Abstract: Mindfulness involves awareness of present experience (Brown & Ryan, 2003), and is related to online vigilance (OV; Johannes et al., 2018). OV denotes the preoccupation with the online world and is linked to smartphone addiction (Throuvala et al., 2020). This study investigated the indirect relationship of mindfulness as a predictor of smartphone use via online vigilance. Undergraduate iPhone users (N = 189) completed the CAMS-R and FMI mindfulness measures (Feldman et al., 2006; Walach et al., 2006), the Online Vigilance Scale (Reinecke et al., 2018), and estimated the proportion of total leisure screen time spent on a smartphone over other devices. Smartphone use was assessed via the Screen Time setting on iPhones. Greater mindfulness indirectly (via OV) predicted lower overall smartphone use, social use, and estimated leisure smartphone screen time. Results suggest that mindfulness training may help to reduce excessive smartphone use by reducing mental preoccupation with the online world.

1. INTRODUCTION
• Mindfulness involves awareness of present experience and has
mediated online vigilance and well-being (Brown & Ryan, 2003;
Johannes et al., 2018).
• Online Vigilance (OV) denotes preoccupation with the online
world and is linked with smartphone addiction (Throuvala et al.,
2020).
• Purpose: To assess the extent to which mindfulness predicts
smartphone use via online vigilance.
METHODS
N = 189 (18 to 34, M-age = 20.0; 78 males, 41% of sample)
Measures: (1) Cognitive and Affective Mindfulness Scale-Revised
(CAMS-R); (2) Freiburg Mindfulness Inventory (FMI);
(3) Online Vigilance Scale; (4) Smartphone Use (assessed via screen
time in iPhone settings); (5) “How much of your leisure screen time
(i.e., not work-related) is spent on your smartphone rather than
other devices (laptop, desktop computer, tablet)?”
CAMS-R and FMI (r = .64) were combined to form a composite
mindfulness measure via averaging their standard (z) scores.
RESULTS & DISCUSSION
Online Vigilance (OV) mediates relationship between mindfulness
and smartphone (1) daily average use; (2) weekly social use (social
media plus messaging); and (3) leisure online time with smartphone.
Implications:
(1) The more mindful one is the less likely they are to develop
smartphone addiction due to reduced preoccupation with the
online world.
(2) Mindfulness training may help to reduce excessive smartphone
use by reducing mental preoccupation with the online world.
(3) Mindfulness training may be especially helpful in reducing
excessive smartphone use for social networking, which was the
most frequent smartphone use.
Mindfulness and Smartphone Use
Mediated by Online Vigilance
Zachary I. Wunder*, Megan Phillips, Gregory A.
Norville, Lara L. Jones, Ph.D.
Contact: Zachary Wunder Email: Gn9687@wayne.edu
Online vigilance mediates
mindfulness as a predictor of
smartphone use (daily average,
social, and for online leisure).
More About the Measures
CAMS-R (Feldman et al., 2006); 12 items which assess
four aspects of mindfulness: acendon, present-
focus, awareness, and acceptance. For example, “I
can accept things I cannot change.”
Freiburg Mindfulness (Walach et al., 2006); 14 items
intended to assess all components of mindfulness
such as, “I accept unpleasant experiences.”
Online Vigilance (Reinecke et al., 2018); 12 items
across three dimensions:
OV-Salience: “My thoughts oeen drie to online
content.”
OV-Monitoring: “I constantly monitor what is
happening online.”
OV-ReacWvity: “When I receive an online message, I
immediately give it my full acendon.”
More Results
CorrelaWons:
• ObjecWve Smartphone Use (hrs): Daily Average (+)
related to OV (r = .26). Social Use (+) related to OV
(r = .27).
• EsWmated Smartphone Use (self-report): Leisure
(+) related to OV (r = .26).
• Mindfulness Composite: (-) related to OV (r = -.18)
(consistent with Johannes et al., 2018)
No effects:
• No direct or indirect effects of mindfulness on
weekly entertainment use, number of phone
pickups, or number of nodficadons.
LimitaWons and Future DirecWons
• LimitaWon: Only iPhone users were included in the
study, though no differences in results is expected
between iPhone and Android users.
• Future DirecWons: Invesdgate (1) factors that
predict mindfulness and (2) best methods for
overcoming inhibitors of mindfulness culdvadon.
Mindfulness
Online
Vigilance
Daily Average
Use
Mindfulness
Online
Vigilance
Weekly Social
Use
Mindfulness
Online
Vigilance
% of Leisure Time on
Smartphone
-.17* .26*
.27*
-.18*
-.18*
-.07
.03
.22*
-.22*
Standardized Indirect effect: -.0490, SE = .02, 95% CI: -.1073, -.0075
Standardized Indirect effect: -.0391 , SE = .02, 95% CI: -.0804, -.0047
Standardized Indirect effect: -.0444, SE = .03, 95% CI: -.1018, -.0037 * p < .05
* p < .01
* p < .05