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
Open accessAbstract
BACKGROUND: Physical inactivity is one of the leading risk factors contributing to the rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. We developed a mobile phone app, FeatForward, to increase the level of physical activity in patients with cardiometabolic risk (CMR) factors. This intervention is expected to result in an overall improvement in patient health outcomes. OBJECTIVE: The objective of this study is to evaluate the effect of a mobile phone-based app, FeatForward, on physical activity levels and other CMR factors in patients with chronic conditions. METHODS: The study will be implemented as a 2-arm randomized controlled trial with 300 adult patients with chronic conditions over a 6-month follow-up period. Participants will be assigned to either the intervention group receiving the FeatForward app and standard care versus a control group who will receive only usual care. The difference in physical activity levels between the control group and intervention group will be measured as the primary outcome. We will also evaluate the effect of this intervention on secondary measures including clinical outcome changes in global CMR factors (glycated hemoglobin, fasting blood glucose, blood pressure, waist circumference, Serum lipids, C-reactive protein), health-related quality of life, health care usage, including attendance of scheduled clinic visits and hospitalizations, usability, and satisfaction, participant engagement with the FeatForward app, physician engagement with physician portal, and willingness to engage in physical activity. Instruments that will be used in evaluating secondary outcomes include the Short-Form (SF)-12, app usability and satisfaction questionnaires, physician satisfaction questionnaire. The intention-to-treat approach will be used to evaluate outcomes. All outcomes will be measured longitudinally at baseline, midpoint (3 months), and 6 months. Our primary outcome, physical activity, will be assessed by mixed-model analysis of variance with intervention assignment as between-group factor and time as within-subject factor. A similar approach will be used to analyze continuous secondary outcomes while categorical outcomes will be analyzed by chi-square test. RESULTS: The study is still in progress and we hope to have the results by the end of 2016. CONCLUSIONS: The mobile phone-based app, FeatForward, could lead to significant improvements in physical activity and other CMR factors in patients.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Researchers evaluated a range of measures that aligned with their target chronic condition, such as blood glucose, weight, symptoms, patient-reported outcomes, diary entries, and steps.
“Researchers evaluated a range of measures that aligned with their target chronic condition, such as blood glucose [56,60,61,64,73], weight [56,73,74], symptoms [66,68,69], patient-reported outcomes [38,46,52,65,71], diary entries [47,66], and steps [51].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [56]
Reviewed researchers measured time spent on unique sections, unique pages, length of a unique session, length between unique sessions, and total time spent on an app.
“Researchers measured the time spent on unique sections of an app [66], the time spent on unique pages [56], the length of a unique session [38,42,43,71], the length between unique sessions [72], and the total time spent on an app [62,68,73].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [56]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 15 · Cited by 11
- Authors
- Agboola, S., Palacholla, R. S., Centi, A., Kvedar, J., & Jethwani, K.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Agboola, Stephen
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2016-05-12
- Volume
- 5
- Issue
- 2
- Pages
- e84
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC4882414
- OpenAlex
- W2370754342
- Cited by
- 11
- References
- 15
- Citations by year
- 2023: 1 · 2022: 1 · 2020: 2 · 2019: 5 · 2018: 1 · 2017: 1
- FWCI
- 4.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Samsung
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- physical activity, cardiometabolic risk factors, mobile health apps, chronic disease, multimodal intervention, HbA1c, blood pressure, serum lipids, C-reactive protein, health-related quality of life, physician engagement, physical inactivity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review