Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study

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Background Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. Objective This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). Methods A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the “Staff Hours” app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data’s nested structure. Results Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night’s midpoint of sleep (β=–0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA’s activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. Conclusions This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies.

Section: Discussionsupporting

confidence: 94%

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities

Chen,

Lin,

Chang

et al. 2024

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“…We referred to a recent study [ 16 ] to guide our determination of sample size. That study used a similar methodology with 66 participants, including 33 patients with insomnia and 33 healthy controls, over a minimum duration of 4 weeks.…”

Section: Methodsmentioning

confidence: 99%

“…That study used a similar methodology with 66 participants, including 33 patients with insomnia and 33 healthy controls, over a minimum duration of 4 weeks. This previous study [ 16 ] generated a comprehensive data set encompassing 2097 person-days and aimed to investigate the correlations between specific indicators and self-reported depressive symptoms, as well as sleep quality. It also sought to compare app-defined circadian rhythm and sleep indicators with actigraphy-derived measures.…”

Section: Methodsmentioning

confidence: 99%

“…Surpassing the participant number [ 16 ] in the study we referenced, our larger sample size granted us the necessary statistical power to delve into the intricate relationships between multiple variables within our analytical framework. Additionally, the varied composition of our participant groups significantly strengthened the validity and applicability of our results.…”

Section: Methodsmentioning

confidence: 99%

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Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study

Chuang,

Lin,

Lee

et al. 2024

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Background This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. Objective We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. Methods We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. Results Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. Conclusions Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

Rest-Activity Rhythm Differences in Acute Rehabilitation Between Poststroke Patients and Non–Brain Disease Controls: Comparative Study

Liang,

Wu,

Lin

et al. 2024

Background Circadian rhythm disruptions are a common concern for poststroke patients undergoing rehabilitation and might negatively impact their functional outcomes. Objective Our research aimed to uncover unique patterns and disruptions specific to poststroke rehabilitation patients and identify potential differences in specific rest-activity rhythm indicators when compared to inpatient controls with non–brain-related lesions, such as patients with spinal cord injuries. Methods We obtained a 7-day recording with a wearable actigraphy device from 25 poststroke patients (n=9, 36% women; median age 56, IQR 46-71) and 25 age- and gender-matched inpatient control participants (n=15, 60% women; median age 57, IQR 46.5-68.5). To assess circadian rhythm, we used a nonparametric method to calculate key rest-activity rhythm indicators—relative amplitude, interdaily stability, and intradaily variability. Relative amplitude, quantifying rest-activity rhythm amplitude while considering daily variations and unbalanced amplitudes, was calculated as the ratio of the difference between the most active 10 continuous hours and the least active 5 continuous hours to the sum of these 10 and 5 continuous hours. We also examined the clinical correlations between rest-activity rhythm indicators and delirium screening tools, such as the 4 A’s Test and the Barthel Index, which assess delirium and activities of daily living. Results Patients who had a stroke had higher least active 5-hour values compared to the control group (median 4.29, IQR 2.88-6.49 vs median 1.84, IQR 0.67-4.34; P=.008). The most active 10-hour values showed no significant differences between the groups (stroke group: median 38.92, IQR 14.60-40.87; control group: median 31.18, IQR 18.02-46.84; P=.93). The stroke group presented a lower relative amplitude compared to the control group (median 0.74, IQR 0.57-0.85 vs median 0.88, IQR 0.71-0.96; P=.009). Further analysis revealed no significant differences in other rest-activity rhythm metrics between the two groups. Among the patients who had a stroke, a negative correlation was observed between the 4 A’s Test scores and relative amplitude (ρ=–0.41; P=.045). Across all participants, positive correlations emerged between the Barthel Index scores and both interdaily stability (ρ=0.34; P=.02) and the most active 10-hour value (ρ=0.42; P=.002). Conclusions This study highlights the relevance of circadian rhythm disruptions in poststroke rehabilitation and provides insights into potential diagnostic and prognostic implications for rest-activity rhythm indicators as digital biomarkers.