(Bloomberg) — A tech-led rebound in stocks faded as caution prevailed on Wall Street ahead of a deluge of economic data and concerns over the Federal Reserve’s ability to slash interest rates in December. Bonds dropped.
The relief brought by the end of a historic US shutdown gave way to volatility this week as various Fed speakers damped wagers on policy easing. Hot areas favored by momentum traders such as artificial-intelligence whipsawed. Bitcoin was barely up for 2025. After briefly erasing a 1.4% slide, the S&P 500 closed little changed. Nvidia Corp. rose ahead of its earnings.
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The outlook for lower rates favoring Corporate America alongside booming AI prospects have powered a torrid surge since the April meltdown, making many traders look past high valuations to keep chasing the market higher.
Earnings for most big tech companies have been in line or above expectations, though the outlook has been murky when it comes to where borrowing costs are headed. As Nvidia gets ready to report Wednesday, options traders are pricing in a 6.2% stock swing in either direction – its highest implied move in a year.
“Its earnings will be a huge test for the markets and the AI-trade, and could either ease fears about AI valuations or inflame them considerably,” said Kyle Rodda at Capital.com.
Also next week, big box retailers like Walmart Inc. and Target Corp. will report their results, offering a read on the state of consumer spending – the main engine of the American economy.
The S&P 500 held above its 50-day moving average after briefly falling below it. A gauge of megacaps halted a three-day rout.
The yield on 10-year Treasuries climbed three basis points to 4.15%. UK markets got hit as speculation about the budget heightened worries about the nation’s finances. Oil rose as geopolitical risks mount from Russia to Iran.
“Stocks should bounce back here, but the dip buyers have been burned lately, so it might be a slow move back up to regain confidence,” said Bob Lang founder of Explosive Options.
We also saw some pretty clear rotation this week into health care primarily and consumer staples – looking like they have bottomed, according to Ken Mahoney at Mahoney Asset Management.
“Not really what you want to see if you are in the AI trade or adjacent stocks,” he said. “This is a unique circumstance where it feels like a mini bear market in some stocks” even though the S&P 500 is not that far from its highs.
“What’s happened recently in the market isn’t even close to a tech wreck, but it may be a bit of a tech reckoning,” said Daniel Skelly, head of Morgan Stanley’s Wealth Management Market Research & Strategy Team.
The recent volatility hasn’t altered the longer-term bullish case for the AI leadership, he said. But health care remains one of the market’s key overlooked stories.
Breadth deterioration in equities remains an ongoing concern, indicating a more tactical defensive stance and sector rotation, according to Craig Johnson at Piper Sandler.
Still, the S&P 500 managed to hold above its average price of the past 50 days. Failure to do so would invite a deeper pullback, Johnson noted.
“The general trend has been to buy the dip, which could provide a respite,” said Melissa Brown at SimCorp. “Retail investors may be spooked temporarily, but are likely to come back in if they believe the long-term story driving many of the names that have been gutted remains intact.”
Brown notes that a real rebound, though, may have to wait until government data starts flowing again and investors get a better read on the state of the economy and inflation.
“But it will only be a recovery if the economy continues to grow and inflation does not,” she said.
As labor-market and inflation reports return, fundamentals should help distinguish a true trend from emotion-driven selling, making the recent pullback feel more like a reset than a turning point, according to Mark Hackett at Nationwide.
A slew of Fed officials have expressed skepticism over the need for a cut in December – or outright opposed one. It remains unclear whether they can persuade enough voting members of the Federal Open Market Committee, given that a number of them are worried about job weakness.
Their remarks came less than a month after Chair Jerome Powell warned that a December cut is far from a “foregone conclusion.”
Markets have taken note of the volume of comments from the so-called inflation hawks. Investors have marked down the odds of a rate cut in December to less than 50%. Before the Fed’s October meeting, they were almost fully pricing in a reduction.
“The tough but business-as-usual wrestling match over a December rate cut risks morphing into a crisis of governance at the Fed, with implications that extend well beyond whether it does or does not cut then,” said Krishna Guha at Evercore. “Absent miraculous clarification from limited data, Powell is in a rough spot. We urge cool heads and compromise.”
Guha says that he still leans toward a “hawkish cut,” but the odds have diminished.
“Our expectation for a soft October employment report and under-control October core CPI inflation should settle the internal debate at the FOMC in favor of an additional 25 basis-point rate cut,” said Gennadiy Goldberg at TD Securities. “With that said, the decision is likely to be contentious, with a high possibility of additional hawkish dissents.”
Despite the cautious rhetoric from Fed officials this week, Ulrike Hoffmann-Burchardi at UBS Global Wealth Management says any decision will ultimately be data-dependent.
She noted that even if the official October jobs report does not include the unemployment rate, the payrolls figure should still provide a good indication of the health of the labor market. Some private data alonsgide sentiment surveys should also allow the Fed to continue its rate-cutting cycle if inflation remains under control, she added.
As traders geared up for a deluge of economic data that will shape the Fed outlook, this week’s bout of risk aversion deepened the selloff in Bitcoin from a record high reached in early October.
The largest digital-asset sank below $95,000. The crypto market remains under strain after $19 billion in liquidations on Oct. 10 in turn erased over $1 trillion from the total market value of all cryptocurrencies, CoinGecko data shows.
Corporate Highlights:
Alphabet Inc.’s Google plans to invest $40 billion in three new Texas data centers, ramping up its footprint as competitors such as OpenAI and Anthropic PBC map out their own multibillion-dollar bets in the state. Google has offered to tweak its ad tech products to settle a European Union order after a near-€3 billion ($3.4 billion) antitrust penalty, stopping short of a partial breakup watchdogs favor. The cost of protecting Oracle Corp.’s debt against default is surging by the most since 2021, as jittery investors and lenders rush to hedge against the billions of dollars the software giant is pouring into artificial intelligence. Applied Materials Inc. suffered a sales decline last quarter and predicted another drop in the current period, though the chip-equipment maker sees demand improving in the second half of 2026. Walmart Inc. Chief Executive Officer Doug McMillon, who over a decade ushered the big-box behemoth into the Internet age, will retire in February. He’ll be replaced by US head John Furner — long viewed as the heir apparent. Warner Bros. Discovery Inc. amended the contract of Chief Executive Officer David Zaslav to ensure his stock options remain eligible to vest even if the media company is sold. Merck & Co. agreed to acquire Cidara Therapeutics Inc., a biotech company developing a flu treatment, as part of its ongoing efforts to make up for the upcoming patent loss of its blockbuster cancer drug Keytruda. Bristol Myers Squibb Co. fell after one of its most important experimental medicines appeared unlikely to benefit patients who had suffered a heart complication, another setback for the drugmaker’s product pipeline. Boeing Co. stands to win most of a major order from Flydubai for single-aisle aircraft, though Airbus SE still has a long-shot chance to pry some business from an airline that’s never ordered from the European planemaker. Emirates is planning to use SpaceX’s Starlink to upgrade the onboard Wi-Fi in its fleet, according to people familiar with the matter, even though the service isn’t currently approved by the government. BlackRock Inc. has agreed to pay up to €2 billion ($2.33 billion) to form a data center venture with Spanish engineering firm ACS SA. American Tower Corp. and European buyout firm EQT AB are among parties weighing bids for French tower company TDF Infrastructure, people with knowledge of the matter said. A group of First Brands Group creditors is demanding new, independent advisers for company units that issued nearly $2.5 billion in off-balance-sheet debt, claiming conflicts of interest threaten to disrupt the sprawling insolvency case of auto-parts maker. JBS NV, the world’s largest meat supplier, reported a quarterly operating loss at its US beef business as a shortage of cattle continues to hit margins at the unit. BHP Group Ltd. is liable to compensate hundreds of thousands of victims of a devastating dam collapse in Brazil, a London judge ruled, moving closer to a potential multi-billion dollar payout a decade after the disaster. Nu Holdings Ltd. said artificial intelligence features it started to deploy in Brazil helped the fintech increase credit-card limits for some clients, boosting third-quarter revenue and profit. Sigma Lithium Corp. stocks rose as investors focused on the company’s forecast to resume mining operations by the end of the month, despite another quarter of cash burn, lower sales and production volumes. Allianz SE, the German insurer that owns bond manager Pacific Investment Management Co., raised its outlook for full-year profit after third-quarter earnings rose, driven by its property-casualty insurance and asset management businesses. Siemens Energy AG substantially raised its mid-term financial targets on strong demand for gas turbines and data center equipment as well as restructuring progress at its Gamesa wind turbine unit. Richemont sales climbed as shoppers from the US to China snapped up the luxury group’s pricey Cartier and Van Cleef & Arpels jewelry. Jaguar Land Rover Automotive Plc swung to a £559 million ($735 million) quarterly loss and slashed its guidance after a cyberattack temporarily halted production at the UK’s largest automaker. Some of the main moves in markets:
Stocks
The S&P 500 was little changed as of 4 p.m. New York time The Nasdaq 100 was little changed The Dow Jones Industrial Average fell 0.7% The MSCI World Index fell 0.3% Bloomberg Magnificent 7 Total Return Index rose 0.2% The Russell 2000 Index rose 0.2% Currencies
The Bloomberg Dollar Spot Index was little changed The euro fell 0.1% to $1.1621 The British pound fell 0.2% to $1.3169 The Japanese yen was little changed at 154.53 per dollar Cryptocurrencies
Bitcoin fell 4.1% to $94,701.4 Ether fell 1% to $3,147.81 Bonds
The yield on 10-year Treasuries advanced three basis points to 4.15% Germany’s 10-year yield advanced three basis points to 2.72% Britain’s 10-year yield advanced 14 basis points to 4.57% The yield on 2-year Treasuries advanced two basis points to 3.61% The yield on 30-year Treasuries advanced four basis points to 4.75% Commodities
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The current society has also entered a new era of digitalization, and the internet penetration rate is rising every year []. According to the Statistical Report on Internet Development in China, the number of netizens has grown from 620,000 in 1997 to 1.108 billion in 2024, and the internet penetration rate has risen to 78.6% []. The internet has gradually penetrated all aspects of personal life, and because mobile phones are powerful and widely used, they have had an important impact on people’s lives. Some studies have found that it has changed the way people consume information, reshaped people’s communication mode [], facilitated people’s daily life, exacerbated the digital divide [], and even had an impact on people’s mental health [-].
Related studies have found a significant association between mobile internet use (MIU) and depressive symptoms [-]. Notably, some studies have suggested that internet use, even for health purposes, may be associated with increased rates of depressive symptoms []. In addition, internet addiction and unhealthy internet-based behavior patterns (eg, excessive passive scrolling, exposure to cyberbullying, or engagement in upward social comparison) have been shown to exacerbate depressive symptoms in adolescent populations []. However, in a broader social context, for adults in general, the judicious use of the mobile internet—characterized by purposeful activities such as maintaining meaningful social connections, accessing evidence-based health information, and participating in supportive internet-based communities—appears to provide relief from depressive symptoms [-]. For older people in particular, the available evidence suggests that the internet may have a more positive impact. This may be due to the enhancement of their social connections and support networks through internet-based communication, for example, which indirectly reduces depressive symptom levels [,,]. Although cross-sectional studies have repeatedly validated these ideas, there remains a relative paucity of research on the long-term relationship between the two []. Therefore, more longitudinal studies are needed to further explore how the general population can prevent or alleviate depressive symptoms through appropriate MIU.
Social support plays an important explanatory role in the negative correlation between mobile internet access and depressive symptoms. First, there is a strong link between MIU and social support [-]. For example, a Finnish study noted that cell phones can provide users with information and emotional support []. Another survey covering 7 European countries also showed that the use of the internet promotes the establishment of social support networks and improves the subjective health of individuals compared with other devices. In addition, several studies have found that the internet has become an important tool for maintaining and enhancing interpersonal relationships, and that people gain the necessary support and satisfaction from internet-based social interactions. On the other hand, social support is widely recognized as one of the key factors in maintaining an individual’s mental health []. A large body of literature shows that a good social support system is associated with lower rates of depressive symptoms [-]. Conversely, when a person lacks strong social support, they experience fewer positive emotions and their overall mental health declines, which increases the risk of depressive symptoms. These findings emphasize the importance of building a healthy social support environment for the prevention and treatment of depressive symptoms.
Many studies examining the association between MIU and depressive symptoms are limited by their design. Most rely on cross-sectional data [], which cannot establish longitudinal relationship or determine whether frequent MIU leads to depression or vice versa. Furthermore, inconsistent definitions and measurements of social support across studies hinder the comparability and generalizability of findings [,]. Although recent longitudinal cohort studies have begun to clarify temporal relationships [,], many still lack comprehensive models and diverse populations.
Objectives
To address these gaps, our study uses a longitudinal design to examine the long-term effects of changes in MIU behavior on individuals’ social support and depressive symptoms. It uses analytical methods such as the least absolute shrinkage and selection operator (LASSO) regression and cross-lagged models to distinguish the mental health impacts of different usage patterns. Leveraging internationally recognized scales and multistage population surveys, the study systematically analyzes the longitudinal mechanisms and mediating pathways linking mobile internet usage, social support, and depressive symptoms to enhance the generalizability and reliability of findings. Ultimately, it aims to provide scientific evidence and intervention strategies for promoting healthy MIU, strengthening social support, and alleviating depressive symptoms. The conceptual framework and mediation model are presented in .
Figure 1. Analytical models of mobile internet use, social support, and depressive symptoms: (A) mediation model, and (B) cross-lagged panel model.
Methods
This prospective cohort study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [].
Study Design
This longitudinal study used data collected across 2 waves (2019 and 2023) from rural residents in Taierzhuang District, Zaozhuang City, Shandong Province, China. Using a random cluster sampling approach, we selected 1 village from each of the 3 towns in Taierzhuang District, then recruited residents aged 18 years and older from these areas.
Participants
The baseline survey (wave 1) was conducted in November 2019 and included 879 participants. The follow-up survey (wave 2) was administered in August 2023. Among the original participants, 51 had passed away, 145 had relocated away from the village, and 194 could not be contacted or declined to participate in the follow-up. Ultimately, 489 rural residents provided complete responses in both waves, representing the final sample for this 4-year longitudinal analysis.
Data Collection
The implementation process of the 2023 Longitudinal Survey of Rural Residents is as follows. First, we communicated with and obtained support from doctors at local township health centers. We fully explained the survey content to residents beforehand to ensure voluntary participation and obtain signed informed consent forms (illiterate residents were assisted by their legal guardians). Second, trained graduate students conducted standardized one-on-one face-to-face surveys to ensure accurate questionnaire administration; during data cleaning, we identified minimal missing data, with only the follow-up social support scale having 6 missing values (1.23% of observations). Little’s Missing Completely at Random (MCAR) test was conducted (χ20<0.001), which supports the conclusion that the data were missing completely at random. Given the low missing rate and MCAR pattern, we imputed these missing values using the mean of the available social support scores. All other variables had complete data. The entire process followed the principle of informed consent, and data quality was ensured through standardized procedures.
Ethical Considerations
This research protocol received ethical approval from the Institutional Review Board at the Shandong University School of Public Health (Approval LL20190802). Written informed consent was obtained from all participants before enrollment. In cases involving illiterate or semiliterate individuals, consent was secured from their legal guardians. All data collected were anonymized through the use of unique identification codes. In compliance with institutional review board policies, participants did not receive financial compensation to prevent any potential coercion. The manuscript includes no personally identifiable information, and no images necessitating additional consent are present. The study was conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki and relevant Chinese regulations pertaining to research involving human participants.
Measurement
Sociodemographic Variables
In this survey, the demographic variables of sex, age, ethnicity, marital status, education level, occupation, height, and weight were collected. Sex was measured as male (1) and female (0). According to the definition of the World Health Organization, age is divided into the non–older adult population group (≤60 y old) and the older adult population group (>60 y old). The ethnicity was measured by Han Chinese (1) and others (0), and the marriage was measured by married (1) and others (0). The education level is measured by illiteracy (1), primary school (2), and junior high school (3), and the occupation is measured by farmers (1) and others (0). BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²).
MIU
MIU was measured using the following two questions, drawing upon previous literature []: “Do you go online?” The answers are divided into 3 categories: “independent internet access,” “internet access with the help of others,” and “no.” If someone answers “independent internet access” and “internet access with the help of others,” they need to answer the next question, “What are your internet tools?” The answer is set to multiple topics, including “desktop computer,” “laptop,” “tablet,” “mobile phone,” and “other devices.” If the answer option includes the “mobile phone” option, it is defined as “mobile internet user” (1); if not, it is defined as “other”(0).
Social Support
The Perceived Social Support Scale is used to assess the level of social support in this study []. This scale consists of 12 questions, each with 7 answers: “strongly disagree,” “disagree,” “slightly disagree,” “neutral,” “agree,” “slightly agree,” and “strongly agree.” The answer scores correspond to 1‐7 points. It includes 3 dimensions, namely “family support,” “friend support,” and “others support” []. Each dimension contains 4 questions, with a score of 4‐28 and a total score of 12‐84. The higher the score, the higher the social support. The Perceived Social Support Scale is currently a very popular and widely used scale internationally, with excellent reliability and validity. In previous studies, the Cronbach α coefficient for this scale was 0.96 []. In this study, the Cronbach α coefficients for the scale are 0.90 (wave 1) and 0.92 (wave 2), indicating excellent reliability.
Depression Symptoms
The Center for Epidemiological Studies-Depression Scale (CES-D) was used in this survey, specifically designed to evaluate the frequency of current depressive symptoms, with a focus on depressive emotions or mood []. It is important to note that the CES-D serves as a screening tool rather than a diagnostic instrument. This scale consists of 20 questions, reflecting 4 aspects of depressive symptoms: depressed affect, positive affect, somatic problems and retarded activity, and interpersonal relationship problems []. The answer includes: “occasionally or none (<1 d),” “sometimes (1‐2 d),” “frequent or half the time (3‐4 d),” and “most of the time or continuously (5‐7 d).” Respondents choose the appropriate answer based on their actual situation and frequency of feeling in the past week. Each question is assigned a score of 0‐3 points, with 4 items indicating nondepression and requiring reverse scoring. The total score range is 0‐60 points, and the higher the score, the higher the frequency of depression occurrence. For interpretive context, a score of 16 or higher is widely used in screening settings to indicate a possible depressive state. Referencing other studies on Chinese residents, the Cronbach α coefficient for this scale is 0.895 []. In this study, the Cronbach α coefficients for the scale are 0.91 (wave 1) and 0.90 (wave 2), indicating excellent reliability.
Statistical Methods
Quality Control and Single Factor Analysis
After the survey was completed, EpiData software (version 3.1; EpiData Association) was used to input the paper data obtained from the survey into a computer, forming an electronic version of the data. Adopting a dual input format ensures the accuracy of data input, followed by data cleaning and analysis. Descriptive analysis and univariate analysis were conducted on demographic variables and core variables.
Multifactor Analysis Based on LASSO Regression
LASSO regression can solve the problem of multicollinearity, and its core idea is to reduce unimportant coefficients to 0 by introducing a penalty function. This algorithm not only achieves the visibility of model parameters, but also plays a role in feature selection [].
LASSO regression analysis was conducted with R software (version 4.4.2; R Core Team) to control demographic variables and test the relationship between MIU, social support, and depressive symptoms. This version is widely used in medical data and statistics [,]. Model 1 examines the association between baseline MIU and follow-up depression, controlling for demographic variables. Model 2 adds baseline social support to test its potential mediating role between MIU and depression. Model 3 evaluates the relationship between baseline MIU and follow-up social support, controlling for covariates.
Robustness Analysis
To ensure the reliability of the LASSO regression results, we conducted comprehensive robustness analyses comprising bootstrap stability assessment with 1000 replications to evaluate variable selection consistency, complemented by sensitivity analysis examining variable selection patterns across different regularization parameters (including λ.min, λ.1se, and modified penalty strengths of 0.5×λ.min and 2×λ.min). Variables demonstrating selection frequencies exceeding 70% across these analyses were considered to exhibit high stability, providing robust evidence for the consistency of our identified predictors.
Analysis of Cross-Lagged Effect
We used Mplus (version 8.3; Muthen & Muthen) to conduct a cross-lagged panel model analysis, controlling for age, BMI, sex, ethnicity, marital status, occupation, and education. The model examined the reciprocal relationships between MIU, social support, and depressive symptoms across the 2 waves of measurement, and all path coefficients were analyzed. P<.05 indicates statistical significance. Mplus 8.3 offers advanced capabilities for handling complex models and missing data, providing greater flexibility in modeling scenarios where other software may not fully recognize the model. Using Mplus 8.3 ensures the reliability of results []. Finally, the cross-lagged model between MIU, social support, and depressive symptoms was established. The path coefficient ρ reflects the strength and direction of the longitudinal relationship between variables across time points. A significant cross-lagged path coefficient (eg, from baseline MIU to follow-up social support) suggests a longitudinal relationship in that direction. The relative magnitude and significance of the reciprocal paths (eg, social support to depression vs depression to social support) are compared to determine bidirectional or unidirectional relationships and determine whether variables have a 1-way or 2-way impact. If it is bidirectional, the degree of influence can be determined [].
Results
Participants
In this longitudinal study, 489 rural residents successfully completed a questionnaire survey (mean age 63.30, SD 13.35 y; n=310, 63.39% female). Among them, the average age of the nonolder adult population group (age ≤60 y) was 50.00 (SD 8.82) years, while the average age of the older adult group (age >60 y) was 72.36 (SD 6.61) years. The participant flow is summarized in . describes the sample distribution of social demographics, MIU, social support, and depressive symptoms. The number of mobile internet users at baseline was 110, accounting for 22.49%, while the number of people who did not know how to use the internet was 379, accounting for 77.51%. The average scores of the social support scale at baseline and follow-up were 59.15 (SD 14.39) and 59.94 (SD 14.97), respectively. The average scores of the depression scale at baseline and follow-up were 10.39 (SD 10.59) and 10.74 (SD 11.19), respectively.
Figure 2. Inclusion and exclusion of rural resident samples standard.
Table 1. Sample description and univariate analysis about the factors associated with depressive symptoms and social support (n=489).
Variables
Participants
Depressive symptoms at follow-up
Social support at follow-up
Mean (SD)
t test (df), F test (df), or r value
P value
Mean (SD)
t test (df), F test (df), or r value
P value
Total, n (%)
489 (100)
10.74 (11.19)
—
—
59.94 (14.97)
—
—
Age (years), mean (SD)
63.30 (13.35)
3.326 (487)
<.001
−5.028 (487)
<.001
Nonolder adult population
50.00 (8.82)
8.72 (9.48)
63.97 (14.08)
The older adults population
72.36 (6.61)
12.12 (12.04)
57.20 (14.97)
BMI at baseline (kg/m2), mean (SD)
23.99 (3.47)
—
−0.091 (487)
.04
—
−0.034 (487)
.46
BMI at follow-up (kg/m2), mean (SD)
23.89 (3.50)
—
−0.098 (487)
.03
—
0.034 (487)
.46
Sex, n (%)
−1.942 (487)
.053
−1.307 (487)
.19
Male
179 (36.61)
9.45 (9.56)
58.78 (14.92)
Female
310 (63.39)
11.49 (11.99)
60.61 (14.99)
Ethnicity, n (%)
1.122 (487)
.26
0.079 (487)
.94
Han
476 (97.34)
10.84 (11.27)
59.95 (15.05)
Others
13 (2.66)
7.31 (7.47)
59.62 (12.39)
Marital status, n (%)
−5.691 (487)
<.001
2.061 (487)
.04
Married
398 (81.39)
9.41 (9.77)
60.61 (14.63)
Others
91 (18.61)
16.58 (14.70)
57.03 (16.16)
Education, n (%)
12.069 (2)
<.001
6.206 (2)
.002
Illiteracy
209 (42.74)
13.54 (12.94)
57.34 (14.69)
Primary school
137 (28.02)
9.07 (8.90)
60.90 (15.08)
Middle school and above
143 (29.24)
8.27 (9.45)
62.82 (14.74)
Occupation, n (%)
0.614 (487)
.54
1.014 (487)
.31
Farmers
311 (63.6)
10.98 (11.99)
60.46 (15.01)
Others
178 (36.4)
10.33 (9.65)
59.03 (14.91)
Mobile internet use at baseline, n (%)
−2.981 (487)
.003
4.663 (487)
<.001
Mobile internet users
110 (22.49)
7.96 (8.82)
65.68 (13.46)
Others
379 (77.51)
11.55 (11.68)
58.27 (15.00)
Social support at baseline, mean (SD)
59.15 (14.39)
—
−0.332 (487)
<.001
—
0.263 (487)
<.001
Depressive symptoms at baseline, mean (SD)
10.39 (10.59)
—
0.526 (487)
<.001
—
−0.226 (487)
<.001
aNot applicable.
bt test scores.
cr value.
dF test scores.
Descriptive Data
shows the results of these demographic characteristics and single-factor analysis. The results showed a positive correlation between age group (t487=3.326, P<.001), education level (F2=12.069, P<.001), baseline depression (r487=0.526, P<.001), and follow-up depression. BMI at baseline (r=−0.091, P=.04), BMI at follow-up (r=−0.098, P=.03), marital status (t487=−5.691, P<.001), MIU at baseline (t487=−2.981, P=.003), and social support at baseline (r=−0.332, P<.001) were negatively correlated with depression at follow-up. Marital status (t487=2.061, P=.04), education level (t487=6.206, P=.002), MIU at baseline (t487=4.663, P<.001) and social support at baseline (r=0.263, P<.001) were positively correlated with social support at follow-up. Age group (t487=−5.028, P<.001) and depressive symptoms at baseline (r487=−0.226, P<.001) were negatively correlated with social support at follow-up. All tests are 2-tailed as presented in .
Main Results
Multivariate Analysis Based on LASSO Regression
Based on the preset models 1‐3, variables are included in the LASSO regression model, and the best lambda value is selected as the screening criterion. According to -, significant variables are retained, respectively. In model 1, the non–older adult population, BMI at baseline, BMI at follow-up, male, ethnic Han, married, primary school, middle school and above, and mobile internet users are retained. In model 2, BMI at baseline, BMI at follow-up, male, ethnic Han, married, primary school, middle school and above, and social support at baseline are retained. In model 3, non–older adult population, BMI at baseline, male, married, occupation farmers, and mobile internet users are retained. The best lambda value and the coefficients under the best lambda value are presented in .
Figure 3. (A) Cross-validation error curve, (B) least absolute shrinkage and selection operator coefficient path diagram, and (C) relationship between non-zero coefficient quantity and λ for least absolute shrinkage and selection operator regression of model 1. LASSO: least absolute shrinkage and selection operator. Figure 4. (A) Cross-validation error curve; (B) LASSO coefficient path diagram; and (C) relationship between nonzero coefficient quantity and λ forLASS0 regression of model 2. LASSO: least absolute shrinkage and selection operator. Figure 5. (A) Cross-validation error curve; (B)LASSO coefficient path diagram; and (C) relationship between non-zero coefficient quantity and λ for LASSO regression of model 3. LASSO: least absolute shrinkage and selection operator.
The robustness analyses demonstrated consistently high stability across all 3 models. For depression symptom predictors, bootstrap stability analysis revealed high selection frequencies for marital status (100%), primary education (97.4%), secondary education (96.3%), male (88.2%), and MIU (78.7%). In the social support mediation pathway, both baseline social support and marital status demonstrated perfect selection consistency (100%) across all bootstrap replications and maintained selection under all regularization conditions. Furthermore, MIU exhibited near-perfect selection stability (98.9%) in predicting social support. Sensitivity analyses confirmed consistent variable selection patterns across different regularization parameters for all key predictors, providing robust evidence for the reliability of the identified relationships.
Table 2. Least absolute shrinkage and selection operator regression analysis for the factors associated with social support and depressive symptoms.
Variables
Model 1
Model 2
Model 3
Constant
10.74233129
10.74233129
59.9399586
Age (years)
The non–older adult population
−0.21301654
—
2.1070368
The elderly population
—
—
—
BMI at baseline (kg/m2)
−0.07730491
−0.14305917
−0.8245169
BMI at follow up (kg/m2)
−0.49373459
−0.45496701
—
Sex
Male
−0.5016278
−0.36233779
−0.4075946
Female
—
—
—
Ethnicity
Han
0.28272745
0.12709935
—
Others
—
—
—
Marital status
Married
−2.14018878
−1.94364746
0.4366721
Others
—
—
—
Education
Illiteracy
—
—
—
Primary school
−1.15823898
−1.10337027
0.3243639
Middle school and above
−1.17625388
−0.7891779
0.5941248
Occupation
Farmers
—
—
1.3140195
Others
—
—
—
Mobile internet use at baseline
Mobile internet users
−0.42342155
−0.02666954
1.7695324
Others
—
—
—
Social support at baseline
—
−3.12424953
—
Best λ
0.1880534
0.2078466
0.2695602
aThe numbers in the table represent the coefficients under the best λ value.
bdependent variable is depressive symptoms at follow-up
cdependent variable is social support at follow-up.
dNot applicable.
Cross-Lagged Model
illustrates the correlation between MIU, social support, and depressive symptoms measured in the 2 periods. MIU, social support, and depressive symptoms were significantly correlated (all P<.001). This laid the foundation for us to establish a cross-lag model. The results of the correlation test are presented in .
Table 3. The correlation analysis of mobile internet use, social support, and depressive symptoms.
Variables
Value, mean (SD)
1
2
3
4
5
Mobile internet use at baseline
—
—
—
—
—
—
Mobile internet use at follow-up
—
0.506c (P<.001)
—
—
—
—
Social support at baseline
59.15 (14.39)
0.216c (P<.001)
0.169c (P<.001)
—
—
—
Social support at follow-up
59.94 (14.97)
0.207c (P<.001)
0.260c (P<.001)
0.263c (P<.001)
—
—
Depressive symptoms at baseline
10.39 (10.59)
−0.188c (P<.001)
−0.187c (P<.001)
−0.487c (P<.001)
-0.226c (P<.001)
—
Depressive symptoms at follow-up
10.74 (11.19)
−0.134c (P=.003)
−0.165c (P<.001)
−0.332c (P<.001)
−0.369c (P<.001)
0.526c (P<.001)
aNot applicable.
bThe lower triangular part of the correlation matrix is presented. The upper triangular part is omitted as it is symmetric to the lower triangular part.
cr value.
shows the significance of the relationship and path between the 3 variables in 2 periods. The relationship between baseline MIU and follow-up social support was significant (ρ=0.097, P=.049), and there was a 1-way relationship, indicating that baseline MIU had the same direction for follow-up social support. The relationship between social support and depressive symptoms is significant, and there is a significant bidirectional relationship. Baseline social support inversely predicts follow-up depressive symptoms (ρ=−0.096, P=.03), and baseline depressive symptoms also inversely predict follow-up social support (ρ=−0.109, P=.04). Baseline social support has a greater impact on follow-up depressive symptoms. The results of the cross-lagged model are illustrated in . All parameters presented are standardized path coefficients.
Figure 6. Output model of the cross-lagged panel model of mobile internet use, social support, and depression after adding control variables.
Discussion
Key Results
This study delved into the longitudinal relationship between MIU, social support, and depressive symptoms among community residents, using cross-lagged modeling to track and analyze the potential interactions among the 3 during this 4-year period. The results indicated that MIU was significantly negatively correlated with depressive symptoms, suggesting that mobile Internet access and use may help alleviate depressive symptoms over time. Specifically, mobile internet appears to enhance social support and reduce depressive symptoms through several pathways; it helps users maintain and expand social connections, especially long-distance relationships, providing emotional support and a sense of belonging []. It offers platforms for accessing health information and joining interest communities, boosting self-efficacy and coping abilities; and positive internet-based interactions promote positive emotions and buffer stress [,,]. Crucially, the mental health impact of mobile internet depends heavily on usage patterns: purposeful use for communication and learning often yields positive outcomes, whereas passive consumption or excessive use may increase psychological risks. Therefore, promoting healthy usage behaviors, not just access, should be a key focus for future digital health interventions.
Social support plays a longitudinal mediating role between MIU and depressive symptoms. Specifically, early mobile internet usage enhances later levels of social support through multiple behavioral and psychological mechanisms. By breaking temporal and spatial constraints, mobile internet enables individuals to more conveniently maintain long-distance relationships, access timely emotional support, and join internet-based communities based on shared interests, thereby significantly increasing their perceived social support []. This enhanced support system further alleviates depressive symptoms by providing emotional validation, reducing loneliness, and strengthening a sense of belonging []. Thus, while MIU does not directly reduce depression, it indirectly promotes mental health improvement by expanding and consolidating social support networks.
Higher levels of social support at baseline predicted significant reductions in depressive symptoms at subsequent follow-up. This phenomenon is consistent with extensive current research findings of a stable negative association between social support and depressive symptoms [,]. For the adolescent population, perceived social support is a key factor in preventing mental health problems and warding off the onset of depressive symptoms during the transition to adulthood []. In contrast, in the older population, research has shown that the impact of stressful life events on depressed mood in older adults can be effectively buffered by active participation in family and social activities, reducing the degree of mean difference []. In particular, lack of social support has been identified as a significant predictor of depressive symptoms in older men. In particular, lack of social support has been identified as a significant predictor of depressive symptoms in older men []. Notably, emotional support is widely recognized as a highly consistent and important protective factor against depressive symptoms in adults []. Thus, whether targeting adolescents, older adults, or the general population, enhancing social support networks is an effective strategy for alleviating depressive symptoms and promoting mental health.
Depressive symptoms at baseline similarly predicted lower levels of social support during the follow-up phase. This phenomenon can be explained through the perspectives of social behavior modification and the stress buffering model. From the social behavior modification angle, core depressive symptoms—such as persistent low mood, diminished interest, and cognitive deficits—reduce the frequency and quality of social interactions, thereby weakening the size and effectiveness of social support networks []. From the stress buffering model perspective, effective social support requires individuals to actively seek and accept it []. However, depression impairs both the willingness and ability to seek support, leading to isolation and reduced help-seeking. These behaviors erode the support system, as social support requires reciprocal interaction that becomes strained. Critically, depression distorts the perception of available support, diminishing its effectiveness. This creates a vicious cycle: diminished support exacerbates depression, which further weakens social connections.
Marital status and education significantly influence the relationship between social support and depressive symptoms. From a social capital theory perspective, marriage enhances resilience to stress by expanding individuals’ networks of strong ties (such as family and friends) and weak ties, thereby providing multidimensional support—emotional, instrumental, and informational—and reducing the risk of depressive symptoms []. Research suggested that the quality of marital intimacy negatively correlates with depression severity. Conversely, higher educational attainment often helps individuals cope more effectively with psychological stress by enhancing cognitive flexibility, promoting resource acquisition strategies, and boosting self-efficacy, thereby reducing depressive symptoms []. Education also indirectly promotes mental health by expanding social network diversity and elevating socioeconomic status. Consequently, marriage and education, as key social structural factors, play crucial roles in the mechanisms linking social support and depressive symptoms.
Innovations
In this study, a longitudinal data design with 2 time points was adopted to enhance the reliability of inferring relationships between variables across both time periods. The application of the LASSO regression method for variable selection and regularization has improved the explanatory power and prediction accuracy of the model. Combined with cross-lag panel model analysis, this paper reveals the dynamic interaction mechanism between MIU, social support, and depressive symptoms.
Limitations
This study has several limitations that should be considered. First, the reliance on self-reported questionnaires may introduce social desirability or recall bias. Second, there is no unified measurement standard for MIU, which may lead to reporting bias and limit the nuanced interpretation of usage patterns, as we did not quantitatively assess the duration, frequency, or specific behavioral characteristics of internet use. Third, although the sample size was sufficient for primary analyses, it may restrict subgroup analyses and generalizability, particularly given that the study focused solely on mobile internet access without including other digital devices (eg, desktops or laptops). In addition, while LASSO regression provides robust variable selection, its coefficient estimates do not permit traditional CIs due to inherent selection bias and nonstandard sampling distributions introduced by the regularization process. However, we addressed this limitation through comprehensive robustness checks, including bootstrap stability analysis and sensitivity testing across different regularization parameters, which demonstrated high consistency in variable selection. Finally, many potential influencing factors of depressive symptoms may not have been fully accounted for in the analysis. These limitations should be addressed in future studies.
Conclusion
This study analyzed the relationship between MIU, social support, and depressive symptoms through longitudinal data. LASSO regression shows that social support plays a completely mediating role between MIU and depressive symptoms. The cross-lag model shows that MIU positively predicts social support, and there is a 2-way reverse predictive relationship between social support and depressive symptoms. These results reveal the complex impact of digital technology use on mental health, emphasizing the importance of promoting healthy internet-based social interactions for maintaining psychological well-being. In addition, it is recommended to develop digital tools that enhance real-life social connections and consider individual internet-based behavior patterns in mental health interventions. Future research should further explore the universality of this relationship across different cultural backgrounds in order to, first, develop more effective strategies for promoting mental health. Second, develop community-based digital literacy programs tailored to rural residents, focusing on how to use mobile internet for meaningful social interactions and reliable health information acquisition. Third, integrate internet-based social support enhancement into existing mental health interventions. For example, train primary health care providers to guide patients on using mobile platforms to maintain and strengthen social connections. Fourth, design public health campaigns that promote the conscious use of mobile internet for strengthening real-life social relationships, rather than passive or excessive consumption. Fifth, collaborate with telecommunications companies to improve network infrastructure in rural areas and develop affordable data plans, ensuring equitable digital access for mental health promotion. Finally, incorporate assessments of internet use patterns into routine mental health screenings in primary care settings to identify individuals at risk of problematic use and provide early intervention.
The authors thank all participants for their active involvement and to the researchers for their invaluable assistance. We are deeply grateful for your invaluable contributions and unwavering support throughout this endeavor. This work was supported by the National Natural Science Foundation of China. The funder had no role in study design, data collection, analysis, interpretation, or writing of the manuscript.
The data supporting the findings of this study are not publicly available due to confidentiality and ownership considerations, as they were collected specifically for this research and do not originate from a public source. However, deidentified data can be made available from the corresponding author upon reasonable request for research purposes.
None declared.
Edited by Amaryllis Mavragani, Stefano Brini; submitted 26.Apr.2025; peer-reviewed by Yu Akaishi, Zhen Gui; final revised version received 30.Sep.2025; accepted 30.Sep.2025; published 14.Nov.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
NEW YORK, Nov. 14 (Xinhua) — Oil prices climbed on Friday.
The West Texas Intermediate for December delivery added 1.40 U.S. dollars, or 2.39 percent, to settle at 60.09 dollars a barrel on the New York Mercantile Exchange. Brent crude for January delivery increased by 1.38 dollars, or 2.19 percent, to settle at 64.39 dollars a barrel on the London ICE Futures Exchange. ■
Flooding has closed part of the railway line in Cornwall, Great Western Railway (GWR) has confirmed.
Train services running between Liskeard and Looe have been cancelled and disruption is expected until the end of the day, the firm said.
It said Network Rail was monitoring water levels and inspections would need to be carried out before the line was reopened.
GWR said it was sorry for the disruption and added it had requested replacement transport options between Liskeard and Looe – and would give details once they had been confirmed.
Carbon Capture Materials Research Report 2025-2030, Profiles of Key Players – Ecolab, BASF, DOW, Mitsubishi, Solvay, Air Products and Chemicals, Tosoh, Honeywell International, and Zeochem – ResearchAndMarkets.com Business Wire
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NEW YORK, Nov 14 (Reuters) – Tiger Global Management, the hedge fund founded and led by Chase Coleman, slashed its stake in Facebook parent Meta Platforms (META.O), opens new tab during the third quarter, according to a filing released on Friday.
During the quarter ended September 30, Tiger Global reduced its holdings in Meta by 62.6% to 2.8 million of the company’s shares, valuing the stake at about $2.1 billion. The firm also dissolved its positions in some other high-profile names including drugmakers Eli Lilly (LLY.N), opens new tab, Novo Nordisk (NOVOb.CO), opens new tab and cybersecurity firm CrowdStrike (CRWD.O), opens new tab.
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Tiger Global, which is an offshoot of famed investor Julian Robertson’s firm and is part of a cohort of stock-picking funds popularly known as Tiger Cubs, took new positions in streaming giant Netflix (NFLX.O), opens new tab and buy-now-pay-later firm Klarna (KLAR.N), opens new tab.
Tiger Global ended the first half of 2025 up roughly 4.5%, trailing most of its top multi-strategy fund peers.
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Anirban Sen is the Editor in Charge of Market Structure at Reuters in New York where he leads the news agency’s coverage of stock exchanges, and market-making firms including Jane Street and Citadel Securities. Previously Anirban was M&A Editor at Reuters, leading a team of reporters who regularly broke market-moving news about the biggest deals in corporate America. Some of his scoops have included Mars’ $36 billion deal for snack maker Kellanova, design software firm Synopsys’ $35 billion deal for Ansys, and buyout firm GTCR’s $18.5 billion deal for merchant services provider Worldpay. In 2023, Anirban was part of a Reuters team that won a Gerald Loeb Award for the agency’s coverage of the collapse of FTX. After starting with Reuters in Bangalore in 2009, he left in 2013 to work as a technology deals reporter in several leading business news outlets in India, including The Economic Times and Mint. Anirban rejoined Reuters in 2019 as Editor in Charge, Finance, to lead a team of reporters in India, covering everything from investment banking to venture capital.
Thinking about whether Digi International could be a hidden gem or overpriced stock? You are not alone, especially if you are curious about what really makes a tech company worth its current price.
In just the past week, Digi International’s stock jumped 8.4%, adding to a 31.5% gain year to date and an impressive 127.7% surge over the past five years.
Much of this momentum has been fueled by recent headlines around Digi’s new product launches and key partnerships in the Internet of Things space. These developments have given investors plenty to think about regarding Digi’s growth prospects and overall risk profile.
Despite this buzz, Digi International scores only 2 out of 6 on our valuation checks for undervalued companies. This suggests there is room for debate on how attractive it is today. We will break down the main valuation approaches, and at the end, reveal an even better way to put these numbers in context.
Digi International scores just 2/6 on our valuation checks. See what other red flags we found in the full valuation breakdown.
The Discounted Cash Flow (DCF) model estimates a company’s value by projecting its future cash flows and discounting them back to today’s dollars. In essence, it answers the question of what all of Digi International’s expected future profits are worth right now.
Digi International’s current Free Cash Flow is $105.8 million. According to analysts, Free Cash Flow is expected to grow steadily, reaching around $107.6 million by the end of fiscal 2027. Moving further, projections using modest growth rates suggest Free Cash Flow could grow to $151.3 million by 2035. It is important to note that only the first few years are based on analyst estimates, with long-term figures extrapolated by Simply Wall St.
Based on the 2 Stage Free Cash Flow to Equity model, this steady cash generation gives Digi International an intrinsic value of $59.21 per share. With the stock currently trading about 34.9% below this fair value estimate, the DCF model points to the shares being significantly undervalued at current prices.
Result: UNDERVALUED
Our Discounted Cash Flow (DCF) analysis suggests Digi International is undervalued by 34.9%. Track this in your watchlist or portfolio, or discover 876 more undervalued stocks based on cash flows.
DGII Discounted Cash Flow as at Nov 2025
Head to the Valuation section of our Company Report for more details on how we arrive at this Fair Value for Digi International.
The Price-to-Earnings (PE) ratio is a widely used metric for valuing profitable companies like Digi International because it directly links the market’s expectations to a company’s bottom-line earnings. Investors often look to the PE ratio as a quick gauge of whether a stock seems reasonably priced in the context of its profitability.
It is important to consider that what counts as a normal or fair PE ratio depends on growth expectations and risk. Fast-growing or less risky companies tend to justify higher PE ratios, while slower-growing or riskier ones usually trade at lower valuations.
Digi International currently trades at a PE of 35.1x. This is above both its peer average of 22.5x and the wider communications industry average of 31.7x. A more refined benchmark is Simply Wall St’s proprietary “Fair Ratio,” which calculates what Digi’s PE should be after factoring in its earnings growth forecast, margins, market cap, industry, and risk profile. For Digi International, the Fair Ratio is 27.9x.
The Fair Ratio provides a more personalized benchmark than simply comparing with peers or industry averages, as it accounts for the unique combination of the company’s growth, profitability, and business risks.
Because Digi International’s current PE of 35.1x is notably higher than its Fair Ratio of 27.9x, the stock appears to be trading above what is considered justified based on these factors.
Result: OVERVALUED
NasdaqGS:DGII PE Ratio as at Nov 2025
PE ratios tell one story, but what if the real opportunity lies elsewhere? Discover 1402 companies where insiders are betting big on explosive growth.
Earlier we mentioned there is an even better way to understand valuation, so let’s introduce you to Narratives. Narratives are a simple but powerful tool that allow you to connect your outlook on Digi International—your story about its future opportunities, risks, and business drivers—with a clear financial forecast and a personalized estimate of fair value.
With Narratives, investors go beyond just plugging in numbers by describing the assumptions, confidence, and reasoning that shape their expectations around revenue growth, profit margins, and overall company performance. Narratives are easy to create and update on Simply Wall St’s Community page, where millions of investors share their perspectives and debate fair value using both numbers and analysis.
Narratives help make buy and sell decisions easier by directly comparing your own Fair Value estimate for Digi International against the current share price. As new information emerges, such as news, earnings releases, or management updates, these Narratives are refreshed automatically so your view stays current and relevant.
For example, some investors may build a bullish Narrative around Digi’s rapid shift to recurring software revenue, supporting a fair value up to $50 per share, while more cautious users might focus on margin risks and prefer a lower fair value, closer to $30. By making your assumptions explicit and comparing them with others, Narratives empower you to invest with clarity and confidence.
Do you think there’s more to the story for Digi International? Head over to our Community to see what others are saying!
NasdaqGS:DGII Community Fair Values as at Nov 2025
This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.
Companies discussed in this article include DGII.
Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com
