A web-based physical activity intervention targeting affect regulation: a randomized feasibility trial.

Early career professionals (ECPs) are a critical target for physical activity (PA) promotion. Affect contributes to an established PA intention-behaviour gap and is pertinent among ECPs. OBJECTIVE: The purpose of this study was to examine the feasibility and acceptability of a web-based intervention and explore the effects on secondary outcomes (moderate-to-vigorous PA (MVPA), emotion regulation, multi-process action control constructs). METHODS: Adults aged 25-44 who were employed at least part-time in a desk-based job and not meeting PA guidelines (<150 min MVPA) were recruited and randomized into a 6-week online intervention integrating acceptance and commitment principles and affect regulation strategies, or a control group. RESULTS: Forty adults were recruited and randomized to the web-based intervention (n = 21) and waitlist control (n = 19). The recruitment rate was 29%, retention was 75%, engagement was 68%, and satisfaction was high in both quantitative and qualitative assessment. Participants allocated to the intervention improved MVPA (ηp2=0.30), emotion regulation (ηp2 =0.49), behavioural regulation (ηp2=0.53), affective attitude (ηp2=0.23), identity (ηp2=0.24), and constructs of mindfulness (ηp2=0.44), and valued living (ηp2=0.20). CONCLUSIONS: Primary outcomes concerning feasibility were adequate and secondary outcomes improved, suggesting a full-scale randomized controlled trial is feasible with minor modifications. A large-scale study is warranted to establish intervention effectiveness.

A Web-Based Intervention for Insufficiently Active College Students: Feasibility and Preliminary Efficacy.

BACKGROUND: One third of college students do not achieve aerobic activity levels recommended for physical and mental health. The web-based "I Can Be Active!" intervention was designed to help college students increase their physical activity. The intervention was grounded in the Multi-Process Action Control (M-PAC) framework which emphasizes translating intention into sustainable action. OBJECTIVE: The primary purpose was to evaluate the feasibility of the intervention with insufficiently active young adult college students. The secondary purposes were to describe the preliminary effects of the intervention on: (1) the M-PAC constructs and (2) physical activity. METHODS: Twenty-one college students, ages 18 to 24, were enrolled in the pre-post quasi-experimental study to test the 8-week intervention during Spring 2021. Data were collected via self-report questionnaires, web-analytics, and interviews. Feasibility outcomes included recruitment, retention, acceptability, practicality, and implementation. Preliminary efficacy outcomes were self-report M-PAC constructs and physical activity. Data analyses included descriptive statistics, t tests, Wilcoxon signed rank tests, Hedge's g, and thematic analysis. RESULTS: Recruitment and retention rates were 70% and 71%, respectively. Participants reacted positively to the program, content, and features, except the manual entry step tracker and private social media group. Positive trends and significant increases were found in the regulatory and reflexive M-PAC constructs (self-regulation, habit, and identity) and physical activity. CONCLUSIONS: Findings support the feasibility and preliminary effects of the intervention for insufficiently active college students and highlight implications for intervention refinement. Future research will test intervention effectiveness using a randomized controlled trial with a larger diverse sample of college students.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Development and usability testing of a technology-based intervention for promoting physical activity among post-treatment cancer survivors (WExercise) using the multi-process action control framework.

Background: To promote physical activity in post-treatment cancer survivors, a mobile application WExercise was developed using the Multi-Process Action Control Framework. It contains 10 weekly online lesson to facilitate reflective, regulatory, and reflexive processes to help participants to form and sustain physical activity behavior. Objectives: To test the usability and acceptability of WExercise in post-treatment cancer survivors. Methods: This study involved four phases: (1) preparing application content, (2) expert panel review (comprising oncology healthcare workers, exercise specialists, and behavior change researchers), (3) developing the app, and (4) usability test. The usability test was conducted cross-sectionally using direct observation of application navigation tasks, a quantitative survey, and qualitative interviews among 10 post-treatment cancer survivors. Results: In Phase 2, the expert panel rated the application highly on relevance, accuracy, comprehensiveness, meaningfulness, and easiness to understand (average score = 3.83 out of 4). The application was developed accordingly. In Phase 4, the System Usability Score was 75 %, greater than the cut-off point. Participants gave the items assessing acceptance of the application positive ratings (e.g., satisfaction = 4.30 out of 5). Based on the performance and feedback, the application was modified, including adjusting the font size and improving the visualization of buttons. Conclusion: Overall, experts and potential users considered the application relevant, usable, and acceptable. It has the full potential for further testing in a larger trial for its effectiveness in promoting physical activity in cancer survivors.

Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study.

BACKGROUND: Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility. OBJECTIVE: This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content. METHODS: During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases. RESULTS: In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention. CONCLUSIONS: ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input.