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Xue Q, Yang J, Wang H, Zhang D. How and When Leisure Crafting Enhances College Students’ Well-Being: A (Quantitative) Weekly Diary Study. Psychol Res Behav Manag 2022; 15:273-290. [PMID: 35210877 PMCID: PMC8857993 DOI: 10.2147/prbm.s344717] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/24/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Qing Xue
- Shandong Youth University of Political Science, Jinan, People's Republic of China
| | - Jinxin Yang
- University of Jinan, Jinan, People's Republic of China
| | - Huatian Wang
- Industrial Engineering and Innovation Science, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Deyu Zhang
- College of Foreign Languages, Ocean University of China, Qingdao, People's Republic of China
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2
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Crochiere RJ, Zhang FZ, Juarascio AS, Goldstein SP, Thomas JG, Forman EM. Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse. Transl Behav Med 2021; 11:2099-2109. [PMID: 34529044 DOI: 10.1093/tbm/ibab123] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions. Passive sensors also can measure lapse risk factors and may offer advantages over EMA (e.g., objective, automatic, semicontinuous data collection), but currently can measure only a few lapse predictors, a notable limitation. This study preliminarily compared the burden and accuracy of commercially available sensors versus established EMA in lapse prediction. N = 23 adults with overweight/obesity completed a 6-week commercial app-based weight loss program. Participants wore a Fitbit, enabled GPS tracking, completed EMA, and reported on EMA and sensor burden poststudy via a 5-point Likert scale. Sensed risk factors were physical activity and sleep (accelerometer), geolocation (GPS), and time, from which 233 features (measurable characteristics of sensor signals) were extracted. EMA measured 19 risk factors, lapse, and categorized GPS into meaningful geolocations. Two supervised binary classification models (LASSO) were created: the sensor model predicted lapse with 63% sensitivity (true prediction rate of lapse) and 60% specificity (true prediction rate of non-lapse) and EMA model with 59% sensitivity and 72% specificity. EMA model accuracy was higher, but self-reported EMA burden (M = 2.96, SD = 1.02) also was higher (M = 1.50, SD = 0.94). EMA model accuracy was superior, but EMA burden was higher than sensor burden. Findings highlight the promise of sensors in contributing to lapse prediction, and future research may use EMA, sensors, or both depending on prioritization of accuracy versus participant burden.
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Affiliation(s)
- Rebecca J Crochiere
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, PA 19104, USA
| | - Fengqing Zoe Zhang
- The Miriam Hospital's Weight Control and Diabetes Research Center, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Adrienne S Juarascio
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, PA 19104, USA
| | - Stephanie P Goldstein
- The Miriam Hospital's Weight Control and Diabetes Research Center, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - J Graham Thomas
- The Miriam Hospital's Weight Control and Diabetes Research Center, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Evan M Forman
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, PA 19104, USA
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3
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Laranjo L, Quiroz JC, Tong HL, Arevalo Bazalar M, Coiera E. A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study. J Med Internet Res 2020; 22:e19991. [PMID: 33289670 PMCID: PMC7755540 DOI: 10.2196/19991] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/06/2020] [Accepted: 11/11/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users. OBJECTIVE This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features. METHODS This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups-underweight-normal and overweight-obese BMI-using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis. RESULTS In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years; 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants' BMI decreased by 0.34 kg/m2 (P<.001), with a loss of 0.86 kg/m2 in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with individuals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app's self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370; P<.001). Most participants mentioned a desire for a more personalized intervention. CONCLUSIONS This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match individual preferences and needs.
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Affiliation(s)
- Liliana Laranjo
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.,Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Juan C Quiroz
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.,Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia
| | - Huong Ly Tong
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | | | - Enrico Coiera
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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4
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Abstract
PURPOSE OF REVIEW This review synthesizes recent research on remotely delivered interventions for obesity treatment, including summarizing outcomes and challenges to implementing these treatments as well as outlining recommendations for clinical implementation and future research. RECENT FINDINGS There are a wide range of technologies used for delivering obesity treatment remotely. Generally, these treatments appear to be acceptable and feasible, though weight loss outcomes are mixed. Engagement in these interventions, particularly in the long term, is a significant challenge. Newer technologies are rapidly developing and enable tailored and adaptable interventions, though research in this area is in its infancy. Further research is required to optimize potential benefits of remotely delivered interventions for obesity.
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Affiliation(s)
- Lauren E Bradley
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, 1645 W. Jackson Blvd. Suite 400, Chicago, IL, 60612, USA.
| | - Christine E Smith-Mason
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, 1645 W. Jackson Blvd. Suite 400, Chicago, IL, 60612, USA
| | - Joyce A Corsica
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, 1645 W. Jackson Blvd. Suite 400, Chicago, IL, 60612, USA
| | - Mackenzie C Kelly
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, 1645 W. Jackson Blvd. Suite 400, Chicago, IL, 60612, USA
| | - Megan M Hood
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, 1645 W. Jackson Blvd. Suite 400, Chicago, IL, 60612, USA
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5
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Tong HL, Coiera E, Tong W, Wang Y, Quiroz JC, Martin P, Laranjo L. Efficacy of a Mobile Social Networking Intervention in Promoting Physical Activity: Quasi-Experimental Study. JMIR Mhealth Uhealth 2019; 7:e12181. [PMID: 30920379 PMCID: PMC6458538 DOI: 10.2196/12181] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 12/13/2018] [Accepted: 01/30/2019] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Technological interventions such as mobile apps, Web-based social networks, and wearable trackers have the potential to influence physical activity; yet, only a few studies have examined the efficacy of an intervention bundle combining these different technologies. OBJECTIVE This study aimed to pilot test an intervention composed of a social networking mobile app, connected with a wearable tracker, and investigate its efficacy in improving physical activity, as well as explore participant engagement and the usability of the app. METHODS This was a pre-post quasi-experimental study with 1 arm, where participants were subjected to the intervention for a 6-month period. The primary outcome measure was the difference in daily step count between baseline and 6 months. Secondary outcome measures included engagement with the intervention and system usability. Descriptive and inferential statistical tests were conducted; posthoc subgroup analyses were carried out for participants with different levels of steps at baseline, app usage, and social features usage. RESULTS A total of 55 participants were enrolled in the study; the mean age was 23.6 years and 28 (51%) were female. There was a nonstatistically significant increase in the average daily step count between baseline and 6 months (mean change=14.5 steps/day, P=.98, 95% CI -1136.5 to 1107.5). Subgroup analysis comparing the higher and lower physical activity groups at baseline showed that the latter had a statistically significantly higher increase in their daily step count (group difference in mean change from baseline to 6 months=3025 steps per day, P=.008, 95% CI 837.9-5211.8). At 6 months, the retention rate was 82% (45/55); app usage decreased over time. The mean system usability score was 60.1 (SD 19.2). CONCLUSIONS This study showed the preliminary efficacy of a mobile social networking intervention, integrated with a wearable tracker to promote physical activity, particularly for less physically active subgroups of the population. Future research should explore how to address challenges faced by physically inactive people to provide tailored advices. In addition, users' perspectives should be explored to shed light on factors that might influence their engagement with the intervention.
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Affiliation(s)
- Huong Ly Tong
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - William Tong
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Ying Wang
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Juan C Quiroz
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Paige Martin
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Liliana Laranjo
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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6
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Cornelius J, Kennedy A, Wesslen R. An Examination of Twitter Data to Identify Risky Sexual Practices Among Youth and Young Adults in Botswana. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E656. [PMID: 30813392 PMCID: PMC6406710 DOI: 10.3390/ijerph16040656] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 02/18/2019] [Accepted: 02/19/2019] [Indexed: 12/21/2022]
Abstract
Botswana has the third highest rate of HIV infection, as well as one of the highest mobile phone density rates in the world. The rate of mobile cell phone adoption has increased three-fold over the past 10 years. Due to HIV infection rates, youth and young adults are the primary target for prevention efforts. One way to improve prevention efforts is to examine how risk reduction messages are disseminated on social media platforms such as Twitter. Thus, to identify key words related to safer sex practices and HIV prevention, we examined three months of Twitter data in Botswana. 1 December 2015, was our kick off date, and we ended data collection on 29 February 2016. To gather the tweets, we searched for HIV-related terms in English and in Setswana. From the 140,240 tweets collected from 251 unique users, 576 contained HIV-related terms. A representative sample of 25 active Twitter users comprised individuals, one government site and 2 organizations. Data revealed that tweets related to HIV prevention and AIDS did not occur more frequently during the month of December when compared to January and February (t = 3.62, p > 0.05). There was no significant difference between the numbers of HIV related tweets that occurred from 1 December 2015 to 29 February 2016 (F = 32.1, p > 0.05). The tweets occurred primarily during the morning and evening hours and on Tuesdays followed by Thursdays and Fridays. The least number of tweets occurred on Sunday. The highest number of followers was associated with the Botswana government Twitter site. Twitter analytics was found to be useful in providing insight into information being tweeted regarding risky sexual behaviors.
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Affiliation(s)
- Judith Cornelius
- School of Nursing, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA.
| | - Anna Kennedy
- School of Social Work, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA.
| | - Ryan Wesslen
- Computing and Information Systems, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA.
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7
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Tong HL, Coiera E, Laranjo L. Using a Mobile Social Networking App to Promote Physical Activity: A Qualitative Study of Users' Perspectives. J Med Internet Res 2018; 20:e11439. [PMID: 30578201 PMCID: PMC6320410 DOI: 10.2196/11439] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 09/06/2018] [Accepted: 09/08/2018] [Indexed: 01/30/2023] Open
Abstract
Background Despite many health benefits of physical activity, nearly a third of the world’s adult population is insufficiently active. Technological interventions, such as mobile apps, wearable trackers, and Web-based social networks, offer great promise in promoting physical activity, but little is known about users’ acceptability and long-term engagement with these interventions. Objective The aim of this study was to understand users’ perspectives regarding a mobile social networking intervention to promote physical activity. Methods Participants, mostly university students and staff, were recruited using purposive sampling techniques. Participants were enrolled in a 6-month feasibility study where they were provided with a wearable physical activity tracker (Fitbit Flex 2) and a wireless scale (Fitbit Aria) integrated with a social networking mobile app (named “fit.healthy.me”). We conducted semistructured, in-depth qualitative interviews and focus groups pre- and postintervention, which were recorded and transcribed verbatim. The data were analyzed in Nvivo 11 using thematic analysis techniques. Results In this study, 55 participants were enrolled; 51% (28/55) were females, and the mean age was 23.6 (SD 4.6) years. The following 3 types of factors emerged from the data as influencing engagement with the intervention and physical activity: individual (self-monitoring of behavior, goal setting, and feedback on behavior), social (social comparison, similarity and familiarity between users, and participation from other users in the network), and technological. In addition, automation and personalization were observed as enhancing the delivery of both individual and social aspects. Technological limitations were mentioned as potential barriers to long-term usage. Conclusions Self-regulatory techniques and social factors are important to consider when designing a physical activity intervention, but a one-size-fits-all approach is unlikely to satisfy different users’ preferences. Future research should adopt innovative research designs to test interventions that can adapt and respond to users’ needs and preferences throughout time.
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Affiliation(s)
- Huong Ly Tong
- Centre for Health Informatics, Australian Institute of Health Innovation, Sydney, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Sydney, Australia
| | - Liliana Laranjo
- Centre for Health Informatics, Australian Institute of Health Innovation, Sydney, Australia
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Henriksen A, Haugen Mikalsen M, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, Grimsgaard S. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. J Med Internet Res 2018; 20:e110. [PMID: 29567635 PMCID: PMC5887043 DOI: 10.2196/jmir.9157] [Citation(s) in RCA: 216] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 12/18/2017] [Accepted: 01/06/2018] [Indexed: 01/05/2023] Open
Abstract
Background New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.
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Affiliation(s)
- André Henriksen
- Department of Community Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | - Martin Haugen Mikalsen
- Department of Computer Science, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | | | - Miroslav Muzny
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.,Spin-Off Company and Research Results Commercialization Center, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Gunnar Hartvigsen
- Department of Computer Science, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | - Laila Arnesdatter Hopstock
- Department of Health and Care Sciences, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | - Sameline Grimsgaard
- Department of Community Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
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