Healthy Adolescents in School-based PopulationNCT05912439
Smartphone-based Health Behaviour Intervention for Adolescents
A study testing SidekickHealth in people with Healthy Adolescents in School-based Population, run by University of Iceland at 1 site in Iceland.
Trial 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. Read more about this study
- Who is it for?
- Ages 13 to 16 yearsHealthy volunteers can take part
- Conditions the study is for
- Healthy Adolescents in School-based Population
- What is being tested?
- SidekickHealth
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 670 participants
- Where
- 1 siteIceland
- Study dates
- 15 Aug 2017to 1 Dec 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 13 to 16 years
- All sexes
- Healthy volunteers accepted
- Child
Who may be able to join
- 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.
Who cannot join
- Exclusion criteria are diagnosis of severe disorder of intellectual development and/or physical-, developmental- and mental illness significantly restricting ability to use mobile apps.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Smartphone Based Health Behaviour Intervention for Adolescents; Usage and Daily Attrition Rates.
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.
Read the 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.
- Being tested
- SidekickHealth
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SidekickHealthBehavioural
Also called SidekickHealth mHealth application
Usage of mobile application called SidekickHealth.
| Study group | Role | What they receive |
|---|---|---|
| Control | No intervention | 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. |
| Treatment-As-Usual | Active comparator | 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. Receives: Behavioral: SidekickHealth |
| Intervention | Experimental | 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. Receives: Behavioral: SidekickHealth |
What is being measured
The primary outcome is the main question the study is designed to answer.
Application usage
Time stamp in days, minutes, and seconds off each health activity completed within the mobile application
Measured: From admission to discharge, up to 6 weeks.
Secondary outcomes (3)
Anxiety and depression symptoms
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
Measured: From admission to discharge, up to 6 weeks.
General self-effifcacy
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.
Measured: From admission to discharge, up to 6 weeks.
Sleeping habits
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.
Measured: From admission to discharge, up to 6 weeks.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jun 2023
- Study start15 Aug 2017
- Primary completion1 May 2023
- Study completion1 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (36)Papers the sponsor has linked to this study.
- Other trials for Healthy Adolescents in School-based PopulationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Parallel groups
- 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)
- Enrolment
- 670 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Iceland
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05912439
- Sponsor's protocol ID
- UI-2023-mHealth
- Status last verified
- May 2023
- First submitted
- 30 May 2023
- Last updated
- 22 Jun 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
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.
PubMed 33802278Kormendi A. [Smartphone usage among adolescents]. Psychiatr Hung. 2015;30(3):297-302. Hungarian.
PubMed 26471031Birnbaum 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.
PubMed 25808317Lawlor, 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.
PubMed 31742562McLaughlin 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.
PubMed 22873479Radomski 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.
PubMed 30720436IQVIA 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.
PubMed 22818941Lee 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.
PubMed 22818936Ding 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.
PubMed 27475266Sember 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.
PubMed 32160216World Health Organization (2010). Global Recommendations on Physical Activity for Health, Executive Summary. Geneva, Switzerland: World Health Organization. Available from: https://www.ncbi.nlm.nih.gov/books/NBK305060/
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
Study at a glance
From the registry record, last updated by the sponsor on 22 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Smartphone Based Health Behaviour Intervention for Adolescents; Usage and Daily Attrition Rates.
- ClinicalTrials.gov ID
- NCT05912439
- Acronym / sponsor ID
- UI-2023-mHealth
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 670 participants (estimated)
- Lead sponsor
- University of IcelandUniversity, hospital or other
- Study start
- 15 Aug 2017 (actual)
- Primary completion
- 1 May 2023 (actual)
- Study completion
- 1 Dec 2023 (estimated)
- First posted
- 22 Jun 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 3
- 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.
| Arm | Type | Receives |
|---|---|---|
| Control | No intervention | –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. |
| Treatment-As-Usual | Active comparator | Behavioral: SidekickHealthFor 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. |
| Intervention | Experimental | Behavioral: SidekickHealthFor 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. |
Who may qualify
- Ages
- 13 to 16 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- Exclusion criteria are diagnosis of severe disorder of intellectual development and/or physical-, developmental- and mental illness significantly restricting ability to use mobile apps.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SidekickHealthAlso: SidekickHealth mHealth applicationUsage of mobile application called SidekickHealth. | Behavioural | Intervention, Treatment-As-Usual |
Primary outcome
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
3 secondary outcomes
Anxiety and depression symptoms
Time frame: From admission to discharge, up to 6 weeks.
General self-effifcacy
Time frame: From admission to discharge, up to 6 weeks.
Sleeping habits
Time frame: From admission to discharge, up to 6 weeks.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jun 2023.
- Background: PubMed 33802278Jones 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.
- Background: PubMed 26471031Kormendi A. [Smartphone usage among adolescents]. Psychiatr Hung. 2015;30(3):297-302. Hungarian.
- Background: PubMed 25808317Birnbaum 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.
- BackgroundLawlor, 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
- Background: PubMed 31742562Pretorius 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.
- Background: PubMed 22873479McLaughlin 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.
- Background: PubMed 30720436Radomski 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.
- BackgroundIQVIA 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
- BackgroundChan, 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
- Background: PubMed 22818941Kohl 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.
- Background: PubMed 22818936Lee 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.
- Background: PubMed 27475266Ding 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.
- Individual participant dataSharing planned: No. 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.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing SidekickHealth in people with Healthy Adolescents in School-based Population, run by University of Iceland at 1 site in Iceland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 Jun 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Iceland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Application usage (time frame: From admission to discharge, up to 6 weeks.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05912439, which also lists 36 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jun 2023; AzTrials last refreshed its copy on 31 Oct 2026.