Evidence map›Paper›PMID 28304332›Full record

ArticleSensors (Basel, Switzerland)2017

A Wellness Mobile Application for Smart Health: Pilot Study Design and Results.

Giovanna Sannino, Manolo Forastiere, Giuseppe De Pietro

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

3 authors.

Giovanna SanninoInstitute of High Performance Computing and Networking (ICAR) of National Research Council (CNR), 80131 Naples, Italy. giovanna.sannino@icar.cnr.it.
Manolo ForastiereNEATEC S.p.A., Pozzuoli, 80078 Naples, Italy. m.forastiere@neatec.it.
Giuseppe De PietroInstitute of High Performance Computing and Networking (ICAR) of National Research Council (CNR), 80131 Naples, Italy. giuseppe.depietro@icar.cnr.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wellness is one of the main factors crucial in the avoidance of illness or disease. Experience has shown that healthy lifestyle programs are an important strategy to prevent the major shared risk factors for many diseases including cardiovascular diseases, strokes, diabetes, obesity, and hypertension. Within the ambit of the Smart Health 2.0 project, a Wellness App has been developed which has the aim of providing people with something similar to a personal trainer. This Wellness App is able to gather information about the subject, to classify her/him by evaluating some of her/his specific characteristics (physical parameters and lifestyle) and to make personal recommendations to enhance her/his well-being. The application can also give feedback on the effectiveness of the specified characteristics by monitoring their evolution over time, and can provide a positive incentive to stimulate the subject to achieve her/his wellness goals. In this paper, we present a pilot study conducted in Calabria, a region of Italy, aimed at an evaluation of the validity, usability, and navigability of the app, and of people's level of satisfaction with it. The preliminary results show an average score of 77.16 for usability and of 76.87 for navigability, with an improvement of the Wellness Index with a significance average of 95% and of the Mediterranean Adequacy Index with a significance average of as high as 99%.

Indexed as

Mobile ApplicationsFemaleHumansItalyMalePilot ProjectsResearch Designlifestyle monitoringpilot studyWellness App

Identifiers

PMID28304332
PMCPMC5375897

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.