A Multimodal mHealth Intervention (FeatForward) to Improve Physical Activity Behavior in Patients with High Cardiometabolic Risk Factors: Rationale and Protocol for a Randomized Controlled Trial

Agboola, Stephen; Palacholla, Ramya; Centi, Amanda; Kvedar, Joseph C.; Jethwani, Kamal

doi:10.2196/resprot.5489

Cited by 11 publications

(15 citation statements)

References 14 publications

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“…Users’ views on whether the app or wearable increased, or will continue to increase and promote, physical activity [ 39 , 52 , 70 , 75 , 79 , 94 , 103 , 113 , 122 , 123 , 126 , 143 , 145 ]…”

Section: Resultsmentioning

confidence: 99%

“…Users’ perceived usefulness or helpfulness of the app or wearable [ 39 , 74 , 103 , 116 ] and its components [ 52 , 59 , 136 , 165 , 166 ] in self-monitoring [ 54 ], supporting fitness and physical activity [ 118 , 136 ], and supporting them to stay motivated [ 163 ]…”

Section: Resultsmentioning

confidence: 99%

“…Ability of the app or wearable to provide answers to health-related questions [ 160 ] and insight into physical activity or health conditions [ 52 ]…”

Section: Resultsmentioning

confidence: 99%

“…Health care professionals’ perceptions of the usefulness of information about patients’ physical activity or health condition and whether it supported engagement with patients’ home care [ 52 ]…”

Section: Resultsmentioning

confidence: 99%

“…Whether the app or wearable was noticed and remarked upon by others [ 79 , 128 ] or prompted discussion with others [ 52 ].…”

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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Ability of the app or wearable to provide answers to health-related questions [ 160 ] and insight into physical activity or health conditions [ 52 ]…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Whether the app or wearable was noticed and remarked upon by others [ 79 , 128 ] or prompted discussion with others [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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

Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials

et al. 2018

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Despite growing interest in remote patient monitoring, limited evidence exists to substantiate claims of its ability to improve outcomes. Our aim was to evaluate randomized controlled trials (RCTs) that assess the effects of using wearable biosensors (e.g. activity trackers) for remote patient monitoring on clinical outcomes. We expanded upon prior reviews by assessing effectiveness across indications and presenting quantitative summary data. We searched for articles from January 2000 to October 2016 in PubMed, reviewed 4,348 titles, selected 777 for abstract review, and 64 for full text review. A total of 27 RCTs from 13 different countries focused on a range of clinical outcomes and were retained for final analysis; of these, we identified 16 high-quality studies. We estimated a difference-in-differences random effects meta-analysis on select outcomes. We weighted the studies by sample size and used 95% confidence intervals (CI) around point estimates. Difference-in-difference point estimation revealed no statistically significant impact of remote patient monitoring on any of six reported clinical outcomes, including body mass index (−0.73; 95% CI: −1.84, 0.38), weight (−1.29; −3.06, 0.48), waist circumference (−2.41; −5.16, 0.34), body fat percentage (0.11; −1.56, 1.34), systolic blood pressure (−2.62; −5.31, 0.06), and diastolic blood pressure (−0.99; −2.73, 0.74). Studies were highly heterogeneous in their design, device type, and outcomes. Interventions based on health behavior models and personalized coaching were most successful. We found substantial gaps in the evidence base that should be considered before implementation of remote patient monitoring in the clinical setting.

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Access to mobile phone and willingness to receive mHealth services among patients with diabetes in Northwest Ethiopia: a cross-sectional study

et al. 2019

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ObjectivesThis study aimed at determining access to mobile phone and willingness to receive mobile phone-based diabetes health services as well as identify associated factors in Northwest Ethiopia.DesignAn institution-based cross-sectional survey was conducted from February to March 2016.ParticipantsSystematic randomly selected 423 patients with diabetes.SettingUniversity of Gondar Hospital diabetic clinic.Main outcome measuresThe main outcome measure was willingness to receive diabetic health service via mobile phone voice call or messaging services.ResultsOut of 423 patients with diabetes, 329 (77.8%) had access to a mobile phone. Among the latter, 232 (70.5%) were willing to receive mobile phone-based health services. The educational status of patients (adjusted OR (AOR): 2.6 (95% CI: 1.2 to 5.58)), route of medication (AOR: 3.2 (95% CI: 1.44 to 7.1)), transportation mechanism (AOR: 4.1 (95% CI: 1.2 to 13.57)), travel time to health facility (AOR: 0.3 (95% CI: 0.12 to 0.82)), current use of mobile phone as appointment reminder (AOR: 2.6 (95% CI: 1.07 to 6.49)) and locking mobile phone with passwords (AOR: 4.6 (95% CI: 1.63 to 12.95)) were significantly associated with the willingness to receive mobile phone-based diabetic health services.ConclusionAccess to a mobile phone and willingness to receive mobile phone-based health services were high. Educational status, route of medication, transportation mechanism, time to reach the service, using mobile phone as appointment reminder and locking mobile phone with passwords were significantly associated factors. Given the high proportion of access and willingness of patients to receive mobile phone-based health services, mHealth interventions could be helpful.

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A Multimodal mHealth Intervention (FeatForward) to Improve Physical Activity Behavior in Patients with High Cardiometabolic Risk Factors: Rationale and Protocol for a Randomized Controlled Trial

Cited by 11 publications

References 14 publications

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

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

Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials

Access to mobile phone and willingness to receive mHealth services among patients with diabetes in Northwest Ethiopia: a cross-sectional study

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