Melanie Falck

Jean-Claude Martin

Christine Le Scanff

Céline Clavel
email: celine.clavel@lisn.fr

Impact of a Regulatory Fit Motivational Virtual Coach on Users during Physical Activity

Keywords: Regulatory Focus, Virtual Coach, Motivation, Motivational Message

Introduction: Despite the well-known benefits of physical activity, inactivity remains a challenge. Current technologies, through motivational messages, attempt to address this but often overlook individual differences. Objective:

To assess the influence of a user's motivational profile on the effectiveness of messages from a virtual coach during exercise. Method: 67 participants completed two stepping sessions. A virtual coach provided them with motivational messages, tailored or not to their profile, using vocal and facial expressions.

Results: The coach's presence did not change overall activity. However, prevention-oriented participants were more responsive to a tailored coach, increasing their activity, and less efficient with a mismatched coach. Intrinsic motivation was pivotal, especially with a promotion agent. Conclusion: Personalizing multimodal feedback from a virtual agent proves beneficial. The effect hinges on the user's profile and personality, key elements for physical activity support tools.

Introduction

The benefits of regular physical activity are well-established, with research showing that it can have both preventative and curative effects, improve quality of life, and provide pleasure [START_REF] Cekin | Psychological Benefits of Regular Physical Activity: Evidence from Emerging Adults[END_REF]. However, sedentary lifestyles are becoming increasingly prevalent, with lack of motivation, fatigue, and fear of movement among older adults or those with disabilities being major contributing factors [START_REF] Bethancourt | Barriers to and Facilitators of Physical Activity Program Use among Older Adults[END_REF].

To address this issue, various interventions have been developed to promote physical activity, such as using mental simulation to help individuals envision themselves engaging in the activity [START_REF] Meslot | Theory-Based Interventions Combining Mental Simulation and Planning Techniques to Improve Physical Activity: Null Results from Two Randomized Controlled Trials[END_REF], or utilizing motivational interviewing techniques [START_REF] Kanaoka | Designing a Motivational Agent for Behavior Change in Physical Activity[END_REF]. Tailored health messages have also been shown to be effective in encouraging physical activity [START_REF] Martinez | Examining the Use of Message Tailoring to Promote Physical Activity among Medically Underserved Adults[END_REF]. However, these studies often rely on self-reported measures of behavioral intentions rather than actual behavior change and tend to focus on demographic characteristics rather than the psychological profile of the individual [START_REF] Gellert | An Age-Tailored Intervention Sustains Physical Activity Changes in Older Adults: A Randomized Controlled Trial[END_REF].

Research suggests that motivation plays a significant role not only in performance but also in engagement, continuation, and completion of physical activity [START_REF] Perski | Conceptualising Engagement with Digital Behaviour Change Interventions: A Systematic Review Using Principles from Critical Interpretive Synthesis[END_REF]. Therefore, understanding the motivational profiles and strategies of individuals is a promising approach to promoting physical activity. The objective of our research is to expand the knowledge of psychological factors that should be considered in the design of technologies, specifically virtual coaches, to promote physical activity. Specifically, we aim to assess the impact of motivational messages displayed by a virtual coach on an individual's motivation and commitment to continue physical activity, with a focus on messages that are better tailored to the user's profile to impact physical activity continuation.

Conceptual Background and Hypothesis

Regulatory Focus Theory (RFT)

Regulatory Focus Theory (RFT) is a theoretical framework developed by Higgins [START_REF] Higgins | Beyond Pleasure and Pain[END_REF] that explains motivation as a result of self-regulation processes that are aimed at achieving a desired end state. The theory postulates the existence of two distinct and independent self-regulatory guidance systems: promotion focus, which is oriented towards advancement and progress, and prevention focus, which is oriented towards safety and security [START_REF] Scholer | Promotion and Prevention Systems: Regulatory Focus Dynamics within Self-Regulatory Hierarchies[END_REF].

According to RFT, individuals have a chronic regulatory focus, which refers to a tendency to prefer one self-regulatory guidance system over the other. However, this preference can change depending on the specific situation or through experimental manipulation (situational regulatory focus) [START_REF] Shah | Performance Incentives and Means: How Regulatory Focus Influences Goal Attainment[END_REF]. The theory also suggests that the motivational strategy can be influenced by environmental factors [START_REF] Higgins | Making a Good Decision: Value from Fit[END_REF].

Regulatory Fit and Its Impact

Regulatory fit theory (RFT) proposes that the match or mismatch between an individual's motivational strategy and the means used to achieve the objective can influence their motivation [START_REF] Higgins | Making a Good Decision: Value from Fit[END_REF]. When the match is adequate, it produces a state called regulatory fit, which induces in the individual a sense of truth about the objective pursued. A meta-analysis by [START_REF] Motyka | Regulatory Fit: A Meta-Analytic Synthesis[END_REF] found that more than a hundred articles report a strong impact of regulatory fit on people's assessment, intention and behaviour in various fields such as persuasion (e.g. [START_REF] Lee | Bringing the Frame into Focus: The Influence of Regulatory Fit on Processing Fluency and Persuasion[END_REF], consumer purchasing behavior (e.g. Av- [START_REF] Avnet | Locomotion, Assessment, and Regulatory Fit: Value Transfer from "How" to "What[END_REF] and health issues(e.g. [START_REF] Rothman | The Strategic Use of Gain-and Loss-Framed Messages to Promote Healthy Behavior: How Theory Can Inform Practice[END_REF].

Previous Research on Physical Activity and Sport Using RFT

Previous research on the use of Regulatory Focus Theory (RFT) in the field of physical activity and sport has primarily focused on analyzing the links between performance and motivational strategies through different types of instructions or messages. According to a meta-analysis by [START_REF] Motyka | Regulatory Fit: A Meta-Analytic Synthesis[END_REF], studies have supported the hypothesis that a simple, non-personalized message is not sufficient to optimize performance and that an understanding of an individual's motivational orientation is necessary. However, relatively few studies have been conducted under actual practice conditions. Studies by Latimer et al. [START_REF] Latimer | A Field Experiment Testing the Utility of Regulatory Fit Messages for Promoting Physical Activity[END_REF] and Plessner et al. [START_REF] Plessner | Regulatory Fit as a Determinant of Sport Performance: How to Succeed in a Soccer Penalty-Shooting[END_REF] have shown positive effects of regulatory fit on physical activity performance, but other studies, such as those by [START_REF] Daryanto | Service Firms and Customer Loyalty Programs: A Regulatory Fit Perspective of Reward Preferences in a Health Club Setting[END_REF] and [START_REF] Martinez | Examining the Use of Message Tailoring to Promote Physical Activity among Medically Underserved Adults[END_REF], have yielded mixed results (for a review see [START_REF] Ludolph | Does Regulatory Fit Lead to More Effective Health M[END_REF]. A major limitation of these studies is that they often rely on self-reported data, which is prone to errors such as memory bias and social desirability bias [START_REF] Kay | Regulatory Fit Improves Fitness for People with Low Exercise Experience[END_REF]. Additionally, many studies have focused on measuring the impact of regulatory fit on intentions to engage in physical activity rather than on actual behavior change [START_REF] Pfeffer | Regulatory Fit Messages and Physical Activity Motivation[END_REF].

Despite mixed results, the meta-analysis by Motyka et al. [START_REF] Motyka | Regulatory Fit: A Meta-Analytic Synthesis[END_REF]) revealed the importance of certain methodological factors, such as a greater effect of regulatory fit on behavior when the experiment is based on an individual's chronic regulatory focus rather than an experimentally induced strategy. However, few researchers have examined the relationship between regulatory focus and the different forms of motivation proposed by Self-Determination Theory (SDT). Further research is needed to fully understand the potential of RFT in promoting physical activity and to identify effective strategies for different individuals based on their motivational orientations.

SDT, proposed by Deci and Ryan [START_REF] Deci | Intrinsic Motivation and Self-Determination in Human Behavior[END_REF], suggests that motivation is not simply a quantitative concept, but one that can be qualitatively distinguished into different forms. According to SDT, motivation is driven by the satisfaction of three fundamental needs: autonomy, competence, and relatedness [START_REF] Baumeister | The Need to Belong: Desire for Interpersonal Attachments as a Fundamental Human Motivation[END_REF][START_REF] Hui | Loving Freedom: Concerns with Promotion or Prevention and the Role of Autonomy in Relationship Well-Being[END_REF] observed a relationship between these fundamental needs and regulatory focus, with promotion-focused individuals placing a greater emphasis on autonomy.

Furthermore, Lalot, Quiamzade, & Zerhouni [START_REF] Lalot | Regulatory Focus and Self-Determination Motives Interact to Predict Students' Nutrition-Habit Intentions[END_REF] suggested that there may be an interaction between intrinsic and extrinsic motivations, and message framing (promotion versus prevention). Prevention-oriented individuals may be more sensitive to obligations and duties and thus may respond better to normative pressure and the need for social acceptance. Similarly, promotion-oriented individuals may be more motivated by the desire to achieve one's ideal self, or the need for accomplishment.

In the context of physical activity, Laroche, Roussel, Cury, & Boiché (Laroche

Research on Virtual Coaching and Its Impact on Health Behavior

Lastly, research in computer science has explored virtual coaching and more particularly the design and contribution of virtual coaches to support individuals in their behaviour change. [START_REF] Kamali | Virtual Coaches for Older Adults' Wellbeing: A Systematic Review[END_REF] presented a systematic review of virtual coaches specifically aimed at improving or maintaining older adults' health. They report promising benefits of applying virtual trainers to promote health behaviour [START_REF] Luerssen | Virtual Agents as a Service: Applications in Healthcare[END_REF] but also reported the lack of rigorous evaluations. They also observed a lack of consensus regarding the definition of virtual coach and noted the diversity of theoretical frameworks that are used to design e-coaching interventions.

The feedback provided by these systems is also the subject of much research. Several meta-analyses have noted the importance of the underlying theoretical context in designing motivational messages, particularly messages targeting affective attitudes, self-efficacy, and self-regulation (Op den [START_REF] Op Den Akker | Tailored Motivational Message Generation: A Model and Practical Framework for Real-Time Physical Activity Coaching[END_REF].

In a research on the impact of motivational messages also focusing on virtual coaches, Lucas, Krämer, Peters, Mell and Gratch [START_REF] Lucas | Effects of Perceived Agency and Message Tone in Responding to a Virtual Personal Trainer[END_REF] measured the performance of 212 participants during a fitness bike session in a laboratory experiment. The same positive or negative motivational messages were communicated by a real coach via Skype, a virtual coach, or an avatar. They concluded that the greater the coach's behavioural realism, the more the negative messages are accepted, or even lead to more physical effort than positive messages.

Objectives of the Current Study and Its Potential Contribution to the Field

The current study aims to investigate the relationship between regulatory focus and motivation in the context of virtual coaching for physical activity. We evaluate the impact of personalized messages delivered by a virtual coach during physical activity and examine the relationship between regulatory focus and motivation. By doing so, we hope to gain a deeper understanding of how virtual coaching can be optimized to suit different individuals based on their motivational orientations. Our specific hypotheses are: H1: Interventions that include messages framed in terms of promotion versus prevention delivered by a virtual coach will increase engagement and continuation in physical exercise, particularly when the intervention aligns with the participant's regulatory focus.

H2: Individuals with a high level of self-determined motivation will persist more in physical exercise than those with a low level of self-determined motiva-Psychology tion.

H3: Promotion-oriented participants with high levels of intrinsic motivation will be less affected by the fit effect compared to prevention-oriented participants.

H4: The effectiveness of messages framed in terms of promotion versus prevention will depend on the individual's intrinsic and extrinsic motivation for physical activity and regulatory focus profile, with a positive influence on persistence in physical activity for those with high promotion and intrinsic motivation scores, particularly when interacting with a promotion-oriented agent.

Method

Participants

The sample for this study consisted of 69 adults (25 women and 44 men) with an average age of 35.2 years (ranging from 18 to 61 years). In terms of body composition, the average body mass index (BMI) for participants was 23.3, which falls within the normal range (BMI 18.5 to 25). The sample represented a range of body compositions, including normal, underweight, and overweight, but excluded individuals with anorexia or morbid obesity.

Protocol

The study was conducted in a laboratory setting and consisted of two phases. The initial phase entailed administering a battery of self-report measures to determine the regulatory focus orientation, motivational state, and physical activity level of each participant.

In the second phase, participants performed two sessions of physical activity on a stepper, with a break in between to allow for rest. The first session was the same for all participants and was a controlled trial. The task was to take as many steps as possible, with a maximum of 1500 steps. Participants' progress was tracked using a connected watch, and feedback (the achieved number of steps) was displayed every 65 seconds on a computer screen facing the participant (Figure 1). The second session was the experimental trial. Participants were randomly assigned to one of three conditions: Fit, NoFit, or Control. In the Fit condition, participants received personalized motivational messages that aligned with their regulatory focus orientation (promotion or prevention). In the NoFit condition, participants received non-personalized motivational messages that did not align with their regulatory focus orientation. In the Control condition, participants received no motivational messages.

In the Fit and NoFit conditions, a virtual coach was displayed on a screen in front of the participant and remained present throughout the exercise. The virtual coach communicated motivational messages designed to fit either the Promotion or Prevention profile of the participant. The messages were formulated in a positive form for promotion-oriented participants, focusing on the goal as the desired end state, self-esteem, and accessibility of the goal. This translates into messages such as: "You have taken x steps. Keep up the good work!" or "

Step by step, you will reach your goal, be brave, you have everything to go far" or "Nothing is impossible with a valiant heart, it's time to give it your all". For prevention-oriented participants, messages focused on avoiding an undesired end state, not deviating from the goal, effective behavior, and a sense of duty.

This translates into messages such as: "You have taken x steps, keep this attitude and there is no reason for you to fail", or "Continue like this, you must not slacken your efforts", "Congratulations, by continuing like this, you will preserve your achievements". During this second experimental trial, the task was also to take as many steps as possible, with a maximum of 1500 steps. Participants' progress was always tracked using a connected watch. Feedback was provided on a computer screen every 65 seconds according to the experimental condition.

After the second session, participants completed a final questionnaire to assess their perception of the virtual coach (if applicable). Data were collected and analysed to examine the impact of personalized messages on engagement and continuation in physical exercise, and the relationship between regulatory focus and motivation.

Measures

Behavioral measures. We recorded the number of steps and the duration of the exercise for each participant and each session. We also computed cadence (walking rate in steps per minute) by dividing the number of steps by the duration of the exercise and analyzed the change in this ratio between the two sessions (control trial and experimental trial).

Psychological measures. All predictor variables were assessed prior to the second phase. Participants completed two self-report questionnaires and provided responses to several demographic and physical activity-related questions.

The Regulatory Focus Questionnaire-Proverbs Form (RFQ-PF, [START_REF] Faur | Measuring Chronic Regulatory Focus with Proverbs: The Developmental and Psychometric Properties of a French Scale[END_REF] was utilized to measure chronic regulatory focus, and the 28-item General Motivation Scale (EMG-28; [START_REF] Guay | On the Hierarchical Structure of Self-Determined Motivation: A Test of Top-Down and Bottom-Up Effects[END_REF] was used to evaluate participants' Psychology motivation levels. This scale is composed of 7 dimensions that characterize various types of motivation, ranging from intrinsic motivation to motivation.

Results

In this section, we present the results of our study. We examined the distribution of our data and found that the Kolmogorov-Smirnov tests supported the assumption of normality (p > 0.05). We then conducted an ANOVA to test hypothesis H1 and linear regression to explore hypotheses H2 to H5. For all analyses, we report the significance level (p < 0.05) and effect size, as measured by eta-squared (η 2 ) and R-squared (R 2 ). In addition, in order to comply with APA standards, we report the means, standard deviations, and sample sizes for each group in the tables.

The Impact of Virtual Coaching on Cadence

To examine the overall impact of the virtual coach on physical effort, we compared the average cadence for each experimental condition. Results indicated a significant increase in the average step-to-time ratio between the two sessions regardless of the condition (F(1, 66) = 59.924, p < 0.00001, η 2 = 0.476). However, there was no significant difference in the change in the cadence between the experimental conditions (F(2, 64) = 0.97041, p = 0.38443). These findings suggest that the mere display of a virtual coach and motivational messages did not lead to greater physical exertion when they were not tailored to the participant's profile. Additionally, there was no significant interaction effect between the virtual agent's characteristics and the participant's profile on the change in cadence (F(2, 61) = 1.2889, p = 0.28297).

Independent-sample T-tests were conducted, and results, presented in Figure 2, revealed a trending effect among participants with the Prevention profile in the NoFit condition compared to the Fit condition (t(15) = 1.75759, p = 0.099) and the Control condition (t(14) = -1.76835, p = 0.099). However, no significant effects were observed for the Promotion profile. Among participants with a Promotion profile, performance progression was similar across the three conditions.

Relationship between Participants' Regulatory Focus and Change in Cadence across Sessions

A linear regression was conducted to examine the relationship between promotion scale scores and changes in cadence across sessions. Results showed that there was a significant positive correlation between the promotion scores of participants and changes in cadence when subjects were in the Fit condition and interacted with a promotion-oriented virtual coach (F(2.21) = 7.02, p < 0.015;

Adjusted R 2 = 0.216). This suggests that as the promotion scores of participants increase, the cadence also increases across sessions (beta adjusted = 9.25). Additionally, a significant linear regression was found when participants had high prevention scale scores and were in the Fit condition, interacting with a prevention-oriented virtual coach (F(1, 22) = 4.41, p < 0.047, adjusted R 2 = 0.167). In this condition, the higher the prevention scale score of participants, the greater the increase in cadence across sessions (beta adjusted = 10.19). These results align with our H1 hypothesis, revealing a correlation between the virtual coach's profile and the agent's profile. This correlation varies depending on the participant's promotion or prevention profile. The results only partially support H1, indicating that there is no main effect of fit on cadence across sessions. However, the results also suggest that prevention-oriented participants are more sensitive to the alignment of the virtual coach's profile with their own profile, compared to promotion-oriented participants. Linear regressions further demonstrate that the greater the alignment between the participant's and virtual coach's profile, the more significant the change in cadence.

Correlation between Forms of Motivation and Change in Cadence across Sessions

In order to examine the relationship between different forms of motivation and the change in the cadence across sessions, simple linear regression analyses were conducted. Results revealed that intrinsic motivation to know (IM-to know) did not have a significant effect on the change in the ratio, regardless of the experimental condition.

Furthermore, results revealed that intrinsic motivation to accomplish things (IM-to accomplish) was found to have a significant linear relationship with the change in cadence across sessions, as evidenced by a regression equation of F(1, 65) = 7.93, p < 0.0064, and an adjusted R 2 = 0.095. This relationship was observed across all groups and indicated that a higher score in IM-to accomplish was associated with a greater positive change in the ratio between sessions. However, when the relationship was examined separately for each experi-mental condition, it was found to be only statistically significant in the Fit condition (F(1, 22) = 5.61, p < 0.027, adjusted R 2 = 0.167) and not in the Control or No Fit conditions.

No significant relationships were found between the change in cadence and intrinsic motivation to experience stimulation, extrinsic motivation, or motivation.

These results support our H2 hypothesis, which posits that individuals with a high level of self-determined motivation will exhibit greater persistence in physical exercise than those with a low level of self-determined motivation. Furthermore, the data revealed that intrinsic motivation to accomplish things is specifically related to the evolution of the cadence between the two sessions. These findings contribute to a deeper understanding of the relationship between motivation and persistence in physical exercise.

Correlation between Motivation and Regulatory Focus

As expected, correlations were observed between regulatory orientation and motivational profile. The higher the subjects' scores in promotion, the higher their scores in intrinsic motivation to know (IM-to know) (r(65) = 0.29; p < 0.05), intrinsic motivation toward accomplishments (IM-to accomplish things) (r(65) = 0.44; p < 0.05), intrinsic motivation to experience stimulation (IM-to experience stimulation) (r(65) = 0.44; p < 0.05), and identified extrinsic motivation (r(65) = 0.29; p < 0.05). For prevention-oriented participants, it was found that the higher the participant's score in prevention, the higher the score for motivation (r(65) = 0.26; p < 0.05), extrinsic introjected motivation (r(65) = 0.33; p < 0.05) and IM-to experience stimulation (r(65) = 0.26; p < 0.05).

These correlations vary according to context, i.e., the type of agent: promotion versus prevention, or the level of adaptation of the agent's profile to the user's profile: Fit versus NoFit.

A positive correlation was observed between the promotion scale score and intrinsic motivation (IM-to-know, r(21) = 0.54, p < 0.05; IM-to-accomplish things, r(21) = 0.66, p < 0.05) when participants interacted with a promotion-oriented virtual coach. Additionally, a positive correlation was observed between the prevention scale score and extrinsic motivation with introjected regulation (r(21) = 0.57, p < 0.05). These results suggest that as the promotion score increased, so did the intrinsic motivation score. Conversely, as the prevention score increased, the extrinsic motivation score with introjected regulation also increased.

When participants interacted with a prevention-oriented virtual coach, a correlation was observed between the score of the participant on the prevention scale and the IM-to-know (r(21) = 0.43; p < 0.05), the IM-to-accomplish things (r(21) = 0.42; p < 0.05), and IM-to-experience stimulation (r(21) = 0.58; p < 0.05), as well as with motivation (r(21) = 0.43; p < 0.05). Results indicate that the higher the prevention score, the higher the score for intrinsic motivation and motivation. Additionally, a correlation was found between the promotion score and IM-to-experience stimulation (r(21) = 0.54; p < 0.05). Thus, when participants interacted with a prevention-oriented virtual coach, the higher their promotion score, the higher their intrinsic motivation to experience stimulation.

In the Fit condition, i.e., when participants interacted with a virtual coach which displayed motivational messages adapted to their profile, we observed significant positive correlations between the prevention score and the scores of intrinsic motivation to know (r(22) = 0.53; p < 0.05), to accomplish things (r(22) = 0.48; p < 0.05), and to experience stimulation (r(22) = 0.50; p < 0.05). These results indicate that as the prevention score increased, the intrinsic motivation score also increased.

In the NoFIT condition, i.e., when participants interacted with a virtual coach which displayed motivational messages not adapted to their profile, we observed significant positive correlations between the promotion score and the scores of intrinsic motivation-to know (r(20) = 0.46 p < 0.05), to accomplish things (r(20) = 0.81; p < 0.05), to experience stimulation (r(20) = 0.54; p < 0.05). These results

suggest that as the promotion score increased, the intrinsic motivation score also increased. Additionally, there is a significant positive correlation between the prevention score and extrinsic motivation with introjected regulation (r(20) = 0.48, p < 0.05).

These results partially confirm hypotheses H3 and H4. In the presence of a promotion agent, there is a correlation between the participant's promotion score and their intrinsic motivation to know and accomplish things. Similarly, in the presence of a prevention agent, there is a correlation between the participant's prevention score and their intrinsic motivation or motivation. However, this correlation between the participant's focus and motivation varies depending on context. In the Fit condition, there was a correlation between the prevention score and intrinsic motivation. In the NoFit condition, there was a correlation between the promotion score and intrinsic motivation. This seems to suggest that the effectiveness of framed messages in terms of promotion or prevention depends on the individual's intrinsic and extrinsic motivation profile for physical activity and their regulatory focus. However, these results do not show any link with the change in cadence.

Correlation between Cadence, Motivation and Regulatory Focus

The results partially support the hypothesis that there is a correlation between cadence, and motivation, and regulatory focus. In the Fit condition, significant correlations were observed between cadence and prevention score (r(22) = 0.41, p < 0.05) as well as intrinsic motivation to accomplish score (r(22) = 0.45, p < 0.05). Participants who interacted with a virtual coach adapted to their profile demonstrated an increase in cadence between the two sessions, with higher scores in prevention or intrinsic motivation to accomplish being positively correlated with a higher cadence. However, in the NoFit condition, there was no significant correlation between cadence and either motivation or profile. Additionally, no significant correlation was found in the Control condition, indicating no link between cadence and the participants' motivational profile or regulatory focus. Thus, the impact of a prevention or promotion-framed message depends on the individual's intrinsic and extrinsic motivation for physical activity and their regulatory focus. However, these results do not reveal any relationship with cadence.

Discussion

The purpose of this study was to investigate the relationship between regulatory focus and motivation, and their impact on users' physical activity. We also wanted to assess how a virtual coach can influence users' performance. We combined two widely used theories in health psychology, the Regulatory Focus Theory and the Self-Determination Theory. We hypothesized that there would be a fit between regulatory orientation and motivation forms. Our findings partially support this hypothesis.

We first tested whether a virtual coach providing motivational feedback during an exercise would improve participants' performance. Contrary to our expectations, our results did not show any significant improvement in performance between participants with and without the virtual coach. However, we observed that the participants' cadence increased significantly for all participants between the 1st and 2nd exercise, regardless of the presence of a virtual coach.

Our results are consistent with previous research, suggesting that individuals with a promotion focus are less sensitive to external elements that modify their behaviour, and those with a prevention focus are more sensitive to external elements that modify their behaviour (as expected, these participants were more sensitive to the context). Our results also confirm the results of other studies in the field of physical activity and sport [START_REF] Motyka | Regulatory Fit: A Meta-Analytic Synthesis[END_REF][START_REF] Plessner | Regulatory Fit as a Determinant of Sport Performance: How to Succeed in a Soccer Penalty-Shooting[END_REF][START_REF] Daryanto | Service Firms and Customer Loyalty Programs: A Regulatory Fit Perspective of Reward Preferences in a Health Club Setting[END_REF] which underline that the effectiveness of the motivational messages depends on the adequacy between the user profile and the content of the message. These motivational messages do not need to be positive to be effective. Additionally, our findings suggest that intrinsic motivation impacts cadence more than promotion-oriented messages. This raises questions about the design of our promotion-oriented messages, indicating that they may not be perceived as promotional enough by our participants.

The study we presented in this article displays some limitations, such as measuring the level of motivation using self-reported responses, and the number of steps, which might not be the most accurate indicators of motivation. In addition, this study was conducted in a laboratory setting, so it is unclear how well the results would generalize to real-world situations. Future research should focus on studying the long-term effects of virtual coaches on physical activity and consider the potential influence of the virtual agent's appearance and gender.

Figure 1 .

 1 Figure 1. Experimental setup.

Figure 2 .

 2 Figure 2. Relation between Cadence (walking rate), the profiles of the user and the profile of the coach.

Conclusion

In conclusion, this study provides valuable insight into how regulatory focus and motivation impact users' physical activity. It underscores the importance of aligning the feedback provided by a virtual coach with the user's regulatory orientation to maximize the effectiveness of an intervention. While our current findings have set a foundation, more nuanced explorations are essential to better understand the multifaceted relationship between regulatory orientation, motivation, and physical activity. As technology and health psychology continue to intersect, future studies may look at diversifying the virtual coaching methods and tailoring interventions to individual profiles more comprehensively, thereby enhancing the effectiveness of virtual coaching in promoting physical activity and overall well-being.

Ethical Considerations

All procedures involving human participants were carried out in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the ethics committee of Université Paris Saclay under reference CER-Paris-Saclay-2018-037.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.


Agnès Réjasse

Florent Dupriez

Thomas Lefebvre

Alfredo Rios

Christina Nielsen-Leroux

Impact

Impact and persistence of the opportunistic bacteria Serratia marcescens in the feed and in Tenebrio molitor larvae under mass rearing conditions

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Quantification and persistence of Serratia marcescens in Tenebrio molitor

Stress factors impact on Tm rearing with Sm Serratia marcescens 5 µm

Serratia marscecens is a redpigmented Gram negative enterobacterium. This bacteria is a common opportunistic pathogen of insect and humans.

Tenebrio molitor

Tenebrio molitor is a beetle of the family Tenebrionidae. As all holometabolous insects this species grows through four life stages: egg, larva, pupa, and adult. The total life cycle from egg to adult is approximately 90 days.  Tenebrio molitor is only weakly affected by the presence of Serratia marcescens even under stressed conditions.

 Serratia marcescens can survive in small quantities in the feed substrate or in the larva ( gut and feces.)

 Sanitation of the final insect product is necessary to remove the presence of Serratia marcescens.

 This pathogen could be considered as a potential marker in the assessment of infectious pressure. Contact : agnes.rejasse@inra.fr, christina.nielsen-leroux@inra.fr , tle@ynsect.com

Conclusions

CONCLUSION

• The q-PCR allows Serratia detection at a very low threshold.

• Q-PCR is precise for quantification only in samples of easy DNA extraction. • Large variation was observed among replicates.

• qPCR technique is too expensive for persistence analysis.

• Need another method = specific Erythritol growth medium. (ref. Irving J. and al.(1972). Applied Microbiology)

Detection and persistence ? Study of stress factors

Protocol for the applied combined stress

Impact of starvation and larval density on Individual Mean Weight Gain Impact of Serratia exposure and sieving on mortality

A 2-level factorial experiment was designed to apply the following stress factors:  Starvation (+) /continuous feeding (-)  Automatic sieving (+) / handmade sieving (-)  High density (0,8g/cm²) (+) / low density (0,4g/cm²) (-)  Serratia exposure (+) / no exposure to Serratia (-)

Larval containers were provided with humid (30%) wheat bran. Starvation was brought by the removal of feed from the container the day prior to data recording while the continuous fed larvae received supplementary feeding in the event that they had finished their feed.

The figure on the left shows the relative quantity of feed provided and the frequency of data recording .

One Key Performance Indicator (KPI) used in insect rearing is the Individual Mean Weight Percentage Gain (IMWG%.) This indicator shows the relative growth of larvae in regards to their initial weight.

The figure on the left shows IMWG% according to the combined stress factors: density(-/+) and starvation (-/+).

An analysis of variance (ANOVA) indicated the presence of an interaction between density and starvation on IMWG%.

The highest IMWG% was observed for the larvae reared at high density and continuous feeding. A 31% decrease in IMWG% was observed between the high-density groups if the starvation stress was applied.

CONCLUSION

At low density conditions starvation seemed not to have an impact on IMWG%. At high densities however, starvation presented a large negative impact on IMWG%.

Larvae exposed to Serratia presented a higher mortality than larvae that were not exposed to the pathogen. The mortality was approximately twice as high in the Serratia exposed larvae.

CONCLUSION

Serratia has an impact on mortality of Tenebrio molitor but not enough to risk a collapse of the larval production.

Ynsect is developing innovative techniques to prevent infectious risks by sampling and monitoring the rearing production through the use of embedded sensors and molecular methods for the detection of pathogens. The development of knowledge and techniques in insect health for this new industry is essential. The future is built today through collaborative projects between companies and academic laboratories such as the Micalis team of INRA.

Perspectives

Feces from 5 larvae are collected at different timepoints. Serratia is detected from diluted feces on erythritol agar.

Quantity of

  Impact and persistence of the opportunistic bacteria Serratia marcescens in the feed and in Tenebrio molitor larvae under mass rearing conditions Detection of Serratia marcescens by qPCR ADN extraction from gut or feces or larvae (Kit Mo Bio ®) qPCR kit qPCR test Serratia marcescens (PCR Max ®) Quantification of bacteria Tenebrio molitor larvae were fed with Serratia contaminated feed during 17 hours with 9 .10 5 bacteria/Control of specific medium : Bacterial counts in 50 mg samples

  Industrial insect rearing aims to produce large quantities of high quality insects, following safety and secure sanitary conditions. Diseases in insect rearing caused by pathogens can compromise insect quality and production. Therefore, it is important to gain knowledge on pathogens characteristics such as prevalence, virulence, means for detection and persistence. In addition, knowledge on the behavior of human opportunistic pathogens in insect rearing is of importance for sanitary risk issues. It is also important to assess the influence of biotic and abiotic rearing conditions on insect's sensitivity to pathogens. This study focuses on the opportunistic pathogen Serratia marcescens on the industrial farming of Tenebrio molitor.Objectives : evaluate the impact and persistence of Serratia marcescens (Sm) in Tenebrio molitor (Tm) rearing by: -Evaluating the impact of biotic/abiotic stresses on Tm -Assessing the performance of methods for Sm detection -Characterizing the persistence of Sm in Tm larvae and feed Agnès Rejasse 1 , Florent Dupriez 2 , Thomas Lefebvre 2 , Alfredo Rios 2 , Christina Nielsen-LeRoux 1 1 INRA MICALIS, AgroParisTech-Univ. Paris Saclay, 78350 Jouy-en-Josas, France. 2 YNSECT, Genopole, 1 rue Pierre Fontaine, 91058 Evry

  The figure on the left shows the mortality rate in percentage according to the the combined stress factors: Serratia (-/+) and Sieving (-/+).This study belongs to a research project funded by the INRA and CIRAD GloFoods metaprogram based on a specific collaboration between Ynsect and INRA Micalis teams. This project is intended to extend to other partners.

  Quantity of Serratia in bran : detected on erythritol plate from bran soaked with culture (6.10 9 CFU) and incubated at 30°CCFU bacteria in branSerratia CFU on erythritol medium

Persistence of Serratia in feed (wheat bran) and in feces of Tenebrio molitor CONCLUSION

	Serratia persists in low quantities in Tenebrio over time but a
	large variation was present among replicates: After 24H of ad
	contaminated wheat bran
	after 7 days with a 5 log reduction ( 100.000 fold less)
	• CFU counting on selective medium is less precise in quantity
	than qPCR but indicates viability not only presence

• No Serratia was found in Tm and feed from the rearing • Low Persistence of Serratia in d libitum ingestion of bran soaked with Sm, the amount of Sm found, in the larva is about 5.103 bacteria (decrease of 5 log or 10,000 times more than in the bran at 24 H) Sm can persist in the gut and reappears when the larvae feed again.


J P Giovanangeli

N Reul

M H Garat

H Branger

Some Aspects of Wind-Wave Coupling at High Winds: an Experimental Study

Experiments were conducted in the IRPHE \Vind-wave facility in order to study the evidence and occurrence of air-fl ow separation above short wind waves. An original air-flow separation probe was de.signed and a Particle Image Velocimetry (PIV) technique in the air close to the surface \Vas carried on. Measurements above steep waves clearly sl10\v air-fl.ow separation at the crest, organised ''ortex motions with reverse flow above tl1e leeward face and reattachment on the windward face of tlie downstream wave. Occurrence of air-fio,v separation grows linearly with wave steepness and decreases with wa\•e age. T}1e associated eff ects on \Vind-\vave coupling are investigated.

Introduction

Momentum and energy fluxes from wind to waves are still not well understood, more particularly at high winds. This is due to the lack of knowledge on the structure of pressure and velocity fields in the close vicinity of steep and breaking waves. Some authors {[l, 2, 3, 4, 5, 6, 7]) argued that over steep laboratory wind waves, air-fl ow separation (AFS) could occur leading to significant changes on the wind-wave coupling and on the momentum and energy fluxes between air and water flows. However, as mentioned in {8], the results concerned a parti cular range of laboratory wind waves or quasi-steady breaking waves. No direct observations of the spatial structure of the air fl ow in the close vicinity of short wind-forced steep gravity waves were systematically done.

This paper reports upon a complete series of experiments conducted in the large IRPHE wind-wave tank in order to study the structure of air flow above short and steep wind-forced waves. First we developed an original method ca pable to detect systematically the occurrence of air-flow sep aration at the wave crest and to quantify its effects upon momentum and energy fluxes from wind to wave. Then, we used a new and powerful method based upon air-flow visu alisation technique: the Particle Imaging Velocimetry (PIV). The results show definitely the occurrence of AFS process, its dependence on sea state and its sig nificant effects upon the structure of the turbulent boundary layer and wind-wave coupling. These results are of crucial importance to modelize and to parametrize wind-wave interactions particularly for young seas.

Experimental procedures

A first series of experiments have been conducted in the large IRPHE wind-wave tank (60 m long, 3 m wide and 3 m high) described in details by [9]. A second series using PIV have been conducted in the (l/5)'h scale model of the large facility. A sophisticated experimental set-up has been used. It consisted in a)

three capacitive wave gauges allowing to determine a1nplitudes, along-wind and cross-wind slopes of the waves; b) an original pressure probe developed at IRPHE to measure the air static-pressure fluctuations [10); c) an X-wire to determine longitudinal and vertical turbulent wind velocity fluctuations; and d) a wave follower to locate the probes at a constant height above the instantaneous 'vater surface in presence of waves. Some experiments l1ave been done over combination of paddle and wind waves. For these runs, particular attention was taken in order to avoid parasitical noise induced by the paddle displacements. The procedure is described in details in [11].

A key part of the investigation was to develop an original method to detect systematically reverse upstream flow above the water surface induced by AFS as observed by [START_REF] Kawai | Bound[END_REF] and [12]. The second one was to operate the PIV visualisation technique in order to give the spatial and temporal features of the air flow in the close vicinity of the waves.

2.1

The air-llo\v separation probe Starting from the idea that AFS can induce reverse flow very close to the leeward face of the wave [12], the AFS probe detector consisted in two DANTEC Rll cylindrical films separated 0.3 mm apart in the mean wind direction. The cold film was located upstream the hot film. Finally, the two films were mounted on a wave follower to explore the air flow in the close vicinity of the water surface in presence of waves and particularly in the troughs. In absence of AFS, the air flow is flowing downstream, the thermal wake of the hot film is driven downstream and then the upstream cold wire does not detect any air-flow temperature fluctuation.

On the other hand, if AFS occurs, it could generate upstream local recirculations, and then locally the cold film will detect a temperature fluctuation due to the thermal wake of the downstream hot film. The efficiency of the method has been proved by a complete series of tests in [13).

2.2

The PIV method

The air flow above unsteady breaking waves was measured with a particle image velocimetry system based on the cross-correlation technique. Unsteady breaking waves were generated at a fixed location in the (1/5)'h scale model of the large facility by focussing packet of waves generated by a wavemaker and forced by the wind. A 12 mJ doubled pulsed Nd:YAG laser was used to provide a light sheet about 1 mm thick directed downward at the measurements site. The air-flow was seeded with water particles with diameters about 10 µm. Images of these illuminated particles were captured with a 80C42 Doublein1age 700 Dantec cam era using a high-performance progressive scan interline CCD ship. The effective resolution of the CCD camera was of 768 (horizontal) x 484 (vertical) pixels. The can1era and the light sheet were slightly inclined at 10° in order to detect the free surface. Flow above wave groups were then analysed with the Dantec Flowmap

Processor for different wind speeds, wave group amplitudes and breaking point positions.

Results and discussions

First we studied the statistical link between the structure of the turbulent air flow and the water surface displacements. We investigated the evolution with height of the coherency function between along-wind wave slope and the static pressure fluctuations in the air for different wind velocities. As expected, for a given mean wind velocity, the coherency function decreases as the height increases but for a given height, the coherency increases as the mean wind increases. This suggests here that larger is the wind higher is the coupling between the air flow and the \vaves.

Usually it is assumed that the exponential decay in amplitude with the height of the wave-induced fluctuations evolves as e -ak z with a a decay parameter taken as a constant close to the unity and k the wavenumber of the wave. By mea suring the evolution with height of the correlation function between air-pressure fluctuations and longitudinal wave slope, we estimated the vertical evolution of the amplitude of wave-induced pressure fluctuations. The decay coefficient a was found to be wave-age dependent. The coefficient is close to 1.5 and decreases as the wave age decreases. This result suggests again that wind-wave coupling increases as the wave age decreases, as predicted by others [14,15].

To quantify the wind-wave coupling, we determined the contribution of wave induced organised motions to the total stress for different sea states. This was done by others (15,16], considering the ratio rw/r where rw is the wave-induced stress and r = pu*2 is the total stress. In this work, the organised wave-induced stress has been considered by comparison between the form drag (correlation (p'q,),=q between pressure fluctuations p1 at the surface and longitudinal wave slopes q,) and the total stress r. As it was not possible to measure directly the air-pressure fluctuation at the water surface, the form drag (p'q,),=q was estimated at the height z and extrapolated at z = q, taking into account the decay coefficient value a as a function of wave age c/u*. The contribution of the wave-induced motions to the total stress is not constant but depends upon the wave age as shown in Figure 1. One can observe that the ratio {p't/x)x=• /r, which can be considered as a \Vind-,vave coupling parameter, is maximum close to c/u* = 2, for example, for young gravity waves. This is consistent with results obtained by others [12,16,17,18). We can observe that for older gravity waves (c/u* > 3) the wind-wave coupling parameter decreases as the wave age increases and tends to reach the value of 20% which is in agreement with {19] and [20].

Finally, for very young waves the contribution of the form drag to the total stress decreases. This suggest that for these waves the viscous stress takes a prominent part to support momentum llux from wind to waves. Finally we found that the AFS process induces large spikes on the instan taneous product p'T/z just behind the highest crest and on the instantaneous product u ' w' in phase with the trough of the highest wave. This seems to be in accordance with A FS which induces a reattachment point shifted in the down wind direction from the separation point located close to the highest wave crest.

\ " \ ifJ I ' ifJ 0.7 f \ Q) � 0 \ - (f) 0.6 \ \ ro \ \ 80 - 0 0.5 \ f- �o" -- CJ) \ ro 0.4 O' � ' 0 Q\ ' 0.3 " ' E 0 ' i{ � " 0 ' • ' LL 0.2 ' • ' ' ' 0.1 0 0 1 2 3 4 5 6 • C / U
To consider the effects of AFS process upon the wind-wave coupling, condi tional sampling have been applied to each u ' w' rf and T/z time series. Data corresponding to the cases where AFS occurs and where AFS does not occur have been considered separately. Figure 1 presents the evolution of the wind wave coupling parameter (p'11,)z=" /r, as a function of the wave age with and without considering AFS. This figure shows clearly the role played by AFS in the wind-wave coupling. One can observe that the effect of AFS is maximum for c/u* close to 2 and the form drag is increased by a factor of two when AFS occurs. The wind-wave coupling parameter is largely enhanced by the AFS.

Nevertheless one can note that detection of AFS has been done here on the cri terion of occurrence of reverse flow above the leeward face of the waves. This seems highly selective and one can argue that the measured AFS occurrence could be underestimated.

In order to verify definitely the existence of AFS another series of experi ments have been conducted in the (1/5)'h scale model of the large wind-wave tank. In this set of experiments the Particle Imaging Velocimetry method de scribed above has been operated.

  Now the question arises: why wind-wave coupling by means of coherent pres sure fluctuations increases as the '\Vave age of gravity waves decreases? Tl1e struc ture of the velocity and pressure fields in the air fl ow has been investigated by using the AFS probe detector simultaneously \vith the pressure probe, hot-\vire anemometers and wave gauges. Figure2displays the frequency of occurrence of

Figure 1 .

 1 Figure 1. Contribution of the form drag to the total stress as a functio1:1 of wave age c / u� ( waves without air-flow separation; ---total wavefield )

Figure 2 .Figure 3 .

 23 Figure 2. Occurrence (in %) of air-flow separation as function of \vave steepness. {o wind waves; + wind waves plus paddle waves)

Figure 4

 4 presents examples of PIV air-flow measurements. Above the steeper wave instantaneous wind-speed vectors pro cessed by PIV clearly proves the occurrence of air-flow separation downwind the crest. When AFS occurs, PIV picture shows a} accelerated flow over the crest, b) well organised vortex motions with reverse flow above the leeward face, and c) reattachment on the windward face of the downstream wave. Data acquisi tion of p' u' and w' have been done simultaneously, and we project to correlate the occurrence of AFS observed by PIV with changes in momentum and energy fluxes over steep or breaking waves. Organised vortex motions on the leeward face of the waves could explain why the contribution of form drag to total stress increases as wave steepness increases. AFS should be taken into account to mo delize wind-wave coupling. PIV results show that the water surface should notbe considered as a streamline function as it is supposed in theoretical present models. Finally AFS suggests to not consider the existence of a constant shear layer in the close vicinity of the surface waves as it has been argued by others(19,21].

Figt1re 4 .4

 4 Figt1re 4. PlV measuren1ents above \vaves; top: without significant air-fio,v separa tlo11; bottom: evidence of air-flow separation \Vith accelerated flow and col1erent vortex motio11s

Ackno;vledgements

This work was supported by Grants ONR 14-93, DRET 94-147, CNRS/PATOJVI 91-1231 and ATP/PATOM 96-423.