasibility. After having developed an improved version of the app, a controlled prospective study will be implemented under these operating conditions to assess the m-SBI efficacy on patients’ lifestyles change in the 3-month following the visit. Other development steps include the m-SBI implementation in primary care and longer term follow-up.
Footnotes
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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The CHU of Rennes funded the present research and the Fondation Nominoë funded the tablet-based application. Camille Forcier received a PhD grant from the University of Rennes.
Ethical Approval: The CHU of Rennes institutional review board approved the study protocol (approval number: 21.37, date: April 19th 2021).
Consent to Participate: All patients provided informed consent before participating.
ORCID iD: Aymery Constant 
https://orcid.org/0000-0001-9282-3036
References
- 1. Lawrence W, Black C, Tinati T, et al. ‘Making every contact count’: evaluation of the impact of an intervention to train health and social care practitioners in skills to support health behaviour change. J Health Psychol. 2016;21(2):138-151. doi: 10.1177/1359105314523304 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Fisher EB, Fitzgibbon ML, Glasgow RE, et al. Behavior matters. Am J Prev Med. 2011;40(5):e15-e30. doi: 10.1016/j.amepre.2010.12.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Descatha A, Fadel M, Constant A, Schnebelen C. Opportunities for making every contact count approaches in workplaces. Occup Med Oxf Engl. 2024;74(7):470-471. doi: 10.1093/occmed/kqae031 [DOI] [PubMed] [Google Scholar]
- 4. Copenhagen: WHO Regional Office for Europe. Integrated Brief Interventions for Noncommunicable Disease Risk Factors in Primary Care: The Manual. BRIEF Project. Copenhagen: WHO Regional Office for Europe; 2022. [Google Scholar]
- 5. Weinehall L, Johansson H, Sorensen J, Jerdén L, May J, Jenkins P. Counseling on lifestyle habits in the United States and Sweden: a report comparing primary care health professionals’ perspectives on lifestyle counseling in terms of scope, importance and competence. BMC Fam Pract. 2014;15:83. doi: 10.1186/1471-2296-15-83 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Jay M, Schlair S, Caldwell R, Kalet A, Sherman S, Gillespie C. From the patient’s perspective: the impact of training on resident physician’s obesity counseling. J Gen Intern Med. 2010;25(5):415-422. doi: 10.1007/s11606-010-1299-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28(6):690-701. doi: 10.1037/a0016136 [DOI] [PubMed] [Google Scholar]
- 8. Ogunleye A, Osunlana A, Asselin J, Cave A, Sharma AM, Campbell-Scherer DL. The 5As team intervention: bridging the knowledge gap in obesity management among primary care practitioners. BMC Res Notes. 2015;8:810. doi: 10.1186/s13104-015-1685-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Rueda-Clausen CF, Benterud E, Bond T, Olszowka R, Vallis MT, Sharma AM. Effect of implementing the 5As of obesity management framework on provider-patient interactions in primary care. Clin Obes. 2014;4(1):39-44. doi: 10.1111/cob.12038 [DOI] [PubMed] [Google Scholar]
- 10. Tansil KA, Esser MB, Sandhu P, et al. Alcohol electronic screening and brief intervention: a community guide systematic review. Am J Prev Med. 2016;51(5):801-811. doi: 10.1016/j.amepre.2016.04.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Jophlin L, Liu TY, McClain CJ. Nutritional deficiencies in alcohol use disorder/alcohol-associated liver disease. Curr Opin Gastroenterol. 2024;40(2):112-117. doi: 10.1097/MOG.0000000000000999 [DOI] [PubMed] [Google Scholar]
- 12. Sobral-Oliveira MB, Faintuch J, Guarita DR, Oliveira CP, Carrilho FJ. Nutritional profile of asymptomatic alcoholic patients. Arq Gastroenterol. 2011;48(2):112-118. doi: 10.1590/s0004-28032011000200006 [DOI] [PubMed] [Google Scholar]
- 13. Webb MJ, Kauer SD, Ozer EM, Haller DM, Sanci LA. Does screening for and intervening with multiple health compromising behaviours and mental health disorders amongst young people attending primary care improve health outcomes? A systematic review. BMC Fam Pract. 2016;17(1):104. doi: 10.1186/s12875-016-0504-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res. 2017;17(1):88. doi: 10.1186/s12913-017-2031-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Sidani S, Braden CJ. Nursing and Health Interventions: Design, Evaluation, and Implementation. John Wiley & Sons; 2021. [Google Scholar]
- 16. Wangler J, Jansky M. Primary care involvement in clinical research: prerequisites, motivators, and barriers: results from a study series. Arch Public Health. 2024;82(1):41. doi: 10.1186/s13690-024-01272-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. AbdulRaheem Y. Unveiling the significance and challenges of integrating prevention levels in healthcare practice. J Prim Care Community Health. 2023;14:21501319231186500. doi: 10.1177/21501319231186500 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Castetbon K, Méjean C, Deschamps V, et al. Dietary behaviour and nutritional status in underprivileged people using food aid (ABENA study, 2004-2005). J Hum Nutr Diet. 2011;24(6):560-571. doi: 10.1111/j.1365-277X.2011.01158.x [DOI] [PubMed] [Google Scholar]
- 19. Gache P, Michaud P, Landry U, et al. The Alcohol Use Disorders Identification Test (AUDIT) as a screening tool for excessive drinking in primary care: reliability and validity of a French version. Alcohol Clin Exp Res. 2005;29(11):2001-2007. doi: 10.1097/01.alc.0000187034.58955.64 [DOI] [PubMed] [Google Scholar]
- 20. Wanner M, Hartmann C, Pestoni G, Martin BW, Siegrist M, Martin-Diener E. Validation of the Global Physical Activity Questionnaire for self-administration in a European context. BMJ Open Sport Exerc Med. 2017;3(1):e000206. doi: 10.1136/bmjsem-2016-000206 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Lewis MA, Neighbors C. Social norms approaches using descriptive drinking norms education: a review of the research on personalized normative feedback. J Am Coll Health J ACH. 2006;54(4):213-218. doi: 10.3200/JACH.54.4.213-218 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Neighbors C, Lewis MA, Bergstrom RL, Larimer ME. Being controlled by normative influences: self-determination as a moderator of a normative feedback alcohol intervention. Health Psychol Off. 2006;25(5):571-579. doi: 10.1037/0278-6133.25.5.571 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Miller WR, Rollnick S. Motivational Interviewing: Preparing People for Change. 2nd edn. The Guilford Press; 2002:xx, 428. [Google Scholar]
- 24. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51(3):390-395. doi: 10.1037//0022-006x.51.3.390 [DOI] [PubMed] [Google Scholar]
- 25. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-340. doi: 10.2307/249008 [DOI] [Google Scholar]
- 26. Koopmans B, Nielen MM, Schellevis FG, Korevaar JC. Non-participation in population-based disease prevention programs in general practice. BMC Public Health. 2012;12(1):1-10. doi: 10.1186/1471-2458-12-856 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Clark AM, King-Shier KM, Duncan A, et al. Factors influencing referral to cardiac rehabilitation and secondary prevention programs: a systematic review. Eur J Prev Cardiol. 2013;20(4):692-700. doi: 10.1177/2047487312447846 [DOI] [PubMed] [Google Scholar]
- 28. Williams NH, Hendry M, France B, Lewis R, Wilkinson C. Effectiveness of exercise-referral schemes to promote physical activity in adults: systematic review. Br J Gen Pract. 2007;57(545):979-986. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Ju I, Banks E, Calabria B, et al. General practitioners’ perspectives on the prevention of cardiovascular disease: systematic review and thematic synthesis of qualitative studies. BMJ Open. 2018;8(11):e021137. doi: 10.1136/bmjopen-2017-021137 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Pescheny JV, Pappas Y, Randhawa G. Facilitators and barriers of implementing and delivering social prescribing services: a systematic review. BMC Health Serv Res. 2018;18(1):86. doi: 10.1186/s12913-018-2893-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Kent-Marvick J, Knysheva M, Gibson B, Simonsen SE. Why do people choose to enroll or not enroll in the national diabetes prevention program lifestyle change program? A mixed-methods analysis from a sample of adults with a prediabetes diagnosis. J Prim Care Community Health. 2024;15:21501319241282862. doi: 10.1177/21501319241282862 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. McCurdie T, Sanderson P, Aitken LM, Liu D. Two sides to every story: the dual perspectives method for examining interruptions in healthcare. Appl Ergon. 2017;58:102-109. doi: 10.1016/j.apergo.2016.05.012 [DOI] [PubMed] [Google Scholar]
- 33. Zurynski Y, Smith C, Siette J, Easpaig BNG, Simons M, Knaggs GT. Identifying enablers and barriers to referral, uptake and completion of lifestyle modification programmes: a rapid literature review. BMJ Open. 2021;11(3):e045094. doi: 10.1136/bmjopen-2020-045094 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Almeida FA, Smith-Ray RL, Dzewaltowski DA, et al. An interactive computer session to initiate physical activity in sedentary cardiac patients: randomized controlled trial. J Med Internet Res. 2015;17(8):e206. doi: 10.2196/jmir.3759 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Hrolfsdottir L, Halldorsson TI, Birgisdottir BE, Hreidarsdottir IT, Hardardottir H, Gunnarsdottir I. Development of a dietary screening questionnaire to predict excessive weight gain in pregnancy. Matern Child Nutr. 2019;15(1):e12639. doi: 10.1111/mcn.12639 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Murali S, Arab L, Vargas R, Rastogi A, Ang A, Shetty N. Internet-based tools to assess diet and provide feedback in chronic kidney disease stage IV: a pilot study. J Ren Nutr. 2013;23(2):e33-e42. doi: 10.1053/j.jrn.2012.05.001 [DOI] [PubMed] [Google Scholar]
- 37. Nagykaldi ZJ, Voncken-Brewster V, Aspy CB, Mold JW. Novel computerized health risk appraisal may improve longitudinal health and wellness in primary care: a pilot study. Appl Clin Inform. 2013;4(1):75-87. doi: 10.4338/ACI-2012-10-RA-0048 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Ozanne E, Karliner LS, Tice JA, et al. An intervention tool to increase patient–physician discussion of lifestyle risk factors for breast cancer. J Womens Health. 2019;28(11):1468-1475. doi: 10.1089/jwh.2018.7026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Katz DA, Graber M, Lounsbury P, et al. Multiple risk factor counseling to promote heart-healthy lifestyles in the chest pain observation unit: pilot randomized controlled trial. Acad Emerg Med. 2017;24(8):968-982. doi: 10.1111/acem.13231 [DOI] [PubMed] [Google Scholar]
- 40. McClish D, Carcaise-Edinboro P, Esinhart H, Wilson DB, Bean MK. Differences in response to a dietary intervention between the general population and first-degree relatives of colorectal cancer patients. J Nutr Educ Behav. 2014;46(5):376-383. doi: 10.1016/j.jneb.2014.02.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults. JMIR MHealth UHealth. 2015;3(2):e42. doi: 10.2196/mhealth.4160 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Nguyen M, Fujioka J, Wentlandt K, et al. Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care. BMC Palliat Care. 2020;19(1):138. doi: 10.1186/s12904-020-00644-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A systematic review of the technology acceptance model in health informatics. Appl Clin Inform. 2018;9(3):604-634. doi: 10.1055/s-0038-1668091 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Henney AE, Gillespie CS, Alam U, Hydes TJ, Cuthbertson DJ. Ultra-processed food intake is associated with non-alcoholic fatty liver disease in adults: a systematic review and meta-analysis. Nutrients. 2023;15(10):2266. doi: 10.3390/nu15102266 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci IS. 2011;6:42. doi: 10.1186/1748-5908-6-42 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Greaves CJ, Sheppard KE, Abraham C, et al. Systematic review of reviews of intervention components associated with increased effectiveness in dietary and physical activity interventions. BMC Public Health. 2011;11(1):119. doi: 10.1186/1471-2458-11-119 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Sheehan J, Laver K, Bhopti A, et al. Methods and effectiveness of communication between hospital allied health and primary care practitioners: a systematic narrative review. J Multidiscip Healthc. 2021;14:493-511. doi: 10.2147/JMDH.S295549 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Meyer N, Hellbrecht I, Breuing J, et al. Heterogeneous methodology in the development of patient versions of clinical practice guidelines: a scoping review. J Clin Epidemiol. 2023;161:53-64. doi: 10.1016/j.jclinepi.2023.07.005 [DOI] [PubMed] [Google Scholar]