Journal of technology in behavioral science最新文献

Background: Meditation-based interventions (MBIs) hold promise for enhancing health and well-being. However, substantial barriers impede engagement in traditional forms of these interventions. Innovations in mobile health offer an avenue for overcoming barriers associated with traditional MBIs. A particular mobile health innovation - Ecological Momentary Interventions (EMIs) - has the potential to enhance the accessibility, acceptability, and efficacy of MBIs. However, there is limited research on EMIs integrated into MBIs (i.e., meditation-based [MB]-EMIs). This conceptual paper aims to make a theoretical case for MB-EMIs to highlight their potential and to inform future studies on MB-EMIs.

Methods: We discuss the historical context, conceptual foundations, motivation for adoption, and empirical evidence supporting the potential of EMIs. Additionally, we explore the conceptual intersections between EMIs and both traditional contemplative sources and contemporary secular MBIs. Furthermore, we describe empirical studies on MB-EMIs.

Findings/results: Studies have demonstrated diverse approaches to integrating EMIs into MBIs. These studies exhibit variability in key dimensions, including the MBI with which the EMI is integrated and the characteristics of the EMI itself. MB-EMIs have therapeutic potential, but there are many important scientific questions about them that have not yet been answered.

Conclusions: Future studies should continue to examine the impact and safety of meditation-based EMIs, leverage innovations in passive data collection, explore user experiences, develop these interventions for and with marginalized populations, and emphasize informal meditation practice. As the field matures, systematic reviews or meta-analyses will be essential to map the full landscape of MB-EMIs.

CBT-i Coach is a free, publicly available mobile health application (app) that provides users with the core intervention components of cognitive behavioral therapy for insomnia (CBT-i). By making these components directly accessible to users, CBT-i Coach potentially circumvents barriers to accessing CBT-i, such as a scarcity of and lack of referral to trained providers. It may also serve a preventative function by helping address sleep disruption before it reaches clinical levels. However, no study to date has investigated the potential effectiveness of CBT-i Coach for either of these two purposes in public, naturalistic use. This study observed public CBT-i Coach use over an 18-month period to investigate whether users reporting either clinical or subclinical insomnia symptoms (N = 1618) showed improvement in insomnia symptoms, sleep efficiency, and sleep quality while using the app and whether clinically meaningful engagement with the app was associated with improvements. Users' insomnia symptoms, sleep efficiency, and sleep quality improved while using CBT-i Coach (d = 0.57, 0.41, and 0.24, respectively). Regarding clinically meaningful engagement, greater engagement with the My Sleep (b = -0.004) and Tools (b = -0.050) sections were associated with lower final insomnia symptoms, and greater engagement with the My Sleep section (b = 0.012) was associated with higher final sleep quality. CBT-i Coach therefore appears to be a valuable public health tool to improve sleep. It may also provide a preventative service by addressing sleep disruption among those with subclinical symptom levels who otherwise might not have access to CBT-i.

Objective: Passive sensing applications are limited by their inability to determine who is using a device, a critical concern in child mobile device use research, where devices are often shared between siblings or between a child and their parent. Our previous work leveraged behavioral biometrics to identify a target child user; however, it is unknown what type of training data is necessary for optimal model performance. This study evaluated model performance across different characteristics of training data.

Methods: Thirty-six children (11.3 ± 0.9 years, 56% female) self-selected a video or a game on iPads for 10 min while laying and for another 5 min while sitting. The SensorLog application captured iPad accelerometer and gyroscope data while the child interacted with the device. Machine learning algorithms including Neural Network (NN), Random Forest (RF), k-Nearest Neighbors (k-NN), and SwipeFormer were applied to determine the most important aspects of training data to optimize model performance. The aspects of training data evaluated included (1) varying the length (i.e., seconds of training data), (2) varying the user position (i.e., sitting, laying), and (3) varying the time proximity between training and testing data. F1 score was used to evaluate model performance.

Results: The SwipeFormer F1 scores were lowest when the training data was further from the test data (0 when training data was 11 min away from test data) and highest when training data was close to test data (0.91 when training data was the minute preceding test data). The SwipeFormer F1 scores were highest when predicting the user laying from laying (0.97) and sitting from sitting (0.94), and lowest when predicting the user sitting from laying (0) and laying from sitting (0). The length of training data had little impact on performance, with a SwipeFormer F1 score of 0.91 when training on one minute of data and a SwipeFormer F1 score of 0.94 when training on twelve minutes of data.

Discussion: Because researchers would likely be predicting users at different timepoints than their training data, research should focus on improving model performance for identifying users independent of time proximity for training and test data.

Purpose: The current pilot study examines engagement with and preliminary effectiveness of an mHealth intervention designed for teens with eating disorders (EDs) to delineate specific user characteristics associated with intervention engagement and the impact of this engagement on ED symptoms.

Methods: Teens 14-17 years old with or at high-risk for an ED were recruited from social media (n=29) and provided access to an mHealth intervention for 2 months. At baseline, participants were surveyed on ED and other mental health symptoms and demographics. Bivariate analyses were used to examine associations between baseline characteristics and time spent in the app (<10 vs. ≥ 10 minutes). Qualitative feedback from participants on intervention content and usability was also collected and reported.

Results: Out of the 29 participants, 22 (76%) utilized the app at least once after gaining access. The median number of logins for these users was 6, with an interquartile range spanning from 3 to 15. Over half of teens spent 10 minutes or more engaging with the app during the study period (n=15, 52%). Compared to those who spent less than 10 minutes with the app, those who spent more than 10 minutes engaging with the app were slightly younger, more likely to endorse less chronic ED symptoms, and less likely to report social anxiety disorder (ps < 0.05).

Conclusion: Teens' distinct user characteristics impact rates of uptake and engagement with an ED-focused mHealth intervention and should be considered in the design and iteration of these tools. mHealth tools have the potential to improve ED recovery outcomes among teens, and future studies should further evaluate the effectiveness of these tools and integration of content to support severe ED symptoms and other comorbid mental health issues.

Background: Self-monitoring technology (e.g., smartphone applications) aids weight loss, but its role in weight maintenance remains under studied.

Purpose: To evaluate use and perceptions of self-monitoring technologies in National Weight Control Registry (NWCR) participants (adults who have maintained a ≥30lbs loss for ≥1 year) who maintained versus regained weight.

Methods: NWCR participants completed an online survey about self-monitoring technology use and perceptions. Of 1,000 invited participants, 794 completed the survey. Those who reported gaining ≥2.3kg (5lbs) in the past year were categorized as the "regain" group (40.8%); those reporting <2.3kg gain were the "maintain" group (59.2%).

Results: The sample (n=794) was mostly female, White, middle-aged adults. "Regain" was more interested in technology than paper-based methods to self-monitor weight (p<.01) and diet (p<.01) but not exercise (p=.23) than "maintain". There were no differences in wearable trackers interest, most valued features, or use barriers, but the "regain" group was more likely to report guilt, discouragement, body image concerns, and anxiety about weight loss when using behavior-tracking technologies (p<.001); rates of discontinuation from these feelings or unhealthy weight control practices were not different between groups.

Conclusions: This appears to be the first study investigating naturalistic use of self-monitoring technology in a demographically homogenous sample maintaining significant weight loss. The "regain" group was more likely to use self-monitoring technology but reported more tracking-associated negative feelings. Future research must determine how to support individuals emotionally and with weight maintenance when self-monitoring contributes to negative byproducts. Other work should identify the optimal elements of self-monitoring technology for weight loss maintenance.

The impact of social media (SM) use ('screentime') on adolescent mental health has been the focus of increasing concern, despite mixed findings from empirical research. Current methodological approaches rely on self-reported SM use, which has limited accuracy and obscure the dynamic interplay of SM use and mental health. Smartphone-based mobile sensing offers new opportunities to gain insights into adolescents' SM use patterns and behaviors, particularly at an idiographic level. Considerations and challenges of smartphone sensing methods for capturing adolescents' SM use patterns and behaviors in clinical psychological science are discussed in the context of a pilot study using smartphone-based sensing with adolescents. The pilot study included 19 adolescents (Mean age = 15.84; 68% boys; 79% White) who installed a passive monitoring application (AWARE) on their phones for 31 (SD = 5.6) days. Descriptive data of sensing acceptability and feasibility are presented based on participant ratings and data yield ratio of usable data (74.18%). Sensing yielded 10,038 hourly observations collected from the 'application foreground' sensor across all participants from social media apps, and a total of 645 applications used. Categorization of SM apps were coded (kappa >.90) into 'social networking' (N = 20 apps) and 'broader SM' (N = 41) and compared to both Play Store-defined SM apps (N = 26) and popular SM apps based on Common Sense Media Survey (N = 9). Descriptive data on extracted behavioral features (duration, checking) from SM use categories (binned hourly and daily) are presented. Challenges, opportunities, and future directions of sensing methods for SM use are discussed to inform our understanding of its impacts on mental health and to improve the rigor of SM research in clinical psychological science.

Supplementary information: The online version contains supplementary material available at 10.1007/s41347-024-00443-5.

Automated coaching messages for weight control can save time and costs, but their repetitive, generic nature may limit their effectiveness compared to human coaching. Large language model (LLM) based artificial intelligence (AI) chatbots, like ChatGPT, could offer more personalized and novel messages to address repetition with their data-processing abilities. While LLM AI demonstrates promise to encourage healthier lifestyles, studies have yet to examine the feasibility and acceptability of LLM-based BWL coaching. Eighty-seven adults in a weight-loss trial (BMI ≥ 27 kg/m²) rated ten coaching messages' helpfulness (five human-written, five ChatGPT-generated) using a 5-point Likert scale, providing additional open-ended feedback to justify their ratings. Participants also identified which messages they believed were AI-generated. The evaluation occurred in two phases: messages in Phase 1 were perceived as impersonal and negative, prompting revisions for messages in Phase 2. In Phase 1, AI-generated messages were rated less helpful than human-written ones, with 66% receiving a helpfulness rating of 3 or higher. However, in Phase 2, the AI messages matched the human-written ones regarding helpfulness, with 82% scoring three or above. Additionally, 50% were misidentified as human-written, suggesting AI's sophistication in mimicking human-generated content. A thematic analysis of open-ended feedback revealed that participants appreciated AI's empathy and personalized suggestions but found them more formulaic, less authentic, and too data-focused. This study reveals the preliminary feasibility and perceived helpfulness of LLM AIs, like ChatGPT, in crafting potentially effective weight control coaching messages. Our findings also underscore areas for future enhancement.

Supplementary information: The online version contains supplementary material available at 10.1007/s41347-025-00491-5.

Time spent on social media has been an inconsistent predictor of mental health outcomes in young people. However, most studies have assessed social media use globally, with few investigations of the relative influence of specific social media platforms, which may partially account for mixed findings. Furthermore, studies often focus on a single mental health outcome, limiting understanding of how social media relates to psychological well-being. The purpose of the current study was to examine associations between time spent on multiple popular social media platforms and a variety of mental health-related outcomes in a sample of young adults. Participants included 575 young adults who completed an online survey assessing self-reported time spent on Twitter, TikTok, YouTube, Instagram, Reddit, Snapchat, and Facebook as well as depression, anxiety, PTSD, loneliness, friend support, and self-esteem. Path analyses showed that in the overall sample, greater use of Tiktok and YouTube were consistently associated with more mental health issues, whereas greater use of Snapchat was associated with fewer mental health issues. Models examining results for men and women separately suggested that use of Tiktok was more relevant in women's mental health, whereas use of Reddit was more relevant in men's mental health. Findings highlight that associations are not uniform across social media platforms. More research is needed that compares individual platforms and their relationship to psychological well-being as well as future studies examining how gender impacts findings.

Adolescent social media serves a broad range of functions, which may be helpful for some and harmful for others. During the COVID-19 lockdown, social media evolved considerably, occupying an even more central role in adolescents' lives. This study leverages a new approach to measuring social media use behaviors-passive smartphone sensing. Specifically, we aimed to test if and how adolescents self-regulate their social media use in response to how they feel during and after use. This study followed 19 adolescents for 1 month. Participants completed baseline measures, assessing demographic and clinical characteristics. We used passive smartphone sensing to measure objective social media use behaviors ("screen time" and checking) for a 1-month period. Adolescents also completed daily diary questions on their mood. Analyses took an idiographic (n = 1) approach. Dynamic structural equation models tested daily and next-day relationships between social media use behaviors and mood for each adolescent. Most adolescents (n = 13 of 19) did not self-regulate their social media use in relation to their mood. Most importantly, they did not use it less when they felt more negative mood during use. That said, some adolescents (n = 6) did alter their social media use behaviors depending on their mood. Each adolescent's pattern of social media use and mood was also qualitatively interpreted within their context of demographic (e.g., experience of holding a minoritized identity) and clinical characteristics (e.g., history of suicidal thoughts and behaviors). These results highlight the next steps for possible intervention points to help adolescents adjust their use patterns to maximize mental health benefits while minimizing possible harm. Findings also begin to develop a template for applying social media use recommendations, while centering the experiences of individual adolescents.