Evaluation of a Therapeutic Education Application in the Treatment of Young People with Moderate or Problematic Screen Use (PHONIX CARE)

Evaluation D'une Application D'éducation Thérapeutique Dans La Prise En Charge De Jeunes Ayant Un Usage Modéré Ou Problématique Des Écrans

This single-center, controlled, and randomized study evaluates the effectiveness of the Phonix Care app in regulating screen use among young people aged 11 to 25. Faced with high and often concerning levels of screen consumption among youth, this research aims to provide an innovative intervention method beyond current psychotherapeutic and pharmacological approaches, which are often limited by the risk of relapse and the difficulty in delaying the short-term rewards offered by screen activities [1, 2, 3]. Phonix Care is designed to encourage awareness and self-regulation of screen use, thus promoting more responsible and autonomous behavior.

The primary outcome measure is based on a problematic screen use score derived from the Digital Addiction Scale. Secondary objectives include examining the effects of the app on screen consumption, physical health, mental health, and motivation towards studies, measured through a series of questionnaires and objective evaluations.

The study is conducted on 138 subjects, divided into two groups: an experimental group and a control group, over a participation period of six months. Statistical analyses will include descriptive analyses, multiple linear regression, and mediation models to assess the impact of Phonix Care.

The expected outcomes of this research include significant contributions to the scientific literature regarding screen use among youth, as well as advances in adolescent and young adult health and psychology. In practice, the evaluation of Phonix Care could lead to the development of an effective medical device to quantify and treat problematic screen use, offering a complementary therapy to existing methods to prevent or remedy this issue.

1. Winkler A, Dörsing B, Rief W, Shen Y, Glombiewski JA. Treatment of Internet addiction: A meta-analysis. Clinical Psychology Review. 2013;33(2):317-29. https://doi.org/10.1016/j.cpr.2012.12.005
2. Xu LX, Wu LL, Geng XM, Wang ZL, Guo XY, Song KR, Liu GQ, Deng LY, Zhang JT, Potenza MN. A review of psychological interventions for Internet addiction. Psychiatry Research. 2021;302: 114016. https://doi.org/10.1016/j.psychres.2021.114016
3. Zajac K, Ginley MK, Chang R, Petry NM. Treatments for Internet gaming disorder and Internet addiction: A systematic review. Psychology of Addictive Behaviors. 2017;31(8):979-94. https://doi.org/10.1037/adb0000315

Study Overview

• Status: Completed
• Conditions: Internet Addiction Disorder
• Intervention / Treatment: Device: Phonix Care

Detailed Description

Quality assurance: A risk analysis of our application was conducted by an external organization, Surgiqual Institute. Their audit validated that our cybersecurity systems and risk management procedures were state-of-the-art in compliance with medical legislation applicable to our application. They produced a document to state that, based on their audit, they affirm the responsibility for ensuring the technical and legislative compliance of our application.

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Data checks: each data type had to match with a user profile template (JSON FORMAT) :

• unique_encrypted_code_name (characters AND number)
• usage_data (characters)
• date (characters : DD-MM-YY)
• screen_type (characters)
• app_name : (characters)
• usage_data_per_app : usage_value (number)
• questionnaire_data (characters)
• date (characters : DD-MM-YY)
• questionnaire_name (characters)
• questionnaire_item : questionnaire_response (number)
• additional_personal_data (characters)

Source data verification: a preliminary technical study (with 15 participants) was conducted to:

• Validate that the self-reported hourly screen usage in the technical study corresponded to the data passively collected by our telemetric measurement applications over one month.
• Confirm that the self-reported responses to online questionnaires matched the actual answers provided by the participants.
• Ensure that each participant's key could be used only once.
• Verify that participants assigned to the experimental group couldn't bypass the application's restrictions.

Data dictionary:

• Daily application usage data (Source : application Phonix Care) :

  • First opening schedule
  • Last opening schedule
  • Opening Frequency
  • Usage duration

• Questionnaires responses (Source : the participant through the application Phonix Care)

  o Digital Addiction Scale

  • Regulation of Screen Time Consumption
  • International Physical Activity Questionnaire
  • Sleep Schedules diary
  • Revised Screen for Child Anxiety Related Emotional Disorders (SCARED-R)
  • University of Laval Loneliness Scale (ULS)
  • Rosenberg Self-Esteem Scale
  • Education Motivation Scale (EMS)
• Experimental arm only : specific screen rules during the 5-months intervention period and the number of challenges that were completed

Standard Operating Procedures (SOPs) were split into 10 steps :

1. Patient Recruitment:

   • Patient recruitment procedures will be conducted in accordance with the study protocol.
   • Recruitment efforts will be documented and tracked using electronic records maintained within the clinical trial management system (CTMS).

2. Data Collection:

   • Data collection will be performed using the Phonix Care application for daily application usage data.

   • Participants will input responses to questionnaires directly into the Phonix Care application.
   • For participants in the experimental arm, specific screen rules adherence and challenge completion will be recorded within the Phonix Care application.

3. Data Management:

   • Data collected from the Phonix Care application will be securely transmitted and stored on a dedicated health server hosted by a certified data management provider (AZNETWORK).
   • Access to the data will be restricted to authorized personnel only, with appropriate user permissions assigned based on roles and responsibilities.
   • Pseudo-anonymization procedures will be implemented to protect participant confidentiality.
   • Regular data backups will be performed to ensure data integrity and availability.

4. Data Analysis:

   • Data analysis will be conducted using statistical software approved by the study investigators (notably R, SPSS and Python).

   • Analysis will include aggregating daily application usage data, questionnaire responses, and experimental arm-specific data to assess intervention efficacy and participant outcomes.

5. Reporting for Adverse Events:

   • Any adverse events reported by participants will be documented in a dedicated electronic data capture system.
   • Adverse events will be promptly reviewed by the study investigators and reported to the appropriate regulatory authorities as per regulatory requirements.

6. Change Management:

   • Any modifications to the study protocol or data management procedures will be documented and approved by the study sponsor and ethics committee.
   • Changes will be communicated to relevant study personnel, and updated procedures will be implemented accordingly.

7. Quality Assurance:

   • Regular quality checks will be conducted to ensure data accuracy and consistency.
   • Data validation checks will be performed to identify any discrepancies or anomalies in the collected data.
   • Internal audits will be conducted periodically to review data management procedures and compliance with SOPs.

8. Training and Compliance:

   • Study personnel involved in data collection and management will receive training on SOPs and data handling procedures.
   • Compliance with SOPs will be monitored and enforced throughout the duration of the study.

9. Record Keeping:

   • All study-related documentation, including SOPs, data management logs, and training records, will be maintained in a secure electronic repository and duplicated to a secured space into a specific room of the AGEIS laboratory.

   • Records will be retained in accordance with regulatory requirements and study protocol specifications.

10. Documentation and Archiving:

    • Upon study completion, all study documentation will be archived for future reference and audit purposes.
    • Archiving procedures will adhere to regulatory guidelines and institutional policies.

Sample size assessment: To evaluate the effectiveness of Phonix Care using the overall score from the Digital Addiction Scale by Hawi et al. (2019), with an average Cohen's effect size d= 0.30 to 0.40 and a standard deviation of 19.25 (mean= 56.3), here are the necessary sample sizes for different statistical powers (1-β), with a significance level of α= 0.05:

80% power: from 96 to 174 participants required. 85% power: from 110 to 200 participants required. 90% power: from 129 to 233 participants required.

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Plan for missing data: We conduct an analysis of the missing data mechanism according to the rules set by Little and Rubin. Although it is very rare, if we validate the hypothesis that the missing data are completely random (Missing Completely At Random), we conduct the analyses using the incomplete data set. This data set will not bias the estimates. The most likely case is the validation of the Missing At Random hypothesis, which suggests that the missing data are due to one or more factors in our possession (e.g., experimental condition, threshold of problematic use), we proceed with multiple imputations before conducting our analyses. To determine if certain factors can explain whether the data are missing or not, we use logistic regression analyses via the GLM package on R Studio version 4.0.2. In the case of multiple imputations, we use the MICE package on R Studio version 4.0.2.

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Statistical analysis plan: We first proceed with the descriptive analysis of screen usage profiles and the number of profiles observed in our sample. We expect to observe at least three usage profiles: moderate, intensive, and problematic. To do this, we use the K-means clustering method. Next, the variables measured by questionnaire undergo longitudinal confirmatory factor analyses to ensure that, despite experimentation, we observe some longitudinal invariance of the measurement constructs (i.e., weak invariance). For our primary research objective, we conduct analyses using multiple linear regression. By controlling for certain factors that may have an effect on problematic screen usage (e.g., gender, age), we evaluate the simple effects of digital addiction scores before the study and the assignment group, and then the interaction effect between this addiction score and the assignment group on digital addiction scores at the end of the study. To address our secondary objectives, we conduct multiple linear regression and mediation analyses for each of the secondary objective variables as dependent variables in linear regressions and as mediation variables.

• Study Type: Interventional
• Enrollment (Actual): 139
• Phase: Not Applicable

Contacts and Locations

Study Locations

• France
  • Saint-Martin-d'Hères, France, 38400
    • PupilLab

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• To have an Android-compatible smartphone.
• To be enrolled in middle school, high school, or university.
• To be between 11 and 25 years old.

Exclusion Criteria:

• To have a poor understanding of the French language.
• To have participated in another interventional study in the same field within the last six months.
• To undergo psychological and/or medical follow-up related to screen addiction.
• To undergo pharmacological treatment for screen addiction disorder.
• To exceed the VRB threshold of 4500 euros.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Phonix Care experimental group; The experimental arm involves modifying the functionalities of the user's digital devices (e.g., computer, smartphone, tablet, gaming console). The objective is to allow individuals access only to essential digital functionalities such as calls, alarms, work tools, camera, and unlock recreational digital functionalities only if the user engages in non-digital leisure activities (e.g., cultural, sports, family, artistic activities). The smartphone sensors validate the activities performed to earn digital time that can be spent by the user. Gradually, the user progresses a virtual animal until reaching the third stage of therapeutic education. A phoenix will evolve simultaneously with the user when they engage in non-digital recreational activities. For 5 months, all participants' cross-platform screen-usage data are monitored with fine granularity, including the frequency of app openings, schedules of opening, and names of app openings.	Device: Phonix Care; Phonix Care consists of a 5-month digital therapeutic program that encourages the user to engage in non-digital activities through pre-defined screen rules and off-screen challenges validated by smartphone sensors.
No Intervention: Observational group; For 5 months, all participants' cross-platform screen-usage data are monitored with fine granularity, including the frequency of app openings, schedules of opening, and names of app openings.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Digital Addiction Scale	The primary outcome measure is a problematic screen usage score ranging from 25 to 125 points, calculated from an online assessment questionnaire translated into French from the Digital Addiction Scale (Hawi et al., 2019). Participants respond to 25 statements in which they are asked to select the option that best reflects their thoughts on their screen usage, with the following response options: 1 "never"; 2 "rarely"; 3 "sometimes"; 4 "often"; and 5 "always". The statements describe nine criteria related to problematic screen usage: preoccupation; tolerance; deprivation; conflicts; associated problems; deception; attraction to other activities; relapse; and mood modification. The higher the participants' total score, the more problematic their screen usage is reported. This information is collected at two measurement times: before and after the experiment. We control for the pre-experiment level, and our primary outcome measure refers to the measurement taken after the experiment.	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Regulation of Screen Time Consumption	The regulation of screen consumption is measured using a global score based on objective data through the Phonix Care tool (connection time on each screen and disconnection time on each screen; names of applications/software; number of times applications/software are accessed each day; duration of use for each application/software; time of first use for each application/software; time of last use for each application/software). This score will be defined after processing the data from the exploratory study to aid in diagnosis.	pre-intervention (T0), post-intervention (T0 + 5 months), with a completion duration of 0 minutes (data is collected passively).
International Physical Activity Questionnaire	Physical activity and sedentary behavior: we assess the number of physical activities conducted over 7 days and sedentary behavior using a French translation of Craig et al.'s (2003) IPAQ. Three types of physical activities are targeted by the questionnaire: vigorous physical activities, moderate activities, and walking (i.e., number of days, number of hours, and minutes). Additionally, to measure subjects' sedentary behavior, the number of days, hours, and minutes spent sitting over the past 7 days is also requested.	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.
Sleep Schedules diary	Quality and quantity of sleep: We assess sleep daily for 7 days using an online diary to be filled out each morning by the subject. This is a subjective but scientifically reliable method, used for 30 years to evaluate vigilance disorders (Bastuji & Jouvet, 1985).	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.
Revised Screen for Child Anxiety Related Emotional Disorders (SCARED-R)	To measure depression (10 statements), general anxiety (10 statements), and social phobia (5 statements), we use the Revisited Child Anxiety and Depression Scale (RCADS) translated into French by Bouvard et al. (2012). Subjects are required to indicate, for the 25 statements of the questionnaire, how often each thing happens to them. For each category, we calculate an average score from the responses to items ranging from 0 to 3, where 0 corresponds to "never," 1 to "sometimes," 2 to "often," and 3 to "always." The higher the subjects report that situations always happen to them, the more they report psychological distress.	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.
University of Laval Loneliness Scale (ULS)	The University of Laval Loneliness Scale (ESUL), consisting of 20 statements, is a French translation of the UCLA-R Loneliness Scale (de Grâce et al., 1993). For each statement, subjects indicate the frequency with which each statement describes well what they feel (e.g., "My interests and ideas are not shared by those around me"). The response scale ranges from 1 "never" to 4 "always." The more subjects report that situations always happen to them, the more they report social isolation.	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.
Rosenberg Self-Esteem Scale	It is measured by a questionnaire (EES-10 by Rosenberg, 2008) and consists of 10 statements. Subjects will need to indicate their agreement with each statement, knowing that 1 corresponds to "not at all agree", 2 to "rather disagree", 3 to "rather agree", and 4 to "completely agree".	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.
Education Motivation Scale (EMS)	We assess motivation in studies using the EME-C28 questionnaire by Vallerand et al. (1989). This questionnaire consists of 28 statements distributed across 7 subscales. These subscales measure three types of intrinsic motivation (to know, to accomplish, and to stimulate), three types of extrinsic motivation (identified, introjected, external), and amotivation. Participants are required to indicate the extent to which each statement currently corresponds to one of the reasons why they pursue their studies, with a response of 1 indicating that the reason does not correspond to them at all, and 7 indicating that it corresponds to them completely.	pre-intervention (T0), post-intervention (T0 + 5 months), with an estimated completion duration of 15 minutes.

Collaborators and Investigators

• Sponsor: University Hospital, Grenoble
• Collaborators: Institut National de la Santé Et de la Recherche Médicale, France; University Grenoble Alps; Centre National de la Recherche Scientifique, France; Laboratoire de Psychologie et NeuroCognition; Université Savoie Mont Blanc; Laboratoire Autonomie, Gérontologie, E-santé, Imagerie et Société (AGEIS)
• Investigators: Principal Investigator: Alexandre BELLIER, MD, PhD, Centre d'Investigation Clinique - CHU Grenoble Alpes / Département d'Anatomie (LADAF) - Université Grenoble Alpes / Laboratoire AGEIS - Université Grenoble Alpes

Publications and helpful links

General Publications

• Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003 Aug;35(8):1381-95. doi: 10.1249/01.MSS.0000078924.61453.FB.
• Young KS. Cognitive behavior therapy with Internet addicts: treatment outcomes and implications. Cyberpsychol Behav. 2007 Oct;10(5):671-9. doi: 10.1089/cpb.2007.9971.
• Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Prev Med Rep. 2018 Oct 18;12:271-283. doi: 10.1016/j.pmedr.2018.10.003. eCollection 2018 Dec.
• Clark NM, Zimmerman BJ. A social cognitive view of self-regulated learning about health. Health Educ Behav. 2014 Oct;41(5):485-91. doi: 10.1177/1090198114547512.
• Notara V, Vagka E, Gnardellis C, Lagiou A. The Emerging Phenomenon of Nomophobia in Young Adults: A Systematic Review Study. Addict Health. 2021 Apr;13(2):120-136. doi: 10.22122/ahj.v13i2.309.
• Ratan ZA, Parrish AM, Zaman SB, Alotaibi MS, Hosseinzadeh H. Smartphone Addiction and Associated Health Outcomes in Adult Populations: A Systematic Review. Int J Environ Res Public Health. 2021 Nov 22;18(22):12257. doi: 10.3390/ijerph182212257.
• Adelantado-Renau M, Moliner-Urdiales D, Cavero-Redondo I, Beltran-Valls MR, Martinez-Vizcaino V, Alvarez-Bueno C. Association Between Screen Media Use and Academic Performance Among Children and Adolescents: A Systematic Review and Meta-analysis. JAMA Pediatr. 2019 Nov 1;173(11):1058-1067. doi: 10.1001/jamapediatrics.2019.3176.
• Kapp C, Perlini T, Baggio S, Stephan P, Urrego AR, Rengade CE, Macias M, Hainard N, Halfon O. [Psychometric properties of the Consumer Satisfaction Questionnaire (CSQ-8) and the Helping Alliance Questionnaire (HAQ)]. Sante Publique. 2014 May-Jun;26(3):337-44. French.
• Johnson JG, Cohen P, Kasen S, Brook JS. Extensive television viewing and the development of attention and learning difficulties during adolescence. Arch Pediatr Adolesc Med. 2007 May;161(5):480-6. doi: 10.1001/archpedi.161.5.480.
• Pilatti A, Bravo AJ, Michelini Y, Aguirre P, Pautassi RM. Self-control and problematic use of social networking sites: Examining distress tolerance as a mediator among Argentinian college students. Addict Behav Rep. 2021 Oct 21;14:100389. doi: 10.1016/j.abrep.2021.100389. eCollection 2021 Dec.
• Wartberg L, Zieglmeier M, Kammerl R. An Empirical Exploration of Longitudinal Predictors for Problematic Internet Use and Problematic Gaming Behavior. Psychol Rep. 2021 Apr;124(2):543-554. doi: 10.1177/0033294120913488. Epub 2020 Apr 1.
• Chen YL, Gau SS. Sleep problems and internet addiction among children and adolescents: a longitudinal study. J Sleep Res. 2016 Aug;25(4):458-65. doi: 10.1111/jsr.12388. Epub 2016 Feb 8.
• Royant-Parola S, Londe V, Trehout S, Hartley S. [The use of social media modifies teenagers' sleep-related behavior]. Encephale. 2018 Sep;44(4):321-328. doi: 10.1016/j.encep.2017.03.009. Epub 2017 Jun 8. French.
• Zagalaz-Sanchez ML, Cachon-Zagalaz J, Sanchez-Zafra M, Lara-Sanchez A. Mini Review of the Use of the Mobile Phone and Its Repercussion in the Deficit of Physical Activity. Front Psychol. 2019 Jun 6;10:1307. doi: 10.3389/fpsyg.2019.01307. eCollection 2019.
• Prizant-Passal S, Shechner T, Aderka IM. Social anxiety and Internet use A meta-analysis: What do we know? What are we missing? Computers in Human Behavior. 2016;62 :221-9. https://doi.org/10.1016/j.chb.2016.04.003
• Twenge JM. More Time on Technology, Less Happiness Associations Between Digital-Media Use and Psychological Well-Being. Current Directions in Psychological Science. 2019;28(4):372-9.https://doi.org/10.1177/0963721419838244
• Deci EL, Ryan RM. Intrinsic Motivation and Self-Determination in Human Behavior. Boston, MA: Springer US; 1985. https://doi.org/10.1007/978-1-4899-2271-7
• Deci EL, Ryan RM. Favoriser la motivation optimale et la santé mentale dans les divers milieux de vie. Canadian Psychology/Psychologie canadienne. 2008 ; 49(1) : 24-34. https://doi.org/10.1037/0708-5591.49.1.24
• Mills DJ, Milyavskaya M, Mettler J, Heath NL. Exploring the pull and push underlying problem video game use: A Self-Determination Theory approach. Personality and Individual Differences. 2018;135: 176-81. https://doi.org/10.1016/j.paid.2018.07.007
• Meng SQ, Cheng JL, Li YY, Yang XQ, Zheng JW, Chang XW, Shi Y, Chen Y, Lu L, Sun Y, Bao YP, Shi J. Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clin Psychol Rev. 2022 Mar;92:102128. doi: 10.1016/j.cpr.2022.102128. Epub 2022 Jan 25.
• Harris B, Regan T, Schueler J, Fields SA. Problematic Mobile Phone and Smartphone Use Scales: A Systematic Review. Front Psychol. 2020 May 5;11:672. doi: 10.3389/fpsyg.2020.00672. eCollection 2020.
• Ryding FC, Kuss DJ. Passive objective measures in the assessment of problematic smartphone use: A systematic review. Addict Behav Rep. 2020 Jan 27;11:100257. doi: 10.1016/j.abrep.2020.100257. eCollection 2020 Jun.
• King D, Delfabbro P. Internet Gaming Disorder: Theory, Assessment, Treatment, and Prevention. Academic Press; 2018. 294 p.
• Radtke T, Apel T, Schenkel K, Keller J, von Lindern E. Digital detox: An effective solution in the smartphone era? A systematic literature review. Mobile Media & Communication. 2021:205015792110286. https://doi.org/10.1177/20501579211028647
• Winkler A, Dorsing B, Rief W, Shen Y, Glombiewski JA. Treatment of internet addiction: a meta-analysis. Clin Psychol Rev. 2013 Mar;33(2):317-29. doi: 10.1016/j.cpr.2012.12.005. Epub 2013 Jan 5.
• Xu LX, Wu LL, Geng XM, Wang ZL, Guo XY, Song KR, Liu GQ, Deng LY, Zhang JT, Potenza MN. A review of psychological interventions for internet addiction. Psychiatry Res. 2021 Aug;302:114016. doi: 10.1016/j.psychres.2021.114016. Epub 2021 May 21.
• Zajac K, Ginley MK, Chang R, Petry NM. Treatments for Internet gaming disorder and Internet addiction: A systematic review. Psychol Addict Behav. 2017 Dec;31(8):979-994. doi: 10.1037/adb0000315. Epub 2017 Sep 18.
• Przepiorka AM, Blachnio A, Miziak B, Czuczwar SJ. Clinical approaches to treatment of Internet addiction. Pharmacol Rep. 2014 Apr;66(2):187-91. doi: 10.1016/j.pharep.2013.10.001. Epub 2014 Mar 2.
• Hawi NS, Samaha M, Griffiths MD. The Digital Addiction Scale for Children: Development and Validation. Cyberpsychol Behav Soc Netw. 2019 Dec;22(12):771-778. doi: 10.1089/cyber.2019.0132. Epub 2019 Nov 22.
• Bouvard M, Dacquin F, Denis A. Étude de la validité de l'échelle d'anxiété et de dépression révisée (RCADS) et de la grille d'évaluation des troubles anxieux forme révisée (SCARED-R). Journal de Thérapie Comportementale et Cognitive. 2012 ; 22 (4) : 175-81. https://doi.org/10.1016/j.jtcc.2012.09.003
• de Grâce GR, Joshi P, Pelletier R. L'Échelle de solitude de l'Université Laval (ÉSUL) : validation canadienne-française du UCLA Loneliness Scale. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement. 1993 ; 25 (1) : 12-27. https://doi.org/10.1037/h0078812
• Crépin N, Delerue F. Echelle d'Estime de Soi de Rosenberg. Institut Régional du Bien-être, de la Médecine et du Sport Santé. 2008.
• Vallerand RJ, Blais MR, Brière NM, Pelletier LG. Construction et validation de l'échelle de motivation en éducation (EME). Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement. 1989 ; 21(3) : 323-49. https://doi.org/10.1037/h0079855
• Bastuji H, Jouvet M. [Value of the sleep diary in the study of vigilance dis]. Electroencephalogr Clin Neurophysiol. 1985 Apr;60(4):299-305. doi: 10.1016/0013-4694(85)90003-3. French.
• Little RJ, Rubin DB. Statistical Analysis with Missing Data. Wiley & Sons, Incorporated, John; 2019. 464 p.
• Brown TA. Confirmatory Factor Analysis for Applied Research, Second Edition. Guilford Publications; 2015. 462 p.
• Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition: A Regression-Based Approach. The Guilford Press; 2022. 692 p

Helpful Links

• Website where participants could register to the study, get additional informations, download the digital therapeutic program and fill questionnaires.
• Mildeca [En ligne]. Les français " addicts " à leurs écrans ? Publication des résultats du premier Baromètre MILDECA/Harris Interactive sur les usages d'écrans et les problématiques associées ; 2021
• Rapport du Haut Conseil de la santé publique [En ligne]. Effets de l'exposition des enfants et des jeunes aux écrans ; 2019.
• Rapport du Haut Conseil de la santé publique [En ligne]. Effets de l'exposition des enfants et des jeunes aux écrans (seconde partie) : de l'usage excessif à la dépendance ; 2021.
• Médiamétrie [En ligne]. La parentalité à l'épreuve du numérique. Observatoire de la Parentalité & de l'Education Numérique et UNAF ; 2020.

Study record dates

Study Major Dates

• Study Start (Actual): March 3, 2023
• Primary Completion (Actual): January 16, 2024
• Study Completion (Actual): January 16, 2024

Study Registration Dates

• First Submitted: October 16, 2024
• First Submitted That Met QC Criteria: October 16, 2024
• First Posted (Actual): October 18, 2024

Study Record Updates

• Last Update Posted (Actual): October 18, 2024
• Last Update Submitted That Met QC Criteria: October 16, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: Mental Health; Digital Therapeutics; Young People; Physical Health; Problematic Screen Use; Therapeutic Education Application
• Additional Relevant MeSH Terms: Compulsive Behavior; Impulsive Behavior; Behavior, Addictive; Technology Addiction; Internet Addiction Disorder
• Other Study ID Numbers: 38RC22.0299; 2022-A02645-38 (Registry Identifier: ID-RCB); 22.04601.000170 (Other Identifier: SI Number (research ethics committees Number))

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: There is no IPD sharing plan.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No