BACKGROUND Physical inactivity is a global health issue, and mobile health (mHealth) applications (apps) are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released in markets. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity. OBJECTIVE To investigate the usage patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample. METHODS We recruited N = 20,573 online panels who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. RESULTS Approximately a quarter of the sample were identified as app users who showed similar demographic characteristics documented in the literature; that is, they were younger, had a higher income, received higher education, and were more active than non-app users. Our results revealed unique associations between demographic variables and specific app functions (e.g., sensor information, journaling, and GPS were more frequently used by men than women). Another important finding is that people typically use two different functions within an app (IQR: 1-4 functions), and the most common pattern was to use sensor information (i.e., self-monitoring) and one other function such as goal setting or reminders. CONCLUSIONS Regardless of the current trend of app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Objective: The Transtheoretical Model (TTM) has been the basis of health promotion programs, which are, for example, used to tailor behavioral interventions according to the stages of change. Empirical studies have shown that the TTM effectively describes the processes of behavioral adaptation to acquire healthier lifestyles; however, it has been argued that TTM-based interventions are not superior to non-TTM-based interventions for promoting physical activity (PA). Evidence has also highlighted some inconsistencies with theoretical assumptions, especially regarding how each process-of-change strategy emerges across the stages. Therefore, we investigated (a) how well the TTM describes the distributional characteristics of PA levels as well as other relevant variables (e.g., process of change, self-efficacy) across stages, and (b) how predictive the TTM variables are of PA levels within each stage. Methods: We analyzed data from 20,581 Japanese-speaking adults who completed online questionnaires on PA and TTM variables. Results: The results replicated previous findings that stage membership is associated with PA, the process of change, decisional balance, and self-efficacy, albeit with inconclusive evidence of temptations. Regression analyses revealed that some processes of change (self-revaluation, reinforcement management, and self-liberation) were more predictive of PA in pre-action stages than in post-action stages; self-efficacy was predictive of PA only in the maintenance stage but not in the other stages. Conclusions: Overall, the data support the theoretical assumptions of the TTM, but the stage specificity of the active processes may not always be consistent with the theory.

Computational modeling of behavior is becoming a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. In this approach, the reliability of the parameter estimates is an important issue. The most commonly used metric to assess it is the test-retest reliability, which quantifies the consistency of the parameter estimates obtained with multiple measurements. The reliability could be increased by improving the parameter estimation methods as well as the design of behavioral tasks. Studies using hierarchical (Bayesian) models have reported significant improvements in test-retest reliability. Hierarchical models assume prior distributions for individual parameter estimates. In this report, we point out that the test-retest reliability of parameter estimates based on prior distributions are strongly affected by heterogeneity in the sample population, possibly leading to overestimation of the reliability. When test-retest reliability is increased using prior distributions, it may indicate the presence of heterogeneity in the population and the need for subgrouping.

BACKGROUND Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA. OBJECTIVE We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA. METHODS This study was a single-blind, pilot randomized control trial in which 70 adults (44 women; mean age of 40.5 years; closed user group) were allocated to one of three BCT conditions: self-monitoring (feedback on participants’ own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for four weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and post-intervention (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed. RESULTS Participants showed excellent adherence (mean valid-wear time of Fitbit = 26.4 out of 28 days; 94.39 %), no dropout was recorded. No significant changes were found in self-reported total PA (dz < 0.27, Ps > .05). Fitbit-assessed step count during the intervention period was slightly higher in the goal-setting and social-comparison groups than in the self-monitoring group although the effects did not reach statistical significance (Ps = .052 and .057). However, more than half of the participants in the precontemplation stage (n = 27 out of 46) reported progress to a higher stage across the three conditions. Additionally, significant increases were detected for several aspects of motivation (i.e., integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal-setting group (P = .039). CONCLUSIONS Fitbit-implemented BCTs promote readiness and motivation for PA although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA. CLINICALTRIAL https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b

Classifying individuals based on personality and other characteristics is a common statistical approach used in marketing, medicine, and social sciences. This approach has several advantages: it improves the simplicity of data, helps data-driven decision-making, and guides intervention strategies such as personalized care. On the other hand, continuous variables are often used to classify individuals, meaning that dimensional information is reduced to several discrete classes (of individuals) and thus much information is lost through this process. Although the loss of information may be practically or pragmatically acceptable, how much information is lost and what influence this decision has on predicting external outcomes has not been systematically investigated. Therefore, in this study, we examined the predictive performance of the classification approach compared with the dimensional approach by analyzing survey data obtained from approximately 20,000 individuals concerning physical activity and psychological traits, including the Big Five personality traits. First, we classified individuals based on the dimensional data of their psychological traits and obtained several different cluster solutions. Second, these clusters were used to predict the levels of physical activity (i.e., the classification approach), which were then compared with the predictions made by the raw dimensional scales of psychological traits (i.e., the dimensional approach). The results showed that the four-cluster solution, which was supported by the standard criterion for determining the number of clusters, achieved no more than 60% explanatory power of the dimensional approach. To achieve a comparable prediction accuracy, the number of clusters must be increased to at least 20. These findings imply that the cluster solution suggested by the conventional statistical criteria may not be optimal when clusters are used to predict external outcomes.