Digital Location Tracking: A Preliminary Investigation of Parents’ Use of Digital Technology to Monitor their Adolescent’s Location

Abstract

The emergence of digital technologies has changed the dynamic of parent-adolescent relationships. Parents can now use digital technologies to monitor their adolescent’s physical location. Yet, to date, no known research has examined the extent to which digital location tracking occurs in parent-adolescent dyads, and how tracking links to adolescent adjustment. The current research examined digital location tracking in a large sample of adolescents (N = 729; M_age = 15.03). Overall, about half of parents and adolescents reported digital location tracking. Girls and younger adolescents were more likely to be tracked, and tracking was associated with greater externalizing problems and alcohol consumption; however, these associations were not robust across multiple informants and sensitivity analyses. Positive linkages with externalizing problems and cannabis use were in part contingent on age and positive parenting, with associations emerging for older adolescents and adolescents who report lower positive parenting. Older adolescents are increasingly striving for independence and autonomy, and adolescents who perceive lower positive parenting may view digital tracking as controlling and intrusive. However, results were not robust after statistical correction. This brief report is intended to serve as a preliminary investigation into digital location tracking, and future research is needed to determine the directionality of associations. Possible consequences of parental digital tracking require careful consideration by researchers in order to provide guidance on the best practices for engaging in digital monitoring while nurturing and respecting the parent-adolescent relationship.

Contemporary parent-adolescent relationships now transcend online spaces, with digital environments providing parents with new tools to monitor their adolescents (Modecki et al., 2022). One-third of parents in a representative U.S. sample report using digital software to track their children’s (aged 5 – 11) location (Auxier et al., 2020). However, there is little research on the use of these tools among adolescents. Traditionally, tracking children’s location becomes increasingly difficult as they grow more independent in adolescence (Dishion & McMahon, 1998). It is unknown to what extent parents engage in digital location tracking, how reports of tracking may differ by informant (adolescent versus parent), and how parental tracking may relate to their adolescent’s psychosocial adjustment. The current research addresses these questions in a large sample of adolescents.

Digital tracking behavior can increase parental knowledge. Parental knowledge is associated with greater adjustment, including lower internalizing problems, externalizing problems, and substance use (Hoeve et al., 2009; Kerr & Stattin, 2000; Racz & McMahon, 2011). However, associations with adjustment vary depending on how knowledge is gained. Knowledge is acquired by numerous sources, including parental solicitation and child disclosure (Stattin & Kerr, 2000). Child disclosure involves children openly communicating with their parents without prompting, whereas parental solicitation involves parents actively seeking information from their children (Stattin & Kerr, 2000). When knowledge is gained through child disclosure, associations with improved adjustment are observed (Kapetanovic, Bohlin, et al., 2020; Kapetanovic, Skoog, et al., 2020; Keijers et al., 2010; Kerr et al., 2010). Conversely, when parents engage in soliciting practices, poorer adjustment may be observed (Kapetanovic, Bohlin, et al., 2020; Kapetanovic, Skoog, et al., 2020; Kerr et al., 2010).

Adolescents may perceive that parental solicitation is invasive (e.g., Hawk et al., 2008; Pettit et al., 2001), as well as potentially disruptive to their attempts at achieving autonomy (cf. McCurdy et al., 2020). Scholars have drawn from theories of self-determination (Ryan & Deci, 2000) and psychological reactance (Miron & Brehm, 2006) in suggesting that overly controlling parental behaviors can undermine adolescents’ attempts for autonomy, which, in turn, can increase externalizing and internalizing problems (Kakihara & Tilton-Weaver, 2009; Van Petegem et al., 2015). Indeed, greater parental restrictions may be linked to greater internalizing problems through adolescents’ feelings of being overcontrolled, perhaps especially for older adolescents (Kakihara et al., 2010). Perceived solicitation is linked to greater delinquent behavior when controlling for other parent-child practices, including child disclosure (Kerr & Stattin, 2000).

Given this existing literature, the association between digital location tracking and adolescent adjustment may vary depending on features of the parent-child relationship. If adolescents perceive digital location tracking as concern and care, and/or if adolescents willingly allow their parents to digitally monitor their location (perhaps representing a form of child disclosure), then associations with improved adjustment may be observed. Conversely, if adolescents perceive digital location tracking as overly controlling, then associations with poorer adjustment may be observed. Considering key characteristics of adolescents and the general nature of their relationship with their parents may help elucidate these associations.

Likely of importance is the extent to which a parent is perceived to engage in positive parenting practices. If digital location tracking is paired with positive parenting behavior, then this may reflect a more authoritative parenting style, in which parental solicitation (via tracking) is reflecting warm concern for the adolescent’s safety and well-being (cf. Kapetanovic, Bohlin, et al., 2020). However, if digital location tracking is paired with lower levels of positive parenting behavior, then this may reflect a more authoritarian parenting style, in which parental solicitation (via tracking) is perceived as cold, demanding, and an invasion of privacy (cf. Kapetanovic, Bohlin, et al., 2020). Digital location tracking was expected to be associated with higher adjustment (lower internalizing problems, externalizing problems, and substance use) when positive parenting was perceived to be higher, but poorer adjustment when positive parenting was perceived to be lower. In addition, parental knowledge decreases across adolescence, whereas adolescent secrecy increases (Lionetti et al., 2019), reflecting adolescents’ increasing desires for autonomy and independence as they age. Older adolescents may perceive digital location tracking as particularly controlling and intrusive. Therefore, it was expected that digital location tracking would be associated with poorer adjustment for older adolescents.

Method

Sample

This study used data from Wave 3 (2018) of a longitudinal study based in a Southeastern state in the United States. Participants were included if they had data on adolescent-reported and/or parent-reported digital location tracking. The full sample included 729 participants, with data available for 686 adolescents¹ and 646 parents (574 participants had both adolescent and parent tracking data, 72 had parent data only, 83 had adolescent data only). The adolescent sample was, on average, 15.03 years old (SD = 1.14; Range = 12-18), 55% female and 45% male, and 57% White, 23% Black/African American, 10% Hispanic, and 10% Multiracial/Other. The parent sample was, on average, 44.71 years old (SD = 6.86), 90% female, and 65% White, 23% Black/African American, 7% Hispanic, and 5% Multiracial/Other.

Study procedures were approved by the Duke University Institutional Review Board. The initial wave of the study took place in 2015. Adolescents were drawn from students enrolled in the statewide public-school population, as determined by records from the state’s Department of Public Instruction. An initial sample of 2,104 adolescents was recruited and completed surveys via telephone, with the sample representative of the public-school population on sex, race/ethnicity, and socioeconomic status. Most (n = 1,867) agreed to be contacted for future waves. Subsequent contact was made via telephone and email, with consent/assent and survey procedures occurring online. Some loss of sample representativeness occurred between baseline and the wave included in the current research, in which the current sample was more likely female, more likely white and less likely Hispanic, younger, and less likely to engage in substance use at Wave 1 compared to those who only participated at baseline (see supplement).

Measures

Digital Location Tracking

Adolescents responded to the item, “How often does your parent monitor your location using technology or an app (e.g., Find My iPhone, Find My Friends, mSpy, Life360)?” using a 1 – 6 scale (1 = Never, 6 = Several times each day); this item did not necessarily target the parent participating in the study. Parents answered the same item to report how often they monitored their adolescent’s location on a different scale ranging from 1 (Never) to 5 (Always). We examined these items both continuously (on their existing scales) and dichotomously for two reasons. It could be that simply engaging in any digital location tracking behavior, regardless of the extent, is key for outcomes. Moreover, as digital location tracking can be done quickly and covertly, adolescents may struggle with accurately reporting quantity.

Internalizing Problems

Adolescents reported on their psychological distress using six items (Kessler et al., 2002), which served as an indicator of internalizing problems. Problems were reported over the extent they were experienced in the last month (1 = None of the time, 5 = All of the time). Sample items include “Hopeless” and “Worthless” (α = .88).

Externalizing Problems

Adolescents reported on their frequency of engaging in aggressive (physical and relational) and deviant behavior over the past thirty days using 25 items (Miller-Johnson et al., 2004). Items were responded to using a 0 (Never) to 5 (20 or more times) response scale. A sample item includes “Shoved or pushed another kid” (α = .92).

Substance Use

Adolescents reported on whether they have ever consumed more than a few sips of alcohol. If they answered affirmatively, they were directed to an item assessing past year alcohol use, with the response scale ranging from 0 (0 occasions/none) to 6 (40 or more occasions). Participants who reported no lifetime use were recoded as ‘0’ on past year use. Past year cannabis use was assessed with the item, “On how many occasions (if any) in the past year have you used marijuana (weed, pot) or hashish (hash, hash oil)?”; all participants responded to this item regardless of lifetime use, using the same response scale as the alcohol item. Adolescents reported on whether they have ever used or smoked an e-cigarette. If they answered affirmatively, they were directed to an item assessing past month e-cigarette use, with the response scale ranging from 0 (Not at all) to 6 (More than 5 times a day). Participants who reported no lifetime e-cigarette use were recoded as ‘0’ on past month use. All items were adapted from the Monitoring the Future survey (Johnston et al., 2011).

Moderators

Adolescents self-reported age. Using the Alabama Parenting Questionnaire-Short Form (Elgar et al., 2007), adolescents reported on three items assessing positive parenting behaviors. The response scale ranged from 1 (Never) to 5 (Always), and a sample item includes “Your parent lets you know when you are doing a good job with something” (α = .89).

Covariates

Key demographic variables were controlled for in central analyses to account for potential links with adjustment: gender, race/ethnicity, and perceived socioeconomic status (SES). Adolescents reported on perceived SES by responding to, “In your opinion, how is your family doing financially?”, with response options ranging from 0 (We do not have enough money to meet our basic needs) to 3 (We have enough money to do almost anything we want). In addition, because digital location tracking may be contingent on neighborhood safety, this construct was controlled. Adolescents responded to 15 items (adapted from Evenson et al., 2006) assessing neighborhood safety (e.g., “My neighborhood is safe from crime”; α = .78), with response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Finally, to better determine unique links with adjustment above and beyond traditional parental monitoring/knowledge, we included adolescent-reported parental knowledge in adolescent-report models, and parent-reported parental knowledge in parent-report models. Adolescents responded to five items (e.g., “How much do your parents try to know who you spend time with?”; α = .72); parents responded to the same five items but framed from the parent’s perspective (α = .73; Fletcher et al., 2004). Response options ranged from 1 (They/I don’t try) to 3 (They/I try a lot).

Transparency and Openness

This study was not preregistered. Code and data are on Open Science Framework²: https://osf.io/9kwx8/. Sample size was determined based on successful outreach to participants included in the larger longitudinal study (N = 2,104); all relevant data exclusions, manipulations, and measures are noted.

Results

1. What share of adolescents and parents report digital location tracking?

Out of 657 adolescents with digital location tracking data, 329 (50%) report that their parents monitor their location. Out of 646 parents with data, 326 (51%) report that they monitor their adolescent’s location. Out of 574 parent-adolescent dyads in which both adolescent and parent-reported data were available, 211 (37%) were in agreement that the parent did not engage in digital location tracking, whereas 206 (36%) were in agreement that the parent did engage in digital location tracking. In 78 dyads (14%), the adolescent reported that their parent tracked their location but the parent reported that they did not. In 79 dyads (14%), the parent reported that they tracked their adolescent’s location but the adolescent reported that they did not.

2. How does digital location tracking relate to demographics and adjustment?

Parent-reported and adolescent-reported digital location tracking were highly correlated (r = .53, p < .001). Descriptive statistics and correlations for continuous variables are in Table 1. Only (weak, positive) correlations with parental knowledge were found. Additionally, there were no associations with race or gender (ps > .053). Parent-reported and adolescent-reported digital location tracking were next examined dichotomously. Adolescents who reported tracking were younger (M = 14.91 versus M = 15.16; t (655) = 2.84, p = .005; d = .22), and had higher externalizing problems (M = 0.29 versus M = 0.21; t (655) = −2.25, p = .025; d = .18), parental knowledge (M = 2.68 versus M = 2.53, t (654) = −4.79, p < .001; d = .37), and parent-reported knowledge (M = 2.88 versus M = 2.84, t (575) = −2.08, p = .038; d = .17). For parent-reported tracking, parents were more likely to report tracking for girls than boys (46% of boys and 54% of girls were tracked; χ² (1) = 4.13, p = .042), reported greater parental knowledge (M = 2.89 versus M = 2.82, t (644) = −3.22, p = .001; d = .25), and had adolescents who reported greater knowledge (M = 2.67 versus M = 2.58, t (599) = −2.94, p = .003; d = .24).

Table 1.

Correlations and Descriptive Statistics for Continuous Variables

Adolescent-Report Tracking		Parent-Report Tracking	Mean (SD)
Age	−.03	−.05	15.03 (1.14)
SES	.03	.03	1.91 (0.69)
Neighborhood Safety	.05	.02	3.70 (0.61)
Adolescent-Report Knowledge	.19***	.11**	2.61 (0.41)
Parent-Report Knowledge	.10*	.11**	2.86 (0.26)
Positive Parenting	.07	−.01	3.78 (1.10)
Internalizing Problems	.02	−.01	2.26 (0.89)
Externalizing Problems	.03	.00	0.25 (0.41)
Past Year Alcohol	.07	.07	0.35 (0.93)
Past Year Cannabis	.04	.03	0.28 (1.00)
Past Month E-Cigarette	.06	.03	0.24 (0.88)
Mean (SD)	2.26 (1.62)	2.21 (1.46)
Percent Score ‘1’	50%	49%
Percent Score ‘2’	19%	15%
Percent Score ‘3’	8%	13%
Percent Score ‘4’	8%	10%
Percent Score ‘5’	10%	13%
Percent Score ‘6’	6%	N/A

Note. ‘Percent Score’ values indicate the percentage of participants who endorsed each response item option. For example, 50% of adolescents responded with a score of ‘1’ (Never) for if their parents track them, whereas 19% reported with a score of ‘2’ (Hardly Ever).

^***p < .001

^**p < .01

^*p < .05

Finally, a series of ANCOVAs tested if dyad type (as reported in Research Question 1) was differentially associated with adjustment outcomes, controlling for age, gender, race/ethnicity, SES, neighborhood safety, positive parenting, and parental knowledge. The overall ANCOVA was significant for alcohol consumption (p = .020). Bonferroni-corrected pairwise comparisons indicated that in dyads in which the parent and child agreed that there was tracking, adolescents reported higher alcohol consumption (M = 0.45) than dyads in which the parent and child agreed that there was no tracking (M = 0.26; p = .016); the discrepant dyads fell in between (child reported tracking and parent did not M = 0.38; parent reported tracking and child did not M = 0.32). The overall analysis was significant for externalizing problems (p = .030), but Bonferroni-corrected pairwise comparisons were not significant (ps > .076).

3. Do age and positive parenting moderate associations between tracking and adjustment?

Models were run in M_plus (Version 8.7; Muthén & Muthén, 1998-2017) to test associations between digital location tracking and adjustment, with age and positive parenting included as moderators. Models were run for each tracking variable³ separately. Results were nearly identical for continuous and dichotomous tracking; because of this, the presentation of results focuses on dichotomous tracking (see Tables S1 and S2 for full results), with notable deviations from continuous tracking noted (see Tables S3 and S4 for full results). Dichotomous tracking variables were effect-coded −1 = no tracking, 1 = tracking. Due to skewness in externalizing problems and substance use, models were estimated and tested using maximum likelihood with robust standard errors⁴. Interactions were probed using the Johnson-Neyman technique, in which we examined at which raw values of the moderator the associations between tracking and each outcome went from non-significant to significant. Significance was determined by the 95% confidence intervals, with associations significant if the interval did not overlap with zero. Covariates included gender, race/ethnicity, SES, neighborhood safety, and parental knowledge. Full information maximum likelihood was used for missingness; missingness on outcome variables varied between 6.9 – 7.5%. Models were run hierarchically, in which Model 1 tested main effects, Model 2 added two-way interactions, and Model 3 added the three-way interaction. Due to the large number of tests, Benjamini-Hochberg corrections were applied to each model. No significant associations of interest emerged for internalizing problems or e-cigarette use.

Externalizing Problems

There was a main effect for child-reported tracking, in which adolescents who reported tracking reported higher externalizing problems (β = .13). This association was qualified by two two-way interactions, in which tracking interacted with age (p = .043) and positive parenting (p = .027). Tracking was associated with greater externalizing problems when positive parenting was valued 4.44 and lower (on a 1-5 scale; b = 0.03, 95% CI [0.00, 0.07]) and at ages 14.46 and older (b = 0.03, 95% CI [0.00, 0.07]; Figure 1). When applying the Benjamini-Hochberg correction, only the main effect remained significant; the interaction terms⁵ were no longer significant (corrected ps > .083).

Figure 1. Johnson-Neyman Plot Depicting Age Interaction for Externalizing Problems.

Note. The association between tracking and externalizing problems was significant at ages on the right of the dotted line. Dark grey areas indicate areas of significance

Alcohol Use

A main effect⁶ was observed for both child- and parent-reported tracking, in which tracking was associated with greater alcohol consumption (βs = .09 - .10). When applying the correction, only the child-reported tracking association remained (parent-reported corrected p = .073).

Cannabis Use

A two-way interaction⁷ was observed between child-reported tracking and positive parenting (p = .042). Tracking was associated with greater cannabis use only when positive parenting was 3.45 or lower (b = 0.09, 95% CI [0.00, 0.17]. When applying the correction, this interaction was not significant (corrected p = .113).

Discussion

With digital tracking technologies, parents can have greater knowledge of their children’ activities. Considering that increasing drives for autonomy and independence are major adolescent milestones (McCurdy et al., 2020), an understanding of how the use of these tools relates to adolescent adjustment is crucial. Parents must be provided with information necessary to ensure that these technologies are not detrimental to their child’s well-being.

What share of adolescents and parents report digital location tracking?

Half of parents and adolescents reported digital location tracking, notably more than the one-third of parents who reported tracking their child’s location in a previous representative sample (Auxier et al., 2020). This is likely because of differences in sample age: whereas we focused on older adolescents, past research focused on youth aged 5 – 11 (Auxier et al., 2020), many of whom may not have a device that allows for tracking. Indeed, older children in this study were more likely to be tracked than younger children, with 41% of children aged 9 to 11 tracked (Auxier et al., 2020), suggesting that tracking behavior increases with child age (to a point).

Although most parent-adolescent dyads agree on whether the parent is tracking, there were notable minorities in which (a) adolescents reported tracking and parents did not, and (b) parents reported tracking and adolescents did not. For the former group, as only one caregiver report was obtained, some adolescents may simply be reporting on the behavior of another caregiver. Other adolescents may erroneously believe they are being tracked. Future research should examine the reasons surrounding these incorrect perceptions, particularly in the context of child disclosure and parental solicitation. For example, some adolescents may perceive that their parents have access to their location through their smartphone plan, but the parent may not have this knowledge or use this feature. Such adolescents may perceive that this disclosure of their location is simply a parameter of youth smartphone ownership. Associations with adjustment may vary dramatically compared to other adolescents, such as those whose parents may have falsely told them that they engage in tracking, perhaps in an attempt to combat problematic behavior. Although the parent may not actually have the intention of using this tool, this example may nonetheless represent a form of manipulative solicitation, adolescents’ perceptions of which may bear consequences for their adjustment. Additionally, for the group in which parents reported tracking and adolescents did not, these adolescents may simply be unaware of the tools at their parents’ disposal. Importantly, adolescents may respond very differently if the parent’s tracking was discovered, as some may perceive these tools as intrusive forms of solicitation.

How does digital location tracking relate to demographics and adjustment?

Of note, digital location tracking was only weakly correlated with parent- and adolescent-reports of parental knowledge, suggesting that digital location tracking may be a related-yet-distinct construct that is unique to the digital age. There was evidence that girls and younger adolescents were more likely to be tracked. Previous research on parental monitoring and knowledge suggests that as adolescents age, parents may increasingly grant drives for autonomy, resulting in less monitoring behavior and ultimately lower knowledge (Lionetti et al., 2019). This may be reflected in the digital age, in which parental relinquishment of digital tracking apps may be a new way of showcasing respect for adolescents’ increasing bids for autonomy. Additionally, findings with gender reflect prior research on parental monitoring and knowledge, in which monitoring and knowledge is higher for girls (Kerr & Stattin, 2000). The greater use of tracking apps for girls may reflect a greater concern for adolescent girls’ safety and behavior, whereas some parents may adapt a “boys will be boys” approach for sons and forgo tracking (Hawk et al., 2008). Implications for adjustment may vary depending on how parents’ engagement in tracking is communicated to the adolescent. Past research indicates that compared to boys, adolescent girls are more likely to disclose to their parents, yet also more likely to have information solicited by their parents (Kerr & Stattin, 2000). Adjustment among girls who willingly allow their parents to track their location, perhaps for safety purposes, may differ from that of girls whose parents forcefully solicit this information.

Past research supports the importance of this distinction regardless of gender, as parental monitoring may be associated with maladjustment if monitoring results in feelings of overcontrol and invasion (Kakihara & Tilton-Weaver, 2009; Van Petegem et al., 2015), whereas child disclosure is associated with positive outcomes and may reflect a high-quality parent-child relationship (e.g., Keijers et al., 2010; Kerr et al., 2010). If parents have realistic safety concerns for their child, it may be fruitful to clearly and warmly communicate these concerns to them, and how digital location tracking may allow an adolescent to have greater freedom (e.g., going out without parental supervision) while the parent has an unobtrusive tool to monitor safety. The reasoning that parents communicate for tracking, and how this communication unfolds, may matter more than the tracking behavior itself.

Evidence linking digital location tracking to adjustment was weak. There was support that digital location tracking was associated with greater externalizing problems and alcohol consumption; however, these associations were inconsistent across models. These discrepant findings could be due to adolescent perceptions of tracking, and how tracking behavior unfolds. It is possible that in the current sample, digital location tracking is ill-received by adolescents, thus representing a form of invasive parental solicitation which has been found to be associated with poorer adjustment (Kakihara & Tilton-Weaver, 2009; Van Petegem et al., 2015). Parents may also engage in tracking after their adolescents engage in problematic acting out behavior, such as externalizing problems and alcohol use. In this case, tracking may manifest not from prior safety concerns, but from lack of trust or concerns about future behavior. Future longitudinal studies should examine potential bidirectional associations. For example, implementing tracking after problem behavior occurs may result in a downward spiral in which the adolescent perceives tracking as unwanted solicitation, further heightening the risk of problem behavior.

Do age and positive parenting moderate associations between tracking and adjustment?

There was little consistent evidence of moderation by age and positive parenting. Although several interactions emerged in the expected direction, in which digital location tracking was associated with greater externalizing problems and cannabis use at only lower levels of positive parenting and/or older age, these interactions were not robust to statistical correction. Given ongoing debate regarding multiple test correction in multiple regression (Anderson, 2022), we stress that these findings (or lack thereof) are merely preliminary. Much work is to be done examining linkages between digital location tracking and adjustment, and what individual characteristics may influence the strength and direction of these associations.

Stronger associations with age and positive parenting may emerge when incorporating perceptions of digital location tracking. The weak associations in which tracking was linked with greater externalizing problems and cannabis use at lower positive parenting may indicate tracking that is conducted in a way that mirrors parental solicitation. Stronger and more consistent linkages may emerge when digital location tracking is measured in terms of the degree to which it is conducted in parallel with parental solicitation, versus child disclosure. A similar approach may be fruitful for examining associations with age. Nearly 90% of our sample was concentrated between ages 13 and 16. Future research can adapt a wider range of ages to better determine developmental windows in which digital location tracking may be more strongly associated with adjustment, for better or for worse. As adolescents approach 18, they may increasingly view digital location tracking as intrusive. Additionally, adolescents may increasingly view “problem” behaviors such as alcohol consumption as normative of late adolescence and not necessarily problematic (cf. Tan, 2012), whereas their parents view these as behaviors that require increased monitoring. There may also be unique cohort effects as parents grow accustomed to tracking tools. Links with adjustment may vary depending on if a parent suddenly begins tracking an older adolescent after years of adolescent smartphone ownership, compared to if the parent engaged in tracking since initial smartphone ownership.

Limitations and Conclusions

This study is limited by a narrow focus on the measurement of digital location tracking, the lack of inclusion of multiple caregiver reports, its cross-sectional design, the presence of attrition, and variation in response formats for substance use outcomes. It is our hope that this preliminary report will set a foundation for future research examining digital location tracking and its associations with adolescent adjustment. In particular, it is critical to consider motivations and perceptions of digital location tracking, in that associations with adjustment may vary depending on whether adolescents willingly disclose their location to their parents, versus if their location is solicited by their parents, especially if the latter is paired with little open communication regarding the reasoning for tracking. Weak moderation effects by age and positive parenting were observed in the current research; these associations may serve as a springboard for future studies, in which the possible moderation role of these constructs can be explored in conjunction with perceptions of digital location tracking. If digital location tracking is paired with warm and supportive parenting, then deleterious associations with adjustment may not be observed. However, if tracking is accompanied by less positive involvement with the adolescent, or if it used in later adolescence when it may be developmentally inappropriate, links with poorer adjustment may occur. Additional research examining digital location tracking will hopefully spark conversations not only among researchers and educators but also between parents and their children about the role that technology should play in contemporary parenting.