Work and Non-Work Physical Activity Predict Real-Time Smoking Level and Urges in Young Adults

The association between different types of physical activity and smoking behavior

BACKGROUND

Smoking is harmful, which has become a major public health burden. Physical activity may be related to smoking. Physical activity is one of the current methods for smoking control and smoking cessation. Different types of physical activity may have different effect on smoking behavior.

OBJECTIVE

The purpose of this study was to identify the direction and extent of the impact of different types of physical activity above moderate intensity (including work physical activity, recreational physical activity, commuter physical activity and sedentary behavior) on smoking behavior.

MATERIALS AND METHODS

In this study, a total of 2,015 individuals (1,233 males and 782 females, mean age 54.02±17.31 years) was selected from the representative population aged 20 and above in the National Health and Nutrition Survey of the United States from 2017 to 2018. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) ; the tobacco use questionnaire (SMQ) was used to determine whether the sample had smoking behavior at this stage. Binary Logistic regression analysis was performed with various physical activities as independent variables and smoking behavior as dependent variables. All data were analyzed through Statistical Product and Service Solutions (SPSS) 26.0.

RESULTS

After adjusted for all confounding variables, physical activity at work was close to significantly associated with smoking behavior (P=0.053), odds ratio (OR) =1.135 (95%Cl: 0.999-1.289). Recreational physical activity was significantly associated with smoking behavior (P < 0.001), OR=0.729 (95%Cl: 0.639-0.832). Commuting physical activity was significantly associated with smoking behavior (P < 0.001), OR=1.214 (95%Cl:1.048-1.405). Sedentary behavior was significantly associated with smoking behavior (P < 0.001), OR=1.363 (95%Cl: 1.154-1.611).

CONCLUSIONS

Given that different types of physical activity have different associations with smoking behavior. Therefore, when physical activity is used as a tobacco control measurement, it is necessary to pay attention to the type and environment of physical activity. Recreational physical activities should be appropriately increased, sedentary behavior should be reduced, and smoking prohibit environment should be expanded as far as possible to achieve better clinical intervention effects.

Use of Electronic Ecological Momentary Assessment Methodologies in Physical Activity, Sedentary Behavior, and Sleep Research in Young Adults: Systematic Review

BACKGROUND

Recent technological advances allow for the repeated sampling of real-time data in natural settings using electronic ecological momentary assessment (eEMA). These advances are particularly meaningful for investigating physical activity, sedentary behavior, and sleep in young adults who are in a critical life stage for the development of healthy lifestyle behaviors.

OBJECTIVE

This study aims to describe the use of eEMA methodologies in physical activity, sedentary behavior, and sleep research in young adults.

METHODS

The PubMed, CINAHL, PsycINFO, Embase, and Web of Science electronic databases were searched through August 2022. Inclusion criteria were use of eEMA; sample of young adults aged 18 to 25 years; at least 1 measurement of physical activity, sedentary behavior, or sleep; English language; and a peer-reviewed report of original research. Study reports were excluded if they were abstracts, protocols, or reviews. The risk of bias assessment was conducted using the National Heart, Lung, and Blood Institute's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Screening, data extraction, and risk of bias assessments were conducted by independent authors, with discrepancies resolved by consensus. Descriptive statistics and narrative synthesis were used to identify overarching patterns within the following categories guided by the Checklist for Reporting Ecological Momentary Assessments Studies: study characteristics, outcomes and measures, eEMA procedures, and compliance.

RESULTS

The search resulted in 1221 citations with a final sample of 37 reports describing 35 unique studies. Most reports (28/37, 76%) were published in the last 5 years (2017-2022), used observational designs (35/37, 95%), consisted of samples of college students or apprentices (28/35, 80%), and were conducted in the United States (22/37, 60%). The sample sizes ranged from 14 to 1584 young adults. Physical activity was measured more frequently (28/37, 76%) than sleep (16/37, 43%) or sedentary behavior (4/37, 11%). Of the 37 studies, 11 (30%) reports included 2 movement behaviors and no reports included 3 movement behaviors. eEMA was frequently used to measure potential correlates of movement behaviors, such as emotional states or feelings (25/37, 68%), cognitive processes (7/37, 19%), and contextual factors (9/37, 24%). There was wide variability in the implementation and reporting of eEMA procedures, measures, missing data, analysis, and compliance.

CONCLUSIONS

The use of eEMA methodologies in physical activity, sedentary behavior, and sleep research in young adults has greatly increased in recent years; however, reports continue to lack standardized reporting of features unique to the eEMA methodology. Additional areas in need of future research include the use of eEMA with more diverse populations and the incorporation of all 3 movement behaviors within a 24-hour period. The findings are intended to assist investigators in the design, implementation, and reporting of physical activity, sedentary behavior, and sleep research using eEMA in young adults.

TRIAL REGISTRATION

PROSPERO CRD42021279156; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021279156.

mHealth technology for ecological momentary assessment in physical activity research: a systematic review

OBJECTIVE

To systematically review the publications on ecological momentary assessment (EMA) relating to physical activity (PA) behavior in order to classify the methodologies, and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. As a result of this review, we want to ask if there is enough evidence to propose the use of the term "mEMA" (mobile-based EMA).

DESIGN

A systematic review according to PRISMA Statement (PROSPERO registration: CRD42018088136).

METHOD

Four databases (PsycINFO, CINALH, Medline and Web of Science Core Collection) were searched electronically from 2008 to February 2018.

RESULTS

A total of 76 studies from 297 potential articles on the use of EMA and PA were included in this review. It was found that 71% of studies specifically used "EMA" for assessing PA behaviors but the rest used other terminology that also adjusted to the inclusion criteria. Just over half (51.3%) of studies (39) used mHealth technology, mainly smartphones, for collecting EMA data. The majority (79.5%) of these studies (31 out of 39) were published during the last 4 years. On the other hand, 58.8% of studies that only used paper-and-pencil were published during the first 3 years of the 10-year period analyzed. An accelerometer was the main built-in sensor used for collecting PA behavior by means of mHealth (69%). Most of the studies were carried out on young-adult samples, with only three studies in older adults. Women were included in 60% of studies, and healthy people in 82%. The studies lasted between 1 and 7 days in 57.9%, and between three and seven assessments per day were carried out in 37%. The most popular topics evaluated together with PA were psychological state and social and environmental context.

CONCLUSIONS

We have classified the EMA methodologies used for assessing PA behaviors. A total of 71% of studies used the term "EMA" and 51.3% used mHealth technology. Accelerometers have been the main built-in sensor used for collecting PA. The change of trend in the use of tools for EMA in PA coincides with the technological advances of the last decade due to the emergence of smartphones and mHealth technology. There is enough evidence to use the term mEMA when mHealth technology is being used for monitoring real-time lifestyle behaviors in natural situations. We define mEMA as the use of mobile computing and communication technologies for the EMA of health and lifestyle behaviors. It is clear that the use of mHealth is increasing, but there is still a lot to be gained from taking advantage of all the capabilities of this technology in order to apply EMA to PA behavior. Thus, mEMA methodology can help in the monitoring of healthy lifestyles under both subjective and objective perspectives. The tendency for future research should be the automatic recognition of the PA of the user without interrupting their behavior. The ecological information could be completed with voice messages, image captures or brief text selections on the touch screen made in real time, all managed through smartphone apps. This methodology could be extended when EMA combined with mHealth are used to evaluate other lifestyle behaviors.

Spirituality/Religiosity (SpR), Leisure-Time Physical Activity, and Sedentary Behaviour in Students at a Catholic University

Some studies have found an association between spirituality and/or religiosity (SpR) and cardiovascular health, for reasons which remain unclear. This study explores whether SpR is linked to physical activity whilst young, which in turn is linked to long-term cardiovascular health. Students at a Catholic University in Australia completed a survey combining the SpREUK-P SF 17 SpR questionnaire with elements of the long-form 7-day recall International Physical Activity Questionnaire. Respondents who scored highly in the unconventional spiritual practices components of SpREUK-P engaged in moderate intensity physical activity more frequently. This finding may have implications for health promotion.

mActive-Smoke: A Prospective Observational Study Using Mobile Health Tools to Assess the Association of Physical Activity With Smoking Urges

BACKGROUND

Evidence that physical activity can curb smoking urges is limited in scope to acute effects and largely reliant on retrospective self-reported measures. Mobile health technologies offer novel mechanisms for capturing real-time data of behaviors in the natural environment.

OBJECTIVE

This study aimed to explore this in a real-world longitudinal setting by leveraging mobile health tools to assess the association between objectively measured physical activity and concurrent smoking urges in a 12-week prospective observational study.

METHODS

We enrolled 60 active smokers (≥3 cigarettes per day) and recorded baseline demographics, physical activity, and smoking behaviors using a Web-based questionnaire. Step counts were measured continuously using the Fitbit Charge HR. Participants reported instantaneous smoking urges via text message using a Likert scale ranging from 1 to 9. On study completion, participants reported follow-up smoking behaviors in an online exit survey.

RESULTS

A total of 53 participants (aged 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite) recorded at least 6 weeks of data and were thus included in the analysis. We recorded 15,365 urge messages throughout the study, with a mean of 290 (SD 62) messages per participant. Mean urge over the course of the study was positively associated with daily cigarette consumption at follow-up (Pearson r=.33; P=.02). No association existed between daily steps and mean daily urge (beta=-6.95×10-3 per 1000 steps; P=.30). Regression models of acute effects, however, did reveal modest inverse associations between steps within 30-, 60-, and 120-min time windows of a reported urge (beta=-.0191 per 100 steps, P<.001). Moreover, 6 individuals (approximately 10% of the study population) exhibited a stronger and consistent inverse association between steps and urge at both the day level (mean individualized beta=-.153 per 1000 steps) and 30-min level (mean individualized beta=-1.66 per 1000 steps).

CONCLUSIONS

Although there was no association between objectively measured daily physical activity and concurrently self-reported smoking urges, there was a modest inverse relationship between recent step counts (30-120 min) and urge. Approximately 10% of the individuals appeared to have a stronger and consistent inverse association between physical activity and urge, a provocative finding warranting further study.

Exercise decreases risk of future active disease in patients with inflammatory bowel disease in remission

BACKGROUND

Although exercise impacts quality of life in patients with inflammatory bowel disease, little is known about its role in disease activity. Among patients with inflammatory bowel disease in remission, we aimed to evaluate the association between exercise and subsequent active disease.

METHODS

We performed a prospective study using the Crohn's and Colitis Foundation of America Partners' internet-based cohort of individuals with self-reported inflammatory bowel disease. We identified participants in remission, defined as short Crohn's disease activity index <150 or simple clinical colitis activity index ≤2. The primary exposure was exercise status, measured using the validated Godin leisure-time activity index. The primary study outcome, assessed after 6 months, was active disease defined using the above disease activity index thresholds. We used bivariate and multivariate analyses to describe the independent association between exercise and risk of active disease.

RESULTS

We identified 1308 patients with Crohn's disease (CD) and 549 with ulcerative or indeterminate colitis (UC/IC) in remission, of whom 227 (17.4%) with CD and 135 (24.6%) with UC/IC developed active disease after 6 months. Higher exercise level was associated with decreased risk of active disease for CD (adjusted risk ratio: 0.72, 95% confidence interval: 0.55-0.94) and UC/IC (adjusted risk ratio: 0.78, 95% confidence interval: 0.54-1.13).

CONCLUSIONS

In patients with CD in remission, those with higher exercise levels were significantly less likely to develop active disease at 6 months. In patients with UC/IC in remission, patients with higher exercise levels were less likely to develop active disease at 6 months; however this was not statistically significant.