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Leonard KS, de Brito JN, Larouche ML, Rydell SA, Mitchell NR, Pereira MA, Buman MP. Effect of Weight Goals on Sitting and Moving During a Worksite Sedentary Time Reduction Intervention. TRANSLATIONAL JOURNAL OF THE AMERICAN COLLEGE OF SPORTS MEDICINE 2022; 7:e000210. [PMID: 36213514 PMCID: PMC9534174 DOI: 10.1249/tjx.0000000000000210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Introduction/Purpose Although many US adults report trying to lose weight, little research has examined weight loss goals as a motivator for reducing workplace sitting and increasing physical activity. This exploratory analysis examined weight goals and the association with changes in workplace sitting, physical activity, and weight. Methods Employees (N = 605) were drawn from worksites participating in Stand and Move at Work. Worksites (N = 24) were randomized to a multilevel behavioral intervention with (STAND+) or without (MOVE+) sit-stand workstations for 12 months; MOVE+ worksites received sit-stand workstations from 12 to 24 months. At each assessment (baseline and 3, 12, and 24 months), participants were weighed and wore activPAL monitors. Participants self-reported baseline weight goals and were categorized into the "Lose Weight Goal" (LWG) group if they reported trying to lose weight or into the "Other Weight Goal" (OWG) group if they did not. Results Generalized linear mixed models revealed that within STAND+, LWG and OWG had similar sitting time through 12 months. However, LWG sat significantly more than OWG at 24 months. Within MOVE+, sitting time decreased after introduction of sit-stand workstations for LWG and OWG, although LWG sat more than OWG. Change in physical activity was minimal and weight remained stable in all groups. Conclusions Patterns of change in workplace sitting were more favorable in OWG relative to LWG, even in the absence of notable weight change. Expectations of weight loss might be detrimental for reductions in workplace sitting. Interventionists may want to emphasize non-weight health benefits of reducing workplace sitting.
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Affiliation(s)
- Krista S. Leonard
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Junia N. de Brito
- School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | | | - Sarah A. Rydell
- School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | | | - Mark A. Pereira
- School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Matthew P. Buman
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
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McGowan A, Sittig S, Bourrie D, Benton R, Iyengar S. The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation. JMIR Mhealth Uhealth 2022; 10:e40576. [PMID: 36103226 PMCID: PMC9520383 DOI: 10.2196/40576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/04/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users.
Objective
This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions.
Methods
An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics.
Results
The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; P<.001), with an adjusted R2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (P<.001) and ≥60 years (P<.001). Model 2 was significant (F13,6536=341.035; P<.001), with an adjusted R2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness.
Conclusions
This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category.
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Affiliation(s)
- Aleise McGowan
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS, United States
| | - Scott Sittig
- University of Louisiana at Lafayette, Lafayette, LA, United States
| | - David Bourrie
- University of South Alabama, Mobile, AL, United States
| | - Ryan Benton
- University of South Alabama, Mobile, AL, United States
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Ong AKS, Prasetyo YT, Yuduang N, Nadlifatin R, Persada SF, Robas KPE, Chuenyindee T, Buaphiban T. Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137979. [PMID: 35805634 PMCID: PMC9265314 DOI: 10.3390/ijerph19137979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 02/08/2023]
Abstract
With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.
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Affiliation(s)
- Ardvin Kester S. Ong
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
| | - Yogi Tri Prasetyo
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
- Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taiwan
- Correspondence: ; Tel.: +63(2)-8247-5000 (ext. 6202)
| | - Nattakit Yuduang
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
- School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
| | - Reny Nadlifatin
- Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia;
| | - Satria Fadil Persada
- Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta 11480, Indonesia;
| | - Kirstien Paola E. Robas
- School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines; (A.K.S.O.); (N.Y.); (K.P.E.R.)
| | - Thanatorn Chuenyindee
- Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand; (T.C.); (T.B.)
| | - Thapanat Buaphiban
- Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand; (T.C.); (T.B.)
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Influencing Factors of Acceptance and Use Behavior of Mobile Health Application Users: Systematic Review. Healthcare (Basel) 2021; 9:healthcare9030357. [PMID: 33809828 PMCID: PMC8004182 DOI: 10.3390/healthcare9030357] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 11/17/2022] Open
Abstract
Purpose/Significance: Mobile health applications provide a convenient way for users to obtain health information and services. Studying the factors that influence users’ acceptance and use of mobile health applications (apps or Apps) will help to improve users’ actual usage behavior. Method/Process: Based on the literature review method and using the Web of Science core database as the data source, this paper summarizes the relevant research results regarding the influencing factors of the acceptance and use behavior of mobile health application users and makes a systematic review of the influencing factors from the perspectives of the individual, society, and application (app or App) design. Result/Conclusion: In terms of the individual dimension, the users’ behavior is influenced by demographic characteristics and motivations. Social attributes, source credibility, and legal issues all affect user behavior in the social dimension. In the application design dimension, functionality, perceived ease of use and usefulness, security, and cost are the main factors. At the end of the paper, suggestions are given to improve the users’ acceptability of mobile health applications and improve their use behavior.
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Marchant G, Bonaiuto F, Bonaiuto M, Guillet Descas E. Exercise and Physical Activity eHealth in COVID-19 Pandemic: A Cross-Sectional Study of Effects on Motivations, Behavior Change Mechanisms, and Behavior. Front Psychol 2021; 12:618362. [PMID: 33692722 PMCID: PMC7937732 DOI: 10.3389/fpsyg.2021.618362] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/12/2021] [Indexed: 11/20/2022] Open
Abstract
OBJECTIVES The aims of this research were (1) to compare the levels of physical activity of eHealth users and non-users, (2) to determine the effects of these technologies on motivations, and (3) to establish the relationship that could exist between psychological constructs and physical activity behaviors. METHODS This cross-sectional study involved 569 adults who responded to an online questionnaire during confinement in France. The questions assessed demographics, usage of eHealth for exercise and physical activity, and behavioral levels. The questionnaire also measured the constructs of Social Cognitive Theory, the Theory of Planned Behavior, and automaticity facets toward eHealth for exercise and physical activity. RESULTS Participants who were users of eHealth for exercise and physical activity presented significantly higher levels of vigorous physical activity and total physical activity per week than non-users (p < 0.001). The chi-square test showed significant interactions between psychological constructs toward eHealth (i.e., self-efficacy, behavioral attitudes, intentions, and automaticity) and physical activity levels (all interactions were p < 0.05). Self-efficacy was significantly and negatively correlated with walking time per week. Concerning the automaticity facets, efficiency was positive and significantly correlated with vigorous physical activity levels per week (p < 0.05). Then, regressions analyses showed that self-efficacy and automaticity efficiency explained 5% of the variance of walking minutes per week (ß = -0.27, p < 0.01) and vigorous physical activity per week (ß = 0.20, p < 0.05), respectively. CONCLUSION This study has shown that people during confinement looked for ways to stay active through eHealth. However, we must put any technological solution into perspective. The eHealth offers possibilities to stay active, however its benefits and the psychological mechanisms affected by it remains to be demonstrated: eHealth could be adapted to each person and context.
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Affiliation(s)
- Gonzalo Marchant
- Laboratory of Vulnerabilities and Innovation in Sport, UFR STAPS, Claude Bernard University Lyon 1, Lyon, France
| | | | - Marino Bonaiuto
- CIRPA – Interuniversity Research Centre in Environmental Psychology, Department of Psychology of Developmental and Socialization Processes, Sapienza University of Rome, Rome, Italy
| | - Emma Guillet Descas
- Laboratory of Vulnerabilities and Innovation in Sport, UFR STAPS, Claude Bernard University Lyon 1, Lyon, France
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Rising CJ, Jensen RE, Moser RP, Oh A. Characterizing the US Population by Patterns of Mobile Health Use for Health and Behavioral Tracking: Analysis of the National Cancer Institute's Health Information National Trends Survey Data. J Med Internet Res 2020; 22:e16299. [PMID: 32406865 PMCID: PMC7256752 DOI: 10.2196/16299] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 12/23/2019] [Accepted: 02/03/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Multiple types of mobile health (mHealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. However, despite their availability, some individuals do not own, do not realize they own, or own but do not use these technologies. Others may use mHealth devices, but their use varies in tracking health, behaviors, and goals. Examining patterns of mHealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mHealth interventions for diverse audiences. OBJECTIVE The aim of this study was to identify patterns of mHealth use for health and behavioral tracking in the US adult population and to characterize the population according to those patterns. METHODS We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6789) to characterize respondents according to 5 mutually exclusive reported patterns of mHealth use for health and behavioral tracking: (1) mHealth nonowners and nonusers report not owning or using devices to track health, behaviors, or goals; (2) supertrackers track health or behaviors and goals using a smartphone or tablet plus other devices (eg, Fitbit); (3) app trackers use only a smartphone or tablet; (4) device trackers use only nonsmartphone or nontablet devices and do not track goals; and (5) nontrackers report having smartphone or tablet health apps but do not track health, behaviors, or goals. RESULTS Being in the mHealth nonowners and nonusers category (vs all mHealth owners and users) is associated with males, older age, lower income, and not being a health information seeker. Among mHealth owners and users, characteristics of device trackers and supertrackers were most distinctive. Compared with supertrackers, device trackers have higher odds of being male (odds ratio [OR] 2.22, 95% CI 1.55-3.19), older age (vs 18-34 years; 50-64 years: OR 2.83, 95% CI 1.52-5.30; 65+ years: OR 6.28, 95% CI 3.35-11.79), have an annual household income of US $20,000 to US $49,999 (vs US $75,000+: OR 2.31, 95% CI 1.36-3.91), and have a chronic condition (OR 1.69, 95% CI 1.14-2.49). Device trackers also have higher odds of not being health information seekers than supertrackers (OR 2.98, 95% CI 1.66-5.33). CONCLUSIONS Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mHealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to those of mHealth nonowners and nonusers. Our results suggest patterns of mHealth use may inform how to target mHealth interventions to enhance reach and facilitate healthy behaviors.
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Affiliation(s)
- Camella J Rising
- Health Communication and Informatics Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, United States
| | - Roxanne E Jensen
- Outcomes Research Branch, Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, United States
| | - Richard P Moser
- Office of the Associate Director, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, United States
| | - April Oh
- Health Communication and Informatics Research Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, United States
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