Enhancing physical activity promotion in midlife women with technology-based self-monitoring and social connectivity: A pilot study

Butryn, Meghan L.; Arigo, Danielle; Raggio, Greer A.; Colasanti, Marie; Forman, Evan M.

doi:10.1177/1359105314558895

Cited by 46 publications

(76 citation statements)

References 24 publications

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“…Number of log-ins [ 83 , 137 ], number of times app opened [ 92 , 103 ], number of days device worn [ 139 , 165 , 166 ], self-reported frequency of viewing activity trackers [ 136 ]…”

Section: Resultsmentioning

confidence: 99%

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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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“…Number of log-ins [ 83 , 137 ], number of times app opened [ 92 , 103 ], number of days device worn [ 139 , 165 , 166 ], self-reported frequency of viewing activity trackers [ 136 ]…”

Section: Resultsmentioning

confidence: 99%

“…Use of social features, including self-reported frequency of viewing social media messages [ 139 ], number of social media messages sent [ 50 , 106 , 130 , 140 ], number of times leader board page accessed [ 139 ], number of likes or posts on Facebook [ 61 ], number of YouTube video views [ 160 ]…”

Section: Resultsmentioning

confidence: 99%

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

McCallum¹,

Rooksby²,

Gray³

2018

JMIR Mhealth Uhealth

111

View full text Add to dashboard Cite

show abstract

“…Common types of activity data from these devices include number of steps, time spent in physical activity by intensity, and time spent “idle.” The devices also have additional functions that can be used to support behavior change, such as goal setting (eg, 10,000 steps per day), prompts/cues, and social networking and accountability [ 7 ]. The uptake of these devices, both in the consumer market and in research, has been rapid [ 8 - 10 ]. It is estimated that more than 50 million smartwatch and health and fitness trackers were sold worldwide in 2015 and it is predicted that this figure will reach more than 80 million in 2016 [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry

Gomersall¹,

Ng²,

Burton³

et al. 2016

J Med Internet Res

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BackgroundActivity trackers are increasingly popular with both consumers and researchers for monitoring activity and for promoting positive behavior change. However, there is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings.ObjectiveThe aim was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior with data from the ActiGraph GT3X+ accelerometer in a free-living context.MethodsThirty-two participants were recruited using convenience sampling; 29 provided valid data for this study (female: 90%, 26/29; age: mean 39.6, SD 11.0 years). On two occasions for 7 days each, participants wore an ActiGraph GT3X+ accelerometer on their right hip and either a hip-worn Fitbit One (n=14) or wrist-worn Jawbone UP (n=15) activity tracker. Daily estimates of steps and very active minutes were derived from the Fitbit One (n=135 days) and steps, active time, and longest idle time from the Jawbone UP (n=154 days). Daily estimates of steps, MVPA, and longest sedentary bout were derived from the corresponding days of ActiGraph data. Correlation coefficients and Bland-Altman plots with examination of systematic bias were used to assess convergent validity and agreement between the devices and the ActiGraph. Cohen’s kappa was used to assess the agreement between each device and the ActiGraph for classification of active versus inactive (≥10,000 steps per day and ≥30 min/day of MVPA) comparable with public health guidelines.ResultsCorrelations with ActiGraph estimates of steps and MVPA ranged between .72 and .90 for Fitbit One and .56 and .75 for Jawbone UP. Compared with ActiGraph estimates, both devices overestimated daily steps by 8% (Fitbit One) and 14% (Jawbone UP). However, mean differences were larger for daily MVPA (Fitbit One: underestimated by 46%; Jawbone UP: overestimated by 50%). There was systematic bias across all outcomes for both devices. Correlations with ActiGraph data for longest idle time (Jawbone UP) ranged from .08 to .19. Agreement for classifying days as active or inactive using the ≥10,000 steps/day criterion was substantial (Fitbit One: κ=.68; Jawbone UP: κ=.52) and slight-fair using the criterion of ≥30 min/day of MVPA (Fitbit One: κ=.40; Jawbone UP: κ=.14).ConclusionsThere was moderate-strong agreement between the ActiGraph and both Fitbit One and Jawbone UP for the estimation of daily steps. However, due to modest accuracy and systematic bias, they are better suited for consumer-based self-monitoring (eg, for the public consumer or in behavior change interventions) rather than to evaluate research outcomes. The outcomes that relate to health-enhancing MVPA (eg, “very active minutes” for Fitbit One or “active time” for Jawbone UP) and sedentary behavior (“idle time” for Jawbone UP) should be used with caution by consumers and researchers alike.

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“…In addition to addressing psychological needs for competence, incorporation of social components may support motivation through promoting relatedness to other service users. Research on such technologies in exercise interventions suggests that use can be quickly integrated in participants’ lives [ 24 ] and may increase physical activity levels [ 25 – 29 ]; however, overall, the evidence is equivocal [ 23 ]. Furthermore, little is known about the acceptability of these technologies to ERS populations or if the benefits will remain once the initial novelty has ceased.…”

Section: Introductionmentioning

confidence: 99%

Protocol for a feasibility randomised controlled trial of the use of Physical ACtivity monitors in an Exercise Referral Setting: the PACERS study

Hawkins

Edwards

Charles

et al. 2017

Pilot Feasibility Stud

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BackgroundExercise referral schemes are recommended by the National Institute for Clinical Excellence (NICE) for physical activity promotion among inactive patients with health conditions or risk factors. Whilst there is evidence for the initial effectiveness and cost-effectiveness of such schemes for increasing physical activity, evidence of long-term effects is limited. Techniques such as goal setting, self-monitoring and personalised feedback may support motivation for physical activity. Technologies such as activity monitoring devices provide an opportunity to enhance delivery of motivational techniques. This paper describes the PACERS study protocol, which aims to assess the feasibility and acceptability of implementing an activity monitor within the existing Welsh National Exercise Referral Scheme (NERS) and proposed evaluation methodology for a full-scale randomised controlled trial.Methods/designThe PACERS study consists of a pilot randomised controlled trial, process evaluation and exploratory economic analyses. Participants will be recruited from the generic pathway of the Welsh NERS and will be randomly assigned to receive the intervention or usual practice. Usual practice is a 16-week structured exercise programme; the intervention consists of an accelerometry-based activity monitor (MyWellnessKey) and an associated web platform (MyWellnessCloud). The primary outcomes are predefined progression criteria assessing the acceptability and feasibility of the intervention and feasibility of the proposed evaluation methodology. Postal questionnaires will be completed at baseline (time 0: T0), 16 weeks after T0 (T1) and 12 months after T0 (T2). Routinely collected data will also be accessed at the same time points. A sub-sample of intervention participants and exercise referral staff will be interviewed following initiation of intervention delivery and at the end of the study.DiscussionThe PACERS study seeks to assess the feasibility of adding a novel motivational component to an existing effective intervention in order to enhance effects on physical activity and support longer-term maintenance. The study will provide insight into the acceptability of activity-monitoring technologies to an exercise referral population and delivery staff. Data from this study will be used to determine whether and how to proceed to a full-scale trial of effectiveness of the intervention, including any necessary refinements to intervention implementation or the proposed evaluation methodology.Trial registration ISRCTN85785652

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Enhancing physical activity promotion in midlife women with technology-based self-monitoring and social connectivity: A pilot study

Cited by 46 publications

References 24 publications

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

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

Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry

Protocol for a feasibility randomised controlled trial of the use of Physical ACtivity monitors in an Exercise Referral Setting: the PACERS study

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