scholarly journals PT663. Comparison of wearable activity tracker to actigraphy for sleep evaluation and circadian rhythm measurement.

2016 ◽  
Vol 19 (Suppl_1) ◽  
pp. 42-42
Keyword(s):  
Circadian Rhythm ◽  
Activity Tracker ◽  
Wearable Activity Tracker

scholarly journals Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study (Preprint)

2020 ◽  
Author(s):  
Chul-Hyun Cho ◽  
Taek Lee ◽  
Jung-Been Lee ◽  
Ju Yeon Seo ◽  
Hee-Jung Jee ◽  
...  
Keyword(s):  
Circadian Rhythm ◽  
Health Behaviors ◽  
Mood Disorders ◽  
Learning Algorithm ◽  
Smartphone App ◽  
Face To Face ◽  
Activity Tracker ◽  
Wearable Activity Tracker ◽  
Giving Feedback ◽  
Depressive Episodes

BACKGROUND Smartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world. OBJECTIVE We aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state. METHODS In total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model. RESULTS The CRM group had 96.7% fewer total depressive episodes (n/year; exp β=0.033, <i>P</i>=.03), 99.5% shorter depressive episodes (total; exp β=0.005, <i>P</i>&lt;.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, <i>P</i>&lt;.001), 97.4% fewer total mood episodes (exp β=0.026, <i>P</i>=.008), and 98.9% shorter mood episodes (total; exp β=0.011, <i>P</i>&lt;.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group. CONCLUSIONS The CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app. CLINICALTRIAL ClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657

scholarly journals Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study

10.2196/21283 ◽  
2020 ◽  
Vol 7 (8) ◽  
pp. e21283 ◽  
Author(s):  
Chul-Hyun Cho ◽  
Taek Lee ◽  
Jung-Been Lee ◽  
Ju Yeon Seo ◽  
Hee-Jung Jee ◽  
...  
Keyword(s):  
Circadian Rhythm ◽  
Health Behaviors ◽  
Mood Disorders ◽  
Learning Algorithm ◽  
Smartphone App ◽  
Face To Face ◽  
Activity Tracker ◽  
Wearable Activity Tracker ◽  
Giving Feedback ◽  
Depressive Episodes

Background Smartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world. Objective We aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state. Methods In total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model. Results The CRM group had 96.7% fewer total depressive episodes (n/year; exp β=0.033, P=.03), 99.5% shorter depressive episodes (total; exp β=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, P<.001), 97.4% fewer total mood episodes (exp β=0.026, P=.008), and 98.9% shorter mood episodes (total; exp β=0.011, P<.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group. Conclusions The CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app. Trial Registration ClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657

A personalised smartphone-delivered just-in-time adaptive intervention (JITABug) to increase physical activity in older adults: A feasibility pilot study (Preprint)

2021 ◽  
Author(s):  
Jacqueline Louise Mair ◽  
Lawrence Hayes ◽  
Amy Campbell ◽  
Duncan Buchan ◽  
Chris Easton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Data Collection ◽  
Real Time ◽  
Behaviour Change ◽  
Time Of Day ◽  
Activity Level ◽  
Just In Time ◽  
Activity Tracker ◽  
Wearable Activity Tracker

BACKGROUND Just-in-time-adaptive-interventions (JITAIs) provide real-time ‘in the moment’ behaviour change support to people when they need it most. JITAIs could be a viable way to provide personalised physical activity support to older adults in the community. However, it is unclear how feasible it is to remotely deliver a physical activity intervention via a smartphone to older adults, or how acceptable older adults would find a JITAI targeting physical activity in everyday life. OBJECTIVE (1) to describe the development of “JITABug”, a personalised smartphone and activity tracker delivered JITAI designed to support older adults to increase or maintain their physical activity level; (2) to explore the acceptability of JITABug in a free-living setting, and (3) to assess the feasibility of conducting an effectiveness trial of the JITABug intervention. METHODS The intervention development process was underpinned by the Behaviour Change Wheel. The intervention consisted of a wearable activity tracker (Fitbit) and a companion smartphone app (JITABug) which delivered goal setting, planning, reminders, and just-in-time adaptive messages to encourage achievement of personalised physical activity goals. Message delivery was tailored based on time of day, real-time physical activity tracker data, and weather conditions. We tested the feasibility of remotely delivering the JITAI with older adults in a 6-week trial using a mixed-methods approach. Data collection involved assessment of physical activity by accelerometery and activity tracker, self-reported mood and mental wellbeing via ecological momentary assessment, and contextual information on physical activity via voice memos. Feasibility and acceptability outcomes included: (1) recruitment capability and adherence to the intervention; (2) intervention delivery ‘in the wild’; (3) appropriateness of data collection methodology; (4) adverse events and; (5) participant satisfaction. RESULTS Of 46 recruited older adults (aged 56-72 years old), 65% completed the intervention. The intervention was successfully delivered as intended; 27 participants completed the intervention independently, 94% of physical activity messages were successfully delivered, and 99% of Fitbit and 100% of weather data calls were successful. Wrist-worn accelerometer data were obtained from 96% at baseline and 96% at follow up. On average, participants recorded 8/16 (50%) voice memos, 3/8 (38%) mood assessments, and 2/4 (50%) wellbeing assessments via the app. Overall acceptability of the intervention was very good (77% satisfaction). Participant feedback suggested that more diverse and tailored physical activity messages, app usage reminders, technical refinements regarding real-time data syncing, and an improved user interface could improve the intervention and make it more appealing. CONCLUSIONS This study suggests that a smartphone delivered JITAI utilizing a wearable activity tracker is an acceptable way to support physical activity in older adults in the community. Overall, the intervention is feasible, however based on user feedback, the JITABug app requires further technical refinements that may enhance usage, engagement, and user satisfaction before moving to effectiveness trials. CLINICALTRIAL Non-Applicable

scholarly journals Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Samantha F. Ehrlich ◽  
Jill M. Maples ◽  
Cristina S. Barroso ◽  
Kathleen C. Brown ◽  
David R. Bassett ◽  
...  
Keyword(s):  
Physical Activity ◽  
Gestational Diabetes ◽  
Goal Setting ◽  
Equivalence Testing ◽  
Structured Interviews ◽  
Self Monitoring ◽  
Activity Tracker ◽  
Wearable Activity Tracker ◽  
Stepping In Place ◽  
Monitoring Devices

Abstract Background Activity monitoring devices may be used to facilitate goal-setting, self-monitoring, and feedback towards a step-based physical activity (PA) goal. This study examined the performance of the wrist-worn Fitbit Charge 3™ (FC3) and sought opinions on walking and stepping-in-place from women with gestational diabetes (GDM). Methods Participants completed six 2-min metronome-assisted over ground bouts that varied by cadence (67, 84, or 100 steps per minute) and mode (walking or stepping-in-place; N = 15), with the sequence randomized. Steps were estimated by FC3 and measured, in duplicate, by direct observation (hand-tally device, criterion). Equivalence testing by the two one-sided tests (TOST) method assessed agreement within ± 15%. Mean absolute percent error (MAPE) of steps were compared to 10%, the accuracy standard of the Consumer Technology Association (CTA)™. A subset (n = 10) completed a timed, 200-m self-paced walk to assess natural walking pace and cadence. All participants completed semi-structured interviews, which were transcribed and analyzed using descriptive and interpretive coding. Results Mean age was 27.0 years (SD 4.2), prepregnancy BMI 29.4 kg/m2 (8.3), and gestational age 32.8 weeks (SD 2.6). The FC3 was equivalent to hand-tally for bouts of metronome-assisted walking and stepping-in-place at 84 and 100 steps per minute (i.e., P < .05), although walking at 100 steps per minute (P = .01) was no longer equivalent upon adjustment for multiple comparisons (i.e., at P < .007). The FC3 was equivalent to hand-tally during the 200-m walk (i.e., P < .001), in which mean pace was 68.2 m per minute (SD 10.7), or 2.5 miles per hour, and mean cadence 108.5 steps per minute (SD 6.5). For walking at 84 and 100 steps per minute, stepping-in-place at 100 steps per minute, and the 200-m walk, MAPE was within 10%, the accuracy standard of the CTA™. Interviews revealed motivation for PA, that stepping-in-place was an acceptable alternative to walking, and competing responsibilities made it difficult to find time for PA. Conclusions The FC3 appears to be a valid step counter during the third trimester, particularly when walking or stepping-in-place at or close to women’s preferred cadence.

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