Physical Activity and Depressive Mood in the Daily Life of Older Adults

Gruenenfelder-Steiger, Andrea E.; Katana, Marko; Martin, Annika; Aschwanden, Damaris; Koska, Julia L.; Kündig, Yvonne; Pfister-Lipp, Eliane; Allemand, Mathias

doi:10.1024/1662-9647/a000172

Cited by 13 publications

(16 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In fact, most studies investigating mobile health tracking have focused on young or middle-aged individuals or examined the effects only in individuals who are already physically active (Dallinga et al, 2015). However, older adults in particular might profit from innovative approaches, such as mobile physical activity tracking for individual health promotion and prophylaxis, since an appropriate level of physical activity can contribute to healthier aging processes (e.g., Gruenfelder-Steiger et al, 2017; Landi et al, 2007; Peel, McClure, & Bartlett, 2005). Moreover, an increasing number of older individuals have already started to use new mobile devices, and the potential uses of technology for coping with everyday life have been well documented (e.g., Seifert & Schelling, 2016; Schulz et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Willingness of Older Adults to Share Mobile Health Data with Researchers

2018

View full text Add to dashboard Cite

Abstract. This study explored the use of wearable devices to track self-recorded health data and the willingness to share this data with researchers. Participants aged ≥ 50 years (n = 1,013) were interviewed in a representative telephone survey. Results indicated that 43.3% of all participants used one or more mobile devices (activity tracker, smartwatch, smartphone, or tablet), and that 27.6% used those devices for the purposes of recording health data. Additionally, 57.2% of the participants who tracked their health data were willing to share it with researchers. Income significantly contributed to predicting this willingness, whereas other independent variables were not significant predictors. This study indicates a relatively positive overall willingness to share self-recorded mobile health data with the science community.

show abstract

Section: Introductionmentioning

confidence: 99%

Willingness of Older Adults to Share Mobile Health Data with Researchers

2018

View full text Add to dashboard Cite

show abstract

“…This involved exploring additional passive sensing data, such as typing patterns, phone call habits, and biometric parameters. The majority of these studies (n = 16) were conducted in Western countries, including North America (n = 9) [ 19 – 27 ] and Europe (n = 7) [ 28 – 34 ], while the rest were conducted in Asia (n = 3) [ 35 – 37 ] and Australia (n = 2) [ 38 , 39 ]. The subjects of 11 studies were from community settings [ 20 , 21 , 23 , 26 – 28 , 30 , 32 – 34 , 37 ] and eight studies were selected from facility-based centers [ 19 , 22 , 24 , 25 , 31 , 36 , 38 , 39 ].…”

Section: Resultsmentioning

confidence: 99%

Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review

Adhibai,

Kosiyaporn,

Markchang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Background The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly. Method The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis. Results The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects. Conclusion Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.

show abstract

“…The level of physical activity, which can be measured using bodyworn accelerometers (see section 2.1), has been identified as a potential predictor of human emotions [74] and depressive moods [75]. Zhang et al were able to recognize emotional states of test subjects (happy, neutral, and angry) with fair accuracy, relying only on accelerometer data from smart wristbands [76].…”

Section: Mood and Emotion Recognitionmentioning

confidence: 99%

Privacy implications of accelerometer data

Kröger

Raschke

Bhuiyan

2019

Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

View full text Add to dashboard Cite

Accelerometers are sensors for measuring acceleration forces. They can be found embedded in many types of mobile devices, including tablet PCs, smartphones, and smartwatches. Some common uses of built-in accelerometers are automatic image stabilization, device orientation detection, and shake detection. In contrast to sensors like microphones and cameras, accelerometers are widely regarded as not privacy-intrusive. This sentiment is reflected in protection policies of current mobile operating systems, where third-party apps can access accelerometer data without requiring security permission. It has been shown in experiments, however, that seemingly innocuous sensors can be used as a side channel to infer highly sensitive information about people in their vicinity. Drawing from existing literature, we found that accelerometer data alone may be sufficient to obtain information about a device holder's location, activities, health condition, body features, gender, age, personality traits, and emotional state. Acceleration signals can even be used to uniquely identify a person based on biometric movement patterns and to reconstruct sequences of text entered into a device, including passwords. In the light of these possible inferences, we suggest that accelerometers should urgently be re-evaluated in terms of their privacy implications, along with corresponding adjustments to sensor protection mechanisms.

show abstract

Physical Activity and Depressive Mood in the Daily Life of Older Adults

Cited by 13 publications

References 47 publications

Willingness of Older Adults to Share Mobile Health Data with Researchers

Willingness of Older Adults to Share Mobile Health Data with Researchers

Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review

Privacy implications of accelerometer data

Contact Info

Product

Resources

About