Adolescent Digital Technology Interactions and Importance: Associations with Depression and Well-Being

Abstract

Adolescents' digital technology use is nearly ubiquitous and has been associated with health benefits and risks, including risks of depression. The Adolescents' Digital Technology Interactions and Importance (ADTI) scale provides a novel approach to measuring how adolescents prioritize their technology use. The purpose of this study was to investigate the relationship between adolescents' technology interactions and mental health measures, including depression and mental well-being. This cross-sectional online survey study recruited adolescents aged 12–18 years using Qualtrics panels. Survey measures included the ADTI and assessments of depression and well-being. Analyses included the Kruskal–Wallis test and multivariate logistic regression analyses. The 4,592 participants had a mean age of 15.6 years (SD = 1.68), 46.4 percent were female, 66.9 percent were Caucasian, and 74.5 percent lived in a household with an income above the poverty line. The median ADTI total score was 48 (range 18–90), 23.0 percent (n = 1,055) of participants were categorized for at risk for depression and 54.8 percent (n = 2,477) of participants were categorized as high mental well-being. Participants with higher ADTI total scores were more likely to be at risk for depression (odds ratio [OR] = 1.059, 95 percent confidence interval [CI]: 1.054–1.064). Furthermore, participants with a higher ADTI total score were more likely to have a higher mental well-being (OR = 1.015, 95 percent CI: 1.012–1.019). We found that ADTI total scores were significantly higher both among adolescents who screened positive for depression and among adolescents with higher mental well-being. This intriguing finding suggests that it is possible that digital technology use intensifies either the positive or the negative mental states that adolescents bring to their online environment.

Introduction

Adolescents have the highest rates of social media use of any age group, almost half report they are connected “almost constantly.”¹ Adolescents' digital technology use is associated with benefits to their well-being, including connection, social support, and entertainment.^2–4 However, previous studies suggest associations between digital technology use and poorer mental health.^5–7 Previous studies of mental health and social media have typically measured self-reported⁸ or passively observed⁹ frequency of use. Measuring the quantity of time is subject to challenges such as recall bias and being limited to measuring a single platform or device.¹⁰ To better understand the relationship between adolescent technology use and mental health, novel assessment approaches are needed.

One new method to measure adolescent technology use is the Adolescents' Digital Technology Interactions and Importance (ADTI) scale.¹¹ This scale assesses how adolescents interact with technology and the perceived importance of such interactions. This scale may allow new insights into the quality of technology use, and how the importance of technology interactions may be associated with mental health and well-being. Thus, the purpose of this study was to investigate the relationship between adolescents' technology interactions and mental health measures, including depression and mental well-being.

Materials and Methods

This cross-sectional survey was conducted during February–March 2019. The Institutional Review Board at the relevant university approved this study.

Setting and participants

Our goal was to achieve a national sample of adolescent survey respondents. Compared with traditional survey approaches, such as in person, online survey panels offer a broader reach.¹² We selected Qualtrics panels given evidence that they can achieve demographic representation within a 10 percent range of their corresponding values in the U.S. population.¹³

A Qualtrics survey manager recruited adult panel participants between February and March 2019 who indicated they had adolescent children aged 12–18 years old and spoke English. Parents who fit this criterion received information about the study and provided informed consent for their child's participation if the child was under age 18 years. After parental consent, the adolescent was given study information and an opportunity to provide assent. Adolescents who were aged 18 years provided consent.

Eligibility for this study was limited to 12- to 18-year-old English-speaking U.S. residents. Qualtrics established parameters to recruit participants with race/ethnicity representative of the U.S. census population.¹³

Survey measures

Technology interactions and importance

Technology interactions and their importance were measured using the ADTI scale (ADTI included as Supplementary Data), which has been validated in previous study.^11,14 This scale includes 18 items and 3 subscales. The three subscales included (1) ADTI-1: technology to bridge online/offline experiences, (2) ADTI-2: technology to go outside one's identity or offline environment, and (3) ADTI-3: technology for social connection. For each item, participants were asked: “How important, if at all, is it for you to use media and technology platforms for the following purposes?” Participants responded using a 5-point Likert scale ranging from “not at all important” to “extremely important.” The Cronbach's alpha scores were 0.87 (subscale 1), 0.90 (subscale 2), and 0.82 (subscale 3), 0.92 for the total scale.

Mental health outcomes

Depression was measured with the Patient Health Questionnaire (PHQ)-9.¹⁵ This 9-item scale asks participants how often they have experienced depression symptoms in the past 2 weeks. Example items included “little interest or pleasure in doing things” and “feeling down, depressed or hopeless.” Response options used a 4-point Likert scale from not at all to nearly every day, and the Cronbach's alpha was 0.82.¹⁶

Mental well-being was measured using the Short Warwick-Edinburgh Mental Well-being Scale.¹⁷ This scale is a shorter form of the Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), which was designed to monitor well-being of populations. The shorter 7-item scale relates more to functions than feelings, and asks participants to indicate how often they agree with the statements for the past 2 weeks. Example items include “I've been feeling optimistic about the future” and “I've been feeling relaxed.” Response options included a 5-point Likert scale from none of the time to all of the time. The internal consistency reliability of the SWEMWBS was strong (Pearson Separation Index = 0.84).

Demographic variables

Demographic data included age, gender, and race. We collected race information to ensure our survey met our demographic distribution goals.

Analysis

We dichotomized the PHQ-9 summary score to focus on clinically relevant differences in depression risk. We used the cutoff of 10, with 1–10 representing not at risk for depression and 11 and above representing at risk for depression, as this categorization has been validated for adolescent populations.¹⁸ Since there is no established cutoff for the SWEMWBS available for classifying mental well-being status into clinical relevant categories, we dichotomized the mental well-being summary score by the median, that is, ≥27 for high level of mental well-being and <27 for low level of mental well-being. We also conducted a nonparametric Spearman correlation analysis to evaluate the relationship between depression and well-being.

Since the distributions of the ADTI total and subscale scores were skewed to the left, the comparisons of ADTI total and subscale scores between mental health outcomes were conducted using the nonparametric Wilcoxon rank sum test. The ADTI total and subscale scores were summarized in graphical format using box and whisker plots.

Multivariate logistic regression analyses were conducted to evaluate whether ADTI total and subscale scores predict depression risk and mental well-being status. Age (as binary groups), gender, race, and poverty were included as covariates in the multivariate models. The results were reported in term of odds ratios (ORs) and corresponding 95 percent confidence intervals (CI). All reported p values were two-sided and p < 0.05 was used to define statistical significance. Statistical analyses were conducted using SAS software (SAS Institute, Inc., Cary, NC), version 9.4.

Results

The 4,592 participants had a mean age of 15.6 years (SD = 1.68), 46.4 percent were female, 66.9 percent were Caucasian, and 74.5 percent lived in a household with an income above the poverty line. Table 1 includes demographic information.

Table 1.

Demographic Information for 12- to 18-Year-Old Participants Recruited Through Qualtrics (n = 4,592)

Variables	Categories	n (percent)
Age	12–14	2,200 (48.1)
Age	15–18	2,376 (51.9)
Gender	Female	2,130 (46.4)
	Male	2,392 (52.1)
	Other	70 (1.5)
Race	White	3,070 (66.9)
	Black	699 (15.2)
	Native	152 (3.3)
	Asian	235 (5.1)
	Multiracial	221 (4.8)
	Other	215 (4.7)
Family income at federal poverty level	No	3,402 (74.5)
Family income at federal poverty level	Yes	1,166 (25.5)

The median ADTI total score was 48 (range 18–90), for ADTI-1 the median score was 17 (range 6–30), for ADTI-2 the median score was 13 (range 7–35), and for ADTI-3 the median score was 17 (range 5–25). For the PHQ-9 the mean score was 5.5 (SD = 6.9) and 23.0 percent (n = 1,055) of participants were categorized as at risk for depression. For the SWEMWBS, the mean score was 26.6 (SD = 5.0) and 54.8 percent (n = 2,477) of participants were categorized as high mental well-being. There was a moderate negative correlation observed between the PHQ-9 and well-being scales with r = −0.26 (95 percent CI = −0.29 to −0.24).

ADTI and depression

Participants who met criteria for depression had significantly higher ADTI total scores compared with those without depression, with a median of 62 (range 18–90) for participants meeting criteria for depression versus a median of 44 (range 18–90) for participants without meeting criteria for depression (p < 0.0001). In addition, participants who met criteria for depression had higher ADTI-1, ADTI-2, and ADTI-3 scores, with a median of 21 (range 6–30) versus 16 (range 6–30) (p < 0.0001) for ADTI-1, a median of 23 (range 7–35) versus 11 (range 7–35) (p < 0.0001) for ADTI-2, and a median of 19 (range 5–25) versus 16 (range 5–25) (p < 0.0001) for ADTI-3 (Fig. 1).

FIG. 1.

Box plots showing ADTI scale scores by (A) depression presence or absence summary scores and (B) high and low well-being summary scores. ADTI, Adolescents' Digital Technology Interactions and Importance.

ADTI and mental well-being

Participants with higher mental well-being had significantly higher ADTI total scores, with a median of 50 (range 18–90) versus median 46 (range 18–90) (p < 0.0001). Furthermore, participants with higher mental well-being had significantly higher ADTI-1, ADTI-2, and ADTI-3 score (Fig. 1).

Multivariate analyses

Participants with higher ADTI total scores were more likely to be at risk for depression (adjusted OR [aOR] = 1.059, 95 percent CI: 1.054–1.064). Participants with higher ADTI-1 subscale scores (aOR = 1.126, 95 percent CI: 1.113–1.140), ADTI-2 subscale scores (aOR = 1.161, 95 percent CI: 1.149–1.173), and ADTI-3 subscale scores (OR = 1.098, 95 percent CI: 1.083–1.114) were also more likely to be at risk for depression.

Furthermore, we found that participants with a higher ADTI total score were more likely to have a higher mental well-being (aOR = 1.015, 95 percent CI: 1.012–1.019). Participants with a higher ADTI-1 (aOR = 1.046, 95 percent CI: 1.037–1.056), ADTI-2 (aOR = 1.027, 95 percent CI: 1.019–1.035), and ADTI-3 (aOR = 1.040, 95 percent CI: 1.029–1.051) were also more likely to score higher for mental well-being.

Discussion

In this study, we investigated the relationship between adolescents' perceived importance of digital technology and associations with depression and mental well-being. We found that ADTI total scores were higher both among adolescents who screened positive for depression and among adolescents with higher mental well-being. This intriguing finding suggests that a higher value placed on technology use was associated with both higher likelihood of depression and higher likelihood of mental well-being.

Therefore, it is possible that digital technology use intensifies either the positive or the negative mental states that adolescents bring to their online environment. Alternatively, it may be that each of these two mental states drives the perceived importance of technology use for its own independent set of reasons. For example, adolescents at risk of depression may be more likely to describe technology as important because they utilize it to regulate their moods, whereas adolescents with higher well-being may value technology simply because it brings them enjoyment.

Our multivariate analyses confirmed that adolescents with higher ADTI scores were more likely to be at risk for depression as well as more likely to have higher mental well-being. Although the aORs were all highly significant, the ORs values were quite small, suggesting that these differences in ADTI scores by depression or well-being may not be highly clinically significant. This finding is supported by a meta-analysis¹⁹ of studies examining quantity of social media time, this study found small correlations between time spent on social media and well-being, as well as weak correlations with depression. Other investigators have argued that media use may negatively impact some adolescents but caution that overstating these relationships to apply to adolescents as a whole is not warranted.²⁰

Our study is limited by the use of Qualtrics panels for recruitment. A growing literature uses Qualtrics to obtain youth samples for surveys on media.^21,22 We obtained our sample using nonprobability methods; thus, it is vulnerable to selection biases such as self-referral and nonresponse biases. However, previous studies support that demographics of samples recruited by Qualtrics have been within 10 percent of the general U.S. population.¹³ Our data were collected through self-report, which may be prone to social desirability bias; however, Internet-based administration can encourage accurate reporting for sensitive items.^23–25

In conclusion, this study is unique in our approach of considering the importance of technology alongside both negative outcomes, such as depression, and positive outcomes, such as well-being. This study's strength is thus in its consideration of both negative and positive outcomes from technology use. Future studies and analyses can use tools such as the ADTI to parse out what specific technology interactions are associated with depression compared with well-being, among adolescents or specific adolescent populations.²⁶ Future studies should evaluate whether placing importance on different aspects of technology use, measured through the ADTI subscales, is associated with negative and positive mental health consequences.

Footnotes

Acknowledgment

The authors would like to acknowledge the contributions of Christine Richards in formatting this article.

Authors' Contributions

Dr. Moreno contributed to the conception of the study, the data collection instruments, intervention design, the interpretation of the data, and drafting and editing the article. Ms. Binger contributed to the conception of the study, data collection instruments, interpretation of the data, drafting, and editing the article. Mr. Minich contributed to interpretation of data, writing, and editing the article. Ms. Zhao and Dr. Eickhoff contributed to the conception of the study, statistical analyses, interpretation of the data, drafting, and editing the article.

Author Disclosure Statement

No authors have conflicts of interest to disclose.

Funding Information

This study was funded by a research agreement with Facebook.

Supplementary Material

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Supplementary Material

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