Beyond screentime: a 7-day mobile tracking study among college students to disentangle smartphone screentime and content effects on sleep

Figures & data

Table 1. App categories and descriptions.

Category	Description	Example*
Generic app categories
General Top 100	Apps from Top 100 most frequently used apps which could not be categorised.	n/a
Non Top 100	Apps outside Top 100 most frequently used apps.	n/a
Home Lock Screen	Unlocking of the phone.	n/a
Content specific app categories
1. Video Content	Apps to stream video content, including short and longform	YouTube
2. Social Media: Platforms (PF)	Apps to access image based social media platforms	Instagram
3. Social Media: Interpersonal communication (IC)	Apps for communication and messaging tailored to 1-to-1 or (small) group conversations	WhatsApp
4. Gaming	Apps to play (casual) smartphone games	Dots & Co
5. Work/Study	Productivity apps mainly used for non-entertainment purposes	Canvas (student app) Gmail
6. Music/Audio	Apps that provide audio content	Spotify
7. Relaxation	Apps used to relax, focus, and meditate	Calm

Note. The top panel lists generic app categories that do not match content specific categories which were theory driven.

Table 2. Means and standard deviations.

	Evening: post-7pm		Pre-bedtime 2 hours before bedtime
	M	SD	M	SD
Sleep parameters
Number of Awakenings, count	1.27	1.30	1.24	1.29
Duration in minutes	472.68	108.37	477.46	109.63
Quality (low = 1, high = 5)	2.95	0.99	2.94	1.02
Latency in minutes	23.49	27.07	24.07	27.23
Time per app category in minutes
General Top 100	19.81	24.23	5.88	10.67
Non Top 100	7.01	19.85	1.26	3.35
Home Lock Screen	8.89	6.81	1.96	1.62
Video Content	11.68	24.30	6.01	14.55
Social Media: Platforms	23.32	27.05	6.22	9.67
Social media: Interpersonal communication	25.44	33.37	5.21	8.91
Gaming	1.32	7.60	0.94	5.02
Work/Study	4.96	7.60	0.98	2.41
Music/Audio	5.99	15.16	1.52	5.46
Relaxation	0.75	7.86	0.27	3.27

Table 3. Correlations of main constructs with sleep indicators.

	Sleep
	1	2	3	4
Sleep
1. Awakenings
2. Sleep duration	.15**
3. Sleep quality	−.03	0.31***
4. Sleep latency	.11*	−0.04	−0.21***	-
Apps
Generic app categories
1. General top 100	−.10	−0.08	−0.09	0.08
2. Non top 100	−.05	−0.08	−0.14**	0.16**
3. Home lock screen	−.05	−0.13*	−0.11*	0.05
Content app categories
1. Video content	−.07	0.09	0.09	0.13*
2. Social media: PF^a	−.06	−0.07	0.07	−0.03
3. Social media: IC^a	−.03	−0.10	−0.14**	0.04
4. Gaming	.00	0.04	0.11*	−0.06
5. Work/study	−.09	−0.04	−0.11*	0.05
6. Music/audio	.05	−0.09	0.02	−0.01
7. Meditation	−.06	0.06	0.17**	0.00

Note. Zero-order pearson correlations with pairwise-deletion.

^a PF = platforms, IC = interpersonal communication. *p < .05, **p < .01, ***p < .001. Analyses based on evening use data.

Table 4. Number of nighttime awakenings and sleep duration predicted by app time and count.

	Night time awakenings^a				Sleep duration^b
	App time		App count		App time		App count
	7 pm to sleep	2 hours pre sleep	7 pm to sleep	2 hours pre sleep	7 pm to sleep	2 hours pre sleep	7 pm to sleep	2 hours pre sleep
Intercept	1.18 (0.01)	1.14 (0.01)	1.18 (0.01)	1.18 (0.01)	473.16 (6.18)***	477.82 (6.53)***	473.13 (6.10)***	477.65 (6.50)***
App usage cwc	1.002 (0.00)**	1.002 (0.00)**	1.00 (0.00)	0.99 (0.00)	−0.10 (0.09)	0.15 (0.33)	−1.35 (0.45)**	1.90 (1.66)
App usage cmc	1.001 (0.00)	1.001 (0.00)	0.99 (0.00)	1.00 (0.00)	−0.07 (0.12)	0.44 (0.46)	−0.51 (0.37)	−0.35 (2.43)
Model fit
AIC	1122.6	1122.6	953.6	1128.4	4620.71	3929.48	4594.40	3922.64
BIC	1138.4	1138.4	968.7	1138.7	4640.38	3948.33	4614.06	3941.50
Log Likelihood	−557.3	−557.3	−472.8	−560.2	−2305.35	−1959.74	−2292.20	−1956.32
No of obs	377	377	321	377	377	321	377	321
No group ID	75	75	71	75	75	71	75	71
Var: ID (SD)	0.16 (0.40)	0.16 (0.40)	0.17 (0.40)	0.17 (0.41)	568.4 (23.84)	375.9 (19.39)	554.2 (23.54)	355.4 (18.85)
Var: Res σ^2	0.61	0.61	0.63	0.61	11220.1	11720.3	10941.9	11729.0
Marginal R^2/conditional R^2	0.026/0.230	0.026/0.230	0.017/0.225	0.006/0.217	0.004/0.052	0.004/0.035	0.029/0.076	0.004/0.033
ICC	0.21	0.21	0.21	0.21	0.05	0.03	0.05	0.03

Note. Unstandardised regression coefficients, standard errors in parentheses.

^a Generalised linear mixed model fit by maximum likelihood, Laplace Approximation.

^b Generalised linear mixed model fit by maximum likelihood. cwc = person-mean centred predictor, cmc = centred person mean. *p < .05, **p < .01, ***p < .001.

Table 5. Sleep quality and sleep latency predicted by app time and count.

	Sleep quality				Sleep latency
	App time		App count		App time		App count
	7 pm to sleep	2 hours pre sleep	7 pm to sleep	7 pm to sleep	7 pm to sleep	2 hours pre sleep	7 pm to sleep	7 pm to sleep
Intercept	2.95 (0.69)***	2.94 (0.08)***	2.95 (0.07)***	2.94 (0.08)***	22.48 (2.11)***	22.93 (2.31)***	22.55 (2.08)***	22.94 (2.31)***
App usage cwc	−0.0004 (0.0007)	−0.0014 (0.0028)	−0.005 (0.004)	−0.0001 (0.014)	0.01 (0.02)	0.07 (0.07)	−0.03 (0.09)	−0.20 (0.33)
App usage cmc	0.0003 (0.001)	0.0059 (0.0052)	−0.007 (0.004)	0.005 (0.027)	0.03 (0.04)	−0.08 (0.15)	0.21 (0.12)	−0.54 (0.83)
Model fit
AIC	1060.27	922.05	1050.23	917.01	3468.04	2934.12	3440.86	2927.96
BIC	1079.84	940.83	1069.80	935.79	3476.42	2952.89	3460.43	2946.73
Log likelihood	−525.13	−456.02	−520.12	−453.50	−1719.23	−1462.06	−1715.43	−1458.98
No of obs	370	316	370	316	370	317	370	316
No group ID	75	71	75	71	75	71	75	71
Var: ID (SD)	0.17 (0.42)	0.20 (0.45)	0.16 (0.40)	0.20 (0.45)	219.4 (14.81)	251.9 (15.87)	211.4 (14.54)	251.4 (15.86)
Var: Res σ^2	0.81	0.84	0.81	0.85	501.75	472.82	501.50	473.83
Marginal R^2/conditional R^2	0.001/0.177	0.008/0.198	0.019/0.179	0.000/0.193	0.005/0.308	0.004/0.350	0.018/0.309	0.004/0.349
ICC	0.18	0.19	0.16	0.19	0.30	0.35	0.30	0.35

Note. Generalised linear mixed model fit by maximum likelihood. Unstandardised regression coefficients, standard errors in parentheses. Model 1 = grand-mean centred predictor, Model 2 = person-mean centred predictor and centred person mean. *p < .05, **p < .01, ***p < .001.

Table 6. Time spent per app type in the evening and pre-bedtime predicting sleep.

	Awakenings^a		Duration^b		Quality		Latency^b
	Evening	Pre-bed	Evening	Pre-bed	Evening	Pre-bed	Evening	Pre-bed
Intercept	1.38 (0.41)	1.18 (0.41)	494.81 (10.88)	473.36 (11.4)	3.06 (0.10)	2.97 (0.11)	20.85 (3.11)	20.16 (3.12)
Top 100	0.99* (0.00)	0.99 (0.01)	−0.19 (0.24)	−0.49 (0.59)	−0.00 (0.00)	0.00 (0.01)	0.02 (0.06)	−0.05 (0.14)
Non-top 100	1.00 (0.00)	0.96 (0.02)	−0.18 (0.35)	−3.13 (1.81)	−0.00 (0.00)	−0.02 (0.02)	0.20* (0.09)	0.29 (0.40)
Home/lock	1.00 (0.01)	1.11*** (0.03)	−1.40 (0.97)	3.19 (3.96)	−0.01 (0.01)	−0.01 (0.04)	0.03 (0.23)	0.21 (0.85)
Video	1.00 (0.00)	1.00 (0.00)	0.48* (0.24)	0.69 (0.43)	0.00 (0.00)	−0.00 (0.00)	0.07 (0.06)	−0.02 (0.09)
Social media: PF	1.00 (0.00)	0.99 (0.01)	−0.25 (0.22)	0.49 (0.64)	0.00 (0.00)	0.01* (0.01)	−0.05 (0.05)	−0.08 (0.14)
Social media: IC	1.00 (0.00)	1.00 (0.01)	−0.16 (0.20)	−0.27 (0.71)	−0.00 (0.00)	−0.01 (0.01)	0.03 (0.05)	−0.26 (0.15)
Gaming	1.00 (0.01)	0.99 (0.01)	0.19 (0.76)	2.47 (1.24)	0.01 (0.01)	0.02 (0.01)	−0.04 (0.19)	0.04 (0.32)
Work/study	0.99 (0.01)	0.97 0.03	0.54 (0.89)	−0.57 (2.53)	0.00 (0.01)	−0.07** (0.02)	−0.17 (0.21)	3.22*** (0.51)
Music/audio	1.00 (0.00)	0.99 (0.01)	−0.57 (0.37)	−2.07 (1.13)	0.00 (0.00)	−0.01 (0.01)	0.05 (0.09)	0.72** (0.23)
Relaxation	0.98 (0.01)	0.99 (0.03)	0.92 (0.73)	1.88 (1.90)	0.02** (0.01)	0.04* (0.02)	0.03 (0.17)	0.13 (0.41)
Model fit
AIC	1130.9	953.6	4604.4	3907.3	1122.6	963.6	3473.7	2904.9
BIC	1178.1	998.8	4655.5	3956.3	1173.4	1012.4	3524.5	2953.7
Log likelihood	−553.43	−464.8	−2289.2	−1940.6	−548.3	−468.8	−1723.8	−1439.4
No of obs	377	321	377	321	370	316	370	316
No group ID	75	71	75	71	75	71	75	71
Var: residual σ^2	0.61	0.63	11149.06	11625.09	0.81	0.80	507.31	400.42
Marginal/conditional R^2	.067/.265	0.083/0.278	0.045/0.076	0.053/0.064	0.069/0.181	0.087/0.246	0.027/0.305	0.107/0.470
ICC	0.21	0.21	0.03	0.01	0.12	0.17	0.29	0.41

^a Generalised linear mixed model fit by maximum likelihood (Laplace approximation; Family: poisson, log); coefficients are odds ratios.

^b Linear mixed model fit by REML^,.t-tests use Satterthwaite's method, lmerModLmerTest. Uncl = unclassified; *p < 0.05, **p < 0.01, ***p < 0.001.

Data availability

The data that support the findings of this study are openly available at https://osf.io/scvta/?view_only=3ae25ae4dd024248a4f9efe31ec80aba.