Smartphone-based Health Behaviour Intervention for Adolescents

• ClinicalTrials.gov Identifier: NCT05912439
• Recruitment Status: Enrolling by invitation
• First Posted: June 22, 2023
• Last Update Posted: June 22, 2023
• Sponsor: University of Iceland
• Information provided by (Responsible Party): University of Iceland

Study Description

Brief Summary:

Despite most adolescents having access to smartphones, few of them seem to use mobile health (mHealth) applications for health improvement, highlighting the apparent lack of interest in mHealth applications among adolescents. Adolescent mHealth interventions have been burdened with high attrition rates, where attrition is often measured at two time points. Research on these interventions among adolescents have frequently lacked detailed time related attrition data alongside analysis of attrition reasons through usage. The objective is to obtain daily attrition rates among adolescents in an mHealth intervention called SidekickHealth and gain a deeper understanding of attrition patterns and reasons along with the function of motivational support, such as altruistic rewards, through analysis of application usage data.

Condition or disease	Intervention/treatment	Phase
Healthy Adolescents in School-based Population	Behavioral: SidekickHealth	Not Applicable

Detailed Description:

Throughout the past decade ownership and access to smartphones and mobile devices has grown profoundly among adolescents and youth worldwide. The growth has been such that smartphone ownership or access among US adolescents was 95% four years ago and had increased by 23% in the four years prior. Similar development was observed in the majority of developed economies where adolescent smartphone access and ownership is above the 90th percentile. Smartphones are so widely distributed and used that approximately 45% of adolescents spend nearly all waking hours online. However, modest projections of daily usage is that many spend way less time online each day though it is usually more than 4 hours.

Extreme smartphone usage in adolescent and youth populations has been extensively covered but a more positive side to mobile usage is that a significant proportion of adolescents seek health information and clinical help online through their mobile devices, providing ample opportunities to reach at risk adolescents with science based methods focusing on health improvement. Health problems, i.e. mental health and lifestyle disease, disproportionally burden lower SES groups as well as diverse minority groups and smartphones could become a vital tool in eliminating such disparities since smartphone access and ownership is not related to SES status, gender or race in diverse economies. The mHealth market is steadily becoming saturated with applications and yearly increase in number of applications available has skyrocketed in recent years, with estimated 350.000 mHealth applications currently on the market. However, only 8% of adolescents seem to use health applications to improve their health, highlighting the apparent gap between easy access, extensive daily usage and lack of interest in mHealth applications among adolescents.

Lack of physical activity has been labelled a global pandemic and reported as the 4th leading global cause of death. Physical inactivity increases risk of lifestyle diseases, such as heart disease, type 2 diabetes and cancer, resulting in over 5 million annual global deaths. Further, the estimated annual financial burden of physical inactivity is nearly 54 billion USD in health care costs around the world. There seems to be a drop in physical activity in adolescence and a large part of adolescents are under the recommended physical activity levels provided by the World Health Organization (WHO). Lack of sufficient physical activity tends to continue into adulthood and research suggests that the majority of adolescents in the EU do not even reach 30% of recommended daily physical activity. Further, adolescents seem to have the unhealtiest diet of all age groups and adolescence is a particular susceptibility period to weight gain. Research has repeatedly revealed a significant relationship between nutritional behavior and physical activity in terms of weight management. A tremendous increase in global adolescent obesity has been witnessed in the past decades and prevalence for instance tripled since 1975. Cost-effective interventions to increase physical activity and improve nutritional behavior in adolescent populations are therefore direly needed.

Physical inactivity and inadequate nutritional habits are often interrelated to disabling emotional problems and integrated strategies should include all three pillars to improve physical as well as mental well-being in adolescent populations. Mobile health interventions targeting disabling emotional problems in adolescent populations have revealed encouraging outcome, despite the fact that attrition rates in these interventions are generally high. Varying definitions of attrition have complicated research on this topic but attrition is defined as leaving treatment before obtaining a required level of improvement or completing intervention goals. Research on mental mHealth interventions among adolescents have frequently lacked detailed time related attrition data alongside accurate definitions and analysis of attrition reasons though recent studies show promise in that regard. Attrition is regularly reported at two distinct points of time; intervention start and at end of intervention. A continuous measure of usage vs. non-usage in mHealth interventions for adolescents while simultaneously obtaining detailed usage data in order to prevent or delay exact times of attrition in future interventions would perhaps be an improved representation of attrition.

Increased knowledge on actual attrition factors and patterns in adolescent populations from mHealth interventions are direly needed. Obtaining a better understanding of how motivational support motivates adolescents to use mHealth applications and why they maintain or lose interest in using them to improve their health is of vital importance. Motivational support in mHealth interventions, defined as strategies to enhance motivation and counter attrition to overcome behavior change barriers, often include goal-setting, feedback, social support and rewards. Systematic reviews examining possible drivers behind usage point to group and task customization, localization, functional user support, gamification of health tasks and immediate visual but simplified feedback on user action while while gender-related motivational support features could be contributing factors. Timing of tailored motivational support, through just-in-time adaptive interventions (JITAIs), should be considered as well when implementing adolescent mHealth interventions since time-based individualization could counter high attritions rates. Given the magnitude of reported health problems among adolescents and lack of cost-effective health behaviour interventions specifically developed for adolescent populations, the need for better understanding of attrition reasons in adolescent mHealth interventions is massive. The purpose of this study is firstly to seek richer understanding of continuous attrition rates from a mHealth intervention called SidekickHealth in an adolescent population and what effects motivational support has on attrition rates. Secondly, the aim is to examine effectiveness of the intervention with the aim to increase daily mental, nutritional and physical health behaviour.

Study Design

• Study Type: Interventional (Clinical Trial)
• Estimated Enrollment: 670 participants
• Allocation: Randomized
• Intervention Model: Parallel Assignment
• Intervention Model Description: The study is a randomised controlled study. Group randomisation is used to distinguish three participating schools into control, treatment-as-usual (TAU) and intervention groups. Measures are obtained at baseline and 42 days later. Participants in both the TAU group and the intervention group receive an approximately 10 minutes long introduction regarding the study specifications and the application. The control group receive no further contact, access to the application or information until study-end questionnaire measures. Participants in the intervention group are randomly assigned to teams consisting of 8 individuals that collectively and individually compete in point collection through completion of in-app health tasks. Participation in the TAU group and intervention group is defined as downloading the Sidekick app and completing at least 3 health exercises within it.
• Masking: None (Open Label)
• Masking Description: Masking (blinding) procedures is not done after initial randomisation on group (school) level, since participants are aware of the intervention design, that is whether or not they use a mHealth application
• Primary Purpose: Basic Science
• Official Title: Smartphone Based Health Behaviour Intervention for Adolescents; Usage and Daily Attrition Rates.
• Actual Study Start Date: August 15, 2017
• Actual Primary Completion Date: May 1, 2023
• Estimated Study Completion Date: December 1, 2023

Arms and Interventions

Arm	Intervention/treatment
No Intervention: Control; Measures for participants in control group are obtained at baseline and 42 days later. The control group receives no further contact, access to the mHealth application or information until study-end questionnaire measures are provided.
Active Comparator: Treatment-As-Usual; For participants in Treatment-As-Usual (TAU) group measures are obtained at baseline and 42 days later. Participants receive an approximately 10 minutes long introduction regarding study specifications and the mHealth application. Active participation in TAU group is defined as downloading the Sidekick app and completing at least 3 health exercises within it. Time of exercise is defined as the timestamp on completion of exercise within any of the three types of exercise categories (physical activity, nutrition and mental health) of the app. Exercise frequency refers to how often a given exercise was completed by a participant in TAU group. Time of attrition is defined as the time stamp of last completing health exercise within the Sidekick throughout intervention period. Participants in TAU group use the application individually throughout trial period without any motivational support.	Behavioral: SidekickHealth; Usage of mobile application called SidekickHealth.; Other Name: SidekickHealth mHealth application
Experimental: Intervention; For participants in intervention group measures are obtained at baseline and 42 days later. Participants receive an approximately 10 minutes long introduction regarding study specifications and the mHealth application. Active participation in intervention group is defined as downloading the Sidekick app and completing at least 3 health exercises within it. Time of exercise is defined as the timestamp on completion of exercise within any of the three types of exercise categories (physical activity, nutrition and mental health) of the app. Exercise frequency refers to how often a given exercise was completed by a participant in TAU group. Time of attrition is defined as the time stamp of last completing health exercise within the Sidekick throughout intervention period. Participants in intervention group receive weekly motivational support in form of individual and group feedback on usage, participation in friendly health task competitions and weekly altruistic rewards for usage.	Behavioral: SidekickHealth; Usage of mobile application called SidekickHealth.; Other Name: SidekickHealth mHealth application

Outcome Measures

Primary Outcome Measures :

1. Application usage [ Time Frame: From admission to discharge, up to 6 weeks. ]
   Time stamp in days, minutes, and seconds off each health activity completed within the mobile application

Secondary Outcome Measures :

1. Anxiety and depression symptoms [ Time Frame: From admission to discharge, up to 6 weeks. ]
   Anxiety and depressive symptoms are assessed using the Revised Children´s Anxiety and Depression Scale (RCADS), a self-report assessment tool for children and youth. The scale is a four point Likert scale, spans 47 questions and is divided into 6 subscales; separation anxiety symptoms, general anxiety symptoms, obsessive-compulsion symptoms, social anxiety symptoms, panic symptoms, depression symptoms. A T-score over 65 marked a clinical cut-off point. The inventory's psychometrics have been studied with acceptable findings in both US and Icelandic paediatric populations
2. General self-effifcacy [ Time Frame: From admission to discharge, up to 6 weeks. ]
   General Self Efficacy Scale (GSE), a 10 item self-report questionnaire with ranging total scores from 10 to 40, is used to measure self-efficacy levels where higher score yielding increasing self-efficacy. Acceptable psychometric properties for the questionnaire have been obtained and it is used globally in youth populations.
3. Sleeping habits [ Time Frame: From admission to discharge, up to 6 weeks. ]
   BEARS sleep screening algorithm is used to evaluate participants´ behavioural sleeping problems. It is a screening instrument for children from 2 to 18 years old, a binary (0-1) parental or self-assessment tool which psychometrics have been studied with acceptable findings in paediatric populations. Self-assessment is applied in study population.

Eligibility Criteria

• Ages Eligible for Study: 13 Years to 16 Years (Child)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes

Criteria

Inclusion Criteria:

• All children attending the oldest 3 classes in three participating public elementary schools in Iceland are eligible participants. All children in public schools in the municipality are equipped with an iPad from 10 years of age.

Exclusion Criteria:

• Exclusion criteria are diagnosis of severe disorder of intellectual development and/or physical-, developmental- and mental illness significantly restricting ability to use mobile apps.

Contacts and Locations

Locations

Iceland

University of Iceland

Reykjavík, Reykjavik, Iceland, 101

Sponsors and Collaborators

University of Iceland

  Study Documents (Full-Text)

Documents provided by University of Iceland:

Study Protocol and Statistical Analysis Plan  [PDF] June 9, 2023

More Information

Publications of Results:

Egilsson E, Bjarnason R, Njardvik U. Usage and Weekly Attrition in a Smartphone-Based Health Behavior Intervention for Adolescents: Pilot Randomized Controlled Trial. JMIR Form Res. 2021 Feb 17;5(2):e21432. doi: 10.2196/21432.

Bear HA, Ayala Nunes L, DeJesus J, Liverpool S, Moltrecht B, Neelakantan L, Harriss E, Watkins E, Fazel M. Determination of Markers of Successful Implementation of Mental Health Apps for Young People: Systematic Review. J Med Internet Res. 2022 Nov 9;24(11):e40347. doi: 10.2196/40347.

Other Publications:

Jones EAK, Mitra AK, Bhuiyan AR. Impact of COVID-19 on Mental Health in Adolescents: A Systematic Review. Int J Environ Res Public Health. 2021 Mar 3;18(5):2470. doi: 10.3390/ijerph18052470.

Kormendi A. [Smartphone usage among adolescents]. Psychiatr Hung. 2015;30(3):297-302. Hungarian.

Birnbaum ML, Rizvi AF, Confino J, Correll CU, Kane JM. Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Interv Psychiatry. 2017 Aug;11(4):290-295. doi: 10.1111/eip.12237. Epub 2015 Mar 23. Erratum In: Early Interv Psychiatry. 2017 Dec;11(6):539.

Lawlor, A. & Kirakowski, J. (2014). Online support groups for mental health: A space for challenging self-stigma or a means of social avoidance? Computers in Human Behavior, 32, 152-161. https://doi.org/10.1016/j.chb.2013.11.015

Pretorius C, Chambers D, Coyle D. Young People's Online Help-Seeking and Mental Health Difficulties: Systematic Narrative Review. J Med Internet Res. 2019 Nov 19;21(11):e13873. doi: 10.2196/13873.

McLaughlin KA, Costello EJ, Leblanc W, Sampson NA, Kessler RC. Socioeconomic status and adolescent mental disorders. Am J Public Health. 2012 Sep;102(9):1742-50. doi: 10.2105/AJPH.2011.300477. Epub 2012 Feb 16.

Radomski AD, Wozney L, McGrath P, Huguet A, Hartling L, Dyson MP, Bennett K, Newton AS. Design and Delivery Features That May Improve the Use of Internet-Based Cognitive Behavioral Therapy for Children and Adolescents With Anxiety: A Realist Literature Synthesis With a Persuasive Systems Design Perspective. J Med Internet Res. 2019 Feb 5;21(2):e11128. doi: 10.2196/11128.

IQVIA Institute. (2021, July 1). Digital Health Trends 2021. IQVIA. https://www.iqvia.com/-/media/iqvia/pdfs/institute-reports/digital-health-trends-2021/iqvia-institute-digital-health-trends-2021.pdf

Chan, A., Kow, R. & Cheng, J. K. (2017). Adolescents' perceptions on smartphone applications (apps) for health management. Journal of Mobile Technology in Medicine, 6(2), 47-55. https://doi.org/10.7309/jmtm.6.2.6

Kohl HW 3rd, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Kahlmeier S; Lancet Physical Activity Series Working Group. The pandemic of physical inactivity: global action for public health. Lancet. 2012 Jul 21;380(9838):294-305. doi: 10.1016/S0140-6736(12)60898-8.

Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012 Jul 21;380(9838):219-29. doi: 10.1016/S0140-6736(12)61031-9.

Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, Pratt M; Lancet Physical Activity Series 2 Executive Committee. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016 Sep 24;388(10051):1311-24. doi: 10.1016/S0140-6736(16)30383-X. Epub 2016 Jul 28.

Sember V, Jurak G, Kovac M, Duric S, Starc G. Decline of physical activity in early adolescence: A 3-year cohort study. PLoS One. 2020 Mar 11;15(3):e0229305. doi: 10.1371/journal.pone.0229305. eCollection 2020.

Global Recommendations on Physical Activity for Health. Geneva: World Health Organization; 2010. Available from http://www.ncbi.nlm.nih.gov/books/NBK305057/

OECD (2016). Health at a Glance: Europe 2016 - State of Health in the EU Cycle. Paris, France: OECD Publishing. https://doi.org/10.1787/9789264265592-en

Telama R, Yang X, Viikari J, Valimaki I, Wanne O, Raitakari O. Physical activity from childhood to adulthood: a 21-year tracking study. Am J Prev Med. 2005 Apr;28(3):267-73. doi: 10.1016/j.amepre.2004.12.003.

Craigie AM, Lake AA, Kelly SA, Adamson AJ, Mathers JC. Tracking of obesity-related behaviours from childhood to adulthood: A systematic review. Maturitas. 2011 Nov;70(3):266-84. doi: 10.1016/j.maturitas.2011.08.005. Epub 2011 Sep 15.

Rose T, Barker M, Maria Jacob C, Morrison L, Lawrence W, Strommer S, Vogel C, Woods-Townsend K, Farrell D, Inskip H, Baird J. A Systematic Review of Digital Interventions for Improving the Diet and Physical Activity Behaviors of Adolescents. J Adolesc Health. 2017 Dec;61(6):669-677. doi: 10.1016/j.jadohealth.2017.05.024. Epub 2017 Aug 16.

Kouvari M, Karipidou M, Tsiampalis T, Mamalaki E, Poulimeneas D, Bathrellou E, Panagiotakos D, Yannakoulia M. Digital Health Interventions for Weight Management in Children and Adolescents: Systematic Review and Meta-analysis. J Med Internet Res. 2022 Feb 14;24(2):e30675. doi: 10.2196/30675.

WHO (2021, June 9th). Obesity and Overweight. World Health Organization. Retrieved January 26, 2023, from https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#cms

Eysenbach G. The law of attrition. J Med Internet Res. 2005 Mar 31;7(1):e11. doi: 10.2196/jmir.7.1.e11.

Meyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis. J Med Internet Res. 2020 Sep 29;22(9):e20283. doi: 10.2196/20283.

Topooco N, Bylehn S, Dahlstrom Nysater E, Holmlund J, Lindegaard J, Johansson S, Aberg L, Bergman Nordgren L, Zetterqvist M, Andersson G. Evaluating the Efficacy of Internet-Delivered Cognitive Behavioral Therapy Blended With Synchronous Chat Sessions to Treat Adolescent Depression: Randomized Controlled Trial. J Med Internet Res. 2019 Nov 1;21(11):e13393. doi: 10.2196/13393.

Adelman CB, Panza KE, Bartley CA, Bontempo A, Bloch MH. A meta-analysis of computerized cognitive-behavioral therapy for the treatment of DSM-5 anxiety disorders. J Clin Psychiatry. 2014 Jul;75(7):e695-704. doi: 10.4088/JCP.13r08894.

Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry. 2013 Jul-Aug;35(4):332-8. doi: 10.1016/j.genhosppsych.2013.03.008. Epub 2013 May 8.

Maenhout L, Peuters C, Cardon G, Crombez G, DeSmet A, Compernolle S. Nonusage Attrition of Adolescents in an mHealth Promotion Intervention and the Role of Socioeconomic Status: Secondary Analysis of a 2-Arm Cluster-Controlled Trial. JMIR Mhealth Uhealth. 2022 May 10;10(5):e36404. doi: 10.2196/36404.

Twomey C, O'Reilly G, Byrne M, Bury M, White A, Kissane S, McMahon A, Clancy N. A randomized controlled trial of the computerized CBT programme, MoodGYM, for public mental health service users waiting for interventions. Br J Clin Psychol. 2014 Nov;53(4):433-50. doi: 10.1111/bjc.12055. Epub 2014 May 15.

Melville KM, Casey LM, Kavanagh DJ. Dropout from Internet-based treatment for psychological disorders. Br J Clin Psychol. 2010 Nov;49(Pt 4):455-71. doi: 10.1348/014466509X472138. Epub 2009 Oct 1.

Mitchell, A. J. & Selmes, T. (2007). Why don't patients attend their appointments? Maintaining engagement with psychiatric services. Advances in psychiatric treatment, 13(6).423-434. https://doi.org/10.1192/apt.bp.106.003202

Vigerland S, Lenhard F, Bonnert M, Lalouni M, Hedman E, Ahlen J, Olen O, Serlachius E, Ljotsson B. Internet-delivered cognitive behavior therapy for children and adolescents: A systematic review and meta-analysis. Clin Psychol Rev. 2016 Dec;50:1-10. doi: 10.1016/j.cpr.2016.09.005. Epub 2016 Sep 20.

Jeminiwa RN, Hohmann NS, Fox BI. Developing a Theoretical Framework for Evaluating the Quality of mHealth Apps for Adolescent Users: A Systematic Review. J Pediatr Pharmacol Ther. 2019 Jul-Aug;24(4):254-269. doi: 10.5863/1551-6776-24.4.254.

Palos-Sanchez PR, Saura JR, Rios Martin MA, Aguayo-Camacho M. Toward a Better Understanding of the Intention to Use mHealth Apps: Exploratory Study. JMIR Mhealth Uhealth. 2021 Sep 9;9(9):e27021. doi: 10.2196/27021.

Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med. 2018 May 18;52(6):446-462. doi: 10.1007/s12160-016-9830-8.

• Responsible Party: University of Iceland
• ClinicalTrials.gov Identifier: NCT05912439
• Other Study ID Numbers: UI-2023-mHealth
• First Posted: June 22, 2023
• Last Update Posted: June 22, 2023
• Last Verified: May 2023

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No
• Plan Description: The plan is not to share individual participant data since this is a study with Icelandic adolescent participants and the Icelandic Data Protection and the Processing of Personal Data (DPPPD) laws are very strict on the matter.

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No