Limits...
A classification scheme for analyzing mobile apps used to prevent and manage disease in late life.

Wang A, An N, Lu X, Chen H, Li C, Levkoff S - JMIR Mhealth Uhealth (2014)

Bottom Line: 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.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).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gerontechnology Lab, Hefei University of Technology, Hefei, China.

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.

No MeSH data available.


Selection of apps sample in August 2012.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4114434&req=5

figure2: Selection of apps sample in August 2012.

Mentions: Sample selection was based on the following inclusion and exclusion criteria. Inclusion criteria included: (1) apps that one had to pay for, as free apps often became upgraded apps into paid ones, leading to the possible double coding of the same app (eg, Vital mHealth released two versions of the mobile app “PainMonitoring”, a free version with limited functionality and a paid version that recorded and monitored a user’s pain level); and (2) apps whose written descriptions were in English for the purpose of global access and use. Exclusion criteria included: (1) apps misclassified and misplaced under the “Fitness and Health” category, as determined by the manufacturers’ descriptions of function not being associated with health or health care, such as bread maker timer assistant and journey scene records; and (2) apps that did not specifically relate to health or health care of older adults (eg, apps for baby health-nursing, apps for disseminating pregnancy prevention, and birth control techniques). Apps with explicit age information and/or illustrations of older adults as their target population directly helped us to identify apps for older adults. For those apps without this information, there is typically a description of the targeted health condition and functional status for the app. Knowing the health condition being targeted, as well as the functional status, enabled us to identify health-related apps for elders with specific health conditions and levels of functional status. In sum, we relied on the three types of data (ie, age information/target population, type of health condition, and functional status) to identify health and fitness apps that were not relevant for older adults. Taking August 2012 as an example, on the basis of these inclusion/exclusion criteria, there were 657 apps in the health and fitness category, with 445/657 (67.7%) free apps, 61/657 (9.3%) non-English apps, 4/657 (0.6%) misclassified apps, and 99/657 (15.1%) apps not applicable to older adults. Thus, the final study sample for August 2012 consisted of 48/657 (7.3%) apps that could be employed for older adults’ health and health care (Figure 2). App samples selected from August 2011 and June 2011 are represented in Figures 3 and 4, respectively.


A classification scheme for analyzing mobile apps used to prevent and manage disease in late life.

Wang A, An N, Lu X, Chen H, Li C, Levkoff S - JMIR Mhealth Uhealth (2014)

Selection of apps sample in August 2012.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4114434&req=5

figure2: Selection of apps sample in August 2012.
Mentions: Sample selection was based on the following inclusion and exclusion criteria. Inclusion criteria included: (1) apps that one had to pay for, as free apps often became upgraded apps into paid ones, leading to the possible double coding of the same app (eg, Vital mHealth released two versions of the mobile app “PainMonitoring”, a free version with limited functionality and a paid version that recorded and monitored a user’s pain level); and (2) apps whose written descriptions were in English for the purpose of global access and use. Exclusion criteria included: (1) apps misclassified and misplaced under the “Fitness and Health” category, as determined by the manufacturers’ descriptions of function not being associated with health or health care, such as bread maker timer assistant and journey scene records; and (2) apps that did not specifically relate to health or health care of older adults (eg, apps for baby health-nursing, apps for disseminating pregnancy prevention, and birth control techniques). Apps with explicit age information and/or illustrations of older adults as their target population directly helped us to identify apps for older adults. For those apps without this information, there is typically a description of the targeted health condition and functional status for the app. Knowing the health condition being targeted, as well as the functional status, enabled us to identify health-related apps for elders with specific health conditions and levels of functional status. In sum, we relied on the three types of data (ie, age information/target population, type of health condition, and functional status) to identify health and fitness apps that were not relevant for older adults. Taking August 2012 as an example, on the basis of these inclusion/exclusion criteria, there were 657 apps in the health and fitness category, with 445/657 (67.7%) free apps, 61/657 (9.3%) non-English apps, 4/657 (0.6%) misclassified apps, and 99/657 (15.1%) apps not applicable to older adults. Thus, the final study sample for August 2012 consisted of 48/657 (7.3%) apps that could be employed for older adults’ health and health care (Figure 2). App samples selected from August 2011 and June 2011 are represented in Figures 3 and 4, respectively.

Bottom Line: 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.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).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gerontechnology Lab, Hefei University of Technology, Hefei, China.

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.

No MeSH data available.