Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial.

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 controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 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 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (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=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). 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 (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, 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=.04). 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. TRIAL REGISTRATION: Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b.

Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters.

Computational modeling of behavior is increasingly being adopted as a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. This approach involves estimating parameters in a computational (or cognitive) model that represents the computational processes of the underlying behavior. In this approach, the reliability of the parameter estimates is an important issue. The use of hierarchical (Bayesian) approaches, which place a prior on each model parameter of the individual participants, is thought to improve the reliability of the parameters. However, the characteristics of reliability in parameter estimates, especially when individual-level priors are assumed, as in hierarchical models, have not yet been fully discussed. Furthermore, the suitability of different reliability measures for assessing parameter reliability is not thoroughly understood. In this study, we conduct a systematic examination of these issues through theoretical analysis and numerical simulations, focusing specifically on reinforcement learning models. We note that the heterogeneity in the estimation precision of individual parameters, particularly with priors, can skew reliability measures toward individuals with higher precision. We further note that there are two factors that reduce reliability, namely estimation error and intersession variation in the true parameters, and we discuss how to evaluate these factors separately. Based on the considerations of this study, we present several recommendations and cautions for assessing the reliability of the model parameters.

Association of brain-autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning.

The total amount of mental activity applied to working memory at a given point in time is called cognitive load, which is an important factor in various activities in daily life. We have proposed new feature quantities that reflect the time-series changes in the power of typical frequency bands in electroencephalogram (EEG) for use in examining the relationship between brain activity and behavior under cognitive load. We also measured heart rate variability (HRV) and spontaneous skin conductance responses (SCR) to examine functional associations among brain activity, autonomic activity, and behavior under cognitive load. Additionally, we applied our machine learning model previously developed using EEG to the estimation of arousal level to interpret the brain-autonomic-behavior functional association under cognitive load. Experimental data from 12 healthy undergraduate students showed that participants with higher levels of infra-slow fluctuations of alpha power have more cognitive resources and thus can process information under cognitive load more efficiently. In addition, HRV reflecting parasympathetic activity correlated with task accuracy. The arousal level estimated using our machine learning model showed its robust relationship with EEG. Despite the limitation of the sample size, the results of this pilot study suggest that the information processing efficiency of the brain under cognitive load is reflected by time-series fluctuations in EEG, which are associated with an individual's task performance. These findings can contribute to the evaluation of the internal state of humans associated with cognitive load and the prediction of human behaviors in various situations under cognitive load.

Deficits in proactive avoidance and neural responses to drinking motives in problem drinkers.

Physical pain and negative emotions represent two distinct drinking motives that contribute to harmful alcohol use. Proactive avoidance which can reduce problem drinking in response to these motives appears to be impaired in problem drinkers. However, proactive avoidance and its underlying neural deficits have not been assessed experimentally. How these deficits inter-relate with drinking motives to influence alcohol use also remains unclear. The current study leveraged neuroimaging data collected in forty-one problem and forty-one social drinkers who performed a probabilistic learning go/nogo task that involved proactive avoidance of painful outcomes. We characterized the regional brain responses to proactive avoidance and identified the neural correlates of drinking to avoid physical pain and negative emotions. Behavioral results confirmed problem drinkers' proactive avoidance deficits in learning rate and performance accuracy, both which were associated with greater alcohol use. Imaging findings in problem drinkers showed that negative emotions as a drinking motive predicted attenuated right insula activation during proactive avoidance. In contrast, physical pain motive predicted reduced right putamen response. These regions' activations as well as functional connectivity with the somatomotor cortex also demonstrated a negative relationship with drinking severity and positive relationship with proactive avoidance performance. Path modeling further delineated the pathways through which physical pain and negative emotions, along with alcohol use severity, influenced the neural and behavioral measures of proactive avoidance. Taken together, the current findings provide experimental evidence for proactive avoidance deficits in problem drinkers and establish the link between their neural underpinnings and alcohol misuse.

The Neural Correlates of Individual Differences in Reinforcement Learning during Pain Avoidance and Reward Seeking.

Organisms learn to gain reward and avoid punishment through action-outcome associations. Reinforcement learning (RL) offers a critical framework to understand individual differences in this associative learning by assessing learning rate, action bias, pavlovian factor (i.e., the extent to which action values are influenced by stimulus values), and subjective impact of outcomes (i.e., motivation to seek reward and avoid punishment). Nevertheless, how these individual-level metrics are represented in the brain remains unclear. The current study leveraged fMRI in healthy humans and a probabilistic learning go/no-go task to characterize the neural correlates involved in learning to seek reward and avoid pain. Behaviorally, participants showed a higher learning rate during pain avoidance relative to reward seeking. Additionally, the subjective impact of outcomes was greater for reward trials and associated with lower response randomness. Our imaging findings showed that individual differences in learning rate and performance accuracy during avoidance learning were positively associated with activities of the dorsal anterior cingulate cortex, midcingulate cortex, and postcentral gyrus. In contrast, the pavlovian factor was represented in the precentral gyrus and superior frontal gyrus (SFG) during pain avoidance and reward seeking, respectively. Individual variation of the subjective impact of outcomes was positively predicted by activation of the left posterior cingulate cortex. Finally, action bias was represented by the supplementary motor area (SMA) and pre-SMA whereas the SFG played a role in restraining this action tendency. Together, these findings highlight for the first time the neural substrates of individual differences in the computational processes during RL.

Revisiting the Transtheoretical Model for Physical Activity: A Large-Scale Cross-Sectional Study on Japanese-Speaking Adults.

BACKGROUND: 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. PURPOSE: 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 20,573 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-reevaluation, reinforcement management, and self-liberation) were more predictive of PA in pre-active 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.

The Transtheoretical Model has been the basis of many behavioral interventions for promoting physical activity. One of the key concepts of the model is the stage of change, which is a framework to help understand the readiness to begin physical activity and exercise. The model assumes five progressive stages of behavior change (e.g., the precontemplation stage, where people have no intention to change behavior; the maintenance stage, where people have continued physical activity for a long enough period), through which individuals acquire an active lifestyle. The model also assumes that different strategies for behavior change are appropriate at different stages and, confidence and attitudes toward physical activity vary dynamically across stages. The current study examined how valid these theoretical assumptions using data from 20,573 Japanese-speaking adults. The data overall supported the assumptions of the Transtheoretical Model, for example, highlighting the importance of enhancing awareness about the causes and (dis)advantages of being (in)active at earlier stages. Although some inconsistencies were identified (some strategies were not as useful as the model assumed), these findings may suggest that the Transtheoretical Model holds universal theoretical value as a descriptive model of behavioral change for active lifestyle across Western and East Asian populations.

Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey.

Background: Physical inactivity is a global health issue, and mobile health (mHealth) 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. 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: This study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample. Methods: We recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used. Results: Approximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders. Conclusions: Regardless of the current trend in 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.

A learning mechanism shaping risk preferences and a preliminary test of its relationship with psychopathic traits.

People tend to avoid risk in the domain of gains but take risks in the domain of losses; this is called the reflection effect. Formal theories of decision-making have provided important perspectives on risk preferences, but how individuals acquire risk preferences through experiences remains unknown. In the present study, we used reinforcement learning (RL) models to examine the learning processes that can shape attitudes toward risk in both domains. In addition, relationships between learning parameters and personality traits were investigated. Fifty-one participants performed a learning task, and we examined learning parameters and risk preference in each domain. Our results revealed that an RL model that included a nonlinear subjective utility parameter and differential learning rates for positive and negative prediction errors exhibited better fit than other models and that these parameters independently predicted risk preferences and the reflection effect. Regarding personality traits, although the sample sizes may be too small to test personality traits, increased primary psychopathy scores could be linked with decreased learning rates for positive prediction error in loss conditions among participants who had low anxiety traits. The present findings not only contribute to understanding how decision-making in risky conditions is influenced by past experiences but also provide insights into certain psychiatric problems.

The Effect of Reduced Learning Ability on Avoidance in Psychopathy: A Computational Approach.

Individuals with psychopathy often show deficits in learning, which often have negative consequences. Several theories have been proposed to explain psychopathic behaviors, but the learning mechanisms in psychopathy are still unclear. To clarify the learning anomalies in psychopathy, we fitted reinforcement learning (RL) models to behavioral data. We conducted two experiments to examine the effect of psychopathy as a group difference (Experiment 1) and as a continuum (Experiment 2). Forty-three undergraduates (in Experiment 1) and fifty-five undergraduate and graduate students (in Experiment 2) performed a go/no-go based learning task with accompanying rewards or punishments. Although we observed no differences in learning performance among the levels of psychopathic traits, the learning rate for the positive prediction error in the loss domain was lower for those with high-psychopathic trait than for those with low-psychopathic trait. This finding indicates that individuals with high-psychopathic traits update an action value less when they avoid a negative outcome. Our model can represent previous theories under a computational framework and provide a new perspective on impaired learning in psychopathy.