The Implication of Wearables and the Factors Affecting Their Usage among Recreationally Active People

Factors and Design Features Influencing the Continued Use of Wearable Devices

The initial healthy uptake of wearable devices is not necessarily accompanied by sustained or continued use. Accordingly, this study investigates the factors influencing the continuous use of wearable devices with a particular emphasis on design features. We complemented the expectation-confirmation model (ECM) theoretical foundation with various design features such as trust, readability, dialogue support, personalization, device battery, appeal, and social support. The study employs a simultaneous mixed method research design denoted as QUANT + qual. The quantitative analysis leverages partial least squares structural equation modeling (PLS-SEM) using survey data collected from wearable device users. The qualitative analysis complements the quantitative focus of the research by providing insights into the results obtained from the quantitative analysis. We found that subjects tend to use wearables daily (60%) or several times a week (33%), and 91% plan to use them even more. Subjects indicated multiple usages for wearables. Most subjects were using wearables for healthcare and wellness (61%) or sports and fitness (54%) and had smartwatches wearable type (74%). The model explains 24.1% (p < 0.01) of the variance of continued intention to use. As a theoretical contribution, the findings support using the ECM as a theoretical foundation for explaining the continued use of wearables. Partial least squares (PLS) and qualitative data analysis highlight the relative importance that wearable users place on perceived usefulness. Most notable are tracking functions and design features such as device battery, integration with other apps/devices, dialogue support, and appeal.

Upper extremity kinematics during walking gait changes through pregnancy

BACKGROUND

Thirty percent of adults in the United States use wearable fitness devices as of 2020 [1], such as fitness watches, to monitor and track health and physical activity parameters. Physical changes during pregnancy may impact wrist worn device accuracy. The arms may be needed as compensation during walking because thorax axial rotation may be inhibited by pelvic tilt during pregnancy [2].

METHODS

To examine arm motion changes, twenty-three pregnant women (28 ± 4 y) were tested in four-week intervals ( ± 2 weeks) at 18-, 22-, 26-, 30- and 34-weeks' gestation. Kinematic data were measured during self-selected speed walking. Segment angles and angular velocities were analyzed over time. Linear regressions were used to analyze the correlations between arm motion and the other kinematic variables.

RESULTS

Arm range of motion significantly increased (p = 0.006) over gestation, but leg, thorax, and pelvis range of motions did not significantly change. Arm range of motion was correlated with pelvis (r² =0.311, p = 0.001, β = 1.724) and leg (r² = 0.285, p = 0.004, β = 1.520) range of motion and gait velocity (r² =0.566, p = 0.001, β = 39.110). Arm velocities significantly increased (p < 0.012), as did leg velocities (p < 0.022) over gestation time, but thorax and pelvis rotational velocities did not significantly change over time. Arm velocity was correlated with leg velocity in both flexion (r² =0.598, p = 0.001, β = 1.61) and extension (r² =0.568, p = 0.001, β = 1.35).

SIGNIFICANCE

Arm swing increases over the course of gestation during walking, which does not follow the exact pattern of changes seen in the legs, thorax, and pelvis. These results show that a typical gait analysis of lower body motions may miss important biomechanical changes or compensations at different points over pregnancy. Future studies should examine why these changes may occur. Studies should also be conducted to see if arm changes impact outcome parameters from fitness watches and affect their validity as an exercise tracker during pregnancy.

Monitoring sleep using smartphone data in a population of college students

Sleep is fundamental to all health, especially mental health. Monitoring sleep is thus critical to delivering effective healthcare. However, measuring sleep in a scalable way remains a clinical challenge because wearable sleep-monitoring devices are not affordable or accessible to the majority of the population. However, as consumer devices like smartphones become increasingly powerful and accessible in the United States, monitoring sleep using smartphone patterns offers a feasible and scalable alternative to wearable devices. In this study, we analyze the sleep behavior of 67 college students with elevated levels of stress over 28 days. While using the open-source mindLAMP smartphone app to complete daily and weekly sleep and mental health surveys, these participants also passively collected phone sensor data. We used these passive sensor data streams to estimate sleep duration. These sensor-based sleep duration estimates, when averaged for each participant, were correlated with self-reported sleep duration (r = 0.83). We later constructed a simple predictive model using both sensor-based sleep duration estimates and surveys as predictor variables. This model demonstrated the ability to predict survey-reported Pittsburgh Sleep Quality Index (PSQI) scores within 1 point. Overall, our results suggest that smartphone-derived sleep duration estimates offer practical results for estimating sleep duration and can also serve useful functions in the process of digital phenotyping.

Impact of Activity Tracker Usage in Combination with a Physical Activity Intervention on Physical and Cognitive Parameters in Healthy Adults Aged 60+: A Randomized Controlled Trial

Regular physical activity (PA) is of central importance for healthy aging and has a well-known impact on helping older adults maintain their cognitive and physical health. Thus, we aimed to compare the effectiveness of two physical activity interventions primarily conducted at home (print-based or web-based vs. web-based plus the use of an activity tracker) on cognitive and physical health parameters in older adults. Data of participants (n = 551, 60–80 years) were analyzed after being randomly allocated to a waitlist control group (CG), a web-based or print-based intervention group (IG) or a web-based intervention group that also included the use of an activity tracker (AG). Measured parameters were grip strength, endurance (two-minute step test), gait speed (four-meter walk test), cognition (Simon task; balanced integration score (BIS), reaction time and accuracy) and physical self-concept (Physical Self-Description Questionnaire (PSDQ)). We found the highest effect sizes in all measured dimensions for AG (grip strength, endurance, gait speed, reaction time, physical self-concept), followed by IG (endurance, gait speed, reaction time, physical self-concept) and CG (endurance, gait speed, BIS). Findings suggest that a combined web-based and activity tracker intervention may improve physical functions, physical self-concept, and cognition in community-dwelling older adults.

Distinct physical activity and sedentary behavior trajectories in older adults during participation in a physical activity intervention: a latent class growth analysis

BACKGROUND

This study aimed to identify latent moderate-to-vigorous intensity physical activity (MVPA) and sedentary behavior (SB) trajectories in older adults participating in a randomized intervention trial and to explore associations with baseline social-cognitive predictors.

METHODS

Data were assessed at baseline (T0, participants were inactive or had recently become active), after a ten-week physical activity intervention (T1), and a second 24-week intervention phase (T2). Latent class growth analysis was used on accelerometer-assessed weekly MVPA and daily SB, respectively (n = 215 eligible participants). Activity changes within trajectory classes and baseline social-cognitive predictor differences between trajectory classes were analyzed.

RESULTS

A "stable insufficient MVPA" (n = 197, p for difference in MVPA level at T0 and T2 (pT0-T2) = .789, effect size (Cohen's d) = .03) and a "stable high MVPA" trajectory (n = 18, pT0-T2 = .137, d = .39), as well as a "slightly decreasing high SB" (n = 63, p for difference in SB (pT0-T2) = .022, d = .36) and a "slightly increasing moderate SB" trajectory (n = 152, pT0-T2 = .019, d = .27) emerged. Belonging to the "stable high MVPA" trajectory was associated with higher action planning levels compared to the "stable insufficient MVPA" trajectory (M = 5.46 versus 4.40, d = .50). Belonging to the "decreasing high SB" trajectory was associated with higher action self-efficacy levels compared to the "increasing moderate SB" trajectory (M = 5.27 versus 4.72, d = .33).

CONCLUSIONS

Change occurred heterogeneously in latent (not directly observed) subgroups, with significant positive trajectories only observed in the highly sedentary.

TRIAL REGISTRATION

German Registry of Clinical Trials, DRKS00016073 , Registered 10 January 2019.