8  Appendices

8.1 Appendix A — Supplementary Material for Chapter 3

8.1.1 Participants

Of the 3,065 participants that originally signed up for participation, 1,449 (47.3%) responded to the onboarding invitation, provided informed consent and began receiving questionnaires. After removing participants that did not meet our eligibility criteria or who completed too few ESM questionnaires (pre-registered minimum of 8), the data of 1,315 participants were retained for formal analyses.

Persons willing to sign up for our study were first directed to a webpage that provided them with a study overview, and then were directed to complete an online form that verified they met our eligibility criteria: that they were aged 18 years or over, owned a smartphone, and were willing to complete the two-week ESM phase. After signing up, participants immediately received an automated email that provided full study information. Up to one week afterwards, participants received an invitation email which contained a link that directed them to our onboarding website. Here, they once more found all study information (presented both in print and in a video format) as well as detailed instructions to download and install one or two smartphone apps. Participants could also opt to have their computer monitored (e.g., screentime), but this data is not the focus of the current study, so it is not discussed.

8.1.2 Onboarding and ESM

In the experience sampling app, participants provided informed consent and received an intake questionnaire that measured their age, education status, and occupation, as well as other factors not of focal interest to the present study (a full method is available at our OSF page). Immediately after, participants began receiving ESM questionnaires according to a mixed-sampling scheme that contained semi-random and fixed elements. Following the initial notification, each ESM questionnaire remained available to the participant for 45 min. A reminder was sent out if the questionnaire had not been responded to 30 min after the initial notification. In total, participants received ESM questionnaires for 14 full days. After completing the ESM phase, participants received an outtake questionnaire that also measured factors that are not of direct interest to the present study (e.g., Trait Anxiety).

The first questionnaire of the day contained twenty-six items, the last questionnaire thirty-four, and the four in-between twenty-four items. Each questionnaire was identical in content, apart from the question’s stem. Also, each questionnaire was presented to participants in a random order, to counter order effects.

8.1.3 Data preparation

Raw smartphone trace data was processed using Python packages pandas and Dask, and afterwards merged with the ESM data. This combined dataset enabled calculation of the three behavioral features. Additional data preparation (e.g., centering, lagging, etc.), as well as formal analyses, were conducted in R, using the packages dplyr, esmpack, misty (for grand-mean centering), and lavaan (for CFA and multilevel SEM models).

We first examined data for outliers and data errors, and whether model assumptions were met for later analyses (e.g., linearity). Then, select variables (e.g., mental fatigue) were lagged to the next dataset row to enable modeling across adjoining timepoints. For each participant, variables were only lagged when the next dataset row also represented the next received questionnaire for that person (thus, data from questionnaire 4, for instance, would only be able to predict data from questionnaire 5 for that same participant), and belonged to the same day (thus, data from the last questionnaire of the day could not predict data from the first questionnaire of the next day, an important analytic decision given the extensive temporal gap that would otherwise result between these questionnaires).

Following, level 2 versions of select level 1 variables (e.g., mental fatigue) were created to enable later analyses (i.e., the level 2 variable represented the average of level 1 variable values for a given participant). Level 1 (observation level) variables were then person-mean centered, to ensure model coefficients (in our Multilevel SEM) characterized the pure within-person relationship (i.e., with between-person variance removed). The centered behavioral features were additionally standardized at the within-person level to ensure model variables shared similar scales. Level 2 (person-level) variables were grand-mean centered to facilitate intercept interpretation. However, level 2 variables were only needed to saturate the level 2 model and enable multilevel SEM functionality with lavaan.

All code used in processing the data can be found on osf, under Data and Code / code. Please refer to the folder’s README.txt for further information on which file contains which data processing steps.

The Android tracking app encountered issues on certain devices (Xiaomi, Huawei and Samsung). The main issue was the smartphone OS restricting access to different tracking data after a certain period of time. In response, we set up a monitoring dashboard to look for instances of potential data loss and could then troubleshoot with our participants. By providing device-specific instructions (see https://dontkillmyapp.com/) we could often pinpoint the cause and restore access.

8.1.4 Original Models

To keep the results section concise, only the results of the finalized models were discussed. The original models (with poor model fit), and the step-by-step procedure is detailed on osf. If you would like to run through the code itself, you can find an R Markdown file under Data and Code / code / 06_structural_models.Rmd. Alternatively, you can find a rendered version of this markdown file with the output of these models under: Data and Code / code / rendered_notebooks / 06_structural_models.html. This html page takes you through all the data analysis steps, informing about all analytical decisions taken.

8.2 Appendix B — Supplementary Material for Chapter 4

8.2.1 Mediation Models Output

Table 8.1: Full mediation model output.

Outcome	Predictor	B	SE	LL	UL	p	β
chat duration
rushed	chat duration (c)	−.001	.007	−.014	.013	.916	−.000
rushed	work activity	.471	.045	.383	.558	.000	.138
rushed	morning	−.016	.019	−.054	.021	.398	−.004
rushed	evening	−.005	.019	−.041	.032	.795	−.001
juggling load	chat duration (a)	.025	.003	.019	.032	.000	.037
juggling load	work activity	.594	.016	.563	.625	.000	.431
juggling load	morning	−.033	.008	−.048	−.017	.000	−.022
juggling load	evening	.008	.008	−.009	.024	.362	.005
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.021	.003	.016	.027	.000	.013
total	c+(a*b)	.021	.007	.007	.034	.003	.012
chat frequency
rushed	chat frequency (c)	−.003	.007	−.016	.011	.700	−.002
rushed	work activity	.471	.045	.383	.558	.000	.138
rushed	morning	−.017	.019	−.055	.021	.383	−.005
rushed	evening	−.005	.019	−.041	.032	.804	−.001
juggling load	chat frequency (a)	.023	.003	.016	.029	.000	.033
juggling load	work activity	.594	.016	.563	.625	.000	.431
juggling load	morning	−.031	.008	−.047	−.016	.000	−.021
juggling load	evening	.008	.008	−.008	.024	.336	.006
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.019	.003	.014	.024	.000	.011
total	c+(a*b)	.016	.007	.002	.030	.024	.010
chat notifications
rushed	chat notifications (c)	.014	.007	−.000	.029	.053	.008
rushed	work activity	.468	.045	.381	.556	.000	.137
rushed	morning	−.011	.019	−.049	.027	.565	−.003
rushed	evening	−.006	.019	−.043	.030	.738	−.002
juggling load	chat notifications (a)	.028	.003	.022	.034	.000	.041
juggling load	work activity	.591	.016	.559	.622	.000	.428
juggling load	morning	−.028	.008	−.044	−.012	.000	−.019
juggling load	evening	.008	.008	−.008	.024	.336	.006
rushed	juggling load (b)	.838	.035	.770	.906	.000	.339
ab	a*b	.023	.003	.018	.029	.000	.014
total	c+(a*b)	.038	.008	.023	.053	.000	.022
chat fragmentation
rushed	chat fragmentation (c)	.003	.006	−.010	.015	.686	.002
rushed	work activity	.470	.045	.383	.558	.000	.138
rushed	morning	−.016	.019	−.053	.022	.418	−.004
rushed	evening	−.005	.019	−.042	.031	.778	−.002
juggling load	chat fragmentation (a)	.026	.003	.019	.032	.000	.038
juggling load	work activity	.594	.016	.562	.625	.000	.430
juggling load	morning	−.033	.008	−.049	−.017	.000	−.022
juggling load	evening	.007	.008	−.009	.023	.387	.005
rushed	juggling load (b)	.839	.035	.770	.907	.000	.339
ab	a*b	.022	.003	.016	.027	.000	.013
total	c+(a*b)	.024	.007	.011	.037	.000	.014
social duration
rushed	social duration (c)	.003	.007	−.011	.016	.713	.001
rushed	work activity	.471	.045	.383	.558	.000	.138
rushed	morning	−.016	.019	−.053	.022	.406	−.004
rushed	evening	−.005	.019	−.042	.031	.777	−.002
juggling load	social duration (a)	.007	.003	.001	.014	.024	.010
juggling load	work activity	.597	.016	.565	.628	.000	.433
juggling load	morning	−.038	.008	−.053	−.022	.000	−.026
juggling load	evening	.010	.008	−.007	.026	.244	.007
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.006	.003	.001	.011	.024	.003
total	c+(a*b)	.009	.007	−.006	.023	.245	.005
social frequency
rushed	social frequency (c)	−.007	.007	−.021	.006	.295	−.004
rushed	work activity	.470	.045	.383	.558	.000	.138
rushed	morning	−.017	.019	−.055	.021	.375	−.005
rushed	evening	−.004	.019	−.040	.033	.838	−.001
juggling load	social frequency (a)	.009	.003	.003	.016	.005	.013
juggling load	work activity	.596	.016	.565	.628	.000	.432
juggling load	morning	−.037	.008	−.053	−.021	.000	−.025
juggling load	evening	.009	.008	−.007	.025	.271	.006
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.008	.003	.002	.013	.005	.004
total	c+(a*b)	.001	.007	−.014	.015	.937	.000
social notifications
rushed	social notifications (c)	−.005	.008	−.021	.011	.545	−.002
rushed	work activity	.471	.045	.384	.558	.000	.138
rushed	morning	−.017	.019	−.054	.021	.382	−.005
rushed	evening	−.004	.019	−.041	.032	.816	−.001
juggling load	social notifications (a)	.006	.004	−.001	.014	.093	.007
juggling load	work activity	.596	.016	.565	.627	.000	.432
juggling load	morning	−.037	.008	−.053	−.021	.000	−.025
juggling load	evening	.010	.008	−.006	.026	.237	.007
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.005	.003	−.001	.012	.093	.002
total	c+(a*b)	.000	.009	−.017	.017	.971	.000
social fragmentation
rushed	social fragmentation (c)	.004	.007	−.010	.017	.574	.002
rushed	work activity	.471	.045	.383	.558	.000	.138
rushed	morning	−.016	.019	−.053	.022	.412	−.004
rushed	evening	−.005	.019	−.042	.031	.770	−.002
juggling load	social fragmentation (a)	.011	.003	.005	.018	.001	.015
juggling load	work activity	.596	.016	.565	.628	.000	.432
juggling load	morning	−.037	.008	−.053	−.021	.000	−.025
juggling load	evening	.009	.008	−.007	.025	.271	.006
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.009	.003	.004	.015	.001	.005
total	c+(a*b)	.013	.007	−.001	.027	.068	.007
email duration
rushed	email duration (c)	.018	.006	.006	.030	.004	.010
rushed	work activity	.469	.045	.382	.556	.000	.138
rushed	morning	−.014	.019	−.051	.024	.467	−.004
rushed	evening	−.006	.019	−.042	.031	.754	−.002
juggling load	email duration (a)	.016	.003	.010	.023	.000	.023
juggling load	work activity	.595	.016	.563	.626	.000	.431
juggling load	morning	−.036	.008	−.052	−.020	.000	−.025
juggling load	evening	.010	.008	−.007	.026	.239	.007
rushed	juggling load (b)	.838	.035	.770	.906	.000	.339
ab	a*b	.014	.003	.008	.019	.000	.008
total	c+(a*b)	.031	.007	.019	.044	.000	.018
email frequency
rushed	email frequency (c)	.015	.006	.002	.028	.021	.009
rushed	work activity	.469	.045	.382	.557	.000	.138
rushed	morning	−.013	.019	−.051	.024	.486	−.004
rushed	evening	−.006	.019	−.042	.031	.758	−.002
juggling load	email frequency (a)	.019	.003	.013	.025	.000	.027
juggling load	work activity	.594	.016	.563	.625	.000	.431
juggling load	morning	−.034	.008	−.050	−.019	.000	−.024
juggling load	evening	.010	.008	−.007	.026	.249	.007
rushed	juggling load (b)	.838	.035	.770	.906	.000	.339
ab	a*b	.016	.003	.011	.021	.000	.009
total	c+(a*b)	.031	.007	.017	.045	.000	.018
email notifications
rushed	email notifications (c)	.021	.009	.003	.039	.025	.010
rushed	work activity	.466	.045	.379	.554	.000	.137
rushed	morning	−.013	.019	−.051	.024	.488	−.004
rushed	evening	−.004	.019	−.041	.032	.827	−.001
juggling load	email notifications (a)	.030	.004	.021	.038	.000	.035
juggling load	work activity	.589	.016	.558	.621	.000	.427
juggling load	morning	−.034	.008	−.050	−.018	.000	−.023
juggling load	evening	.012	.008	−.004	.028	.155	.008
rushed	juggling load (b)	.838	.035	.770	.906	.000	.339
ab	a*b	.025	.004	.018	.032	.000	.012
total	c+(a*b)	.046	.010	.026	.065	.000	.022
email fragmentation
rushed	email fragmentation (c)	.018	.006	.006	.030	.003	.010
rushed	work activity	.469	.045	.382	.556	.000	.137
rushed	morning	−.014	.019	−.051	.024	.480	−.004
rushed	evening	−.006	.019	−.043	.030	.741	−.002
juggling load	email fragmentation (a)	.018	.003	.011	.024	.000	.025
juggling load	work activity	.594	.016	.563	.625	.000	.431
juggling load	morning	−.035	.008	−.051	−.020	.000	−.024
juggling load	evening	.009	.008	−.007	.026	.258	.007
rushed	juggling load (b)	.838	.035	.770	.906	.000	.339
ab	a*b	.015	.003	.009	.020	.000	.009
total	c+(a*b)	.033	.006	.020	.045	.000	.019
w. comm. duration
rushed	w. comm. duration (c)	.010	.012	−.013	.033	.379	.003
rushed	work activity	.470	.045	.383	.557	.000	.138
rushed	morning	−.016	.019	−.053	.022	.406	−.004
rushed	evening	−.005	.019	−.041	.032	.792	−.001
juggling load	w. comm. duration (a)	.022	.006	.010	.034	.000	.017
juggling load	work activity	.595	.016	.563	.626	.000	.431
juggling load	morning	−.038	.008	−.054	−.022	.000	−.026
juggling load	evening	.011	.008	−.006	.027	.201	.007
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.019	.005	.009	.029	.000	.006
total	c+(a*b)	.029	.012	.005	.053	.017	.009
w. comm. frequency
rushed	w. comm. frequency (c)	.008	.012	−.017	.032	.530	.002
rushed	work activity	.470	.045	.383	.558	.000	.138
rushed	morning	−.016	.019	−.053	.022	.406	−.004
rushed	evening	−.005	.019	−.041	.032	.793	−.001
juggling load	w. comm. frequency (a)	.023	.006	.010	.035	.000	.017
juggling load	work activity	.594	.016	.563	.626	.000	.431
juggling load	morning	−.037	.008	−.053	−.022	.000	−.026
juggling load	evening	.011	.008	−.006	.027	.198	.008
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.019	.005	.009	.029	.000	.006
total	c+(a*b)	.027	.013	.001	.053	.044	.008
w. comm. notifications
rushed	w. comm. notifications (c)	.038	.017	.005	.071	.026	.010
rushed	work activity	.467	.045	.380	.555	.000	.137
rushed	morning	−.015	.019	−.053	.022	.423	−.004
rushed	evening	−.004	.019	−.040	.033	.833	−.001
juggling load	w. comm. notifications (a)	.030	.008	.015	.046	.000	.020
juggling load	work activity	.593	.016	.562	.625	.000	.430
juggling load	morning	−.037	.008	−.053	−.022	.000	−.026
juggling load	evening	.011	.008	−.005	.028	.169	.008
rushed	juggling load (b)	.838	.035	.770	.906	.000	.339
ab	a*b	.025	.007	.012	.038	.000	.007
total	c+(a*b)	.063	.018	.028	.098	.000	.017
w. comm. fragmentation
rushed	w. comm. fragmentation (c)	.010	.011	−.013	.032	.407	.003
rushed	work activity	.470	.045	.383	.557	.000	.138
rushed	morning	−.016	.019	−.053	.022	.405	−.004
rushed	evening	−.005	.019	−.041	.032	.791	−.001
juggling load	w. comm. fragmentation (a)	.024	.006	.012	.035	.000	.018
juggling load	work activity	.595	.016	.563	.626	.000	.431
juggling load	morning	−.038	.008	−.053	−.022	.000	−.026
juggling load	evening	.011	.008	−.006	.027	.204	.007
rushed	juggling load (b)	.839	.035	.771	.907	.000	.339
ab	a*b	.020	.005	.010	.030	.000	.006
total	c+(a*b)	.029	.012	.006	.053	.013	.009
Average
rushed	juggling load	.839	.035	.770	.907	.000	.339
juggling load	work activity	.594	.016	.563	.626	.000	.431
rushed	work activity	.470	.045	.382	.557	.000	.138
rushed	morning	−.015	.019	−.053	.022	.431	−.004
rushed	evening	−.005	.019	−.042	.032	.788	−.001
juggling load	morning	−.035	.008	−.051	−.019	.000	−.024
juggling load	evening	.010	.008	−.007	.026	.257	.007

(16 models: four features across four app categories)

8.2.2 Person-Specific Output

Table 8.2: Person-specific effects: Feeling Rushed.

	Mean	SD	2.5%	97.5%	neg (sig.) %	neg (non-sig.) %	non-existent %	pos (non-sig.) %	pos (sig.) %
Chat
chat duration	.03	.09	−.16	.21	0.26	14.99	67.70	17.05	0.00
chat frequency	.02	.10	−.17	.22	0.65	18.99	60.59	19.25	0.52
chat notifications	.05	.11	−.17	.27	0.65	27.65	47.67	22.61	1.42
chat fragmentation	.03	.07	−.11	.17	0.00	7.75	82.56	9.69	0.00
Social
social duration	.01	.09	−.16	.18	0.13	9.17	81.40	9.04	0.26
social frequency	.00	.09	−.16	.17	0.39	9.69	79.07	10.72	0.13
social notifications	.01	.08	−.15	.16	0.26	7.49	85.01	7.24	0.00
social fragmentation	.02	.07	−.13	.16	0.13	5.94	87.21	6.59	0.13
Email
email duration	.04	.06	−.09	.16	0.00	3.62	88.63	7.75	0.00
email frequency	.04	.08	−.13	.20	0.00	16.93	65.63	16.80	0.65
email notifications	.06	.14	−.21	.33	0.65	23.77	54.78	18.99	1.81
email fragmentation	.04	.05	−.06	.14	0.00	1.16	94.06	4.65	0.13
Work communication
w. comm. duration	.03	.05	−.07	.14	0.00	1.03	96.25	2.71	0.00
w. comm. frequency	.03	.09	−.14	.21	0.00	3.36	92.76	3.88	0.00
w. comm. notifications	.08	.15	−.21	.37	0.39	6.98	86.05	5.68	0.90
w. comm. fragmentation	.03	.04	−.04	.11	0.00	0.00	98.06	1.94	0.00

(parameter estimates, heterogeneity interval, and random effects distribution)

Table 8.3: Person-specific effects: Juggling Load.

	Mean	SD	2.5%	97.5%	neg (sig.) %	neg (non-sig.) %	non-existent %	pos (non-sig.) %	pos (sig.) %
Chat
chat duration	.03	.05	−.06	.12	0.13	4.91	84.11	8.79	2.07
chat frequency	.03	.05	−.07	.12	0.39	7.49	80.49	9.17	2.45
chat notifications	.03	.05	−.07	.13	0.39	10.98	75.71	10.98	1.94
chat fragmentation	.03	.05	−.07	.13	0.00	6.07	80.62	11.50	1.81
Social
social duration	.01	.03	−.06	.08	0.26	2.33	93.28	3.62	0.52
social frequency	.01	.04	−.07	.09	0.13	3.10	90.05	6.20	0.52
social notifications	.01	.03	−.06	.08	0.00	1.55	94.44	3.62	0.39
social fragmentation	.01	.04	−.06	.09	0.26	2.07	92.89	3.75	1.03
Email
email duration	.02	.05	−.08	.11	0.00	2.07	87.21	9.43	1.29
email frequency	.02	.05	−.08	.12	0.13	4.39	83.33	10.21	1.94
email notifications	.03	.06	−.09	.15	0.52	19.51	63.31	12.66	4.01
email fragmentation	.02	.05	−.08	.12	0.00	2.33	86.18	10.47	1.03
Work communication
w. comm. duration	.02	.05	−.07	.11	0.00	0.13	98.97	0.90	0.00
w. comm. frequency	.02	.05	−.07	.12	0.00	0.78	96.51	2.58	0.13
w. comm. notifications	.03	.07	−.10	.16	0.52	4.91	88.89	4.52	1.16
w. comm. fragmentation	.03	.04	−.06	.11	0.00	0.00	99.22	0.78	0.00

(parameter estimates, heterogeneity interval, and random effects distribution)

Table 8.4: Cross-level interactions: Fixed effects estimates.

	Rushed — Chat Notifications					Rushed — Email Notifications
	Model 1	Model 2	Model 3	Model 4		Model 5	Model 6	Model 7	Model 8
Intercept	3.504***	2.780***	2.897***	2.725***		3.504***	2.780***	2.897***	2.725***
Chat / Email	0.153***	0.090***	0.084***	0.044		0.145***	0.152***	0.112***	0.095**
Age	−0.015***					−0.015***
Chat/Email × Age	−0.002*					−0.000
Gender		0.271***					0.271***
Chat/Email × Gender		−0.000					−0.023
Parenthood			0.074					0.074
Chat/Email × Parenthood			0.011					0.046*
Segmentation				0.093*					0.093*
Chat/Email × Segmentation				0.020*					0.021
Participants	772	763	772	622		772	763	772	622
Observations	42,761	42,355	42,761	35,051		42,761	42,355	42,761	35,051
Marginal R² / Conditional R²	0.012 / 0.418	0.009 / 0.419	0.003 / 0.418	0.006 / 0.393		0.013 / 0.420	0.011 / 0.421	0.005 / 0.420	0.008 / 0.395
	Juggling Load — Chat Notifications					Juggling Load — Email Notifications
	Model 9	Model 10	Model 11	Model 12		Model 13	Model 14	Model 15	Model 16
Intercept	0.559***	0.493***	0.417***	0.426***		0.559***	0.492***	0.416***	0.427***
Chat / Email	0.097***	0.065***	0.058***	0.033**		0.109***	0.094***	0.081***	0.041*
Age	−0.001					−0.001
Chat/Email × Age	−0.001*					−0.001
Gender		0.032					0.033
Chat/Email × Gender		−0.004					−0.005
Parenthood			0.167***					0.167***
Chat/Email × Parenthood			0.008					0.016
Segmentation				0.044*					0.044***
Chat/Email × Segmentation				0.012*					0.022***
Participants	772	763	772	622		772	763	772	622
Observations	42,764	42,358	42,764	35,053		42,764	42,358	42,764	35,053
Marginal R² / Conditional R²	0.009 / 0.208	0.009 / 0.207	0.023 / 0.207	0.012 / 0.187		0.012 / 0.212	0.012 / 0.212	0.026 / 0.212	0.016 / 0.193

* p < .05 ** p < .01 *** p < .001. Cross-level interactions used unstandardized dependent variables (rushed, juggling load) and within-person standardized smartphone measures (chat and email notifications). Our OSF page includes the full model output.

8.3 Appendix C — Supplementary Material for Chapter 6

Step-by-Step Guide for Collecting Log Data from iOS, macOS, and iPadOS

8.3.1 Preparation

Device Requirements:

• iOS (minimum iOS 26) and iPadOS (minimum iPadOS 26) data can only be captured if the respondent also has a Mac (minimum MacOS 26) linked to the same Apple ID. The device of the Researcher can be a Mac or Windows.

Tool Requirements:

• You’ll need an internet connection to access the marimo notebook OR install docker and download the tool to parse the files locally.

To use the tool:

• Navigate to the publicly available Marimo Notebook: https://molab.marimo.io/notebooks/nb_iVkKovhPYZBGnMVggpXWSL/app
  • Drag and drop the collected files of the participants

OR

• Run a local instance of the Marimo Notebook through docker:
  • Install Docker desktop
  • Download the tool from the Project OSF: https://osf.io/6puxm/?view_only=b44063a220204bdca33b964e2e8a4ebd
  • Run from the Root Repo:

docker load -i ios-biome-screen-time.tar
docker run -p 8080:8080 kvgaever/ios-biome-screen-time:latest

• Go to http://localhost:8080
• Drag and drop the collected files of the participants

8.3.2 Step 1. Set-up — On Device

Ensure all devices use the same Apple ID. Enable Screen Time Synchronization:

On Mac:

1. Open System Preferences.
2. Click on Screen Time.
3. Scroll down and toggle Share Across Devices to ON.

On iPhone:

1. Open Settings.
2. Tap on Screen Time.
3. Scroll down and toggle Share Across Devices to ON.

On iPad:

1. Open Settings.
2. Tap on Screen Time.
3. Scroll down and toggle Share Across Devices to ON.

On Apple Watch: No extra settings need to be changed.

Once synchronization is enabled, the Mac will store a file containing log data from all the devices that are synchronized.

8.3.3 Step 2. Data Collection

Wait for the desired logging period (e.g., 14 days) to collect comprehensive data. The database will store use data of the last 4 weeks or since synchronization was enabled, whichever is shorter.

8.3.4 Step 3. Data Retrieval — On Device and Transfer

Locate Sync.db:

1. Open Finder on the Mac.
2. From the top menu, select Go > Go to Folder…
3. Enter the following path: ~/Library/Biome/sync/Sync.db

Locate and compress the biome files:

1. Open Finder on the Mac.
2. From the top menu, select Go > Go to Folder…
3. Enter the following path: ~/Library/Biome/streams/restricted/
4. Locate the folder named App.InFocus.
5. Zip the App.InFocus folder.

Retrieve the folder and the Sync.db file: Let the respondents donate the compressed folder and the Sync.db file using a data donation method.

8.3.5 Step 4. Data Parsing — On Device of Researcher

Open the Tool & drag and drop the sync.db file and the compressed folder to the right location and download the dataset in a usable format.