| Literature DB >> 36011162 |
Daniel M Sop1,2, Taylor Crouch3, Yue Zhang1, Thokozeni Lipato1, John Wilson2, Wally R Smith1.
Abstract
Prescription opioid nonadherence, specifically opioid misuse, has contributed to the opioid epidemic and opioid-related mortality in the US. Popular methods to measure and control opioid adherence have limitations, but mobile health, specifically smartphone applications, offers a potentially useful technology for this purpose. We developed, tested, and validated the OpPill application using the Mobile Applications Rating Scale (MARS), a validated tool for assessing the quality of mobile health apps. The MARS contains four scales (range of each scale = 0-4) that rate Engagement, Functionality, Aesthetics, and Information Quality. It also assesses subjective quality, relevance, and overall application impact. Our application was built to be a mobile monitoring and reporting system intended to enhance opioid adherence by collecting data and providing systematic feedback on pain and opioid use. Patients (n = 28) all had one of various SCD genotypes, were ages 19 to 59 years (mean 36.56), 53.6% were female, and 39.3% had completed some college. Patients rated the OpPill application highly on all four scales: Engagement, 3.93 ± 0.73; Functionality, 4.54 ± 0.66; Aesthetics, 3.92 ± 0.81; Information, 3.91 ± 0.87. The majority of patients found the application to be relevant for their care. A total of 96% reported the information within the app was complete, while 4% estimated the information to be minimal or overwhelming. Patients (91.7%) overwhelmingly reported that the quality of information as it pertained to SCD patients was relevant; only 8.3% found the application to be poorly relevant to SCD. Similarly, patients (91.7%) overwhelmingly rated both the application's performance and ease of use positively. The large majority of participants (85.7%) found the application to be interesting to use, while 74% found it entertaining. All users found the application's navigation to be logical and accurate with consistent and intuitive gestural design. We conclude that the OpPill application, specifically targeted to monitor opioid use and pain and opioid behavior in patients with Chronic Non-Cancer Pain, was feasible and rated by SCD patients as easy-to-use using a validated rating tool.Entities:
Keywords: chronic condition; mHealth; medical apps; opioids; pain management; sickle cell disease
Year: 2022 PMID: 36011162 PMCID: PMC9407817 DOI: 10.3390/healthcare10081506
Source DB: PubMed Journal: Healthcare (Basel) ISSN: 2227-9032
Figure 1OpPill Application Screenshots.
Figure 2MARS tool category scores.
Figure 3MARS Tool category distribution.
Figure 4Application subjective quality ratings.
Spearman Rank Correlation/ANOVA*—Application Impact.
| Application Impact | ||||
|---|---|---|---|---|
| Engagement | Functionality | Aesthetics | Information | |
| Awareness | 0.60 (0.0032) ** | 0.08 (0.7343) | 0.45 (0.0281) ** | 0.54 (0.0110) ** |
| Knowledge | 0.56 (0.0057) ** | 0.0020 (0.9929) | 0.34 (0.0989) | 0.42 (0.0578) |
| Attitude | 0.67 (0.0006) ** | 0.18 (0.4298) | 0.46 (0.0288) ** | 0.47 (0.0385) ** |
| Intention to Change | 0.61 (0.0020) ** | 0.09 (0.6838) | 0.46 (0.0237) ** | 0.57 (0.0075) ** |
| Help Seeking | 0.55 (0.0063) ** | −0.04 (0.8458) | 0.29 (0.1726) | 0.29 (0.2011) |
| Behavior Change | 0.54 (0.0101) ** | 0.21 (0.3380) | 0.58 (0.0039) ** | 0.43 (0.0561) |
| Age | −0.15 (0.3310) | 0.06 (0.9100) | −0.25 (0.0879) | −0.03 (0.8949) |
| Gender | 0.008 (0.6454) * | 0.000014 (0.9849) * | 0.009 (0.6461) * | 0.01 (0.6349) * |
| Education | 0.24 (0.2566) * | 0.07 (0.9083) * | 0.20 (0.4575) * | 0.19 (0.5726) * |
| Income | 0.18 (0.5514) * | 0.22 (0.4566) * | 0.11 (0.8554) * | 0.28 (0.4384) * |
| Genotype | 0.40 (0.0588) * | 0.28 (0.1343) * | 0.39 (0.1075) * | 0.27 (0.2868) * |
| Willingness to Recommend | 0.41 (0.0425) ** | 0.73 (<0.0001) ** | 0.58 (0.0019) ** | 0.40 (0.0308) ** |
| Frequency of Usage | 0.08 (0.5634) | 0.24 (0.1738) | 0.57 (0.0040) ** | 0.41 (0.0711) |
| Willingness to Pay | 0.53(0.0023) ** | 0.30 (0.1235) | 0.23 (0.1781) | 0.32 (0.1631) |
| Application Rating | 0.73 (<0.0001) ** | 0.64 (0.0008) ** | 0.78 (<0.0001) ** | 0.73 (<0.0001) ** |
* ANOVA for Gender, Education, Income, and Genotype (Categorical variables). ** Indicates statistical significance.
Figure 5Sickle cell-specific application quality scale distribution.
Mean Difference (p-Value) and Effect Size of Each Application Category Between Gender, Education, and Income.
| Mean Diff. (|t|) | Cohen’s d (Effect Size) | ||
|---|---|---|---|
| Gender (M–F) | |||
| Engagement | −0.005 | 0.98 | 0.01 |
| Functionality | −0.08 | 0.76 | 0.12 |
| Aesthetics | 0.15 | 0.65 | 0.18 |
| Information | −0.18 | 0.64 | 0.20 |
| Education | |||
| Engagement | −0.09 | 0.77 | 0.12 |
| Functionality | 0.07 | 0.81 | 0.11 |
| Aesthetics | −0.15 | 0.73 | 0.18 |
| Information | −0.14 | 0.75 | 0.16 |
| Income | |||
| Engagement | −0.08 | 0.80 | 0.11 |
| Functionality | −0.5 | 0.05 * | 2.6 |
| Aesthetics | 0.09 | 0.80 | 0.12 |
| Information | 0.3 | 0.39 | 0.42 |
* Indicates Statistical significance.