Evidence mapPaperPMID 40340833Full record

SynthesisJournal of medical Internet research2025

Effectiveness of Mobile Health Interventions for Reducing Sitting Time in Older Adults: Systematic Review and Meta-Analysis.

Siqing Chen, Chenchen Wang, Albert Ko, Carol Ewing Garber, Edward Giovannucci, Yuting Yang, Matthew Stults-Kolehmainen, Lili Yang

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Siqing ChenDepartment of Nursing, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang Province, China.ORCID https://orcid.org/0000-0001-8683-4206
Chenchen WangDepartment of Nursing, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.ORCID https://orcid.org/0009-0004-2630-4149
Albert KoDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0009-0004-5939-1947
Carol Ewing GarberDepartment of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0002-1268-576X
Edward GiovannucciDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.ORCID https://orcid.org/0000-0002-6123-0219
Yuting YangDepartment of Nursing, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang Province, China.ORCID https://orcid.org/0000-0002-9367-9942
Matthew Stults-KolehmainenDepartment of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0001-5361-9614
Lili YangDepartment of Nursing, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang Province, China.ORCID https://orcid.org/0000-0002-4587-1438

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile health (mHealth) provides health information through electronic devices, even at home. The escalating prevalence of sedentary behaviors among older adults, which leads to increased adverse health outcomes, underscores the pressing need for a comprehensive understanding of the effectiveness of mHealth interventions.

objectiveThis study aims to examine the effectiveness of mHealth interventions in the sitting time of older adults (age 55 years).

methodsA systematic review and meta-analysis of randomized controlled trials was conducted to evaluate the effects of mHealth interventions on total sitting time during waking hours, excluding sleep. A literature search was conducted using multiple databases, including PubMed, Embase, Web of Science, and Cochrane, covering articles published from the inception of each database through October 2023. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were explicitly applied to structure this report. Methodological quality was assessed using the Cochrane Risk of Bias (ROB 2) tool for randomized controlled trials and the Methodological Index for Non-Randomized Studies (MINORS) tool for nonrandomized studies. Two independent reviewers screened the studies, extracted data, and assessed methodological quality using established criteria. Meta-analyses were performed using Review Manager (version 5.4; Cochrane Collaboration).

resultsTen studies were identified, of which 3 were included in the meta-analysis, while the remaining 7 were assessed exclusively in the systematic review. The interventions predominantly took place in community settings (n=3) and home-based settings (n=3). Three studies aimed to decrease sedentary behavior and 7 aimed to increase physical activity. The interventions were primarily conducted once daily (n=7) via mobile devices such as smartphones (n=7) and typically involved a single intervention delivered at different time intervals, such as every 15, 20, or 30 minutes (n=4). The interventions typically lasted 12 weeks (n=4) and used objective assessment tools, such as the ActiGraph GT3X+ (n=8). The included studies applied the habit formation theory (n=1), the self-efficacy theory (n=1), the social cognitive theory (n=1), and the social-ecological theory (n=1) as frameworks. Additionally, behavior change techniques, including "goal setting," "problem-solving," "action planning," and "review behavior goal(s)" (n=6), were used. Meta-analysis of the 3 studies included showed a significant decrease in sedentary behavior with mHealth interventions compared with conventional or no interventions (weighted mean difference [WMD]=59.1 min/d, 95% CI 99.1 to 20.2; P=.003).

conclusionsmHealth interventions effectively reduce sitting time in older adults. Strategies using interventions with specific frequencies and durations, dedicated mobile monitoring devices, and behavior change techniques showed the potential to reduce sedentary behavior among older adults. These results also underscore the potential of mHealth as a key tool for promoting the well-being of older adults through technology-driven public health efforts.

trial registrationPROSPERO CRD42023443926; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023443926.

Indexed as

Sedentary BehaviorSitting PositionTelemedicineAgedFemaleHumansMaleMiddle AgedRandomized Controlled Trials as Topiccommunityhealth behaviormeta-analysisolder adultssedentary behaviorsystematic review

Identifiers

PMID40340833
PMCPMC12101137

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.