Tweeting to Health

Chung, Arlene E.; Skinner, Asheley Cockrell; Hasty, Stephanie E.; Perrin, Eliana M.

doi:10.1177/0009922816653385

Cited by 104 publications

(52 citation statements)

References 13 publications

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“…The majority employed either a CCT design (n=7) or an RCT design (n=15). All interventions addressed weight management; however, the behavioral focus of each intervention differed: a total of 10 focused on both healthy eating and PA [61,62,64-67,69,71,73,74], 7 focused on multiple behaviors (eg, healthy eating, PA, stress management, and sleep) [57,59,60,72,76-78], 3 focused on self-weighing [63,70], 3 focused on PA only [58,68,79], and 1 focused on healthy eating only [75]. The number of participants within each of the studies ranged from 12 to 2621, with a mean sample size of 468 participants.…”

Section: Resultsmentioning

confidence: 99%

“…The duration of interventions ranged from 6 weeks to 24 months (mean=22 weeks), with an average retention rate at the final point of data collection of 79%. In terms of outcomes, 12 out of the 24 studies did not report any statistically significant changes in the weight-related measure(s) [57,58,61,65,66,68,71-73,77-79], 8 reported significant positive weight-related changes (eg, maintenance of a healthy weight or reversal of small gains) [60,62-64,70,74,76], and 4 reported mixed outcomes [59,67,69,75]. Refer to Multimedia Appendix 3 for a summary of individual study characteristics.…”

Section: Resultsmentioning

confidence: 99%

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Weight Management in Young Adults: Systematic Review of Electronic Health Intervention Components and Outcomes

et al. 2019

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Background Young adulthood is a vulnerable period for unhealthy lifestyle adoption and excess weight gain. Scant attention has been focused on developing and evaluating effective weight gain prevention strategies for this age group. Electronic health (eHealth) offers potential as a cost-effective means of delivering convenient, individually-tailored, and contextually-meaningful interventions at scale. Objective The primary aim of this systematic review was to locate and synthesize the evidence on eHealth weight management interventions targeting young adults, with a particular focus on (eHealth) intervention components and outcomes. Methods A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy was executed across the following electronic databases: Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, EBSCO, EMBASE, Emerald, Education Resources Information Center, Medical Literature Analysis and Retrieval System Online, Ovid, ProQuest, PsycINFO, PubMed, Science Direct, Scopus, and Web of Science. Furthermore, 2 reviewers independently assessed records for eligibility: peer-reviewed, published in English, and report evaluations of eHealth weight management interventions targeting healthy young adults (aged 18-35 years). Data were then extracted from studies that met the criteria for inclusion. The methodological quality of studies was independently assessed by 2 reviewers using the Effective Public Health Practice Project’s (EPHPP) quality assessment tool. A comprehensive narrative evidence synthesis was then completed. Results Out of the 1301 studies assessed for eligibility, 24 met the criteria for inclusion. According to the EPHPP quality assessment tool, overall, 19 studies were as rated weak, 5 as moderate, and none as strong. The narrative synthesis of intervention outcomes found 8 studies reported positive weight-related outcomes, 4 reported mixed outcomes, and 12 did not report any significant changes in weight-related outcomes. The narrative synthesis of (eHealth) intervention components led to 3 levels of classification. A total of 14 studies were classified as Web-based , 3 as mobile-based , and 7 as multicomponent interventions. Following the narrative synthesis, 5 key strategies were thematically identified: self-regulation (goal setting and self-monitoring), tailored or personalized feedback, contact with an interventionist, social support, and behavioral prompts (nudges and reminders) and booster messages. Conclusions Findings highlight the limited evidence base for eHealth weight management interventions targeting young adults. The complex nature of weight management presents an ongoing challenge for interventionists to identify what works, for whom, ho...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Weight Management in Young Adults: Systematic Review of Electronic Health Intervention Components and Outcomes

et al. 2019

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“…The majority of studies (90.9%, 101/111) used sensors to measure physical activity. These were most often the in-device sensors used to deliver feedback on physical activity (67.6%, 75/111) (eg, Fitbit [ 105 , 162 ]). Some studies used external sensors (eg, Acti-Graph GT3X [ActiGraph, Shalimar, FL, USA], Sensewear Armband [BodyMedia, Inc., Pittsburgh, PA], Omron pedometer [Omron Healthcare, Inc., Bannockburn, I]), instead of, or in triangulation with, in-device sensors (23.4%, 26/111).…”

Section: Resultsmentioning

confidence: 99%

“…Likelihood of users recommending the app or wearable to a friend or other people [ 116 , 139 , 162 , 165 , 166 ]…”

Section: Resultsmentioning

confidence: 99%

Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review

McCallum¹,

Rooksby²,

Gray³

2018

JMIR Mhealth Uhealth

111

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BackgroundAlthough many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work.ObjectivesThis study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed.MethodAn interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis.ResultsA total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance).ConclusionsThe rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid res...

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“…For example, mobile technologies could be used to collect diet and activity data to identify at-risk individuals and to mediate an intervention program. Small-scale intervention trials have been promising (13).…”

Section: Prevent Disease and Maintain Healthmentioning

confidence: 99%