1
|
Messer LH, D’Souza E, Merchant G, Mueller L, Farnan J, Habif S, Pinsker JE. Smartphone Bolus Feature Increases Number of Insulin Boluses in People With Low Bolus Frequency. J Diabetes Sci Technol 2024; 18:10-13. [PMID: 37605474 PMCID: PMC10899852 DOI: 10.1177/19322968231191796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
BACKGROUND The t:connect mobile app from Tandem Diabetes Care recently added a feature to allow t:slim X2 insulin pump users to initiate an insulin bolus from their personal smartphone. User experience and user interface considerations prioritized safety and ease of use, and we examined whether the smartphone bolus feature changed bolus behavior in individuals who bolused less than three times/day. METHODS We performed a retrospective analysis of t:slim X2 insulin pump users in the United States who had remotely updated their insulin pump software to be compatible with the smartphone bolus version of the app and who gave less than three boluses per day prior to the smartphone bolus update. RESULTS Of the 4470 early adopters who met these criteria, the median number of boluses was 2.2 per day (prior to smartphone bolus update) versus 2.7 per day (after smartphone bolus update), equating to approximately half a bolus more delivered per day (P < .001). Overall, a median of one bolus per day was administered by smartphone app as opposed to being initiated from the screen on the insulin pump. CONCLUSION This analysis found a significant increase in bolusing behavior among early adopters of the smartphone bolus feature of the t:connect mobile app.
Collapse
|
2
|
Merchant G, Valentine K, Hevener W, Willes L, Ta D, Hernandez R, Gagnon R, Chen K, Blase A. 0682 Evaluation Of An Incentive-based Intervention To Improve 90-day Adherence In Pap-naive Patients. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Although PAP therapy is the gold standard treatment for obstructive sleep apnea, adherence to treatment is suboptimal. Without sustained therapy adherence, patients are at risk of serious negative health outcomes. The objective of this study was to test whether a digitally delivered monetary and social reward program helped patients new to PAP therapy. Financial incentive schemes are effective in helping patients adhere to difficult medication or therapy plans. Additionally, there is an abundance of evidence that social support is a critical component to long-term health behavior change.
Methods
This prospective, randomized, single site pilot is evaluating the effectiveness of an app-based intervention in helping patients adhere to PAP therapy. The financial incentive design leverages loss aversion, and the social incentive design leverages the strength of close ties and variable reinforcement. The primary endpoint is mean PAP usage at 3 months. Secondary endpoints include Medicare compliance, change in functional status, and baseline scores of perceived disease severity, claustrophobia, coping skills, and health literacy as moderators of the intervention’s effectiveness. Study recruitment is ongoing, with an expected sample size of 150 subjects.
Results
Of the 132 subjects enrolled, 56% are male, 61% are Caucasian, and 65% are married. The mean age is 49.6 ± 12.0 years and mean BMI is 32.4 ± 8.4 kg/m2. Additional demographics such as income level, education level, and number of children along with the primary and secondary endpoints will be presented. A subgroup analysis of the primary endpoint will be generated for subjects identified as strugglers within the first 3 days of usage.
Conclusion
The results of this study will provide insight into methods such as financial and social incentives delivered via a smartphone on initial compliance with PAP therapy, as well as provide more information on the behavioral change associated with beginning PAP therapy.
Support
ResMed
Collapse
Affiliation(s)
| | | | | | - L Willes
- Willes Consulting Group, Inc, Encinitas, CA
| | - D Ta
- ResMed Science Center, San Diego, CA
| | | | | | | | - A Blase
- ResMed Science Center, San Diego, CA
| |
Collapse
|
3
|
Camplain R, Sotres-Alvarez D, Alvarez C, Wilson R, Perreira KM, Castañeda SF, Merchant G, Gellman MD, Chambers EC, Gallo LC, Evenson KR. The association of acculturation with accelerometer-assessed and self-reported physical activity and sedentary behavior: The Hispanic Community Health Study/Study of Latinos. Prev Med Rep 2020; 17:101050. [PMID: 32021761 PMCID: PMC6994298 DOI: 10.1016/j.pmedr.2020.101050] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 12/16/2019] [Accepted: 01/11/2020] [Indexed: 01/09/2023] Open
Abstract
Associations between acculturation and physical activity varied across measurements. Higher social acculturation was positively associated with self-reported physical activity. Language acculturation, but not language preference, was positively associated with leisure-time physical activity. Language acculturation, but not language preference, was negatively associated with occupational physical activity. Among workers, greater acculturation was associated with lower occupational physical activity. Longer residency in the US was associated with higher accelerometer-assessed moderate-to-vigorous physical activity. Most acculturation measures were positively associated with self-report, but not accelerometer-assessed sedentary behavior.
The adoption of US culture among immigrants has been associated with higher leisure-time physical activity and sedentary behavior. However, most research to date assesses this association using single measures of acculturation and physical activity. Our objective was to describe the cross-sectional association between acculturation and both physical activity and sedentary behavior among US Hispanic/Latino adults. Participants included Hispanic/Latinos 18–74 years living in four US locations enrolled in the Hispanic Community Health Study/Study of Latinos from 2008 to 2011. Acculturation was measured using acculturation scales (language and social), years in the US, language preference, and age at immigration. Physical activity and sedentary behavior were measured using the Global Physical Activity Questionnaire (N = 15,355) and Actical accelerometer (N = 11,954). Poisson, logistic, and linear regression were used, accounting for complex design and sampling weights. English-language preference was positively associated with self-reported leisure-time and transportation physical activity and accelerometer-assessed moderate-to-vigorous physical activity (MVPA). Social acculturation was positively associated with self-reported leisure-time and transportation physical activity and MVPA. Years in the US and age at immigration were positively associated with accelerometer-assessed MVPA. Language acculturation, years in the US, and age at immigration were associated with occupational physical activity among those who reported employment. Most acculturation measures were associated with self-reported sitting but not with accelerometer-assessed sedentary behavior. Different measures of acculturation, capturing various domains acculturation, were associated with physical activity and sedentary behavior. However, the direction of the association was dependent on the measures of acculturation physical activity/sedentary behavior, highlighting the complexity of these relationships.
Collapse
Affiliation(s)
- Ricky Camplain
- Northern Arizona University, Center for Health Equity Research, 1395 S. Knoles Drive, ARD Building, Suite 140, Flagstaff, AZ 86011, USA.,University of North Carolina, Department of Epidemiology, 123 W. Franklin St. Building C CB 8050, Suite 410, Chapel Hill, NC 27599-8050, USA
| | - Daniela Sotres-Alvarez
- University of North Carolina at Chapel Hill, Department of Biostatistics, 123 W. Franklin St., Building C, Suite 450, Chapel Hill, NC 27516, USA
| | - Carolina Alvarez
- University of North Carolina at Chapel Hill, Thurston Arthritis Research Center, 3300 Thurston Bldg., CB #7280, Chapel Hill, NC 27599-7280, USA
| | - Rebbecca Wilson
- University of North Carolina at Chapel Hill, Department of Biostatistics, 123 W. Franklin St., Building C, Suite 450, Chapel Hill, NC 27516, USA
| | - Krista M Perreira
- University of North Carolina, Department of Social Medicine, 333 S. Columbia St., 342B MacNider Hall, Chapel Hill, NC 27599, USA
| | - Sheila F Castañeda
- San Diego State University, South Bay Latino Research Center, 780 Bay Blvd, Sute 200 Chula Vista, CA 91910, USA
| | - Gina Merchant
- University of California, San Diego, Department of Medicine, 9500 Gilman Drive #0881, La Jolla, CA 92093-0881, USA
| | - Marc D Gellman
- University of Miami, Department of Psychology, Flipse Building, 5665 Ponce de Leon Blvd., Coral Gables, FL 33146, USA
| | - Earle C Chambers
- Albert Einstein College of Medicine, Department of Family and Social Medicine, Jack and Pearl Resnick Campus, 1300 Morris Park Avenue, Block Room 408, Bronx, NY 10461, USA
| | - Linda C Gallo
- San Diego State University, Department of Psychology, 5500 Campanile Drive, San Diego, CA 92182-4611, USA
| | - Kelly R Evenson
- University of North Carolina, Department of Epidemiology, 123 W. Franklin St. Building C CB 8050, Suite 410, Chapel Hill, NC 27599-8050, USA
| |
Collapse
|
4
|
Tate DF, Lytle L, Polzien K, Diamond M, Leonard KR, Jakicic JM, Johnson KC, Olson CM, Patrick K, Svetkey LP, Wing RR, Lin PH, Coday M, Laska MN, Merchant G, Czaja SJ, Schulz R, Belle SH. Deconstructing Weight Management Interventions for Young Adults: Looking Inside the Black Box of the EARLY Consortium Trials. Obesity (Silver Spring) 2019; 27:1085-1098. [PMID: 31135102 PMCID: PMC6749832 DOI: 10.1002/oby.22506] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 03/10/2019] [Indexed: 11/09/2022]
Abstract
OBJECTIVE The goal of the present study was to deconstruct the 17 treatment arms used in the Early Adult Reduction of weight through LifestYle (EARLY) weight management trials. METHODS Intervention materials were coded to reflect behavioral domains and behavior change techniques (BCTs) within those domains planned for each treatment arm. The analytical hierarchy process was employed to determine an emphasis profile of domains in each intervention. RESULTS The intervention arms used BCTs from all of the 16 domains, with an average of 29.3 BCTs per intervention arm. All 12 of the interventions included BCTs from the six domains of Goals and Planning, Feedback and Monitoring, Social Support, Shaping Knowledge, Natural Consequences, and Comparison of Outcomes; 11 of the 12 interventions shared 15 BCTs in common across those six domains. CONCLUSIONS Weight management interventions are complex. The shared set of BCTs used in the EARLY trials may represent a core intervention that could be studied to determine the required emphases of BCTs and whether additional BCTs add to or detract from efficacy. Deconstructing interventions will aid in reproducibility and understanding of active ingredients.
Collapse
Affiliation(s)
- Deborah F. Tate
- Departments of Health Behavior and Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Leslie Lytle
- Departments of Health Behavior and Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kristen Polzien
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Molly Diamond
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelsey R. Leonard
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - John M. Jakicic
- Physical Activity and Weight Management Research Center, Department of Health and Physical Activity, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Karen C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | | | - Kevin Patrick
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla CA, USA
| | - Laura P. Svetkey
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Rena R. Wing
- The Miriam Hospital, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Pao-Hwa Lin
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Mathilda Coday
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Melissa N. Laska
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Gina Merchant
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla CA, USA
| | - Sara J. Czaja
- Department of Psychiatry and Behavioral Sciences, University of Miami, Miami, FL, USA
| | - Richard Schulz
- Department of Psychology and University Center for Social and Urban Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven H. Belle
- Graduate School of Public Health, Epidemiology & Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|
5
|
Merchant G, Blase A, Willes L, Benjafield A. 0531 Patients Who Immediately Struggle With CPAP: Identifying A Patient Population In Need Of Early Intervention. Sleep 2019. [DOI: 10.1093/sleep/zsz067.529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
6
|
Moller AC, Merchant G, Conroy DE, West R, Hekler E, Kugler KC, Michie S. Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential. J Behav Med 2017; 40:85-98. [PMID: 28058516 PMCID: PMC5532801 DOI: 10.1007/s10865-016-9818-7] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 12/20/2016] [Indexed: 11/25/2022]
Abstract
As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.
Collapse
Affiliation(s)
- Arlen C Moller
- Illinois Institute of Technology, Chicago, IL, USA.
- Northwestern University, Chicago, IL, USA.
| | - Gina Merchant
- University of California, San Diego, San Diego, CA, USA
| | - David E Conroy
- The Pennsylvania State University, State College, PA, USA
| | | | | | - Kari C Kugler
- The Pennsylvania State University, State College, PA, USA
| | | |
Collapse
|
7
|
Merchant G, Weibel N, Pina L, Griswold WG, Fowler JH, Ayala GX, Gallo LC, Hollan J, Patrick K. Face-to-Face and Online Networks: College Students' Experiences in a Weight-Loss Trial. J Health Commun 2017; 22:75-83. [PMID: 28060581 PMCID: PMC6534122 DOI: 10.1080/10810730.2016.1250847] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This study aimed to understand how college students participating in a 2-year randomized controlled trial (Project SMART: Social and Mobile Approach to Reduce Weight; N = 404) engaged their social networks and used social and mobile technologies to try and lose weight. Participants in the present study (n = 20 treatment, n = 18 control) were approached after a measurement visit and administered semi-structured interviews. Interviews were analyzed using principles from grounded theory. Treatment group participants appreciated the timely support provided by the study and the integration of content across multiple technologies. Participants in both groups reported using non-study-designed apps to help them lose weight, and many participants knew one another outside of the study. Individuals talked about weight-loss goals with their friends face to face and felt accountable to follow through with their intentions. Although seeing others' success online motivated many, there was a range of perceived acceptability in talking about personal health-related information on social media. The findings from this qualitative study can inform intervention trials using social and mobile technologies to promote weight loss. For example, weight-loss trials should measure participants' use of direct-to-consumer technologies and interconnectivity so that treatment effects can be isolated and cross-contamination accounted for.
Collapse
Affiliation(s)
- Gina Merchant
- a Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Qualcomm Institute/Calit 2 , University of California San Diego , La Jolla , California , USA
- b Department of Biomedical Informatics , University of California San Diego , La Jolla , California , USA
| | - Nadir Weibel
- a Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Qualcomm Institute/Calit 2 , University of California San Diego , La Jolla , California , USA
- c Department of Computer Science and Engineering , University of California San Diego , La Jolla , California , USA
| | - Laura Pina
- a Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Qualcomm Institute/Calit 2 , University of California San Diego , La Jolla , California , USA
- c Department of Computer Science and Engineering , University of California San Diego , La Jolla , California , USA
| | - William G Griswold
- a Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Qualcomm Institute/Calit 2 , University of California San Diego , La Jolla , California , USA
- c Department of Computer Science and Engineering , University of California San Diego , La Jolla , California , USA
| | - James H Fowler
- a Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Qualcomm Institute/Calit 2 , University of California San Diego , La Jolla , California , USA
- d Department of Medicine , School of Medicine, University of California San Diego , La Jolla , California , USA
| | - Guadalupe X Ayala
- e Division of Health Promotion, Graduate School of Public Health , San Diego State University , San Diego , California , USA
| | - Linda C Gallo
- f San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology , San Diego , California , USA
| | - James Hollan
- g Department of Cognitive Science , University of California San Diego , La Jolla , California , USA
| | - Kevin Patrick
- a Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Qualcomm Institute/Calit 2 , University of California San Diego , La Jolla , California , USA
| |
Collapse
|
8
|
Godino JG, Merchant G, Norman GJ, Donohue MC, Marshall SJ, Fowler JH, Calfas KJ, Huang JS, Rock CL, Griswold WG, Gupta A, Raab F, Fogg BJ, Robinson TN, Patrick K. Using social and mobile tools for weight loss in overweight and obese young adults (Project SMART): a 2 year, parallel-group, randomised, controlled trial. Lancet Diabetes Endocrinol 2016; 4:747-755. [PMID: 27426247 PMCID: PMC5005009 DOI: 10.1016/s2213-8587(16)30105-x] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 05/21/2016] [Accepted: 05/24/2016] [Indexed: 11/26/2022]
Abstract
BACKGROUND Few weight loss interventions are evaluated for longer than a year, and even fewer employ social and mobile technologies commonly used among young adults. We assessed the efficacy of a 2 year, theory-based, weight loss intervention that was remotely and adaptively delivered via integrated user experiences with Facebook, mobile apps, text messaging, emails, a website, and technology-mediated communication with a health coach (the SMART intervention). METHODS In this parallel-group, randomised, controlled trial, we enrolled overweight or obese college students (aged 18-35 years) from three universities in San Diego, CA, USA. Participants were randomly assigned (1:1) to receive either the intervention (SMART intervention group) or general information about health and wellness (control group). We used computer-based permuted-block randomisation with block sizes of four, stratified by sex, ethnicity, and college. Participants, study staff, and investigators were masked until the intervention was assigned. The primary outcome was objectively measured weight in kg at 24 months. Differences between groups were evaluated using linear mixed-effects regression within an intention-to-treat framework. Objectively measured weight at 6, 12, and 18 months was included as a secondary outcome. The trial is registered with ClinicalTrials.gov, number NCT01200459. FINDINGS Between May 18, 2011, and May 17, 2012, 404 individuals were randomly assigned to the intervention (n=202) or control (n=202). Participants' mean (SD) age was 22·7 (3·8) years. 284 (70%) participants were female and 125 (31%) were Hispanic. Mean (SD) body-mass index at baseline was 29·0 (2·8) kg/m(2). At 24 months, weight was assessed in 341 (84%) participants, but all 404 were included in analyses. Weight, adjusted for sex, ethnicity, and college, was not significantly different between the groups at 24 months (-0·79 kg [95% CI -2·02 to 0·43], p=0·204). However, weight was significantly less in the intervention group compared with the control group at 6 months (-1·33 kg [95% CI -2·36 to -0·30], p=0·011) and 12 months (-1·33 kg [-2·30 to -0·35], p=0·008), but not 18 months (-0·67 kg [95% CI -1·69 to 0·35], p=0·200). One serious adverse event in the intervention group (gallstones) could be attributable to rapid and excessive weight loss. INTERPRETATION Social and mobile technologies did not facilitate sustained reductions in weight among young adults, although these approaches might facilitate limited short-term weight loss. FUNDING The National Heart, Lung, and Blood Institute of the National Institutes of Health (U01 HL096715).
Collapse
Affiliation(s)
- Job G Godino
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Gina Merchant
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Gregory J Norman
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Michael C Donohue
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Simon J Marshall
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - James H Fowler
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Division of Global Public Health, University of California, San Diego, La Jolla, CA, USA; Department of Political Science, University of California, San Diego, La Jolla, CA, USA
| | - Karen J Calfas
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Jeannie S Huang
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Division of Pediatric Gastroenterology, University of California, San Diego, La Jolla, CA, USA
| | - Cheryl L Rock
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - William G Griswold
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA
| | - Anjali Gupta
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Development, Aging, and Regeneration, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Fredric Raab
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA
| | - B J Fogg
- Behavior Design Laboratory, Human Sciences and Technologies Advanced Research Institute, Stanford University, Stanford, CA, USA
| | - Thomas N Robinson
- Stanford Solutions Science Laboratory, Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Kevin Patrick
- Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA; Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA.
| |
Collapse
|
9
|
Yue Xu S, Nelson S, Kerr J, Godbole S, Patterson R, Merchant G, Abramson I, Staudenmayer J, Natarajan L. Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity. Stat Methods Med Res 2016; 27:1168-1186. [PMID: 27405327 DOI: 10.1177/0962280216657119] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Physical inactivity is a recognized risk factor for many chronic diseases. Accelerometers are increasingly used as an objective means to measure daily physical activity. One challenge in using these devices is missing data due to device nonwear. We used a well-characterized cohort of 333 overweight postmenopausal breast cancer survivors to examine missing data patterns of accelerometer outputs over the day. Based on these observed missingness patterns, we created psuedo-simulated datasets with realistic missing data patterns. We developed statistical methods to design imputation and variance weighting algorithms to account for missing data effects when fitting regression models. Bias and precision of each method were evaluated and compared. Our results indicated that not accounting for missing data in the analysis yielded unstable estimates in the regression analysis. Incorporating variance weights and/or subject-level imputation improved precision by >50%, compared to ignoring missing data. We recommend that these simple easy-to-implement statistical tools be used to improve analysis of accelerometer data.
Collapse
Affiliation(s)
- Selene Yue Xu
- 1 Department of Mathematics, UC San Diego, La Jolla, USA
| | - Sandahl Nelson
- 2 Graduate School of Public Health, San Diego State University, San Diego, USA.,3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA
| | - Jacqueline Kerr
- 3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.,4 Moores UC San Diego Cancer Center, UC San Diego, La Jolla, USA.,5 Center for Wireless and Population Health Sciences, UC San Diego, La Jolla, USA
| | - Suneeta Godbole
- 5 Center for Wireless and Population Health Sciences, UC San Diego, La Jolla, USA
| | - Ruth Patterson
- 3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.,4 Moores UC San Diego Cancer Center, UC San Diego, La Jolla, USA
| | - Gina Merchant
- 5 Center for Wireless and Population Health Sciences, UC San Diego, La Jolla, USA
| | - Ian Abramson
- 1 Department of Mathematics, UC San Diego, La Jolla, USA
| | - John Staudenmayer
- 6 Department of Mathematics and Statistics, University of Massachusetts, Amherst, USA
| | - Loki Natarajan
- 3 Department of Family Medicine and Public Health, UC San Diego, La Jolla, USA.,4 Moores UC San Diego Cancer Center, UC San Diego, La Jolla, USA
| |
Collapse
|
10
|
Kerr J, Takemoto M, Bolling K, Atkin A, Carlson J, Rosenberg D, Crist K, Godbole S, Lewars B, Pena C, Merchant G. Two-Arm Randomized Pilot Intervention Trial to Decrease Sitting Time and Increase Sit-To-Stand Transitions in Working and Non-Working Older Adults. PLoS One 2016; 11:e0145427. [PMID: 26735919 PMCID: PMC4703201 DOI: 10.1371/journal.pone.0145427] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 12/02/2015] [Indexed: 11/19/2022] Open
Abstract
Background Excessive sitting has been linked to poor health. It is unknown whether reducing total sitting time or increasing brief sit-to-stand transitions is more beneficial. We conducted a randomized pilot study to assess whether it is feasible for working and non-working older adults to reduce these two different behavioral targets. Methods Thirty adults (15 workers and 15 non-workers) age 50–70 years were randomized to one of two conditions (a 2-hour reduction in daily sitting or accumulating 30 additional brief sit-to-stand transitions per day). Sitting time, standing time, sit-to-stand transitions and stepping were assessed by a thigh worn inclinometer (activPAL). Participants were assessed for 7 days at baseline and followed while the intervention was delivered (2 weeks). Mixed effects regression analyses adjusted for days within participants, device wear time, and employment status. Time by condition interactions were investigated. Results Recruitment, assessments, and intervention delivery were feasible. The ‘reduce sitting’ group reduced their sitting by two hours, the ‘increase sit-to-stand’ group had no change in sitting time (p < .001). The sit-to-stand transition group increased their sit-to-stand transitions, the sitting group did not (p < .001). Conclusions This study was the first to demonstrate the feasibility and preliminary efficacy of specific sedentary behavioral goals. Trial Registration clinicaltrials.gov NCT02544867
Collapse
Affiliation(s)
- Jacqueline Kerr
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
- * E-mail:
| | - Michelle Takemoto
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| | - Khalisa Bolling
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| | - Andrew Atkin
- UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, CB2 0QQ, United Kingdom
| | - Jordan Carlson
- Children's Mercy Hospital, Kansas City, Missouri, United States of America
| | - Dori Rosenberg
- Group Health Research Institute, Seattle, Washington, United States of America
| | - Katie Crist
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| | - Suneeta Godbole
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| | - Brittany Lewars
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| | - Claudia Pena
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| | - Gina Merchant
- Department of Family Medicine and Public Health, University of California, San Diego, California, United States of America
| |
Collapse
|
11
|
Merchant G, Buelna C, Castañeda SF, Arredondo EM, Marshall SJ, Strizich G, Sotres-Alvarez D, Chambers EC, McMurray RG, Evenson KR, Stoutenberg M, Hankinson AL, Talavera GA. Accelerometer-measured sedentary time among Hispanic adults: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Prev Med Rep 2015; 2:845-53. [PMID: 26844159 PMCID: PMC4721303 DOI: 10.1016/j.pmedr.2015.09.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Excessive sedentary behavior is associated with negative health outcomes independent of physical activity. Objective estimates of time spent in sedentary behaviors are lacking among adults from diverse Hispanic/Latino backgrounds. The objective of this study was to describe accelerometer-assessed sedentary time in a large, representative sample of Hispanic/Latino adults living in the United States, and compare sedentary estimates by Hispanic/Latino background, sociodemographic characteristics and weight categories. This study utilized baseline data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) that included adults aged 18-74 years from four metropolitan areas (N = 16,415). Measured with the Actical accelerometer over 6 days, 76.9% (n = 12,631) of participants had > 10 h/day and > 3 days of data. Participants spent 11.9 h/day (SD 3.0), or 74% of their monitored time in sedentary behaviors. Adjusting for differences in wear time, adults of Mexican background were the least (11.6 h/day), whereas adults of Dominican background were the most (12.3 h/day), sedentary. Women were more sedentary than men, and older adults were more sedentary than younger adults. Household income was positively associated, whereas employment was negatively associated, with sedentary time. There were no differences in sedentary time by weight categories, marital status, or proxies of acculturation. To reduce sedentariness among these populations, future research should examine how the accumulation of various sedentary behaviors differs by background and region, and which sedentary behaviors are amenable to intervention.
Collapse
Affiliation(s)
- Gina Merchant
- Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, United States
- Center for Wireless and Population Health Systems, University of California, San Diego, San Diego, CA, United States
- Corresponding author.
| | - Christina Buelna
- Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States
| | - Sheila F. Castañeda
- Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States
| | - Elva M. Arredondo
- Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States
| | - Simon J. Marshall
- Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, United States
- Center for Wireless and Population Health Systems, University of California, San Diego, San Diego, CA, United States
| | - Garrett Strizich
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Daniela Sotres-Alvarez
- Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Earle C. Chambers
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Robert G. McMurray
- Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Kelly R. Evenson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Mark Stoutenberg
- Department of Public Health Sciences, University of Miami, Miami, FL, United States
| | - Arlene L. Hankinson
- Chronic Disease Division, Chicago Department of Public Health, Chicago, IL, United States
| | - Gregory A. Talavera
- Institute for Behavioral and Community Health, Graduate School of Public Health, San Diego State University, San Diego, CA, United States
| |
Collapse
|
12
|
Qi Q, Strizich G, Merchant G, Sotres-Alvarez D, Buelna C, Castañeda SF, Gallo LC, Cai J, Gellman MD, Isasi CR, Moncrieft AE, Sanchez-Johnsen L, Schneiderman N, Kaplan RC. Objectively Measured Sedentary Time and Cardiometabolic Biomarkers in US Hispanic/Latino Adults: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Circulation 2015; 132:1560-9. [PMID: 26416808 DOI: 10.1161/circulationaha.115.016938] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 08/03/2015] [Indexed: 01/17/2023]
Abstract
BACKGROUND Sedentary behavior is recognized as a distinct construct from lack of moderate-vigorous physical activity and is associated with deleterious health outcomes. Previous studies have primarily relied on self-reported data, whereas data on the relationship between objectively measured sedentary time and cardiometabolic biomarkers are sparse, especially among US Hispanics/Latinos. METHODS AND RESULTS We examined associations of objectively measured sedentary time (via Actical accelerometers for 7 days) and multiple cardiometabolic biomarkers among 12 083 participants, aged 18 to 74 years, from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Hispanics/Latinos of diverse backgrounds (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) were recruited from 4 US cities between 2008 and 2011. Sedentary time (<100 counts/min) was standardized to 16 hours/d of wear time. The mean sedentary time was 11.9 hours/d (74% of accelerometer wear time). After adjustment for moderate-vigorous physical activity and confounding variables, prolonged sedentary time was associated with decreased high-density lipoprotein cholesterol (P=0.04), and increased triglycerides, 2-hour glucose, fasting insulin, and homeostatic model assessment of insulin resistance (all P<0.0001). These associations were generally consistent across age, sex, Hispanic/Latino backgrounds, and physical activity levels. Even among individuals meeting physical activity guidelines, sedentary time was detrimentally associated with several cardiometabolic biomarkers (diastolic blood pressure, high-density lipoprotein cholesterol, fasting and 2-hour glucose, fasting insulin and homeostatic model assessment of insulin resistance; all P<0.05). CONCLUSIONS Our large population-based, objectively derived data showed deleterious associations between sedentary time and cardiometabolic biomarkers, independent of physical activity, in US Hispanics/Latinos. Our findings emphasize the importance of reducing sedentary behavior for the prevention of cardiometabolic diseases, even in those who meet physical activity recommendations.
Collapse
Affiliation(s)
- Qibin Qi
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.).
| | - Garrett Strizich
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Gina Merchant
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Daniela Sotres-Alvarez
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Christina Buelna
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Sheila F Castañeda
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Linda C Gallo
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Jianwen Cai
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Marc D Gellman
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Carmen R Isasi
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Ashley E Moncrieft
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Lisa Sanchez-Johnsen
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Neil Schneiderman
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| | - Robert C Kaplan
- From Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY (Q.Q., G.S., C.R.I., R.C.K.); San Diego State University, Graduate School of Public Health, San Diego, CA (G.M., C.B., S.F.C.); University of North Carolina, Collaborative Studies Coordinating Center, Department of Biostatistics, Chapel Hill, NC (D.S.-A., J.C.); San Diego State University, Department of Psychology, San Diego, CA (L.C.G.); University of Miami, Department of Psychology, Miami, FL (M.D.G., A.E.M., N.S.); and University of Illinois at Chicago, Department of Psychiatry, Chicago, IL (L.S.-J.)
| |
Collapse
|
13
|
Abstract
OBJECTIVE To identify current distracted driving (DD) behaviors among college students, primarily those involving cell phone use, and elucidate the opinions of the students on the most effective deterrent or intervention for reducing cell phone use. METHODS Students enrolled at 12 colleges and universities were recruited to participate in an online, anonymous survey. Recruitment was done via school-based list-serves and posters. School sizes ranged from 476 to over 30,000. The validated survey included 38 questions; 17 were specifically related to distracted driving. RESULTS Four thousand nine hundred sixty-four participants completed the surveys; the average age was 21.8, 66% were female, 82.7% were undergraduates, and 47% were white/non-Hispanic. Additionally, 4,517 (91%) reported phoning and/or texting while driving; 4,467 (90%) of drivers said they talk on the phone while driving; 1,241 (25%) reported using a hands-free device "most of the time"; 4,467 (90%) of drivers reported texting while driving; 2,488 (50%) reported sending texts while driving on the freeway; 2,978 (60%) while in stop-and-go traffic or on city streets; and 4,319 (87%) at traffic lights. Those who drove more often were more likely to drive distracted. When asked about their capability to drive distracted, 46% said they were capable or very capable of talking on a cell phone and driving, but they felt that only 8.5% of other drivers were capable. In a multivariate model, 9 predictors explained 44% of the variance in DD, which was statistically significant, F (17, 4945) = 224.31; P <.0001; R(2) = 0.44. The four strongest predictors (excluding driving frequency) were self-efficacy (i.e., confidence) in driving while multitasking (β = 0.37), perception of safety of multitasking while driving (β = 0.19), social norms (i.e., observing others multitasking while driving; β = 0.29), and having a history of crashing due to multitasking while driving (β = 0.11). CONCLUSIONS Distracted driving is a highly prevalent behavior among college students who have higher confidence in their own driving skills and ability to multitask than they have in other drivers' abilities. Drivers' self-efficacy for driving and multitasking in the car, coupled with a greater likelihood of having witnessed DD behaviors in others, greatly increased the probability that a student would engage in DD. Most students felt that policies, such as laws impacting driving privilege and insurance rate increases, would influence their behavior.
Collapse
Affiliation(s)
- Linda Hill
- a University of California , San Diego , California
| | | | | | | | | | | |
Collapse
|
14
|
Merchant G, Weibel N, Patrick K, Fowler JH, Norman GJ, Gupta A, Servetas C, Calfas K, Raste K, Pina L, Donohue M, Griswold WG, Marshall S. Click "like" to change your behavior: a mixed methods study of college students' exposure to and engagement with Facebook content designed for weight loss. J Med Internet Res 2014; 16:e158. [PMID: 24964294 PMCID: PMC4090380 DOI: 10.2196/jmir.3267] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Revised: 03/29/2014] [Accepted: 04/28/2014] [Indexed: 11/13/2022] Open
Abstract
Background Overweight or obesity is prevalent among college students and many gain weight during this time. Traditional face-to-face weight loss interventions have not worked well in this population. Facebook is an attractive tool for delivering weight loss interventions for college students because of its popularity, potential to deliver strategies found in successful weight loss interventions, and ability to support ongoing adaptation of intervention content. Objective The objective of this study was to describe participant exposure to a Facebook page designed to deliver content to overweight/obese college students in a weight loss randomized controlled trial (N=404) and examine participant engagement with behavior change campaigns for weight loss delivered via Facebook. Methods The basis of the intervention campaign model were 5 self-regulatory techniques: intention formation, action planning, feedback, goal review, and self-monitoring. Participants were encouraged to engage their existing social network to meet their weight loss goals. A health coach moderated the page and modified content based on usage patterns and user feedback. Quantitative analyses were conducted at the Facebook post- and participant-level of analysis. Participant engagement was quantified by Facebook post type (eg, status update) and interaction (eg, like) and stratified by weight loss campaign (sequenced vs nonsequenced). A subset of participants were interviewed to evaluate the presence of passive online engagement or “lurking.” Results The health coach posted 1816 unique messages to the study’s Facebook page over 21 months, averaging 3.45 posts per day (SD 1.96, range 1-13). In all, 72.96% (1325/1816) of the posts were interacted with at least once (eg, liked). Of these, approximately 24.75% (328/1325) had 1-2 interactions, 23.39% (310/1325) had 3-5 interactions, 25.13% (333/1325) had 6-8 interactions, and 41 posts had 20 or more interactions (3.09%, 41/1325). There was significant variability among quantifiable (ie, visible) engagement. Of 199 participants in the final intervention sample, 32 (16.1%) were highly active users and 62 (31.2%) never visibly engaged with the intervention on Facebook. Polls were the most popular type of post followed by photos, with 97.5% (79/81) and 80.3% (386/481) interacted with at least once. Participants visibly engaged less with posts over time (partial r=–.33; P<.001). Approximately 40% of the participants interviewed (12/29, 41%) reported passively engaging with the Facebook posts by reading but not visibly interacting with them. Conclusions Facebook can be used to remotely deliver weight loss intervention content to college students with the help of a health coach who can iteratively tailor content and interact with participants. However, visible engagement with the study’s Facebook page was highly variable and declined over time. Whether the level of observed engagement is meaningful in terms of influencing changes in weight behaviors and outcomes will be evaluated at the completion of the overall study.
Collapse
Affiliation(s)
- Gina Merchant
- Center for Wireless and Population Health Systems, The Qualcomm Institute/Calit2, Department of Family and Preventive Medicine, University of California San Diego, La Jolla, CA, United States.
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Patrick K, Marshall SJ, Davila EP, Kolodziejczyk JK, Fowler JH, Calfas KJ, Huang JS, Rock CL, Griswold WG, Gupta A, Merchant G, Norman GJ, Raab F, Donohue MC, Fogg BJ, Robinson TN. Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). Contemp Clin Trials 2014; 37:10-8. [PMID: 24215774 PMCID: PMC3910290 DOI: 10.1016/j.cct.2013.11.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Revised: 10/29/2013] [Accepted: 11/01/2013] [Indexed: 12/01/2022]
Abstract
PURPOSE To describe the theoretical rationale, intervention design, and clinical trial of a two-year weight control intervention for young adults deployed via social and mobile media. METHODS A total of 404 overweight or obese college students from three Southern California universities (M(age) = 22( ± 4) years; M(BMI) = 29( ± 2.8); 70% female) were randomized to participate in the intervention or to receive an informational web-based weight loss program. The intervention is based on behavioral theory and integrates intervention elements across multiple touch points, including Facebook, text messaging, smartphone applications, blogs, and e-mail. Participants are encouraged to seek social support among their friends, self-monitor their weight weekly, post their health behaviors on Facebook, and e-mail their weight loss questions/concerns to a health coach. The intervention is adaptive because new theory-driven and iteratively tailored intervention elements are developed and released over the course of the two-year intervention in response to patterns of use and user feedback. Measures of body mass index, waist circumference, diet, physical activity, sedentary behavior, weight management practices, smoking, alcohol, sleep, body image, self-esteem, and depression occur at 6, 12, 18, and 24 months. Currently, all participants have been recruited, and all are in the final year of the trial. CONCLUSION Theory-driven, evidence-based strategies for physical activity, sedentary behavior, and dietary intake can be embedded in an intervention using social and mobile technologies to promote healthy weight-related behaviors in young adults.
Collapse
Affiliation(s)
- K Patrick
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States.
| | - S J Marshall
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - E P Davila
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - J K Kolodziejczyk
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States; Graduate School of Public Health, San Diego State University, San Diego, CA 92182, United States
| | - J H Fowler
- Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States; Medical Genetics Division and Political Science Department, University of California, San Diego, La Jolla, CA 92093, United States
| | - K J Calfas
- Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - J S Huang
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Rady Children's Hospital, San Diego, CA 92123, United States; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, United States
| | - C L Rock
- Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - W G Griswold
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, United States
| | - A Gupta
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States
| | - G Merchant
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States; Graduate School of Public Health, San Diego State University, San Diego, CA 92182, United States
| | - G J Norman
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - F Raab
- Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - M C Donohue
- Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States
| | - B J Fogg
- Behavior Design Lab, Human Sciences and Technologies Advanced Research Institute, Stanford University, Stanford, CA 94305, United States
| | - T N Robinson
- Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA 94305, United States
| |
Collapse
|
16
|
Affiliation(s)
- Simon J Marshall
- Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, 92093-0901, USA.
| | | |
Collapse
|
17
|
Merchant G, Pulvers K, Brooks RD, Edwards J. Coping with the urge to smoke: A real-time analysis. Res Nurs Health 2012; 36:3-15. [DOI: 10.1002/nur.21520] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/23/2012] [Indexed: 11/12/2022]
|
18
|
Abstract
The present study examined the association between positive traits, pain catastrophizing, and pain perceptions. We hypothesized that pain catastrophizing would mediate the relationship between positive traits and pain. First, participants (n = 114) completed the Trait Hope Scale, the Life Orientation Test- Revised, and the Pain Catastrophizing Scale. Participants then completed the experimental pain stimulus, a cold pressor task, by submerging their hand in a circulating water bath (0º Celsius) for as long as tolerable. Immediately following the task, participants completed the Short-Form McGill Pain Questionnaire (MPQ-SF). Pearson correlation found associations between hope and pain catastrophizing (r = -.41, p < .01) and MPQ-SF scores (r = -.20, p < .05). Optimism was significantly associated with pain catastrophizing (r = -.44, p < .01) and MPQ-SF scores (r = -.19, p < .05). Bootstrapping, a non-parametric resampling procedure, tested for mediation and supported our hypothesis that pain catastrophizing mediated the relationship between positive traits and MPQ-SF pain report. To our knowledge, this investigation is the first to establish that the protective link between positive traits and experimental pain operates through lower pain catastrophizing.
Collapse
Affiliation(s)
- Anna Hood
- Department of Psychology, California State University San Marcos, San Marcos, California, 92026, USA
| | | | | | | | | |
Collapse
|
19
|
O'Laughlin K, Go S, Gabayan G, Iqbal E, Merchant G, Lopez-Freeman R, Zucker M, Hoffman J, Mower W. 190: Fear of Brain Herniation From Lumbar Puncture: Do History and Physical Exam Indicate Abnormalities on Head Computed Tomography? Ann Emerg Med 2009. [DOI: 10.1016/j.annemergmed.2009.06.218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
20
|
|
21
|
|