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arxiv: 1906.10166 · v2 · pith:QVTMHXXJnew · submitted 2019-06-24 · 💻 cs.HC

Challenges and Opportunities of Big Data in Healthcare Mobile Applications

Mohsen Aghabozorgi Nafchi (1) , Maryam Aghabozorgi Nafchi (2) ((1) Shiraz University , (2) Shahrekord University) This is my paper

Pith reviewed 2026-05-25 17:03 UTC · model grok-4.3

classification 💻 cs.HC
keywords big datahealthcaremobile applicationschallengesopportunitiesdata analysishealth systems
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The pith

Healthcare mobile apps generate growing volumes of data that bring both challenges and opportunities for improving healthcare systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines the rapid increase in data collected by mobile healthcare applications and its implications for users and developers. It frames this volume as a source of both practical difficulties in data handling and potential benefits for system design. The central question posed is whether the accumulating data can support the creation of new healthcare tools and lead to better health outcomes. The discussion covers these aspects in detail as a review of the topic.

Core claim

The volume of data gathered by healthcare mobile applications is increasing day by day and presents critical challenges and opportunities for designing new tools in healthcare systems and improving health condition.

What carries the argument

Big data generated by healthcare mobile applications, which instantly gather and analyze user data to support health-related functions.

If this is right

  • Developers face new demands to manage data scale while building health tools.
  • Users may benefit if data analysis yields improved health monitoring features.
  • Healthcare systems could incorporate app-derived insights into broader services.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The review format leaves open how specific technical solutions might address the challenges mentioned.
  • Connections could be drawn to data privacy regulations that affect mobile health data use.
  • Future work might test whether particular app categories produce more usable data than others.

Load-bearing premise

The premise that the increasing volume of data from mobile apps can be leveraged to design new tools and improve health conditions.

What would settle it

Empirical evidence that data volume growth from these apps has not led to any measurable new tool designs or health improvements despite years of collection.

read the original abstract

The health and various ways to improve healthcare systems are one of the most concerns of human in history. By the growth of mobile technology, different mobile applications in the field of the healthcare system are developed. These mobile applications instantly gather and analyze the data of their users to help them in the health area. This volume of data will be a critical problem. Big data in healthcare mobile applications have its challenges and opportunities for the users and developers. Does this amount of gathered data which is increasing day by day can help the human to design new tools in healthcare systems and improve health condition? In this chapter, we will discuss meticulously the challenges and opportunities of big data in the healthcare mobile applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript is a qualitative discussion chapter reviewing challenges (e.g., privacy, scalability) and opportunities (e.g., analytics for health improvement) in big data from healthcare mobile applications. It poses whether the increasing volume of gathered data can help design new tools and improve health conditions, answering via general discussion rather than evidence-based analysis.

Significance. The topic is relevant to mHealth, but the paper offers no new empirical findings, derivations, case studies, or falsifiable predictions. Its value, if any, is limited to serving as a high-level overview; it does not advance the field or provide actionable insights beyond definitional statements.

major comments (1)
  1. Abstract: the central premise—that increasing data volume 'can help the human to design new tools... and improve health condition'—is posed as an open question but receives no supporting analysis, evidence, or even structured review of prior work, leaving the discussion unsupported.
minor comments (2)
  1. Abstract contains grammatical issues (e.g., 'one of the most concerns', 'By the growth of mobile technology') that should be corrected for clarity.
  2. The manuscript would benefit from explicit section headings, citations to specific mHealth studies, and concrete examples of the challenges/opportunities mentioned.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We respond point-by-point to the major comment below, clarifying the scope of this discussion chapter.

read point-by-point responses

  1. Referee: Abstract: the central premise—that increasing data volume 'can help the human to design new tools... and improve health condition'—is posed as an open question but receives no supporting analysis, evidence, or even structured review of prior work, leaving the discussion unsupported.

    Authors: The abstract introduces the motivating question to frame the chapter. The manuscript is a qualitative review chapter that then examines challenges (privacy, scalability, security) and opportunities (analytics for personalized interventions, population health insights) drawn from the mHealth literature. It structures the discussion around these themes to explore how growing data volumes from mobile applications can inform tool design and health outcomes. As a review rather than an empirical study, it synthesizes prior work rather than generating new data or predictions; this format is standard for discussion chapters and provides the requested structured overview. revision: no

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a purely discursive review chapter with no equations, derivations, fitted parameters, predictions, or first-principles results. Its central premise—that rising data volume from healthcare mobile apps creates challenges and opportunities—is definitional to the topic and posed rhetorically in the abstract, then addressed via discussion rather than any claim that reduces to its own inputs by construction. No self-citations or ansatzes are present, so no load-bearing circular steps exist.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced because the paper contains no derivations, models, or quantitative claims.

pith-pipeline@v0.9.0 · 5662 in / 891 out tokens · 21659 ms · 2026-05-25T17:03:33.782641+00:00 · methodology

discussion (0)

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Reference graph

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