User Characteristics and Parenting Practices Associated with Adolescents' Initial Use of a Lifestyle Behavior Modification Intervention

Abstract

Background and Purpose:

E-health interventions can provide Canadian adolescents (13–17 years old) with personalized support to help them modify their obesogenic behaviors. However, use of e-health interventions among adolescents has not been extensively examined. This study examined user characteristics and parenting practices associated with adolescents' initial use of the Aim2Be app; a health behavior modification intervention delivered through a smartphone app.

Methods:

A total of 371 adolescent–parent dyads completed a baseline assessment and were invited to use the Aim2Be app. Mean adolescent age was 14.9 years and 50.1% were male (n = 186). Mean adult age was 44.1 years and 34.7% were male (n = 129). Using Mplus (v.8), path analyses were completed to identify adolescent characteristics and parenting practices that were significantly associated with initial use of the app. Analyses were then stratified to explore whether these associations were confounded by parents' gender.

Results:

79.2% of adolescents (n = 294) initially used the Aim2Be app. Adolescent engagement in healthy behaviors was directly associated with increased odds of using the app (odds ratio [OR] = 1.08; 95% confidence interval [CI] = 1.01–1.14), whereas autonomous motivation was indirectly associated (OR = 1.02; 95% CI = 1.00–1.04). Structure parenting practices were indirectly associated with increased odds of using the app (OR = 1.02; 95% CI = 1.00–1.04). When analyses were stratified by parent's gender, differences in the associations emerged.

Conclusions:

Both user characteristics and parenting practices were significantly associated with adolescents' initial use of Aim2Be. These findings will help inform future e-health interventions increase user engagement by identifying the characteristics of individuals who are not accessing the intervention, as well as identifying factors of the household environment that support use.

Introduction

The majority of Canadian adolescents exhibit obesogenic behaviors (i.e., inadequate levels of physical activity and intake of fruits/vegetables, as well as high screen time and intake of sugary drinks and/or processed food).^1–3 Consequently, this population is faced with a greater risk of various acute and chronic comorbidities associated with excess adiposity.⁴ To effectively treat and manage adolescent obesity, lifestyle behavior modification interventions need to be multifaceted and target the adolescent's diet, physical activity, and sedentary behaviors.⁵ In addition, these interventions need to promote lifelong weight management; therefore, many are delivered within a family-based context, as the household environment is a key target for intervention.⁶

Parenting practices are often targeted as part of these interventions, since parents can shape children's health behaviors as they play a critical role in socializing children to adopt healthy behaviors.⁷ Although inconsistencies exist in the literature, the type of parenting practices parents use have varying effects on children's behaviors such as physical activity,^8,9 dietary habits,¹⁰ and screen time.^9,11 For example, some studies suggest controlling parenting practices undermine children self-regulatory processes that support the adoption of healthy behaviors and find structure and autonomy supportive parenting practices to support better self-regulatory processes.¹² Although there is a lot more evidence of how parenting practices influence children's obesogenic behaviors,^8–11 there is some evidence to suggest that parents continue to play a central role during adolescence.¹³ In a sample of 11- to 16-year-old children, parenting practices as well as adolescents' intrinsic motivation significantly predicted adherence to an e-health intervention,¹³ highlighting the continued role of parents throughout adolescence for supporting healthy lifestyle behaviors.

One major limitation regarding family-based interventions is that they are typically resource intensive as they are delivered in-person¹⁴; thus, new delivery processes need to be developed and evaluated to improve the reach of childhood obesity interventions. One type of intervention that has gained popularity over the past decade are e-health interventions designed to be delivered through an Internet platform.¹⁵ E-health interventions are cost-effective and can provide adolescents with tailored information and resources, along with guidance and supportive feedback to help them address a health-related goal.¹⁶ In addition, there is preliminary evidence to suggest that e-health interventions can be effective at improving adolescents' health outcomes.¹⁷ Finally, these types of interventions are scalable and have the potential to reach the majority of Canadian youth, as cell phone and Internet usage among this population is ubiquitous.¹⁸ However, limited reach and poor user engagement remain a major issue among e-health lifestyle behavior modification interventions,^19,20 which can ultimately jeopardize the effectiveness of these programs.²¹

Although many studies have examined factors associated with adoption of e-health interventions aimed at modifying obesogenic behaviors among adults,^22,23 very few have focused on adolescents²⁴ and none have specifically focused on initial use. To address this research gap, this study examined the associations between adolescent-level characteristics and parenting practices with adolescents' initial use of the Aim2Be app; a health behavior modification intervention program delivered through a smartphone app guided by Social Cognitive Theory²⁵ and Player Experience and Need Satisfaction Model²⁶—a model based on Self-Determination Theory.²⁷ Specifically, this study examined whether adolescents' health behaviors and motivation, as well as parenting practices were associated with initial app use. In addition, this study explored whether these associations were confounded by parents' gender. This study focused on adolescents' motivation and parenting practices to align with the theoretical underpinning of Aim2Be.²⁸ From Self-Determination Theory, it was hypothesized that adolescents with high levels of autonomous or controlled motivation and poor health behaviors would be more likely to initially use the Aim2Be app.^26,27 As Social Cognitive Theory assumes behaviors and intrapersonal factors are also influenced by the social environment, this study also assumed that the household environment, specifically positive parenting practices (i.e., higher levels of autonomy supportive and structure parenting practices), would be associated with initial app use.^25,28

Focusing on initial use is critical as this is the most important component of app adoption, as without use there cannot be any adoption. Furthermore, understanding the predictors of initial use serves to evaluate whether an app reaches its intended audience and to gain an in-depth understanding who the app appeals—which are critical components for impacting the health behaviors of adolescents in a meaningful way.

Methods

Study Design

This study is a secondary analysis of the baseline data collected as part of the evaluation of Aim2Be app version 2.1. Data were collected from March to October 2018 and ethics approval was granted by the institutional ethics board (No. H16-03090).

Study Participants

Participants were recruited from a Canadian market research company that recruits members through web advertisements and random digit dialing. Parents (N = 1418) were initially screened to ensure that they met the eligibility criteria (primary caregiver of a 13- to 17-year-old adolescent, both parent and adolescent literate in English and able to read at the grade 5 level, and having Internet access at home). Eligible parents were asked to provide consent to be contacted by research staff from the University of British Columbia. Table 1 presents the demographic characteristics of the sample (n = 371).

Table 1.

Sociodemographic Characteristics of the Study Sample (n = 371)

	n (%)	Mean ± SD	Range
Adolescent sample
Age	371 (100)	14.9 years ±1.5	13–17
Gender
Male	187 (50.4)
Female	184 (49.6)
Ethnicity
White	258 (69.5)
Chinese	21 (5.7)
South East Asian/South Asian/Japanese	19 (5.1)
Aboriginal/white and aboriginal	22 (5.9)
Other	51 (13.8)
Parent sample
Age	368 (99.2)	46.6 years ±6.6	27–66
Missing	3 (0.8)
Gender
Male	129 (34.8)
Female	242 (65.2)
Marital status
Married/common law	311 (83.8)
Single/divorced/widowed	60 (16.2)
Household income
<$50,000	53 (14.3)
$50,000–$99,999	134 (36.1)
$100,000–$150,000+	148 (39.9)
Missing	36 (9.7)
Highest level of education attained
High school or less	86 (23.2)
College diploma or trade certificate	98 (26.4)
Bachelor's degree	140 (37.7)
Above bachelor's degree	47 (12.7)

SD, standard deviation.

Study Protocol

Once parents and adolescents consented to participate in the study, they completed the baseline assessment in the Research Electronic Data Capture platform^29,30 and were provided with an enrollment code and a download link to their respective version of the Aim2Be app. Participants were then asked to enroll, login, and use the app as they see fit. Participants accessed the app for 4.5 months and received a nominal cash incentive for completing the assessments.

Intervention Description

Aim2Be is a gamified phone app and website designed to provide Canadian families (specifically adolescent/parent dyads) with strategies to guide long-term behavior change with regard to physical activity, sedentary behaviors, healthy eating, and sleep. An innovative aspect of Aim2Be is that it supports behavior change by linking change in health behavior with environmental concerns (with β-testing demonstrating families' appreciation for the integrated concepts³¹). At its core, Aim2Be is strongly grounded in theories of health behaviors (Social Cognitive Theory²⁵ and Player Experience and Need Satisfaction Model²⁶—a model based on Self-Determination Theory²⁷). It aims to (1) support both individual and familial changes; (2) support the development of self-regulatory processes that increase self-efficacy and intrinsic motivation to ensure maintenance of health behaviors (e.g., incorporates goal setting, planning, and monitoring of health behaviors as well as it integrates graded task to support self-efficacy); and (3) incorporates gamification elements to make the experience enjoyable and increase motivation to change health behaviors. The theoretical underpinning, as well as the rationale for selecting the various theories used in Aim2Be is described elsewhere.²⁸

The Aim2Be adolescent app incorporates a number of features to support adolescents' journey to behavior change. Aim2Be provides adolescents with autonomy over their health journey, as adolescents choose the behaviors they would like to change based on reflection and self-discovery tools. After their onboarding process adolescents typically start their journey by selecting an Aim from a list of 15 (drop the pop, be a mindful eater, be a veggie fan, be sugar smart, be well rested, break your addiction, be social, be body positive, be brainy, step it up, use less plastic, cook and enjoy, stand up for others, be outdoorsy, dine out right, and power up your day). Some aims are designed to link health promotion with environmental issues as this concept resonated with adolescents and parents³¹; however, the underlying health behaviors targeted remained the behaviors associated with obesity. Adolescents can select to work on up to three aims at a time. Each aim has three stages that require adolescents to complete tasks that support their self-regulatory processes. The app also includes a check-in section where the participant can monitor and follow their change in health behaviors; and a resources section that includes short articles that users can read to self-discover ways of changing their health behaviors.

To support enjoyment and maintenance of behavior change, Aim2Be includes the following gamified features: daily bonus, daily quick wins (on the spot actions that relate to their selected aims), quizzes, collecting currencies that can be used to purchase collectibles or stories, collectibles that can be purchased, and stories that allow the reader to choose the adventures for the main characters in the story.

Finally, providing social support is a core element of the app. In their journey to change their health behaviors adolescents are provided with (1) a social wall to interact with other adolescents, (2) access to health coach who can answer questions as they move through their journey, and (3) there is a parent companion app that encourages parents to initiate changes in the home environment and to provide the needed social support (further description elsewhere²⁸).

Study Measures

Adolescents' initial use of Aim2Be

Adolescents' initial use of Aim2Be was measured objectively using web-analytic data provided by the app developers, Ayogo Health, Inc. Adolescents who downloaded, enrolled, and accessed the app were defined as initial users, whereas the others were classified as nonusers.

Adolescents' health behaviors

Adolescents' health behaviors score was a composite score for physical activity, dietary, and screen time behaviors. Adolescents' physical activity was measured using the Physical Activity Questionnaire for Older Children³² (test retest reliability ranging from 0.75 to 0.82 in children and correlation with peak oxygen uptake of 0.52 in adolescents³³). The adolescents reported average minutes of moderate-to-vigorous physical activity per day. Adolescents' dietary behaviors was measured using items taken from the National Youth Physical Activity and Nutrition Study³⁴ [validation against 24-hour dietary recall showed significant correlation for intake of beverages (0.26–0.49), but fruit and vegetable intake tends to be significantly higher^35,36]. Participants recorded, in 0.5 servings increment, the amount of fruit and vegetable servings they consumed in the previous day (not including 100% fruit or vegetable juices, or fried potatoes). In addition, participants were asked to record how many times they consumed (1) 100% fruit juices, (2) regular soda/pop, and (3) other beverages, including fruit-flavored drinks, sports or high energy drinks, blended sweetened tea or coffee drinks, as well as other sweetened drinks. Servings of fruit and vegetable and daily intake of sugar-sweetened beverage were used in the analyses.

Adolescents' screen time was assessed using Rosenberg et al.'s³⁷ Sedentary Behavior Questionnaire that has been used with adolescents (although validation exists in adults, Rosenberg's questionnaire has been found to be sensitive to changes in obesity interventions conducted among adolescents³⁸). Specifically, four sedentary behaviors were measured: watching TV; playing computer or video game; using a computer, tablet, or mobile device outside of school work or paid work; and talking or texting on a cell phone. Participants reported the length of time they spent being sedentary on their most recent weekday and weekend day (8 items in total). Adolescents' average daily sedentary hours was used for the analyses.

An overall health behavior indicator was calculated by first dichotomizing each behavior at the median split and summing them across behaviors. The score ranged from 0 to 4; “4/4” a healthy lifestyle relative to the study sample, whereas “0/4” an unhealthy lifestyle relative to the study sample.

Adolescents' autonomous and controlled motivations

Adolescents' autonomous and controlled motivations were measured using 16 items that were adapted from the Family Life, Activity, Sun, Health, and Eating (FLASHE) study.³⁹ The FLASHE motivation scale included four items for each health behavior, where two of the items measured controlled motivation (i.e., external and introject regulation) and two items measured autonomous motivation (i.e., integrated regulation and intrinsic motivation). These four items were repeated for each behavior (i.e., physical activity, fruits and vegetables intake, consumption of sugar-sweetened beverages, and screen time) and measured on a 5-point Likert scale.

Similar to the health behavior index, an indicator of both adolescents' autonomous and controlled motivation was computed. For each behavior, autonomous motivation was dichotomized by first averaging the two items and recoding score <3 as 0 (low) and those >3 as 1 (high). The score on all health behaviors were summed, resulting in a score that ranged from 0 to 4 (“4/4” was autonomously motivated on all health behaviors, whereas “0/4” was not autonomously motivated). The same scoring was used for controlled motivation.

Parenting practices

Parenting practices were measured using questionnaires drawn from the FLASHE study.³⁹ Twenty six items assessed, on a 5-point Likert scale, parents' level of control, structure, and autonomy-supportive practices that they implement with regard to their adolescent's fruit and vegetable intake (7 items); sugar-sweetened beverages and junk food intake (7 items); physical activity (6 items); and screen time (6 items). Examples of control parenting practices include coercive behaviors, such as threats and bribes. Structure parenting practices involves the structuring of the household environment to promote certain behaviors (e.g., rules and limits, modeling of ideal behavior). Finally, autonomy-supportive practices include child involvement and education, where adolescents are allowed to develop self-regulatory skills through age-appropriate exploration.

An index score for each parenting practice (control, structure, and autonomy support) was computed by averaging the survey items associated with each parenting practice within every health behavior. Scores were dichotomized (i.e., scores <3 recoded as 0 and >3 recoded as 1) and then summed across the four health behaviors for each parenting practice. This resulted in a control, structure, and autonomy supportive parenting score that ranged from 0 to 4 (“4/4” utilized the parenting practice for all behaviors and “0/4” did not use that practice for any of the behaviors).

Data Analysis

Path analyses were used to determine whether adolescent characteristics and parenting practices were associated with adolescents' initial use of Aim2Be. Stratified analyses were used to determine whether the effects of parenting practices were the same if the survey was completed by the mother or father. All models were adjusted for adolescent's age and gender, whereas a significance level of 5% were used. In addition, residual plots were evaluated to ensure that regression assumptions were not violated. Analyses were conducted with Mplus version 8 (Ref.⁴⁰).

Missing Data

In total, 28 parent–adolescent dyads (7.5% of the total sample) contained missing information on the motivation or parenting practice indicators. If the indicator variable for a given health behavior was an average of two items, the index score was computed if an answer was provided on at least one of the items. In total, 23 dyads were recovered and only 5 dyads (1.3% of the total sample) contained missing information.

Results

Descriptive Statistics and Univariate Analysis

Table 2 presents the descriptive statistics of the dependent variable, independent variables, and mediators, as well as the univariate analyses of the independent variables, mediators, and covariates. In the univariate analyses, adolescents' health behavior was the only variable that was significantly associated with app adoption (β = 0.17). Thus, adolescents' odds of initially using the app increased if they reported engaging in healthier behaviors.

Table 2.

Descriptive Statistics and Univariate Analyses for Dependent Variables, Independent Variables, Mediators, and Covariates

					Dependent variable
					Initial app use
	n (%)	Mean (SD)	Median (IQR)	Range	B	95% CI	p
Dependent variable
Initial app use
Yes	294 (79.2)
No	77 (20.8)
Independent variables
Autonomy practices		1.6 (1.2)	1 (2)	0–4	−0.03	(−0.17 to 0.11)	0.66
Structure practices		2.6 (1.1)	3 (2)	0–4	−0.06	(−0.20 to 0.08)	0.40
Control practices		2.1 (1.4)	2 (2)	0–4	−0.06	(−0.21 to 0.08)	0.39
Mediators
Health behaviors		2.0 (1.1)	2 (2)	0–4	0.17	(0.03 to 0.31)	0.02
Autonomous motivation		2.2 (1.1)	2 (2)	0–4	0.01	(−0.13 to 0.15)	0.90
Controlled motivation		2.2 (1.4)	2 (2)	0–4	0.09	(−0.05 to 0.22)	0.22
Covariates
Adolescent age					−0.11	(−0.25 to 0.03)	0.11
Adolescent gender^a					0.07	(−0.07 to 0.21)	0.32

Bold: p < 0.05.

Reference: male.

β, standardized estimate; CI, confidence interval; IQR, interquartile range.

Characteristics Associated with Adolescents' Adoption of Aim2Be

Table 3 displays the associations between adolescent characteristics and parenting practices with Aim2Be adoption, adjusted for adolescent's age and gender. Table 4 displays the results stratified by gender of parents. Finally, Figure 1 summarizes the results from Tables 3 and 4.

Figure 1.

Graphical summaries of the associations between adolescent characteristics and parenting practices and initial app use, adjusted for adolescent's age and gender. Full sample (n = 366). Indirect path: (1) Structure parenting practices had an indirect effect on initial use but not specific indirect paths were identified (OR = 1.02). (2) Autonomous motivation was indirectly associated with initial use through health behaviors (OR = 1.02). (3) Autonomy parenting practices was indirectly associated with health behaviors through autonomous motivation (β = 0.06). Mother sample (n = 239). Indirect paths: (1) Autonomous motivation had an indirect effect on initial use through health behaviors (OR = 1.01). Father sample (n = 127). Indirect path: (1) Autonomy practices had an indirect effect on initial use through autonomous motivation (β = 0.09). OR, odds ratio.

Table 3.

Associations between Adolescents' Characteristics and Parenting Practices with Adolescent's Initial App Use, Adjusted for Adolescent's Gender and Age (n = 366)

	Dependent variable		Mediators
	Initial app use		Autonomous motivation	Controlled motivation	Health behaviors
	Direct effect	Indirect effect	Direct effect	Direct effect	Direct effect	Indirect effect
	OR (95% CI)	OR (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)
Independent variables
Autonomy practices	1.01 (0.94 to 1.08)	1.00 (0.98 to 1.01)	0.18 (0.06 to 0.29)		−0.04 (−0.17 to 0.08)	0.06 (0.02 to 0.10)³
Structure practices	0.97 (0.90 to 1.03)	1.02 (1.00 to 1.04)¹	0.03 (−0.08 to 0.15)	0.22 (0.11 to 0.32)	−0.03 (0.00 to 0.22)	0.03 (−0.01 to 0.08)
Control practices	0.98 (0.90 to 1.05)	1.01 (0.99 to 1.03)		0.20 (0.10 to 0.31)	0.11 (−0.16 to 0.09)	0.02 (−0.00 to 0.05)
Mediators
Health behaviors	1.08 (1.02 to 1.15)
Autonomous motivation	0.96 (0.90 to 1.03)	1.02 (1.00 to 1.05)²			0.32 (0.22 to 0.42)
Controlled motivation	1.05 (0.99 to 1.13)	1.01 (1.00 to 1.02)			0.11 (0.00 to 0.22)
Covariates
Adolescent age	0.96 (0.90 to 1.02)		0.02 (−0.80 to 0.12)	0.05 (−0.05 to 0.15)	−0.12 (−0.22 to −0.02)
Adolescent gender^a	1.02 (0.96 to 1.08)		0.10 (−0.00 to 0.20)	0.04 (−0.06 to 0.13)	−0.03 (−0.13 to 0.07)

Indirect path: (1) Structure parenting practices had an indirect effect on initial app use but no specific indirect paths were identified. (2) The indirect effect between autonomous motivation and initial app use occurs through health behaviors. (3) The indirect effect between autonomy parenting practices and health behaviors occurs through autonomous motivation.

Bold: p < 0.05.

Reference: male.

OR, odds ratio.

Table 4.

Associations between Adolescents' Characteristics and Parenting Practices with Adolescent's Initial App Use, among Mothers (n = 239) and Fathers (n = 127): Adjusted for Adolescent's Gender and Age

	Dependent variable		Mediators
	Initial app use		Autonomous motivation	Controlled motivation	Health behaviors
	Direct effect	Indirect effect	Direct effect	Direct effect	Direct effect	Indirect effect
	OR (95% CI)	OR (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)
Mother sample (n = 239)
Independent variables
Autonomy practices	0.98 (0.89 to 1.07)	0.99 (0.98 to 1.01)	0.14 (−0.01 to 0.28)		−0.04 (−0.20 to 0.12)	0.04 (−0.01 to 0.09)
Structure practices	0.99 (0.91 to 1.07)	1.01 (0.99 to 1.04)	0.05 (−0.10 to 0.19)	0.25 (0.13 to 0.38)	0.07 (−0.08 to 0.21)	0.05 (−0.01 to 0.11)
Control practices	1.00 (0.91 to 1.09)	1.01 (0.98 to 1.03)		0.20 (0.07 to 0.33)	0.01 (−0.15 to 0.16)	0.03 (−0.00 to 0.06)
Mediators
Health behaviors	1.11 (1.03 to 1.19)
Autonomous motivation	0.95 (0.88 to 1.03)	1.01 (1.00 to 1.06)¹			0.31 (0.19 to 0.43)
Controlled motivation	1.01 (0.94 to 1.10)	1.02 (1.00 to 1.03)			0.15 (0.01 to 0.28)
Covariates
Adolescent age	0.98 (0.91 to 1.06)		0.04 (−0.09 to 0.17)	0.13 (0.01 to 0.26)	−0.10 (−0.22 to −0.02)
Adolescent gender¹	1.01 (0.94 to 1.08)		0.10 (−0.02 to 0.23)	0.02 (−0.10 to 0.14)	0.04 (−0.13 to 0.07)
Father sample (n = 127)
Independent variables
Autonomy practices	1.08 (0.95 to 1.23)	0.99 (0.96 to 1.03)	0.27 (0.08 to 0.45)		−0.09 (−0.29 to 0.12)	0.09 (0.01 to 0.17)²
Structure practices	0.95 (0.84 to 1.07)	1.03 (0.99 to 1.07)	0.01 (−0.18 to 0.20)	0.17 (−0.01 to 0.35)	0.26 (0.08 to 0.43)	0.01 (−0.06 to 0.08)
Control practices	0.92 (0.86 to 1.04)	1.02 (0.99 to 1.06)		0.21 (0.03 to 0.39)	−0.10 (−0.31 to 0.11)	0.01 (−0.03 to 0.05)
Mediators
Health behaviors	1.04 (0.93 to 1.16)
Autonomous motivation	0.98 (0.87 to 1.10)	1.01 (0.98 to 1.05)			0.34 (0.17 to 0.52)
Controlled motivation	1.13 (1.01 to 1.25)	1.00 (0.99 to 1.01)			0.04 (−0.14 to 0.22)
Covariates
Adolescent age	0.93 (0.84 to 1.04)		−0.02 (−0.19 to 0.15)	−0.10 (−0.28 to 0.07)	−0.21 (−0.38 to −0.05)
Adolescent gender^a	1.02 (0.93 to 1.13)		0.12 (−0.05 to 0.29)	0.10 (−0.06 to 0.27)	−0.20 (−0.35 to −0.04)

Indirect paths: (1) Among mothers, the indirect effect between autonomous motivation and Aim2Be initial app use occurs through the health behaviors. (2) Among fathers, the indirect effect between autonomy practices and health behaviors occurs through autonomous motivation.

Bold: p < 0.05.

Reference: male.

Full sample

Adolescents' health behaviors and autonomous motivation, in addition to structure parenting practices had a direct or indirect effect on initial use of Aim2Be (Table 3). Specifically, health behaviors had a positive direct effect on adolescents' adoption (odds ratio [OR] = 1.08; p < 0.05), where a one standard deviation increase in adolescent's health behaviors score was associated with an 8% increase in the odds of initially using Aim2Be. Adolescents' autonomous motivation had a positive, indirect effect on initial use, which occurred through the adolescents' health behaviors (OR = 1.02; p < 0.05). Specifically, a one standard deviation increase in adolescent's autonomous motivation was associated with a 2% increase in the odds of initially using Aim2Be. Finally, parental use of structure practices also had a positive indirect effect on adolescents' initial use (OR = 1.02; p < 0.05). However, since none of the specific indirect paths were significant, the results suggest that it was the combined effect of structure practices on autonomous and controlled motivation that resulted in increase in initial use. Specifically, one standard deviation increase in structure practices was associated with a 2% increase in the adolescent's odds of initially using Aim2Be.

With regard to the characteristics of the adolescents, autonomy supportive parenting practices was positively associated with autonomous motivation (β = 0.18; p < 0.05) and had a positive indirect association with adolescents' health behaviors (β = 0.06; p < 0.05). Autonomous motivation was positively associated with adolescents' health behaviors (β = 0.32; p < 0.05). Control parenting practices was positively associated with controlled motivation (β = 0.20; p < 0.05), meaning that increased use of controlling practices was associated with greater controlled motivation among adolescents. Finally, adolescent's age was negatively associated with health behaviors (β = −0.12; p < 0.05), which indicated that older adolescents engaged in poorer health behaviors, as compared with younger adolescents.

Mother and father sample

When the analyses were stratified by parent's gender, difference in the associations emerged (Table 4; Fig. 1). Specifically, among mothers, the associations between adolescents' health behaviors and autonomous motivation with initial app use remained significant (ORs = 1.11 and 1.01, respectively). These results suggest that adolescents who engaged in healthy behaviors and had higher levels of autonomous motivation had greater odds of initially using the Aim2Be app. However, the indirect effect between structure parenting practices and adolescent's initial app use no longer remained significant.

Among fathers, the direct and indirect associations between adolescents' health behaviors, autonomous motivation, and structure practices with initial app use were not significant. Rather, a new path emerged. Specifically, controlled motivation was the only variable that was positively associated with initial app use (OR = 1.13; p < 0.05). Specifically, one standard deviation increase in adolescents' controlled motivation score was associated with a 13% increase in their odds of initially using Aim2Be.

Discussion

This is the first study that examined whether adolescents' health behaviors and motivation, as well as parenting practices and parental gender were associated with adolescents' initial use of an app. The findings highlight that adolescents' characteristics and parenting practices had both a direct and indirect effect on initial app use. Furthermore, the mechanisms through which parenting practices influenced adolescents' initial use of Aim2Be were not the same in the mother and father samples. Overall, these results emphasize the importance of considering the effects of both adolescent and parent characteristics when attempting to reach the intended audience with their e-health intervention aimed at changing obesity-related behaviors.

A key finding of this study was that adolescents who reported engaging in healthier behaviors and were more autonomously motivated had greater odds of initially using Aim2Be. Within the literature, predictors of e-health intervention use have mainly been examined among adults,^22,23,41 with the exception of one study that examined willingness to use e-health interventions among adolescents.²⁴ Among adult samples, studies have found that participants who reported healthier lifestyle behaviors^22,23 and who had higher levels of motivation⁴¹ were more likely to use e-health interventions. The same associations were observed in this study, indicating that adolescents who may have benefited the most from this program (i.e., poor health behaviors and/or low motivation) were not accessing it. In contrast, one adolescent study contradicts the study results, as they reported that poorer health behaviors and high BMI levels were associated with increased willingness to use e-health interventions.²⁴ However, that study²⁴ differed in the following ways: it focused on willingness and not actual use; and adolescents were recruited from a health care context and not from the general population. These differences may explain the differences with our study. Overall, our findings stress the importance of examining adolescents' characteristics, as a way to ensure that the target population is accessing and benefiting from the program.

Another key finding was that parenting practices influenced adolescents' initial use of Aim2Be. However, what was unexpected was that structure parenting practices had an indirect effect on initial use through both autonomous and controlled motivation. Although at first this may contradict principles from Self-Determination Theory,²⁷ past studies have shown that among adolescents, both autonomous and controlled motivation were positively associated with adolescents' health behavior.^42–44 Adolescents may be in a transitional period of their development where autonomous motivation may start to emerge. Although the process through which adolescents become autonomously motivated is unclear, adolescents may transition from controlled to autonomous motivation in a structured household environment, where their innate need of competence is satisfied and their values and identity mature.⁴⁵ Overall, these findings suggest that greater insight is needed to better understand the types of supportive environments (including physical and social) that are required to motivate adolescents to engage with e-health interventions.

Finally, the results from the stratified analyses indicated that the mechanism of associations differ between parent gender subgroups, where autonomous processes (i.e., adolescents' autonomous motivation and health behaviors) drove the associations among mothers, whereas controlled processes (i.e., adolescents' controlled motivation) drove the associations among fathers. These results may in part be reflective of the gender differences in parental expectations with regard to adolescents' obesogenic behaviors. Specifically, studies have found that fathers use significantly more controlling practices with regard to adolescent's physical activity levels and dietary behaviours.⁴⁶ In addition, mothers were found to use different supportive practices,^46,47 which are known to support autonomous processes. Overall, the results of this study indicate that future research should focus on understanding the role and influence of parent–child relationships with regard to adolescents' interest in using an app focused on health behaviors.

The results of this study need to take into consideration its limitations. About 21% of adolescents opted to not download and use the app and while this study focused on motivation and parenting practices, it is possible that other factors that were not measured could have influenced their decisions to not use or download the app (e.g., how the app was described to them). Participants' responses to the baseline questionnaire are subject to social desirability and recall bias. Although validated questionnaires were used, many had to be modified; thus, the sensitivity of these questionnaires may have been reduced. This study specifically focused on initial app use and as such the findings of this study do not generalize to adoption. Although initial app use is a prerequisite to app adoption, the predictors of adoption are likely different. Despite these limitations, this study was one of the first study to explore whether the mechanisms of adolescents' initial app use differed between a mother versus father sample. In addition, the study sample was representative of the Canadian population^48–50; thus, the results may generalize to other Canadian families.

Conclusion

By analyzing patterns of initial use, this study provided greater insight into the adolescent and parental characteristics that were associated with adolescents' initial use Aim2Be. This study identified a subgroup of adolescents (i.e., those with better health behaviors, who were autonomously motivated, and lived within a structured household environment) who had greater odds of initially using the app. In addition, as parental gender was a significant confounder in the association between motivation and health behaviors, it highlighted that when fathers are co-participating in the intervention with their adolescents they are likely activating different mechanisms to support their adolescents change in health behaviors. Future research can build upon the findings of this study to improve strategies to broaden the reach and use of e-health interventions. Ensuring that e-health interventions can effectively reach and be used by their intended/target population(s) is an important initial step to impact the health behaviors of adolescents in a meaningful way.

Footnotes

Authors' Contributions

C.P. conceived the study under the guidance of L.C.M. L.C.M. oversaw the data collection of this study. G.E.F. and M.G. provided input in the conceptualization of the study. C.P. conducted the analyses under the guidance of L.C.M. C.P. drafted the article with substantial contributions from L.C.M. All authors have read, edited, and approved the final article.

Ethical Statement

The Children's and Women Research Ethics Board at the University of British Columbia approved the protocol and consent form for this study (No. H16-03090), which align with institutional and national policies (i.e., bound to Tri-Council Policy Statement: Ethical Conduct for Research Involving humans in accordance with the recent Helsinki's Declaration). All participants consented to be part of this study and have agreed to have the results published in aggregate form.

Funding Information

Funding for this research has been received through a contract from the Childhood Obesity Foundation, which received funds from the Public Health Agency of Canada (Project No. 1617-HQ-000046, Contact No. 604-251-2229) and matched financial and in-kind funds from Ayogo Health, Inc., Merck Canada, Inc., Heart and Stroke, Obesity Canada, Diabetes Canada, Craving Change, Canadian Society for Exercise Physiology, David Suzuki Foundation, and Pacific Blue Cross Foundation. The Childhood Obesity Foundation owns the content of Aim2Be, which has been developed with expert input. Ayogo Health, Inc. contributed financial and technical resources to develop the Aim2Be app, which is powered by Ayogo's Empower platform. C.P. received University of British Columbia Graduate Support funding and Canadian Institutes of Health Research Frederick Banting and Charles Best Graduate Scholarship award. L.C.M. received salary support from the BC Children's Hospital Research Institute.

Author Disclosure Statement

C.P., G.E.F., and M.G. have no competing interests to declare. L.C.M. received funding to conduct the evaluation of the Aim2Be, which is developed by the Childhood Obesity Foundation with funds as described earlier. L.C.M. does not have any ownership in the Aim2Be app.

References

Statistics Canada. Health Fact Sheets: Physical Activity and Screen Time among Canadian Children and Youth, 2016 and 2017 [Internet]. Ottawa: Statistics Canada, April 2019. Available at https://www150.statcan.gc.ca/n1/pub/82-625-x/2019001/article/00003-eng.htm Last accessed August 4, 2019.

Colapinto

, Graham

, St-Pierre

. Trends and correlates of frequency of fruit and vegetable consumption, 2007 to 2014. Health Rep, 2018; 29:9–14.

Langlois

, Garriguet

, Gonzalez

, et al. Change in total sugars consumption among Canadian children and adults. Health Rep, 2019; 30:10–19.

Guh

, Zhang

, Bansback

, et al. The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health, 2009; 9:88.

Al-Khudairy

, Loveman

, Colquitt

, et al. Diet, physical activity and behavioural interventions for the treatment of overweight or obese adolescents aged 12 to 17 years. Cochrane Database Syst Rev, 2017; 6:CD012691.

Gruber

, Haldeman

. Using the family to combat childhood and adult obesity. Prev Chronic Dis, 2009; 6:A106.

McCoby

, Martin

. Socialization in the context of the family: Parent-child interaction [Internet]. In: Mussen PH, Hetherington EM (eds), Handbook of Child Psychology: Vol. 4. Socialization, Personality, and Social Development. New York: Wiley, 1983, pp. 1–101.

Jago

, Davison

, Brockman

, et al. Parenting styles, parenting practices, and physical activity in 10- to 11-year olds. Prev Med, 2011; 52:44–47.

Maitland

, Stratton

, Foster

, et al. A place for play? The influence of the home physical environment on children's physical activity and sedentary behaviour. Int J Behav Nutr Phys Act, 2013; 10:99.

10.

Ventura

, Birch

. Does parenting affect children's eating and weight status?. Int J Behav Nutr Phys Act, 2008; 5:15.

11.

Jago

, Davison

, Thompson

, et al. Parental sedentary restriction, maternal parenting style, and television viewing among 10- to 11-year-olds. Pediatrics, 2011; 128:e572–e578.

12.

Vaughn

, Ward

, Fisher

, et al. Fundamental constructs in food parenting practices: A content map to guide future research. Nutr Rev, 2016; 74:98–117.

13.

Mâsse

, Watts

, Barr

, et al. Individual and household predictors of adolescents' adherence to a web-based intervention. Ann Behav Med, 2015; 49:371–383.

14.

Quattrin

, Cao

, Paluch

, et al. Cost-effectiveness of family-based obesity treatment. Pediatrics, 2017; 140:e20162755

15.

Eysenbach

. What is e-health?. J Med Internet Res, 2001; 3:e20.

16.

Ahern

. Challenges and opportunities of eHealth research. Am J Prev Med, 2007; 32:S75–S82.

17.

Fedele

, Cushing

, Fritz

, et al. Mobile health interventions for improving health outcomes in youth: A meta-analysis. JAMA Pediatr, 2017; 171:461–469.

18.

Statistics Canada. A Portrait of Canadian Youth: March 2019 Updates [Internet]. Ottawa: Statistics Canada, May 2019. Available at https://www150.statcan.gc.ca/n1/pub/11-631-x/11-631-x2019003-eng.htm Last accessed August 4, 2019.

19.

Kohl

, Crutzen

, de Vries

. Online prevention aimed at lifestyle behaviors: A systematic review of reviews. J Med Internet Res, 2013; 15:e146.

20.

Kelders

, Kok

, Ossebaard

, et al. Persuasive system design does matter: A systematic review of adherence to web-based interventions. J Med Internet Res, 2012; 14:e152.

21.

Donkin

, Christensen

, Naismith

, et al. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res, 2011; 13:e52.

22.

Kelders

, Van Gemert-Pijnen

JEWC

, Werkman

, et al. Effectiveness of a web-based intervention aimed at healthy dietary and physical activity behavior: A randomized controlled trial about users and usage. J Med Internet Res, 2011; 13:e32.

23.

Glasgow

, Nelson

, Kearney

, et al. Reach, engagement, and retention in an internet-based weight loss program in a multi-site randomized controlled trial. J Med Internet Res, 2007; 9:e11.

24.

Tercyak

, Abraham

, Graham

, et al. Association of multiple behavioral risk factors with adolescents' willingness to engage in eHealth promotion. J Pediatr Psychol, 2009; 34:457–469.

25.

Bandura

. Social cognitive theory: An agentic perspective. Annu Rev Psychol, 2001; 52:1–26.

26.

Ryan

, Rigby

, Przybylski

. The motivational pull of video games: A self-determination theory approach. Motiv Emot, 2006; 30:344–360.

27.

Ryan

, Deci

. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol, 2000; 55:68–78.

28.

Mâsse

, Vlaar

, Macdonald

, et al. Aim2Be an mHealth intervention for children with overweight and obesity: Study protocol for a randomized controlled trial. Trials, 2020; 21:132.

29.

Harris

, Taylor

, Thielke

, et al. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform, 2009; 42:377–381.

30.

Harris

, Taylor

, Minor

, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform, 2019; 95:103208.

31.

Jogova

, Song

JE-S

, Campbell

, et al. Process evaluation of the Living Green, Healthy and Thrifty (LiGHT) web-based child obesity management program: Combining health promotion with ecology and economy. Can J Diabetes, 2013; 37:72–81.

32.

Kowalski

, Crocker

, Donen

. The physical activity questionnaire for older children (PAQ-C) and adolescents (PAQ-A) manual. Coll Kinesiol Univ Saskatchewan, 2004; 87:1–38.

33.

Bervoets

, Van Noten

, Van Roosbroeck

, et al. Reliability and validity of the Dutch Physical Activity Questionnaires for Children (PAQ-C) and Adolescents (PAQ-A). Arch Public Health, 2014; 72:47.

34.

Centers for Disease Control and

Prevention

. National Youth Physical Activity and Nutritional Study (NYPANS) Updated: June 14, 2018. Available at https://www.cdc.gov/healthyyouth/data/yrbs/nypans.htm Last accessed June 1, 2020.

35.

Eaton

, Olsen

EOM

, Brener

, et al. A comparison of fruit and vegetable intake estimates from three survey question sets to estimates from 24-hour dietary recall interviews. J Acad Nutr Diet, 2013; 113:1165–1174.

36.

Olsen

EOM

, Eaton

, Sohyun

, et al. Comparing methods for assessing beverage intake among high school students. Am J Health Behav, 2014; 38:114–123.

37.

Rosenberg

, Norman

, Wagner

, et al. Reliability and validity of the Sedentary Behavior Questionnaire (SBQ) for adults. J Phys Act Health, 2010; 7:697–705.

38.

French

, Gerlach

, Mitchell

, et al. Household obesity prevention: Take Action—a group-randomized trial. Obesity, 2011; 19:2082–2088.

39.

Nebeling

, Hennessy

, Oh

, et al. The FLASHE study: Survey development, dyadic perspectives, and participant characteristics. Am J Prev Med, 2017; 52:839–848.

40.

Muthén

, Muthén

. Mplus User's Guide. Los Angeles, CA: Muthén & Muthén, 1998–2017.

41.

Perski

, Blandford

, West

, et al. Conceptualising engagement with digital behaviour change interventions: A systematic review using principles from critical interpretive synthesis. Transl Behav Med, 2017; 7:254–267.

42.

Verloigne

, De Bourdeaudhuij

, Tanghe

, et al. Self-determined motivation towards physical activity in adolescents treated for obesity: An observational study. Int J Behav Nutr Phys Act, 2011; 8:97.

43.

Parks

, Blaser

, Smith

, et al. Correlates of fruit and vegetable intake among parents and adolescents: Findings from the Family Life, Activity, Sun, Health, and Eating (FLASHE) study. Public Health Nutr, 2018; 21:2079–2087.

44.

Lubans

, Lonsdale

, Plotnikoff

, et al. Development and evaluation of the Motivation to Limit Screen-time Questionnaire (MLSQ) for adolescents. Prev Med, 2013; 57:561–566.

45.

Deci

, Ryan

. Motivation, personality, and development within embedded social contexts: An overview of self-determination theory [Internet]. In: Ryan RM (ed), The Oxford Handbook of Human Motivation. Oxford: Oxford University Press, 2012, pp. 85–107.

46.

Lloyd

, Lubans

, Plotnikoff

, et al. Maternal and paternal parenting practices and their influence on children's adiposity, screen-time, diet and physical activity. Appetite, 2014; 79:149–157.

47.

Davison

, Cutting

, Birch

. Parents' activity-related parenting practices predict girls' physical activity. Med Sci Sports Exerc, 2003; 35:1589–1595.

48.

Statistics Canada. Canadian Demographics at a Glance: Second Edition [Internet]. Ottawa: Statistics Canada, 2016. Available at https://www150.statcan.gc.ca/n1/en/pub/91-003-x/91-003-x2014001- eng.pdf?st = AQmc0PUQ Last accessed August 4, 2019.

49.

Statistics Canada. Immigration and Ethnocultural Diversity: Key Results from the 2016 Census [Internet]. Ottawa: Statistics Canada, 2017. Available at https://www150.statcan.gc.ca/n1/daily-quotidien/171025/dq171025b-eng.htm Last accessed August 4, 2019.

50.

Statistics Canada. Income Highlight Tables, 2016 Census [Internet]. Ottawa: Statistics Canada, 2017. Available at https://www12.statcan.gc.ca/censusrecensement/2016/dp-pd/hlt-fst/inc-rev/index-eng.cfm Last accessed August 4, 2019.