ArticleNursing research
Efficacy of Osteoporosis Prevention Smartphone App.
Article in Nursing research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of menopause apps with an emphasis on osteoporosis.BMC women's health · 2023Pooled it
- Behavior Change Techniques in Digital Health Interventions for Midlife Women: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- Article
- Enhancing Behavioural Changes: A Narrative Review on the Effectiveness of a Multifactorial APP-Based Intervention Integrating Physical Activity.International journal of environmental research and public health · 2024Review
- Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review.Journal of medical Internet research · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundThe Striving to be Strong study tested the efficacy of a multifaceted, theory-based, complex osteoporosis prevention smartphone application (app). We hypothesized use of the app would improve bone mineral density and trabecular bone scores.
methodsThe study was a three-group, prospective, repeated-measure, longitudinal randomized trial. Baseline sample consisted of 290 healthy women between 40 and 60 years of age. Participants were randomly assigned to one of three groups: "Striving," a dynamically tailored, person-centered app; "Boning Up," a standardized osteoporosis-education e-book; and "Wait List," a participant's choice of intervention in the final 3 months of the 12-month study. Participants had or were provided a smart phone. Bone mineral density and trabecular bone scores were measured using dual-energy X-ray absorptiometry at baseline and 12 months. To assess engagement in health behavior change processes, ecological momentary assessments were administered via text messaging during the 12 months participants actively used the app.
resultsThe final sample reflects an 89.6% retention rate. There were decreases in bone mineral density over time but not among the three groups. The percentage of bone density lost over 12 months was lower than expected. Trabecular bone scores were not different over time or by group but improved across all three groups. DISCUSSION: Small but positive results were observed across all groups, suggesting one or more aspects of participation might have affected outcomes, including dissemination of the intervention across groups, retention without participation, ecological momentary assessments functioning as both an intervention and measure, and selective engagement in research-based recommendations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.