Smartphone-based Health Behaviour Intervention for Adolescents

Study Overview
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Brief Summary
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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
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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.

Official Title
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Smartphone Based Health Behaviour Intervention for Adolescents; Usage and Daily Attrition Rates.

Conditions
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Healthy Adolescents in School-based Population

Intervention / Treatment
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* Behavioral: SidekickHealth


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 13 Years
Maximum Age: 16 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional 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)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: Control<br>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<br>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<br>* Usage of mobile application called SidekickHealth.<br>* Other names: SidekickHealth mHealth application;|
| Experimental: Intervention<br>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<br>* Usage of mobile application called SidekickHealth.<br>* Other names: SidekickHealth mHealth application;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Application usage | Time stamp in days, minutes, and seconds off each health activity completed within the mobile application | From admission to discharge, up to 6 weeks. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 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. | 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. | From admission to discharge, up to 6 weeks. |