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    Smartphone addiction and associated psychological factors

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    The use of smartphone technology has increased drastically resulting in a risk of addiction to certain web applications, such as social networking sites (SNS) that are easily accessible via smartphones. A major concern regarding the increased use of SNS sites is the risk of an increase in narcissism amongst users of SNS. The present study examined the relationship between smartphone use, narcissistic tendencies, and personality as predictors of smartphone addiction. A self-selected sample of 256 smartphone users (M = 29.2; SD = 9.49) completed an online survey. The results revealed that 13.3% of the sample was classified as addicted to smartphones. Regression analysis revealed that narcissism, openness, neuroticism, and age were linked to smartphone addiction. Therefore, it is suggested that smartphones encourage narcissism, even in non-narcissistic users. Future research requires more in-depth qualitative data, addiction scale comparisons, and comparison of use with, and without, SNS access. Further, it is advised that prospective buyers of smartphones be pre-warned of the potential addictive properties of new technology

    ๋…ธ์ธ์—์„œ ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ๊ณผ ๋…ธ์‡ ์˜ ์—ฐ๊ด€์„ฑ ๋ฐ ๊ด€๋ จ์š”์ธ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑดํ•™์ „๊ณต),2019. 8. ์กฐ์„ฑ์ผ.Introduction: With worldwide aging, there are many ongoing studies to further identify the risk factors and ways to prevent age-related conditions. The most actively studied areas include frailty in the elderly. Rapid development and increasing use of smartphones have come to play an important role in health industries. Despite the increasing importance of smartphone use for sustaining healthy life, no large study has reported the characteristics of elderly smartphone users. Our hypothesis is that the ownership of a smartphone is inversely associated with frailty because smartphone owners can benefit from various health applications to manage their health, and the use of smartphone itself can be a good cognitive exercise that can help prevent frailty. Therefore, the aim of this study is to describe the various sociodemographic and medical characteristics of the elderly smartphone users and non-users, and to identify the association between the use of smartphones and frailty. The obtained information may be helpful to screening frailty in small clinics. Methods: We used the baseline data of the Korean Frailty and Aging Cohort Study which is a nationwide cohort study conducted to identify and prevent the factors contributing to aging and frailty. The data of a total of 2935 participants were analyzed for various demographic, socioeconomic, cognitive, and functional characteristics as well as frailty. Frailty was defined using Fried frailty index. The characteristics of the participants were described in terms of smartphone ownership, and multiple logistic regression analysis was performed to assess the association between the use of smartphones and frailty. Results: Out of 2935 participants aged between 70 and 84, 1404 (47.8%) participants were using smartphones, and 1531 (52.2%) participants were using cellphones other than smartphones or did not own a cellphone. The mean age of all participants was 76.0 years old. The smartphone users were more likely to be male (53.3%), with higher educational and economic background compared to non-users. They were also more likely to be in a marital relationship and not living alone, but received less social support, and exhibited poorer daily functional abilities. However, they exhibited higher cognitive capabilities, and more importantly, less frail in all aspects of frailty criteria compared to smartphone non-users. The odds ratio of the association between smartphone ownership and frailty was 0.47, 95% confidence interval 039-055, after adjusting for various related factors. Conclusion: Ownership of a smartphone is a result of multifactorial circumstances and conditions as is frailty. Smartphone non-users in this study were more frail than smartphone users, and exhibited poorer cognitive abilities while maintaining better daily functional abilities and social interaction. Therefore, it is our conclusion that the ownership of a smartphone in older adults represents many background factors that are often linked to frailty in an inverse manner, and a simple question or identification of ones type of phone may be used in conjunction with other methods to screen frailty in older adults.๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  : ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๊ณ ๋ นํ™”๊ฐ€ ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ ๋งŒ์„ฑ์งˆํ™˜์„ ์˜ˆ๋ฐฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ„ํ—˜์š”์†Œ์™€ ๋ฐฉ๋ฒ•์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜๋Š” ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋…ธ์‡ ๋Š” ๊ฐ€์žฅ ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋Š” ๋ถ„์•ผ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋…ธ์‡  ์—ฐ๊ตฌ์™€ ๋™์‹œ์— ์ง€๋‚œ ์ˆ˜๋…„๊ฐ„ ์Šค๋งˆํŠธํฐ์€ ๋น ๋ฅธ ๋ฐœ์ „๊ณผ ์„ฑ์žฅ์„ ๋ณด์—ฌ ์ด์ œ ๊ฑด๊ฐ• ์‚ฌ์—…์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ฑด๊ฐ•ํ•œ ์‚ถ์˜ ์˜์œ„์— ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์˜ ์ค‘์š”์„ฑ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋…ธ์ธ ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์ž๋“ค์˜ ํŠน์„ฑ์— ๊ด€ํ•œ ๋Œ€๊ทœ๋ชจ ์—ฐ๊ตฌ๋Š” ๋ฏธ๋ฏธํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐ€์„ค์€ ์Šค๋งˆํŠธํฐ ์†Œ์œ ์ž๋“ค์€ ๊ฑด๊ฐ•์„ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์Šค๋งˆํŠธํฐ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์Šค๋งˆํŠธํฐ์˜ ์‚ฌ์šฉ ์ž์ฒด๊ฐ€ ์ข‹์€ ์ธ์ง€๊ธฐ๋Šฅ ๋ฐœ๋‹ฌ์šด๋™์ด ๋˜๊ธฐ ๋•Œ๋ฌธ์— ์Šค๋งˆํŠธํฐ ์†Œ์œ ์ž๋“ค์—๊ฒŒ ๋…ธ์‡ ๊ฐ€ ๋œ ์กด์žฌํ•  ๊ฒƒ์ด๋ผ๋Š” ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋…ธ์ธ ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์ž ๋ฐ ๋น„์‚ฌ์šฉ์ž์˜ ๋‹ค์–‘ํ•œ ์‚ฌํšŒ์ธ๊ตฌํ•™์  ๋ฐ ์˜ํ•™์  ํŠน์„ฑ์„ ๊ธฐ์ˆ ํ•˜๊ณ , ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ๊ณผ ๋…ธ์‡ ์˜ ์—ฐ๊ด€์„ฑ์„ ํ™•์ธํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์–ป์€ ์ •๋ณด๋Š” ์†Œ๊ทœ๋ชจ ์˜๋ฃŒ์‹œ์„ค์—์„œ ๋…ธ์‡ ๋ฅผ ์„ ๋ณ„ํ•˜๋Š”๋ฐ ํฐ ๋„์›€์ด ๋  ๊ฒƒ์ด๋‹ค. ๋ฐฉ๋ฒ• : ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ๋…ธ์ธ๋…ธ์‡ ์ฝ”ํ˜ธํŠธ์‚ฌ์—…(KFACS)์˜ ์ž๋ฃŒ๋ฅผ 1์ฐจ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ง„ํ–‰๋˜์—ˆ๋‹ค. KFACS๋Š” ๋…ธํ™” ๋ฐ ๋…ธ์‡ ์™€ ๊ด€๋ จํ•œ ์ธ์ž๋“ค์„ ์ฐพ์•„๋‚ด๊ณ  ์ด๋ฅผ ์˜ˆ๋ฐฉํ•˜๊ณ ์ž ์ง„ํ–‰๋˜๊ณ  ์žˆ๋Š” ํ•œ๊ตญ์˜ ๊ตญ๊ฐ€๊ธฐ๋ฐ˜ ์ฝ”ํ˜ธํŠธ ์—ฐ๊ตฌ์ด๋‹ค. ๋งŒ 70์„ธ์—์„œ 84์„ธ์˜ ์ด 2935๋ช… ์ฐธ๊ฐ€์ž์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์ธ๊ตฌํ•™์ , ์‚ฌํšŒ๊ฒฝ์ œ์ , ์ธ์ง€์  ๋ฐ ๊ธฐ๋Šฅ์  ํŠน์„ฑ๊ณผ ๊ด€๋ จ๋œ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ ๋…ธ์‡ ์™€์˜ ๊ด€๋ จ์„ฑ ๋˜ํ•œ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋…ธ์‡ ๋Š” Fried ๋…ธ์‡ ์ง€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ •์˜ํ•˜์˜€๋‹ค. ์ฐธ๊ฐ€์ž๋“ค์˜ ํŠน์„ฑ์„ ์Šค๋งˆํŠธํฐ ์†Œ์œ  ์œ ๋ฌด์— ๋”ฐ๋ผ ๊ธฐ์ˆ ํ•˜์˜€์œผ๋ฉฐ ๋กœ์ง€์Šคํ‹ฑ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ๊ณผ ๋…ธ์‡ ์˜ ์—ฐ๊ด€์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ : ์ด 2935๋ช…์˜ ์ฐธ๊ฐ€์ž ์ค‘ 1404๋ช…(47.8%)์ด ์Šค๋งˆํŠธํฐ์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ 1531๋ช…(52.2%)๋Š” ์Šค๋งˆํŠธํฐ์ด ์•„๋‹Œ ํ•ธ๋“œํฐ์„ ์‚ฌ์šฉํ•˜๊ฑฐ๋‚˜ ํ•ธ๋“œํฐ์„ ๊ฐ€์ง€๊ณ  ์žˆ์ง€ ์•Š์•˜๋‹ค. ์ฐธ๊ฐ€์ž๋“ค์˜ ํ‰๊ท  ์—ฐ๋ น์€ 76.0์„ธ์˜€๋‹ค. ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์ž๋“ค์€ ๋‚จ์„ฑ์ด ๋” ๋งŽ์•˜์œผ๋ฉฐ (53.3%), ๋น„์‚ฌ์šฉ์ž๋“ค์— ๋น„ํ•ด ๊ต์œก ๋ฐ ๊ฒฝ์ œ์  ์ˆ˜์ค€์ด ๋†’์•˜๋‹ค. ์‚ฌ์šฉ์ž๋“ค์€ ๋˜ํ•œ ๊ฒฐํ˜ผ์ƒํƒœ์ธ ๋น„์œจ์ด ๋†’๊ณ  ๋…๊ฑฐ์˜ ๋น„์œจ์ด ๋‚ฎ์•˜์œผ๋‚˜ ์‚ฌํšŒ์  ์ƒํ˜ธ๊ด€๊ณ„๊ฐ€ ์ ์—ˆ๊ณ  ๋” ๋‚ฎ์€ ์ผ์ƒ์  ๋Šฅ๋ ฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ด์— ๋ฐ˜ํ•ด ์ธ์ง€๊ธฐ๋Šฅ์€ ๋” ๋›ฐ์–ด๋‚ฌ์œผ๋ฉฐ, ํŠนํžˆ ๋…ธ์‡ ๊ธฐ์ค€์˜ 5๊ฐ€์ง€ ํ•ญ๋ชฉ ๋ชจ๋‘์—์„œ ๋น„์‚ฌ์šฉ์ž๋“ค๋ณด๋‹ค ๋›ฐ์–ด๋‚œ ์ƒํƒœ๋ฅผ ๋ณด์˜€๋‹ค. ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ๊ณผ ๋…ธ์‡ ์˜ ์—ฐ๊ด€์„ฑ์˜ ๊ต์ฐจ๋น„๋Š” ๋‹ค์–‘ํ•œ ๊ด€๋ จ ์š”์†Œ๋ฅผ ๋ณด์ •ํ•œ ํ›„ 0.47์ด์—ˆ์œผ๋ฉฐ 95% ์‹ ๋ขฐ๊ตฌ๊ฐ„์€ 0.39~0.55์˜€๋‹ค. ๊ฒฐ๋ก  : ์Šค๋งˆํŠธํฐ์˜ ์†Œ์œ ๋Š” ๋…ธ์‡ ์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์š”์†Œ๊ฐ€ ์ƒํ˜ธ์ž‘์šฉํ•œ ๊ฒฐ๊ณผ๋ฌผ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์Šค๋งˆํŠธํฐ ๋น„์‚ฌ์šฉ์ž๋“ค์ด ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์ž๋“ค๋ณด๋‹ค ๋” ๋›ฐ์–ด๋‚œ ์ผ์ƒ์  ๋Šฅ๋ ฅ๊ณผ ์‚ฌํšŒ์  ์ƒํ˜ธ๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ์œผ๋‚˜ ์ธ์ง€๊ธฐ๋Šฅ์ด ๋” ๋ถ€์กฑํ•˜์˜€์œผ๋ฉฐ ๋” ๋†’์€ ๋…ธ์‡ ์˜ ๋น„์œจ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ๊ณ ๋ น์ž์˜ ์Šค๋งˆํŠธํฐ ์†Œ์œ ๋Š” ์ข…์ข… ๋…ธ์‡ ์™€ ์—ญ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์ธ์ž๋ฅผ ๋Œ€๋ณ€ํ•œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์–ด๋–ค ํƒ€์ž…์˜ ํ•ธ๋“œํฐ์„ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ๊ฐ„๋‹จํžˆ ์งˆ๋ฌธ์ด ๋‹ค๋ฅธ ๋…ธ์‡  ๊ด€๋ จ ์„ ๋ณ„๊ฒ€์‚ฌ์™€ ํ•จ๊ป˜ ์ž‘์€ ์˜๋ฃŒ๊ธฐ๊ด€์—์„œ ๋…ธ์‡ ๋ฅผ ์„ ๋ณ„ํ•˜๊ธฐ ์œ„ํ•œ ์ข‹์€ ๋ฐฉ๋ฒ•์ด ๋  ์ˆ˜ ์žˆ๊ฒ ๋‹ค.Chapter 1. Introduction 1 1.1. Aging society and frailty 1 1.2. Adoption of smartphones 1 1.2.1. mHealth 1 1.2.2. Characteristics of elderly smartphone adopters 2 1.3. Frailty and smartphones 3 1.4. Objective 4 Chapter 2. Materials and Methods 6 2.1. Study Design and Population 6 2.2. Definition of Frailty 6 2.3. Other covariates of interest 7 2.4. Statistical Methods 9 Chapter 3. Results 10 3.1. General characteristics of the participants 10 3.2. Distribution of smartphone use and prevalence of frailty 15 3.3. Social, functional, and cognitive assessments 21 3.4. Association between smartphone use and frailty 26 Chapter 4. Discussion 32 4.1. Characteristics of elderly smartphone users and non-users 32 4.2. Digital frailty 36 4.3. Strengths and limitations 38 Chapter 5. Conclusion 41 References 42 Abstract in Korean 46Maste

    Secure Pick Up: Implicit Authentication When You Start Using the Smartphone

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    We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background, our system implicitly observes users' phone pick-up movements, the way they bend their arms when they pick up a smartphone to interact with the device, to authenticate the users. Our SPU outperforms the state-of-the-art implicit authentication mechanisms in three main aspects: 1) SPU automatically learns the user's behavioral pattern without requiring a large amount of training data (especially those of other users) as previous methods did, making it more deployable. Towards this end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW) algorithm to effectively quantify similarities between users' pick-up movements; 2) SPU does not rely on a remote server for providing further computational power, making SPU efficient and usable even without network access; and 3) our system can adaptively update a user's authentication model to accommodate user's behavioral drift over time with negligible overhead. Through extensive experiments on real world datasets, we demonstrate that SPU can achieve authentication accuracy up to 96.3% with a very low latency of 2.4 milliseconds. It reduces the number of times a user has to do explicit authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies (SACMAT) 201

    Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

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    Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

    Why Do People Adopt, or Reject, Smartphone Password Managers?

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    People use weak passwords for a variety of reasons, the most prescient of these being memory load and inconvenience. The motivation to choose weak passwords is even more compelling on Smartphones because entering complex passwords is particularly time consuming and arduous on small devices. Many of the memory- and inconvenience-related issues can be ameliorated by using a password manager app. Such an app can generate, remember and automatically supply passwords to websites and other apps on the phone. Given this potential, it is unfortunate that these applications have not enjoyed widespread adoption. We carried out a study to find out why this was so, to investigate factors that impeded or encouraged password manager adoption. We found that a number of factors mediated during all three phases of adoption: searching, deciding and trialling. The studyโ€™s findings will help us to market these tools more effectively in order to encourage future adoption of password managers

    Are HIV smartphone apps and online interventions fit for purpose?

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    Sexual health is an under-explored area of Human-Computer Interaction (HCI), particularly sexually transmitted infections such as HIV. Due to the stigma associated with these infections, people are often motivated to seek information online. With the rise of smartphone and web apps, there is enormous potential for technology to provide easily accessible information and resources. However, using online information raises important concerns about the trustworthiness of these resources and whether they are fit for purpose. We conducted a review of smartphone and web apps to investigate the landscape of currently available online apps and whether they meet the diverse needs of people seeking information on HIV online. Our functionality review revealed that existing technology interventions have a one-size-fits-all approach and do not support the breadth and complexity of HIV-related support needs. We argue that technology-based interventions need to signpost their offering and provide tailored support for different stages of HIV, including prevention, testing, diagnosis and management

    A realisation of ethical concerns with smartphone personal health monitoring apps

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    The pervasiveness of smartphones has facilitated a new way in which owners of devices can monitor their health using applications (apps) that are installed on their smartphones. Smartphone personal health monitoring (SPHM) collects and stores health related data of the user either locally or in a third party storing mechanism. They are also capable of giving feedback to the user of the app in response to conditions are provided to the app therefore empowering the user to actively make decisions to adjust their lifestyle. Regardless of the benefits that this new innovative technology offers to its users, there are some ethical concerns to the user of SPHM apps. These ethical concerns are in some way connected to the features of SPHM apps. From a literature survey, this paper attempts to recognize ethical issues with personal health monitoring apps on smartphones, viewed in light of general ethics of ubiquitous computing. The paper argues that there are ethical concerns with the use of SPHM apps regardless of the benefits that the technology offers to users due to SPHM appsโ€™ ubiquity leaving them open to known and emerging ethical concerns. The paper then propose a need further empirical research to validate the claim
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