Association Between Serious Psychological Distress and Loneliness During the COVID-19 Pandemic: A Cross-Sectional Study with Pregnant Japanese Women

Takeda, Takashi; Yoshimi, Kana; Kai, Sayaka; Inoue, Fumi

doi:10.2147/ijwh.s338596

Cited by 9 publications

(7 citation statements)

References 35 publications

(45 reference statements)

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“… 28 Some past studies in Japan have used a cutoff point of 10 points, and this criterion was also adopted. 21 , 29 …”

Section: Methodsmentioning

confidence: 99%

“… 20 The association between severe psychological distress (SPD) and loneliness was shown in a previous report on psychological distress among Japanese pregnant women conducted in June 2021. 21 Stressful living, coupled with vulnerability to stress, causes psychological distress and elevates the risk of psychiatric disorders, including depression and anxiety; however, its association with PMDs is unclear. Moreover, the relationship between loneliness and PMDs has not yet been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Association Between Loneliness, Premenstrual Symptoms, and Other Factors During the COVID-19 Pandemic: A Cross-Sectional Study with Japanese High School Students

Takeda¹,

Yoshimi²,

Kai³

et al. 2023

IJWH

Self Cite

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Purpose Adolescence is a period of transition from childhood to adulthood where people are vulnerable to stress. The COVID-19 pandemic continues to cause sustained stress in the population. Since the COVID-19 pandemic, social isolation and loneliness have increased. Loneliness is associated with increased stress, psychological distress, and a higher risk of mental illnesses, such as depression. This study examined the association between loneliness, premenstrual symptoms, and other factors in the era of the COVID-19 pandemic among adolescent females in Japan. Patients and Methods A school-based cross-sectional survey of 1450 adolescent female students in Japan was conducted in mid-December of 2021. Specifically, paper-based questionnaires were distributed in class, and the responses were collected. The Premenstrual Symptoms Questionnaire (PSQ), 6-item Kessler Psychological Distress Scale, 3-item Revised UCLA Loneliness Scale (R-UCLA), and Fear of COVID-19 Scale were used as measurement tools. The prevalence of loneliness was defined as a total R-UCLA score ≥ 6. Results The prevalence of loneliness was 29.0%. The prevalence of serious psychological distress was also high (8.2%), especially in the lonely group (16.0%). Multivariable regression analysis identified the following factors associated with loneliness: second year (odds ratio [OR] 1.53; 95% confidence interval [CI] 1.09–2.14), longer internet use (OR, 1.11; 95% CI, 1.02–1.20), total PSQ score (OR 1.08; 95% CI 1.06–1.11), and psychological distress (OR 1.05; 95% CI 1.01–1.08). Conclusion Adolescent females in Japan showed a high prevalence of loneliness. School year (2nd year), longer periods of internet use, premenstrual symptom severity, and psychological distress were independently associated with loneliness. For clinicians and school health professionals, special concern should be given to the psychological health of adolescent females during the COVID-19 pandemic.

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“… 28 Some past studies in Japan have used a cutoff point of 10 points, and this criterion was also adopted. 21 , 29 …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association Between Loneliness, Premenstrual Symptoms, and Other Factors During the COVID-19 Pandemic: A Cross-Sectional Study with Japanese High School Students

Takeda¹,

Yoshimi²,

Kai³

et al. 2023

IJWH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recruitment was performed via maternity clinics or social media advertisements for two groups of pregnant women with different inclusion criteria from January 2019 to March 2020. The first group included women with a history of preterm birth (gestational weeks [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] or late miscarriage (gestational weeks [12][13][14][15][16][17][18][19][20][21][22]. The second group consisted of women with a history of previous full-term uncomplicated pregnancy and no pregnancy losses.…”

Section: Participants and Recruitmentmentioning

confidence: 99%

“…Other studies showed that loneliness is significantly associated with postpartum depression [11, 12]. It was also shown that, in the COVID-19 pandemic, loneliness during pregnancy was associated with serious psychological distress [13], anxiety [14], cognitive distortion [10], higher level of perceived stress [6], and lower level of social support [6, 7]. Maternal loneliness increases the risk of respiratory tract infections in newborn babies [15].…”

Section: Introductionmentioning

confidence: 99%

Predicting maternal social loneliness by passive sensing with wearable devices

Sarhaddi

Azimi

Niela‐Vilén

et al. 2022

Preprint

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Background Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness non-invasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. Objective The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period based on and to identify the important objective physiological parameters in loneliness detection. Methods We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks postpartum. Responses to this questionnaire and the background information of the participants were collected via our customized cross-platform mobile application. We leveraged participants’ smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores >= 12) and non-loneliness (scores < 12). We developed decision tree and gradient boosting models to predict loneliness. We evaluated the models by using a leave-one-participant-out cross validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. Results The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1 scores of 0.871 and 0.897, respectively. Our results also show that loneliness is highly associated with activity intensity, activity distribution during the day, resting heart rate (HR), and resting heart rate variability (HRV). Conclusion Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1 score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being by early detection of loneliness.

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“…Other studies showed that loneliness was significantly associated with postpartum depression [ 14 , 15 ]. It was also shown that, in the COVID-19 pandemic, loneliness during pregnancy was associated with serious psychological distress [ 16 ], anxiety [ 17 ], cognitive distortion [ 13 ], higher level of perceived stress [ 9 ], and a lower level of social support [ 9 , 10 ]. Maternal loneliness increases the risk of respiratory tract infections in newborn babies [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study

Sarhaddi¹,

Azimi²,

Niela‐Vilén³

et al. 2023

JMIR Form Res

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Background Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. Objective The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period and identify the important objective physiological parameters in loneliness detection. Methods We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks post partum. Responses to this questionnaire and background information of the participants were collected through our customized cross-platform mobile app. We leveraged participants’ smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores≥12) and nonloneliness (scores<12). We developed decision tree and gradient-boosting models to predict loneliness. We evaluated the models by using leave-one-participant-out cross-validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. Results The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1-scores of 0.897 and 0.872, respectively. Our results also show that loneliness is highly associated with activity intensity and activity distribution during the day. In addition, resting heart rate (HR) and resting HR variability (HRV) were correlated with loneliness. Conclusions Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1-score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being through early detection of loneliness.

show abstract

Association Between Serious Psychological Distress and Loneliness During the COVID-19 Pandemic: A Cross-Sectional Study with Pregnant Japanese Women

Cited by 9 publications

References 35 publications

Association Between Loneliness, Premenstrual Symptoms, and Other Factors During the COVID-19 Pandemic: A Cross-Sectional Study with Japanese High School Students

Association Between Loneliness, Premenstrual Symptoms, and Other Factors During the COVID-19 Pandemic: A Cross-Sectional Study with Japanese High School Students

Predicting maternal social loneliness by passive sensing with wearable devices

Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study

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