Person-specific dose-finding for a digital messaging intervention to promote physical activity

The Specification of a Computational Model of Physical Activity Habit

The influence of habit on physical activity is computationally modeled as the aggregated influence of past behavioral choices a person makes in a given context. We hypothesize that the influence of habit on behavior can be enhanced through engagement of the target behavior in a particular context or weakened through engagement of alternative behaviors in that context.

Physical Activity Dependence on Relative Temperature and Humidity Characteristics in a Young, Insufficiently Active Population: A Weather Typing Analysis

BACKGROUND

Physical activity (PA) is an important contributor to one's physical and mental health both acutely and across the lifespan. Much research has done on the ambient environment's impact on PA; however, these studies have used absolute values of atmospheric measures such as temperature and humidity, which vary spatiotemporally and make comparisons between studies which differ in location or time of year difficult to square with one another.

METHODS

Here, we employ the Global Weather Type Classification, Version 2, to determine the combined impact of temperature and humidity on PA in a sample of insufficiently active young adults. We conducted secondary analyses of data from a single-group behavioral intervention trial that varied the number of digital messages sent daily. Young adults (n = 81) wore Fitbit Versa smartwatches for a 6-month period sometime between April 2019 and July 2020, and location was tracked using a custom smartphone application.

RESULTS

Mixed linear models indicated that, across 8179 person-days, PA was significantly lower on days with humid conditions and significantly higher on warm dry days, though the latter relationship was no longer significant when controlling for timing in relation to the COVID-19 pandemic declaration. Demographic factors did not affect the relationship between weather and PA.

CONCLUSIONS

Results are a first step in providing additional guidance for encouraging PA in insufficiently active individuals given forecasted daily weather conditions. Future work should examine seasonal variability in the weather type-PA relationship without the influence of a world-altering event influencing results.

Wearable device adherence among insufficiently-active young adults is independent of identity and motivation for physical activity

Wearable devices are increasingly being integrated to improve prevention, chronic disease management and rehabilitation. Inferences about individual differences in device-measured physical activity depends on devices being worn long enough to obtain representative samples of behavior. Little is known about how psychological factors are associated with device wear time adherence. This study evaluated associations between identity, behavioral regulations, and device wear adherence during an ambulatory monitoring period. Young adults who reported insufficient physical activity (N = 271) were recruited for two studies before and after the SARS-COVID-19 pandemic declaration. Participants completed a baseline assessment and wore an Actigraph GT3X + accelerometer on their waist for seven consecutive days. Multiple linear regression indicated that wear time was positively associated with age, negatively associated with integrated regulation for physical activity, and greater after (versus before) the pandemic declaration. Overall, the model accounted for limited variance in device wear time. Exercise identity and exercise motivation were not associated with young adults' adherence to wearing the physical activity monitors. Researchers and clinicians can use wearable devices with young adults with minimal concern about systematic motivational biases impacting adherence to device wear.

Association between Weather and Self-Monitored Steps in Individuals with Prediabetes and Type 2 Diabetes in Sweden over Two Years

BACKGROUND

Many studies have identified key factors affecting the rates of engagement in physical activity in older adults with chronic disease. Environmental conditions, such as weather variations, can present challenges for individuals with chronic diseases, such as type 2 diabetes when engaging in physical activity. However, few studies have investigated the influence of weather on daily steps in people with chronic diseases, especially those with prediabetes and type 2 diabetes.

OBJECTIVE

This study investigated the association between weather variations and daily self-monitored step counts over two years among individuals with prediabetes and type 2 diabetes in Sweden.

METHODS

The study is a secondary analysis using data from the Sophia Step Study, aimed at promoting physical activity among people with prediabetes and type 2 diabetes, which recruited participants from two urban primary care centers in Stockholm and one rural primary care center in southern Sweden over eight rounds. This study measured physical activity using step counters (Yamax Digiwalker SW200) and collected self-reported daily steps. Environmental factors such as daily average temperature, precipitation, and hours of sunshine were obtained from the Swedish Meteorological and Hydrological Institute. A robust linear mixed-effects model was applied as the analysis method.

RESULTS

There was no association found between weather variations and the number of steps taken on a daily basis. The analysis indicated that only 10% of the variation in daily steps could be explained by the average temperature, precipitation, and sunshine hours after controlling for age, gender, and BMI. Conversely, individual factors explained approximately 38% of the variation in the observations.

CONCLUSION

This study revealed that there was no association between weather conditions and the number of daily steps reported by individuals with prediabetes and type 2 diabetes taking part in a physical activity intervention over two years. Despite the weather conditions, women and younger people reported more steps than their male and older counterparts.

Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis

BACKGROUND

Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions.

OBJECTIVE

The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques.

METHODS

We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively.

RESULTS

Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages.

CONCLUSIONS

No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

Dose-response relations between the frequency of two types of momentary feedback prompts and daily physical activity

OBJECTIVE

Self-monitoring and behavioral feedback are widely used to help people monitor progress toward daily physical activity goals. Little information exists about the optimal dosing parameters for these techniques or if they are interchangeable in digital physical activity interventions. This study used a within-person experimental design to evaluate associations between the frequency of two different prompt types (one for each technique) and daily physical activity.

METHOD

Insufficiently active young adults were assigned monthly physical activity goals and wore smartwatches with activity trackers for 3 months. They received zero to six randomly selected and timed watch-based prompts each day, with individual prompts either providing behavioral feedback or prompting the participant to self-monitor.

RESULTS

Physical activity increased significantly over the 3-month period (step counts d = 1.03; moderate-to-vigorous physical activity duration d = 0.99). Mixed linear models revealed that daily step counts were positively associated with the frequency of daily self-monitoring prompts up to approximately three prompts/day (d = 0.22) after which additional prompts provided minimal or reduced benefit. Daily step counts were not associated with the frequency of behavioral feedback prompts. Daily moderate-to-vigorous physical activity was not associated with the frequency of either prompt.

CONCLUSIONS

Self-monitoring and behavioral feedback are not interchangeable behavior change techniques in digital physical activity interventions, and only self-monitoring prompts show signs of a dose-response association with physical activity volume. Activity trackers, such as smartwatches and mobile apps, should provide an option to replace behavioral feedback prompts with self-monitoring prompts to promote physical activity among insufficiently active young adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration

BACKGROUND

The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity.

PURPOSE

This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions.

METHODS

Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020.

RESULTS

Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration.

CONCLUSIONS

Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

Climate change, behavior change and health: a multidisciplinary, translational and multilevel perspective

The climate crisis provides a critical new lens through which health and health behaviors need to be viewed. This paper has three goals. First, it provides background on the climate crisis, the role of human behavior in creating this crisis, and the health impacts of climate change. Second, it proposes a multilevel, translational approach to investigating health behavior change in the context of the climate crisis. Third, it identifies specific challenges and opportunities for increasing the rigor of behavioral medicine research in the context of the climate crisis. The paper closes with a call for behavioral medicine to be responsive to the climate crisis.

Dynamic models of stress-smoking responses based on high-frequency sensor data

Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.