title: A classification scheme for analyzing mobile apps used to prevent and manage disease in late life.
abstract: BACKGROUND
  There are several mobile apps that offer tools for disease prevention and management among older adults, and promote health behaviors that could potentially reduce or delay the onset of disease. A classification scheme that categorizes apps could be useful to both older adult app users and app developers.
  
  
  OBJECTIVE
  The objective of our study was to build and evaluate the effectiveness of a classification scheme that classifies mobile apps available for older adults in the "Health & Fitness" category of the iTunes App Store.
  
  
  METHODS
  We constructed a classification scheme for mobile apps according to three dimensions: (1) the Precede-Proceed Model (PPM), which classifies mobile apps in terms of predisposing, enabling, and reinforcing factors for behavior change; (2) health care process, specifically prevention versus management of disease; and (3) health conditions, including physical health and mental health. Content analysis was conducted by the research team on health and fitness apps designed specifically for older adults, as well as those applicable to older adults, released during the months of June and August 2011 and August 2012. Face validity was assessed by a different group of individuals, who were not related to the study. A reliability analysis was conducted to confirm the accuracy of the coding scheme of the sample apps in this study.
  
  
  RESULTS
  After applying sample inclusion and exclusion criteria, a total of 119 apps were included in the study sample, of which 26/119 (21.8%) were released in June 2011, 45/119 (37.8%) in August 2011, and 48/119 (40.3%) in August 2012. Face validity was determined by interviewing 11 people, who agreed that this scheme accurately reflected the nature of this application. The entire study sample was successfully coded, demonstrating satisfactory inter-rater reliability by two independent coders (95.8% initial concordance and 100% concordance after consensus was reached). The apps included in the study sample were more likely to be used for the management of disease than prevention of disease (109/119, 91.6% vs 15/119, 12.6%). More apps contributed to physical health rather than mental health (81/119, 68.1% vs 47/119, 39.5%). Enabling apps (114/119, 95.8%) were more common than reinforcing (20/119, 16.8%) or predisposing apps (10/119, 8.4%).
  
  
  CONCLUSIONS
  The findings, including face validity and inter-rater reliability, support the integrity of the proposed classification scheme for categorizing mobile apps for older adults in the "Health and Fitness" category available in the iTunes App Store. Using the proposed classification system, older adult app users would be better positioned to identify apps appropriate for their needs, and app developers would be able to obtain the distributions of available mobile apps for health-related concerns of older adults more easily.
authors.affiliation: Gerontechnology Lab, Hefei University of Technology, Hefei, China.
authors.forename: Aiguo
authors.initials: A
authors.lastname: Wang
authors.forename: Ning
authors.initials: N
authors.lastname: An
authors.forename: Xin
authors.initials: X
authors.lastname: Lu
authors.forename: Hongtu
authors.initials: H
authors.lastname: Chen
authors.forename: Changqun
authors.initials: C
authors.lastname: Li
authors.forename: Sue
authors.initials: S
authors.lastname: Levkoff
keywords: Precede-Proceed Model (PPM); app; health care process; mHealth; management; mental health; physical health; prevention
references.citation: Clin Psychol Psychother. 2011 Jan-Feb;18(1):75-9
references.pmid: 20309842
references.citation: Am Psychol. 2000 Jan;55(1):110-21
references.pmid: 11392855
references.citation: J Med Internet Res. 2012 Nov 21;14(6):e161
references.pmid: 23171838
references.citation: BMJ. 2001 Feb 24;322(7284):443-4
references.pmid: 11222406
references.citation: Telemed J E Health. 2004 Summer;10(2):170-83
references.pmid: 15319047
references.citation: J Med Internet Res. 2013 Jun 14;15(6):e120
references.pmid: 23770578
references.citation: Health Econ. 2004 Jun;13(6):543-62
references.pmid: 15185385
references.citation: J Med Internet Res. 2012 May 14;14(3):e72
references.pmid: 22584372
references.citation: Biomed Eng Online. 2011 Apr 05;10:24
references.pmid: 21466669
references.citation: J Med Internet Res. 2012 Oct 18;14(5):e144
references.pmid: 23079075
references.citation: J Med Internet Res. 2013 Apr 18;15(4):e86
references.pmid: 23598614
references.citation: J Med Internet Res. 2005 Jul 01;7(3):e23
references.pmid: 15998614