Browsing by Author "Loganathar, Priya"

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    Quality of Mobile Apps for Care Partners of People With Alzheimer Disease and Related Dementias: Mobile App Rating Scale Evaluation
    (JMIR Publications, 2022-03) Werner, Nicole E.; Brown, Janetta C.; Loganathar, Priya; Holden, Richard J.; Medicine, School of Medicine
    Background: Over 11 million care partners in the United States who provide care to people living with Alzheimer disease and related dementias (ADRD) cite persistent and pervasive unmet needs related to their caregiving role. The proliferation of mobile apps for care partners has the potential to meet care partners’ needs, but the quality of apps is unknown. Objective: This study aims to evaluate the quality of publicly available apps for care partners of people living with ADRD and identify design features of low- and high-quality apps to guide future research and user-centered app development. Methods: We searched the US Apple App and Google Play stores with the criteria that included apps needed to be available in the US Google Play or Apple App stores, accessible to users out of the box, and primarily intended for use by an informal (family or friend) care partner of a person living with ADRD. We classified and tabulated app functionalities. The included apps were then evaluated using the Mobile App Rating Scale (MARS) using 23 items across 5 dimensions: engagement, functionality, aesthetics, information, and subjective quality. We computed descriptive statistics for each rating. To identify recommendations for future research and app development, we categorized rater comments on score-driving factors for each MARS rating item and what the app could have done to improve the item score. Results: We evaluated 17 apps. We found that, on average, apps are of minimally acceptable quality. Functionalities supported by apps included education (12/17, 71%), interactive training (3/17, 18%), documentation (3/17, 18%), tracking symptoms (2/17, 12%), care partner community (3/17, 18%), interaction with clinical experts (1/17, 6%), care coordination (2/17, 12%), and activities for the person living with ADRD (2/17, 12%). Of the 17 apps, 8 (47%) had only 1 feature, 6 (35%) had 2 features, and 3 (18%) had 3 features. The MARS quality mean score across apps was 3.08 (SD 0.83) on the 5-point rating scale (1=inadequate to 5=excellent), with apps scoring highest on average on functionality (mean 3.37, SD 0.99) and aesthetics (mean 3.24, SD 0.92) and lowest on average on information (mean 2.95, SD 0.95) and engagement (mean 2.76, SD 0.89). The MARS subjective quality mean score across apps was 2.26 (SD 1.02). Conclusions: We identified apps whose mean scores were more than 1 point below minimally acceptable quality, whereas some were more than 1 point above. Many apps had broken features and were rated as below acceptable for engagement and information. Minimally acceptable quality is likely to be insufficient to meet care partner needs. Future research should establish minimum quality standards across dimensions for care partner mobile apps. Design features of high-quality apps identified in this study can provide the foundation for benchmarking these standards.
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    Quality of Mobile Apps for Care Partners of People With Alzheimer Disease and Related Dementias: Mobile App Rating Scale Evaluation
    (JMIR, 2022-03-29) Werner, Nicole E.; Brown, Janetta C.; Loganathar, Priya; Holden, Richard J.; Medicine, School of Medicine
    Background: Over 11 million care partners in the United States who provide care to people living with Alzheimer disease and related dementias (ADRD) cite persistent and pervasive unmet needs related to their caregiving role. The proliferation of mobile apps for care partners has the potential to meet care partners' needs, but the quality of apps is unknown. Objective: This study aims to evaluate the quality of publicly available apps for care partners of people living with ADRD and identify design features of low- and high-quality apps to guide future research and user-centered app development. Methods: We searched the US Apple App and Google Play stores with the criteria that included apps needed to be available in the US Google Play or Apple App stores, accessible to users out of the box, and primarily intended for use by an informal (family or friend) care partner of a person living with ADRD. We classified and tabulated app functionalities. The included apps were then evaluated using the Mobile App Rating Scale (MARS) using 23 items across 5 dimensions: engagement, functionality, aesthetics, information, and subjective quality. We computed descriptive statistics for each rating. To identify recommendations for future research and app development, we categorized rater comments on score-driving factors for each MARS rating item and what the app could have done to improve the item score. Results: We evaluated 17 apps. We found that, on average, apps are of minimally acceptable quality. Functionalities supported by apps included education (12/17, 71%), interactive training (3/17, 18%), documentation (3/17, 18%), tracking symptoms (2/17, 12%), care partner community (3/17, 18%), interaction with clinical experts (1/17, 6%), care coordination (2/17, 12%), and activities for the person living with ADRD (2/17, 12%). Of the 17 apps, 8 (47%) had only 1 feature, 6 (35%) had 2 features, and 3 (18%) had 3 features. The MARS quality mean score across apps was 3.08 (SD 0.83) on the 5-point rating scale (1=inadequate to 5=excellent), with apps scoring highest on average on functionality (mean 3.37, SD 0.99) and aesthetics (mean 3.24, SD 0.92) and lowest on average on information (mean 2.95, SD 0.95) and engagement (mean 2.76, SD 0.89). The MARS subjective quality mean score across apps was 2.26 (SD 1.02). Conclusions: We identified apps whose mean scores were more than 1 point below minimally acceptable quality, whereas some were more than 1 point above. Many apps had broken features and were rated as below acceptable for engagement and information. Minimally acceptable quality is likely to be insufficient to meet care partner needs. Future research should establish minimum quality standards across dimensions for care partner mobile apps. Design features of high-quality apps identified in this study can provide the foundation for benchmarking these standards.
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    User Personas to Guide Technology Intervention Design to Support Caregiver-Assisted Medication Management
    (Oxford, 2022-11) Linden, Anna; Loganathar, Priya; Holden, Richard; Boustani, Malaz; Campbell, Noll; Ganci, Aaron; Werner, Nicole; Herron School of Art
    Informal caregivers often help manage medications for people with ADRD. Caregiver-assisted medication management has the potential to optimize outcomes for caregivers and people with ADRD, but is often associated with suboptimal outcomes. We used the user-centered design persona method to represent the needs of ADRD caregivers who manage medications for people with ADRD to guide future design decisions for technology interventions. Data were collected through virtual contextual inquiry in which caregivers (Nf24) sent daily multimedia text messages depicting medication management activities for seven days each, followed by an interview that used the messages as prompts to understand medication management needs. We applied the persona development method to the data to identify distinct caregiver personas, i.e., evidence-derived groups of prospective users of a future intervention. We used team-based affinity diagramming to organize information about participants based on intragroup (dis)similarities, to create meaningful clusters representing intervention-relevant attributes. We then used group consensus discussion to create personas based on attribute clusters. The six identified attributes differentiating personas were: 1. medication acquisition, 2. medication organization, 3. medication administration, 4. monitoring symptoms, 5. care network, 6. technology preferences. Three personas were identified based on differences on those attributes: Regimented Ruth (independent, proactive, tech savvy, controls all medications), Intuitive Ian (collaborative, uses own judgment, some technology, provides some medication autonomy), Passive Pamela (reactive, easy going, technology novice, provides full medication autonomy). These personas can be used to guide technology intervention design by evaluating how well intervention designs support each of them.