Feasibility and Acceptability of a Mobile-Assisted Screening and Brief Intervention for Multiple Health Behaviors in Medical Settings

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 questionnaire¹⁸; the Alcohol Use Disorders Identification Test—Consumption; (AUDIT-C)¹⁹; and the International Physical Activity Questionnaire²⁰ 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 change²⁴ (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
Variables	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 min^34
-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

Declaration of Conflicting Interests

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

Funding

The author(s) 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

References

Lawrence

Black

Tinati

, 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

Fisher

Fitzgibbon

Glasgow

, et al. Behavior matters. Am J Prev Med. 2011;40(5):e15-e30. doi:10.1016/j.amepre.2010.12.031

Descatha

Fadel

Constant

Schnebelen

Opportunities for making every contact count approaches in workplaces. Occup Med Oxf Engl. 2024;74(7):470-471. doi:10.1093/occmed/kqae031

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.

Weinehall

Johansson

Sorensen

Jerdén

May

Jenkins

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

Jay

Schlair

Caldwell

Kalet

Sherman

Gillespie

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

Michie

Abraham

Whittington

McAteer

Gupta

Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28(6):690-701. doi:10.1037/a0016136

Ogunleye

Osunlana

Asselin

Cave

Sharma

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

Rueda-Clausen

Benterud

Bond

Olszowka

Vallis

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

10.

Tansil

Esser

Sandhu

, 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

11.

Jophlin

Liu

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

12.

Sobral-Oliveira

Faintuch

Guarita

Oliveira

Carrilho

FJ.

Nutritional profile of asymptomatic alcoholic patients. Arq Gastroenterol. 2011;48(2):112-118. doi:10.1590/s0004-28032011000200006

13.

Webb

Kauer

Ozer

Haller

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

14.

Sekhon

Cartwright

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

15.

Sidani

Braden

CJ.

Nursing and Health Interventions: Design, Evaluation, and Implementation. John Wiley & Sons; 2021.

16.

Wangler

Jansky

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

17.

AbdulRaheem

Unveiling the significance and challenges of integrating prevention levels in healthcare practice. J Prim Care Community Health. 2023;14:21501319231186500. doi:10.1177/21501319231186500

18.

Castetbon

Méjean

Deschamps

, 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

19.

Gache

Michaud

Landry

, 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

20.

Wanner

Hartmann

Pestoni

Martin

Siegrist

Martin-Diener

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

21.

Lewis

Neighbors

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

22.

Neighbors

Lewis

Bergstrom

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

23.

Miller

Rollnick

Motivational Interviewing: Preparing People for Change. 2nd edn. The Guilford Press; 2002:xx, 428.

24.

Prochaska

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

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

26.

Koopmans

Nielen

Schellevis

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

27.

Clark

King-Shier

Duncan

, 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

28.

Williams

Hendry

France

Lewis

Wilkinson

Effectiveness of exercise-referral schemes to promote physical activity in adults: systematic review. Br J Gen Pract. 2007;57(545):979-986.

29.

Banks

Calabria

, 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

30.

Pescheny

Pappas

Randhawa

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

31.

Kent-Marvick

Knysheva

Gibson

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

32.

McCurdie

Sanderson

Aitken

Liu

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

33.

Zurynski

Smith

Siette

Easpaig

BNG

Simons

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

34.

Almeida

Smith-Ray

Dzewaltowski

, 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

35.

Hrolfsdottir

Halldorsson

Birgisdottir

Hreidarsdottir

Hardardottir

Gunnarsdottir

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

36.

Murali

Arab

Vargas

Rastogi

Ang

Shetty

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

37.

Nagykaldi

Voncken-Brewster

Aspy

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

38.

Ozanne

Karliner

Tice

, 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

39.

Katz

Graber

Lounsbury

, 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

40.

McClish

Carcaise-Edinboro

Esinhart

Wilson

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

41.

Rabbi

Pfammatter

Zhang

Spring

Choudhury

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

42.

Nguyen

Fujioka

Wentlandt

, 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

43.

Rahimi

Nadri

Lotfnezhad Afshar

Timpka

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

44.

Henney

Gillespie

Alam

Hydes

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

45.

Michie

van Stralen

West

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

46.

Greaves

Sheppard

Abraham

, 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

47.

Sheehan

Laver

Bhopti

, 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

48.

Meyer

Hellbrecht

Breuing

, 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

Feasibility and Acceptability of a Mobile-Assisted Screening and Brief Intervention for Multiple Health Behaviors in Medical Settings

Abstract

Introduction:

Methods:

Results:

Conclusions:

Keywords

Introduction

Methods

Design of the Mobile-Assisted Screening and Brief Intervention (m-SBI) CliniCAP®

Study Design and Participants

Recruitment

Measures

Socio-demographics and behavioral characteristics

Feasibility

Management

Acceptability

Statistical Analysis

Results

Recruitment

Description of Study Sample

Feasibility and Management

Acceptability

Discussion

Conclusions

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Approval

Consent to Participate

ORCID iD

References

Design of the Mobile-Assisted Screening and Brief Intervention (m-SBI) CliniCAP^®