Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making Framework

Impact of health information seeking behavior and digital health literacy on self-perceived health and depression symptoms among older adults in the United States

BACKGROUND

Understanding the impact of digital health literacy and health information-seeking behavior on the self-perceived health and depression symptoms of older adults is crucial, particularly as the number of older internet users is increasing.

METHODS

This study utilized data from the Health Information National Trends Survey to examine the relationship between these factors and the health outcomes of adults aged 50 and above.

RESULTS

The study found that digital health literacy has a positive but non-significant relationship with self-perceived health when other factors are considered. However, education level and body mass index consistently predicted self-perceived health. Moreover, higher digital health literacy was associated with a reduced likelihood of perceived depression symptoms, even after adjusting for demographic and health-related factors.

CONCLUSIONS

These findings highlight the importance of digital health literacy in the mental well-being of older adults and provide insights for shaping future health policies and interventions.

Preferences for Text Messaging Supports During Youth Transition to Adult Mental Health Services: Theory-Informed Modified e-Delphi Study

BACKGROUND

For many young people, the transition from child to adult mental health services is a vulnerable time associated with treatment disengagement and illness progression. Providing service information and options to youth, appealing to them, and tailoring to their needs during this period could help overcome systematic barriers to a successful transition. We know little about how SMS text message-based interventions might be leveraged to support the motivational, informational, and behavioral needs of youth during this time. Ascertaining youth preferences for the content and functionality of an SMS text message service could inform prototype development.

OBJECTIVE

This study investigated consensus preferences among youth on important content, technology features, and engagement supports to inform a transition-focused SMS text message service.

METHODS

A modified e-Delphi survey design was used to collect demographics, current levels of technology use, importance ratings on message content, preferred technical features, and barriers and enablers to engagement for youth in Canada aged 16-26 years who have accessed mental health services within the past 5 years. Survey items on content were categorized according to the information-motivation-behavioral skills (IMB) model. Survey items on technical features were categorized according to the persuasive system design (PSD) model. A predefined consensus rating matrix and descriptive statistics were used to characterize the sample. The high consensus threshold was 70%.

RESULTS

A total of 100 participants, predominantly non-White (n=47, 47%), aged 20-26 years (n=59, 59%), and who had first accessed mental health services between the ages of 13 and 19 years (n=60, 60%), were selected. The majority (n=90, 90%) identified as daily SMS text message users. A high level of consensus on importance ratings was reported in 45% (9/20) of content items based on the IMB model. There were higher levels of consensus on importance ratings related to behavior domain items (3/3, 100%) than information domain items (4/9, 44%) or motivation domain items (2/8, 25%). A high level of consensus on importance ratings was reported in only 19% (4/21) of feature and functionality items based on the PSD model. Among PSD model categories, there was a high level of consensus on importance ratings in 8% (1/12) of the primary task support domain items and 100% (3/3) of the system credibility support domain items. None of the dialogue-support and social-support domain items met the high level of consensus thresholds. In total, 27% (27/100) of youth indicated that the most significant enabler for engaging with a transition-focused SMS text message intervention was the personalization of text messages.

CONCLUSIONS

Scientists developing next-generation SMS text messaging interventions for this population need to consider how levels of consensus on different features may impact feasibility and personalization efforts. Youth can (and should) play an integral role in the development of these interventions.

Spanish validation of the simple lifestyle indicator questionnaire: validity and reliability analysis

Introduction

It has been shown that lifestyle is a highly modifiable determinant having a direct effect on the health status. Therefore, short and simple questionnaires assessing the lifestyle of the general and clinical population are needed to create interventions on behavioral aspects that can improve the health status. The Simple Lifestyle Indicator Questionnaire (SLIQ) is a validated health scale in English that combines five lifestyle factors: diet, exercise, alcohol consumption, tobacco use, and stress level. The objective of this study was to validate the SLIQ questionnaire in Spanish by analyzing the scale's validity and reliability. Its discriminatory power of the scale was also examined by evaluating the differences in health outcomes according to the levels of adherence to a healthy lifestyle.

Methods

The sample consisted of 745 participants with an average age of 39.94 (SD: 16.99). A transcultural adaptation process was carried out to validate the SLIQ questionnaire in the Spanish context, to determinate the structural equivalence of the Spanish version as compared to the English version, and to assess the psychometric properties of the scale. PREDIMED and IPAQ scales were used to analyze the convergent validity of the Spanish version of the SLIQ regarding to diet and exercise, and the questionnaires SF-12 and DASS-21 questionnaires were used to assess the capacity of the Spanish version of the SLIQ to discriminate health status related to different levels of reported lifestyles.

Results

Regarding validity, the results indicate significant correlations between the different dimensions of the SLIQ questionnaire and those used as a reference. As for reliability, the test-retest analyses reveal a high temporal consistency for the scores obtained on the questionnaire. Finally, the differences found in anxiety, depression, and quality of life, with regard to the different levels of adherence in the SLIQ questionnaire, suggest that the questionnaire's Spanish version has adequate discriminatory power.

Discussion

The obtained correlation coefficients between the SLIQ and the other standardized measures pointed out the adequate convergent validity of the instrument. Moreover, the test-retest results demonstrated the stability of the results obtained through this questionnaire. Finally, the lifestyle categories derived from the SLIQ showed a high ability to discriminate between participants' health profiles. Hence, it can be concluded that the Spanish version of the SLIQ questionnaire is a valid and reliable tool for the quick and effective assessment of lifestyle.

Habit formation of preventive behaviours during the COVID-19 pandemic: a longitudinal study of physical distancing and hand washing

Background

Since the outbreak of the COVID-19 pandemic, physical distancing and hand washing have been used as effective means to reduce virus transmission in the Netherlands. However, these measures pose a societal challenge as they require people to change their customary behaviours in various contexts. The science of habit formation is potentially useful for informing policy-making in public health, but the current literature largely overlooked the role of habit in predicting and explaining these preventive behaviours. Our research aimed to describe habit formation processes of physical distancing and hand washing and to estimate the influences of habit strength and intention on behavioural adherence.

Methods

A longitudinal survey was conducted between July and November 2020 on a representative Dutch sample (n = 800). Respondents reported their intentions, habit strengths, and adherence regarding six context-specific preventive behaviours on a weekly basis. Temporal developments of the measured variables were visualized, quantified, and mapped onto five distinct phases of the pandemic. Regression models were used to test the effects of intention, habit strength, and their interaction on behavioural adherence.

Results

Dutch respondents generally had strong intentions to adhere to all preventive measures and their adherence rates were between 70% and 90%. They also self-reported to experience their behaviours as more automatic over time, and this increasing trend in habit strength was more evident for physical-distancing than for hand washing behaviours. For all six behaviours, both intention and habit strength predicted subsequent adherence (all ps < 2e-16). In addition, the predictive power of intention decreased over time and was weaker for respondents with strong habits for physical distancing when visiting supermarkets (B = -0.63, p <.0001) and having guests at home (B = -0.54, p <.0001) in the later phases of the study, but not for hand washing.

Conclusions

People’s adaptations to physical-distancing and hand washing measures involve both intentional and habitual processes. For public health management, our findings highlight the importance of using contextual cues to promote habit formation, especially for maintaining physical-distancing practices. For habit theories, our study provides a unique dataset that covers multiple health behaviours in a critical real-world setting.

Frameworks for Implementation, Uptake, and Use of Cardiometabolic Disease-Related Digital Health Interventions in Ethnic Minority Populations: Scoping Review

BACKGROUND

Digital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the implementation of digital health interventions. We considered frameworks to include any models, theories, or taxonomies that describe or predict implementation, uptake, and use of digital health interventions.

OBJECTIVE

We aimed to assess how health inequalities are addressed in frameworks relevant to the implementation, uptake, and use of digital health interventions; health and ethnic inequalities; and interventions for cardiometabolic disease.

METHODS

SCOPUS, PubMed, EMBASE, Google Scholar, and gray literature were searched to identify papers on frameworks relevant to the implementation, uptake, and use of digital health interventions; ethnically or culturally diverse populations and health inequalities; and interventions for cardiometabolic disease. We assessed the extent to which frameworks address health inequalities, specifically ethnic inequalities; explored how they were addressed; and developed recommendations for good practice.

RESULTS

Of 58 relevant papers, 22 (38%) included frameworks that referred to health inequalities. Inequalities were conceptualized as society-level, system-level, intervention-level, and individual. Only 5 frameworks considered all levels. Three frameworks considered how digital health interventions might interact with or exacerbate existing health inequalities, and 3 considered the process of health technology implementation, uptake, and use and suggested opportunities to improve equity in digital health. When ethnicity was considered, it was often within the broader concepts of social determinants of health. Only 3 frameworks explicitly addressed ethnicity: one focused on culturally tailoring digital health interventions, and 2 were applied to management of cardiometabolic disease.

CONCLUSIONS

Existing frameworks evaluate implementation, uptake, and use of digital health interventions, but to consider factors related to ethnicity, it is necessary to look across frameworks. We have developed a visual guide of the key constructs across the 4 potential levels of action for digital health inequalities, which can be used to support future research and inform digital health policies.

Theory-based habit modeling for enhancing behavior prediction in behavior change support systems

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users' habit strengths can be very useful for behavior change support systems, for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, building on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies on dental behavior change ( N = 36 and N = 75 ), where we instructed participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. The results showed that for the task of predicting future brushing behavior, the theory-based model that computed habit strength achieved an accuracy of 68.6% (Study 1) and 76.1% (Study 2), which outperformed the model that relied on self-reported behavioral determinants but showed no advantage over models that relied on past behavior. We discuss the implications of our results for research on behavior change support systems and habit formation.

A Local Community-Based Social Network for Mental Health and Well-being (Quokka): Exploratory Feasibility Study

BACKGROUND

Developing healthy habits and maintaining prolonged behavior changes are often difficult tasks. Mental health is one of the largest health concerns globally, including for college students.

OBJECTIVE

Our aim was to conduct an exploratory feasibility study of local community-based interventions by developing Quokka, a web platform promoting well-being activity on university campuses. We evaluated the intervention's potential for promotion of local, social, and unfamiliar activities pertaining to healthy habits.

METHODS

To evaluate this framework's potential for increased participation in healthy habits, we conducted a 6-to-8-week feasibility study via a "challenge" across 4 university campuses with a total of 277 participants. We chose a different well-being theme each week, and we conducted weekly surveys to (1) gauge factors that motivated users to complete or not complete the weekly challenge, (2) identify participation trends, and (3) evaluate the feasibility of the intervention to promote local, social, and novel well-being activities. We tested the hypotheses that Quokka participants would self-report participation in more local activities than remote activities for all challenges (Hypothesis H1), more social activities than individual activities (Hypothesis H2), and new rather than familiar activities (Hypothesis H3).

RESULTS

After Bonferroni correction using a Clopper-Pearson binomial proportion confidence interval for one test, we found that there was a strong preference for local activities for all challenge themes. Similarly, users significantly preferred group activities over individual activities (P<.001 for most challenge themes). For most challenge themes, there were not enough data to significantly distinguish a preference toward familiar or new activities (P<.001 for a subset of challenge themes in some schools).

CONCLUSIONS

We find that local community-based well-being interventions such as Quokka can facilitate positive behaviors. We discuss these findings and their implications for the research and design of location-based digital communities for well-being promotion.

Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator

Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with users. Instead of designing such complex strategies manually, reinforcement learning (RL) can be used to adaptively optimize intervention strategies concerning the user’s context. In this paper, we focus on the issue of overwhelming interactions when learning a good adaptive strategy for the user in RL-based mHealth intervention agents. We present a data-driven approach integrating psychological insights and knowledge of historical data. It allows RL agents to optimize the strategy of delivering context-aware notifications from empirical data when counterfactual information (user responses when receiving notifications) is missing. Our approach also considers a constraint on the frequency of notifications, which reduces the interaction burden for users. We evaluated our approach in several simulation scenarios using real large-scale running data. The results indicate that our RL agent can deliver notifications in a manner that realizes a higher behavioral impact than context-blind strategies.