Evidence map›Paper›PMID 33634196›Full record

ReviewmHealth2021

Mobile physical activity planning and tracking: a brief overview of current options and desiderata for future solutions.

Maged N Kamel Boulos, Stephen P Yang

Abstract readReview
In one paragraph

Review in mHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
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

2 authors.

Maged N Kamel BoulosSchool of Information Management, Sun Yat-sen University, Guangzhou 510006, China.
Stephen P YangState University of New York College at Cortland, Cortland, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Consistent and enjoyable physical activity (PA) can be a crucial component to improving or maintaining one's overall health status. Using advanced features on smartphones (GPS, Bluetooth, motion sensing, etc.) coupled with an app or game that is able to assist mobile users to not only track location, but also to interact socially with others based in real-life (IRL), virtual reality (VR), or alternate-reality (ARG), has the potential to give health experts better tools to encourage higher compliance to protocols, rehabilitation, behaviour change and health outcomes. This paper outlines the available mHealth apps that capitalize on pervasive smartphone features coupled with sensors, and suggests which features might impact future PA patterns. The authors argue that the ultimate mobile PA planning and tracking app/platform will be the one capable of supporting both precision and accuracy health (offering truly individualized PA advice and coaching while preserving user privacy) and precision and accuracy public health (providing public health decision makers with community-level PA indicators obtained from app data aggregates of user populations).

Indexed as

digital healthdigital public healthmHealthPhysical activity

Identifiers

PMID33634196
PMCPMC7882264

What Socratic holds

Textmetadata
Read underepoch 390

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.