Author(s):
- Ashley Polhemus
- Sara Simblett
- Erin Dawe Lane
- Benjamin Elliott
- Sagar Jilka
- Esther Negbenose
- Patrick Burke
- Janice Weyer
- Jan Novak
- Marissa F Dockendorf
- Gergely Temesi
- Til Wykes
- RADAR-CNS
Abstract:
Background: Mobile health applications (apps) are promising condition self-management tools for people living with multiple sclerosis (MS). However, most existing apps do not include health tracking features. This gap has been raised as a priority research topic, but the development of new self-management apps will require designers to understand the context and needs of those living with MS. Our aim was to conduct a content analysis of publicly available user reviews of existing MS self-management apps to understand desired features and guide the design of future apps.
Methods: We systematically reviewed MS self-management apps which were publicly available in English on the Google Play and iOS app stores. We then conducted sentiment and content analysis of recent user reviews which referenced health tracking and data visualization to understand self-reported experiences and feedback.
Results: Searches identified 75 unique apps, of which six met eligibility criteria and had reviews. One hundred and thirty-seven user reviews of these apps were eligible, though most were associated with a single app (n=108). Overall, ratings and sentiment scores skewed highly positive (Median [IQR]: Ratings – 5 [4-5], Sentiment scores – 0.70 [0.44-0.86]), though scores of individual apps varied. Content analysis revealed five themes: reasons for app usage, simple user experience, customization and flexibility, feature requests, and technical issues. Reviewers suggested that app customization, interconnectivity, and consolidated access to desired features should be considered in the design of future apps. User ratings weakly correlated with review sentiment scores (ρ = 0.27 [0.11-0.42]).
Conclusions: Self-tracking options in MS apps are currently limited, though the apps that offer these functions are considered useful by individuals with MS. Additional qualitative research is required to understand how specific app features and opportunities for personalization should be incorporated into new self-management tools for this population.
Documentation:
References:
- Likableness ratings of 555 personality-trait words.J. Pers. Soc. Psychol. 1968; 9: 272-279https://doi.org/10.1037/h0025907View in Article
- Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.Psychol. Bull. 1968; 70: 213-220https://doi.org/10.1037/h0026256View in Article
- Cooper V.
- Clatworthy J.
- Whetham J.
- Consortium E.
mHealth interventions to support self-management In HIV: a systematic review.Open AIDS J. 2017; 11: 119-132https://doi.org/10.2174/1874613601711010119View in Article
- De Choudhury M.
- Gamon M.
- Counts S.
- Horvitz E.
Predicting depression via social media.ICWSM. 2013; 13: 1-10View in Article
- Du J.
- Xu J.
- Song H.
- Liu X.
- Tao C.
Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.J. Biomed. Semantics. 2017; 8https://doi.org/10.1186/s13326-017-0120-6View in Article
- The intertwining of body, self and world: A phenomenological study of living with recently-diagnosed multiple sclerosis.J. Phenomenol. Psychol. 2003; 34View in Article
- Giunti G.
- Fernández E.G.
- Zubiete E.D.
- Romero O.R.
Supply and demand in mhealth apps for persons with multiple sclerosis: Systematic search in app stores and scoping literature review.JMIR mHealth uHealth. 2018; https://doi.org/10.2196/10512View in Article
- Giunti G.
- Kool J.
- Rivera Romero O.
- Dorronzoro Zubiete E.
Exploring the Specific Needs of Persons with Multiple Sclerosis for mHealth Solutions for Physical Activity: Mixed-Methods Study.JMIR mHealth uHealth. 2018; 6: e37https://doi.org/10.2196/mhealth.8996View in Article
- Gliddon E.
- Barnes S.J.
- Murray G.
- Michalak E.E.
Online and mobile technologies for self-management in bipolar disorder: a systematic review.Psychiatr. Rehabil. J. 2017; https://doi.org/10.1037/prj0000270View in Article
- Goreis A.
- Felnhofer A.
- Kafka J.X.
- Probst T.
- Kothgassner O.D.
Efficacy of self-management smartphone-based apps for post-traumatic stress disorder symptoms: a systematic review and meta-analysis.Front. Neurosci. 2020; https://doi.org/10.3389/fnins.2020.00003View in Article
- VADER: a parsimonious rule-based model for sentiment analysis of social media text.in: Proceedings of the International AAAI Conference on Web and Social MediaProceedings of the International AAAI Conference on Web and Social Media. 2014: 216-225View in Article
- Li R.
- Liang N.
- Bu F.
- Hesketh T.
The effectiveness of self-management of hypertension in adults using mobile health: Systematic review and meta-analysis.JMIR mHealth uHealth. 2020; https://doi.org/10.2196/17776View in Article
- On a test of whether one of two random variables is stochastically larger than the other.Ann. Math. Stat. 1947; 18: 50-60https://doi.org/10.1214/aoms/1177730491View in Article
- Interrater reliability: The kappa statistic.Biochem. Medica. 2012; 22: 276-282https://doi.org/10.11613/bm.2012.031View in Article
- NHS Apps Library – NHS Digital [WWW Document], n.d. URL https://digital.nhs.uk/services/nhs-apps-library (accessed 4.19.22).View in Article
- Nicholas J.
- Fogarty A.S.
- Boydell K.
- Christensen H.
The reviews are in: a qualitative content analysis of consumer perspectives on apps for bipolar disorder.J. Med. Internet Res. 2017; https://doi.org/10.2196/jmir.7273View in Article
- Nicholas J.
- Larsen M.E.
- Proudfoot J.
- Christensen H.
Mobile apps for bipolar disorder: a systematic review of features and content quality.J. Med. Internet Res. 2015; 17: e4581https://doi.org/10.2196/jmir.4581View in Article
- Olsson M.
- Lexell J.
- Söderberg S.
The meaning of women’s experiences of living with multiple sclerosis.Health Care Women Int. 2008; 29: 416-430https://doi.org/10.1080/07399330701876646View in Article
- ORCHA [WWW Document], n.d. URL https://appfinder.orcha.co.uk/(accessed 4.19.22).View in Article
- Opinion mining and sentiment analysis.Found. Trends Inf. Retr. 2008; 2: 1-135https://doi.org/10.1561/1500000011View in Article
- Depressive moods of users portrayed in twitter.in: Proceedings of the ACM SIGKDD Workshop of Healthcare Informatics. ACM, New York, New York2012: 1-8View in Article
- Plow M.A.
- Finlayson M.
- Rezac M.
A scoping review of self-management interventions for adults with multiple sclerosis.PM R. 2011; 3: 251-262https://doi.org/10.1016/j.pmrj.2010.11.011View in Article
- Polhemus A.M.
- Novak J.
- Majid S.
- Simblett S.K.
- Morris D.
- Bruce S.
- Burke P.
- Dockendorf M.F.
- Temesi G.
- Wykes T.
Data visualization in chronic neurological and mental health condition self-management: a systematic review of user perspectives.JMIR Ment. Heal. 2022; 9: e25249View in Article
- Polhemus A.M.
- Simblett S.
- Dawe-Lane E.
- Gilpin G.
- Elliott B.
- Jilka S.
- Novak J.
- Nica R.
- Temesi G.
- Wykes T.
Health tracking via mobile apps for depression self-management: a qualitative content analysis of user reviews (Preprint).JMIR Hum. Factors. 2022; 9https://doi.org/10.2196/40133View in Article
- Rae-Grant A.D.
- Turner A.P.
- Sloan A.
- Miller D.
- Hunziker J.
- Haselkorn J.K.
Self-management in neurological disorders: Systematic review of the literature and potential interventions in multiple sclerosis care.J. Rehabil. Res. Dev. 2011; 48: 1087-1100https://doi.org/10.1682/JRRD.2010.08.0159View in Article
- mHealth for diabetes support: a systematic review of apps available on the Italian market.mHealth. 2017; 3: 16https://doi.org/10.21037/mhealth.2017.04.06View in Article
- Needs Assessment—mHealth Applications for People Aging with Multiple Sclerosis.J. Healthc. Informatics Res. 2018; 2: 71-98https://doi.org/10.1007/s41666-018-0023-zView in Article
- Salimzadeh Z.
- Damanabi S.
- Kalankesh L.R.
- Ferdousi R.
Mobile applications for multiple sclerosis: A focus on self-management.Acta Inform. Medica. 2019; 27: 12-18https://doi.org/10.5455/aim.2019.27.12-18View in Article
- An analysis of variance test for normality (complete samples).Biometrika. 1965; 52: 591-611https://doi.org/10.1093/biomet/52.3-4.591View in Article
- Spearman Rank Correlation Coefficient
The Concise Encyclopedia of Statistics. Springer, New York2008: 502-505https://doi.org/10.1007/978-0-387-32833-1_379View in Article
- Tausczik Y.R.
- Pennebaker J.W.
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods.J. Lang. Soc. Psychol. 2010; 29: 24-54https://doi.org/10.1177/0261927X09351676View in Article
- Methods for the thematic synthesis of qualitative research in systematic reviews.BMC Med. Res. Methodol. 2008; 8https://doi.org/10.1186/1471-2288-8-45View in Article
- Thompson A.J.
- Banwell B.L.
- Barkhof F.
- Carroll W.M.
- Coetzee T.
- Comi G.
- Correale J.
- Fazekas F.
- Filippi M.
- Freedman M.S.
- Fujihara K.
- Galetta S.L.
- Hartung H.P.
- Kappos L.
- Lublin F.D.
- Marrie R.A.
- Miller A.E.
- Miller D.H.
- Montalban X.
- Mowry E.M.
- Sorensen P.S.
- Tintoré M.
- Traboulsee A.L.
- Trojano M.
- Uitdehaag B.M.J.
- Vukusic S.
- Waubant E.
- Weinshenker B.G.
- Reingold S.C.
- Cohen J.A.
Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria.Lancet. Neurol. 2018; 17: 162-173https://doi.org/10.1016/S1474-4422(17)30470-2View in Article
- Turchioe M.R.
- Myers A.
- Isaac S.
- Baik D.
- Grossman L.V.
- Ancker J.S.
- Creber R.M.
A systematic review of patient-facing visualizations of personal health data.Appl. Clin. Inform. 2019; https://doi.org/10.1055/s-0039-1697592View in Article
- Turpin M.
- Kerr G.
- Gullo H.
- Bennett S.
- Asano M.
- Finlayson M.
Understanding and living with multiple sclerosis fatigue.Br. J. Occup. Ther. 2018; 81: 82-89https://doi.org/10.1177/0308022617728679View in Article
- Wang Y.
- Xue H.
- Huang Y.
- Huang L.
- Zhang D.
A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management.Adv. Nutr. An Int. Rev. J. 2017; 8: 449-462https://doi.org/10.3945/an.116.014100View in Article
