Smartphone keyboard interaction monitoring as an unobtrusive method to approximate rest-activity patterns: Experience Sampling Study Investigating Interindividual and Metric-Specific Variations

Background: Sleep is an important determinant of our health and behavior during the wake phase. To facilitate the monitoring of sleep over prolonged time and across a large number of persons, novel research methods for field assessments are required. The ubiquity of smartphones offers new avenues to detect rest-activity patterns in everyday-life situations in a non-invasive and inexpensive manner and at a large scale. Recent studies provided evidence for the potential of smartphone interactions monitoring as a novel tracking method to approximate rest-activity patterns based on the timing of periods of smartphone activity and inactivity throughout the 24-h day. These findings require further replication and more detailed insights in interindividual variations in the associations and deviations with commonly used metrics to monitor rest-activity patterns in everyday life. Objective: The present study aimed to replicate and expand on earlier findings regarding the associations and deviations between smartphone keyboard-derived and self-reported estimates of the timing of the onset of the rest and active periods and the duration of the rest period. Moreover, we aimed to quantify interindividual variations in the associations and time differences between the two assessment modalities, and investigate to what extent general sleep quality, chronotype and trait self-control moderate these associations and deviations. Methods: Students were recruited to participate in a 7-day experience sampling study with parallel smartphone keyboard interactions monitoring. Multilevel modelling was employed to analyze the data. Results: In total, 157 students participated in the study, with an overall response rate for the diaries of 88.9%. Results revealed moderate to strong relations between the keyboard-derived and self-reported estimates, with stronger associations for the timing-related estimates (beta ranging from .61 to .78) than the duration-related estimates (beta=.51 and beta=.52). The relational strength between the time-related estimates was lower, but did not significantly differ for the duration-related estimates, among students experiencing more disturbances in general sleep quality. Time differences between the keyboard-derived and self-reported estimates were on average small ( Conclusions: We were able to replicate earlier findings regarding the potential of smartphone keyboard interaction monitoring as a method to approximate rest-activity patterns. Complementing earlier research findings, the current study showed that the behavioral proxies obtained from smartphone interactions might be less powerful among students who experienced disturbances in their general sleep quality. The generalization and underlying process of these findings remain to be further investigated

Behavioural biometrics: using smartphone keyboard activity as a proxy for rest-activity patterns

Rest–activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long‐term monitoring of rest–activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest–activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self‐reports within healthy participants. First‐year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user‐reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest–activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long‐term monitoring of (disturbances to) rest–activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep‐related problems, or in populations for whom disturbances in rest–activity patterns are secondary complaints, such as neurological disorders

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed